Confinement in F4 Exceptional Gauge Group Using Domain Structures

NASA Astrophysics Data System (ADS)

Rafibakhsh, Shahnoosh; Shahlaei, Amir

2017-03-01

We calculate the potential between static quarks in the fundamental representation of the F4 exceptional gauge group using domain structures of the thick center vortex model. As non-trivial center elements are absent, the asymptotic string tension is lost while an intermediate linear potential is observed. SU(2) is a subgroup of F4. Investigating the decomposition of the 26 dimensional representation of F4 to the SU(2) representations, might explain what accounts for the intermediate linear potential, in the exceptional groups with no center element.

A unified data representation theory for network visualization, ordering and coarse-graining

PubMed Central

Kovács, István A.; Mizsei, Réka; Csermely, Péter

2015-01-01

Representation of large data sets became a key question of many scientific disciplines in the last decade. Several approaches for network visualization, data ordering and coarse-graining accomplished this goal. However, there was no underlying theoretical framework linking these problems. Here we show an elegant, information theoretic data representation approach as a unified solution of network visualization, data ordering and coarse-graining. The optimal representation is the hardest to distinguish from the original data matrix, measured by the relative entropy. The representation of network nodes as probability distributions provides an efficient visualization method and, in one dimension, an ordering of network nodes and edges. Coarse-grained representations of the input network enable both efficient data compression and hierarchical visualization to achieve high quality representations of larger data sets. Our unified data representation theory will help the analysis of extensive data sets, by revealing the large-scale structure of complex networks in a comprehensible form. PMID:26348923

View-tolerant face recognition and Hebbian learning imply mirror-symmetric neural tuning to head orientation

PubMed Central

Leibo, Joel Z.; Liao, Qianli; Freiwald, Winrich A.; Anselmi, Fabio; Poggio, Tomaso

2017-01-01

SUMMARY The primate brain contains a hierarchy of visual areas, dubbed the ventral stream, which rapidly computes object representations that are both specific for object identity and robust against identity-preserving transformations like depth-rotations [1, 2]. Current computational models of object recognition, including recent deep learning networks, generate these properties through a hierarchy of alternating selectivity-increasing filtering and tolerance-increasing pooling operations, similar to simple-complex cells operations [3, 4, 5, 6]. Here we prove that a class of hierarchical architectures and a broad set of biologically plausible learning rules generate approximate invariance to identity-preserving transformations at the top level of the processing hierarchy. However, all past models tested failed to reproduce the most salient property of an intermediate representation of a three-level face-processing hierarchy in the brain: mirror-symmetric tuning to head orientation [7]. Here we demonstrate that one specific biologically-plausible Hebb-type learning rule generates mirror-symmetric tuning to bilaterally symmetric stimuli like faces at intermediate levels of the architecture and show why it does so. Thus the tuning properties of individual cells inside the visual stream appear to result from group properties of the stimuli they encode and to reflect the learning rules that sculpted the information-processing system within which they reside. PMID:27916522

The Livermore Brain: Massive Deep Learning Networks Enabled by High Performance Computing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, Barry Y.

The proliferation of inexpensive sensor technologies like the ubiquitous digital image sensors has resulted in the collection and sharing of vast amounts of unsorted and unexploited raw data. Companies and governments who are able to collect and make sense of large datasets to help them make better decisions more rapidly will have a competitive advantage in the information era. Machine Learning technologies play a critical role for automating the data understanding process; however, to be maximally effective, useful intermediate representations of the data are required. These representations or “features” are transformations of the raw data into a form where patternsmore » are more easily recognized. Recent breakthroughs in Deep Learning have made it possible to learn these features from large amounts of labeled data. The focus of this project is to develop and extend Deep Learning algorithms for learning features from vast amounts of unlabeled data and to develop the HPC neural network training platform to support the training of massive network models. This LDRD project succeeded in developing new unsupervised feature learning algorithms for images and video and created a scalable neural network training toolkit for HPC. Additionally, this LDRD helped create the world’s largest freely-available image and video dataset supporting open multimedia research and used this dataset for training our deep neural networks. This research helped LLNL capture several work-for-others (WFO) projects, attract new talent, and establish collaborations with leading academic and commercial partners. Finally, this project demonstrated the successful training of the largest unsupervised image neural network using HPC resources and helped establish LLNL leadership at the intersection of Machine Learning and HPC research.« less

Gravity Influences the Visual Representation of Object Tilt in Parietal Cortex

PubMed Central

Angelaki, Dora E.

2014-01-01

Sensory systems encode the environment in egocentric (e.g., eye, head, or body) reference frames, creating inherently unstable representations that shift and rotate as we move. However, it is widely speculated that the brain transforms these signals into an allocentric, gravity-centered representation of the world that is stable and independent of the observer's spatial pose. Where and how this representation may be achieved is currently unknown. Here we demonstrate that a subpopulation of neurons in the macaque caudal intraparietal area (CIP) visually encodes object tilt in nonegocentric coordinates defined relative to the gravitational vector. Neuronal responses to the tilt of a visually presented planar surface were measured with the monkey in different spatial orientations (upright and rolled left/right ear down) and then compared. This revealed a continuum of representations in which planar tilt was encoded in a gravity-centered reference frame in approximately one-tenth of the comparisons, intermediate reference frames ranging between gravity-centered and egocentric in approximately two-tenths of the comparisons, and in an egocentric reference frame in less than half of the comparisons. Altogether, almost half of the comparisons revealed a shift in the preferred tilt and/or a gain change consistent with encoding object orientation in nonegocentric coordinates. Through neural network modeling, we further show that a purely gravity-centered representation of object tilt can be achieved directly from the population activity of CIP-like units. These results suggest that area CIP may play a key role in creating a stable, allocentric representation of the environment defined relative to an “earth-vertical” direction. PMID:25339732

Compression embedding

DOEpatents

Sandford, M.T. II; Handel, T.G.; Bradley, J.N.

1998-07-07

A method and apparatus for embedding auxiliary information into the digital representation of host data created by a lossy compression technique and a method and apparatus for constructing auxiliary data from the correspondence between values in a digital key-pair table with integer index values existing in a representation of host data created by a lossy compression technique are disclosed. The methods apply to data compressed with algorithms based on series expansion, quantization to a finite number of symbols, and entropy coding. Lossy compression methods represent the original data as ordered sequences of blocks containing integer indices having redundancy and uncertainty of value by one unit, allowing indices which are adjacent in value to be manipulated to encode auxiliary data. Also included is a method to improve the efficiency of lossy compression algorithms by embedding white noise into the integer indices. Lossy compression methods use loss-less compression to reduce to the final size the intermediate representation as indices. The efficiency of the loss-less compression, known also as entropy coding compression, is increased by manipulating the indices at the intermediate stage. Manipulation of the intermediate representation improves lossy compression performance by 1 to 10%. 21 figs.

Compression embedding

DOEpatents

Sandford, II, Maxwell T.; Handel, Theodore G.; Bradley, Jonathan N.

1998-01-01

A method and apparatus for embedding auxiliary information into the digital representation of host data created by a lossy compression technique and a method and apparatus for constructing auxiliary data from the correspondence between values in a digital key-pair table with integer index values existing in a representation of host data created by a lossy compression technique. The methods apply to data compressed with algorithms based on series expansion, quantization to a finite number of symbols, and entropy coding. Lossy compression methods represent the original data as ordered sequences of blocks containing integer indices having redundancy and uncertainty of value by one unit, allowing indices which are adjacent in value to be manipulated to encode auxiliary data. Also included is a method to improve the efficiency of lossy compression algorithms by embedding white noise into the integer indices. Lossy compression methods use loss-less compression to reduce to the final size the intermediate representation as indices. The efficiency of the loss-less compression, known also as entropy coding compression, is increased by manipulating the indices at the intermediate stage. Manipulation of the intermediate representation improves lossy compression performance by 1 to 10%.

Motor memory is encoded as a gain-field combination of intrinsic and extrinsic action representations.

PubMed

Brayanov, Jordan B; Press, Daniel Z; Smith, Maurice A

2012-10-24

Actions can be planned in either an intrinsic (body-based) reference frame or an extrinsic (world-based) frame, and understanding how the internal representations associated with these frames contribute to the learning of motor actions is a key issue in motor control. We studied the internal representation of this learning in human subjects by analyzing generalization patterns across an array of different movement directions and workspaces after training a visuomotor rotation in a single movement direction in one workspace. This provided a dense sampling of the generalization function across intrinsic and extrinsic reference frames, which allowed us to dissociate intrinsic and extrinsic representations and determine the manner in which they contributed to the motor memory for a trained action. A first experiment showed that the generalization pattern reflected a memory that was intermediate between intrinsic and extrinsic representations. A second experiment showed that this intermediate representation could not arise from separate intrinsic and extrinsic learning. Instead, we find that the representation of learning is based on a gain-field combination of local representations in intrinsic and extrinsic coordinates. This gain-field representation generalizes between actions by effectively computing similarity based on the (Mahalanobis) distance across intrinsic and extrinsic coordinates and is in line with neural recordings showing mixed intrinsic-extrinsic representations in motor and parietal cortices.

View-Tolerant Face Recognition and Hebbian Learning Imply Mirror-Symmetric Neural Tuning to Head Orientation.

PubMed

Leibo, Joel Z; Liao, Qianli; Anselmi, Fabio; Freiwald, Winrich A; Poggio, Tomaso

2017-01-09

The primate brain contains a hierarchy of visual areas, dubbed the ventral stream, which rapidly computes object representations that are both specific for object identity and robust against identity-preserving transformations, like depth rotations [1, 2]. Current computational models of object recognition, including recent deep-learning networks, generate these properties through a hierarchy of alternating selectivity-increasing filtering and tolerance-increasing pooling operations, similar to simple-complex cells operations [3-6]. Here, we prove that a class of hierarchical architectures and a broad set of biologically plausible learning rules generate approximate invariance to identity-preserving transformations at the top level of the processing hierarchy. However, all past models tested failed to reproduce the most salient property of an intermediate representation of a three-level face-processing hierarchy in the brain: mirror-symmetric tuning to head orientation [7]. Here, we demonstrate that one specific biologically plausible Hebb-type learning rule generates mirror-symmetric tuning to bilaterally symmetric stimuli, like faces, at intermediate levels of the architecture and show why it does so. Thus, the tuning properties of individual cells inside the visual stream appear to result from group properties of the stimuli they encode and to reflect the learning rules that sculpted the information-processing system within which they reside. Copyright © 2017 Elsevier Ltd. All rights reserved.

Learning, memory, and the role of neural network architecture.

PubMed

Hermundstad, Ann M; Brown, Kevin S; Bassett, Danielle S; Carlson, Jean M

2011-06-01

The performance of information processing systems, from artificial neural networks to natural neuronal ensembles, depends heavily on the underlying system architecture. In this study, we compare the performance of parallel and layered network architectures during sequential tasks that require both acquisition and retention of information, thereby identifying tradeoffs between learning and memory processes. During the task of supervised, sequential function approximation, networks produce and adapt representations of external information. Performance is evaluated by statistically analyzing the error in these representations while varying the initial network state, the structure of the external information, and the time given to learn the information. We link performance to complexity in network architecture by characterizing local error landscape curvature. We find that variations in error landscape structure give rise to tradeoffs in performance; these include the ability of the network to maximize accuracy versus minimize inaccuracy and produce specific versus generalizable representations of information. Parallel networks generate smooth error landscapes with deep, narrow minima, enabling them to find highly specific representations given sufficient time. While accurate, however, these representations are difficult to generalize. In contrast, layered networks generate rough error landscapes with a variety of local minima, allowing them to quickly find coarse representations. Although less accurate, these representations are easily adaptable. The presence of measurable performance tradeoffs in both layered and parallel networks has implications for understanding the behavior of a wide variety of natural and artificial learning systems.

Deep Learning and Developmental Learning: Emergence of Fine-to-Coarse Conceptual Categories at Layers of Deep Belief Network.

PubMed

Sadeghi, Zahra

2016-09-01

In this paper, I investigate conceptual categories derived from developmental processing in a deep neural network. The similarity matrices of deep representation at each layer of neural network are computed and compared with their raw representation. While the clusters generated by raw representation stand at the basic level of abstraction, conceptual categories obtained from deep representation shows a bottom-up transition procedure. Results demonstrate a developmental course of learning from specific to general level of abstraction through learned layers of representations in a deep belief network. © The Author(s) 2016.

Emergence of an abstract categorical code enabling the discrimination of temporally structured tactile stimuli

PubMed Central

Rossi-Pool, Román; Salinas, Emilio; Zainos, Antonio; Alvarez, Manuel; Vergara, José; Parga, Néstor; Romo, Ranulfo

2016-01-01

The problem of neural coding in perceptual decision making revolves around two fundamental questions: (i) How are the neural representations of sensory stimuli related to perception, and (ii) what attributes of these neural responses are relevant for downstream networks, and how do they influence decision making? We studied these two questions by recording neurons in primary somatosensory (S1) and dorsal premotor (DPC) cortex while trained monkeys reported whether the temporal pattern structure of two sequential vibrotactile stimuli (of equal mean frequency) was the same or different. We found that S1 neurons coded the temporal patterns in a literal way and only during the stimulation periods and did not reflect the monkeys’ decisions. In contrast, DPC neurons coded the stimulus patterns as broader categories and signaled them during the working memory, comparison, and decision periods. These results show that the initial sensory representation is transformed into an intermediate, more abstract categorical code that combines past and present information to ultimately generate a perceptually informed choice. PMID:27872293

Gravity influences the visual representation of object tilt in parietal cortex.

PubMed

Rosenberg, Ari; Angelaki, Dora E

2014-10-22

Sensory systems encode the environment in egocentric (e.g., eye, head, or body) reference frames, creating inherently unstable representations that shift and rotate as we move. However, it is widely speculated that the brain transforms these signals into an allocentric, gravity-centered representation of the world that is stable and independent of the observer's spatial pose. Where and how this representation may be achieved is currently unknown. Here we demonstrate that a subpopulation of neurons in the macaque caudal intraparietal area (CIP) visually encodes object tilt in nonegocentric coordinates defined relative to the gravitational vector. Neuronal responses to the tilt of a visually presented planar surface were measured with the monkey in different spatial orientations (upright and rolled left/right ear down) and then compared. This revealed a continuum of representations in which planar tilt was encoded in a gravity-centered reference frame in approximately one-tenth of the comparisons, intermediate reference frames ranging between gravity-centered and egocentric in approximately two-tenths of the comparisons, and in an egocentric reference frame in less than half of the comparisons. Altogether, almost half of the comparisons revealed a shift in the preferred tilt and/or a gain change consistent with encoding object orientation in nonegocentric coordinates. Through neural network modeling, we further show that a purely gravity-centered representation of object tilt can be achieved directly from the population activity of CIP-like units. These results suggest that area CIP may play a key role in creating a stable, allocentric representation of the environment defined relative to an "earth-vertical" direction. Copyright © 2014 the authors 0270-6474/14/3414170-11$15.00/0.

Motor Memory Is Encoded as a Gain-Field Combination of Intrinsic and Extrinsic Action Representations

PubMed Central

Brayanov, Jordan B.; Press, Daniel Z.; Smith, Maurice A.

2013-01-01

Actions can be planned in either an intrinsic (body-based) reference frame or an extrinsic (world-based) frame, and understanding how the internal representations associated with these frames contribute to the learning of motor actions is a key issue in motor control. We studied the internal representation of this learning in human subjects by analyzing generalization patterns across an array of different movement directions and workspaces after training a visuomotor rotation in a single movement direction in one workspace. This provided a dense sampling of the generalization function across intrinsic and extrinsic reference frames, which allowed us to dissociate intrinsic and extrinsic representations and determine the manner in which they contributed to the motor memory for a trained action. A first experiment showed that the generalization pattern reflected a memory that was intermediate between intrinsic and extrinsic representations. A second experiment showed that this intermediate representation could not arise from separate intrinsic and extrinsic learning. Instead, we find that the representation of learning is based on a gain-field combination of local representations in intrinsic and extrinsic coordinates. This gain-field representation generalizes between actions by effectively computing similarity based on the (Mahalanobis) distance across intrinsic and extrinsic coordinates and is in line with neural recordings showing mixed intrinsic-extrinsic representations in motor and parietal cortices. PMID:23100418

Language Networks as Models of Cognition: Understanding Cognition through Language

NASA Astrophysics Data System (ADS)

Beckage, Nicole M.; Colunga, Eliana

Language is inherently cognitive and distinctly human. Separating the object of language from the human mind that processes and creates language fails to capture the full language system. Linguistics traditionally has focused on the study of language as a static representation, removed from the human mind. Network analysis has traditionally been focused on the properties and structure that emerge from network representations. Both disciplines could gain from looking at language as a cognitive process. In contrast, psycholinguistic research has focused on the process of language without committing to a representation. However, by considering language networks as approximations of the cognitive system we can take the strength of each of these approaches to study human performance and cognition as related to language. This paper reviews research showcasing the contributions of network science to the study of language. Specifically, we focus on the interplay of cognition and language as captured by a network representation. To this end, we review different types of language network representations before considering the influence of global level network features. We continue by considering human performance in relation to network structure and conclude with theoretical network models that offer potential and testable explanations of cognitive and linguistic phenomena.

Digital image analysis to quantify carbide networks in ultrahigh carbon steels

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hecht, Matthew D.; Webler, Bryan A.; Picard, Yoosuf N., E-mail: ypicard@cmu.edu

A method has been developed and demonstrated to quantify the degree of carbide network connectivity in ultrahigh carbon steels through digital image processing and analysis of experimental micrographs. It was shown that the network connectivity and carbon content can be correlated to toughness for various ultrahigh carbon steel specimens. The image analysis approach first involved segmenting the carbide network and pearlite matrix into binary contrast representations via a grayscale intensity thresholding operation. Next, the carbide network pixels were skeletonized and parceled into braches and nodes, allowing the determination of a connectivity index for the carbide network. Intermediate image processing stepsmore » to remove noise and fill voids in the network are also detailed. The connectivity indexes of scanning electron micrographs were consistent in both secondary and backscattered electron imaging modes, as well as across two different (50 × and 100 ×) magnifications. Results from ultrahigh carbon steels reported here along with other results from the literature generally showed lower connectivity indexes correlated with higher Charpy impact energy (toughness). A deviation from this trend was observed at higher connectivity indexes, consistent with a percolation threshold for crack propagation across the carbide network. - Highlights: • A method for carbide network analysis in steels is proposed and demonstrated. • ImageJ method extracts a network connectivity index from micrographs. • Connectivity index consistent in different imaging conditions and magnifications. • Impact energy may plateau when a critical network connectivity is exceeded.« less

Network representations of angular regions for electromagnetic scattering

PubMed Central

2017-01-01

Network modeling in electromagnetics is an effective technique in treating scattering problems by canonical and complex structures. Geometries constituted of angular regions (wedges) together with planar layers can now be approached with the Generalized Wiener-Hopf Technique supported by network representation in spectral domain. Even if the network representations in spectral planes are of great importance by themselves, the aim of this paper is to present a theoretical base and a general procedure for the formulation of complex scattering problems using network representation for the Generalized Wiener Hopf Technique starting basically from the wave equation. In particular while the spectral network representations are relatively well known for planar layers, the network modelling for an angular region requires a new theory that will be developed in this paper. With this theory we complete the formulation of a network methodology whose effectiveness is demonstrated by the application to a complex scattering problem with practical solutions given in terms of GTD/UTD diffraction coefficients and total far fields for engineering applications. The methodology can be applied to other physics fields. PMID:28817573

Identifying network representation issues with the network trip.

DOT National Transportation Integrated Search

2012-04-23

The purpose of this study was to evaluate the effects of road-network representation on the application of the Network Robustness Index (NRI), using the Chittenden County Regional Transportation Model. The results are expected to improve the requirem...

Activity in the fronto-parietal network indicates numerical inductive reasoning beyond calculation: An fMRI study combined with a cognitive model

PubMed Central

Liang, Peipeng; Jia, Xiuqin; Taatgen, Niels A.; Borst, Jelmer P.; Li, Kuncheng

2016-01-01

Numerical inductive reasoning refers to the process of identifying and extrapolating the rule involved in numeric materials. It is associated with calculation, and shares the common activation of the fronto-parietal regions with calculation, which suggests that numerical inductive reasoning may correspond to a general calculation process. However, compared with calculation, rule identification is critical and unique to reasoning. Previous studies have established the central role of the fronto-parietal network for relational integration during rule identification in numerical inductive reasoning. The current question of interest is whether numerical inductive reasoning exclusively corresponds to calculation or operates beyond calculation, and whether it is possible to distinguish between them based on the activity pattern in the fronto-parietal network. To directly address this issue, three types of problems were created: numerical inductive reasoning, calculation, and perceptual judgment. Our results showed that the fronto-parietal network was more active in numerical inductive reasoning which requires more exchanges between intermediate representations and long-term declarative knowledge during rule identification. These results survived even after controlling for the covariates of response time and error rate. A computational cognitive model was developed using the cognitive architecture ACT-R to account for the behavioral results and brain activity in the fronto-parietal network. PMID:27193284

Activity in the fronto-parietal network indicates numerical inductive reasoning beyond calculation: An fMRI study combined with a cognitive model.

PubMed

Liang, Peipeng; Jia, Xiuqin; Taatgen, Niels A; Borst, Jelmer P; Li, Kuncheng

2016-05-19

Numerical inductive reasoning refers to the process of identifying and extrapolating the rule involved in numeric materials. It is associated with calculation, and shares the common activation of the fronto-parietal regions with calculation, which suggests that numerical inductive reasoning may correspond to a general calculation process. However, compared with calculation, rule identification is critical and unique to reasoning. Previous studies have established the central role of the fronto-parietal network for relational integration during rule identification in numerical inductive reasoning. The current question of interest is whether numerical inductive reasoning exclusively corresponds to calculation or operates beyond calculation, and whether it is possible to distinguish between them based on the activity pattern in the fronto-parietal network. To directly address this issue, three types of problems were created: numerical inductive reasoning, calculation, and perceptual judgment. Our results showed that the fronto-parietal network was more active in numerical inductive reasoning which requires more exchanges between intermediate representations and long-term declarative knowledge during rule identification. These results survived even after controlling for the covariates of response time and error rate. A computational cognitive model was developed using the cognitive architecture ACT-R to account for the behavioral results and brain activity in the fronto-parietal network.

Audio Spectrogram Representations for Processing with Convolutional Neural Networks

NASA Astrophysics Data System (ADS)

Wyse, L.

2017-05-01

One of the decisions that arise when designing a neural network for any application is how the data should be represented in order to be presented to, and possibly generated by, a neural network. For audio, the choice is less obvious than it seems to be for visual images, and a variety of representations have been used for different applications including the raw digitized sample stream, hand-crafted features, machine discovered features, MFCCs and variants that include deltas, and a variety of spectral representations. This paper reviews some of these representations and issues that arise, focusing particularly on spectrograms for generating audio using neural networks for style transfer.

Representational geometry: integrating cognition, computation, and the brain

PubMed Central

Kriegeskorte, Nikolaus; Kievit, Rogier A.

2013-01-01

The cognitive concept of representation plays a key role in theories of brain information processing. However, linking neuronal activity to representational content and cognitive theory remains challenging. Recent studies have characterized the representational geometry of neural population codes by means of representational distance matrices, enabling researchers to compare representations across stages of processing and to test cognitive and computational theories. Representational geometry provides a useful intermediate level of description, capturing both the information represented in a neuronal population code and the format in which it is represented. We review recent insights gained with this approach in perception, memory, cognition, and action. Analyses of representational geometry can compare representations between models and the brain, and promise to explain brain computation as transformation of representational similarity structure. PMID:23876494

[Stakeholder representations of the role of the intermediate level of the DRC health system].

PubMed

Mbeva, Jean Bosco Kahindo; Karemere, Hermès; Schirvel, Carole; Porignon, Denis

2014-01-01

Intermediate health care structures in the DRC were designed during the setting-up of primary health care in a perspective of health district support. This study was designed to describe stakeholder representations of the intermediate level of the DRC health system during the first 30 years of the primary health care system. This case study was based on inductive analysis of data from 27 key informant interviews.. The intermediate level of the health system, lacking sufficient expertise and funding during the 1980s, was confined to inspection and control functions, answering to the central level of the Ministry of health and provincial authorities. Since the 1990s, faced with the pressing demand for support from health district teams, whose self-management had to deal with humanitarian emergencies, the need to integrate vertical programmes, and cope with the logistics of many different actors, the intermediate heath system developed methods and tools to support heath districts. This resulted in a subsidiary model of the intermediate level, the perceived efficacy of which varies according to the province over recent years. The "subsidiary" model of the intermediary health system level seems a good alternative to the "control" model in DRC.

Feature integration and object representations along the dorsal stream visual hierarchy

PubMed Central

Perry, Carolyn Jeane; Fallah, Mazyar

2014-01-01

The visual system is split into two processing streams: a ventral stream that receives color and form information and a dorsal stream that receives motion information. Each stream processes that information hierarchically, with each stage building upon the previous. In the ventral stream this leads to the formation of object representations that ultimately allow for object recognition regardless of changes in the surrounding environment. In the dorsal stream, this hierarchical processing has classically been thought to lead to the computation of complex motion in three dimensions. However, there is evidence to suggest that there is integration of both dorsal and ventral stream information into motion computation processes, giving rise to intermediate object representations, which facilitate object selection and decision making mechanisms in the dorsal stream. First we review the hierarchical processing of motion along the dorsal stream and the building up of object representations along the ventral stream. Then we discuss recent work on the integration of ventral and dorsal stream features that lead to intermediate object representations in the dorsal stream. Finally we propose a framework describing how and at what stage different features are integrated into dorsal visual stream object representations. Determining the integration of features along the dorsal stream is necessary to understand not only how the dorsal stream builds up an object representation but also which computations are performed on object representations instead of local features. PMID:25140147

A Neural Network Architecture For Rapid Model Indexing In Computer Vision Systems

NASA Astrophysics Data System (ADS)

Pawlicki, Ted

1988-03-01

Models of objects stored in memory have been shown to be useful for guiding the processing of computer vision systems. A major consideration in such systems, however, is how stored models are initially accessed and indexed by the system. As the number of stored models increases, the time required to search memory for the correct model becomes high. Parallel distributed, connectionist, neural networks' have been shown to have appealing content addressable memory properties. This paper discusses an architecture for efficient storage and reference of model memories stored as stable patterns of activity in a parallel, distributed, connectionist, neural network. The emergent properties of content addressability and resistance to noise are exploited to perform indexing of the appropriate object centered model from image centered primitives. The system consists of three network modules each of which represent information relative to a different frame of reference. The model memory network is a large state space vector where fields in the vector correspond to ordered component objects and relative, object based spatial relationships between the component objects. The component assertion network represents evidence about the existence of object primitives in the input image. It establishes local frames of reference for object primitives relative to the image based frame of reference. The spatial relationship constraint network is an intermediate representation which enables the association between the object based and the image based frames of reference. This intermediate level represents information about possible object orderings and establishes relative spatial relationships from the image based information in the component assertion network below. It is also constrained by the lawful object orderings in the model memory network above. The system design is consistent with current psychological theories of recognition by component. It also seems to support Marr's notions of hierarchical indexing. (i.e. the specificity, adjunct, and parent indices) It supports the notion that multiple canonical views of an object may have to be stored in memory to enable its efficient identification. The use of variable fields in the state space vectors appears to keep the number of required nodes in the network down to a tractable number while imposing a semantic value on different areas of the state space. This semantic imposition supports an interface between the analogical aspects of neural networks and the propositional paradigms of symbolic processing.

Representational Distance Learning for Deep Neural Networks

PubMed Central

McClure, Patrick; Kriegeskorte, Nikolaus

2016-01-01

Deep neural networks (DNNs) provide useful models of visual representational transformations. We present a method that enables a DNN (student) to learn from the internal representational spaces of a reference model (teacher), which could be another DNN or, in the future, a biological brain. Representational spaces of the student and the teacher are characterized by representational distance matrices (RDMs). We propose representational distance learning (RDL), a stochastic gradient descent method that drives the RDMs of the student to approximate the RDMs of the teacher. We demonstrate that RDL is competitive with other transfer learning techniques for two publicly available benchmark computer vision datasets (MNIST and CIFAR-100), while allowing for architectural differences between student and teacher. By pulling the student's RDMs toward those of the teacher, RDL significantly improved visual classification performance when compared to baseline networks that did not use transfer learning. In the future, RDL may enable combined supervised training of deep neural networks using task constraints (e.g., images and category labels) and constraints from brain-activity measurements, so as to build models that replicate the internal representational spaces of biological brains. PMID:28082889

Representational Distance Learning for Deep Neural Networks.

PubMed

McClure, Patrick; Kriegeskorte, Nikolaus

2016-01-01

Deep neural networks (DNNs) provide useful models of visual representational transformations. We present a method that enables a DNN (student) to learn from the internal representational spaces of a reference model (teacher), which could be another DNN or, in the future, a biological brain. Representational spaces of the student and the teacher are characterized by representational distance matrices (RDMs). We propose representational distance learning (RDL), a stochastic gradient descent method that drives the RDMs of the student to approximate the RDMs of the teacher. We demonstrate that RDL is competitive with other transfer learning techniques for two publicly available benchmark computer vision datasets (MNIST and CIFAR-100), while allowing for architectural differences between student and teacher. By pulling the student's RDMs toward those of the teacher, RDL significantly improved visual classification performance when compared to baseline networks that did not use transfer learning. In the future, RDL may enable combined supervised training of deep neural networks using task constraints (e.g., images and category labels) and constraints from brain-activity measurements, so as to build models that replicate the internal representational spaces of biological brains.

Causal Networks or Causal Islands? The Representation of Mechanisms and the Transitivity of Causal Judgment.

PubMed

Johnson, Samuel G B; Ahn, Woo-kyoung

2015-09-01

Knowledge of mechanisms is critical for causal reasoning. We contrasted two possible organizations of causal knowledge—an interconnected causal network, where events are causally connected without any boundaries delineating discrete mechanisms; or a set of disparate mechanisms—causal islands—such that events in different mechanisms are not thought to be related even when they belong to the same causal chain. To distinguish these possibilities, we tested whether people make transitive judgments about causal chains by inferring, given A causes B and B causes C, that A causes C. Specifically, causal chains schematized as one chunk or mechanism in semantic memory (e.g., exercising, becoming thirsty, drinking water) led to transitive causal judgments. On the other hand, chains schematized as multiple chunks (e.g., having sex, becoming pregnant, becoming nauseous) led to intransitive judgments despite strong intermediate links ((Experiments 1-3). Normative accounts of causal intransitivity could not explain these intransitive judgments (Experiments 4 and 5). Copyright © 2015 Cognitive Science Society, Inc.

Region based route planning - Multi-abstraction route planning based on intermediate level vision processing

NASA Technical Reports Server (NTRS)

Doshi, Rajkumar S.; Lam, Raymond; White, James E.

1989-01-01

Intermediate and high level processing operations are performed on vision data for the organization of images into more meaningful, higher-level topological representations by means of a region-based route planner (RBRP). The RBRP operates in terrain scenarios where some or most of the terrain is occluded, proceeding without a priori maps on the basis of two-dimensional representations and gradient-and-roughness information. Route planning is accomplished by three successive abstractions and yields a detailed point-by-point path by searching only within the boundaries of relatively small regions.

Topic segmentation via community detection in complex networks

NASA Astrophysics Data System (ADS)

de Arruda, Henrique F.; Costa, Luciano da F.; Amancio, Diego R.

2016-06-01

Many real systems have been modeled in terms of network concepts, and written texts are a particular example of information networks. In recent years, the use of network methods to analyze language has allowed the discovery of several interesting effects, including the proposition of novel models to explain the emergence of fundamental universal patterns. While syntactical networks, one of the most prevalent networked models of written texts, display both scale-free and small-world properties, such a representation fails in capturing other textual features, such as the organization in topics or subjects. We propose a novel network representation whose main purpose is to capture the semantical relationships of words in a simple way. To do so, we link all words co-occurring in the same semantic context, which is defined in a threefold way. We show that the proposed representations favor the emergence of communities of semantically related words, and this feature may be used to identify relevant topics. The proposed methodology to detect topics was applied to segment selected Wikipedia articles. We found that, in general, our methods outperform traditional bag-of-words representations, which suggests that a high-level textual representation may be useful to study the semantical features of texts.

Topic segmentation via community detection in complex networks.

PubMed

de Arruda, Henrique F; Costa, Luciano da F; Amancio, Diego R

2016-06-01

Many real systems have been modeled in terms of network concepts, and written texts are a particular example of information networks. In recent years, the use of network methods to analyze language has allowed the discovery of several interesting effects, including the proposition of novel models to explain the emergence of fundamental universal patterns. While syntactical networks, one of the most prevalent networked models of written texts, display both scale-free and small-world properties, such a representation fails in capturing other textual features, such as the organization in topics or subjects. We propose a novel network representation whose main purpose is to capture the semantical relationships of words in a simple way. To do so, we link all words co-occurring in the same semantic context, which is defined in a threefold way. We show that the proposed representations favor the emergence of communities of semantically related words, and this feature may be used to identify relevant topics. The proposed methodology to detect topics was applied to segment selected Wikipedia articles. We found that, in general, our methods outperform traditional bag-of-words representations, which suggests that a high-level textual representation may be useful to study the semantical features of texts.

Representational geometry: integrating cognition, computation, and the brain.

PubMed

Kriegeskorte, Nikolaus; Kievit, Rogier A

2013-08-01

The cognitive concept of representation plays a key role in theories of brain information processing. However, linking neuronal activity to representational content and cognitive theory remains challenging. Recent studies have characterized the representational geometry of neural population codes by means of representational distance matrices, enabling researchers to compare representations across stages of processing and to test cognitive and computational theories. Representational geometry provides a useful intermediate level of description, capturing both the information represented in a neuronal population code and the format in which it is represented. We review recent insights gained with this approach in perception, memory, cognition, and action. Analyses of representational geometry can compare representations between models and the brain, and promise to explain brain computation as transformation of representational similarity structure. Copyright © 2013 Elsevier Ltd. All rights reserved.

Planning Marine Reserve Networks for Both Feature Representation and Demographic Persistence Using Connectivity Patterns

PubMed Central

Bode, Michael; Williamson, David H.; Weeks, Rebecca; Jones, Geoff P.; Almany, Glenn R.; Harrison, Hugo B.; Hopf, Jess K.; Pressey, Robert L.

2016-01-01

Marine reserve networks must ensure the representation of important conservation features, and also guarantee the persistence of key populations. For many species, designing reserve networks is complicated by the absence or limited availability of spatial and life-history data. This is particularly true for data on larval dispersal, which has only recently become available. However, systematic conservation planning methods currently incorporate demographic processes through unsatisfactory surrogates. There are therefore two key challenges to designing marine reserve networks that achieve feature representation and demographic persistence constraints. First, constructing a method that efficiently incorporates persistence as well as complementary feature representation. Second, incorporating persistence using a mechanistic description of population viability, rather than a proxy such as size or distance. Here we construct a novel systematic conservation planning method that addresses both challenges, and parameterise it to design a hypothetical marine reserve network for fringing coral reefs in the Keppel Islands, Great Barrier Reef, Australia. For this application, we describe how demographic persistence goals can be constructed for an important reef fish species in the region, the bar-cheeked trout (Plectropomus maculatus). We compare reserve networks that are optimally designed for either feature representation or demographic persistence, with a reserve network that achieves both goals simultaneously. As well as being practically applicable, our analyses also provide general insights into marine reserve planning for both representation and demographic persistence. First, persistence constraints for dispersive organisms are likely to be much harder to achieve than representation targets, due to their greater complexity. Second, persistence and representation constraints pull the reserve network design process in divergent directions, making it difficult to efficiently achieve both constraints. Although our method can be readily applied to the data-rich Keppel Islands case study, we finally consider the factors that limit the method’s utility in information-poor contexts common in marine conservation. PMID:27168206

Functional brain networks reconstruction using group sparsity-regularized learning.

PubMed

Zhao, Qinghua; Li, Will X Y; Jiang, Xi; Lv, Jinglei; Lu, Jianfeng; Liu, Tianming

2018-06-01

Investigating functional brain networks and patterns using sparse representation of fMRI data has received significant interests in the neuroimaging community. It has been reported that sparse representation is effective in reconstructing concurrent and interactive functional brain networks. To date, most of data-driven network reconstruction approaches rarely take consideration of anatomical structures, which are the substrate of brain function. Furthermore, it has been rarely explored whether structured sparse representation with anatomical guidance could facilitate functional networks reconstruction. To address this problem, in this paper, we propose to reconstruct brain networks utilizing the structure guided group sparse regression (S2GSR) in which 116 anatomical regions from the AAL template, as prior knowledge, are employed to guide the network reconstruction when performing sparse representation of whole-brain fMRI data. Specifically, we extract fMRI signals from standard space aligned with the AAL template. Then by learning a global over-complete dictionary, with the learned dictionary as a set of features (regressors), the group structured regression employs anatomical structures as group information to regress whole brain signals. Finally, the decomposition coefficients matrix is mapped back to the brain volume to represent functional brain networks and patterns. We use the publicly available Human Connectome Project (HCP) Q1 dataset as the test bed, and the experimental results indicate that the proposed anatomically guided structure sparse representation is effective in reconstructing concurrent functional brain networks.

V4 activity predicts the strength of visual short-term memory representations.

PubMed

Sligte, Ilja G; Scholte, H Steven; Lamme, Victor A F

2009-06-10

Recent studies have shown the existence of a form of visual memory that lies intermediate of iconic memory and visual short-term memory (VSTM), in terms of both capacity (up to 15 items) and the duration of the memory trace (up to 4 s). Because new visual objects readily overwrite this intermediate visual store, we believe that it reflects a weak form of VSTM with high capacity that exists alongside a strong but capacity-limited form of VSTM. In the present study, we isolated brain activity related to weak and strong VSTM representations using functional magnetic resonance imaging. We found that activity in visual cortical area V4 predicted the strength of VSTM representations; activity was low when there was no VSTM, medium when there was a weak VSTM representation regardless of whether this weak representation was available for report or not, and high when there was a strong VSTM representation. Altogether, this study suggests that the high capacity yet weak VSTM store is represented in visual parts of the brain. Allegedly, only some of these VSTM traces are amplified by parietal and frontal regions and as a consequence reside in traditional or strong VSTM. The additional weak VSTM representations remain available for conscious access and report when attention is redirected to them yet are overwritten as soon as new visual stimuli hit the eyes.

Predicting behavior change from persuasive messages using neural representational similarity and social network analyses.

PubMed

Pegors, Teresa K; Tompson, Steven; O'Donnell, Matthew Brook; Falk, Emily B

2017-08-15

Neural activity in medial prefrontal cortex (MPFC), identified as engaging in self-related processing, predicts later health behavior change. However, it is unknown to what extent individual differences in neural representation of content and lived experience influence this brain-behavior relationship. We examined whether the strength of content-specific representations during persuasive messaging relates to later behavior change, and whether these relationships change as a function of individuals' social network composition. In our study, smokers viewed anti-smoking messages while undergoing fMRI and we measured changes in their smoking behavior one month later. Using representational similarity analyses, we found that the degree to which message content (i.e. health, social, or valence information) was represented in a self-related processing MPFC region was associated with later smoking behavior, with increased representations of negatively valenced (risk) information corresponding to greater message-consistent behavior change. Furthermore, the relationship between representations and behavior change depended on social network composition: smokers who had proportionally fewer smokers in their network showed increases in smoking behavior when social or health content was strongly represented in MPFC, whereas message-consistent behavior (i.e., less smoking) was more likely for those with proportionally more smokers in their social network who represented social or health consequences more strongly. These results highlight the dynamic relationship between representations in MPFC and key outcomes such as health behavior change; a complete understanding of the role of MPFC in motivation and action should take into account individual differences in neural representation of stimulus attributes and social context variables such as social network composition. Copyright © 2017 Elsevier Inc. All rights reserved.

Similarity networks as a knowledge representation for space applications

NASA Technical Reports Server (NTRS)

Bailey, David; Thompson, Donna; Feinstein, Jerald

1987-01-01

Similarity networks are a powerful form of knowledge representation that are useful for many artificial intelligence applications. Similarity networks are used in applications ranging from information analysis and case based reasoning to machine learning and linking symbolic to neural processing. Strengths of similarity networks include simple construction, intuitive object storage, and flexible retrieval techniques that facilitate inferencing. Therefore, similarity networks provide great potential for space applications.

Visualizing the engram: learning stabilizes odor representations in the olfactory network.

PubMed

Shakhawat, Amin M D; Gheidi, Ali; Hou, Qinlong; Dhillon, Sandeep K; Marrone, Diano F; Harley, Carolyn W; Yuan, Qi

2014-11-12

The nature of memory is a central issue in neuroscience. How does our representation of the world change with learning and experience? Here we use the transcription of Arc mRNA, which permits probing the neural representations of temporally separated events, to address this in a well characterized odor learning model. Rat pups readily associate odor with maternal care. In pups, the lateralized olfactory networks are independent, permitting separate training and within-subject control. We use multiday training to create an enduring memory of peppermint odor. Training stabilized rewarded, but not nonrewarded, odor representations in both mitral cells and associated granule cells of the olfactory bulb and in the pyramidal cells of the anterior piriform cortex. An enlarged core of stable, likely highly active neurons represent rewarded odor at both stages of the olfactory network. Odor representations in anterior piriform cortex were sparser than typical in adult rat and did not enlarge with learning. This sparser representation of odor is congruent with the maturation of lateral olfactory tract input in rat pups. Cortical representations elsewhere have been shown to be highly variable in electrophysiological experiments, suggesting brains operate normally using dynamic and network-modulated representations. The olfactory cortical representations here are consistent with the generalized associative model of sparse variable cortical representation, as normal responses to repeated odors were highly variable (∼70% of the cells change as indexed by Arc). Learning and memory modified rewarded odor ensembles to increase stability in a core representational component. Copyright © 2014 the authors 0270-6474/14/3415394-08$15.00/0.

Network Analysis of Students' Use of Representations in Problem Solving

NASA Astrophysics Data System (ADS)

McPadden, Daryl; Brewe, Eric

2016-03-01

We present the preliminary results of a study on student use of representations in problem solving within the Modeling Instruction - Electricity and Magnetism (MI-E&M) course. Representational competence is a critical skill needed for students to develop a sophisticated understanding of college science topics and to succeed in their science courses. In this study, 70 students from the MI-E&M, calculus-based course were given a survey of 25 physics problem statements both pre- and post- instruction, covering both Newtonian Mechanics and Electricity and Magnetism (E&M). For each problem statement, students were asked which representations they would use in that given situation. We analyze the survey results through network analysis, identifying which representations are linked together in which contexts. We also compare the representation networks for those students who had already taken the first-semester Modeling Instruction Mechanics course and those students who had taken a non-Modeling Mechanics course.

Calibrating Bayesian Network Representations of Social-Behavioral Models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Whitney, Paul D.; Walsh, Stephen J.

2010-04-08

While human behavior has long been studied, recent and ongoing advances in computational modeling present opportunities for recasting research outcomes in human behavior. In this paper we describe how Bayesian networks can represent outcomes of human behavior research. We demonstrate a Bayesian network that represents political radicalization research – and show a corresponding visual representation of aspects of this research outcome. Since Bayesian networks can be quantitatively compared with external observations, the representation can also be used for empirical assessments of the research which the network summarizes. For a political radicalization model based on published research, we show this empiricalmore » comparison with data taken from the Minorities at Risk Organizational Behaviors database.« less

Deep Logic Networks: Inserting and Extracting Knowledge From Deep Belief Networks.

PubMed

Tran, Son N; d'Avila Garcez, Artur S

2018-02-01

Developments in deep learning have seen the use of layerwise unsupervised learning combined with supervised learning for fine-tuning. With this layerwise approach, a deep network can be seen as a more modular system that lends itself well to learning representations. In this paper, we investigate whether such modularity can be useful to the insertion of background knowledge into deep networks, whether it can improve learning performance when it is available, and to the extraction of knowledge from trained deep networks, and whether it can offer a better understanding of the representations learned by such networks. To this end, we use a simple symbolic language-a set of logical rules that we call confidence rules-and show that it is suitable for the representation of quantitative reasoning in deep networks. We show by knowledge extraction that confidence rules can offer a low-cost representation for layerwise networks (or restricted Boltzmann machines). We also show that layerwise extraction can produce an improvement in the accuracy of deep belief networks. Furthermore, the proposed symbolic characterization of deep networks provides a novel method for the insertion of prior knowledge and training of deep networks. With the use of this method, a deep neural-symbolic system is proposed and evaluated, with the experimental results indicating that modularity through the use of confidence rules and knowledge insertion can be beneficial to network performance.

Deep graphs—A general framework to represent and analyze heterogeneous complex systems across scales

NASA Astrophysics Data System (ADS)

Traxl, Dominik; Boers, Niklas; Kurths, Jürgen

2016-06-01

Network theory has proven to be a powerful tool in describing and analyzing systems by modelling the relations between their constituent objects. Particularly in recent years, a great progress has been made by augmenting "traditional" network theory in order to account for the multiplex nature of many networks, multiple types of connections between objects, the time-evolution of networks, networks of networks and other intricacies. However, existing network representations still lack crucial features in order to serve as a general data analysis tool. These include, most importantly, an explicit association of information with possibly heterogeneous types of objects and relations, and a conclusive representation of the properties of groups of nodes as well as the interactions between such groups on different scales. In this paper, we introduce a collection of definitions resulting in a framework that, on the one hand, entails and unifies existing network representations (e.g., network of networks and multilayer networks), and on the other hand, generalizes and extends them by incorporating the above features. To implement these features, we first specify the nodes and edges of a finite graph as sets of properties (which are permitted to be arbitrary mathematical objects). Second, the mathematical concept of partition lattices is transferred to the network theory in order to demonstrate how partitioning the node and edge set of a graph into supernodes and superedges allows us to aggregate, compute, and allocate information on and between arbitrary groups of nodes. The derived partition lattice of a graph, which we denote by deep graph, constitutes a concise, yet comprehensive representation that enables the expression and analysis of heterogeneous properties, relations, and interactions on all scales of a complex system in a self-contained manner. Furthermore, to be able to utilize existing network-based methods and models, we derive different representations of multilayer networks from our framework and demonstrate the advantages of our representation. On the basis of the formal framework described here, we provide a rich, fully scalable (and self-explanatory) software package that integrates into the PyData ecosystem and offers interfaces to popular network packages, making it a powerful, general-purpose data analysis toolkit. We exemplify an application of deep graphs using a real world dataset, comprising 16 years of satellite-derived global precipitation measurements. We deduce a deep graph representation of these measurements in order to track and investigate local formations of spatio-temporal clusters of extreme precipitation events.

Deep graphs-A general framework to represent and analyze heterogeneous complex systems across scales.

PubMed

Traxl, Dominik; Boers, Niklas; Kurths, Jürgen

2016-06-01

Network theory has proven to be a powerful tool in describing and analyzing systems by modelling the relations between their constituent objects. Particularly in recent years, a great progress has been made by augmenting "traditional" network theory in order to account for the multiplex nature of many networks, multiple types of connections between objects, the time-evolution of networks, networks of networks and other intricacies. However, existing network representations still lack crucial features in order to serve as a general data analysis tool. These include, most importantly, an explicit association of information with possibly heterogeneous types of objects and relations, and a conclusive representation of the properties of groups of nodes as well as the interactions between such groups on different scales. In this paper, we introduce a collection of definitions resulting in a framework that, on the one hand, entails and unifies existing network representations (e.g., network of networks and multilayer networks), and on the other hand, generalizes and extends them by incorporating the above features. To implement these features, we first specify the nodes and edges of a finite graph as sets of properties (which are permitted to be arbitrary mathematical objects). Second, the mathematical concept of partition lattices is transferred to the network theory in order to demonstrate how partitioning the node and edge set of a graph into supernodes and superedges allows us to aggregate, compute, and allocate information on and between arbitrary groups of nodes. The derived partition lattice of a graph, which we denote by deep graph, constitutes a concise, yet comprehensive representation that enables the expression and analysis of heterogeneous properties, relations, and interactions on all scales of a complex system in a self-contained manner. Furthermore, to be able to utilize existing network-based methods and models, we derive different representations of multilayer networks from our framework and demonstrate the advantages of our representation. On the basis of the formal framework described here, we provide a rich, fully scalable (and self-explanatory) software package that integrates into the PyData ecosystem and offers interfaces to popular network packages, making it a powerful, general-purpose data analysis toolkit. We exemplify an application of deep graphs using a real world dataset, comprising 16 years of satellite-derived global precipitation measurements. We deduce a deep graph representation of these measurements in order to track and investigate local formations of spatio-temporal clusters of extreme precipitation events.

Expertise in Auditing: Case Representation Differences between Economy Students, Novice, Intermediate and Expert Auditors.

ERIC Educational Resources Information Center

Vaatstra, Rina F.; And Others

Medical expertise research methods were used to explore the relationship between auditing expertise and case representation. Subjects were 8 first-year economy students, 8 fourth-year auditing students, 8 postgraduate students in auditing, and 8 experienced auditors in the Netherlands, ranging in experience from only a limited knowledge of…

Differential equations for loop integrals in Baikov representation

NASA Astrophysics Data System (ADS)

Bosma, Jorrit; Larsen, Kasper J.; Zhang, Yang

2018-05-01

We present a proof that differential equations for Feynman loop integrals can always be derived in Baikov representation without involving dimension-shift identities. We moreover show that in a large class of two- and three-loop diagrams it is possible to avoid squared propagators in the intermediate steps of setting up the differential equations.

Priming Contour-Deleted Images: Evidence for Immediate Representations in Visual Object Recognition.

ERIC Educational Resources Information Center

Biederman, Irving; Cooper, Eric E.

1991-01-01

Speed and accuracy of identification of pictures of objects are facilitated by prior viewing. Contributions of image features, convex or concave components, and object models in a repetition priming task were explored in 2 studies involving 96 college students. Results provide evidence of intermediate representations in visual object recognition.…

The neural representation of objects formed through the spatiotemporal integration of visual transients

PubMed Central

Erlikhman, Gennady; Gurariy, Gennadiy; Mruczek, Ryan E.B.; Caplovitz, Gideon P.

2016-01-01

Oftentimes, objects are only partially and transiently visible as parts of them become occluded during observer or object motion. The visual system can integrate such object fragments across space and time into perceptual wholes or spatiotemporal objects. This integrative and dynamic process may involve both ventral and dorsal visual processing pathways, along which shape and spatial representations are thought to arise. We measured fMRI BOLD response to spatiotemporal objects and used multi-voxel pattern analysis (MVPA) to decode shape information across 20 topographic regions of visual cortex. Object identity could be decoded throughout visual cortex, including intermediate (V3A, V3B, hV4, LO1-2,) and dorsal (TO1-2, and IPS0-1) visual areas. Shape-specific information, therefore, may not be limited to early and ventral visual areas, particularly when it is dynamic and must be integrated. Contrary to the classic view that the representation of objects is the purview of the ventral stream, intermediate and dorsal areas may play a distinct and critical role in the construction of object representations across space and time. PMID:27033688

ESL Students' Interaction in Second Life: Task-Based Synchronous Computer-Mediated Communication

ERIC Educational Resources Information Center

Jee, Min Jung

2010-01-01

The purpose of the present study was to explore ESL students' interactions in task-based synchronous computer-mediated communication (SCMC) in Second Life, a virtual environment by which users can interact through representational figures. I investigated Low-Intermediate and High-Intermediate ESL students' interaction patterns before, during, and…

node2vec: Scalable Feature Learning for Networks

PubMed Central

Grover, Aditya; Leskovec, Jure

2016-01-01

Prediction tasks over nodes and edges in networks require careful effort in engineering features used by learning algorithms. Recent research in the broader field of representation learning has led to significant progress in automating prediction by learning the features themselves. However, present feature learning approaches are not expressive enough to capture the diversity of connectivity patterns observed in networks. Here we propose node2vec, an algorithmic framework for learning continuous feature representations for nodes in networks. In node2vec, we learn a mapping of nodes to a low-dimensional space of features that maximizes the likelihood of preserving network neighborhoods of nodes. We define a flexible notion of a node’s network neighborhood and design a biased random walk procedure, which efficiently explores diverse neighborhoods. Our algorithm generalizes prior work which is based on rigid notions of network neighborhoods, and we argue that the added flexibility in exploring neighborhoods is the key to learning richer representations. We demonstrate the efficacy of node2vec over existing state-of-the-art techniques on multi-label classification and link prediction in several real-world networks from diverse domains. Taken together, our work represents a new way for efficiently learning state-of-the-art task-independent representations in complex networks. PMID:27853626

A polygon soup representation for free viewpoint video

NASA Astrophysics Data System (ADS)

Colleu, T.; Pateux, S.; Morin, L.; Labit, C.

2010-02-01

This paper presents a polygon soup representation for multiview data. Starting from a sequence of multi-view video plus depth (MVD) data, the proposed representation takes into account, in a unified manner, different issues such as compactness, compression, and intermediate view synthesis. The representation is built in two steps. First, a set of 3D quads is extracted using a quadtree decomposition of the depth maps. Second, a selective elimination of the quads is performed in order to reduce inter-view redundancies and thus provide a compact representation. Moreover, the proposed methodology for extracting the representation allows to reduce ghosting artifacts. Finally, an adapted compression technique is proposed that limits coding artifacts. The results presented on two real sequences show that the proposed representation provides a good trade-off between rendering quality and data compactness.

Character recognition using a neural network model with fuzzy representation

NASA Technical Reports Server (NTRS)

Tavakoli, Nassrin; Seniw, David

1992-01-01

The degree to which digital images are recognized correctly by computerized algorithms is highly dependent upon the representation and the classification processes. Fuzzy techniques play an important role in both processes. In this paper, the role of fuzzy representation and classification on the recognition of digital characters is investigated. An experimental Neural Network model with application to character recognition was developed. Through a set of experiments, the effect of fuzzy representation on the recognition accuracy of this model is presented.

Decision support systems and methods for complex networks

DOEpatents

Huang, Zhenyu [Richland, WA; Wong, Pak Chung [Richland, WA; Ma, Jian [Richland, WA; Mackey, Patrick S [Richland, WA; Chen, Yousu [Richland, WA; Schneider, Kevin P [Seattle, WA

2012-02-28

Methods and systems for automated decision support in analyzing operation data from a complex network. Embodiments of the present invention utilize these algorithms and techniques not only to characterize the past and present condition of a complex network, but also to predict future conditions to help operators anticipate deteriorating and/or problem situations. In particular, embodiments of the present invention characterize network conditions from operation data using a state estimator. Contingency scenarios can then be generated based on those network conditions. For at least a portion of all of the contingency scenarios, risk indices are determined that describe the potential impact of each of those scenarios. Contingency scenarios with risk indices are presented visually as graphical representations in the context of a visual representation of the complex network. Analysis of the historical risk indices based on the graphical representations can then provide trends that allow for prediction of future network conditions.

Generalized intermediate long-wave hierarchy in zero-curvature representation with noncommutative spectral parameter

NASA Astrophysics Data System (ADS)

Degasperis, A.; Lebedev, D.; Olshanetsky, M.; Pakuliak, S.; Perelomov, A.; Santini, P. M.

1992-11-01

The simplest generalization of the intermediate long-wave hierarchy (ILW) is considered to show how to extend the Zakharov-Shabat dressing method to nonlocal, i.e., integro-partial differential, equations. The purpose is to give a procedure of constructing the zero-curvature representation of this class of equations. This result obtains by combining the Drinfeld-Sokolov formalism together with the introduction of an operator-valued spectral parameter, namely, a spectral parameter that does not commute with the space variable x. This extension provides a connection between the ILWk hierarchy and the Saveliev-Vershik continuum graded Lie algebras. In the case of ILW2 the Fairlie-Zachos sinh-algebra was found.

Contrasting Intermediation Practices in Various Advisory Service Networks in the Case of the French Ecophyto Plan

ERIC Educational Resources Information Center

Cerf, M.; Bail, Le; Lusson, J. M.; Omon, B.

2017-01-01

Purpose: To highlight the way a public policy aiming to achieve a 50% decrease of pesticides use in France reframed advice provision in public and private networks. Design/methodology/approach: We developed a framework to analyze intermediation in a public funded network, a farmers' association, and a network of co-operatives. The framework…

Dynamics of scene representations in the human brain revealed by magnetoencephalography and deep neural networks

PubMed Central

Cichy, Radoslaw Martin; Khosla, Aditya; Pantazis, Dimitrios; Oliva, Aude

2017-01-01

Human scene recognition is a rapid multistep process evolving over time from single scene image to spatial layout processing. We used multivariate pattern analyses on magnetoencephalography (MEG) data to unravel the time course of this cortical process. Following an early signal for lower-level visual analysis of single scenes at ~100 ms, we found a marker of real-world scene size, i.e. spatial layout processing, at ~250 ms indexing neural representations robust to changes in unrelated scene properties and viewing conditions. For a quantitative model of how scene size representations may arise in the brain, we compared MEG data to a deep neural network model trained on scene classification. Representations of scene size emerged intrinsically in the model, and resolved emerging neural scene size representation. Together our data provide a first description of an electrophysiological signal for layout processing in humans, and suggest that deep neural networks are a promising framework to investigate how spatial layout representations emerge in the human brain. PMID:27039703

Hyper-heuristic Evolution of Dispatching Rules: A Comparison of Rule Representations.

PubMed

Branke, Jürgen; Hildebrandt, Torsten; Scholz-Reiter, Bernd

2015-01-01

Dispatching rules are frequently used for real-time, online scheduling in complex manufacturing systems. Design of such rules is usually done by experts in a time consuming trial-and-error process. Recently, evolutionary algorithms have been proposed to automate the design process. There are several possibilities to represent rules for this hyper-heuristic search. Because the representation determines the search neighborhood and the complexity of the rules that can be evolved, a suitable choice of representation is key for a successful evolutionary algorithm. In this paper we empirically compare three different representations, both numeric and symbolic, for automated rule design: A linear combination of attributes, a representation based on artificial neural networks, and a tree representation. Using appropriate evolutionary algorithms (CMA-ES for the neural network and linear representations, genetic programming for the tree representation), we empirically investigate the suitability of each representation in a dynamic stochastic job shop scenario. We also examine the robustness of the evolved dispatching rules against variations in the underlying job shop scenario, and visualize what the rules do, in order to get an intuitive understanding of their inner workings. Results indicate that the tree representation using an improved version of genetic programming gives the best results if many candidate rules can be evaluated, closely followed by the neural network representation that already leads to good results for small to moderate computational budgets. The linear representation is found to be competitive only for extremely small computational budgets.

MetaJC++: A flexible and automatic program transformation technique using meta framework

NASA Astrophysics Data System (ADS)

Beevi, Nadera S.; Reghu, M.; Chitraprasad, D.; Vinodchandra, S. S.

2014-09-01

Compiler is a tool to translate abstract code containing natural language terms to machine code. Meta compilers are available to compile more than one languages. We have developed a meta framework intends to combine two dissimilar programming languages, namely C++ and Java to provide a flexible object oriented programming platform for the user. Suitable constructs from both the languages have been combined, thereby forming a new and stronger Meta-Language. The framework is developed using the compiler writing tools, Flex and Yacc to design the front end of the compiler. The lexer and parser have been developed to accommodate the complete keyword set and syntax set of both the languages. Two intermediate representations have been used in between the translation of the source program to machine code. Abstract Syntax Tree has been used as a high level intermediate representation that preserves the hierarchical properties of the source program. A new machine-independent stack-based byte-code has also been devised to act as a low level intermediate representation. The byte-code is essentially organised into an output class file that can be used to produce an interpreted output. The results especially in the spheres of providing C++ concepts in Java have given an insight regarding the potential strong features of the resultant meta-language.

Models of Acetylcholine and Dopamine Signals Differentially Improve Neural Representations

PubMed Central

Holca-Lamarre, Raphaël; Lücke, Jörg; Obermayer, Klaus

2017-01-01

Biological and artificial neural networks (ANNs) represent input signals as patterns of neural activity. In biology, neuromodulators can trigger important reorganizations of these neural representations. For instance, pairing a stimulus with the release of either acetylcholine (ACh) or dopamine (DA) evokes long lasting increases in the responses of neurons to the paired stimulus. The functional roles of ACh and DA in rearranging representations remain largely unknown. Here, we address this question using a Hebbian-learning neural network model. Our aim is both to gain a functional understanding of ACh and DA transmission in shaping biological representations and to explore neuromodulator-inspired learning rules for ANNs. We model the effects of ACh and DA on synaptic plasticity and confirm that stimuli coinciding with greater neuromodulator activation are over represented in the network. We then simulate the physiological release schedules of ACh and DA. We measure the impact of neuromodulator release on the network's representation and on its performance on a classification task. We find that ACh and DA trigger distinct changes in neural representations that both improve performance. The putative ACh signal redistributes neural preferences so that more neurons encode stimulus classes that are challenging for the network. The putative DA signal adapts synaptic weights so that they better match the classes of the task at hand. Our model thus offers a functional explanation for the effects of ACh and DA on cortical representations. Additionally, our learning algorithm yields performances comparable to those of state-of-the-art optimisation methods in multi-layer perceptrons while requiring weaker supervision signals and interacting with synaptically-local weight updates. PMID:28690509

Consistent maximum entropy representations of pipe flow networks

NASA Astrophysics Data System (ADS)

Waldrip, Steven H.; Niven, Robert K.; Abel, Markus; Schlegel, Michael

2017-06-01

The maximum entropy method is used to predict flows on water distribution networks. This analysis extends the water distribution network formulation of Waldrip et al. (2016) Journal of Hydraulic Engineering (ASCE), by the use of a continuous relative entropy defined on a reduced parameter set. This reduction in the parameters that the entropy is defined over ensures consistency between different representations of the same network. The performance of the proposed reduced parameter method is demonstrated with a one-loop network case study.

Body representations in the human brain revealed by kinesthetic illusions and their essential contributions to motor control and corporeal awareness.

PubMed

Naito, Eiichi; Morita, Tomoyo; Amemiya, Kaoru

2016-03-01

The human brain can generate a continuously changing postural model of our body. Somatic (proprioceptive) signals from skeletal muscles and joints contribute to the formation of the body representation. Recent neuroimaging studies of proprioceptive bodily illusions have elucidated the importance of three brain systems (motor network, specialized parietal systems, right inferior fronto-parietal network) in the formation of the human body representation. The motor network, especially the primary motor cortex, processes afferent input from skeletal muscles. Such information may contribute to the formation of kinematic/dynamic postural models of limbs, thereby enabling fast online feedback control. Distinct parietal regions appear to play specialized roles in the transformation/integration of information across different coordinate systems, which may subserve the adaptability and flexibility of the body representation. Finally, the right inferior fronto-parietal network, connected by the inferior branch of the superior longitudinal fasciculus, is consistently recruited when an individual experiences various types of bodily illusions and its possible roles relate to corporeal awareness, which is likely elicited through a series of neuronal processes of monitoring and accumulating bodily information and updating the body representation. Because this network is also recruited when identifying one's own features, the network activity could be a neuronal basis for self-consciousness. Copyright © 2015 Elsevier Ireland Ltd and the Japan Neuroscience Society. All rights reserved.

Diminished neural network dynamics in amnestic mild cognitive impairment.

PubMed

Brenner, Einat K; Hampstead, Benjamin M; Grossner, Emily C; Bernier, Rachel A; Gilbert, Nicholas; Sathian, K; Hillary, Frank G

2018-05-05

Mild cognitive impairment (MCI) is widely regarded as an intermediate stage between typical aging and dementia, with nearly 50% of patients with amnestic MCI (aMCI) converting to Alzheimer's dementia (AD) within 30 months of follow-up (Fischer et al., 2007). The growing literature using resting-state functional magnetic resonance imaging reveals both increased and decreased connectivity in individuals with MCI and connectivity loss between the anterior and posterior components of the default mode network (DMN) throughout the course of the disease progression (Hillary et al., 2015; Sheline & Raichle, 2013; Tijms et al., 2013). In this paper, we use dynamic connectivity modeling and graph theory to identify unique brain "states," or temporal patterns of connectivity across distributed networks, to distinguish individuals with aMCI from healthy older adults (HOAs). We enrolled 44 individuals diagnosed with aMCI and 33 HOAs of comparable age and education. Our results indicated that individuals with aMCI spent significantly more time in one state in particular, whereas neural network analysis in the HOA sample revealed approximately equivalent representation across four distinct states. Among individuals with aMCI, spending a higher proportion of time in the dominant state relative to a state where participants exhibited high cost (a measure combining connectivity and distance), predicted better language performance and less perseveration. This is the first report to examine neural network dynamics in individuals with aMCI. Copyright © 2018 Elsevier B.V. All rights reserved.

Subnetwork mining on functional connectivity network for classification of minimal hepatic encephalopathy.

PubMed

Zhang, Daoqiang; Tu, Liyang; Zhang, Long-Jiang; Jie, Biao; Lu, Guang-Ming

2018-06-01

Hepatic encephalopathy (HE), as a complication of cirrhosis, is a serious brain disease, which may lead to death. Accurate diagnosis of HE and its intermediate stage, i.e., minimal HE (MHE), is very important for possibly early diagnosis and treatment. Brain connectivity network, as a simple representation of brain interaction, has been widely used for the brain disease (e.g., HE and MHE) analysis. However, those studies mainly focus on finding disease-related abnormal connectivity between brain regions, although a large number of studies have indicated that some brain diseases are usually related to local structure of brain connectivity network (i.e., subnetwork), rather than solely on some single brain regions or connectivities. Also, mining such disease-related subnetwork is a challenging task because of the complexity of brain network. To address this problem, we proposed a novel frequent-subnetwork-based method to mine disease-related subnetworks for MHE classification. Specifically, we first mine frequent subnetworks from both groups, i.e., MHE patients and non-HE (NHE) patients, respectively. Then we used the graph-kernel based method to select the most discriminative subnetworks for subsequent classification. We evaluate our proposed method on a MHE dataset with 77 cirrhosis patients, including 38 MHE patients and 39 NHE patients. The results demonstrate that our proposed method can not only obtain the improved classification performance in comparison with state-of-the-art network-based methods, but also identify disease-related subnetworks which can help us better understand the pathology of the brain diseases.

Unconstrained handwritten numeral recognition based on radial basis competitive and cooperative networks with spatio-temporal feature representation.

PubMed

Lee, S; Pan, J J

1996-01-01

This paper presents a new approach to representation and recognition of handwritten numerals. The approach first transforms a two-dimensional (2-D) spatial representation of a numeral into a three-dimensional (3-D) spatio-temporal representation by identifying the tracing sequence based on a set of heuristic rules acting as transformation operators. A multiresolution critical-point segmentation method is then proposed to extract local feature points, at varying degrees of scale and coarseness. A new neural network architecture, referred to as radial-basis competitive and cooperative network (RCCN), is presented especially for handwritten numeral recognition. RCCN is a globally competitive and locally cooperative network with the capability of self-organizing hidden units to progressively achieve desired network performance, and functions as a universal approximator of arbitrary input-output mappings. Three types of RCCNs are explored: input-space RCCN (IRCCN), output-space RCCN (ORCCN), and bidirectional RCCN (BRCCN). Experiments against handwritten zip code numerals acquired by the U.S. Postal Service indicated that the proposed method is robust in terms of variations, deformations, transformations, and corruption, achieving about 97% recognition rate.

Emotion regulation, attention to emotion, and the ventral attentional network

PubMed Central

Viviani, Roberto

2013-01-01

Accounts of the effect of emotional information on behavioral response and current models of emotion regulation are based on two opposed but interacting processes: automatic bottom-up processes (triggered by emotionally arousing stimuli) and top-down control processes (mapped to prefrontal cortical areas). Data on the existence of a third attentional network operating without recourse to limited-capacity processes but influencing response raise the issue of how it is integrated in emotion regulation. We summarize here data from attention to emotion, voluntary emotion regulation, and on the origin of biases against negative content suggesting that the ventral network is modulated by exposure to emotional stimuli when the task does not constrain the handling of emotional content. In the parietal lobes, preferential activation of ventral areas associated with “bottom-up” attention by ventral network theorists is strongest in studies of cognitive reappraisal. In conditions when no explicit instruction is given to change one's response to emotional stimuli, control of emotionally arousing stimuli is observed without concomitant activation of the dorsal attentional network, replaced by a shift of activation toward ventral areas. In contrast, in studies where emotional stimuli are placed in the role of distracter, the observed deactivation of these ventral semantic association areas is consistent with the existence of proactive control on the role emotional representations are allowed to take in generating response. It is here argued that attentional orienting mechanisms located in the ventral network constitute an intermediate kind of process, with features only partially in common with effortful and automatic processes, which plays an important role in handling emotion by conveying the influence of semantic networks, with which the ventral network is co-localized. Current neuroimaging work in emotion regulation has neglected this system by focusing on a bottom-up/top-down dichotomy of attentional control. PMID:24223546

Boolean network representation of contagion dynamics during a financial crisis

NASA Astrophysics Data System (ADS)

Caetano, Marco Antonio Leonel; Yoneyama, Takashi

2015-01-01

This work presents a network model for representation of the evolution of certain patterns of economic behavior. More specifically, after representing the agents as points in a space in which each dimension associated to a relevant economic variable, their relative "motions" that can be either stationary or discordant, are coded into a boolean network. Patterns with stationary averages indicate the maintenance of status quo, whereas discordant patterns represent aggregation of new agent into the cluster or departure from the former policies. The changing patterns can be embedded into a network representation, particularly using the concept of autocatalytic boolean networks. As a case study, the economic tendencies of the BRIC countries + Argentina were studied. Although Argentina is not included in the cluster formed by BRIC countries, it tends to follow the BRIC members because of strong commercial ties.

Deep Residual Network Predicts Cortical Representation and Organization of Visual Features for Rapid Categorization.

PubMed

Wen, Haiguang; Shi, Junxing; Chen, Wei; Liu, Zhongming

2018-02-28

The brain represents visual objects with topographic cortical patterns. To address how distributed visual representations enable object categorization, we established predictive encoding models based on a deep residual network, and trained them to predict cortical responses to natural movies. Using this predictive model, we mapped human cortical representations to 64,000 visual objects from 80 categories with high throughput and accuracy. Such representations covered both the ventral and dorsal pathways, reflected multiple levels of object features, and preserved semantic relationships between categories. In the entire visual cortex, object representations were organized into three clusters of categories: biological objects, non-biological objects, and background scenes. In a finer scale specific to each cluster, object representations revealed sub-clusters for further categorization. Such hierarchical clustering of category representations was mostly contributed by cortical representations of object features from middle to high levels. In summary, this study demonstrates a useful computational strategy to characterize the cortical organization and representations of visual features for rapid categorization.

Dependency-based Siamese long short-term memory network for learning sentence representations

PubMed Central

Zhu, Wenhao; Ni, Jianyue; Wei, Baogang; Lu, Zhiguo

2018-01-01

Textual representations play an important role in the field of natural language processing (NLP). The efficiency of NLP tasks, such as text comprehension and information extraction, can be significantly improved with proper textual representations. As neural networks are gradually applied to learn the representation of words and phrases, fairly efficient models of learning short text representations have been developed, such as the continuous bag of words (CBOW) and skip-gram models, and they have been extensively employed in a variety of NLP tasks. Because of the complex structure generated by the longer text lengths, such as sentences, algorithms appropriate for learning short textual representations are not applicable for learning long textual representations. One method of learning long textual representations is the Long Short-Term Memory (LSTM) network, which is suitable for processing sequences. However, the standard LSTM does not adequately address the primary sentence structure (subject, predicate and object), which is an important factor for producing appropriate sentence representations. To resolve this issue, this paper proposes the dependency-based LSTM model (D-LSTM). The D-LSTM divides a sentence representation into two parts: a basic component and a supporting component. The D-LSTM uses a pre-trained dependency parser to obtain the primary sentence information and generate supporting components, and it also uses a standard LSTM model to generate the basic sentence components. A weight factor that can adjust the ratio of the basic and supporting components in a sentence is introduced to generate the sentence representation. Compared with the representation learned by the standard LSTM, the sentence representation learned by the D-LSTM contains a greater amount of useful information. The experimental results show that the D-LSTM is superior to the standard LSTM for sentences involving compositional knowledge (SICK) data. PMID:29513748

Dynamics of scene representations in the human brain revealed by magnetoencephalography and deep neural networks.

PubMed

Martin Cichy, Radoslaw; Khosla, Aditya; Pantazis, Dimitrios; Oliva, Aude

2017-06-01

Human scene recognition is a rapid multistep process evolving over time from single scene image to spatial layout processing. We used multivariate pattern analyses on magnetoencephalography (MEG) data to unravel the time course of this cortical process. Following an early signal for lower-level visual analysis of single scenes at ~100ms, we found a marker of real-world scene size, i.e. spatial layout processing, at ~250ms indexing neural representations robust to changes in unrelated scene properties and viewing conditions. For a quantitative model of how scene size representations may arise in the brain, we compared MEG data to a deep neural network model trained on scene classification. Representations of scene size emerged intrinsically in the model, and resolved emerging neural scene size representation. Together our data provide a first description of an electrophysiological signal for layout processing in humans, and suggest that deep neural networks are a promising framework to investigate how spatial layout representations emerge in the human brain. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Neural Network of Body Representation Differs between Transsexuals and Cissexuals

PubMed Central

Lin, Chia-Shu; Ku, Hsiao-Lun; Chao, Hsiang-Tai; Tu, Pei-Chi; Li, Cheng-Ta; Cheng, Chou-Ming; Su, Tung-Ping; Lee, Ying-Chiao; Hsieh, Jen-Chuen

2014-01-01

Body image is the internal representation of an individual’s own physical appearance. Individuals with gender identity disorder (GID), commonly referred to as transsexuals (TXs), are unable to form a satisfactory body image due to the dissonance between their biological sex and gender identity. We reasoned that changes in the resting-state functional connectivity (rsFC) network would neurologically reflect such experiential incongruence in TXs. Using graph theory-based network analysis, we investigated the regional changes of the degree centrality of the rsFC network. The degree centrality is an index of the functional importance of a node in a neural network. We hypothesized that three key regions of the body representation network, i.e., the primary somatosensory cortex, the superior parietal lobule and the insula, would show a higher degree centrality in TXs. Twenty-three pre-treatment TXs (11 male-to-female and 12 female-to-male TXs) as one psychosocial group and 23 age-matched healthy cissexual control subjects (CISs, 11 males and 12 females) were recruited. Resting-state functional magnetic resonance imaging was performed, and binarized rsFC networks were constructed. The TXs demonstrated a significantly higher degree centrality in the bilateral superior parietal lobule and the primary somatosensory cortex. In addition, the connectivity between the right insula and the bilateral primary somatosensory cortices was negatively correlated with the selfness rating of their desired genders. These data indicate that the key components of body representation manifest in TXs as critical function hubs in the rsFC network. The negative association may imply a coping mechanism that dissociates bodily emotion from body image. The changes in the functional connectome may serve as representational markers for the dysphoric bodily self of TXs. PMID:24465785

Visual Representations of Microcosm in Textbooks of Chemistry: Constructing a Systemic Network for Their Main Conceptual Framework

ERIC Educational Resources Information Center

Papageorgiou, George; Amariotakis, Vasilios; Spiliotopoulou, Vasiliki

2017-01-01

The main objective of this work is to analyse the visual representations (VRs) of the microcosm depicted in nine Greek secondary chemistry school textbooks of the last three decades in order to construct a systemic network for their main conceptual framework and to evaluate the contribution of each one of the resulting categories to the network.…

Social Network Analysis: A New Methodology for Counseling Research.

ERIC Educational Resources Information Center

Koehly, Laura M.; Shivy, Victoria A.

1998-01-01

Social network analysis (SNA) uses indices of relatedness among individuals to produce representations of social structures and positions inherent in dyads or groups. SNA methods provide quantitative representations of ongoing transactional patterns in a given social environment. Methodological issues, applications and resources are discussed…

Representation of Ecological Systems within the Protected Areas Network of the Continental United States

PubMed Central

Aycrigg, Jocelyn L.; Davidson, Anne; Svancara, Leona K.; Gergely, Kevin J.; McKerrow, Alexa; Scott, J. Michael

2013-01-01

If conservation of biodiversity is the goal, then the protected areas network of the continental US may be one of our best conservation tools for safeguarding ecological systems (i.e., vegetation communities). We evaluated representation of ecological systems in the current protected areas network and found insufficient representation at three vegetation community levels within lower elevations and moderate to high productivity soils. We used national-level data for ecological systems and a protected areas database to explore alternative ways we might be able to increase representation of ecological systems within the continental US. By following one or more of these alternatives it may be possible to increase the representation of ecological systems in the protected areas network both quantitatively (from 10% up to 39%) and geographically and come closer to meeting the suggested Convention on Biological Diversity target of 17% for terrestrial areas. We used the Landscape Conservation Cooperative framework for regional analysis and found that increased conservation on some private and public lands may be important to the conservation of ecological systems in Western US, while increased public-private partnerships may be important in the conservation of ecological systems in Eastern US. We have not assessed the pros and cons of following the national or regional alternatives, but rather present them as possibilities that may be considered and evaluated as decisions are made to increase the representation of ecological systems in the protected areas network across their range of ecological, geographical, and geophysical occurrence in the continental US into the future. PMID:23372754

Representation of ecological systems within the protected areas network of the continental United States

USGS Publications Warehouse

Aycrigg, Jocelyn L.; Davidson, Anne; Svancara, Leona K.; Gergely, Kevin J.; McKerrow, Alexa; Scott, J. Michael

2013-01-01

If conservation of biodiversity is the goal, then the protected areas network of the continental US may be one of our best conservation tools for safeguarding ecological systems (i.e., vegetation communities). We evaluated representation of ecological systems in the current protected areas network and found insufficient representation at three vegetation community levels within lower elevations and moderate to high productivity soils. We used national-level data for ecological systems and a protected areas database to explore alternative ways we might be able to increase representation of ecological systems within the continental US. By following one or more of these alternatives it may be possible to increase the representation of ecological systems in the protected areas network both quantitatively (from 10% up to 39%) and geographically and come closer to meeting the suggested Convention on Biological Diversity target of 17% for terrestrial areas. We used the Landscape Conservation Cooperative framework for regional analysis and found that increased conservation on some private and public lands may be important to the conservation of ecological systems in Western US, while increased public-private partnerships may be important in the conservation of ecological systems in Eastern US. We have not assessed the pros and cons of following the national or regional alternatives, but rather present them as possibilities that may be considered and evaluated as decisions are made to increase the representation of ecological systems in the protected areas network across their range of ecological, geographical, and geophysical occurrence in the continental US into the future.

The connection-set algebra--a novel formalism for the representation of connectivity structure in neuronal network models.

PubMed

Djurfeldt, Mikael

2012-07-01

The connection-set algebra (CSA) is a novel and general formalism for the description of connectivity in neuronal network models, from small-scale to large-scale structure. The algebra provides operators to form more complex sets of connections from simpler ones and also provides parameterization of such sets. CSA is expressive enough to describe a wide range of connection patterns, including multiple types of random and/or geometrically dependent connectivity, and can serve as a concise notation for network structure in scientific writing. CSA implementations allow for scalable and efficient representation of connectivity in parallel neuronal network simulators and could even allow for avoiding explicit representation of connections in computer memory. The expressiveness of CSA makes prototyping of network structure easy. A C+ + version of the algebra has been implemented and used in a large-scale neuronal network simulation (Djurfeldt et al., IBM J Res Dev 52(1/2):31-42, 2008b) and an implementation in Python has been publicly released.

Sparse representation of whole-brain fMRI signals for identification of functional networks.

PubMed

Lv, Jinglei; Jiang, Xi; Li, Xiang; Zhu, Dajiang; Chen, Hanbo; Zhang, Tuo; Zhang, Shu; Hu, Xintao; Han, Junwei; Huang, Heng; Zhang, Jing; Guo, Lei; Liu, Tianming

2015-02-01

There have been several recent studies that used sparse representation for fMRI signal analysis and activation detection based on the assumption that each voxel's fMRI signal is linearly composed of sparse components. Previous studies have employed sparse coding to model functional networks in various modalities and scales. These prior contributions inspired the exploration of whether/how sparse representation can be used to identify functional networks in a voxel-wise way and on the whole brain scale. This paper presents a novel, alternative methodology of identifying multiple functional networks via sparse representation of whole-brain task-based fMRI signals. Our basic idea is that all fMRI signals within the whole brain of one subject are aggregated into a big data matrix, which is then factorized into an over-complete dictionary basis matrix and a reference weight matrix via an effective online dictionary learning algorithm. Our extensive experimental results have shown that this novel methodology can uncover multiple functional networks that can be well characterized and interpreted in spatial, temporal and frequency domains based on current brain science knowledge. Importantly, these well-characterized functional network components are quite reproducible in different brains. In general, our methods offer a novel, effective and unified solution to multiple fMRI data analysis tasks including activation detection, de-activation detection, and functional network identification. Copyright © 2014 Elsevier B.V. All rights reserved.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ng, B

This survey gives an overview of popular generative models used in the modeling of stochastic temporal systems. In particular, this survey is organized into two parts. The first part discusses the discrete-time representations of dynamic Bayesian networks and dynamic relational probabilistic models, while the second part discusses the continuous-time representation of continuous-time Bayesian networks.

Randomizing bipartite networks: the case of the World Trade Web.

PubMed

Saracco, Fabio; Di Clemente, Riccardo; Gabrielli, Andrea; Squartini, Tiziano

2015-06-01

Within the last fifteen years, network theory has been successfully applied both to natural sciences and to socioeconomic disciplines. In particular, bipartite networks have been recognized to provide a particularly insightful representation of many systems, ranging from mutualistic networks in ecology to trade networks in economy, whence the need of a pattern detection-oriented analysis in order to identify statistically-significant structural properties. Such an analysis rests upon the definition of suitable null models, i.e. upon the choice of the portion of network structure to be preserved while randomizing everything else. However, quite surprisingly, little work has been done so far to define null models for real bipartite networks. The aim of the present work is to fill this gap, extending a recently-proposed method to randomize monopartite networks to bipartite networks. While the proposed formalism is perfectly general, we apply our method to the binary, undirected, bipartite representation of the World Trade Web, comparing the observed values of a number of structural quantities of interest with the expected ones, calculated via our randomization procedure. Interestingly, the behavior of the World Trade Web in this new representation is strongly different from the monopartite analogue, showing highly non-trivial patterns of self-organization.

Neural basis for dynamic updating of object representation in visual working memory.

PubMed

Takahama, Sachiko; Miyauchi, Satoru; Saiki, Jun

2010-02-15

In real world, objects have multiple features and change dynamically. Thus, object representations must satisfy dynamic updating and feature binding. Previous studies have investigated the neural activity of dynamic updating or feature binding alone, but not both simultaneously. We investigated the neural basis of feature-bound object representation in a dynamically updating situation by conducting a multiple object permanence tracking task, which required observers to simultaneously process both the maintenance and dynamic updating of feature-bound objects. Using an event-related design, we separated activities during memory maintenance and change detection. In the search for regions showing selective activation in dynamic updating of feature-bound objects, we identified a network during memory maintenance that was comprised of the inferior precentral sulcus, superior parietal lobule, and middle frontal gyrus. In the change detection period, various prefrontal regions, including the anterior prefrontal cortex, were activated. In updating object representation of dynamically moving objects, the inferior precentral sulcus closely cooperates with a so-called "frontoparietal network", and subregions of the frontoparietal network can be decomposed into those sensitive to spatial updating and feature binding. The anterior prefrontal cortex identifies changes in object representation by comparing memory and perceptual representations rather than maintaining object representations per se, as previously suggested. Copyright 2009 Elsevier Inc. All rights reserved.

Computational neural networks in chemistry: Model free mapping devices for predicting chemical reactivity from molecular structure

DOE Office of Scientific and Technical Information (OSTI.GOV)

Elrod, D.W.

1992-01-01

Computational neural networks (CNNs) are a computational paradigm inspired by the brain's massively parallel network of highly interconnected neurons. The power of computational neural networks derives not so much from their ability to model the brain as from their ability to learn by example and to map highly complex, nonlinear functions, without the need to explicitly specify the functional relationship. Two central questions about CNNs were investigated in the context of predicting chemical reactions: (1) the mapping properties of neural networks and (2) the representation of chemical information for use in CNNs. Chemical reactivity is here considered an example ofmore » a complex, nonlinear function of molecular structure. CNN's were trained using modifications of the back propagation learning rule to map a three dimensional response surface similar to those typically observed in quantitative structure-activity and structure-property relationships. The computational neural network's mapping of the response surface was found to be robust to the effects of training sample size, noisy data and intercorrelated input variables. The investigation of chemical structure representation led to the development of a molecular structure-based connection-table representation suitable for neural network training. An extension of this work led to a BE-matrix structure representation that was found to be general for several classes of reactions. The CNN prediction of chemical reactivity and regiochemistry was investigated for electrophilic aromatic substitution reactions, Markovnikov addition to alkenes, Saytzeff elimination from haloalkanes, Diels-Alder cycloaddition, and retro Diels-Alder ring opening reactions using these connectivity-matrix derived representations. The reaction predictions made by the CNNs were more accurate than those of an expert system and were comparable to predictions made by chemists.« less

Scaling and correlations in three bus-transport networks of China

NASA Astrophysics Data System (ADS)

Xu, Xinping; Hu, Junhui; Liu, Feng; Liu, Lianshou

2007-01-01

We report the statistical properties of three bus-transport networks (BTN) in three different cities of China. These networks are composed of a set of bus lines and stations serviced by these. Network properties, including the degree distribution, clustering and average path length are studied in different definitions of network topology. We explore scaling laws and correlations that may govern intrinsic features of such networks. Besides, we create a weighted network representation for BTN with lines mapped to nodes and number of common stations to weights between lines. In such a representation, the distributions of degree, strength and weight are investigated. A linear behavior between strength and degree s(k)∼k is also observed.

Overcomplete compact representation of two-particle Green's functions

NASA Astrophysics Data System (ADS)

Shinaoka, Hiroshi; Otsuki, Junya; Haule, Kristjan; Wallerberger, Markus; Gull, Emanuel; Yoshimi, Kazuyoshi; Ohzeki, Masayuki

2018-05-01

Two-particle Green's functions and the vertex functions play a critical role in theoretical frameworks for describing strongly correlated electron systems. However, numerical calculations at the two-particle level often suffer from large computation time and massive memory consumption. We derive a general expansion formula for the two-particle Green's functions in terms of an overcomplete representation based on the recently proposed "intermediate representation" basis. The expansion formula is obtained by decomposing the spectral representation of the two-particle Green's function. We demonstrate that the expansion coefficients decay exponentially, while all high-frequency and long-tail structures in the Matsubara-frequency domain are retained. This representation therefore enables efficient treatment of two-particle quantities and opens a route to the application of modern many-body theories to realistic strongly correlated electron systems.

On a categorial aspect of knowledge representation

NASA Astrophysics Data System (ADS)

Tataj, Emanuel; Mulawka, Jan; Nieznański, Edward

Adequate representation of data is crucial for modeling any type of data. To faithfully present and describe the relevant section of the world it is necessary to select the method that can easily be implemented on a computer system which will help in further description allowing reasoning. The main objective of this contribution is to present methods of knowledge representation using categorial approach. Next to identify the main advantages for computer implementation. Categorical aspect of knowledge representation is considered in semantic networks realisation. Such method borrows already known metaphysics properties for data modeling process. The potential topics of further development of categorical semantic networks implementations are also underlined.

An animated landscape representation of CD4+ T-cell differentiation, variability, and plasticity: Insights into the behavior of populations versus cells

PubMed Central

Rebhahn, Jonathan A; Deng, Nan; Sharma, Gaurav; Livingstone, Alexandra M; Huang, Sui; Mosmann, Tim R

2014-01-01

Recent advances in understanding CD4+ T-cell differentiation suggest that previous models of a few distinct, stable effector phenotypes were too simplistic. Although several well-characterized phenotypes are still recognized, some states display plasticity, and intermediate phenotypes exist. As a framework for reexamining these concepts, we use Waddington's landscape paradigm, augmented with explicit consideration of stochastic variations. Our animation program “LAVA” visualizes T-cell differentiation as cells moving across a landscape of hills and valleys, leading to attractor basins representing stable or semistable differentiation states. The model illustrates several principles, including: (i) cell populations may behave more predictably than individual cells; (ii) analogous to reticulate evolution, differentiation may proceed through a network of interconnected states, rather than a single well-defined pathway; (iii) relatively minor changes in the barriers between attractor basins can change the stability or plasticity of a population; (iv) intrapopulation variability of gene expression may be an important regulator of differentiation, rather than inconsequential noise; (v) the behavior of some populations may be defined mainly by the behavior of outlier cells. While not a quantitative representation of actual differentiation, our model is intended to provoke discussion of T-cell differentiation pathways, particularly highlighting a probabilistic view of transitions between states. PMID:24945794

Galen: a third generation terminology tool to support a multipurpose national coding system for surgical procedures.

PubMed

Trombert-Paviot, B; Rodrigues, J M; Rogers, J E; Baud, R; van der Haring, E; Rassinoux, A M; Abrial, V; Clavel, L; Idir, H

1999-01-01

GALEN has developed a new generation of terminology tools based on a language independent concept reference model using a compositional formalism allowing computer processing and multiple reuses. During the 4th framework program project Galen-In-Use we applied the modelling and the tools to the development of a new multipurpose coding system for surgical procedures (CCAM) in France. On one hand we contributed to a language independent knowledge repository for multicultural Europe. On the other hand we support the traditional process for creating a new coding system in medicine which is very much labour consuming by artificial intelligence tools using a medically oriented recursive ontology and natural language processing. We used an integrated software named CLAW to process French professional medical language rubrics produced by the national colleges of surgeons into intermediate dissections and to the Grail reference ontology model representation. From this language independent concept model representation on one hand we generate controlled French natural language to support the finalization of the linguistic labels in relation with the meanings of the conceptual system structure. On the other hand the classification manager of third generation proves to be very powerful to retrieve the initial professional rubrics with different categories of concepts within a semantic network.

Serial recall, word frequency, and mixed lists: the influence of item arrangement.

PubMed

Miller, Leonie M; Roodenrys, Steven

2012-11-01

Studies of the effect of word frequency in the serial recall task show that lists of high-frequency words are better recalled than lists of low-frequency words; however, when high- and low-frequency words are alternated within a list, there is no difference in the level of recall for the two types of words, and recall is intermediate between lists of pure frequency. This pattern has been argued to arise from the development of a network of activated long-term representations of list items that support the redintegration of all list items in a nondirectional and nonspecific way. More recently, it has been proposed that the frequency effect might be a product of the coarticulation of items at word boundaries and their influence on rehearsal rather than a consequence of memory representations. The current work examines recall performance in mixed lists of an equal number of high- and low-frequency items arranged in contiguous segments (i.e., HHHLLL and LLLHHH), under quiet and articulatory suppression conditions, to test whether the effect is (a) nondirectional and (b) dependent on articulatory processes. These experiments demonstrate that neither explanation is satisfactory, although the results suggest that the effect is mnemonic. A language-based approach to short-term memory is favored with emphasis on the role of speech production processes at output.

A network of spiking neurons for computing sparse representations in an energy efficient way

PubMed Central

Hu, Tao; Genkin, Alexander; Chklovskii, Dmitri B.

2013-01-01

Computing sparse redundant representations is an important problem both in applied mathematics and neuroscience. In many applications, this problem must be solved in an energy efficient way. Here, we propose a hybrid distributed algorithm (HDA), which solves this problem on a network of simple nodes communicating via low-bandwidth channels. HDA nodes perform both gradient-descent-like steps on analog internal variables and coordinate-descent-like steps via quantized external variables communicated to each other. Interestingly, such operation is equivalent to a network of integrate-and-fire neurons, suggesting that HDA may serve as a model of neural computation. We compare the numerical performance of HDA with existing algorithms and show that in the asymptotic regime the representation error of HDA decays with time, t, as 1/t. We show that HDA is stable against time-varying noise, specifically, the representation error decays as 1/t for Gaussian white noise. PMID:22920853

A network of spiking neurons for computing sparse representations in an energy-efficient way.

PubMed

Hu, Tao; Genkin, Alexander; Chklovskii, Dmitri B

2012-11-01

Computing sparse redundant representations is an important problem in both applied mathematics and neuroscience. In many applications, this problem must be solved in an energy-efficient way. Here, we propose a hybrid distributed algorithm (HDA), which solves this problem on a network of simple nodes communicating by low-bandwidth channels. HDA nodes perform both gradient-descent-like steps on analog internal variables and coordinate-descent-like steps via quantized external variables communicated to each other. Interestingly, the operation is equivalent to a network of integrate-and-fire neurons, suggesting that HDA may serve as a model of neural computation. We show that the numerical performance of HDA is on par with existing algorithms. In the asymptotic regime, the representation error of HDA decays with time, t, as 1/t. HDA is stable against time-varying noise; specifically, the representation error decays as 1/√t for gaussian white noise.

Bilingual Lexical Interactions in an Unsupervised Neural Network Model

ERIC Educational Resources Information Center

Zhao, Xiaowei; Li, Ping

2010-01-01

In this paper we present an unsupervised neural network model of bilingual lexical development and interaction. We focus on how the representational structures of the bilingual lexicons can emerge, develop, and interact with each other as a function of the learning history. The results show that: (1) distinct representations for the two lexicons…

Spreading Activation in an Attractor Network with Latching Dynamics: Automatic Semantic Priming Revisited

ERIC Educational Resources Information Center

Lerner, Itamar; Bentin, Shlomo; Shriki, Oren

2012-01-01

Localist models of spreading activation (SA) and models assuming distributed representations offer very different takes on semantic priming, a widely investigated paradigm in word recognition and semantic memory research. In this study, we implemented SA in an attractor neural network model with distributed representations and created a unified…

Forming Tool Use Representations: A Neurophysiological Investigation into Tool Exposure

ERIC Educational Resources Information Center

Mizelle, John Christopher; Tang, Teresa; Pirouz, Nikta; Wheaton, Lewis A.

2011-01-01

Prior work has identified a common left parietofrontal network for storage of tool-related information for various tasks. How these representations become established within this network on the basis of different modes of exposure is unclear. Here, healthy subjects engaged in physical practice (direct exposure) with familiar and unfamiliar tools.…

Disentangling representations of shape and action components in the tool network.

PubMed

Wang, Xiaoying; Zhuang, Tonghe; Shen, Jiasi; Bi, Yanchao

2018-05-30

Shape and how they should be used are two key components of our knowledge about tools. Viewing tools preferentially activated a frontoparietal and occipitotemporal network, with dorsal regions implicated in computation of tool-related actions and ventral areas in shape representation. As shape and manners of manipulation are highly correlated for daily tools, whether they are independently represented in different regions remains inconclusive. In the current study, we collected fMRI data when participants viewed blocks of pictures of four daily tools (i.e., paintbrush, corkscrew, screwdriver, razor) where shape and action (manner of manipulation for functional use) were orthogonally manipulated, to tease apart these two dimensions. Behavioral similarity judgments tapping on object shape and finer aspects of actions (i.e., manners of motion, magnitude of arm movement, configuration of hand) were also collected to further disentangle the representation of object shape and different action components. Information analysis and representational similarity analysis were conducted on regional neural activation patterns of the tool-preferring network. In both analyses, the bilateral lateral occipitotemporal cortex showed robust shape representations but could not effectively distinguish between tool-use actions. The frontal and precentral regions represented kinematic action components, whereas the left parietal region (in information analyses) exhibited coding of both shape and tool-use action. By teasing apart shape and action components, we found both dissociation and association of them within the tool network. Taken together, our study disentangles representations for object shape from finer tool-use action components in the tool network, revealing the potential dissociable roles different tool-preferring regions play in tool processing. Copyright © 2018 Elsevier Ltd. All rights reserved.

The small heat shock protein Hsp27 affects assembly dynamics and structure of keratin intermediate filament networks.

PubMed

Kayser, Jona; Haslbeck, Martin; Dempfle, Lisa; Krause, Maike; Grashoff, Carsten; Buchner, Johannes; Herrmann, Harald; Bausch, Andreas R

2013-10-15

The mechanical properties of living cells are essential for many processes. They are defined by the cytoskeleton, a composite network of protein fibers. Thus, the precise control of its architecture is of paramount importance. Our knowledge about the molecular and physical mechanisms defining the network structure remains scarce, especially for the intermediate filament cytoskeleton. Here, we investigate the effect of small heat shock proteins on the keratin 8/18 intermediate filament cytoskeleton using a well-controlled model system of reconstituted keratin networks. We demonstrate that Hsp27 severely alters the structure of such networks by changing their assembly dynamics. Furthermore, the C-terminal tail domain of keratin 8 is shown to be essential for this effect. Combining results from fluorescence and electron microscopy with data from analytical ultracentrifugation reveals the crucial role of kinetic trapping in keratin network formation. Copyright © 2013 Biophysical Society. Published by Elsevier Inc. All rights reserved.

User-based representation of time-resolved multimodal public transportation networks.

PubMed

Alessandretti, Laura; Karsai, Márton; Gauvin, Laetitia

2016-07-01

Multimodal transportation systems, with several coexisting services like bus, tram and metro, can be represented as time-resolved multilayer networks where the different transportation modes connecting the same set of nodes are associated with distinct network layers. Their quantitative description became possible recently due to openly accessible datasets describing the geo-localized transportation dynamics of large urban areas. Advancements call for novel analytics, which combines earlier established methods and exploits the inherent complexity of the data. Here, we provide a novel user-based representation of public transportation systems, which combines representations, accounting for the presence of multiple lines and reducing the effect of spatial embeddedness, while considering the total travel time, its variability across the schedule, and taking into account the number of transfers necessary. After the adjustment of earlier techniques to the novel representation framework, we analyse the public transportation systems of several French municipal areas and identify hidden patterns of privileged connections. Furthermore, we study their efficiency as compared to the commuting flow. The proposed representation could help to enhance resilience of local transportation systems to provide better design policies for future developments.

User-based representation of time-resolved multimodal public transportation networks

PubMed Central

Alessandretti, Laura; Gauvin, Laetitia

2016-01-01

Multimodal transportation systems, with several coexisting services like bus, tram and metro, can be represented as time-resolved multilayer networks where the different transportation modes connecting the same set of nodes are associated with distinct network layers. Their quantitative description became possible recently due to openly accessible datasets describing the geo-localized transportation dynamics of large urban areas. Advancements call for novel analytics, which combines earlier established methods and exploits the inherent complexity of the data. Here, we provide a novel user-based representation of public transportation systems, which combines representations, accounting for the presence of multiple lines and reducing the effect of spatial embeddedness, while considering the total travel time, its variability across the schedule, and taking into account the number of transfers necessary. After the adjustment of earlier techniques to the novel representation framework, we analyse the public transportation systems of several French municipal areas and identify hidden patterns of privileged connections. Furthermore, we study their efficiency as compared to the commuting flow. The proposed representation could help to enhance resilience of local transportation systems to provide better design policies for future developments. PMID:27493773

Representations and evolutionary operators for the scheduling of pump operations in water distribution networks.

PubMed

López-Ibáñez, Manuel; Prasad, T Devi; Paechter, Ben

2011-01-01

Reducing the energy consumption of water distribution networks has never had more significance. The greatest energy savings can be obtained by carefully scheduling the operations of pumps. Schedules can be defined either implicitly, in terms of other elements of the network such as tank levels; or explicitly, by specifying the time during which each pump is on/off. The traditional representation of explicit schedules is a string of binary values with each bit representing pump on/off status during a particular time interval. In this paper, we formally define and analyze two new explicit representations based on time-controlled triggers, where the maximum number of pump switches is established beforehand and the schedule may contain fewer than the maximum number of switches. In these representations, a pump schedule is divided into a series of integers with each integer representing the number of hours for which a pump is active/inactive. This reduces the number of potential schedules compared to the binary representation, and allows the algorithm to operate on the feasible region of the search space. We propose evolutionary operators for these two new representations. The new representations and their corresponding operations are compared with the two most-used representations in pump scheduling, namely, binary representation and level-controlled triggers. A detailed statistical analysis of the results indicates which parameters have the greatest effect on the performance of evolutionary algorithms. The empirical results show that an evolutionary algorithm using the proposed representations is an improvement over the results obtained by a recent state of the art hybrid genetic algorithm for pump scheduling using level-controlled triggers.

Use of model calibration to achieve high accuracy in analysis of computer networks

DOEpatents

Frogner, Bjorn; Guarro, Sergio; Scharf, Guy

2004-05-11

A system and method are provided for creating a network performance prediction model, and calibrating the prediction model, through application of network load statistical analyses. The method includes characterizing the measured load on the network, which may include background load data obtained over time, and may further include directed load data representative of a transaction-level event. Probabilistic representations of load data are derived to characterize the statistical persistence of the network performance variability and to determine delays throughout the network. The probabilistic representations are applied to the network performance prediction model to adapt the model for accurate prediction of network performance. Certain embodiments of the method and system may be used for analysis of the performance of a distributed application characterized as data packet streams.

Social Representations of Hero and Everyday Hero: A Network Study from Representative Samples

PubMed Central

Keczer, Zsolt; File, Bálint; Orosz, Gábor; Zimbardo, Philip G.

2016-01-01

The psychological investigation of heroism is relatively new. At this stage, inductive methods can shed light on its main aspects. Therefore, we examined the social representations of Hero and Everyday Hero by collecting word associations from two separate representative samples in Hungary. We constructed two networks from these word associations. The results show that the social representation of Hero is more centralized and it cannot be divided into smaller units. The network of Everyday Hero is divided into five units and the significance moves from abstract hero characteristics to concrete social roles and occupations exhibiting pro-social values. We also created networks from the common associations of Hero and Everyday Hero. The structures of these networks show a moderate similarity and the connections are more balanced in case of Everyday Hero. While heroism in general can be the source of inspiration, the promotion of everyday heroism can be more successful in encouraging ordinary people to recognize their own potential for heroic behavior. PMID:27525418

Image reconstruction by domain-transform manifold learning.

PubMed

Zhu, Bo; Liu, Jeremiah Z; Cauley, Stephen F; Rosen, Bruce R; Rosen, Matthew S

2018-03-21

Image reconstruction is essential for imaging applications across the physical and life sciences, including optical and radar systems, magnetic resonance imaging, X-ray computed tomography, positron emission tomography, ultrasound imaging and radio astronomy. During image acquisition, the sensor encodes an intermediate representation of an object in the sensor domain, which is subsequently reconstructed into an image by an inversion of the encoding function. Image reconstruction is challenging because analytic knowledge of the exact inverse transform may not exist a priori, especially in the presence of sensor non-idealities and noise. Thus, the standard reconstruction approach involves approximating the inverse function with multiple ad hoc stages in a signal processing chain, the composition of which depends on the details of each acquisition strategy, and often requires expert parameter tuning to optimize reconstruction performance. Here we present a unified framework for image reconstruction-automated transform by manifold approximation (AUTOMAP)-which recasts image reconstruction as a data-driven supervised learning task that allows a mapping between the sensor and the image domain to emerge from an appropriate corpus of training data. We implement AUTOMAP with a deep neural network and exhibit its flexibility in learning reconstruction transforms for various magnetic resonance imaging acquisition strategies, using the same network architecture and hyperparameters. We further demonstrate that manifold learning during training results in sparse representations of domain transforms along low-dimensional data manifolds, and observe superior immunity to noise and a reduction in reconstruction artefacts compared with conventional handcrafted reconstruction methods. In addition to improving the reconstruction performance of existing acquisition methodologies, we anticipate that AUTOMAP and other learned reconstruction approaches will accelerate the development of new acquisition strategies across imaging modalities.

Image reconstruction by domain-transform manifold learning

NASA Astrophysics Data System (ADS)

Zhu, Bo; Liu, Jeremiah Z.; Cauley, Stephen F.; Rosen, Bruce R.; Rosen, Matthew S.

2018-03-01

Image reconstruction is essential for imaging applications across the physical and life sciences, including optical and radar systems, magnetic resonance imaging, X-ray computed tomography, positron emission tomography, ultrasound imaging and radio astronomy. During image acquisition, the sensor encodes an intermediate representation of an object in the sensor domain, which is subsequently reconstructed into an image by an inversion of the encoding function. Image reconstruction is challenging because analytic knowledge of the exact inverse transform may not exist a priori, especially in the presence of sensor non-idealities and noise. Thus, the standard reconstruction approach involves approximating the inverse function with multiple ad hoc stages in a signal processing chain, the composition of which depends on the details of each acquisition strategy, and often requires expert parameter tuning to optimize reconstruction performance. Here we present a unified framework for image reconstruction—automated transform by manifold approximation (AUTOMAP)—which recasts image reconstruction as a data-driven supervised learning task that allows a mapping between the sensor and the image domain to emerge from an appropriate corpus of training data. We implement AUTOMAP with a deep neural network and exhibit its flexibility in learning reconstruction transforms for various magnetic resonance imaging acquisition strategies, using the same network architecture and hyperparameters. We further demonstrate that manifold learning during training results in sparse representations of domain transforms along low-dimensional data manifolds, and observe superior immunity to noise and a reduction in reconstruction artefacts compared with conventional handcrafted reconstruction methods. In addition to improving the reconstruction performance of existing acquisition methodologies, we anticipate that AUTOMAP and other learned reconstruction approaches will accelerate the development of new acquisition strategies across imaging modalities.

Vector-based navigation using grid-like representations in artificial agents.

PubMed

Banino, Andrea; Barry, Caswell; Uria, Benigno; Blundell, Charles; Lillicrap, Timothy; Mirowski, Piotr; Pritzel, Alexander; Chadwick, Martin J; Degris, Thomas; Modayil, Joseph; Wayne, Greg; Soyer, Hubert; Viola, Fabio; Zhang, Brian; Goroshin, Ross; Rabinowitz, Neil; Pascanu, Razvan; Beattie, Charlie; Petersen, Stig; Sadik, Amir; Gaffney, Stephen; King, Helen; Kavukcuoglu, Koray; Hassabis, Demis; Hadsell, Raia; Kumaran, Dharshan

2018-05-01

Deep neural networks have achieved impressive successes in fields ranging from object recognition to complex games such as Go 1,2 . Navigation, however, remains a substantial challenge for artificial agents, with deep neural networks trained by reinforcement learning 3-5 failing to rival the proficiency of mammalian spatial behaviour, which is underpinned by grid cells in the entorhinal cortex 6 . Grid cells are thought to provide a multi-scale periodic representation that functions as a metric for coding space 7,8 and is critical for integrating self-motion (path integration) 6,7,9 and planning direct trajectories to goals (vector-based navigation) 7,10,11 . Here we set out to leverage the computational functions of grid cells to develop a deep reinforcement learning agent with mammal-like navigational abilities. We first trained a recurrent network to perform path integration, leading to the emergence of representations resembling grid cells, as well as other entorhinal cell types 12 . We then showed that this representation provided an effective basis for an agent to locate goals in challenging, unfamiliar, and changeable environments-optimizing the primary objective of navigation through deep reinforcement learning. The performance of agents endowed with grid-like representations surpassed that of an expert human and comparison agents, with the metric quantities necessary for vector-based navigation derived from grid-like units within the network. Furthermore, grid-like representations enabled agents to conduct shortcut behaviours reminiscent of those performed by mammals. Our findings show that emergent grid-like representations furnish agents with a Euclidean spatial metric and associated vector operations, providing a foundation for proficient navigation. As such, our results support neuroscientific theories that see grid cells as critical for vector-based navigation 7,10,11 , demonstrating that the latter can be combined with path-based strategies to support navigation in challenging environments.

Methods for calculating Protection Equality for conservation planning

PubMed Central

Kuempel, Caitlin D.; McGowan, Jennifer; Beger, Maria; Possingham, Hugh P.

2017-01-01

Protected Areas (PAs) are a central part of biodiversity conservation strategies around the world. Today, PAs cover c15% of the Earth’s land mass and c3% of the global oceans. These numbers are expected to grow rapidly to meet the Convention on Biological Diversity’s Aichi Biodiversity target 11, which aims to see 17% and 10% of terrestrial and marine biomes protected, respectively, by 2020. This target also requires countries to ensure that PAs protect an “ecologically representative” sample of their biodiversity. At present, there is no clear definition of what desirable ecological representation looks like, or guidelines of how to standardize its assessment as the PA estate grows. We propose a systematic approach to measure ecological representation in PA networks using the Protection Equality (PE) metric, which measures how equally ecological features, such as habitats, within a country’s borders are protected. We present an R package and two Protection Equality (PE) measures; proportional to area PE, and fixed area PE, which measure the representativeness of a country’s PA network. We illustrate the PE metrics with two case studies: coral reef protection across countries and ecoregions in the Coral Triangle, and representation of ecoregions of six of the largest countries in the world. Our results provide repeatable transparency to the issue of representation in PA networks and provide a starting point for further discussion, evaluation and testing of representation metrics. They also highlight clear shortcomings in current PA networks, particularly where they are biased towards certain assemblage types or habitats. Our proposed metrics should be used to report on measuring progress towards the representation component of Aichi Target 11. The PE metrics can be used to measure the representation of any kind of ecological feature including: species, ecoregions, processes or habitats. PMID:28199341

Methods for calculating Protection Equality for conservation planning.

PubMed

Chauvenet, Alienor L M; Kuempel, Caitlin D; McGowan, Jennifer; Beger, Maria; Possingham, Hugh P

2017-01-01

Protected Areas (PAs) are a central part of biodiversity conservation strategies around the world. Today, PAs cover c15% of the Earth's land mass and c3% of the global oceans. These numbers are expected to grow rapidly to meet the Convention on Biological Diversity's Aichi Biodiversity target 11, which aims to see 17% and 10% of terrestrial and marine biomes protected, respectively, by 2020. This target also requires countries to ensure that PAs protect an "ecologically representative" sample of their biodiversity. At present, there is no clear definition of what desirable ecological representation looks like, or guidelines of how to standardize its assessment as the PA estate grows. We propose a systematic approach to measure ecological representation in PA networks using the Protection Equality (PE) metric, which measures how equally ecological features, such as habitats, within a country's borders are protected. We present an R package and two Protection Equality (PE) measures; proportional to area PE, and fixed area PE, which measure the representativeness of a country's PA network. We illustrate the PE metrics with two case studies: coral reef protection across countries and ecoregions in the Coral Triangle, and representation of ecoregions of six of the largest countries in the world. Our results provide repeatable transparency to the issue of representation in PA networks and provide a starting point for further discussion, evaluation and testing of representation metrics. They also highlight clear shortcomings in current PA networks, particularly where they are biased towards certain assemblage types or habitats. Our proposed metrics should be used to report on measuring progress towards the representation component of Aichi Target 11. The PE metrics can be used to measure the representation of any kind of ecological feature including: species, ecoregions, processes or habitats.

An exploration of alternative visualisations of the basic helix-loop-helix protein interaction network

PubMed Central

Holden, Brian J; Pinney, John W; Lovell, Simon C; Amoutzias, Grigoris D; Robertson, David L

2007-01-01

Background Alternative representations of biochemical networks emphasise different aspects of the data and contribute to the understanding of complex biological systems. In this study we present a variety of automated methods for visualisation of a protein-protein interaction network, using the basic helix-loop-helix (bHLH) family of transcription factors as an example. Results Network representations that arrange nodes (proteins) according to either continuous or discrete information are investigated, revealing the existence of protein sub-families and the retention of interactions following gene duplication events. Methods of network visualisation in conjunction with a phylogenetic tree are presented, highlighting the evolutionary relationships between proteins, and clarifying the context of network hubs and interaction clusters. Finally, an optimisation technique is used to create a three-dimensional layout of the phylogenetic tree upon which the protein-protein interactions may be projected. Conclusion We show that by incorporating secondary genomic, functional or phylogenetic information into network visualisation, it is possible to move beyond simple layout algorithms based on network topology towards more biologically meaningful representations. These new visualisations can give structure to complex networks and will greatly help in interpreting their evolutionary origins and functional implications. Three open source software packages (InterView, TVi and OptiMage) implementing our methods are available. PMID:17683601

Compression embedding

DOEpatents

Sandford, M.T. II; Handel, T.G.; Bradley, J.N.

1998-03-10

A method of embedding auxiliary information into the digital representation of host data created by a lossy compression technique is disclosed. The method applies to data compressed with lossy algorithms based on series expansion, quantization to a finite number of symbols, and entropy coding. Lossy compression methods represent the original data as integer indices having redundancy and uncertainty in value by one unit. Indices which are adjacent in value are manipulated to encode auxiliary data. By a substantially reverse process, the embedded auxiliary data can be retrieved easily by an authorized user. Lossy compression methods use loss-less compressions known also as entropy coding, to reduce to the final size the intermediate representation as indices. The efficiency of the compression entropy coding, known also as entropy coding is increased by manipulating the indices at the intermediate stage in the manner taught by the method. 11 figs.

Compression embedding

DOEpatents

Sandford, II, Maxwell T.; Handel, Theodore G.; Bradley, Jonathan N.

1998-01-01

A method of embedding auxiliary information into the digital representation of host data created by a lossy compression technique. The method applies to data compressed with lossy algorithms based on series expansion, quantization to a finite number of symbols, and entropy coding. Lossy compression methods represent the original data as integer indices having redundancy and uncertainty in value by one unit. Indices which are adjacent in value are manipulated to encode auxiliary data. By a substantially reverse process, the embedded auxiliary data can be retrieved easily by an authorized user. Lossy compression methods use loss-less compressions known also as entropy coding, to reduce to the final size the intermediate representation as indices. The efficiency of the compression entropy coding, known also as entropy coding is increased by manipulating the indices at the intermediate stage in the manner taught by the method.

Reserve networks based on richness hotspots and representation vary with scale.

PubMed

Shriner, Susan A; Wilson, Kenneth R; Flather, Curtis H

2006-10-01

While the importance of spatial scale in ecology is well established, few studies have investigated the impact of data grain on conservation planning outcomes. In this study, we compared species richness hotspot and representation networks developed at five grain sizes. We used species distribution maps for mammals and birds developed by the Arizona and New Mexico Gap Analysis Programs (GAP) to produce 1-km2, 100-kmn2, 625-km2, 2500-km2, and 10,000-km2 grid cell resolution distribution maps. We used these distribution maps to generate species richness and hotspot (95th quantile) maps for each taxon in each state. Species composition information at each grain size was used to develop two types of representation networks using the reserve selection software MARXAN. Reserve selection analyses were restricted to Arizona birds due to considerable computation requirements. We used MARXAN to create best reserve networks based on the minimum area required to represent each species at least once and equal area networks based on irreplaceability values. We also measured the median area of each species' distribution included in hotspot (mammals and birds of Arizona and New Mexico) and irreplaceability (Arizona birds) networks across all species. Mean area overlap between richness hotspot reserves identified at the five grain sizes was 29% (grand mean for four within-taxon/state comparisons), mean overlap for irreplaceability reserve networks was 32%, and mean overlap for best reserve networks was 53%. Hotspots for mammals and birds showed low overlap with a mean of 30%. Comparison of hotspots and irreplaceability networks showed very low overlap with a mean of 13%. For hotspots, median species distribution area protected within reserves declined monotonically from a high of 11% for 1-km2 networks down to 6% for 10,000-km2 networks. Irreplaceability networks showed a similar, but more variable, pattern of decline. This work clearly shows that map resolution has a profound effect on conservation planning outcomes and that hotspot and representation outcomes may be strikingly dissimilar. Thus, conservation planning is scale dependent, such that reserves developed using coarse-grained data do not subsume fine-grained reserves. Moreover, preserving both full species representation and species rich areas may require combined reserve design strategies.

Deep learning of orthographic representations in baboons.

PubMed

Hannagan, Thomas; Ziegler, Johannes C; Dufau, Stéphane; Fagot, Joël; Grainger, Jonathan

2014-01-01

What is the origin of our ability to learn orthographic knowledge? We use deep convolutional networks to emulate the primate's ventral visual stream and explore the recent finding that baboons can be trained to discriminate English words from nonwords. The networks were exposed to the exact same sequence of stimuli and reinforcement signals as the baboons in the experiment, and learned to map real visual inputs (pixels) of letter strings onto binary word/nonword responses. We show that the networks' highest levels of representations were indeed sensitive to letter combinations as postulated in our previous research. The model also captured the key empirical findings, such as generalization to novel words, along with some intriguing inter-individual differences. The present work shows the merits of deep learning networks that can simulate the whole processing chain all the way from the visual input to the response while allowing researchers to analyze the complex representations that emerge during the learning process.

Temporal and Motor Representation of Rhythm in Fronto-Parietal Cortical Areas: An fMRI Study

PubMed Central

Konoike, Naho; Kotozaki, Yuka; Jeong, Hyeonjeong; Miyazaki, Atsuko; Sakaki, Kohei; Shinada, Takamitsu; Sugiura, Motoaki; Kawashima, Ryuta; Nakamura, Katsuki

2015-01-01

When sounds occur with temporally structured patterns, we can feel a rhythm. To memorize a rhythm, perception of its temporal patterns and organization of them into a hierarchically structured sequence are necessary. On the other hand, rhythm perception can often cause unintentional body movements. Thus, we hypothesized that rhythm information can be manifested in two different ways; temporal and motor representations. The motor representation depends on effectors, such as the finger or foot, whereas the temporal representation is effector-independent. We tested our hypothesis with a working memory paradigm to elucidate neuronal correlates of temporal or motor representation of rhythm and to reveal the neural networks associated with these representations. We measured brain activity by fMRI while participants memorized rhythms and reproduced them by tapping with the right finger, left finger, or foot, or by articulation. The right inferior frontal gyrus and the inferior parietal lobule exhibited significant effector-independent activations during encoding and retrieval of rhythm information, whereas the left inferior parietal lobule and supplementary motor area (SMA) showed effector-dependent activations during retrieval. These results suggest that temporal sequences of rhythm are probably represented in the right fronto-parietal network, whereas motor sequences of rhythm can be represented in the SMA-parietal network. PMID:26076024

In defense of abstract conceptual representations.

PubMed

Binder, Jeffrey R

2016-08-01

An extensive program of research in the past 2 decades has focused on the role of modal sensory, motor, and affective brain systems in storing and retrieving concept knowledge. This focus has led in some circles to an underestimation of the need for more abstract, supramodal conceptual representations in semantic cognition. Evidence for supramodal processing comes from neuroimaging work documenting a large, well-defined cortical network that responds to meaningful stimuli regardless of modal content. The nodes in this network correspond to high-level "convergence zones" that receive broadly crossmodal input and presumably process crossmodal conjunctions. It is proposed that highly conjunctive representations are needed for several critical functions, including capturing conceptual similarity structure, enabling thematic associative relationships independent of conceptual similarity, and providing efficient "chunking" of concept representations for a range of higher order tasks that require concepts to be configured as situations. These hypothesized functions account for a wide range of neuroimaging results showing modulation of the supramodal convergence zone network by associative strength, lexicality, familiarity, imageability, frequency, and semantic compositionality. The evidence supports a hierarchical model of knowledge representation in which modal systems provide a mechanism for concept acquisition and serve to ground individual concepts in external reality, whereas broadly conjunctive, supramodal representations play an equally important role in concept association and situation knowledge.

Benefits and Challenges of Scaling Up Expansion of Marine Protected Area Networks in the Verde Island Passage, Central Philippines.

PubMed

Horigue, Vera; Pressey, Robert L; Mills, Morena; Brotánková, Jana; Cabral, Reniel; Andréfouët, Serge

2015-01-01

Locally-established marine protected areas (MPAs) have been proven to achieve local-scale fisheries and conservation objectives. However, since many of these MPAs were not designed to form ecologically-connected networks, their contributions to broader-scale goals such as complementarity and connectivity can be limited. In contrast, integrated networks of MPAs designed with systematic conservation planning are assumed to be more effective--ecologically, socially, and economically--than collections of locally-established MPAs. There is, however, little empirical evidence that clearly demonstrates the supposed advantages of systematic MPA networks. A key reason is the poor record of implementation of systematic plans attributable to lack of local buy-in. An intermediate scenario for the expansion of MPAs is scaling up of local decisions, whereby locally-driven MPA initiatives are coordinated through collaborative partnerships among local governments and their communities. Coordination has the potential to extend the benefits of individual MPAs and perhaps to approach the potential benefits offered by systematic MPA networks. We evaluated the benefits of scaling up local MPAs to form networks by simulating seven expansion scenarios for MPAs in the Verde Island Passage, central Philippines. The scenarios were: uncoordinated community-based establishment of MPAs; two scenarios reflecting different levels of coordinated MPA expansion through collaborative partnerships; and four scenarios guided by systematic conservation planning with different contexts for governance. For each scenario, we measured benefits through time in terms of achievement of objectives for representation of marine habitats. We found that: in any governance context, systematic networks were more efficient than non-systematic ones; systematic networks were more efficient in broader governance contexts; and, contrary to expectations but with caveats, the uncoordinated scenario was slightly more efficient than the coordinated scenarios. Overall, however, coordinated MPA networks have the potential to be more efficient than the uncoordinated ones, especially when coordinated planning uses systematic methods.

Benefits and Challenges of Scaling Up Expansion of Marine Protected Area Networks in the Verde Island Passage, Central Philippines

PubMed Central

Horigue, Vera; Pressey, Robert L.; Mills, Morena; Brotánková, Jana; Cabral, Reniel; Andréfouët, Serge

2015-01-01

Locally-established marine protected areas (MPAs) have been proven to achieve local-scale fisheries and conservation objectives. However, since many of these MPAs were not designed to form ecologically-connected networks, their contributions to broader-scale goals such as complementarity and connectivity can be limited. In contrast, integrated networks of MPAs designed with systematic conservation planning are assumed to be more effective—ecologically, socially, and economically—than collections of locally-established MPAs. There is, however, little empirical evidence that clearly demonstrates the supposed advantages of systematic MPA networks. A key reason is the poor record of implementation of systematic plans attributable to lack of local buy-in. An intermediate scenario for the expansion of MPAs is scaling up of local decisions, whereby locally-driven MPA initiatives are coordinated through collaborative partnerships among local governments and their communities. Coordination has the potential to extend the benefits of individual MPAs and perhaps to approach the potential benefits offered by systematic MPA networks. We evaluated the benefits of scaling up local MPAs to form networks by simulating seven expansion scenarios for MPAs in the Verde Island Passage, central Philippines. The scenarios were: uncoordinated community-based establishment of MPAs; two scenarios reflecting different levels of coordinated MPA expansion through collaborative partnerships; and four scenarios guided by systematic conservation planning with different contexts for governance. For each scenario, we measured benefits through time in terms of achievement of objectives for representation of marine habitats. We found that: in any governance context, systematic networks were more efficient than non-systematic ones; systematic networks were more efficient in broader governance contexts; and, contrary to expectations but with caveats, the uncoordinated scenario was slightly more efficient than the coordinated scenarios. Overall, however, coordinated MPA networks have the potential to be more efficient than the uncoordinated ones, especially when coordinated planning uses systematic methods. PMID:26288089

The neural representation of social networks.

PubMed

Weaverdyck, Miriam E; Parkinson, Carolyn

2018-05-24

The computational demands associated with navigating large, complexly bonded social groups are thought to have significantly shaped human brain evolution. Yet, research on social network representation and cognitive neuroscience have progressed largely independently. Thus, little is known about how the human brain encodes the structure of the social networks in which it is embedded. This review highlights recent work seeking to bridge this gap in understanding. While the majority of research linking social network analysis and neuroimaging has focused on relating neuroanatomy to social network size, researchers have begun to define the neural architecture that encodes social network structure, cognitive and behavioral consequences of encoding this information, and individual differences in how people represent the structure of their social world. Copyright © 2018 Elsevier Ltd. All rights reserved.

Control of Synchronization Regimes in Networks of Mobile Interacting Agents

NASA Astrophysics Data System (ADS)

Perez-Diaz, Fernando; Zillmer, Ruediger; Groß, Roderich

2017-05-01

We investigate synchronization in a population of mobile pulse-coupled agents with a view towards implementations in swarm-robotics systems and mobile sensor networks. Previous theoretical approaches dealt with range and nearest-neighbor interactions. In the latter case, a synchronization-hindering regime for intermediate agent mobility is found. We investigate the robustness of this intermediate regime under practical scenarios. We show that synchronization in the intermediate regime can be predicted by means of a suitable metric of the phase response curve. Furthermore, we study more-realistic K -nearest-neighbor and cone-of-vision interactions, showing that it is possible to control the extent of the synchronization-hindering region by appropriately tuning the size of the neighborhood. To assess the effect of noise, we analyze the propagation of perturbations over the network and draw an analogy between the response in the hindering regime and stable chaos. Our findings reveal the conditions for the control of clock or activity synchronization of agents with intermediate mobility. In addition, the emergence of the intermediate regime is validated experimentally using a swarm of physical robots interacting with cone-of-vision interactions.

Spatial analysis of bus transport networks using network theory

NASA Astrophysics Data System (ADS)

Shanmukhappa, Tanuja; Ho, Ivan Wang-Hei; Tse, Chi Kong

2018-07-01

In this paper, we analyze the bus transport network (BTN) structure considering the spatial embedding of the network for three cities, namely, Hong Kong (HK), London (LD), and Bengaluru (BL). We propose a novel approach called supernode graph structuring for modeling the bus transport network. A static demand estimation procedure is proposed to assign the node weights by considering the points of interests (POIs) and the population distribution in the city over various localized zones. In addition, the end-to-end delay is proposed as a parameter to measure the topological efficiency of the bus networks instead of the shortest distance measure used in previous works. With the aid of supernode graph representation, important network parameters are analyzed for the directed, weighted and geo-referenced bus transport networks. It is observed that the supernode concept has significant advantage in analyzing the inherent topological behavior. For instance, the scale-free and small-world behavior becomes evident with supernode representation as compared to conventional or regular graph representation for the Hong Kong network. Significant improvement in clustering, reduction in path length, and increase in centrality values are observed in all the three networks with supernode representation. The correlation between topologically central nodes and the geographically central nodes reveals the interesting fact that the proposed static demand estimation method for assigning node weights aids in better identifying the geographically significant nodes in the network. The impact of these geographically significant nodes on the local traffic behavior is demonstrated by simulation using the SUMO (Simulation of Urban Mobility) tool which is also supported by real-world empirical data, and our results indicate that the traffic speed around a particular bus stop can reach a jammed state from a free flow state due to the presence of these geographically important nodes. A comparison of the simulation and the empirical data provides useful information on how bus operators can better plan their routes and deploy stops considering the geographically significant nodes.

Centrality in earthquake multiplex networks

NASA Astrophysics Data System (ADS)

Lotfi, Nastaran; Darooneh, Amir Hossein; Rodrigues, Francisco A.

2018-06-01

Seismic time series has been mapped as a complex network, where a geographical region is divided into square cells that represent the nodes and connections are defined according to the sequence of earthquakes. In this paper, we map a seismic time series to a temporal network, described by a multiplex network, and characterize the evolution of the network structure in terms of the eigenvector centrality measure. We generalize previous works that considered the single layer representation of earthquake networks. Our results suggest that the multiplex representation captures better earthquake activity than methods based on single layer networks. We also verify that the regions with highest seismological activities in Iran and California can be identified from the network centrality analysis. The temporal modeling of seismic data provided here may open new possibilities for a better comprehension of the physics of earthquakes.

Identifying group discriminative and age regressive sub-networks from DTI-based connectivity via a unified framework of non-negative matrix factorization and graph embedding

PubMed Central

Ghanbari, Yasser; Smith, Alex R.; Schultz, Robert T.; Verma, Ragini

2014-01-01

Diffusion tensor imaging (DTI) offers rich insights into the physical characteristics of white matter (WM) fiber tracts and their development in the brain, facilitating a network representation of brain’s traffic pathways. Such a network representation of brain connectivity has provided a novel means of investigating brain changes arising from pathology, development or aging. The high dimensionality of these connectivity networks necessitates the development of methods that identify the connectivity building blocks or sub-network components that characterize the underlying variation in the population. In addition, the projection of the subject networks into the basis set provides a low dimensional representation of it, that teases apart different sources of variation in the sample, facilitating variation-specific statistical analysis. We propose a unified framework of non-negative matrix factorization and graph embedding for learning sub-network patterns of connectivity by their projective non-negative decomposition into a reconstructive basis set, as well as, additional basis sets representing variational sources in the population like age and pathology. The proposed framework is applied to a study of diffusion-based connectivity in subjects with autism that shows localized sparse sub-networks which mostly capture the changes related to pathology and developmental variations. PMID:25037933

A critical review of the allocentric spatial representation and its neural underpinnings: toward a network-based perspective

PubMed Central

Ekstrom, Arne D.; Arnold, Aiden E. G. F.; Iaria, Giuseppe

2014-01-01

While the widely studied allocentric spatial representation holds a special status in neuroscience research, its exact nature and neural underpinnings continue to be the topic of debate, particularly in humans. Here, based on a review of human behavioral research, we argue that allocentric representations do not provide the kind of map-like, metric representation one might expect based on past theoretical work. Instead, we suggest that almost all tasks used in past studies involve a combination of egocentric and allocentric representation, complicating both the investigation of the cognitive basis of an allocentric representation and the task of identifying a brain region specifically dedicated to it. Indeed, as we discuss in detail, past studies suggest numerous brain regions important to allocentric spatial memory in addition to the hippocampus, including parahippocampal, retrosplenial, and prefrontal cortices. We thus argue that although allocentric computations will often require the hippocampus, particularly those involving extracting details across temporally specific routes, the hippocampus is not necessary for all allocentric computations. We instead suggest that a non-aggregate network process involving multiple interacting brain areas, including hippocampus and extra-hippocampal areas such as parahippocampal, retrosplenial, prefrontal, and parietal cortices, better characterizes the neural basis of spatial representation during navigation. According to this model, an allocentric representation does not emerge from the computations of a single brain region (i.e., hippocampus) nor is it readily decomposable into additive computations performed by separate brain regions. Instead, an allocentric representation emerges from computations partially shared across numerous interacting brain regions. We discuss our non-aggregate network model in light of existing data and provide several key predictions for future experiments. PMID:25346679

Reserve networks based on richness hotspots and representation vary with scale

Treesearch

Susan A. Shriner; Kenneth R. Wilson; Curtis H. Flather

2006-01-01

While the importance of spatial scale in ecology is well established, few studies have investigated the impact of data grain on conservation planning outcomes. In this study, we compared species richness hotspot and representation networks developed at five grain sizes. We used species distribution maps for mammals and birds developed by the Arizona and New Mexico Gap...

Profile of High School Students’ Propositional Network Representation when Interpreting Convention Diagrams

NASA Astrophysics Data System (ADS)

Fatiha, M.; Rahmat, A.; Solihat, R.

2017-09-01

The delivery of concepts in studying Biology often represented through a diagram to easily makes student understand about Biology material. One way to knowing the students’ understanding about diagram can be seen from causal relationship that is constructed by student in the propositional network representation form. This research reveal the trend of students’ propositional network representation patterns when confronted with convention diagram. This descriptive research involved 32 students at one of senior high school in Bandung. The research data was acquired by worksheet that was filled by diagram and it was developed according on information processing standards. The result of this research revealed three propositional network representation patterns are linear relationship, simple reciprocal relationship, and complex reciprocal relationship. The dominating pattern is linear form that is simply connect some information components in diagram by 59,4% students, the reciprocal relationship form with medium level by 28,1% students while the complex reciprocal relationship by only 3,1% and the rest was students who failed to connect information components by 9,4%. Based on results, most of student only able to connect information components on the picture in linear form and a few student constructing reciprocal relationship between information components on convention diagram.

Neural Network Model For Fast Learning And Retrieval

NASA Astrophysics Data System (ADS)

Arsenault, Henri H.; Macukow, Bohdan

1989-05-01

An approach to learning in a multilayer neural network is presented. The proposed network learns by creating interconnections between the input layer and the intermediate layer. In one of the new storage prescriptions proposed, interconnections are excitatory (positive) only and the weights depend on the stored patterns. In the intermediate layer each mother cell is responsible for one stored pattern. Mutually interconnected neurons in the intermediate layer perform a winner-take-all operation, taking into account correlations between stored vectors. The performance of networks using this interconnection prescription is compared with two previously proposed schemes, one using inhibitory connections at the output and one using all-or-nothing interconnections. The network can be used as a content-addressable memory or as a symbolic substitution system that yields an arbitrarily defined output for any input. The training of a model to perform Boolean logical operations is also described. Computer simulations using the network as an autoassociative content-addressable memory show the model to be efficient. Content-addressable associative memories and neural logic modules can be combined to perform logic operations on highly corrupted data.

Investigating Trigonometric Representations in the Transition to College Mathematics

ERIC Educational Resources Information Center

Byers, Patricia

2010-01-01

This Ontario-based qualitative study examined secondary school and college textbooks' treatment of trigonometric representations in order to identify potential sources of student difficulties in the transition from secondary school to college mathematics. Analysis of networks, comprised of trigonometric representations, identified numerous issues…

Research in Knowledge Representation for Natural Language Understanding

DTIC Science & Technology

1980-11-01

artificial intelligence, natural language understanding , parsing, syntax, semantics, speaker meaning, knowledge representation, semantic networks...TinB PAGE map M W006 1Report No. 4513 L RESEARCH IN KNOWLEDGE REPRESENTATION FOR NATURAL LANGUAGE UNDERSTANDING Annual Report 1 September 1979 to 31... understanding , knowledge representation, and knowledge based inference. The work that we have been doing falls into three classes, successively motivated by

Research in Knowledge Representation for Natural Language Understanding.

DTIC Science & Technology

1984-09-01

TYPE OF REPORT & PERIOO COVERED RESEARCH IN KNOWLEDGE REPRESENTATION Annual Report FOR NATURAL LANGUAGE UNDERSTANDING 9/1/83 - 8/31/84 S. PERFORMING...nhaber) Artificial intelligence, natural language understanding , knowledge representation, semantics, semantic networks, KL-TWO, NIKL, belief and...attempting to understand and react to a complex, evolving situation. This report summarizes our research in knowledge representation and natural language

Statechart Analysis with Symbolic PathFinder

NASA Technical Reports Server (NTRS)

Pasareanu, Corina S.

2012-01-01

We report here on our on-going work that addresses the automated analysis and test case generation for software systems modeled using multiple Statechart formalisms. The work is motivated by large programs such as NASA Exploration, that involve multiple systems that interact via safety-critical protocols and are designed with different Statechart variants. To verify these safety-critical systems, we have developed Polyglot, a framework for modeling and analysis of model-based software written using different Statechart formalisms. Polyglot uses a common intermediate representation with customizable Statechart semantics and leverages the analysis and test generation capabilities of the Symbolic PathFinder tool. Polyglot is used as follows: First, the structure of the Statechart model (expressed in Matlab Stateflow or Rational Rhapsody) is translated into a common intermediate representation (IR). The IR is then translated into Java code that represents the structure of the model. The semantics are provided as "pluggable" modules.

Cytoskeleton in motion: the dynamics of keratin intermediate filaments in epithelia.

PubMed

Windoffer, Reinhard; Beil, Michael; Magin, Thomas M; Leube, Rudolf E

2011-09-05

Epithelia are exposed to multiple forms of stress. Keratin intermediate filaments are abundant in epithelia and form cytoskeletal networks that contribute to cell type-specific functions, such as adhesion, migration, and metabolism. A perpetual keratin filament turnover cycle supports these functions. This multistep process keeps the cytoskeleton in motion, facilitating rapid and protein biosynthesis-independent network remodeling while maintaining an intact network. The current challenge is to unravel the molecular mechanisms underlying the regulation of the keratin cycle in relation to actin and microtubule networks and in the context of epithelial tissue function.

Cytoskeleton in motion: the dynamics of keratin intermediate filaments in epithelia

PubMed Central

Windoffer, Reinhard; Beil, Michael; Magin, Thomas M.

2011-01-01

Epithelia are exposed to multiple forms of stress. Keratin intermediate filaments are abundant in epithelia and form cytoskeletal networks that contribute to cell type–specific functions, such as adhesion, migration, and metabolism. A perpetual keratin filament turnover cycle supports these functions. This multistep process keeps the cytoskeleton in motion, facilitating rapid and protein biosynthesis–independent network remodeling while maintaining an intact network. The current challenge is to unravel the molecular mechanisms underlying the regulation of the keratin cycle in relation to actin and microtubule networks and in the context of epithelial tissue function. PMID:21893596

Probabilistic representation of gene regulatory networks.

PubMed

Mao, Linyong; Resat, Haluk

2004-09-22

Recent experiments have established unambiguously that biological systems can have significant cell-to-cell variations in gene expression levels even in isogenic populations. Computational approaches to studying gene expression in cellular systems should capture such biological variations for a more realistic representation. In this paper, we present a new fully probabilistic approach to the modeling of gene regulatory networks that allows for fluctuations in the gene expression levels. The new algorithm uses a very simple representation for the genes, and accounts for the repression or induction of the genes and for the biological variations among isogenic populations simultaneously. Because of its simplicity, introduced algorithm is a very promising approach to model large-scale gene regulatory networks. We have tested the new algorithm on the synthetic gene network library bioengineered recently. The good agreement between the computed and the experimental results for this library of networks, and additional tests, demonstrate that the new algorithm is robust and very successful in explaining the experimental data. The simulation software is available upon request. Supplementary material will be made available on the OUP server.

Virtual terrain: a security-based representation of a computer network

NASA Astrophysics Data System (ADS)

Holsopple, Jared; Yang, Shanchieh; Argauer, Brian

2008-03-01

Much research has been put forth towards detection, correlating, and prediction of cyber attacks in recent years. As this set of research progresses, there is an increasing need for contextual information of a computer network to provide an accurate situational assessment. Typical approaches adopt contextual information as needed; yet such ad hoc effort may lead to unnecessary or even conflicting features. The concept of virtual terrain is, therefore, developed and investigated in this work. Virtual terrain is a common representation of crucial information about network vulnerabilities, accessibilities, and criticalities. A virtual terrain model encompasses operating systems, firewall rules, running services, missions, user accounts, and network connectivity. It is defined as connected graphs with arc attributes defining dynamic relationships among vertices modeling network entities, such as services, users, and machines. The virtual terrain representation is designed to allow feasible development and maintenance of the model, as well as efficacy in terms of the use of the model. This paper will describe the considerations in developing the virtual terrain schema, exemplary virtual terrain models, and algorithms utilizing the virtual terrain model for situation and threat assessment.

What Happens to Student Learning When Color Is Added to a New Knowledge Representation Strategy? Implications from Visual Thinking Networking.

ERIC Educational Resources Information Center

Longo, Palma J.

A long-term study was conducted to test the effectiveness of visual thinking networking (VTN), a new generation of knowledge representation strategies with 56 ninth grade earth science students. The recent findings about the brain's organization and processing conceptually ground VTN as a new cognitive tool used by learners when making their…

Graph-Theoretic Properties of Networks Based on Word Association Norms: Implications for Models of Lexical Semantic Memory.

PubMed

Gruenenfelder, Thomas M; Recchia, Gabriel; Rubin, Tim; Jones, Michael N

2016-08-01

We compared the ability of three different contextual models of lexical semantic memory (BEAGLE, Latent Semantic Analysis, and the Topic model) and of a simple associative model (POC) to predict the properties of semantic networks derived from word association norms. None of the semantic models were able to accurately predict all of the network properties. All three contextual models over-predicted clustering in the norms, whereas the associative model under-predicted clustering. Only a hybrid model that assumed that some of the responses were based on a contextual model and others on an associative network (POC) successfully predicted all of the network properties and predicted a word's top five associates as well as or better than the better of the two constituent models. The results suggest that participants switch between a contextual representation and an associative network when generating free associations. We discuss the role that each of these representations may play in lexical semantic memory. Concordant with recent multicomponent theories of semantic memory, the associative network may encode coordinate relations between concepts (e.g., the relation between pea and bean, or between sparrow and robin), and contextual representations may be used to process information about more abstract concepts. Copyright © 2015 Cognitive Science Society, Inc.

Model validation of simple-graph representations of metabolism

PubMed Central

Holme, Petter

2009-01-01

The large-scale properties of chemical reaction systems, such as metabolism, can be studied with graph-based methods. To do this, one needs to reduce the information, lists of chemical reactions, available in databases. Even for the simplest type of graph representation, this reduction can be done in several ways. We investigate different simple network representations by testing how well they encode information about one biologically important network structure—network modularity (the propensity for edges to be clustered into dense groups that are sparsely connected between each other). To achieve this goal, we design a model of reaction systems where network modularity can be controlled and measure how well the reduction to simple graphs captures the modular structure of the model reaction system. We find that the network types that best capture the modular structure of the reaction system are substrate–product networks (where substrates are linked to products of a reaction) and substance networks (with edges between all substances participating in a reaction). Furthermore, we argue that the proposed model for reaction systems with tunable clustering is a general framework for studies of how reaction systems are affected by modularity. To this end, we investigate statistical properties of the model and find, among other things, that it recreates correlations between degree and mass of the molecules. PMID:19158012

Multidimensional Analysis of Linguistic Networks

NASA Astrophysics Data System (ADS)

Araújo, Tanya; Banisch, Sven

Network-based approaches play an increasingly important role in the analysis of data even in systems in which a network representation is not immediately apparent. This is particularly true for linguistic networks, which use to be induced from a linguistic data set for which a network perspective is only one out of several options for representation. Here we introduce a multidimensional framework for network construction and analysis with special focus on linguistic networks. Such a framework is used to show that the higher is the abstraction level of network induction, the harder is the interpretation of the topological indicators used in network analysis. Several examples are provided allowing for the comparison of different linguistic networks as well as to networks in other fields of application of network theory. The computation and the intelligibility of some statistical indicators frequently used in linguistic networks are discussed. It suggests that the field of linguistic networks, by applying statistical tools inspired by network studies in other domains, may, in its current state, have only a limited contribution to the development of linguistic theory.

Drainage fracture networks in elastic solids with internal fluid generation

NASA Astrophysics Data System (ADS)

Kobchenko, Maya; Hafver, Andreas; Jettestuen, Espen; Galland, Olivier; Renard, François; Meakin, Paul; Jamtveit, Bjørn; Dysthe, Dag K.

2013-06-01

Experiments in which CO2 gas was generated by the yeast fermentation of sugar in an elastic layer of gelatine gel confined between two glass plates are described and analyzed theoretically. The CO2 gas pressure causes the gel layer to fracture. The gas produced is drained on short length scales by diffusion and on long length scales by flow in a fracture network, which has topological properties that are intermediate between river networks and hierarchical-fracture networks. A simple model for the experimental system with two parameters that characterize the disorder and the intermediate (river-fracture) topology of the network was developed and the results of the model were compared with the experimental results.

Incoherence-Mediated Remote Synchronization

NASA Astrophysics Data System (ADS)

Zhang, Liyue; Motter, Adilson E.; Nishikawa, Takashi

2017-04-01

In previously identified forms of remote synchronization between two nodes, the intermediate portion of the network connecting the two nodes is not synchronized with them but generally exhibits some coherent dynamics. Here we report on a network phenomenon we call incoherence-mediated remote synchronization (IMRS), in which two noncontiguous parts of the network are identically synchronized while the dynamics of the intermediate part is statistically and information-theoretically incoherent. We identify mirror symmetry in the network structure as a mechanism allowing for such behavior, and show that IMRS is robust against dynamical noise as well as against parameter changes. IMRS may underlie neuronal information processing and potentially lead to network solutions for encryption key distribution and secure communication.

Observability and Controllability of Networks: Symmetry in Representations of Brains and Controllers for Epidemics

NASA Astrophysics Data System (ADS)

Schiff, Steven

Observability and controllability are essential concepts to the design of predictive observer models and feedback controllers of networked systems. We present a numerical and group representational framework, to quantify the observability and controllability of nonlinear networks with explicit symmetries that shows the connection between symmetries and nonlinear measures of observability and controllability. In addition to the topology of brain networks, we have advanced our ability to represent network nodes within the brain using conservation principles and more accurate biophysics that unifies the dynamics of spikes, seizures, and spreading depression. Lastly, we show how symmetries in controller design can be applied to infectious disease epidemics. NIH Grants 1R01EB014641, 1DP1HD086071.

Complexity of the heart rhythm after heart transplantation by entropy of transition network for RR-increments of RR time intervals between heartbeats.

PubMed

Makowiec, Danuta; Struzik, Zbigniew; Graff, Beata; Wdowczyk-Szulc, Joanna; Zarczynska-Buchnowiecka, Marta; Gruchala, Marcin; Rynkiewicz, Andrzej

2013-01-01

Network models have been used to capture, represent and analyse characteristics of living organisms and general properties of complex systems. The use of network representations in the characterization of time series complexity is a relatively new but quickly developing branch of time series analysis. In particular, beat-to-beat heart rate variability can be mapped out in a network of RR-increments, which is a directed and weighted graph with vertices representing RR-increments and the edges of which correspond to subsequent increments. We evaluate entropy measures selected from these network representations in records of healthy subjects and heart transplant patients, and provide an interpretation of the results.

A Tri-network Model of Human Semantic Processing

PubMed Central

Xu, Yangwen; He, Yong; Bi, Yanchao

2017-01-01

Humans process the meaning of the world via both verbal and nonverbal modalities. It has been established that widely distributed cortical regions are involved in semantic processing, yet the global wiring pattern of this brain system has not been considered in the current neurocognitive semantic models. We review evidence from the brain-network perspective, which shows that the semantic system is topologically segregated into three brain modules. Revisiting previous region-based evidence in light of these new network findings, we postulate that these three modules support multimodal experiential representation, language-supported representation, and semantic control. A tri-network neurocognitive model of semantic processing is proposed, which generates new hypotheses regarding the network basis of different types of semantic processes. PMID:28955266

Arrows as anchors: An analysis of the material features of electric field vector arrows

NASA Astrophysics Data System (ADS)

Gire, Elizabeth; Price, Edward

2014-12-01

Representations in physics possess both physical and conceptual aspects that are fundamentally intertwined and can interact to support or hinder sense making and computation. We use distributed cognition and the theory of conceptual blending with material anchors to interpret the roles of conceptual and material features of representations in students' use of representations for computation. We focus on the vector-arrows representation of electric fields and describe this representation as a conceptual blend of electric field concepts, physical space, and the material features of the representation (i.e., the physical writing and the surface upon which it is drawn). In this representation, spatial extent (e.g., distance on paper) is used to represent both distances in coordinate space and magnitudes of electric field vectors. In conceptual blending theory, this conflation is described as a clash between the input spaces in the blend. We explore the benefits and drawbacks of this clash, as well as other features of this representation. This analysis is illustrated with examples from clinical problem-solving interviews with upper-division physics majors. We see that while these intermediate physics students make a variety of errors using this representation, they also use the geometric features of the representation to add electric field contributions and to organize the problem situation productively.

Deep Learning of Orthographic Representations in Baboons

PubMed Central

Hannagan, Thomas; Ziegler, Johannes C.; Dufau, Stéphane; Fagot, Joël; Grainger, Jonathan

2014-01-01

What is the origin of our ability to learn orthographic knowledge? We use deep convolutional networks to emulate the primate's ventral visual stream and explore the recent finding that baboons can be trained to discriminate English words from nonwords [1]. The networks were exposed to the exact same sequence of stimuli and reinforcement signals as the baboons in the experiment, and learned to map real visual inputs (pixels) of letter strings onto binary word/nonword responses. We show that the networks' highest levels of representations were indeed sensitive to letter combinations as postulated in our previous research. The model also captured the key empirical findings, such as generalization to novel words, along with some intriguing inter-individual differences. The present work shows the merits of deep learning networks that can simulate the whole processing chain all the way from the visual input to the response while allowing researchers to analyze the complex representations that emerge during the learning process. PMID:24416300

Shortest path problem on a grid network with unordered intermediate points

NASA Astrophysics Data System (ADS)

Saw, Veekeong; Rahman, Amirah; Eng Ong, Wen

2017-10-01

We consider a shortest path problem with single cost factor on a grid network with unordered intermediate points. A two stage heuristic algorithm is proposed to find a feasible solution path within a reasonable amount of time. To evaluate the performance of the proposed algorithm, computational experiments are performed on grid maps of varying size and number of intermediate points. Preliminary results for the problem are reported. Numerical comparisons against brute forcing show that the proposed algorithm consistently yields solutions that are within 10% of the optimal solution and uses significantly less computation time.

Knowledge representation in metabolic pathway databases.

PubMed

Stobbe, Miranda D; Jansen, Gerbert A; Moerland, Perry D; van Kampen, Antoine H C

2014-05-01

The accurate representation of all aspects of a metabolic network in a structured format, such that it can be used for a wide variety of computational analyses, is a challenge faced by a growing number of researchers. Analysis of five major metabolic pathway databases reveals that each database has made widely different choices to address this challenge, including how to deal with knowledge that is uncertain or missing. In concise overviews, we show how concepts such as compartments, enzymatic complexes and the direction of reactions are represented in each database. Importantly, also concepts which a database does not represent are described. Which aspects of the metabolic network need to be available in a structured format and to what detail differs per application. For example, for in silico phenotype prediction, a detailed representation of gene-protein-reaction relations and the compartmentalization of the network is essential. Our analysis also shows that current databases are still limited in capturing all details of the biology of the metabolic network, further illustrated with a detailed analysis of three metabolic processes. Finally, we conclude that the conceptual differences between the databases, which make knowledge exchange and integration a challenge, have not been resolved, so far, by the exchange formats in which knowledge representation is standardized.

Structure and Evolution of the Foreign Exchange Networks

NASA Astrophysics Data System (ADS)

Kwapień, J.; Gworek, S.; Drożdż, S.

2009-01-01

We investigate topology and temporal evolution of the foreign currency exchange market viewed from a weighted network perspective. Based on exchange rates for a set of 46 currencies (including precious metals), we construct different representations of the FX network depending on a choice of the base currency. Our results show that the network structure is not stable in time, but there are main clusters of currencies, which persist for a long period of time despite the fact that their size and content are variable. We find a long-term trend in the network's evolution which affects the USD and EUR nodes. In all the network representations, the USD node gradually loses its centrality, while, on contrary, the EUR node has become slightly more central than it used to be in its early years. Despite this directional trend, the overall evolution of the network is noisy.

Thermodynamic characterization of networks using graph polynomials

NASA Astrophysics Data System (ADS)

Ye, Cheng; Comin, César H.; Peron, Thomas K. DM.; Silva, Filipi N.; Rodrigues, Francisco A.; Costa, Luciano da F.; Torsello, Andrea; Hancock, Edwin R.

2015-09-01

In this paper, we present a method for characterizing the evolution of time-varying complex networks by adopting a thermodynamic representation of network structure computed from a polynomial (or algebraic) characterization of graph structure. Commencing from a representation of graph structure based on a characteristic polynomial computed from the normalized Laplacian matrix, we show how the polynomial is linked to the Boltzmann partition function of a network. This allows us to compute a number of thermodynamic quantities for the network, including the average energy and entropy. Assuming that the system does not change volume, we can also compute the temperature, defined as the rate of change of entropy with energy. All three thermodynamic variables can be approximated using low-order Taylor series that can be computed using the traces of powers of the Laplacian matrix, avoiding explicit computation of the normalized Laplacian spectrum. These polynomial approximations allow a smoothed representation of the evolution of networks to be constructed in the thermodynamic space spanned by entropy, energy, and temperature. We show how these thermodynamic variables can be computed in terms of simple network characteristics, e.g., the total number of nodes and node degree statistics for nodes connected by edges. We apply the resulting thermodynamic characterization to real-world time-varying networks representing complex systems in the financial and biological domains. The study demonstrates that the method provides an efficient tool for detecting abrupt changes and characterizing different stages in network evolution.

All for one but not one for all: how multiple number representations are recruited in one numerical task.

PubMed

Wood, Guilherme; Nuerk, Hans-Christoph; Moeller, Korbinian; Geppert, Barbara; Schnitker, Ralph; Weber, Jochen; Willmes, Klaus

2008-01-02

Number processing recruits a complex network of multiple numerical representations. Usually the components of this network are examined in a between-task approach with the disadvantage of relying upon different instructions, tasks, and inhomogeneous stimulus sets across different studies. A within-task approach may avoid these disadvantages and access involved numerical representations more specifically. In the present study we employed a within-task approach to investigate numerical representations activated in the number bisection task (NBT) using parametric rapid event-related fMRI. Participants were to judge whether the central number of a triplet was also its arithmetic mean (e.g. 23_26_29) or not (e.g. 23_25_29). Activation in the left inferior parietal cortex was associated with the deployment of arithmetic fact knowledge, while activation of the intraparietal cortex indicated more intense magnitude processing, instrumental aspects of calculation and integration of the base-10 structure of two-digit numbers. These results replicate evidence from the literature. Furthermore, activation in the dorsolateral and ventrolateral prefrontal cortex revealed mechanisms of feature monitoring and inhibition as well as allocation of cognitive resources recruited to solve a specific triplet. We conclude that the network of numerical representations should rather be studied in a within-task approach than in varying between-task approaches.

The association of personal semantic memory to identity representations: insight into higher-order networks of autobiographical contents.

PubMed

Grilli, Matthew D

2017-11-01

Identity representations are higher-order knowledge structures that organise autobiographical memories on the basis of personality and role-based themes of one's self-concept. In two experiments, the extent to which different types of personal semantic content are reflected in these higher-order networks of memories was investigated. Healthy, young adult participants generated identity representations that varied in remoteness of formation and verbally reflected on these themes in an open-ended narrative task. The narrative responses were scored for retrieval of episodic, experience-near personal semantic and experience-far (i.e., abstract) personal semantic contents. Results revealed that to reflect on remotely formed identity representations, experience-far personal semantic contents were retrieved more than experience-near personal semantic contents. In contrast, to reflect on recently formed identity representations, experience-near personal semantic contents were retrieved more than experience-far personal semantic contents. Although episodic memory contents were retrieved less than both personal semantic content types to reflect on remotely formed identity representations, this content type was retrieved at a similar frequency as experience-far personal semantic content to reflect on recently formed identity representations. These findings indicate that the association of personal semantic content to identity representations is robust and related to time since acquisition of these knowledge structures.

A conceptual framework of computations in mid-level vision

PubMed Central

Kubilius, Jonas; Wagemans, Johan; Op de Beeck, Hans P.

2014-01-01

If a picture is worth a thousand words, as an English idiom goes, what should those words—or, rather, descriptors—capture? What format of image representation would be sufficiently rich if we were to reconstruct the essence of images from their descriptors? In this paper, we set out to develop a conceptual framework that would be: (i) biologically plausible in order to provide a better mechanistic understanding of our visual system; (ii) sufficiently robust to apply in practice on realistic images; and (iii) able to tap into underlying structure of our visual world. We bring forward three key ideas. First, we argue that surface-based representations are constructed based on feature inference from the input in the intermediate processing layers of the visual system. Such representations are computed in a largely pre-semantic (prior to categorization) and pre-attentive manner using multiple cues (orientation, color, polarity, variation in orientation, and so on), and explicitly retain configural relations between features. The constructed surfaces may be partially overlapping to compensate for occlusions and are ordered in depth (figure-ground organization). Second, we propose that such intermediate representations could be formed by a hierarchical computation of similarity between features in local image patches and pooling of highly-similar units, and reestimated via recurrent loops according to the task demands. Finally, we suggest to use datasets composed of realistically rendered artificial objects and surfaces in order to better understand a model's behavior and its limitations. PMID:25566044

A conceptual framework of computations in mid-level vision.

PubMed

Kubilius, Jonas; Wagemans, Johan; Op de Beeck, Hans P

2014-01-01

If a picture is worth a thousand words, as an English idiom goes, what should those words-or, rather, descriptors-capture? What format of image representation would be sufficiently rich if we were to reconstruct the essence of images from their descriptors? In this paper, we set out to develop a conceptual framework that would be: (i) biologically plausible in order to provide a better mechanistic understanding of our visual system; (ii) sufficiently robust to apply in practice on realistic images; and (iii) able to tap into underlying structure of our visual world. We bring forward three key ideas. First, we argue that surface-based representations are constructed based on feature inference from the input in the intermediate processing layers of the visual system. Such representations are computed in a largely pre-semantic (prior to categorization) and pre-attentive manner using multiple cues (orientation, color, polarity, variation in orientation, and so on), and explicitly retain configural relations between features. The constructed surfaces may be partially overlapping to compensate for occlusions and are ordered in depth (figure-ground organization). Second, we propose that such intermediate representations could be formed by a hierarchical computation of similarity between features in local image patches and pooling of highly-similar units, and reestimated via recurrent loops according to the task demands. Finally, we suggest to use datasets composed of realistically rendered artificial objects and surfaces in order to better understand a model's behavior and its limitations.

The Cosmic V-Web

NASA Astrophysics Data System (ADS)

Pomarède, Daniel; Hoffman, Yehuda; Courtois, Hélène M.; Tully, R. Brent

2017-08-01

The network of filaments with embedded clusters surrounding voids, which has been seen in maps derived from redshift surveys and reproduced in simulations, has been referred to as the cosmic web. A complementary description is provided by considering the shear in the velocity field of galaxies. The eigenvalues of the shear provide information regarding whether or not a region is collapsing in three dimensions, which is the condition for a knot, expanding in three dimensions, which is the condition for a void, or in the intermediate condition of a filament or sheet. The structures that are quantitatively defined by the eigenvalues can be approximated by iso-contours that provide a visual representation of the cosmic velocity (V) web. The current application is based on radial peculiar velocities from the Cosmicflows-2 collection of distances. The three-dimensional velocity field is constructed using the Wiener filter methodology in the linear approximation. Eigenvalues of the velocity shear are calculated at each point on a grid. Here, knots and filaments are visualized across a local domain of diameter ˜ 0.1c.

[Success factors in the German healthcare market. Hospitals between cluster formation and privatisation].

PubMed

Schmidt, C; Möller, J; Hardt, F; Gabbert, T; Bauer, M

2007-12-01

The German hospital market is in a state of transition due to the introduction of diagnosis-related groups (DRGs) and a constant change of the reimbursement, demographic, economical and technical framework. To date mainly public hospitals were bought by private hospital chains, but this trend has currently reached university hospitals. During recent months a consolidation within the market of private hospitals took place, while new market players such as foreign hospital chains, US universities and private equity firms emerged on the scene. The target of the privatisation process, however, turns more and more to larger hospitals. Central key values remain the cluster formation and centralisation of key competences such as food supply, purchasing and pharmacy. Within a network of clinics the representation of different care components (basic, regular and maximum care provider) and care levels (low, normal, intermediate and intensive care) remain important elements of efficient hospital management. Today, successful hospital operation is based on the successful competition for patients and even more for qualified staff. In this aspect, university hospitals could play a decisive role, because of their combination of maximum acute care provision and educational mandate. No such network has yet been formed due to the different interests of the owners, however, given the new market situation this alternative concept could become more attractive.

Modelling dendritic ecological networks in space: An integrated network perspective

Treesearch

Erin E. Peterson; Jay M. Ver Hoef; Dan J. Isaak; Jeffrey A. Falke; Marie-Josee Fortin; Chris E. Jordan; Kristina McNyset; Pascal Monestiez; Aaron S. Ruesch; Aritra Sengupta; Nicholas Som; E. Ashley Steel; David M. Theobald; Christian E. Torgersen; Seth J. Wenger

2013-01-01

Dendritic ecological networks (DENs) are a unique form of ecological networks that exhibit a dendritic network topology (e.g. stream and cave networks or plant architecture). DENs have a dual spatial representation; as points within the network and as points in geographical space. Consequently, some analytical methods used to quantify relationships in other types of...

The temporal evolution of conceptual object representations revealed through models of behavior, semantics and deep neural networks.

PubMed

Bankson, B B; Hebart, M N; Groen, I I A; Baker, C I

2018-05-17

Visual object representations are commonly thought to emerge rapidly, yet it has remained unclear to what extent early brain responses reflect purely low-level visual features of these objects and how strongly those features contribute to later categorical or conceptual representations. Here, we aimed to estimate a lower temporal bound for the emergence of conceptual representations by defining two criteria that characterize such representations: 1) conceptual object representations should generalize across different exemplars of the same object, and 2) these representations should reflect high-level behavioral judgments. To test these criteria, we compared magnetoencephalography (MEG) recordings between two groups of participants (n = 16 per group) exposed to different exemplar images of the same object concepts. Further, we disentangled low-level from high-level MEG responses by estimating the unique and shared contribution of models of behavioral judgments, semantics, and different layers of deep neural networks of visual object processing. We find that 1) both generalization across exemplars as well as generalization of object-related signals across time increase after 150 ms, peaking around 230 ms; 2) representations specific to behavioral judgments emerged rapidly, peaking around 160 ms. Collectively, these results suggest a lower bound for the emergence of conceptual object representations around 150 ms following stimulus onset. Copyright © 2018 Elsevier Inc. All rights reserved.

Fixed versus mixed RSA: Explaining visual representations by fixed and mixed feature sets from shallow and deep computational models.

PubMed

Khaligh-Razavi, Seyed-Mahdi; Henriksson, Linda; Kay, Kendrick; Kriegeskorte, Nikolaus

2017-02-01

Studies of the primate visual system have begun to test a wide range of complex computational object-vision models. Realistic models have many parameters, which in practice cannot be fitted using the limited amounts of brain-activity data typically available. Task performance optimization (e.g. using backpropagation to train neural networks) provides major constraints for fitting parameters and discovering nonlinear representational features appropriate for the task (e.g. object classification). Model representations can be compared to brain representations in terms of the representational dissimilarities they predict for an image set. This method, called representational similarity analysis (RSA), enables us to test the representational feature space as is (fixed RSA) or to fit a linear transformation that mixes the nonlinear model features so as to best explain a cortical area's representational space (mixed RSA). Like voxel/population-receptive-field modelling, mixed RSA uses a training set (different stimuli) to fit one weight per model feature and response channel (voxels here), so as to best predict the response profile across images for each response channel. We analysed response patterns elicited by natural images, which were measured with functional magnetic resonance imaging (fMRI). We found that early visual areas were best accounted for by shallow models, such as a Gabor wavelet pyramid (GWP). The GWP model performed similarly with and without mixing, suggesting that the original features already approximated the representational space, obviating the need for mixing. However, a higher ventral-stream visual representation (lateral occipital region) was best explained by the higher layers of a deep convolutional network and mixing of its feature set was essential for this model to explain the representation. We suspect that mixing was essential because the convolutional network had been trained to discriminate a set of 1000 categories, whose frequencies in the training set did not match their frequencies in natural experience or their behavioural importance. The latter factors might determine the representational prominence of semantic dimensions in higher-level ventral-stream areas. Our results demonstrate the benefits of testing both the specific representational hypothesis expressed by a model's original feature space and the hypothesis space generated by linear transformations of that feature space.

Reading as Active Sensing: A Computational Model of Gaze Planning in Word Recognition

PubMed Central

Ferro, Marcello; Ognibene, Dimitri; Pezzulo, Giovanni; Pirrelli, Vito

2010-01-01

We offer a computational model of gaze planning during reading that consists of two main components: a lexical representation network, acquiring lexical representations from input texts (a subset of the Italian CHILDES database), and a gaze planner, designed to recognize written words by mapping strings of characters onto lexical representations. The model implements an active sensing strategy that selects which characters of the input string are to be fixated, depending on the predictions dynamically made by the lexical representation network. We analyze the developmental trajectory of the system in performing the word recognition task as a function of both increasing lexical competence, and correspondingly increasing lexical prediction ability. We conclude by discussing how our approach can be scaled up in the context of an active sensing strategy applied to a robotic setting. PMID:20577589

Reading as active sensing: a computational model of gaze planning in word recognition.

PubMed

Ferro, Marcello; Ognibene, Dimitri; Pezzulo, Giovanni; Pirrelli, Vito

2010-01-01

WE OFFER A COMPUTATIONAL MODEL OF GAZE PLANNING DURING READING THAT CONSISTS OF TWO MAIN COMPONENTS: a lexical representation network, acquiring lexical representations from input texts (a subset of the Italian CHILDES database), and a gaze planner, designed to recognize written words by mapping strings of characters onto lexical representations. The model implements an active sensing strategy that selects which characters of the input string are to be fixated, depending on the predictions dynamically made by the lexical representation network. We analyze the developmental trajectory of the system in performing the word recognition task as a function of both increasing lexical competence, and correspondingly increasing lexical prediction ability. We conclude by discussing how our approach can be scaled up in the context of an active sensing strategy applied to a robotic setting.

Building on prior knowledge without building it in.

PubMed

Hansen, Steven S; Lampinen, Andrew K; Suri, Gaurav; McClelland, James L

2017-01-01

Lake et al. propose that people rely on "start-up software," "causal models," and "intuitive theories" built using compositional representations to learn new tasks more efficiently than some deep neural network models. We highlight the many drawbacks of a commitment to compositional representations and describe our continuing effort to explore how the ability to build on prior knowledge and to learn new tasks efficiently could arise through learning in deep neural networks.

Knowledge-base browsing: an application of hybrid distributed/local connectionist networks

NASA Astrophysics Data System (ADS)

Samad, Tariq; Israel, Peggy

1990-08-01

We describe a knowledge base browser based on a connectionist (or neural network) architecture that employs both distributed and local representations. The distributed representations are used for input and output thereby enabling associative noise-tolerant interaction with the environment. Internally all representations are fully local. This simplifies weight assignment and facilitates network configuration for specific applications. In our browser concepts and relations in a knowledge base are represented using " microfeatures. " The microfeatures can encode semantic attributes structural features contextual information etc. Desired portions of the knowledge base can then be associatively retrieved based on a structured cue. An ordered list of partial matches is presented to the user for selection. Microfeatures can also be used as " bookmarks" they can be placed dynamically at appropriate points in the knowledge base and subsequently used as retrieval cues. A proof-of-concept system has been implemented for an internally developed Honeywell-proprietary knowledge acquisition tool. 1.

Enabling large-scale viscoelastic calculations via neural network acceleration

NASA Astrophysics Data System (ADS)

Robinson DeVries, P.; Thompson, T. B.; Meade, B. J.

2017-12-01

One of the most significant challenges involved in efforts to understand the effects of repeated earthquake cycle activity are the computational costs of large-scale viscoelastic earthquake cycle models. Deep artificial neural networks (ANNs) can be used to discover new, compact, and accurate computational representations of viscoelastic physics. Once found, these efficient ANN representations may replace computationally intensive viscoelastic codes and accelerate large-scale viscoelastic calculations by more than 50,000%. This magnitude of acceleration enables the modeling of geometrically complex faults over thousands of earthquake cycles across wider ranges of model parameters and at larger spatial and temporal scales than have been previously possible. Perhaps most interestingly from a scientific perspective, ANN representations of viscoelastic physics may lead to basic advances in the understanding of the underlying model phenomenology. We demonstrate the potential of artificial neural networks to illuminate fundamental physical insights with specific examples.

A new network representation of the metabolism to detect chemical transformation modules.

PubMed

Sorokina, Maria; Medigue, Claudine; Vallenet, David

2015-11-14

Metabolism is generally modeled by directed networks where nodes represent reactions and/or metabolites. In order to explore metabolic pathway conservation and divergence among organisms, previous studies were based on graph alignment to find similar pathways. Few years ago, the concept of chemical transformation modules, also called reaction modules, was introduced and correspond to sequences of chemical transformations which are conserved in metabolism. We propose here a novel graph representation of the metabolic network where reactions sharing a same chemical transformation type are grouped in Reaction Molecular Signatures (RMS). RMS were automatically computed for all reactions and encode changes in atoms and bonds. A reaction network containing all available metabolic knowledge was then reduced by an aggregation of reaction nodes and edges to obtain a RMS network. Paths in this network were explored and a substantial number of conserved chemical transformation modules was detected. Furthermore, this graph-based formalism allows us to define several path scores reflecting different biological conservation meanings. These scores are significantly higher for paths corresponding to known metabolic pathways and were used conjointly to build association rules that should predict metabolic pathway types like biosynthesis or degradation. This representation of metabolism in a RMS network offers new insights to capture relevant metabolic contexts. Furthermore, along with genomic context methods, it should improve the detection of gene clusters corresponding to new metabolic pathways.

Network analysis for the visualization and analysis of qualitative data.

PubMed

Pokorny, Jennifer J; Norman, Alex; Zanesco, Anthony P; Bauer-Wu, Susan; Sahdra, Baljinder K; Saron, Clifford D

2018-03-01

We present a novel manner in which to visualize the coding of qualitative data that enables representation and analysis of connections between codes using graph theory and network analysis. Network graphs are created from codes applied to a transcript or audio file using the code names and their chronological location. The resulting network is a representation of the coding data that characterizes the interrelations of codes. This approach enables quantification of qualitative codes using network analysis and facilitates examination of associations of network indices with other quantitative variables using common statistical procedures. Here, as a proof of concept, we applied this method to a set of interview transcripts that had been coded in 2 different ways and the resultant network graphs were examined. The creation of network graphs allows researchers an opportunity to view and share their qualitative data in an innovative way that may provide new insights and enhance transparency of the analytical process by which they reach their conclusions. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Defining the natural fracture network in a shale gas play and its cover succession: The case of the Utica Shale in eastern Canada

NASA Astrophysics Data System (ADS)

Ladevèze, P.; Séjourné, S.; Rivard, C.; Lavoie, D.; Lefebvre, R.; Rouleau, A.

2018-03-01

In the St. Lawrence sedimentary platform (eastern Canada), very little data are available between shallow fresh water aquifers and deep geological hydrocarbon reservoir units (here referred to as the intermediate zone). Characterization of this intermediate zone is crucial, as the latter controls aquifer vulnerability to operations carried out at depth. In this paper, the natural fracture networks in shallow aquifers and in the Utica shale gas reservoir are documented in an attempt to indirectly characterize the intermediate zone. This study used structural data from outcrops, shallow observation well logs and deep shale gas well logs to propose a conceptual model of the natural fracture network. Shallow and deep fractures were categorized into three sets of steeply-dipping fractures and into a set of bedding-parallel fractures. Some lithological and structural controls on fracture distribution were identified. The regional geologic history and similarities between the shallow and deep fracture datasets allowed the extrapolation of the fracture network characterization to the intermediate zone. This study thus highlights the benefits of using both datasets simultaneously, while they are generally interpreted separately. Recommendations are also proposed for future environmental assessment studies in which the existence of preferential flow pathways and potential upward fluid migration toward shallow aquifers need to be identified.

Effect of planning for connectivity on linear reserve networks.

PubMed

Lentini, Pia E; Gibbons, Philip; Carwardine, Josie; Fischer, Joern; Drielsma, Michael; Martin, Tara G

2013-08-01

Although the concept of connectivity is decades old, it remains poorly understood and defined, and some argue that habitat quality and area should take precedence in conservation planning instead. However, fragmented landscapes are often characterized by linear features that are inherently connected, such as streams and hedgerows. For these, both representation and connectivity targets may be met with little effect on the cost, area, or quality of the reserve network. We assessed how connectivity approaches affect planning outcomes for linear habitat networks by using the stock-route network of Australia as a case study. With the objective of representing vegetation communities across the network at a minimal cost, we ran scenarios with a range of representation targets (10%, 30%, 50%, and 70%) and used 3 approaches to account for connectivity (boundary length modifier, Euclidean distance, and landscape-value [LV]). We found that decisions regarding the target and connectivity approach used affected the spatial allocation of reserve systems. At targets ≥50%, networks designed with the Euclidean distance and LV approaches consisted of a greater number of small reserves. Hence, by maximizing both representation and connectivity, these networks compromised on larger contiguous areas. However, targets this high are rarely used in real-world conservation planning. Approaches for incorporating connectivity into the planning of linear reserve networks that account for both the spatial arrangement of reserves and the characteristics of the intervening matrix highlight important sections that link the landscape and that may otherwise be overlooked. © 2013 Society for Conservation Biology.

Beyond Fine Tuning: Adding capacity to leverage few labels

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hodas, Nathan O.; Shaffer, Kyle J.; Yankov, Artem

2017-12-09

In this paper we present a technique to train neural network models on small amounts of data. Current methods for training neural networks on small amounts of rich data typically rely on strategies such as fine-tuning a pre-trained neural networks or the use of domain-specific hand-engineered features. Here we take the approach of treating network layers, or entire networks, as modules and combine pre-trained modules with untrained modules, to learn the shift in distributions between data sets. The central impact of using a modular approach comes from adding new representations to a network, as opposed to replacing representations via fine-tuning.more » Using this technique, we are able surpass results using standard fine-tuning transfer learning approaches, and we are also able to significantly increase performance over such approaches when using smaller amounts of data.« less

Predicting RNA folding thermodynamics with a reduced chain representation model

PubMed Central

CAO, SONG; CHEN, SHI-JIE

2005-01-01

Based on the virtual bond representation for the nucleotide backbone, we develop a reduced conformational model for RNA. We use the experimentally measured atomic coordinates to model the helices and use the self-avoiding walks in a diamond lattice to model the loop conformations. The atomic coordinates of the helices and the lattice representation for the loops are matched at the loop–helix junction, where steric viability is accounted for. Unlike the previous simplified lattice-based models, the present virtual bond model can account for the atomic details of realistic three-dimensional RNA structures. Based on the model, we develop a statistical mechanical theory for RNA folding energy landscapes and folding thermodynamics. Tests against experiments show that the theory can give much more improved predictions for the native structures, the thermal denaturation curves, and the equilibrium folding/unfolding pathways than the previous models. The application of the model to the P5abc region of Tetrahymena group I ribozyme reveals the misfolded intermediates as well as the native-like intermediates in the equilibrium folding process. Moreover, based on the free energy landscape analysis for each and every loop mutation, the model predicts five lethal mutations that can completely alter the free energy landscape and the folding stability of the molecule. PMID:16251382

Looking for robust properties in the growth of an academic network: the case of the Uruguayan biological research community.

PubMed

Cabana, Alvaro; Mizraji, Eduardo; Pomi, Andrés; Valle-Lisboa, Juan Carlos

2008-04-01

Graph-theoretical methods have recently been used to analyze certain properties of natural and social networks. In this work, we have investigated the early stages in the growth of a Uruguayan academic network, the Biology Area of the Programme for the Development of Basic Science (PEDECIBA). This transparent social network is a territory for the exploration of the reliability of clustering methods that can potentially be used when we are confronted with opaque natural systems that provide us with a limited spectrum of observables (happens in research on the relations between brain, thought and language). From our social net, we constructed two different graph representations based on the relationships among researchers revealed by their co-participation in Master's thesis committees. We studied these networks at different times and found that they achieve connectedness early in their evolution and exhibit the small-world property (i.e. high clustering with short path lengths). The data seem compatible with power law distributions of connectivity, clustering coefficients and betweenness centrality. Evidence of preferential attachment of new nodes and of new links between old nodes was also found in both representations. These results suggest that there are topological properties observed throughout the growth of the network that do not depend on the representations we have chosen but reflect intrinsic properties of the academic collective under study. Researchers in PEDECIBA are classified according to their specialties. We analysed the community structure detected by a standard algorithm in both representations. We found that much of the pre-specified structure is recovered and part of the mismatches can be attributed to convergent interests between scientists from different sub-disciplines. This result shows the potentiality of some clustering methods for the analysis of partially known natural systems.

Application of neural networks to group technology

NASA Astrophysics Data System (ADS)

Caudell, Thomas P.; Smith, Scott D. G.; Johnson, G. C.; Wunsch, Donald C., II

1991-08-01

Adaptive resonance theory (ART) neural networks are being developed for application to the industrial engineering problem of group technology--the reuse of engineering designs. Two- and three-dimensional representations of engineering designs are input to ART-1 neural networks to produce groups or families of similar parts. These representations, in their basic form, amount to bit maps of the part, and can become very large when the part is represented in high resolution. This paper describes an enhancement to an algorithmic form of ART-1 that allows it to operate directly on compressed input representations and to generate compressed memory templates. The performance of this compressed algorithm is compared to that of the regular algorithm on real engineering designs and a significant savings in memory storage as well as a speed up in execution is observed. In additions, a `neural database'' system under development is described. This system demonstrates the feasibility of training an ART-1 network to first cluster designs into families, and then to recall the family when presented a similar design. This application is of large practical value to industry, making it possible to avoid duplication of design efforts.

A comparison of two neural network schemes for navigation

NASA Technical Reports Server (NTRS)

Munro, Paul W.

1989-01-01

Neural networks have been applied to tasks in several areas of artificial intelligence, including vision, speech, and language. Relatively little work has been done in the area of problem solving. Two approaches to path-finding are presented, both using neural network techniques. Both techniques require a training period. Training under the back propagation (BPL) method was accomplished by presenting representations of (current position, goal position) pairs as input and appropriate actions as output. The Hebbian/interactive activation (HIA) method uses the Hebbian rule to associate points that are nearby. A path to a goal is found by activating a representation of the goal in the network and processing until the current position is activated above some threshold level. BPL, using back-propagation learning, failed to learn, except in a very trivial fashion, that is equivalent to table lookup techniques. HIA, performed much better, and required storage of fewer weights. In drawing a comparison, it is important to note that back propagation techniques depend critically upon the forms of representation used, and can be sensitive to parameters in the simulations; hence the BPL technique may yet yield strong results.

GrDHP: a general utility function representation for dual heuristic dynamic programming.

PubMed

Ni, Zhen; He, Haibo; Zhao, Dongbin; Xu, Xin; Prokhorov, Danil V

2015-03-01

A general utility function representation is proposed to provide the required derivable and adjustable utility function for the dual heuristic dynamic programming (DHP) design. Goal representation DHP (GrDHP) is presented with a goal network being on top of the traditional DHP design. This goal network provides a general mapping between the system states and the derivatives of the utility function. With this proposed architecture, we can obtain the required derivatives of the utility function directly from the goal network. In addition, instead of a fixed predefined utility function in literature, we conduct an online learning process for the goal network so that the derivatives of the utility function can be adaptively tuned over time. We provide the control performance of both the proposed GrDHP and the traditional DHP approaches under the same environment and parameter settings. The statistical simulation results and the snapshot of the system variables are presented to demonstrate the improved learning and controlling performance. We also apply both approaches to a power system example to further demonstrate the control capabilities of the GrDHP approach.

A comparison of two neural network schemes for navigation

NASA Technical Reports Server (NTRS)

Munro, Paul

1990-01-01

Neural networks have been applied to tasks in several areas of artificial intelligence, including vision, speech, and language. Relatively little work has been done in the area of problem solving. Two approaches to path-finding are presented, both using neural network techniques. Both techniques require a training period. Training under the back propagation (BPL) method was accomplished by presenting representations of current position, goal position pairs as input and appropriate actions as output. The Hebbian/interactive activation (HIA) method uses the Hebbian rule to associate points that are nearby. A path to a goal is found by activating a representation of the goal in the network and processing until the current position is activated above some threshold level. BPL, using back-propagation learning, failed to learn, except in a very trivial fashion, that is equivalent to table lookup techniques. HIA, performed much better, and required storage of fewer weights. In drawing a comparison, it is important to note that back propagation techniques depend critically upon the forms of representation used, and can be sensitive to parameters in the simulations; hence the BPL technique may yet yield strong results.

Acquisition, representation and rule generation for procedural knowledge

NASA Technical Reports Server (NTRS)

Ortiz, Chris; Saito, Tim; Mithal, Sachin; Loftin, R. Bowen

1991-01-01

Current research into the design and continuing development of a system for the acquisition of procedural knowledge, its representation in useful forms, and proposed methods for automated C Language Integrated Production System (CLIPS) rule generation is discussed. The Task Analysis and Rule Generation Tool (TARGET) is intended to permit experts, individually or collectively, to visually describe and refine procedural tasks. The system is designed to represent the acquired knowledge in the form of graphical objects with the capacity for generating production rules in CLIPS. The generated rules can then be integrated into applications such as NASA's Intelligent Computer Aided Training (ICAT) architecture. Also described are proposed methods for use in translating the graphical and intermediate knowledge representations into CLIPS rules.

Summary of the 1st International Workshop on Networked Reality in Telecommunication

NASA Astrophysics Data System (ADS)

Davis, T.

1994-05-01

s of workshop papers are presented. Networked reality refers to the array of technologies and services involved in collecting a representation of reality at one location and using it to reconstruct an artificial representation of that reality at a remote location. The term encompasses transmission of the required information between the sites, and also includes the psychological, cultural, and legal implications of introducing derived communication systems. Networked reality is clearly derived from the emerging virtual reality technology base but is intended to go beyond the latter to include its integration with the required telecommunication technologies. A noteworthy feature of the Networked Reality '94 technical program is the extent of emphasis on social (particularly medical) impacts of the technology.

Generative models for discovering sparse distributed representations.

PubMed Central

Hinton, G E; Ghahramani, Z

1997-01-01

We describe a hierarchical, generative model that can be viewed as a nonlinear generalization of factor analysis and can be implemented in a neural network. The model uses bottom-up, top-down and lateral connections to perform Bayesian perceptual inference correctly. Once perceptual inference has been performed the connection strengths can be updated using a very simple learning rule that only requires locally available information. We demonstrate that the network learns to extract sparse, distributed, hierarchical representations. PMID:9304685

Electronic filters, signal conversion apparatus, hearing aids and methods

NASA Technical Reports Server (NTRS)

Morley, Jr., Robert E. (Inventor); Engebretson, A. Maynard (Inventor); Engel, George L. (Inventor); Sullivan, Thomas J. (Inventor)

1994-01-01

An electronic filter for filtering an electrical signal. Signal processing circuitry therein includes a logarithmic filter having a series of filter stages with inputs and outputs in cascade and respective circuits associated with the filter stages for storing electrical representations of filter parameters. The filter stages include circuits for respectively adding the electrical representations of the filter parameters to the electrical signal to be filtered thereby producing a set of filter sum signals. At least one of the filter stages includes circuitry for producing a filter signal in substantially logarithmic form at its output by combining a filter sum signal for that filter stage with a signal from an output of another filter stage. The signal processing circuitry produces an intermediate output signal, and a multiplexer connected to the signal processing circuit multiplexes the intermediate output signal with the electrical signal to be filtered so that the logarithmic filter operates as both a logarithmic prefilter and a logarithmic postfilter. Other electronic filters, signal conversion apparatus, electroacoustic systems, hearing aids and methods are also disclosed.

Fidelity of the representation of value in decision-making

PubMed Central

Dowding, Ben A.

2017-01-01

The ability to make optimal decisions depends on evaluating the expected rewards associated with different potential actions. This process is critically dependent on the fidelity with which reward value information can be maintained in the nervous system. Here we directly probe the fidelity of value representation following a standard reinforcement learning task. The results demonstrate a previously-unrecognized bias in the representation of value: extreme reward values, both low and high, are stored significantly more accurately and precisely than intermediate rewards. The symmetry between low and high rewards pertained despite substantially higher frequency of exposure to high rewards, resulting from preferential exploitation of more rewarding options. The observed variation in fidelity of value representation retrospectively predicted performance on the reinforcement learning task, demonstrating that the bias in representation has an impact on decision-making. A second experiment in which one or other extreme-valued option was omitted from the learning sequence showed that representational fidelity is primarily determined by the relative position of an encoded value on the scale of rewards experienced during learning. Both variability and guessing decreased with the reduction in the number of options, consistent with allocation of a limited representational resource. These findings have implications for existing models of reward-based learning, which typically assume defectless representation of reward value. PMID:28248958

Illuminating the conceptual structure of the space of moral violations with searchlight representational similarity analysis.

PubMed

Wasserman, E A; Chakroff, A; Saxe, R; Young, L

2017-10-01

Characterizing how representations of moral violations are organized, cognitively and neurally, is central to understanding how people conceive and judge them. Past work has identified brain regions that represent morally relevant features and distinguish moral domains, but has not yet advanced a broader account of where and on what basis neural representations of moral violations are organized. With searchlight representational similarity analysis, we investigate where category membership drives similarity in neural patterns during moral judgment of violations from two key moral domains: Harm and Purity. Representations converge across domains in a network of regions resembling the mentalizing network. However, Harm and Purity violation representations respectively converge in different regions: precuneus (PC) and left inferior frontal gyrus (LIFG). Examining substructure within moral domains, Harm violations converge in PC regardless of subdomain (physical harms, psychological harms), while Purity subdomains (pathogen-related violations, sex-related violations) converge in distinct sets of regions - mirroring a dissociation observed in principal-component analysis of behavioral data. Further, we find initial evidence for representation of morally relevant features within these two domain-encoding regions. The present analyses offer a case study for understanding how organization within the complex conceptual space of moral violations is reflected in the organization of neural patterns across the cortex. Copyright © 2017 Elsevier Inc. All rights reserved.

Granger Causality Testing with Intensive Longitudinal Data.

PubMed

Molenaar, Peter C M

2018-06-01

The availability of intensive longitudinal data obtained by means of ambulatory assessment opens up new prospects for prevention research in that it allows the derivation of subject-specific dynamic networks of interacting variables by means of vector autoregressive (VAR) modeling. The dynamic networks thus obtained can be subjected to Granger causality testing in order to identify causal relations among the observed time-dependent variables. VARs have two equivalent representations: standard and structural. Results obtained with Granger causality testing depend upon which representation is chosen, yet no criteria exist on which this important choice can be based. A new equivalent representation is introduced called hybrid VARs with which the best representation can be chosen in a data-driven way. Partial directed coherence, a frequency-domain statistic for Granger causality testing, is shown to perform optimally when based on hybrid VARs. An application to real data is provided.

Using topographic networks to build a representation of consciousness.

PubMed

Tinsley, Chris J

2008-04-01

The subject of consciousness has intrigued both psychologists and neuroscientists for many years. Recently, following many recent advances in the emerging field of cognitive neuroscience, there is the possibility that this fundamental process may soon be explained. In particular, there have been dramatic insights gained into the mechanisms of attention, cognition and perception in recent decades. Here, simple network models are proposed which are used to create a representation of consciousness. The models are inspired by the structure of the thalamus and all incorporate topographic layers in their structure. Operation of the models allows filtering of the information reaching the representation according to (1) modality and/or (2) sub-modality, in addition several of the models allowing filtering at the topographic level. The models presented have different structures and employ different integrative mechanisms to produce gating or amplification at different levels; the resultant representations of consciousness are discussed.

Distributed downhole drilling network

DOEpatents

Hall, David R.; Hall, Jr., H. Tracy; Fox, Joe; Pixton, David S.

2006-11-21

A high-speed downhole network providing real-time data from downhole components of a drilling strings includes a bottom-hole node interfacing to a bottom-hole assembly located proximate the bottom end of a drill string. A top-hole node is connected proximate the top end of the drill string. One or several intermediate nodes are located along the drill string between the bottom-hole node and the top-hole node. The intermediate nodes are configured to receive and transmit data packets transmitted between the bottom-hole node and the top-hole node. A communications link, integrated into the drill string, is used to operably connect the bottom-hole node, the intermediate nodes, and the top-hole node. In selected embodiments, a personal or other computer may be connected to the top-hole node, to analyze data received from the intermediate and bottom-hole nodes.

Optimized color decomposition of localized whole slide images and convolutional neural network for intermediate prostate cancer classification

NASA Astrophysics Data System (ADS)

Zhou, Naiyun; Gao, Yi

2017-03-01

This paper presents a fully automatic approach to grade intermediate prostate malignancy with hematoxylin and eosin-stained whole slide images. Deep learning architectures such as convolutional neural networks have been utilized in the domain of histopathology for automated carcinoma detection and classification. However, few work show its power in discriminating intermediate Gleason patterns, due to sporadic distribution of prostate glands on stained surgical section samples. We propose optimized hematoxylin decomposition on localized images, followed by convolutional neural network to classify Gleason patterns 3+4 and 4+3 without handcrafted features or gland segmentation. Crucial glands morphology and structural relationship of nuclei are extracted twice in different color space by the multi-scale strategy to mimic pathologists' visual examination. Our novel classification scheme evaluated on 169 whole slide images yielded a 70.41% accuracy and corresponding area under the receiver operating characteristic curve of 0.7247.

PatterNet: a system to learn compact physical design pattern representations for pattern-based analytics

NASA Astrophysics Data System (ADS)

Lutich, Andrey

2017-07-01

This research considers the problem of generating compact vector representations of physical design patterns for analytics purposes in semiconductor patterning domain. PatterNet uses a deep artificial neural network to learn mapping of physical design patterns to a compact Euclidean hyperspace. Distances among mapped patterns in this space correspond to dissimilarities among patterns defined at the time of the network training. Once the mapping network has been trained, PatterNet embeddings can be used as feature vectors with standard machine learning algorithms, and pattern search, comparison, and clustering become trivial problems. PatterNet is inspired by the concepts developed within the framework of generative adversarial networks as well as the FaceNet. Our method facilitates a deep neural network (DNN) to learn directly the compact representation by supplying it with pairs of design patterns and dissimilarity among these patterns defined by a user. In the simplest case, the dissimilarity is represented by an area of the XOR of two patterns. Important to realize that our PatterNet approach is very different to the methods developed for deep learning on image data. In contrast to "conventional" pictures, the patterns in the CAD world are the lists of polygon vertex coordinates. The method solely relies on the promise of deep learning to discover internal structure of the incoming data and learn its hierarchical representations. Artificial intelligence arising from the combination of PatterNet and clustering analysis very precisely follows intuition of patterning/optical proximity correction experts paving the way toward human-like and human-friendly engineering tools.

Generative Adversarial Networks: An Overview

NASA Astrophysics Data System (ADS)

Creswell, Antonia; White, Tom; Dumoulin, Vincent; Arulkumaran, Kai; Sengupta, Biswa; Bharath, Anil A.

2018-01-01

Generative adversarial networks (GANs) provide a way to learn deep representations without extensively annotated training data. They achieve this through deriving backpropagation signals through a competitive process involving a pair of networks. The representations that can be learned by GANs may be used in a variety of applications, including image synthesis, semantic image editing, style transfer, image super-resolution and classification. The aim of this review paper is to provide an overview of GANs for the signal processing community, drawing on familiar analogies and concepts where possible. In addition to identifying different methods for training and constructing GANs, we also point to remaining challenges in their theory and application.

Dynamical genetic programming in XCSF.

PubMed

Preen, Richard J; Bull, Larry

2013-01-01

A number of representation schemes have been presented for use within learning classifier systems, ranging from binary encodings to artificial neural networks. This paper presents results from an investigation into using a temporally dynamic symbolic representation within the XCSF learning classifier system. In particular, dynamical arithmetic networks are used to represent the traditional condition-action production system rules to solve continuous-valued reinforcement learning problems and to perform symbolic regression, finding competitive performance with traditional genetic programming on a number of composite polynomial tasks. In addition, the network outputs are later repeatedly sampled at varying temporal intervals to perform multistep-ahead predictions of a financial time series.

Learning representation hierarchies by sharing visual features: a computational investigation of Persian character recognition with unsupervised deep learning.

PubMed

Sadeghi, Zahra; Testolin, Alberto

2017-08-01

In humans, efficient recognition of written symbols is thought to rely on a hierarchical processing system, where simple features are progressively combined into more abstract, high-level representations. Here, we present a computational model of Persian character recognition based on deep belief networks, where increasingly more complex visual features emerge in a completely unsupervised manner by fitting a hierarchical generative model to the sensory data. Crucially, high-level internal representations emerging from unsupervised deep learning can be easily read out by a linear classifier, achieving state-of-the-art recognition accuracy. Furthermore, we tested the hypothesis that handwritten digits and letters share many common visual features: A generative model that captures the statistical structure of the letters distribution should therefore also support the recognition of written digits. To this aim, deep networks trained on Persian letters were used to build high-level representations of Persian digits, which were indeed read out with high accuracy. Our simulations show that complex visual features, such as those mediating the identification of Persian symbols, can emerge from unsupervised learning in multilayered neural networks and can support knowledge transfer across related domains.

Wissensstrukturierung im Unterricht: Neuere Forschung zur Wissensreprasentation und ihre Anwendung in der Didaktik (Knowledge Structuring in Instruction: Recent Research on Knowledge Representation and Its Application in the Classroom).

ERIC Educational Resources Information Center

Einsiedler, Wolfgang

1996-01-01

Asks whether theories of knowledge representation provide a basis for the development of theories of knowledge structuring in instruction. Discusses codes of knowledge, surface versus deep structures, semantic networks, and multiple memory systems. Reviews research on teaching, external representation of cognitive structures, hierarchical…

Increasingly complex representations of natural movies across the dorsal stream are shared between subjects.

PubMed

Güçlü, Umut; van Gerven, Marcel A J

2017-01-15

Recently, deep neural networks (DNNs) have been shown to provide accurate predictions of neural responses across the ventral visual pathway. We here explore whether they also provide accurate predictions of neural responses across the dorsal visual pathway, which is thought to be devoted to motion processing and action recognition. This is achieved by training deep neural networks to recognize actions in videos and subsequently using them to predict neural responses while subjects are watching natural movies. Moreover, we explore whether dorsal stream representations are shared between subjects. In order to address this question, we examine if individual subject predictions can be made in a common representational space estimated via hyperalignment. Results show that a DNN trained for action recognition can be used to accurately predict how dorsal stream responds to natural movies, revealing a correspondence in representations of DNN layers and dorsal stream areas. It is also demonstrated that models operating in a common representational space can generalize to responses of multiple or even unseen individual subjects to novel spatio-temporal stimuli in both encoding and decoding settings, suggesting that a common representational space underlies dorsal stream responses across multiple subjects. Copyright © 2015 Elsevier Inc. All rights reserved.

Exploring the evolution of London's street network in the information space: A dual approach

NASA Astrophysics Data System (ADS)

Masucci, A. Paolo; Stanilov, Kiril; Batty, Michael

2014-01-01

We study the growth of London's street network in its dual representation, as the city has evolved over the past 224 years. The dual representation of a planar graph is a content-based network, where each node is a set of edges of the planar graph and represents a transportation unit in the so-called information space, i.e., the space where information is handled in order to navigate through the city. First, we discuss a novel hybrid technique to extract dual graphs from planar graphs, called the hierarchical intersection continuity negotiation principle. Then we show that the growth of the network can be analytically described by logistic laws and that the topological properties of the network are governed by robust log-normal distributions characterizing the network's connectivity and small-world properties that are consistent over time. Moreover, we find that the double-Pareto-like distributions for the connectivity emerge for major roads and can be modeled via a stochastic content-based network model using simple space-filling principles.

Information content of contact-pattern representations and predictability of epidemic outbreaks

PubMed Central

Holme, Petter

2015-01-01

To understand the contact patterns of a population—who is in contact with whom, and when the contacts happen—is crucial for modeling outbreaks of infectious disease. Traditional theoretical epidemiology assumes that any individual can meet any with equal probability. A more modern approach, network epidemiology, assumes people are connected into a static network over which the disease spreads. Newer yet, temporal network epidemiology, includes the time in the contact representations. In this paper, we investigate the effect of these successive inclusions of more information. Using empirical proximity data, we study both outbreak sizes from unknown sources, and from known states of ongoing outbreaks. In the first case, there are large differences going from a fully mixed simulation to a network, and from a network to a temporal network. In the second case, differences are smaller. We interpret these observations in terms of the temporal network structure of the data sets. For example, a fast overturn of nodes and links seem to make the temporal information more important. PMID:26403504

Reservoir Computing Properties of Neural Dynamics in Prefrontal Cortex

PubMed Central

Procyk, Emmanuel; Dominey, Peter Ford

2016-01-01

Primates display a remarkable ability to adapt to novel situations. Determining what is most pertinent in these situations is not always possible based only on the current sensory inputs, and often also depends on recent inputs and behavioral outputs that contribute to internal states. Thus, one can ask how cortical dynamics generate representations of these complex situations. It has been observed that mixed selectivity in cortical neurons contributes to represent diverse situations defined by a combination of the current stimuli, and that mixed selectivity is readily obtained in randomly connected recurrent networks. In this context, these reservoir networks reproduce the highly recurrent nature of local cortical connectivity. Recombining present and past inputs, random recurrent networks from the reservoir computing framework generate mixed selectivity which provides pre-coded representations of an essentially universal set of contexts. These representations can then be selectively amplified through learning to solve the task at hand. We thus explored their representational power and dynamical properties after training a reservoir to perform a complex cognitive task initially developed for monkeys. The reservoir model inherently displayed a dynamic form of mixed selectivity, key to the representation of the behavioral context over time. The pre-coded representation of context was amplified by training a feedback neuron to explicitly represent this context, thereby reproducing the effect of learning and allowing the model to perform more robustly. This second version of the model demonstrates how a hybrid dynamical regime combining spatio-temporal processing of reservoirs, and input driven attracting dynamics generated by the feedback neuron, can be used to solve a complex cognitive task. We compared reservoir activity to neural activity of dorsal anterior cingulate cortex of monkeys which revealed similar network dynamics. We argue that reservoir computing is a pertinent framework to model local cortical dynamics and their contribution to higher cognitive function. PMID:27286251

Risk assessment by dynamic representation of vulnerability, exploitation, and impact

NASA Astrophysics Data System (ADS)

Cam, Hasan

2015-05-01

Assessing and quantifying cyber risk accurately in real-time is essential to providing security and mission assurance in any system and network. This paper presents a modeling and dynamic analysis approach to assessing cyber risk of a network in real-time by representing dynamically its vulnerabilities, exploitations, and impact using integrated Bayesian network and Markov models. Given the set of vulnerabilities detected by a vulnerability scanner in a network, this paper addresses how its risk can be assessed by estimating in real-time the exploit likelihood and impact of vulnerability exploitation on the network, based on real-time observations and measurements over the network. The dynamic representation of the network in terms of its vulnerabilities, sensor measurements, and observations is constructed dynamically using the integrated Bayesian network and Markov models. The transition rates of outgoing and incoming links of states in hidden Markov models are used in determining exploit likelihood and impact of attacks, whereas emission rates help quantify the attack states of vulnerabilities. Simulation results show the quantification and evolving risk scores over time for individual and aggregated vulnerabilities of a network.

Emergent latent symbol systems in recurrent neural networks

NASA Astrophysics Data System (ADS)

Monner, Derek; Reggia, James A.

2012-12-01

Fodor and Pylyshyn [(1988). Connectionism and cognitive architecture: A critical analysis. Cognition, 28(1-2), 3-71] famously argued that neural networks cannot behave systematically short of implementing a combinatorial symbol system. A recent response from Frank et al. [(2009). Connectionist semantic systematicity. Cognition, 110(3), 358-379] claimed to have trained a neural network to behave systematically without implementing a symbol system and without any in-built predisposition towards combinatorial representations. We believe systems like theirs may in fact implement a symbol system on a deeper and more interesting level: one where the symbols are latent - not visible at the level of network structure. In order to illustrate this possibility, we demonstrate our own recurrent neural network that learns to understand sentence-level language in terms of a scene. We demonstrate our model's learned understanding by testing it on novel sentences and scenes. By paring down our model into an architecturally minimal version, we demonstrate how it supports combinatorial computation over distributed representations by using the associative memory operations of Vector Symbolic Architectures. Knowledge of the model's memory scheme gives us tools to explain its errors and construct superior future models. We show how the model designs and manipulates a latent symbol system in which the combinatorial symbols are patterns of activation distributed across the layers of a neural network, instantiating a hybrid of classical symbolic and connectionist representations that combines advantages of both.

Chemical Equilibrium and Polynomial Equations: Beware of Roots.

ERIC Educational Resources Information Center

Smith, William R.; Missen, Ronald W.

1989-01-01

Describes two easily applied mathematical theorems, Budan's rule and Rolle's theorem, that in addition to Descartes's rule of signs and intermediate-value theorem, are useful in chemical equilibrium. Provides examples that illustrate the use of all four theorems. Discusses limitations of the polynomial equation representation of chemical…

Two-stage neural-network-based technique for Urdu character two-dimensional shape representation, classification, and recognition

NASA Astrophysics Data System (ADS)

Megherbi, Dalila B.; Lodhi, S. M.; Boulenouar, A. J.

2001-03-01

This work is in the field of automated document processing. This work addresses the problem of representation and recognition of Urdu characters using Fourier representation and a Neural Network architecture. In particular, we show that a two-stage Neural Network scheme is used here to make classification of 36 Urdu characters into seven sub-classes namely subclasses characterized by seven proposed and defined fuzzy features specifically related to Urdu characters. We show that here Fourier Descriptors and Neural Network provide a remarkably simple way to draw definite conclusions from vague, ambiguous, noisy or imprecise information. In particular, we illustrate the concept of interest regions and describe a framing method that provides a way to make the proposed technique for Urdu characters recognition robust and invariant to scaling and translation. We also show that a given character rotation is dealt with by using the Hotelling transform. This transform is based upon the eigenvalue decomposition of the covariance matrix of an image, providing a method of determining the orientation of the major axis of an object within an image. Finally experimental results are presented to show the power and robustness of the proposed two-stage Neural Network based technique for Urdu character recognition, its fault tolerance, and high recognition accuracy.

Correlational Neural Networks.

PubMed

Chandar, Sarath; Khapra, Mitesh M; Larochelle, Hugo; Ravindran, Balaraman

2016-02-01

Common representation learning (CRL), wherein different descriptions (or views) of the data are embedded in a common subspace, has been receiving a lot of attention recently. Two popular paradigms here are canonical correlation analysis (CCA)-based approaches and autoencoder (AE)-based approaches. CCA-based approaches learn a joint representation by maximizing correlation of the views when projected to the common subspace. AE-based methods learn a common representation by minimizing the error of reconstructing the two views. Each of these approaches has its own advantages and disadvantages. For example, while CCA-based approaches outperform AE-based approaches for the task of transfer learning, they are not as scalable as the latter. In this work, we propose an AE-based approach, correlational neural network (CorrNet), that explicitly maximizes correlation among the views when projected to the common subspace. Through a series of experiments, we demonstrate that the proposed CorrNet is better than AE and CCA with respect to its ability to learn correlated common representations. We employ CorrNet for several cross-language tasks and show that the representations learned using it perform better than the ones learned using other state-of-the-art approaches.

Toward a self-organizing pre-symbolic neural model representing sensorimotor primitives.

PubMed

Zhong, Junpei; Cangelosi, Angelo; Wermter, Stefan

2014-01-01

The acquisition of symbolic and linguistic representations of sensorimotor behavior is a cognitive process performed by an agent when it is executing and/or observing own and others' actions. According to Piaget's theory of cognitive development, these representations develop during the sensorimotor stage and the pre-operational stage. We propose a model that relates the conceptualization of the higher-level information from visual stimuli to the development of ventral/dorsal visual streams. This model employs neural network architecture incorporating a predictive sensory module based on an RNNPB (Recurrent Neural Network with Parametric Biases) and a horizontal product model. We exemplify this model through a robot passively observing an object to learn its features and movements. During the learning process of observing sensorimotor primitives, i.e., observing a set of trajectories of arm movements and its oriented object features, the pre-symbolic representation is self-organized in the parametric units. These representational units act as bifurcation parameters, guiding the robot to recognize and predict various learned sensorimotor primitives. The pre-symbolic representation also accounts for the learning of sensorimotor primitives in a latent learning context.

Toward a self-organizing pre-symbolic neural model representing sensorimotor primitives

PubMed Central

Zhong, Junpei; Cangelosi, Angelo; Wermter, Stefan

2014-01-01

The acquisition of symbolic and linguistic representations of sensorimotor behavior is a cognitive process performed by an agent when it is executing and/or observing own and others' actions. According to Piaget's theory of cognitive development, these representations develop during the sensorimotor stage and the pre-operational stage. We propose a model that relates the conceptualization of the higher-level information from visual stimuli to the development of ventral/dorsal visual streams. This model employs neural network architecture incorporating a predictive sensory module based on an RNNPB (Recurrent Neural Network with Parametric Biases) and a horizontal product model. We exemplify this model through a robot passively observing an object to learn its features and movements. During the learning process of observing sensorimotor primitives, i.e., observing a set of trajectories of arm movements and its oriented object features, the pre-symbolic representation is self-organized in the parametric units. These representational units act as bifurcation parameters, guiding the robot to recognize and predict various learned sensorimotor primitives. The pre-symbolic representation also accounts for the learning of sensorimotor primitives in a latent learning context. PMID:24550798

Standard model of knowledge representation

NASA Astrophysics Data System (ADS)

Yin, Wensheng

2016-09-01

Knowledge representation is the core of artificial intelligence research. Knowledge representation methods include predicate logic, semantic network, computer programming language, database, mathematical model, graphics language, natural language, etc. To establish the intrinsic link between various knowledge representation methods, a unified knowledge representation model is necessary. According to ontology, system theory, and control theory, a standard model of knowledge representation that reflects the change of the objective world is proposed. The model is composed of input, processing, and output. This knowledge representation method is not a contradiction to the traditional knowledge representation method. It can express knowledge in terms of multivariate and multidimensional. It can also express process knowledge, and at the same time, it has a strong ability to solve problems. In addition, the standard model of knowledge representation provides a way to solve problems of non-precision and inconsistent knowledge.

Internet-Based Approaches to Building Stakeholder Networks for Conservation and Natural Resource Management

EPA Science Inventory

Social network analysis (SNA) is based on a conceptual network representation of social interactions and is an invaluable tool for conservation professionals to increase collaboration, improve information flow, and increase efficiency. We present two approaches to constructing i...

Internet-Based Approaches to Building Stakeholder Networks for Conservation and Natural Resource Management.

EPA Science Inventory

Social network analysis (SNA) is based on a conceptual network representation of social interactions and is an invaluable tool for conservation professionals to increase collaboration, improve information flow, and increase efficiency. We present two approaches to constructing in...

Conceptual Hierarchies in a Flat Attractor Network

PubMed Central

O’Connor, Christopher M.; Cree, George S.; McRae, Ken

2009-01-01

The structure of people’s conceptual knowledge of concrete nouns has traditionally been viewed as hierarchical (Collins & Quillian, 1969). For example, superordinate concepts (vegetable) are assumed to reside at a higher level than basic-level concepts (carrot). A feature-based attractor network with a single layer of semantic features developed representations of both basic-level and superordinate concepts. No hierarchical structure was built into the network. In Experiment and Simulation 1, the graded structure of categories (typicality ratings) is accounted for by the flat attractor-network. Experiment and Simulation 2 show that, as with basic-level concepts, such a network predicts feature verification latencies for superordinate concepts (vegetable ). In Experiment and Simulation 3, counterintuitive results regarding the temporal dynamics of similarity in semantic priming are explained by the model. By treating both types of concepts the same in terms of representation, learning, and computations, the model provides new insights into semantic memory. PMID:19543434

Parenclitic networks: uncovering new functions in biological data

PubMed Central

Zanin, Massimiliano; Alcazar, Joaquín Medina; Carbajosa, Jesus Vicente; Paez, Marcela Gomez; Papo, David; Sousa, Pedro; Menasalvas, Ernestina; Boccaletti, Stefano

2014-01-01

We introduce a novel method to represent time independent, scalar data sets as complex networks. We apply our method to investigate gene expression in the response to osmotic stress of Arabidopsis thaliana. In the proposed network representation, the most important genes for the plant response turn out to be the nodes with highest centrality in appropriately reconstructed networks. We also performed a target experiment, in which the predicted genes were artificially induced one by one, and the growth of the corresponding phenotypes compared to that of the wild-type. The joint application of the network reconstruction method and of the in vivo experiments allowed identifying 15 previously unknown key genes, and provided models of their mutual relationships. This novel representation extends the use of graph theory to data sets hitherto considered outside of the realm of its application, vastly simplifying the characterization of their underlying structure. PMID:24870931

Building Hierarchical Representations for Oracle Character and Sketch Recognition.

PubMed

Jun Guo; Changhu Wang; Roman-Rangel, Edgar; Hongyang Chao; Yong Rui

2016-01-01

In this paper, we study oracle character recognition and general sketch recognition. First, a data set of oracle characters, which are the oldest hieroglyphs in China yet remain a part of modern Chinese characters, is collected for analysis. Second, typical visual representations in shape- and sketch-related works are evaluated. We analyze the problems suffered when addressing these representations and determine several representation design criteria. Based on the analysis, we propose a novel hierarchical representation that combines a Gabor-related low-level representation and a sparse-encoder-related mid-level representation. Extensive experiments show the effectiveness of the proposed representation in both oracle character recognition and general sketch recognition. The proposed representation is also complementary to convolutional neural network (CNN)-based models. We introduce a solution to combine the proposed representation with CNN-based models, and achieve better performances over both approaches. This solution has beaten humans at recognizing general sketches.

Social disinhibition is a heritable subphenotype of tics in Tourette syndrome

PubMed Central

Hirschtritt, Matthew E.; Darrow, Sabrina M.; Illmann, Cornelia; Osiecki, Lisa; Grados, Marco; Sandor, Paul; Dion, Yves; King, Robert A.; Pauls, David L.; Budman, Cathy L.; Cath, Danielle C.; Greenberg, Erica; Lyon, Gholson J.; Yu, Dongmei; McGrath, Lauren M.; McMahon, William M.; Lee, Paul C.; Delucchi, Kevin L.; Scharf, Jeremiah M.

2016-01-01

Objective: To identify heritable symptom-based subtypes of Tourette syndrome (TS). Methods: Forty-nine motor and phonic tics were examined in 3,494 individuals (1,191 TS probands and 2,303 first-degree relatives). Item-level exploratory factor and latent class analyses (LCA) were used to identify tic-based subtypes. Heritabilities of the subtypes were estimated, and associations with clinical characteristics were examined. Results: A 6-factor exploratory factor analysis model provided the best fit, which paralleled the somatotopic representation of the basal ganglia, distinguished simple from complex tics, and separated out socially disinhibited and compulsive tics. The 5-class LCA model best distinguished among the following groups: unaffected, simple tics, intermediate tics without social disinhibition, intermediate with social disinhibition, and high rates of all tic types. Across models, a phenotype characterized by high rates of social disinhibition emerged. This phenotype was associated with increased odds of comorbid psychiatric disorders, in particular, obsessive-compulsive disorder and attention-deficit/hyperactivity disorder, earlier age at TS onset, and increased tic severity. The heritability estimate for this phenotype based on the LCA was 0.53 (SE 0.08, p 1.7 × 10−18). Conclusions: Expanding on previous modeling approaches, a series of TS-related phenotypes, including one characterized by high rates of social disinhibition, were identified. These phenotypes were highly heritable and may reflect underlying biological networks more accurately than traditional diagnoses, thus potentially aiding future genetic, imaging, and treatment studies. PMID:27371487

Social disinhibition is a heritable subphenotype of tics in Tourette syndrome.

PubMed

Hirschtritt, Matthew E; Darrow, Sabrina M; Illmann, Cornelia; Osiecki, Lisa; Grados, Marco; Sandor, Paul; Dion, Yves; King, Robert A; Pauls, David L; Budman, Cathy L; Cath, Danielle C; Greenberg, Erica; Lyon, Gholson J; Yu, Dongmei; McGrath, Lauren M; McMahon, William M; Lee, Paul C; Delucchi, Kevin L; Scharf, Jeremiah M; Mathews, Carol A

2016-08-02

To identify heritable symptom-based subtypes of Tourette syndrome (TS). Forty-nine motor and phonic tics were examined in 3,494 individuals (1,191 TS probands and 2,303 first-degree relatives). Item-level exploratory factor and latent class analyses (LCA) were used to identify tic-based subtypes. Heritabilities of the subtypes were estimated, and associations with clinical characteristics were examined. A 6-factor exploratory factor analysis model provided the best fit, which paralleled the somatotopic representation of the basal ganglia, distinguished simple from complex tics, and separated out socially disinhibited and compulsive tics. The 5-class LCA model best distinguished among the following groups: unaffected, simple tics, intermediate tics without social disinhibition, intermediate with social disinhibition, and high rates of all tic types. Across models, a phenotype characterized by high rates of social disinhibition emerged. This phenotype was associated with increased odds of comorbid psychiatric disorders, in particular, obsessive-compulsive disorder and attention-deficit/hyperactivity disorder, earlier age at TS onset, and increased tic severity. The heritability estimate for this phenotype based on the LCA was 0.53 (SE 0.08, p 1.7 × 10(-18)). Expanding on previous modeling approaches, a series of TS-related phenotypes, including one characterized by high rates of social disinhibition, were identified. These phenotypes were highly heritable and may reflect underlying biological networks more accurately than traditional diagnoses, thus potentially aiding future genetic, imaging, and treatment studies. © 2016 American Academy of Neurology.

Institutional Representations of "Spanish" and "Spanglish": Managing Competing Discourses in Heritage Language Instruction

ERIC Educational Resources Information Center

Showstack, Rachel E.

2015-01-01

This case study examined how one instructor navigated between two competing discourses in an intermediate Spanish heritage language (HL) classroom: on the one hand, teaching "Standard Spanish" to help students achieve professional success, and legitimizing home linguistic practices on the other. In addition to expressing both…

Translation Priming Effect in Spanish-English Bilinguals

ERIC Educational Resources Information Center

Ramírez Sarmiento, Albeiro Miguel Ángel

2011-01-01

This article aims to establish the effects of masked priming by translation equivalents in Spanish-English bilinguals with a high-intermediate level of proficiency in their second language. Its findings serve as evidence to support the hypothesis that semantic representations mediate the mental association among non-cognates from a speaker's first…

Developing and Evaluating Animations for Teaching Quantum Mechanics Concepts

ERIC Educational Resources Information Center

Kohnle, Antje; Douglass, Margaret; Edwards, Tom J.; Gillies, Alastair D.; Hooley, Christopher A.; Sinclair, Bruce D.

2010-01-01

In this paper, we describe animations and animated visualizations for introductory and intermediate-level quantum mechanics instruction developed at the University of St Andrews. The animations aim to help students build mental representations of quantum mechanics concepts. They focus on known areas of student difficulty and misconceptions by…

Addiction to melodrama.

PubMed

Stephens, Robert P

2011-01-01

Addiction films have been shaped by the internal demands of a commercial medium. Specifically, melodrama, as a genre, has defined the limits of the visual representation of addiction. Similarly, the process of intermedialization has tended to induce a metamorphosis that shapes disparate narratives with diverse goals into a generic filmic form and substantially alters the meanings of the texts. Ultimately, visual representations shape public perceptions of addiction in meaningful ways, privileging a moralistic understanding of drug addiction that makes a complex issue visually uncomplicated by reinforcing "common sense" ideas of moral failure and redemption. Copyright © 2011 Informa Healthcare USA, Inc.

Reduced Wiener Chaos representation of random fields via basis adaptation and projection

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tsilifis, Panagiotis, E-mail: tsilifis@usc.edu; Department of Civil Engineering, University of Southern California, Los Angeles, CA 90089; Ghanem, Roger G., E-mail: ghanem@usc.edu

2017-07-15

A new characterization of random fields appearing in physical models is presented that is based on their well-known Homogeneous Chaos expansions. We take advantage of the adaptation capabilities of these expansions where the core idea is to rotate the basis of the underlying Gaussian Hilbert space, in order to achieve reduced functional representations that concentrate the induced probability measure in a lower dimensional subspace. For a smooth family of rotations along the domain of interest, the uncorrelated Gaussian inputs are transformed into a Gaussian process, thus introducing a mesoscale that captures intermediate characteristics of the quantity of interest.

Reduced Wiener Chaos representation of random fields via basis adaptation and projection

NASA Astrophysics Data System (ADS)

Tsilifis, Panagiotis; Ghanem, Roger G.

2017-07-01

A new characterization of random fields appearing in physical models is presented that is based on their well-known Homogeneous Chaos expansions. We take advantage of the adaptation capabilities of these expansions where the core idea is to rotate the basis of the underlying Gaussian Hilbert space, in order to achieve reduced functional representations that concentrate the induced probability measure in a lower dimensional subspace. For a smooth family of rotations along the domain of interest, the uncorrelated Gaussian inputs are transformed into a Gaussian process, thus introducing a mesoscale that captures intermediate characteristics of the quantity of interest.

Noise in Attractor Networks in the Brain Produced by Graded Firing Rate Representations

PubMed Central

Webb, Tristan J.; Rolls, Edmund T.; Deco, Gustavo; Feng, Jianfeng

2011-01-01

Representations in the cortex are often distributed with graded firing rates in the neuronal populations. The firing rate probability distribution of each neuron to a set of stimuli is often exponential or gamma. In processes in the brain, such as decision-making, that are influenced by the noise produced by the close to random spike timings of each neuron for a given mean rate, the noise with this graded type of representation may be larger than with the binary firing rate distribution that is usually investigated. In integrate-and-fire simulations of an attractor decision-making network, we show that the noise is indeed greater for a given sparseness of the representation for graded, exponential, than for binary firing rate distributions. The greater noise was measured by faster escaping times from the spontaneous firing rate state when the decision cues are applied, and this corresponds to faster decision or reaction times. The greater noise was also evident as less stability of the spontaneous firing state before the decision cues are applied. The implication is that spiking-related noise will continue to be a factor that influences processes such as decision-making, signal detection, short-term memory, and memory recall even with the quite large networks found in the cerebral cortex. In these networks there are several thousand recurrent collateral synapses onto each neuron. The greater noise with graded firing rate distributions has the advantage that it can increase the speed of operation of cortical circuitry. PMID:21931607

Triple representation of language, working memory, social and emotion processing in the cerebellum: convergent evidence from task and seed-based resting-state fMRI analyses in a single large cohort.

PubMed

Guell, Xavier; Gabrieli, John D E; Schmahmann, Jeremy D

2018-05-15

Delineation of functional topography is critical to the evolving understanding of the cerebellum's role in a wide range of nervous system functions. We used data from the Human Connectome Project (n = 787) to analyze cerebellar fMRI task activation (motor, working memory, language, social and emotion processing) and resting-state functional connectivity calculated from cerebral cortical seeds corresponding to the peak Cohen's d of each task contrast. The combination of exceptional statistical power, activation from both motor and multiple non-motor tasks in the same participants, and convergent resting-state networks in the same participants revealed novel aspects of the functional topography of the human cerebellum. Consistent with prior studies there were two distinct representations of motor activation. Newly revealed were three distinct representations each for working memory, language, social, and emotional task processing that were largely separate for these four cognitive and affective domains. In most cases, the task-based activations and the corresponding resting-network correlations were congruent in identifying the two motor representations and the three non-motor representations that were unique to working memory, language, social cognition, and emotion. The definitive localization and characterization of distinct triple representations for cognition and emotion task processing in the cerebellum opens up new basic science questions as to why there are triple representations (what different functions are enabled by the different representations?) and new clinical questions (what are the differing consequences of lesions to the different representations?). Copyright © 2018 Elsevier Inc. All rights reserved.

Linear network representation of multistate models of transport.

PubMed Central

Sandblom, J; Ring, A; Eisenman, G

1982-01-01

By introducing external driving forces in rate-theory models of transport we show how the Eyring rate equations can be transformed into Ohm's law with potentials that obey Kirchhoff's second law. From such a formalism the state diagram of a multioccupancy multicomponent system can be directly converted into linear network with resistors connecting nodal (branch) points and with capacitances connecting each nodal point with a reference point. The external forces appear as emf or current generators in the network. This theory allows the algebraic methods of linear network theory to be used in solving the flux equations for multistate models and is particularly useful for making proper simplifying approximation in models of complex membrane structure. Some general properties of linear network representation are also deduced. It is shown, for instance, that Maxwell's reciprocity relationships of linear networks lead directly to Onsager's relationships in the near equilibrium region. Finally, as an example of the procedure, the equivalent circuit method is used to solve the equations for a few transport models. PMID:7093425

Ciência & Saúde Coletiva: scientific production analysis and collaborative research networks.

PubMed

Conner, Norma; Provedel, Attilio; Maciel, Ethel Leonor Noia

2017-03-01

The purpose of this metric and descriptive study was to identify the most productive authors and their collaborative research networks from articles published in Ciência & Saúde Coletiva between, 2005, and 2014. Authors meeting the cutoff criteria of at least 10 articles were considered the most productive authors. VOSviewer and Network Workbench technologies were applied for visual representations of collaborative research networks involving the most productive authors in the period. Initial analysis recovered 2511 distinct articles, with 8920 total authors with an average of 3.55 authors per article. Author analysis revealed 6288 distinct authors, 24 of these authors were identified as the most productive. These 24 authors generated 287 articles with an average of 4.31 authors per article, and represented 8 separate collaborative partnerships, the largest of which had 14 authors, indicating a significant degree of collaboration among these authors. This analysis provides a visual representation of networks of knowledge development in public health and demonstrates the usefulness of VOSviewer and Network Workbench technologies in future research.

Chemical space visualization: transforming multidimensional chemical spaces into similarity-based molecular networks.

PubMed

de la Vega de León, Antonio; Bajorath, Jürgen

2016-09-01

The concept of chemical space is of fundamental relevance for medicinal chemistry and chemical informatics. Multidimensional chemical space representations are coordinate-based. Chemical space networks (CSNs) have been introduced as a coordinate-free representation. A computational approach is presented for the transformation of multidimensional chemical space into CSNs. The design of transformation CSNs (TRANS-CSNs) is based upon a similarity function that directly reflects distance relationships in original multidimensional space. TRANS-CSNs provide an immediate visualization of coordinate-based chemical space and do not require the use of dimensionality reduction techniques. At low network density, TRANS-CSNs are readily interpretable and make it possible to evaluate structure-activity relationship information originating from multidimensional chemical space.

Place Cell Networks in Pre-weanling Rats Show Associative Memory Properties from the Onset of Exploratory Behavior.

PubMed

Muessig, L; Hauser, J; Wills, T J; Cacucci, F

2016-08-01

Place cells are hippocampal pyramidal cells that are active when an animal visits a restricted area of the environment, and collectively their activity constitutes a neural representation of space. Place cell populations in the adult rat hippocampus display fundamental properties consistent with an associative memory network: the ability to 1) generate new and distinct spatial firing patterns when encountering novel spatial contexts or changes in sensory input ("remapping") and 2) reinstate previously stored firing patterns when encountering a familiar context, including on the basis of an incomplete/degraded set of sensory cues ("pattern completion"). To date, it is unknown when these spatial memory responses emerge during brain development. Here, we show that, from the age of first exploration (postnatal day 16) onwards, place cell populations already exhibit these key features: they generate new representations upon exposure to a novel context and can reactivate familiar representations on the basis of an incomplete set of sensory cues. These results demonstrate that, as early as exploratory behaviors emerge, and despite the absence of an adult-like grid cell network, the developing hippocampus processes incoming sensory information as an associative memory network. © The Author 2016. Published by Oxford University Press.

JavaGenes: Evolving Graphs with Crossover

NASA Technical Reports Server (NTRS)

Globus, Al; Atsatt, Sean; Lawton, John; Wipke, Todd

2000-01-01

Genetic algorithms usually use string or tree representations. We have developed a novel crossover operator for a directed and undirected graph representation, and used this operator to evolve molecules and circuits. Unlike strings or trees, a single point in the representation cannot divide every possible graph into two parts, because graphs may contain cycles. Thus, the crossover operator is non-trivial. A steady-state, tournament selection genetic algorithm code (JavaGenes) was written to implement and test the graph crossover operator. All runs were executed by cycle-scavagging on networked workstations using the Condor batch processing system. The JavaGenes code has evolved pharmaceutical drug molecules and simple digital circuits. Results to date suggest that JavaGenes can evolve moderate sized drug molecules and very small circuits in reasonable time. The algorithm has greater difficulty with somewhat larger circuits, suggesting that directed graphs (circuits) are more difficult to evolve than undirected graphs (molecules), although necessary differences in the crossover operator may also explain the results. In principle, JavaGenes should be able to evolve other graph-representable systems, such as transportation networks, metabolic pathways, and computer networks. However, large graphs evolve significantly slower than smaller graphs, presumably because the space-of-all-graphs explodes combinatorially with graph size. Since the representation strongly affects genetic algorithm performance, adding graphs to the evolutionary programmer's bag-of-tricks should be beneficial. Also, since graph evolution operates directly on the phenotype, the genotype-phenotype translation step, common in genetic algorithm work, is eliminated.

Visual short-term memory: activity supporting encoding and maintenance in retinotopic visual cortex.

PubMed

Sneve, Markus H; Alnæs, Dag; Endestad, Tor; Greenlee, Mark W; Magnussen, Svein

2012-10-15

Recent studies have demonstrated that retinotopic cortex maintains information about visual stimuli during retention intervals. However, the process by which transient stimulus-evoked sensory responses are transformed into enduring memory representations is unknown. Here, using fMRI and short-term visual memory tasks optimized for univariate and multivariate analysis approaches, we report differential involvement of human retinotopic areas during memory encoding of the low-level visual feature orientation. All visual areas show weaker responses when memory encoding processes are interrupted, possibly due to effects in orientation-sensitive primary visual cortex (V1) propagating across extrastriate areas. Furthermore, intermediate areas in both dorsal (V3a/b) and ventral (LO1/2) streams are significantly more active during memory encoding compared with non-memory (active and passive) processing of the same stimulus material. These effects in intermediate visual cortex are also observed during memory encoding of a different stimulus feature (spatial frequency), suggesting that these areas are involved in encoding processes on a higher level of representation. Using pattern-classification techniques to probe the representational content in visual cortex during delay periods, we further demonstrate that simply initiating memory encoding is not sufficient to produce long-lasting memory traces. Rather, active maintenance appears to underlie the observed memory-specific patterns of information in retinotopic cortex. Copyright © 2012 Elsevier Inc. All rights reserved.

Particulate representation of a chemical reaction mechanism

NASA Astrophysics Data System (ADS)

Lee, Kam-Wah Lucille

1999-09-01

A growing area of interest in chemical education has been the research associated with conceptual understanding at the particulate level. This study investigated the views of 10 university chemistry lecturers, 85 pre-service chemistry teachers and 23 Secondary 3 (equivalent to Year 9) chemistry students about the particulate level of a chemical reaction, namely the heating of copper (II) carbonate. Four characteristic views were identified on the basis of their diagrammatic representations of particles. These were: (a) formation of intermediates; (b) formation of free particles (e. g., atoms or ions); (c) combination of a and b: formation of free particles (e. g., atoms or ions) first, and then intermediates; (d) no mechanism. Both the majority of the lecturers and the pre-service teachers held an identical view about the reaction mechanism, namely that the decomposition of copper (II) carbonate goes through a transition stage by forming intermediates. In contrast, even though the students were familiar with this reaction, about half of them naïvely believed that copper (II) carbonate broke up on heating and the particles recombined directly to form copper (II) oxide and carbon dioxide, the two observed products. About one-third of the students had neither any notion of how the atoms in the copper (II) carbonate lattice interacted and were rearranged in the reaction nor any concept of bond-breaking and reformation in a chemical reaction.

Using Graph Components Derived from an Associative Concept Dictionary to Predict fMRI Neural Activation Patterns that Represent the Meaning of Nouns.

PubMed

Akama, Hiroyuki; Miyake, Maki; Jung, Jaeyoung; Murphy, Brian

2015-01-01

In this study, we introduce an original distance definition for graphs, called the Markov-inverse-F measure (MiF). This measure enables the integration of classical graph theory indices with new knowledge pertaining to structural feature extraction from semantic networks. MiF improves the conventional Jaccard and/or Simpson indices, and reconciles both the geodesic information (random walk) and co-occurrence adjustment (degree balance and distribution). We measure the effectiveness of graph-based coefficients through the application of linguistic graph information for a neural activity recorded during conceptual processing in the human brain. Specifically, the MiF distance is computed between each of the nouns used in a previous neural experiment and each of the in-between words in a subgraph derived from the Edinburgh Word Association Thesaurus of English. From the MiF-based information matrix, a machine learning model can accurately obtain a scalar parameter that specifies the degree to which each voxel in (the MRI image of) the brain is activated by each word or each principal component of the intermediate semantic features. Furthermore, correlating the voxel information with the MiF-based principal components, a new computational neurolinguistics model with a network connectivity paradigm is created. This allows two dimensions of context space to be incorporated with both semantic and neural distributional representations.

Illness representations of depression and perceptions of the helpfulness of social support: comparing depressed and never-depressed persons.

PubMed

Vollmann, Manja; Scharloo, Margreet; Salewski, Christel; Dienst, Alexander; Schonauer, Klaus; Renner, Britta

2010-09-01

Interactions between depressed persons and persons within their social network are often characterized by misunderstanding and unsuccessful social support attempts. These interpersonal problems could be fostered by discrepancies between depressed and never-depressed persons' illness representations of depression and/or discrepancies in the perceived helpfulness of supportive behaviors. Illness representations of depression (IPQ-R) and perceptions of the helpfulness of different social support behaviors (ISU-DYA and ISAD) were assessed in 41 currently depressed persons and 58 persons without a history of depression. Never-depressed persons perceived depression as more controllable by treatment and as less emotionally impairing than depressed persons, but also as having more severe consequences. Never-depressed persons considered activation-oriented support (motivation to approach problems) as more helpful and protection-oriented support (allowance to draw back) as less helpful in comparison to depressed persons. Data were collected in unrelated samples of depressed and never-depressed persons. Discrepancies in illness representations and perceptions of the helpfulness of social support do exist and may be the origin of problematic social interactions between depressed patients and persons within their social network. Therapeutic interventions should address the issue of conflicting perceptions and encourage depressed patients to acknowledge and discuss this topic within their social network. 2010 Elsevier B.V. All rights reserved.

Mechanistic Representation of Soil C Dynamics: for Arctic Ecosystem

NASA Astrophysics Data System (ADS)

Dwivedi, D.; Riley, W. J.; Bisht, G.

2013-12-01

Arctic and sub-Arctic soils store vast amounts of carbon, approximately 1700 billion metric tones of frozen organic carbon. This carbon is susceptible to release to the atmosphere due to environmental changes (e.g., rapidly evolving landscape, warming); however, the mechanisms responsible for this susceptibility of soil organic matter (SOM) are not well understood, and uncertainties exist in terms of their representation in Earth System models. The representation of SOM dynamics in Earth System Models is critical for future climate prediction. To investigate the impacts of various physical (e.g., multi-phase transport, sorption, desorption, temperature), chemical (e.g., pH), and biological (e.g., microbial activity, enzyme dynamics) factors on SOM stability, we have developed CENTURY-like (describing labile and recalcitrant pools) and complex (describing multiple archetypal polymers and monomers C substrate groups) reaction networks. These reaction networks are integrated in a three-dimensional, multi-phase reactive transport solver (PFLOTRAN) and include representations of bacterial and fungal activity as well as population dynamics, gaseous and aqueous advection, and adsorption and desorption. We test and compare these reaction networks in PFLOTRAN to accurately predict depth-resolved soil organic matter (SOM) in the subsurface. We present results showing impacts of abiotic controls (e.g., surface interactions and temperature) on the long-term stabilization of SOM under permafrost conditions.

Pulmonary Nodule Classification with Deep Convolutional Neural Networks on Computed Tomography Images.

PubMed

Li, Wei; Cao, Peng; Zhao, Dazhe; Wang, Junbo

2016-01-01

Computer aided detection (CAD) systems can assist radiologists by offering a second opinion on early diagnosis of lung cancer. Classification and feature representation play critical roles in false-positive reduction (FPR) in lung nodule CAD. We design a deep convolutional neural networks method for nodule classification, which has an advantage of autolearning representation and strong generalization ability. A specified network structure for nodule images is proposed to solve the recognition of three types of nodules, that is, solid, semisolid, and ground glass opacity (GGO). Deep convolutional neural networks are trained by 62,492 regions-of-interest (ROIs) samples including 40,772 nodules and 21,720 nonnodules from the Lung Image Database Consortium (LIDC) database. Experimental results demonstrate the effectiveness of the proposed method in terms of sensitivity and overall accuracy and that it consistently outperforms the competing methods.

Statistics and dynamics of attractor networks with inter-correlated patterns

NASA Astrophysics Data System (ADS)

Kropff, E.

2007-02-01

In an embodied feature representation view, the semantic memory represents concepts in the brain by the associated activation of the features that describe it, each one of them processed in a differentiated region of the cortex. This system has been modeled with a Potts attractor network. Several studies of feature representation show that the correlation between patterns plays a crucial role in semantic memory. The present work focuses on two aspects of the effect of correlations in attractor networks. In first place, it assesses how a Potts network can store a set of patterns with non-trivial correlations between them. This is done through a simple and biologically plausible modification to the classical learning rule. In second place, it studies the complexity of latching transitions between attractor states, and how this complexity can be controlled.

Changes in resting-state connectivity in musicians with embouchure dystonia.

PubMed

Haslinger, Bernhard; Noé, Jonas; Altenmüller, Eckart; Riedl, Valentin; Zimmer, Claus; Mantel, Tobias; Dresel, Christian

2017-03-01

Embouchure dystonia is a highly disabling task-specific dystonia in professional brass musicians leading to spasms of perioral muscles while playing the instrument. As they are asymptomatic at rest, resting-state functional magnetic resonance imaging in these patients can reveal changes in functional connectivity within and between brain networks independent from dystonic symptoms. We therefore compared embouchure dystonia patients to healthy musicians with resting-state functional magnetic resonance imaging in combination with independent component analyses. Patients showed increased functional connectivity of the bilateral sensorimotor mouth area and right secondary somatosensory cortex, but reduced functional connectivity of the bilateral sensorimotor hand representation, left inferior parietal cortex, and mesial premotor cortex within the lateral motor function network. Within the auditory function network, the functional connectivity of bilateral secondary auditory cortices, right posterior parietal cortex and left sensorimotor hand area was increased, the functional connectivity of right primary auditory cortex, right secondary somatosensory cortex, right sensorimotor mouth representation, bilateral thalamus, and anterior cingulate cortex was reduced. Negative functional connectivity between the cerebellar and lateral motor function network and positive functional connectivity between the cerebellar and primary visual network were reduced. Abnormal resting-state functional connectivity of sensorimotor representations of affected and unaffected body parts suggests a pathophysiological predisposition for abnormal sensorimotor and audiomotor integration in embouchure dystonia. Altered connectivity to the cerebellar network highlights the important role of the cerebellum in this disease. © 2016 International Parkinson and Movement Disorder Society. © 2016 International Parkinson and Movement Disorder Society.

Representing Where along with What Information in a Model of a Cortical Patch

PubMed Central

Roudi, Yasser; Treves, Alessandro

2008-01-01

Behaving in the real world requires flexibly combining and maintaining information about both continuous and discrete variables. In the visual domain, several lines of evidence show that neurons in some cortical networks can simultaneously represent information about the position and identity of objects, and maintain this combined representation when the object is no longer present. The underlying network mechanism for this combined representation is, however, unknown. In this paper, we approach this issue through a theoretical analysis of recurrent networks. We present a model of a cortical network that can retrieve information about the identity of objects from incomplete transient cues, while simultaneously representing their spatial position. Our results show that two factors are important in making this possible: A) a metric organisation of the recurrent connections, and B) a spatially localised change in the linear gain of neurons. Metric connectivity enables a localised retrieval of information about object identity, while gain modulation ensures localisation in the correct position. Importantly, we find that the amount of information that the network can retrieve and retain about identity is strongly affected by the amount of information it maintains about position. This balance can be controlled by global signals that change the neuronal gain. These results show that anatomical and physiological properties, which have long been known to characterise cortical networks, naturally endow them with the ability to maintain a conjunctive representation of the identity and location of objects. PMID:18369416

Associative memory - An optimum binary neuron representation

NASA Technical Reports Server (NTRS)

Awwal, A. A.; Karim, M. A.; Liu, H. K.

1989-01-01

Convergence mechanism of vectors in the Hopfield's neural network is studied in terms of both weights (i.e., inner products) and Hamming distance. It is shown that Hamming distance should not always be used in determining the convergence of vectors. Instead, weights (which in turn depend on the neuron representation) are found to play a more dominant role in the convergence mechanism. Consequently, a new binary neuron representation for associative memory is proposed. With the new neuron representation, the associative memory responds unambiguously to the partial input in retrieving the stored information.

Distinct contributions of functional and deep neural network features to representational similarity of scenes in human brain and behavior

PubMed Central

Greene, Michelle R; Baldassano, Christopher; Fei-Fei, Li; Beck, Diane M; Baker, Chris I

2018-01-01

Inherent correlations between visual and semantic features in real-world scenes make it difficult to determine how different scene properties contribute to neural representations. Here, we assessed the contributions of multiple properties to scene representation by partitioning the variance explained in human behavioral and brain measurements by three feature models whose inter-correlations were minimized a priori through stimulus preselection. Behavioral assessments of scene similarity reflected unique contributions from a functional feature model indicating potential actions in scenes as well as high-level visual features from a deep neural network (DNN). In contrast, similarity of cortical responses in scene-selective areas was uniquely explained by mid- and high-level DNN features only, while an object label model did not contribute uniquely to either domain. The striking dissociation between functional and DNN features in their contribution to behavioral and brain representations of scenes indicates that scene-selective cortex represents only a subset of behaviorally relevant scene information. PMID:29513219

Distinct contributions of functional and deep neural network features to representational similarity of scenes in human brain and behavior.

PubMed

Groen, Iris Ia; Greene, Michelle R; Baldassano, Christopher; Fei-Fei, Li; Beck, Diane M; Baker, Chris I

2018-03-07

Inherent correlations between visual and semantic features in real-world scenes make it difficult to determine how different scene properties contribute to neural representations. Here, we assessed the contributions of multiple properties to scene representation by partitioning the variance explained in human behavioral and brain measurements by three feature models whose inter-correlations were minimized a priori through stimulus preselection. Behavioral assessments of scene similarity reflected unique contributions from a functional feature model indicating potential actions in scenes as well as high-level visual features from a deep neural network (DNN). In contrast, similarity of cortical responses in scene-selective areas was uniquely explained by mid- and high-level DNN features only, while an object label model did not contribute uniquely to either domain. The striking dissociation between functional and DNN features in their contribution to behavioral and brain representations of scenes indicates that scene-selective cortex represents only a subset of behaviorally relevant scene information.

Extraction of texture features with a multiresolution neural network

NASA Astrophysics Data System (ADS)

Lepage, Richard; Laurendeau, Denis; Gagnon, Roger A.

1992-09-01

Texture is an important surface characteristic. Many industrial materials such as wood, textile, or paper are best characterized by their texture. Detection of defaults occurring on such materials or classification for quality control anD matching can be carried out through careful texture analysis. A system for the classification of pieces of wood used in the furniture industry is proposed. This paper is concerned with a neural network implementation of the features extraction and classification components of the proposed system. Texture appears differently depending at which spatial scale it is observed. A complete description of a texture thus implies an analysis at several spatial scales. We propose a compact pyramidal representation of the input image for multiresolution analysis. The feature extraction system is implemented on a multilayer artificial neural network. Each level of the pyramid, which is a representation of the input image at a given spatial resolution scale, is mapped into a layer of the neural network. A full resolution texture image is input at the base of the pyramid and a representation of the texture image at multiple resolutions is generated by the feedforward pyramid structure of the neural network. The receptive field of each neuron at a given pyramid level is preprogrammed as a discrete Gaussian low-pass filter. Meaningful characteristics of the textured image must be extracted if a good resolving power of the classifier must be achieved. Local dominant orientation is the principal feature which is extracted from the textured image. Local edge orientation is computed with a Sobel mask at four orientation angles (multiple of (pi) /4). The resulting intrinsic image, that is, the local dominant orientation image, is fed to the texture classification neural network. The classification network is a three-layer feedforward back-propagation neural network.

Sum over Histories Representation for Kinetic Sensitivity Analysis: How Chemical Pathways Change When Reaction Rate Coefficients Are Varied

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bai, Shirong; Davis, Michael J.; Skodje, Rex T.

2015-11-12

The sensitivity of kinetic observables is analyzed using a newly developed sum over histories representation of chemical kinetics. In the sum over histories representation, the concentrations of the chemical species are decomposed into the sum of probabilities for chemical pathways that follow molecules from reactants to products or intermediates. Unlike static flux methods for reaction path analysis, the sum over histories approach includes the explicit time dependence of the pathway probabilities. Using the sum over histories representation, the sensitivity of an observable with respect to a kinetic parameter such as a rate coefficient is then analyzed in terms of howmore » that parameter affects the chemical pathway probabilities. The method is illustrated for species concentration target functions in H-2 combustion where the rate coefficients are allowed to vary over their associated uncertainty ranges. It is found that large sensitivities are often associated with rate limiting steps along important chemical pathways or by reactions that control the branching of reactive flux« less

Measuring distance through dense weighted networks: The case of hospital-associated pathogens

PubMed Central

Smieszek, Timo; Henderson, Katherine L.; Johnson, Alan P.

2017-01-01

Hospital networks, formed by patients visiting multiple hospitals, affect the spread of hospital-associated infections, resulting in differences in risks for hospitals depending on their network position. These networks are increasingly used to inform strategies to prevent and control the spread of hospital-associated pathogens. However, many studies only consider patients that are received directly from the initial hospital, without considering the effect of indirect trajectories through the network. We determine the optimal way to measure the distance between hospitals within the network, by reconstructing the English hospital network based on shared patients in 2014–2015, and simulating the spread of a hospital-associated pathogen between hospitals, taking into consideration that each intermediate hospital conveys a delay in the further spread of the pathogen. While the risk of transferring a hospital-associated pathogen between directly neighbouring hospitals is a direct reflection of the number of shared patients, the distance between two hospitals far-away in the network is determined largely by the number of intermediate hospitals in the network. Because the network is dense, most long distance transmission chains in fact involve only few intermediate steps, spreading along the many weak links. The dense connectivity of hospital networks, together with a strong regional structure, causes hospital-associated pathogens to spread from the initial outbreak in a two-step process: first, the directly surrounding hospitals are affected through the strong connections, second all other hospitals receive introductions through the multitude of weaker links. Although the strong connections matter for local spread, weak links in the network can offer ideal routes for hospital-associated pathogens to travel further faster. This hold important implications for infection prevention and control efforts: if a local outbreak is not controlled in time, colonised patients will appear in other regions, irrespective of the distance to the initial outbreak, making import screening ever more difficult. PMID:28771581

Seeing the world through non rose-colored glasses: anxiety and the amygdala response to blended expressions

PubMed Central

Bishop, Sonia J.; Aguirre, Geoffrey K.; Nunez-Elizalde, Anwar O.; Toker, Daniel

2015-01-01

Anxious individuals have a greater tendency to categorize faces with ambiguous emotional expressions as fearful (Richards et al., 2002). These behavioral findings might reflect anxiety-related biases in stimulus representation within the human amygdala. Here, we used functional magnetic resonance imaging (fMRI) together with a continuous adaptation design to investigate the representation of faces from three expression continua (surprise-fear, sadness-fear, and surprise-sadness) within the amygdala and other brain regions implicated in face processing. Fifty-four healthy adult participants completed a face expression categorization task. Nineteen of these participants also viewed the same expressions presented using type 1 index 1 sequences while fMRI data were acquired. Behavioral analyses revealed an anxiety-related categorization bias in the surprise-fear continuum alone. Here, elevated anxiety was associated with a more rapid transition from surprise to fear responses as a function of percentage fear in the face presented, leading to increased fear categorizations for faces with a mid-way blend of surprise and fear. fMRI analyses revealed that high trait anxious participants also showed greater representational similarity, as indexed by greater adaptation of the Blood Oxygenation Level Dependent (BOLD) signal, between 50/50 surprise/fear expression blends and faces from the fear end of the surprise-fear continuum in both the right amygdala and right fusiform face area (FFA). No equivalent biases were observed for the other expression continua. These findings suggest that anxiety-related biases in the processing of expressions intermediate between surprise and fear may be linked to differential representation of these stimuli in the amygdala and FFA. The absence of anxiety-related biases for the sad-fear continuum might reflect intermediate expressions from the surprise-fear continuum being most ambiguous in threat-relevance. PMID:25870551

Variable disparity estimation based intermediate view reconstruction in dynamic flow allocation over EPON-based access networks

NASA Astrophysics Data System (ADS)

Bae, Kyung-Hoon; Lee, Jungjoon; Kim, Eun-Soo

2008-06-01

In this paper, a variable disparity estimation (VDE)-based intermediate view reconstruction (IVR) in dynamic flow allocation (DFA) over an Ethernet passive optical network (EPON)-based access network is proposed. In the proposed system, the stereoscopic images are estimated by a variable block-matching algorithm (VBMA), and they are transmitted to the receiver through DFA over EPON. This scheme improves a priority-based access network by converting it to a flow-based access network with a new access mechanism and scheduling algorithm, and then 16-view images are synthesized by the IVR using VDE. Some experimental results indicate that the proposed system improves the peak-signal-to-noise ratio (PSNR) to as high as 4.86 dB and reduces the processing time to 3.52 s. Additionally, the network service provider can provide upper limits of transmission delays by the flow. The modeling and simulation results, including mathematical analyses, from this scheme are also provided.

Designing of network planning system for small-scale manufacturing

NASA Astrophysics Data System (ADS)

Kapulin, D. V.; Russkikh, P. A.; Vinnichenko, M. V.

2018-05-01

The paper presents features of network planning in small-scale discrete production. The procedure of explosion of the production order, considering multilevel representation, is developed. The software architecture is offered. Approbation of the network planning system is carried out. This system allows carrying out dynamic updating of the production plan.

Social networks, personal values, and creativity: evidence for curvilinear and interaction effects.

PubMed

Zhou, Jing; Shin, Shung Jae; Brass, Daniel J; Choi, Jaepil; Zhang, Zhi-Xue

2009-11-01

Taking an interactional perspective on creativity, the authors examined the influence of social networks and conformity value on employees' creativity. They theorized and found a curvilinear relationship between number of weak ties and creativity such that employees exhibited greater creativity when their number of weak ties was at intermediate levels rather than at lower or higher levels. In addition, employees' conformity value moderated the curvilinear relationship between number of weak ties and creativity such that employees exhibited greater creativity at intermediate levels of number of weak ties when conformity was low than when it was high. A proper match between personal values and network ties is critical for understanding creativity.

Effects of category-specific costs on neural systems for perceptual decision-making.

PubMed

Fleming, Stephen M; Whiteley, Louise; Hulme, Oliver J; Sahani, Maneesh; Dolan, Raymond J

2010-06-01

Perceptual judgments are often biased by prospective losses, leading to changes in decision criteria. Little is known about how and where sensory evidence and cost information interact in the brain to influence perceptual categorization. Here we show that prospective losses systematically bias the perception of noisy face-house images. Asymmetries in category-specific cost were associated with enhanced blood-oxygen-level-dependent signal in a frontoparietal network. We observed selective activation of parahippocampal gyrus for changes in category-specific cost in keeping with the hypothesis that loss functions enact a particular task set that is communicated to visual regions. Across subjects, greater shifts in decision criteria were associated with greater activation of the anterior cingulate cortex (ACC). Our results support a hypothesis that costs bias an intermediate representation between perception and action, expressed via general effects on frontal cortex, and selective effects on extrastriate cortex. These findings indicate that asymmetric costs may affect a neural implementation of perceptual decision making in a similar manner to changes in category expectation, constituting a step toward accounting for how prospective losses are flexibly integrated with sensory evidence in the brain.

French and Italian National Unions of Students Confronted with International Policymaking on Higher Education

ERIC Educational Resources Information Center

Genicot, Geneviève

2012-01-01

Similarities between French and Italian political culture of student representation include a conflictual culture in a weak national system of intermediation of interests, and a mimetic relationship with national conflictual party politics. New international topics, such as the Bologna Process or the growing commodification of education, have…

Representation & Redistricting.

ERIC Educational Resources Information Center

McGrail, Loren

This guide is targeted to teachers of intermediate or advanced English as a Second Language (ESL). It provides three 1-hour lesson plans on the topic of the importance of participation in the 2000 U.S. census. The paper asserts that it is critical for minorities who have historically been undercounted and under-represented in the U.S. Congress to…

The Problem State: A Cognitive Bottleneck in Multitasking

ERIC Educational Resources Information Center

Borst, Jelmer P.; Taatgen, Niels A.; van Rijn, Hedderik

2010-01-01

The main challenge for theories of multitasking is to predict when and how tasks interfere. Here, we focus on interference related to the problem state, a directly accessible intermediate representation of the current state of a task. On the basis of Salvucci and Taatgen's (2008) threaded cognition theory, we predict interference if 2 or more…

The Historical Representation of Thanksgiving within Primary- and Intermediate-Level Children's Literature

ERIC Educational Resources Information Center

Bickford, John H., III; Rich, Cynthia W.

2015-01-01

State and national initiatives have compelled significant change in English language arts and social studies/history curricula. English language arts teachers are required to balance fiction (or literature) and nonfiction (or informational texts), which is a considerable change for a content area formerly occupied by fiction (National Governors…

"At-Risk" Adolescents: Redefining Competence through the Multiliteracies of Intermediality, Visual Arts, and Representation.

ERIC Educational Resources Information Center

O'Brien, David

2001-01-01

Critiques the characterization of "at-riskness," with a focus on adolescents, in light of new media literacies or "media literacies in new times." Uses this reconstruction to redefine and reposition these learners as capable and innovative. Posits a variety of socially and culturally appropriate literacies, rather than…

The Role of High-Dimensional Diffusive Search, Stabilization, and Frustration in Protein Folding

PubMed Central

Rimratchada, Supreecha; McLeish, Tom C.B.; Radford, Sheena E.; Paci, Emanuele

2014-01-01

Proteins are polymeric molecules with many degrees of conformational freedom whose internal energetic interactions are typically screened to small distances. Therefore, in the high-dimensional conformation space of a protein, the energy landscape is locally relatively flat, in contrast to low-dimensional representations, where, because of the induced entropic contribution to the full free energy, it appears funnel-like. Proteins explore the conformation space by searching these flat subspaces to find a narrow energetic alley that we call a hypergutter and then explore the next, lower-dimensional, subspace. Such a framework provides an effective representation of the energy landscape and folding kinetics that does justice to the essential characteristic of high-dimensionality of the search-space. It also illuminates the important role of nonnative interactions in defining folding pathways. This principle is here illustrated using a coarse-grained model of a family of three-helix bundle proteins whose conformations, once secondary structure has formed, can be defined by six rotational degrees of freedom. Two folding mechanisms are possible, one of which involves an intermediate. The stabilization of intermediate subspaces (or states in low-dimensional projection) in protein folding can either speed up or slow down the folding rate depending on the amount of native and nonnative contacts made in those subspaces. The folding rate increases due to reduced-dimension pathways arising from the mere presence of intermediate states, but decreases if the contacts in the intermediate are very stable and introduce sizeable topological or energetic frustration that needs to be overcome. Remarkably, the hypergutter framework, although depending on just a few physically meaningful parameters, can reproduce all the types of experimentally observed curvature in chevron plots for realizations of this fold. PMID:24739172

Light-front representation of chiral dynamics with Δ isobar and large-N c relations

DOE PAGES

Granados, C.; Weiss, C.

2016-06-13

Transverse densities describe the spatial distribution of electromagnetic current in the nucleon at fixed light-front time. At peripheral distances b = O(M π –1) the densities are governed by chiral dynamics and can be calculated model-independently using chiral effective field theory (EFT). Recent work has shown that the EFT results can be represented in first-quantized form, as overlap integrals of chiral light-front wave functions describing the transition of the nucleon to soft-pion-nucleon intermediate states, resulting in a quantum-mechanical picture of the peripheral transverse densities. We now extend this representation to include intermediate states with Δ isobars and implement relations basedmore » on the large-N c limit of QCD. We derive the wave function overlap formulas for the Δ contributions to the peripheral transverse densities by way of a three-dimensional reduction of relativistic chiral EFT expressions. Our procedure effectively maintains rotational invariance and avoids the ambiguities with higher-spin particles in the light-front time-ordered approach. We study the interplay of πN and πΔ intermediate states in the quantum-mechanical picture of the densities in a transversely polarized nucleon. We show that the correct N c-scaling of the charge and magnetization densities emerges as the result of the particular combination of currents generated by intermediate states with degenerate N and Δ. The off-shell behavior of the chiral EFT is summarized in contact terms and can be studied easily. As a result, the methods developed here can be applied to other peripheral densities and to moments of the nucleon's generalized parton distributions.« less

Electronic filters, repeated signal charge conversion apparatus, hearing aids and methods

NASA Technical Reports Server (NTRS)

Morley, Jr., Robert E. (Inventor); Engebretson, A. Maynard (Inventor); Engel, George L. (Inventor); Sullivan, Thomas J. (Inventor)

1993-01-01

An electronic filter for filtering an electrical signal. Signal processing circuitry therein includes a logarithmic filter having a series of filter stages with inputs and outputs in cascade and respective circuits associated with the filter stages for storing electrical representations of filter parameters. The filter stages include circuits for respectively adding the electrical representations of the filter parameters to the electrical signal to be filtered thereby producing a set of filter sum signals. At least one of the filter stages includes circuitry for producing a filter signal in substantially logarithmic form at its output by combining a filter sum signal for that filter stage with a signal from an output of another filter stage. The signal processing circuitry produces an intermediate output signal, and a multiplexer connected to the signal processing circuit multiplexes the intermediate output signal with the electrical signal to be filtered so that the logarithmic filter operates as both a logarithmic prefilter and a logarithmic postfilter. Other electronic filters, signal conversion apparatus, electroacoustic systems, hearing aids and methods are also disclosed.

Set processing in a network environment. [data bases and magnetic disks and tapes

NASA Technical Reports Server (NTRS)

Hardgrave, W. T.

1975-01-01

A combination of a local network, a mass storage system, and an autonomous set processor serving as a data/storage management machine is described. Its characteristics include: content-accessible data bases usable from all connected devices; efficient storage/access of large data bases; simple and direct programming with data manipulation and storage management handled by the set processor; simple data base design and entry from source representation to set processor representation with no predefinition necessary; capability available for user sort/order specification; significant reduction in tape/disk pack storage and mounts; flexible environment that allows upgrading hardware/software configuration without causing major interruptions in service; minimal traffic on data communications network; and improved central memory usage on large processors.

Deep Supervised, but Not Unsupervised, Models May Explain IT Cortical Representation

PubMed Central

Khaligh-Razavi, Seyed-Mahdi; Kriegeskorte, Nikolaus

2014-01-01

Inferior temporal (IT) cortex in human and nonhuman primates serves visual object recognition. Computational object-vision models, although continually improving, do not yet reach human performance. It is unclear to what extent the internal representations of computational models can explain the IT representation. Here we investigate a wide range of computational model representations (37 in total), testing their categorization performance and their ability to account for the IT representational geometry. The models include well-known neuroscientific object-recognition models (e.g. HMAX, VisNet) along with several models from computer vision (e.g. SIFT, GIST, self-similarity features, and a deep convolutional neural network). We compared the representational dissimilarity matrices (RDMs) of the model representations with the RDMs obtained from human IT (measured with fMRI) and monkey IT (measured with cell recording) for the same set of stimuli (not used in training the models). Better performing models were more similar to IT in that they showed greater clustering of representational patterns by category. In addition, better performing models also more strongly resembled IT in terms of their within-category representational dissimilarities. Representational geometries were significantly correlated between IT and many of the models. However, the categorical clustering observed in IT was largely unexplained by the unsupervised models. The deep convolutional network, which was trained by supervision with over a million category-labeled images, reached the highest categorization performance and also best explained IT, although it did not fully explain the IT data. Combining the features of this model with appropriate weights and adding linear combinations that maximize the margin between animate and inanimate objects and between faces and other objects yielded a representation that fully explained our IT data. Overall, our results suggest that explaining IT requires computational features trained through supervised learning to emphasize the behaviorally important categorical divisions prominently reflected in IT. PMID:25375136

Pulse-firing winner-take-all networks

NASA Technical Reports Server (NTRS)

Meador, Jack L.

1991-01-01

Winner-take-all (WTA) neural networks using pulse-firing processing elements are introduced. In the pulse-firing WTA (PWTA) networks described, input and activation signal shunting is controlled by one shared lateral inhibition signal. This organization yields an O(n) area complexity that is convenient for integrated circuit implementation. Appropriately specified network parameters allow for the accurate continuous evaluation of inputs using a signal representation compatible with established pulse-firing neural network implementations.

Detecting trends in academic research from a citation network using network representation learning

PubMed Central

Mori, Junichiro; Ochi, Masanao; Sakata, Ichiro

2018-01-01

Several network features and information retrieval methods have been proposed to elucidate the structure of citation networks and to detect important nodes. However, it is difficult to retrieve information related to trends in an academic field and to detect cutting-edge areas from the citation network. In this paper, we propose a novel framework that detects the trend as the growth direction of a citation network using network representation learning(NRL). We presume that the linear growth of citation network in latent space obtained by NRL is the result of the iterative edge additional process of a citation network. On APS datasets and papers of some domains of the Web of Science, we confirm the existence of trends by observing that an academic field grows in a specific direction linearly in latent space. Next, we calculate each node’s degree of trend-following as an indicator called the intrinsic publication year (IPY). As a result, there is a correlation between the indicator and the number of future citations. Furthermore, a word frequently used in the abstracts of cutting-edge papers (high-IPY paper) is likely to be used often in future publications. These results confirm the validity of the detected trend for predicting citation network growth. PMID:29782521

Modular representation of layered neural networks.

PubMed

Watanabe, Chihiro; Hiramatsu, Kaoru; Kashino, Kunio

2018-01-01

Layered neural networks have greatly improved the performance of various applications including image processing, speech recognition, natural language processing, and bioinformatics. However, it is still difficult to discover or interpret knowledge from the inference provided by a layered neural network, since its internal representation has many nonlinear and complex parameters embedded in hierarchical layers. Therefore, it becomes important to establish a new methodology by which layered neural networks can be understood. In this paper, we propose a new method for extracting a global and simplified structure from a layered neural network. Based on network analysis, the proposed method detects communities or clusters of units with similar connection patterns. We show its effectiveness by applying it to three use cases. (1) Network decomposition: it can decompose a trained neural network into multiple small independent networks thus dividing the problem and reducing the computation time. (2) Training assessment: the appropriateness of a trained result with a given hyperparameter or randomly chosen initial parameters can be evaluated by using a modularity index. And (3) data analysis: in practical data it reveals the community structure in the input, hidden, and output layers, which serves as a clue for discovering knowledge from a trained neural network. Copyright © 2017 Elsevier Ltd. All rights reserved.

Software GOLUCA: Knowledge Representation in Mental Calculation

ERIC Educational Resources Information Center

Casas-Garcia, Luis M.; Luengo-Gonzalez, Ricardo; Godinho-Lopes, Vitor

2011-01-01

We present a new software, called Goluca (Godinho, Luengo, and Casas, 2007), based on the technique of Pathfinder Associative Networks (Schvaneveldt, 1989), which produces graphical representations of the cognitive structure of individuals in a given field knowledge. In this case, we studied the strategies used by teachers and its relationship…

Systematic Representation of Knowledge of Ecology: Concepts and Relationships.

ERIC Educational Resources Information Center

Garb, Yaakov; And Others

This study describes efforts to apply principles of systematic knowledge representation (concept mapping and computer-based semantic networking techniques) to the domain of ecology. A set of 24 relationships and modifiers is presented that seem sufficient for describing all ecological relationships discussed in an introductory course. Many of…

Learning target masks in infrared linescan imagery

NASA Astrophysics Data System (ADS)

Fechner, Thomas; Rockinger, Oliver; Vogler, Axel; Knappe, Peter

1997-04-01

In this paper we propose a neural network based method for the automatic detection of ground targets in airborne infrared linescan imagery. Instead of using a dedicated feature extraction stage followed by a classification procedure, we propose the following three step scheme: In the first step of the recognition process, the input image is decomposed into its pyramid representation, thus obtaining a multiresolution signal representation. At the lowest three levels of the Laplacian pyramid a neural network filter of moderate size is trained to indicate the target location. The last step consists of a fusion process of the several neural network filters to obtain the final result. To perform this fusion we use a belief network to combine the various filter outputs in a statistical meaningful way. In addition, the belief network allows the integration of further knowledge about the image domain. By applying this multiresolution recognition scheme, we obtain a nearly scale- and rotational invariant target recognition with a significantly decreased false alarm rate compared with a single resolution target recognition scheme.

Mapping higher-order relations between brain structure and function with embedded vector representations of connectomes.

PubMed

Rosenthal, Gideon; Váša, František; Griffa, Alessandra; Hagmann, Patric; Amico, Enrico; Goñi, Joaquín; Avidan, Galia; Sporns, Olaf

2018-06-05

Connectomics generates comprehensive maps of brain networks, represented as nodes and their pairwise connections. The functional roles of nodes are defined by their direct and indirect connectivity with the rest of the network. However, the network context is not directly accessible at the level of individual nodes. Similar problems in language processing have been addressed with algorithms such as word2vec that create embeddings of words and their relations in a meaningful low-dimensional vector space. Here we apply this approach to create embedded vector representations of brain networks or connectome embeddings (CE). CE can characterize correspondence relations among brain regions, and can be used to infer links that are lacking from the original structural diffusion imaging, e.g., inter-hemispheric homotopic connections. Moreover, we construct predictive deep models of functional and structural connectivity, and simulate network-wide lesion effects using the face processing system as our application domain. We suggest that CE offers a novel approach to revealing relations between connectome structure and function.

A neural network model of semantic memory linking feature-based object representation and words.

PubMed

Cuppini, C; Magosso, E; Ursino, M

2009-06-01

Recent theories in cognitive neuroscience suggest that semantic memory is a distributed process, which involves many cortical areas and is based on a multimodal representation of objects. The aim of this work is to extend a previous model of object representation to realize a semantic memory, in which sensory-motor representations of objects are linked with words. The model assumes that each object is described as a collection of features, coded in different cortical areas via a topological organization. Features in different objects are segmented via gamma-band synchronization of neural oscillators. The feature areas are further connected with a lexical area, devoted to the representation of words. Synapses among the feature areas, and among the lexical area and the feature areas are trained via a time-dependent Hebbian rule, during a period in which individual objects are presented together with the corresponding words. Simulation results demonstrate that, during the retrieval phase, the network can deal with the simultaneous presence of objects (from sensory-motor inputs) and words (from acoustic inputs), can correctly associate objects with words and segment objects even in the presence of incomplete information. Moreover, the network can realize some semantic links among words representing objects with shared features. These results support the idea that semantic memory can be described as an integrated process, whose content is retrieved by the co-activation of different multimodal regions. In perspective, extended versions of this model may be used to test conceptual theories, and to provide a quantitative assessment of existing data (for instance concerning patients with neural deficits).

The organization of the human cerebellum estimated by intrinsic functional connectivity

PubMed Central

Krienen, Fenna M.; Castellanos, Angela; Diaz, Julio C.; Yeo, B. T. Thomas

2011-01-01

The cerebral cortex communicates with the cerebellum via polysynaptic circuits. Separate regions of the cerebellum are connected to distinct cerebral areas, forming a complex topography. In this study we explored the organization of cerebrocerebellar circuits in the human using resting-state functional connectivity MRI (fcMRI). Data from 1,000 subjects were registered using nonlinear deformation of the cerebellum in combination with surface-based alignment of the cerebral cortex. The foot, hand, and tongue representations were localized in subjects performing movements. fcMRI maps derived from seed regions placed in different parts of the motor body representation yielded the expected inverted map of somatomotor topography in the anterior lobe and the upright map in the posterior lobe. Next, we mapped the complete topography of the cerebellum by estimating the principal cerebral target for each point in the cerebellum in a discovery sample of 500 subjects and replicated the topography in 500 independent subjects. The majority of the human cerebellum maps to association areas. Quantitative analysis of 17 distinct cerebral networks revealed that the extent of the cerebellum dedicated to each network is proportional to the network's extent in the cerebrum with a few exceptions, including primary visual cortex, which is not represented in the cerebellum. Like somatomotor representations, cerebellar regions linked to association cortex have separate anterior and posterior representations that are oriented as mirror images of one another. The orderly topography of the representations suggests that the cerebellum possesses at least two large, homotopic maps of the full cerebrum and possibly a smaller third map. PMID:21795627

Significance of Input Correlations in Striatal Function

PubMed Central

Yim, Man Yi; Aertsen, Ad; Kumar, Arvind

2011-01-01

The striatum is the main input station of the basal ganglia and is strongly associated with motor and cognitive functions. Anatomical evidence suggests that individual striatal neurons are unlikely to share their inputs from the cortex. Using a biologically realistic large-scale network model of striatum and cortico-striatal projections, we provide a functional interpretation of the special anatomical structure of these projections. Specifically, we show that weak pairwise correlation within the pool of inputs to individual striatal neurons enhances the saliency of signal representation in the striatum. By contrast, correlations among the input pools of different striatal neurons render the signal representation less distinct from background activity. We suggest that for the network architecture of the striatum, there is a preferred cortico-striatal input configuration for optimal signal representation. It is further enhanced by the low-rate asynchronous background activity in striatum, supported by the balance between feedforward and feedback inhibitions in the striatal network. Thus, an appropriate combination of rates and correlations in the striatal input sets the stage for action selection presumably implemented in the basal ganglia. PMID:22125480

Synaptic State Matching: A Dynamical Architecture for Predictive Internal Representation and Feature Detection

PubMed Central

Tavazoie, Saeed

2013-01-01

Here we explore the possibility that a core function of sensory cortex is the generation of an internal simulation of sensory environment in real-time. A logical elaboration of this idea leads to a dynamical neural architecture that oscillates between two fundamental network states, one driven by external input, and the other by recurrent synaptic drive in the absence of sensory input. Synaptic strength is modified by a proposed synaptic state matching (SSM) process that ensures equivalence of spike statistics between the two network states. Remarkably, SSM, operating locally at individual synapses, generates accurate and stable network-level predictive internal representations, enabling pattern completion and unsupervised feature detection from noisy sensory input. SSM is a biologically plausible substrate for learning and memory because it brings together sequence learning, feature detection, synaptic homeostasis, and network oscillations under a single unifying computational framework. PMID:23991161

Exploring the Neural Representation of Novel Words Learned through Enactment in a Word Recognition Task

PubMed Central

Macedonia, Manuela; Mueller, Karsten

2016-01-01

Vocabulary learning in a second language is enhanced if learners enrich the learning experience with self-performed iconic gestures. This learning strategy is called enactment. Here we explore how enacted words are functionally represented in the brain and which brain regions contribute to enhance retention. After an enactment training lasting 4 days, participants performed a word recognition task in the functional Magnetic Resonance Imaging (fMRI) scanner. Data analysis suggests the participation of different and partially intertwined networks that are engaged in higher cognitive processes, i.e., enhanced attention and word recognition. Also, an experience-related network seems to map word representation. Besides core language regions, this latter network includes sensory and motor cortices, the basal ganglia, and the cerebellum. On the basis of its complexity and the involvement of the motor system, this sensorimotor network might explain superior retention for enactment. PMID:27445918

Exploring sets of molecules from patents and relationships to other active compounds in chemical space networks

NASA Astrophysics Data System (ADS)

Kunimoto, Ryo; Bajorath, Jürgen

2017-09-01

Patents from medicinal chemistry represent a rich source of novel compounds and activity data that appear only infrequently in the scientific literature. Moreover, patent information provides a primary focal point for drug discovery. Accordingly, text mining and image extraction approaches have become hot topics in patent analysis and repositories of patent data are being established. In this work, we have generated network representations using alternative similarity measures to systematically compare molecules from patents with other bioactive compounds, visualize similarity relationships, explore the chemical neighbourhood of patent molecules, and identify closely related compounds with different activities. The design of network representations that combine patent molecules and other bioactive compounds and view patent information in the context of current bioactive chemical space aids in the analysis of patents and further extends the use of molecular networks to explore structure-activity relationships.

Standard representation and unified stability analysis for dynamic artificial neural network models.

PubMed

Kim, Kwang-Ki K; Patrón, Ernesto Ríos; Braatz, Richard D

2018-02-01

An overview is provided of dynamic artificial neural network models (DANNs) for nonlinear dynamical system identification and control problems, and convex stability conditions are proposed that are less conservative than past results. The three most popular classes of dynamic artificial neural network models are described, with their mathematical representations and architectures followed by transformations based on their block diagrams that are convenient for stability and performance analyses. Classes of nonlinear dynamical systems that are universally approximated by such models are characterized, which include rigorous upper bounds on the approximation errors. A unified framework and linear matrix inequality-based stability conditions are described for different classes of dynamic artificial neural network models that take additional information into account such as local slope restrictions and whether the nonlinearities within the DANNs are odd. A theoretical example shows reduced conservatism obtained by the conditions. Copyright © 2017. Published by Elsevier Ltd.

Intelligent control and adaptive systems; Proceedings of the Meeting, Philadelphia, PA, Nov. 7, 8, 1989

NASA Technical Reports Server (NTRS)

Rodriguez, Guillermo (Editor)

1990-01-01

Various papers on intelligent control and adaptive systems are presented. Individual topics addressed include: control architecture for a Mars walking vehicle, representation for error detection and recovery in robot task plans, real-time operating system for robots, execution monitoring of a mobile robot system, statistical mechanics models for motion and force planning, global kinematics for manipulator planning and control, exploration of unknown mechanical assemblies through manipulation, low-level representations for robot vision, harmonic functions for robot path construction, simulation of dual behavior of an autonomous system. Also discussed are: control framework for hand-arm coordination, neural network approach to multivehicle navigation, electronic neural networks for global optimization, neural network for L1 norm linear regression, planning for assembly with robot hands, neural networks in dynamical systems, control design with iterative learning, improved fuzzy process control of spacecraft autonomous rendezvous using a genetic algorithm.

Deep Neural Networks: A New Framework for Modeling Biological Vision and Brain Information Processing.

PubMed

Kriegeskorte, Nikolaus

2015-11-24

Recent advances in neural network modeling have enabled major strides in computer vision and other artificial intelligence applications. Human-level visual recognition abilities are coming within reach of artificial systems. Artificial neural networks are inspired by the brain, and their computations could be implemented in biological neurons. Convolutional feedforward networks, which now dominate computer vision, take further inspiration from the architecture of the primate visual hierarchy. However, the current models are designed with engineering goals, not to model brain computations. Nevertheless, initial studies comparing internal representations between these models and primate brains find surprisingly similar representational spaces. With human-level performance no longer out of reach, we are entering an exciting new era, in which we will be able to build biologically faithful feedforward and recurrent computational models of how biological brains perform high-level feats of intelligence, including vision.

Bidirectional Interplay between Vimentin Intermediate Filaments and Contractile Actin Stress Fibers.

PubMed

Jiu, Yaming; Lehtimäki, Jaakko; Tojkander, Sari; Cheng, Fang; Jäälinoja, Harri; Liu, Xiaonan; Varjosalo, Markku; Eriksson, John E; Lappalainen, Pekka

2015-06-16

The actin cytoskeleton and cytoplasmic intermediate filaments contribute to cell migration and morphogenesis, but the interplay between these two central cytoskeletal elements has remained elusive. Here, we find that specific actin stress fiber structures, transverse arcs, interact with vimentin intermediate filaments and promote their retrograde flow. Consequently, myosin-II-containing arcs are important for perinuclear localization of the vimentin network in cells. The vimentin network reciprocally restricts retrograde movement of arcs and hence controls the width of flat lamellum at the leading edge of the cell. Depletion of plectin recapitulates the vimentin organization phenotype of arc-deficient cells without affecting the integrity of vimentin filaments or stress fibers, demonstrating that this cytoskeletal cross-linker is required for productive interactions between vimentin and arcs. Collectively, our results reveal that plectin-mediated interplay between contractile actomyosin arcs and vimentin intermediate filaments controls the localization and dynamics of these two cytoskeletal systems and is consequently important for cell morphogenesis. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Building an information model (with the help of PSL/PSA). [Problem Statement Language/Problem Statement Analyzer

NASA Technical Reports Server (NTRS)

Callender, E. D.; Farny, A. M.

1983-01-01

Problem Statement Language/Problem Statement Analyzer (PSL/PSA) applications, which were once a one-step process in which product system information was immediately translated into PSL statements, have in light of experience been shown to result in inconsistent representations. These shortcomings have prompted the development of an intermediate step, designated the Product System Information Model (PSIM), which provides a basis for the mutual understanding of customer terminology and the formal, conceptual representation of that product system in a PSA data base. The PSIM is initially captured as a paper diagram, followed by formal capture in the PSL/PSA data base.

Efficient computation of parameter sensitivities of discrete stochastic chemical reaction networks.

PubMed

Rathinam, Muruhan; Sheppard, Patrick W; Khammash, Mustafa

2010-01-21

Parametric sensitivity of biochemical networks is an indispensable tool for studying system robustness properties, estimating network parameters, and identifying targets for drug therapy. For discrete stochastic representations of biochemical networks where Monte Carlo methods are commonly used, sensitivity analysis can be particularly challenging, as accurate finite difference computations of sensitivity require a large number of simulations for both nominal and perturbed values of the parameters. In this paper we introduce the common random number (CRN) method in conjunction with Gillespie's stochastic simulation algorithm, which exploits positive correlations obtained by using CRNs for nominal and perturbed parameters. We also propose a new method called the common reaction path (CRP) method, which uses CRNs together with the random time change representation of discrete state Markov processes due to Kurtz to estimate the sensitivity via a finite difference approximation applied to coupled reaction paths that emerge naturally in this representation. While both methods reduce the variance of the estimator significantly compared to independent random number finite difference implementations, numerical evidence suggests that the CRP method achieves a greater variance reduction. We also provide some theoretical basis for the superior performance of CRP. The improved accuracy of these methods allows for much more efficient sensitivity estimation. In two example systems reported in this work, speedup factors greater than 300 and 10,000 are demonstrated.

Hypermedia-Assisted Instruction and Second Language Learning: A Semantic-Network-Based Approach.

ERIC Educational Resources Information Center

Liu, Min

This literature review examines a hypermedia learning environment from a semantic network basis and the application of such an environment to second language learning. (A semantic network is defined as a conceptual representation of knowledge in human memory). The discussion is organized under the following headings and subheadings: (1) Advantages…

Megamap: flexible representation of a large space embedded with nonspatial information by a hippocampal attractor network

PubMed Central

Zhang, Kechen

2016-01-01

The problem of how the hippocampus encodes both spatial and nonspatial information at the cellular network level remains largely unresolved. Spatial memory is widely modeled through the theoretical framework of attractor networks, but standard computational models can only represent spaces that are much smaller than the natural habitat of an animal. We propose that hippocampal networks are built on a basic unit called a “megamap,” or a cognitive attractor map in which place cells are flexibly recombined to represent a large space. Its inherent flexibility gives the megamap a huge representational capacity and enables the hippocampus to simultaneously represent multiple learned memories and naturally carry nonspatial information at no additional cost. On the other hand, the megamap is dynamically stable, because the underlying network of place cells robustly encodes any location in a large environment given a weak or incomplete input signal from the upstream entorhinal cortex. Our results suggest a general computational strategy by which a hippocampal network enjoys the stability of attractor dynamics without sacrificing the flexibility needed to represent a complex, changing world. PMID:27193320

The Delta Connectome: A network-based framework for studying connectivity in river deltas

NASA Astrophysics Data System (ADS)

Passalacqua, Paola

2017-01-01

Many deltas, including the Mississippi River Delta, have been losing land at fast rates compromising the safety and sustainability of their ecosystems. Knowledge of delta vulnerability has raised global concern and stimulated active interdisciplinary research as deltas are densely populated landscapes, rich in agriculture, fisheries, oil and gas, and important means for navigation. There are many ways of looking at this problem which all contribute to a deeper understanding of the functioning of coastal systems. One aspect that has been overlooked thus far, yet fundamental for advancing delta science is connectivity, both physical (how different portions of the system interact with each other) as well as conceptual (pathways of process coupling). In this paper, I propose a framework called Delta Connectome for studying connectivity in river deltas based on different representations of a delta as a network. After analyzing the classic network representation as a set of nodes (e.g., bifurcations and junctions or regions with distinct physical or statistical behavior) and links (e.g., channels), I show that from connectivity considerations the delta emerges as a leaky network that continuously exchanges fluxes of matter, energy, and information with its surroundings and evolves over time. I explore each network representation and show through several examples how quantifying connectivity can bring to light aspects of deltaic systems so far unexplored and yet fundamental to understanding system functioning and informing coastal management and restoration. This paper serves both as an introduction to the Delta Connectome framework as well as a review of recent applications of the concepts of network and connectivity to deltaic systems within the Connectome framework.

ShapeShop: Towards Understanding Deep Learning Representations via Interactive Experimentation.

PubMed

Hohman, Fred; Hodas, Nathan; Chau, Duen Horng

2017-05-01

Deep learning is the driving force behind many recent technologies; however, deep neural networks are often viewed as "black-boxes" due to their internal complexity that is hard to understand. Little research focuses on helping people explore and understand the relationship between a user's data and the learned representations in deep learning models. We present our ongoing work, ShapeShop, an interactive system for visualizing and understanding what semantics a neural network model has learned. Built using standard web technologies, ShapeShop allows users to experiment with and compare deep learning models to help explore the robustness of image classifiers.

Tuning the developing brain to social signals of emotions

PubMed Central

Leppänen, Jukka M.; Nelson, Charles A.

2010-01-01

PREFACE Humans in diverse cultures develop a similar capacity to recognize the emotional signals of different facial expressions. This capacity is mediated by a brain network that involves emotion-related brain circuits and higher-level visual representation areas. Recent studies suggest that the key components of this network begin to emerge early in life. The studies also suggest that initial biases in emotion-related brain circuits and the early coupling of these circuits and cortical perceptual areas provides a foundation for a rapid acquisition of representations of those facial features that denote specific emotions. PMID:19050711

Improving Feature Representation Based on a Neural Network for Author Profiling in Social Media Texts

PubMed Central

2016-01-01

We introduce a lexical resource for preprocessing social media data. We show that a neural network-based feature representation is enhanced by using this resource. We conducted experiments on the PAN 2015 and PAN 2016 author profiling corpora and obtained better results when performing the data preprocessing using the developed lexical resource. The resource includes dictionaries of slang words, contractions, abbreviations, and emoticons commonly used in social media. Each of the dictionaries was built for the English, Spanish, Dutch, and Italian languages. The resource is freely available. PMID:27795703

Representing sentence information

NASA Astrophysics Data System (ADS)

Perkins, Walton A., III

1991-03-01

This paper describes a computer-oriented representation for sentence information. Whereas many Artificial Intelligence (AI) natural language systems start with a syntactic parse of a sentence into the linguist's components: noun, verb, adjective, preposition, etc., we argue that it is better to parse the input sentence into 'meaning' components: attribute, attribute value, object class, object instance, and relation. AI systems need a representation that will allow rapid storage and retrieval of information and convenient reasoning with that information. The attribute-of-object representation has proven useful for handling information in relational databases (which are well known for their efficiency in storage and retrieval) and for reasoning in knowledge- based systems. On the other hand, the linguist's syntactic representation of the works in sentences has not been shown to be useful for information handling and reasoning. We think it is an unnecessary and misleading intermediate form. Our sentence representation is semantic based in terms of attribute, attribute value, object class, object instance, and relation. Every sentence is segmented into one or more components with the form: 'attribute' of 'object' 'relation' 'attribute value'. Using only one format for all information gives the system simplicity and good performance as a RISC architecture does for hardware. The attribute-of-object representation is not new; it is used extensively in relational databases and knowledge-based systems. However, we will show that it can be used as a meaning representation for natural language sentences with minor extensions. In this paper we describe how a computer system can parse English sentences into this representation and generate English sentences from this representation. Much of this has been tested with computer implementation.

Hierarchical Organization of Auditory and Motor Representations in Speech Perception: Evidence from Searchlight Similarity Analysis

PubMed Central

Evans, Samuel; Davis, Matthew H.

2015-01-01

How humans extract the identity of speech sounds from highly variable acoustic signals remains unclear. Here, we use searchlight representational similarity analysis (RSA) to localize and characterize neural representations of syllables at different levels of the hierarchically organized temporo-frontal pathways for speech perception. We asked participants to listen to spoken syllables that differed considerably in their surface acoustic form by changing speaker and degrading surface acoustics using noise-vocoding and sine wave synthesis while we recorded neural responses with functional magnetic resonance imaging. We found evidence for a graded hierarchy of abstraction across the brain. At the peak of the hierarchy, neural representations in somatomotor cortex encoded syllable identity but not surface acoustic form, at the base of the hierarchy, primary auditory cortex showed the reverse. In contrast, bilateral temporal cortex exhibited an intermediate response, encoding both syllable identity and the surface acoustic form of speech. Regions of somatomotor cortex associated with encoding syllable identity in perception were also engaged when producing the same syllables in a separate session. These findings are consistent with a hierarchical account of how variable acoustic signals are transformed into abstract representations of the identity of speech sounds. PMID:26157026

Control of a solar-energy-supplied electrical-power system without intermediate circuitry

NASA Astrophysics Data System (ADS)

Leistner, K.

A computer control system is developed for electric-power systems comprising solar cells and small numbers of users with individual centrally controlled converters (and storage facilities when needed). Typical system structures are reviewed; the advantages of systems without an intermediate network are outlined; the demands on a control system in such a network (optimizing generator working point and power distribution) are defined; and a flexible modular prototype system is described in detail. A charging station for lead batteries used in electric automobiles is analyzed as an example. The power requirements of the control system (30 W for generator control and 50 W for communications and distribution control) are found to limit its use to larger networks.

Quantity Representation in Children and Rhesus Monkeys: Linear Versus Logarithmic Scales

ERIC Educational Resources Information Center

Beran, Michael J.; Johnson-Pynn, Julie S.; Ready, Christopher

2008-01-01

The performances of 4- and 5-year-olds and rhesus monkeys were compared using a computerized task for quantity assessment. Participants first learned two quantity anchor values and then responded to intermediate values by classifying them as similar to either the large anchor or the small anchor. Of primary interest was an assessment of where the…

Revisiting Unemployment in Intermediate Macroeconomics: A New Approach for Teaching Diamond-Mortensen-Pissarides

ERIC Educational Resources Information Center

Bhattacharya, Arghya; Jackson, Paul; Jenkins, Brian C.

2018-01-01

The authors present a version of the Diamond-Mortensen-Pissarides model of unemployment that is accessible to undergraduates and preserve the dynamic structure of the original model. The model is solvable in closed form using basic algebra and admits a graphical representation useful for illustrating a variety of comparative statics. They show how…

Displayed Trees Do Not Determine Distinguishability Under the Network Multispecies Coalescent

PubMed Central

Zhu, Sha; Degnan, James H.

2017-01-01

Abstract Recent work in estimating species relationships from gene trees has included inferring networks assuming that past hybridization has occurred between species. Probabilistic models using the multispecies coalescent can be used in this framework for likelihood-based inference of both network topologies and parameters, including branch lengths and hybridization parameters. A difficulty for such methods is that it is not always clear whether, or to what extent, networks are identifiable—that is whether there could be two distinct networks that lead to the same distribution of gene trees. For cases in which incomplete lineage sorting occurs in addition to hybridization, we demonstrate a new representation of the species network likelihood that expresses the probability distribution of the gene tree topologies as a linear combination of gene tree distributions given a set of species trees. This representation makes it clear that in some cases in which two distinct networks give the same distribution of gene trees when sampling one allele per species, the two networks can be distinguished theoretically when multiple individuals are sampled per species. This result means that network identifiability is not only a function of the trees displayed by the networks but also depends on allele sampling within species. We additionally give an example in which two networks that display exactly the same trees can be distinguished from their gene trees even when there is only one lineage sampled per species. PMID:27780899

Social representations of electricity network technologies: exploring processes of anchoring and objectification through the use of visual research methods.

PubMed

Devine-Wright, Hannah; Devine-Wright, Patrick

2009-06-01

The aim of this study was to explore everyday thinking about the UK electricity network, in light of government policy to increase the generation of electricity from renewable energy sources. Existing literature on public perceptions of electricity network technologies was broadened by adopting a more socially embedded conception of the construction of knowledge using the theory of social representations (SRT) to explore symbolic associations with network technologies. Drawing and association tasks were administered within nine discussion groups held in two places: a Scottish town where significant upgrades to the local transmission network were planned and an English city with no such plans. Our results illustrate the ways in which network technologies, such as high voltage (HV) pylons, are objectified in talk and drawings. These invoked positive as well as negative symbolic and affective associations, both at the level of specific pylons, and the 'National Grid' as a whole and are anchored in understanding of other networks such as mobile telecommunications. We conclude that visual methods are especially useful for exploring beliefs about technologies that are widespread, proximal to our everyday experience but nevertheless unfamiliar topics of everyday conversation.

BiNA: A Visual Analytics Tool for Biological Network Data

PubMed Central

Gerasch, Andreas; Faber, Daniel; Küntzer, Jan; Niermann, Peter; Kohlbacher, Oliver; Lenhof, Hans-Peter; Kaufmann, Michael

2014-01-01

Interactive visual analysis of biological high-throughput data in the context of the underlying networks is an essential task in modern biomedicine with applications ranging from metabolic engineering to personalized medicine. The complexity and heterogeneity of data sets require flexible software architectures for data analysis. Concise and easily readable graphical representation of data and interactive navigation of large data sets are essential in this context. We present BiNA - the Biological Network Analyzer - a flexible open-source software for analyzing and visualizing biological networks. Highly configurable visualization styles for regulatory and metabolic network data offer sophisticated drawings and intuitive navigation and exploration techniques using hierarchical graph concepts. The generic projection and analysis framework provides powerful functionalities for visual analyses of high-throughput omics data in the context of networks, in particular for the differential analysis and the analysis of time series data. A direct interface to an underlying data warehouse provides fast access to a wide range of semantically integrated biological network databases. A plugin system allows simple customization and integration of new analysis algorithms or visual representations. BiNA is available under the 3-clause BSD license at http://bina.unipax.info/. PMID:24551056

Classification and pose estimation of objects using nonlinear features

NASA Astrophysics Data System (ADS)

Talukder, Ashit; Casasent, David P.

1998-03-01

A new nonlinear feature extraction method called the maximum representation and discrimination feature (MRDF) method is presented for extraction of features from input image data. It implements transformations similar to the Sigma-Pi neural network. However, the weights of the MRDF are obtained in closed form, and offer advantages compared to nonlinear neural network implementations. The features extracted are useful for both object discrimination (classification) and object representation (pose estimation). We show its use in estimating the class and pose of images of real objects and rendered solid CAD models of machine parts from single views using a feature-space trajectory (FST) neural network classifier. We show more accurate classification and pose estimation results than are achieved by standard principal component analysis (PCA) and Fukunaga-Koontz (FK) feature extraction methods.

Gender Divergence in Academics' Representation and Research Productivity: A Nigerian Case Study

ERIC Educational Resources Information Center

Opesade, Adeola Omobola; Famurewa, Kofoworola Folakemi; Igwe, Ebelechukwu Gloria

2017-01-01

Gender equity is increasingly seen as an indicator of development and global acceptance in networks of higher education. Despite this, gender divergence in research productivity of academics coupled with under-representation of women in science has been reported to beset female's scholarly activities. Previous studies provide differing results,…

Order Short-Term Memory Capacity Predicts Nonword Reading and Spelling in First and Second Grade

ERIC Educational Resources Information Center

Binamé, Florence; Poncelet, Martine

2016-01-01

Recent theories of short-term memory (STM) distinguish between item information, which reflects the temporary activation of long-term representations stored in the language system, and serial-order information, which is encoded in a specific representational system that is independent of the language network. Some studies examining the…

Comparative analysis of hierarchical triangulated irregular networks to represent 3D elevation in terrain databases

NASA Astrophysics Data System (ADS)

Abdelguerfi, Mahdi; Wynne, Chris; Cooper, Edgar; Ladner, Roy V.; Shaw, Kevin B.

1997-08-01

Three-dimensional terrain representation plays an important role in a number of terrain database applications. Hierarchical triangulated irregular networks (TINs) provide a variable-resolution terrain representation that is based on a nested triangulation of the terrain. This paper compares and analyzes existing hierarchical triangulation techniques. The comparative analysis takes into account how aesthetically appealing and accurate the resulting terrain representation is. Parameters, such as adjacency, slivers, and streaks, are used to provide a measure on how aesthetically appealing the terrain representation is. Slivers occur when the triangulation produces thin and slivery triangles. Streaks appear when there are too many triangulations done at a given vertex. Simple mathematical expressions are derived for these parameters, thereby providing a fairer and a more easily duplicated comparison. In addition to meeting the adjacency requirement, an aesthetically pleasant hierarchical TINs generation algorithm is expected to reduce both slivers and streaks while maintaining accuracy. A comparative analysis of a number of existing approaches shows that a variant of a method originally proposed by Scarlatos exhibits better overall performance.

Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition

PubMed Central

Cadieu, Charles F.; Hong, Ha; Yamins, Daniel L. K.; Pinto, Nicolas; Ardila, Diego; Solomon, Ethan A.; Majaj, Najib J.; DiCarlo, James J.

2014-01-01

The primate visual system achieves remarkable visual object recognition performance even in brief presentations, and under changes to object exemplar, geometric transformations, and background variation (a.k.a. core visual object recognition). This remarkable performance is mediated by the representation formed in inferior temporal (IT) cortex. In parallel, recent advances in machine learning have led to ever higher performing models of object recognition using artificial deep neural networks (DNNs). It remains unclear, however, whether the representational performance of DNNs rivals that of the brain. To accurately produce such a comparison, a major difficulty has been a unifying metric that accounts for experimental limitations, such as the amount of noise, the number of neural recording sites, and the number of trials, and computational limitations, such as the complexity of the decoding classifier and the number of classifier training examples. In this work, we perform a direct comparison that corrects for these experimental limitations and computational considerations. As part of our methodology, we propose an extension of “kernel analysis” that measures the generalization accuracy as a function of representational complexity. Our evaluations show that, unlike previous bio-inspired models, the latest DNNs rival the representational performance of IT cortex on this visual object recognition task. Furthermore, we show that models that perform well on measures of representational performance also perform well on measures of representational similarity to IT, and on measures of predicting individual IT multi-unit responses. Whether these DNNs rely on computational mechanisms similar to the primate visual system is yet to be determined, but, unlike all previous bio-inspired models, that possibility cannot be ruled out merely on representational performance grounds. PMID:25521294

Three-dimensional Organization of Layered Apical Cytoskeletal Networks Associated with Mouse Airway Tissue Development

NASA Astrophysics Data System (ADS)

Tateishi, Kazuhiro; Nishida, Tomoki; Inoue, Kanako; Tsukita, Sachiko

2017-03-01

The cytoskeleton is an essential cellular component that enables various sophisticated functions of epithelial cells by forming specialized subcellular compartments. However, the functional and structural roles of cytoskeletons in subcellular compartmentalization are still not fully understood. Here we identified a novel network structure consisting of actin filaments, intermediate filaments, and microtubules directly beneath the apical membrane in mouse airway multiciliated cells and in cultured epithelial cells. Three-dimensional imaging by ultra-high voltage electron microscopy and immunofluorescence revealed that the morphological features of each network depended on the cell type and were spatiotemporally integrated in association with tissue development. Detailed analyses using Odf2 mutant mice, which lack ciliary basal feet and apical microtubules, suggested a novel contribution of the intermediate filaments to coordinated ciliary beating. These findings provide a new perspective for viewing epithelial cell differentiation and tissue morphogenesis through the structure and function of apical cytoskeletal networks.

Perception for rugged terrain

NASA Technical Reports Server (NTRS)

Kweon, In SO; Hebert, Martial; Kanade, Takeo

1989-01-01

A three-dimensional perception system for building a geometrical description of rugged terrain environments from range image data is presented with reference to the exploration of the rugged terrain of Mars. An intermediate representation consisting of an elevation map that includes an explicit representation of uncertainty and labeling of the occluded regions is proposed. The locus method used to convert range image to an elevation map is introduced, along with an uncertainty model based on this algorithm. Both the elevation map and the locus method are the basis of a terrain matching algorithm which does not assume any correspondences between range images. The two-stage algorithm consists of a feature-based matching algorithm to compute an initial transform and an iconic terrain matching algorithm to merge multiple range images into a uniform representation. Terrain modeling results on real range images of rugged terrain are presented. The algorithms considered are a fundamental part of the perception system for the Ambler, a legged locomotor.

Study on dioxygen reduction by mutational modifications of the hydrogen bond network leading from bulk water to the trinuclear copper center in bilirubin oxidase

DOE Office of Scientific and Technical Information (OSTI.GOV)

Morishita, Hirotoshi; Kurita, Daisuke; Kataoka, Kunishige

2014-07-18

Highlights: • Proton transport pathway in bilirubin oxidase was mutated. • Two intermediates in the dioxygen reduction steps were trapped and characterized. • A specific glutamate for dioxygen reduction by multicopper oxidases was identified. - Abstract: The hydrogen bond network leading from bulk water to the trinuclear copper center in bilirubin oxidase is constructed with Glu463 and water molecules to transport protons for the four-electron reduction of dioxygen. Substitutions of Glu463 with Gln or Ala were attributed to virtually complete loss or significant reduction in enzymatic activities due to an inhibition of the proton transfer steps to dioxygen. The singlemore » turnover reaction of the Glu463Gln mutant afforded the highly magnetically interacted intermediate II (native intermediate) with a broad g = 1.96 electron paramagnetic resonance signal detectable at cryogenic temperatures. Reactions of the double mutants, Cys457Ser/Glu463Gln and Cys457Ser/Glu463Ala afforded the intermediate I (peroxide intermediate) because the type I copper center to donate the fourth electron to dioxygen was vacant in addition to the interference of proton transport due to the mutation at Glu463. The intermediate I gave no electron paramagnetic resonance signal, but the type II copper signal became detectable with the decay of the intermediate I. Structural and functional similarities between multicopper oxidases are discussed based on the present mutation at Glu463 in bilirubin oxidase.« less

Characterization of Folding Mechanisms of Trp-cage and WW-domain by Network Analysis of Simulations with a Hybrid-resolution Model

PubMed Central

Han, Wei; Schulten, Klaus

2013-01-01

In this study, we apply a hybrid-resolution model, namely PACE, to characterize the free energy surfaces (FESs) of trp-cage and a WW domain variant along with the respective folding mechanisms. Unbiased, independent simulations with PACE are found to achieve together multiple folding and unfolding events for both proteins, allowing us to perform network analysis of the FESs to identify folding pathways. PACE reproduces for both proteins expected complexity hidden in the folding FESs, in particular, meta-stable non-native intermediates. Pathway analysis shows that some of these intermediates are, actually, on-pathway folding intermediates and that intermediates kinetically closest to the native states can be either critical on-pathway or off-pathway intermediates, depending on the protein. Apart from general insights into folding, specific folding mechanisms of the proteins are resolved. We find that trp-cage folds via a dominant pathway in which hydrophobic collapse occurs before the N-terminal helix forms; full incorporation of Trp6 into the hydrophobic core takes place as the last step of folding, which, however, may not be the rate-limiting step. For the WW domain variant studied we observe two main folding pathways with opposite orders of formation of the two hairpins involved in the structure; for either pathway, formation of hairpin 1 is more likely to be the rate-limiting step. Altogether, our results suggest that PACE combined with network analysis is a computationally efficient and valuable tool for the study of protein folding. PMID:23915394

Diverse Region-Based CNN for Hyperspectral Image Classification.

PubMed

Zhang, Mengmeng; Li, Wei; Du, Qian

2018-06-01

Convolutional neural network (CNN) is of great interest in machine learning and has demonstrated excellent performance in hyperspectral image classification. In this paper, we propose a classification framework, called diverse region-based CNN, which can encode semantic context-aware representation to obtain promising features. With merging a diverse set of discriminative appearance factors, the resulting CNN-based representation exhibits spatial-spectral context sensitivity that is essential for accurate pixel classification. The proposed method exploiting diverse region-based inputs to learn contextual interactional features is expected to have more discriminative power. The joint representation containing rich spectral and spatial information is then fed to a fully connected network and the label of each pixel vector is predicted by a softmax layer. Experimental results with widely used hyperspectral image data sets demonstrate that the proposed method can surpass any other conventional deep learning-based classifiers and other state-of-the-art classifiers.

Experience with abstract notation one

NASA Technical Reports Server (NTRS)

Harvey, James D.; Weaver, Alfred C.

1990-01-01

The development of computer science has produced a vast number of machine architectures, programming languages, and compiler technologies. The cross product of these three characteristics defines the spectrum of previous and present data representation methodologies. With regard to computer networks, the uniqueness of these methodologies presents an obstacle when disparate host environments are to be interconnected. Interoperability within a heterogeneous network relies upon the establishment of data representation commonality. The International Standards Organization (ISO) is currently developing the abstract syntax notation one standard (ASN.1) and the basic encoding rules standard (BER) that collectively address this problem. When used within the presentation layer of the open systems interconnection reference model, these two standards provide the data representation commonality required to facilitate interoperability. The details of a compiler that was built to automate the use of ASN.1 and BER are described. From this experience, insights into both standards are given and potential problems relating to this development effort are discussed.

Neurofeedback and the Neural Representation of Self: Lessons From Awake State and Sleep.

PubMed

Ioannides, Andreas A

2018-01-01

Neurofeedback has been around for half a century, but despite some promising results it is not yet widely appreciated. Recently, some of the concerns about neurofeedback have been addressed with functional magnetic resonance imaging and magnetoencephalography adding their contributions to the long history of neurofeedback with electroencephalography. Attempts to address other concerns related to methodological issues with new experiments and meta-analysis of earlier studies, have opened up new questions about its efficacy. A key concern about neurofeedback is the missing framework to explain how improvements in very different and apparently unrelated conditions are achieved. Recent advances in neuroscience begin to address this concern. A particularly promising approach is the analysis of resting state of fMRI data, which has revealed robust covariations in brain networks that maintain their integrity in sleep and even anesthesia. Aberrant activity in three brain wide networks (i.e., the default mode, central executive and salience networks) has been associated with a number of psychiatric disorders. Recent publications have also suggested that neurofeedback guides the restoration of "normal" activity in these three networks. Using very recent results from our analysis of whole night MEG sleep data together with key concepts from developmental psychology, cloaked in modern neuroscience terms, a theoretical framework is proposed for a neural representation of the self, located at the core of a double onion-like structure of the default mode network. This framework fits a number of old and recent neuroscientific findings, and unites the way attention and memory operate in awake state and during sleep. In the process, safeguards are uncovered, put in place by evolution, before any interference with the core representation of self can proceed. Within this framework, neurofeedback is seen as set of methods for restoration of aberrant activity in large scale networks. The framework also admits quantitative measures of improvements to be made by personalized neurofeedback protocols. Finally, viewed through the framework developed, neurofeedback's safe nature is revealed while raising some concerns for interventions that attempt to alter the neural self-representation bypassing the safeguards evolution has put in place.

Neurofeedback and the Neural Representation of Self: Lessons From Awake State and Sleep

PubMed Central

Ioannides, Andreas A.

2018-01-01

Neurofeedback has been around for half a century, but despite some promising results it is not yet widely appreciated. Recently, some of the concerns about neurofeedback have been addressed with functional magnetic resonance imaging and magnetoencephalography adding their contributions to the long history of neurofeedback with electroencephalography. Attempts to address other concerns related to methodological issues with new experiments and meta-analysis of earlier studies, have opened up new questions about its efficacy. A key concern about neurofeedback is the missing framework to explain how improvements in very different and apparently unrelated conditions are achieved. Recent advances in neuroscience begin to address this concern. A particularly promising approach is the analysis of resting state of fMRI data, which has revealed robust covariations in brain networks that maintain their integrity in sleep and even anesthesia. Aberrant activity in three brain wide networks (i.e., the default mode, central executive and salience networks) has been associated with a number of psychiatric disorders. Recent publications have also suggested that neurofeedback guides the restoration of “normal” activity in these three networks. Using very recent results from our analysis of whole night MEG sleep data together with key concepts from developmental psychology, cloaked in modern neuroscience terms, a theoretical framework is proposed for a neural representation of the self, located at the core of a double onion-like structure of the default mode network. This framework fits a number of old and recent neuroscientific findings, and unites the way attention and memory operate in awake state and during sleep. In the process, safeguards are uncovered, put in place by evolution, before any interference with the core representation of self can proceed. Within this framework, neurofeedback is seen as set of methods for restoration of aberrant activity in large scale networks. The framework also admits quantitative measures of improvements to be made by personalized neurofeedback protocols. Finally, viewed through the framework developed, neurofeedback’s safe nature is revealed while raising some concerns for interventions that attempt to alter the neural self-representation bypassing the safeguards evolution has put in place. PMID:29755332

Multiphase flow predictions from carbonate pore space images using extracted network models

NASA Astrophysics Data System (ADS)

Al-Kharusi, Anwar S.; Blunt, Martin J.

2008-06-01

A methodology to extract networks from pore space images is used to make predictions of multiphase transport properties for subsurface carbonate samples. The extraction of the network model is based on the computation of the location and sizes of pores and throats to create a topological representation of the void space of three-dimensional (3-D) rock images, using the concept of maximal balls. In this work, we follow a multistaged workflow. We start with a 2-D thin-section image; convert it statistically into a 3-D representation of the pore space; extract a network model from this image; and finally, simulate primary drainage, waterflooding, and secondary drainage flow processes using a pore-scale simulator. We test this workflow for a reservoir carbonate rock. The network-predicted absolute permeability is similar to the core plug measured value and the value computed on the 3-D void space image using the lattice Boltzmann method. The predicted capillary pressure during primary drainage agrees well with a mercury-air experiment on a core sample, indicating that we have an adequate representation of the rock's pore structure. We adjust the contact angles in the network to match the measured waterflood and secondary drainage capillary pressures. We infer a significant degree of contact angle hysteresis. We then predict relative permeabilities for primary drainage, waterflooding, and secondary drainage that agree well with laboratory measured values. This approach can be used to predict multiphase transport properties when wettability and pore structure vary in a reservoir, where experimental data is scant or missing. There are shortfalls to this approach, however. We compare results from three networks, one of which was derived from a section of the rock containing vugs. Our method fails to predict properties reliably when an unrepresentative image is processed to construct the 3-D network model. This occurs when the image volume is not sufficient to represent the geological variations observed in a core plug sample.

Conservation priorities for mammals in megadiverse Mexico: the efficiency of reserve networks.

PubMed

Ceballos, Gerardo

2007-03-01

A major goal of conservation biologists is to identify critical areas for the conservation of biological diversity and then strategically include them in an efficient system of reserves. In general, however, reserve networks have been selected for different objectives, and most countries lack an evaluation of their reserves' ability to represent a percentage of the national diversity. This paper evaluates the effectiveness of a network of reserves to represent the species of mammals in Mexico. The focus of the analyses is on species and site level, evaluating the representation of all terrestrial mammals in the 30 most important reserves. The representation of all species, endemic species, endangered species, and species with restricted distributions in the reserves was assessed and compared. Endemic or endangered species with restricted distributions were expected to be less represented in reserves than were widespread species. The most important reserves for the conservation of mammals were determined with the use of complementarity analyses. Priority sites for the representation of all the species currently absent from the reserve network were then selected. The results have broad applications for conservation. First, 82% of the mammal species from Mexico were represented in the reserve network, which covers a small portion (3.8%) of the country. Second, this percentage is certainly larger as several reserves were not evaluated due to a lack of data. A priority for a national conservation strategy could be to conduct biological surveys in those reserves lacking inventories to evaluate their contribution to conservation. Third, in spite of its demonstrated value, Mexico's reserve network can be improved by designating complementary areas. Additional priority sites, where reserves are required to represent most gap species in the network, were identified. Finally, it is clear that this reserve network has limitations for maintaining biodiversity and ecosystem services at regional scales. A comprehensive conservation strategy has, therefore, to incorporate mechanisms that enhance the value of human-dominated landscapes for the maintenance of biodiversity.

Group Analysis in MNE-Python of Evoked Responses from a Tactile Stimulation Paradigm: A Pipeline for Reproducibility at Every Step of Processing, Going from Individual Sensor Space Representations to an across-Group Source Space Representation

PubMed Central

Andersen, Lau M.

2018-01-01

An important aim of an analysis pipeline for magnetoencephalographic data is that it allows for the researcher spending maximal effort on making the statistical comparisons that will answer the questions of the researcher, while in turn spending minimal effort on the intricacies and machinery of the pipeline. I here present a set of functions and scripts that allow for setting up a clear, reproducible structure for separating raw and processed data into folders and files such that minimal effort can be spend on: (1) double-checking that the right input goes into the right functions; (2) making sure that output and intermediate steps can be accessed meaningfully; (3) applying operations efficiently across groups of subjects; (4) re-processing data if changes to any intermediate step are desirable. Applying the scripts requires only general knowledge about the Python language. The data analyses are neural responses to tactile stimulations of the right index finger in a group of 20 healthy participants acquired from an Elekta Neuromag System. Two analyses are presented: going from individual sensor space representations to, respectively, an across-group sensor space representation and an across-group source space representation. The processing steps covered for the first analysis are filtering the raw data, finding events of interest in the data, epoching data, finding and removing independent components related to eye blinks and heart beats, calculating participants' individual evoked responses by averaging over epoched data and calculating a grand average sensor space representation over participants. The second analysis starts from the participants' individual evoked responses and covers: estimating noise covariance, creating a forward model, creating an inverse operator, estimating distributed source activity on the cortical surface using a minimum norm procedure, morphing those estimates onto a common cortical template and calculating the patterns of activity that are statistically different from baseline. To estimate source activity, processing of the anatomy of subjects based on magnetic resonance imaging is necessary. The necessary steps are covered here: importing magnetic resonance images, segmenting the brain, estimating boundaries between different tissue layers, making fine-resolution scalp surfaces for facilitating co-registration, creating source spaces and creating volume conductors for each subject. PMID:29403349

Group Analysis in MNE-Python of Evoked Responses from a Tactile Stimulation Paradigm: A Pipeline for Reproducibility at Every Step of Processing, Going from Individual Sensor Space Representations to an across-Group Source Space Representation.

PubMed

Andersen, Lau M

2018-01-01

An important aim of an analysis pipeline for magnetoencephalographic data is that it allows for the researcher spending maximal effort on making the statistical comparisons that will answer the questions of the researcher, while in turn spending minimal effort on the intricacies and machinery of the pipeline. I here present a set of functions and scripts that allow for setting up a clear, reproducible structure for separating raw and processed data into folders and files such that minimal effort can be spend on: (1) double-checking that the right input goes into the right functions; (2) making sure that output and intermediate steps can be accessed meaningfully; (3) applying operations efficiently across groups of subjects; (4) re-processing data if changes to any intermediate step are desirable. Applying the scripts requires only general knowledge about the Python language. The data analyses are neural responses to tactile stimulations of the right index finger in a group of 20 healthy participants acquired from an Elekta Neuromag System. Two analyses are presented: going from individual sensor space representations to, respectively, an across-group sensor space representation and an across-group source space representation. The processing steps covered for the first analysis are filtering the raw data, finding events of interest in the data, epoching data, finding and removing independent components related to eye blinks and heart beats, calculating participants' individual evoked responses by averaging over epoched data and calculating a grand average sensor space representation over participants. The second analysis starts from the participants' individual evoked responses and covers: estimating noise covariance, creating a forward model, creating an inverse operator, estimating distributed source activity on the cortical surface using a minimum norm procedure, morphing those estimates onto a common cortical template and calculating the patterns of activity that are statistically different from baseline. To estimate source activity, processing of the anatomy of subjects based on magnetic resonance imaging is necessary. The necessary steps are covered here: importing magnetic resonance images, segmenting the brain, estimating boundaries between different tissue layers, making fine-resolution scalp surfaces for facilitating co-registration, creating source spaces and creating volume conductors for each subject.

On singular cases in the design derivative of Green's functional

NASA Technical Reports Server (NTRS)

Reiss, Robert

1987-01-01

The author's prior development of a general abstract representation for the design sensitivities of Green's functional for linear structural systems is extended to the case where the structural stiffness vanishes at an internal location. This situation often occurs in the optimal design of structures. Most optimality criteria require that optimally designed beams be statically determinate. For clamped-pinned beams, for example, this is possible only if the flexural stiffness vanishes at some intermediate location. The Green's function for such structures depends upon the stiffness and the location where it vanishes. A precise representation for Green's function's sensitivity to the location of vanishing stiffness is presented for beams and axisymmetric plates.

Assessment of habitat representation across a network of marine protected areas with implications for the spatial design of monitoring.

PubMed

Young, Mary; Carr, Mark

2015-01-01

Networks of marine protected areas (MPAs) are being adopted globally to protect ecosystems and supplement fisheries management. The state of California recently implemented a coast-wide network of MPAs, a statewide seafloor mapping program, and ecological characterizations of species and ecosystems targeted for protection by the network. The main goals of this study were to use these data to evaluate how well seafloor features, as proxies for habitats, are represented and replicated across an MPA network and how well ecological surveys representatively sampled fish habitats inside MPAs and adjacent reference sites. Seafloor data were classified into broad substrate categories (rock and sediment) and finer scale geomorphic classifications standard to marine classification schemes using surface analyses (slope, ruggedness, etc.) done on the digital elevation model derived from multibeam bathymetry data. These classifications were then used to evaluate the representation and replication of seafloor structure within the MPAs and across the ecological surveys. Both the broad substrate categories and the finer scale geomorphic features were proportionately represented for many of the classes with deviations of 1-6% and 0-7%, respectively. Within MPAs, however, representation of seafloor features differed markedly from original estimates, with differences ranging up to 28%. Seafloor structure in the biological monitoring design had mismatches between sampling in the MPAs and their corresponding reference sites and some seafloor structure classes were missed entirely. The geomorphic variables derived from multibeam bathymetry data for these analyses are known determinants of the distribution and abundance of marine species and for coastal marine biodiversity. Thus, analyses like those performed in this study can be a valuable initial method of evaluating and predicting the conservation value of MPAs across a regional network.

Beyond the word and image: characteristics of a common meaning system for language and vision revealed by functional and structural imaging.

PubMed

Jouen, A L; Ellmore, T M; Madden, C J; Pallier, C; Dominey, P F; Ventre-Dominey, J

2015-02-01

This research tests the hypothesis that comprehension of human events will engage an extended semantic representation system, independent of the input modality (sentence vs. picture). To investigate this, we examined brain activation and connectivity in 19 subjects who read sentences and viewed pictures depicting everyday events, in a combined fMRI and DTI study. Conjunction of activity in understanding sentences and pictures revealed a common fronto-temporo-parietal network that included the middle and inferior frontal gyri, the parahippocampal-retrosplenial complex, the anterior and middle temporal gyri, the inferior parietal lobe in particular the temporo-parietal cortex. DTI tractography seeded from this temporo-parietal cortex hub revealed a multi-component network reaching into the temporal pole, the ventral frontal pole and premotor cortex. A significant correlation was found between the relative pathway density issued from the temporo-parietal cortex and the imageability of sentences for individual subjects, suggesting a potential functional link between comprehension and the temporo-parietal connectivity strength. These data help to define a "meaning" network that includes components of recently characterized systems for semantic memory, embodied simulation, and visuo-spatial scene representation. The network substantially overlaps with the "default mode" network implicated as part of a core network of semantic representation, along with brain systems related to the formation of mental models, and reasoning. These data are consistent with a model of real-world situational understanding that is highly embodied. Crucially, the neural basis of this embodied understanding is not limited to sensorimotor systems, but extends to the highest levels of cognition, including autobiographical memory, scene analysis, mental model formation, reasoning and theory of mind. Copyright © 2014 Elsevier Inc. All rights reserved.

Assessment of Habitat Representation across a Network of Marine Protected Areas with Implications for the Spatial Design of Monitoring

PubMed Central

Young, Mary; Carr, Mark

2015-01-01

Networks of marine protected areas (MPAs) are being adopted globally to protect ecosystems and supplement fisheries management. The state of California recently implemented a coast-wide network of MPAs, a statewide seafloor mapping program, and ecological characterizations of species and ecosystems targeted for protection by the network. The main goals of this study were to use these data to evaluate how well seafloor features, as proxies for habitats, are represented and replicated across an MPA network and how well ecological surveys representatively sampled fish habitats inside MPAs and adjacent reference sites. Seafloor data were classified into broad substrate categories (rock and sediment) and finer scale geomorphic classifications standard to marine classification schemes using surface analyses (slope, ruggedness, etc.) done on the digital elevation model derived from multibeam bathymetry data. These classifications were then used to evaluate the representation and replication of seafloor structure within the MPAs and across the ecological surveys. Both the broad substrate categories and the finer scale geomorphic features were proportionately represented for many of the classes with deviations of 1-6% and 0-7%, respectively. Within MPAs, however, representation of seafloor features differed markedly from original estimates, with differences ranging up to 28%. Seafloor structure in the biological monitoring design had mismatches between sampling in the MPAs and their corresponding reference sites and some seafloor structure classes were missed entirely. The geomorphic variables derived from multibeam bathymetry data for these analyses are known determinants of the distribution and abundance of marine species and for coastal marine biodiversity. Thus, analyses like those performed in this study can be a valuable initial method of evaluating and predicting the conservation value of MPAs across a regional network. PMID:25760858

Semantic Representation of Newly Learned L2 Words and Their Integration in the L2 Lexicon

ERIC Educational Resources Information Center

Bordag, Denisa; Kirschenbaum, Amit; Rogahn, Maria; Opitz, Andreas

2017-01-01

The present semantic priming study explores the integration of newly learnt L2 German words into the L2 semantic network of German advanced learners. It provides additional evidence in support of earlier findings reporting semantic inhibition effects for emergent representations. An inhibitory mechanism is proposed that temporarily decreases the…

Deep feature representation with stacked sparse auto-encoder and convolutional neural network for hyperspectral imaging-based detection of cucumber defects

USDA-ARS?s Scientific Manuscript database

It is challenging to achieve rapid and accurate processing of large amounts of hyperspectral image data. This research was aimed to develop a novel classification method by employing deep feature representation with the stacked sparse auto-encoder (SSAE) and the SSAE combined with convolutional neur...

Expert identification of visual primitives used by CNNs during mammogram classification

NASA Astrophysics Data System (ADS)

Wu, Jimmy; Peck, Diondra; Hsieh, Scott; Dialani, Vandana; Lehman, Constance D.; Zhou, Bolei; Syrgkanis, Vasilis; Mackey, Lester; Patterson, Genevieve

2018-02-01

This work interprets the internal representations of deep neural networks trained for classification of diseased tissue in 2D mammograms. We propose an expert-in-the-loop inter- pretation method to label the behavior of internal units in convolutional neural networks (CNNs). Expert radiologists identify that the visual patterns detected by the units are correlated with meaningful medical phenomena such as mass tissue and calcificated vessels. We demonstrate that several trained CNN models are able to produce explanatory descriptions to support the final classification decisions. We view this as an important first step toward interpreting the internal representations of medical classification CNNs and explaining their predictions.

ShapeShop: Towards Understanding Deep Learning Representations via Interactive Experimentation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hohman, Frederick M.; Hodas, Nathan O.; Chau, Duen Horng

Deep learning is the driving force behind many recent technologies; however, deep neural networks are often viewed as “black-boxes” due to their internal complexity that is hard to understand. Little research focuses on helping people explore and understand the relationship between a user’s data and the learned representations in deep learning models. We present our ongoing work, ShapeShop, an interactive system for visualizing and understanding what semantics a neural network model has learned. Built using standard web technologies, ShapeShop allows users to experiment with and compare deep learning models to help explore the robustness of image classifiers.

5W1H Information Extraction with CNN-Bidirectional LSTM

NASA Astrophysics Data System (ADS)

Nurdin, A.; Maulidevi, N. U.

2018-03-01

In this work, information about who, did what, when, where, why, and how on Indonesian news articles were extracted by combining Convolutional Neural Network and Bidirectional Long Short-Term Memory. Convolutional Neural Network can learn semantically meaningful representations of sentences. Bidirectional LSTM can analyze the relations among words in the sequence. We also use word embedding word2vec for word representation. By combining these algorithms, we obtained F-measure 0.808. Our experiments show that CNN-BLSTM outperforms other shallow methods, namely IBk, C4.5, and Naïve Bayes with the F-measure 0.655, 0.645, and 0.595, respectively.

Robust spatial memory maps in flickering neuronal networks: a topological model

NASA Astrophysics Data System (ADS)

Dabaghian, Yuri; Babichev, Andrey; Memoli, Facundo; Chowdhury, Samir; Rice University Collaboration; Ohio State University Collaboration

It is widely accepted that the hippocampal place cells provide a substrate of the neuronal representation of the environment--the ``cognitive map''. However, hippocampal network, as any other network in the brain is transient: thousands of hippocampal neurons die every day and the connections formed by these cells constantly change due to various forms of synaptic plasticity. What then explains the remarkable reliability of our spatial memories? We propose a computational approach to answering this question based on a couple of insights. First, we propose that the hippocampal cognitive map is fundamentally topological, and hence it is amenable to analysis by topological methods. We then apply several novel methods from homology theory, to understand how dynamic connections between cells influences the speed and reliability of spatial learning. We simulate the rat's exploratory movements through different environments and study how topological invariants of these environments arise in a network of simulated neurons with ``flickering'' connectivity. We find that despite transient connectivity the network of place cells produces a stable representation of the topology of the environment.

A Self-Organizing Incremental Neural Network based on local distribution learning.

PubMed

Xing, Youlu; Shi, Xiaofeng; Shen, Furao; Zhou, Ke; Zhao, Jinxi

2016-12-01

In this paper, we propose an unsupervised incremental learning neural network based on local distribution learning, which is called Local Distribution Self-Organizing Incremental Neural Network (LD-SOINN). The LD-SOINN combines the advantages of incremental learning and matrix learning. It can automatically discover suitable nodes to fit the learning data in an incremental way without a priori knowledge such as the structure of the network. The nodes of the network store rich local information regarding the learning data. The adaptive vigilance parameter guarantees that LD-SOINN is able to add new nodes for new knowledge automatically and the number of nodes will not grow unlimitedly. While the learning process continues, nodes that are close to each other and have similar principal components are merged to obtain a concise local representation, which we call a relaxation data representation. A denoising process based on density is designed to reduce the influence of noise. Experiments show that the LD-SOINN performs well on both artificial and real-word data. Copyright © 2016 Elsevier Ltd. All rights reserved.

Feature to prototype transition in neural networks

NASA Astrophysics Data System (ADS)

Krotov, Dmitry; Hopfield, John

Models of associative memory with higher order (higher than quadratic) interactions, and their relationship to neural networks used in deep learning are discussed. Associative memory is conventionally described by recurrent neural networks with dynamical convergence to stable points. Deep learning typically uses feedforward neural nets without dynamics. However, a simple duality relates these two different views when applied to problems of pattern classification. From the perspective of associative memory such models deserve attention because they make it possible to store a much larger number of memories, compared to the quadratic case. In the dual description, these models correspond to feedforward neural networks with one hidden layer and unusual activation functions transmitting the activities of the visible neurons to the hidden layer. These activation functions are rectified polynomials of a higher degree rather than the rectified linear functions used in deep learning. The network learns representations of the data in terms of features for rectified linear functions, but as the power in the activation function is increased there is a gradual shift to a prototype-based representation, the two extreme regimes of pattern recognition known in cognitive psychology. Simons Center for Systems Biology.

A network of discrete events for the representation and analysis of diffusion dynamics.

PubMed

Pintus, Alberto M; Pazzona, Federico G; Demontis, Pierfranco; Suffritti, Giuseppe B

2015-11-14

We developed a coarse-grained description of the phenomenology of diffusive processes, in terms of a space of discrete events and its representation as a network. Once a proper classification of the discrete events underlying the diffusive process is carried out, their transition matrix is calculated on the basis of molecular dynamics data. This matrix can be represented as a directed, weighted network where nodes represent discrete events, and the weight of edges is given by the probability that one follows the other. The structure of this network reflects dynamical properties of the process of interest in such features as its modularity and the entropy rate of nodes. As an example of the applicability of this conceptual framework, we discuss here the physics of diffusion of small non-polar molecules in a microporous material, in terms of the structure of the corresponding network of events, and explain on this basis the diffusivity trends observed. A quantitative account of these trends is obtained by considering the contribution of the various events to the displacement autocorrelation function.

Network representation of protein interactions: Theory of graph description and analysis.

PubMed

Kurzbach, Dennis

2016-09-01

A methodological framework is presented for the graph theoretical interpretation of NMR data of protein interactions. The proposed analysis generalizes the idea of network representations of protein structures by expanding it to protein interactions. This approach is based on regularization of residue-resolved NMR relaxation times and chemical shift data and subsequent construction of an adjacency matrix that represents the underlying protein interaction as a graph or network. The network nodes represent protein residues. Two nodes are connected if two residues are functionally correlated during the protein interaction event. The analysis of the resulting network enables the quantification of the importance of each amino acid of a protein for its interactions. Furthermore, the determination of the pattern of correlations between residues yields insights into the functional architecture of an interaction. This is of special interest for intrinsically disordered proteins, since the structural (three-dimensional) architecture of these proteins and their complexes is difficult to determine. The power of the proposed methodology is demonstrated at the example of the interaction between the intrinsically disordered protein osteopontin and its natural ligand heparin. © 2016 The Protein Society.

The role of the episodic buffer in working memory for language processing.

PubMed

Rudner, Mary; Rönnberg, Jerker

2008-03-01

A body of work has accumulated to show that the cognitive process of binding information from different mnemonic and sensory sources as well as in different linguistic modalities can be fractionated from general executive functions in working memory both functionally and neurally. This process has been defined in terms of the episodic buffer (Baddeley in Trends Cogn Sci 4(11):417-423, 2000). This paper considers behavioural, neuropsychological and neuroimaging data that elucidate the role of the episodic buffer in language processing. We argue that the episodic buffer seems to be truly multimodal in function and that while formation of unitary multidimensional representations in the episodic buffer seems to engage posterior neural networks, maintenance of such representations is supported by frontal networks. Although, the episodic buffer is not necessarily supported by executive processes and seems to be supported by different neural networks, it may operate in tandem with the central executive during effortful language processing. There is also evidence to suggest engagement of the phonological loop during buffer processing. The hippocampus seems to play a role in formation but not maintenance of representations in the episodic buffer of working memory.

Deep recurrent neural network reveals a hierarchy of process memory during dynamic natural vision.

PubMed

Shi, Junxing; Wen, Haiguang; Zhang, Yizhen; Han, Kuan; Liu, Zhongming

2018-05-01

The human visual cortex extracts both spatial and temporal visual features to support perception and guide behavior. Deep convolutional neural networks (CNNs) provide a computational framework to model cortical representation and organization for spatial visual processing, but unable to explain how the brain processes temporal information. To overcome this limitation, we extended a CNN by adding recurrent connections to different layers of the CNN to allow spatial representations to be remembered and accumulated over time. The extended model, or the recurrent neural network (RNN), embodied a hierarchical and distributed model of process memory as an integral part of visual processing. Unlike the CNN, the RNN learned spatiotemporal features from videos to enable action recognition. The RNN better predicted cortical responses to natural movie stimuli than the CNN, at all visual areas, especially those along the dorsal stream. As a fully observable model of visual processing, the RNN also revealed a cortical hierarchy of temporal receptive window, dynamics of process memory, and spatiotemporal representations. These results support the hypothesis of process memory, and demonstrate the potential of using the RNN for in-depth computational understanding of dynamic natural vision. © 2018 Wiley Periodicals, Inc.

Verified compilation of Concurrent Managed Languages

DTIC Science & Technology

2017-11-01

designs for compiler intermediate representations that facilitate mechanized proofs and verification; and (d) a realistic case study that combines these...ideas to prove the correctness of a state-of- the-art concurrent garbage collector. 15. SUBJECT TERMS Program verification, compiler design ...Even though concurrency is a pervasive part of modern software and hardware systems, it has often been ignored in safety-critical system designs . A

Exploring Symbolic Competence: Constructing Meaning(s) and Stretching Cultural Imagination in an Intermediate College-Level French Class

ERIC Educational Resources Information Center

Étienne, Corinne; Vanbaelen, Sylvie

2017-01-01

This study, conducted in a 300-level college French class with 15 students, builds on previous research on symbolic competence (Kramsch, 2009, 2011). Using a film scene and a "Semiotic Gap Activity," we examine how students construct meaning. What do students prioritize? What do they bring from their past symbolic representations? Are…

Effective Clipart Image Vectorization through Direct Optimization of Bezigons.

PubMed

Yang, Ming; Chao, Hongyang; Zhang, Chi; Guo, Jun; Yuan, Lu; Sun, Jian

2016-02-01

Bezigons, i.e., closed paths composed of Bézier curves, have been widely employed to describe shapes in image vectorization results. However, most existing vectorization techniques infer the bezigons by simply approximating an intermediate vector representation (such as polygons). Consequently, the resultant bezigons are sometimes imperfect due to accumulated errors, fitting ambiguities, and a lack of curve priors, especially for low-resolution images. In this paper, we describe a novel method for vectorizing clipart images. In contrast to previous methods, we directly optimize the bezigons rather than using other intermediate representations; therefore, the resultant bezigons are not only of higher fidelity compared with the original raster image but also more reasonable because they were traced by a proficient expert. To enable such optimization, we have overcome several challenges and have devised a differentiable data energy as well as several curve-based prior terms. To improve the efficiency of the optimization, we also take advantage of the local control property of bezigons and adopt an overlapped piecewise optimization strategy. The experimental results show that our method outperforms both the current state-of-the-art method and commonly used commercial software in terms of bezigon quality.

Surface-confined Ullmann coupling of thiophene substituted porphyrins

NASA Astrophysics Data System (ADS)

Beggan, J. P.; Boyle, N. M.; Pryce, M. T.; Cafolla, A. A.

2015-09-01

The covalent coupling of (5,10,15,20-tetrabromothien-2-ylporphyrinato)zinc(II) (TBrThP) molecules on the Ag(111) surface has been investigated under ultra-high-vacuum conditions, using scanning tunnelling microscopy and x-ray photoelectron spectroscopy. The findings provide atomic-level insight into surface-confined Ullmann coupling of thiophene substituted porphyrins, analyzing the progression of organometallic intermediate to final coupled state. Adsorption of the TBrThP molecules on the Ag(111) surface at room temperature is found to result in the reductive dehalogenation of the bromothienyl substituents and the subsequent formation of single strand and crosslinked coordination networks. The coordinated substrate atoms bridge the proximal thienyl groups of the organometallic intermediate, while the cleaved bromine atoms are bound on the adjacent Ag(111) surface. The intermediate complex displays a thermal lability at ˜423 K that results in the dissociation of the proximal thienyl groups with the concomitant loss of the surface bound bromine. At the thermally induced dissociation of the intermediate complex the resultant thienylporphyrin derivatives covalently couple, leading to the formation of a polymeric network of thiophene linked and meso-meso fused porphyrins.

A Multiobjective Sparse Feature Learning Model for Deep Neural Networks.

PubMed

Gong, Maoguo; Liu, Jia; Li, Hao; Cai, Qing; Su, Linzhi

2015-12-01

Hierarchical deep neural networks are currently popular learning models for imitating the hierarchical architecture of human brain. Single-layer feature extractors are the bricks to build deep networks. Sparse feature learning models are popular models that can learn useful representations. But most of those models need a user-defined constant to control the sparsity of representations. In this paper, we propose a multiobjective sparse feature learning model based on the autoencoder. The parameters of the model are learnt by optimizing two objectives, reconstruction error and the sparsity of hidden units simultaneously to find a reasonable compromise between them automatically. We design a multiobjective induced learning procedure for this model based on a multiobjective evolutionary algorithm. In the experiments, we demonstrate that the learning procedure is effective, and the proposed multiobjective model can learn useful sparse features.

An Ensemble of Neural Networks for Stock Trading Decision Making

NASA Astrophysics Data System (ADS)

Chang, Pei-Chann; Liu, Chen-Hao; Fan, Chin-Yuan; Lin, Jun-Lin; Lai, Chih-Ming

Stock turning signals detection are very interesting subject arising in numerous financial and economic planning problems. In this paper, Ensemble Neural Network system with Intelligent Piecewise Linear Representation for stock turning points detection is presented. The Intelligent piecewise linear representation method is able to generate numerous stocks turning signals from the historic data base, then Ensemble Neural Network system will be applied to train the pattern and retrieve similar stock price patterns from historic data for training. These turning signals represent short-term and long-term trading signals for selling or buying stocks from the market which are applied to forecast the future turning points from the set of test data. Experimental results demonstrate that the hybrid system can make a significant and constant amount of profit when compared with other approaches using stock data available in the market.

Analysis and Design of Complex Network Environments

DTIC Science & Technology

2014-02-01

entanglements among un- measured variables. This “potential entanglement ” type of network complexity is previously unaddressed in the literature, yet it...Appreciating the power of structural representations that allow for potential entanglement among unmeasured variables to simplify network inference problems...rely on the idea of subsystems and allows for potential entanglement among unmeasured states. As a result, inferring a system’s signal structure

Team knowledge representation: a network perspective.

PubMed

Espinosa, J Alberto; Clark, Mark A

2014-03-01

We propose a network perspective of team knowledge that offers both conceptual and methodological advantages, expanding explanatory value through representation and measurement of component structure and content. Team knowledge has typically been conceptualized and measured with relatively simple aggregates, without fully accounting for differing knowledge configurations among team members. Teams with similar aggregate values of team knowledge may have very different team dynamics depending on how knowledge isolates, cliques, and densities are distributed across the team; which members are the most knowledgeable; who shares knowledge with whom; and how knowledge clusters are distributed. We illustrate our proposed network approach through a sample of 57 teams, including how to compute, analyze, and visually represent team knowledge. Team knowledge network structures (isolation, centrality) are associated with outcomes of, respectively, task coordination, strategy coordination, and the proportion of team knowledge cliques, all after controlling for shared team knowledge. Network analysis helps to represent, measure, and understand the relationship of team knowledge to outcomes of interest to team researchers, members, and managers. Our approach complements existing team knowledge measures. Researchers and managers can apply network concepts and measures to help understand where team knowledge is held within a team and how this relational structure may influence team coordination, cohesion, and performance.

Deep neural networks for texture classification-A theoretical analysis.

PubMed

Basu, Saikat; Mukhopadhyay, Supratik; Karki, Manohar; DiBiano, Robert; Ganguly, Sangram; Nemani, Ramakrishna; Gayaka, Shreekant

2018-01-01

We investigate the use of Deep Neural Networks for the classification of image datasets where texture features are important for generating class-conditional discriminative representations. To this end, we first derive the size of the feature space for some standard textural features extracted from the input dataset and then use the theory of Vapnik-Chervonenkis dimension to show that hand-crafted feature extraction creates low-dimensional representations which help in reducing the overall excess error rate. As a corollary to this analysis, we derive for the first time upper bounds on the VC dimension of Convolutional Neural Network as well as Dropout and Dropconnect networks and the relation between excess error rate of Dropout and Dropconnect networks. The concept of intrinsic dimension is used to validate the intuition that texture-based datasets are inherently higher dimensional as compared to handwritten digits or other object recognition datasets and hence more difficult to be shattered by neural networks. We then derive the mean distance from the centroid to the nearest and farthest sampling points in an n-dimensional manifold and show that the Relative Contrast of the sample data vanishes as dimensionality of the underlying vector space tends to infinity. Copyright © 2017 Elsevier Ltd. All rights reserved.

deepNF: Deep network fusion for protein function prediction.

PubMed

Gligorijevic, Vladimir; Barot, Meet; Bonneau, Richard

2018-06-01

The prevalence of high-throughput experimental methods has resulted in an abundance of large-scale molecular and functional interaction networks. The connectivity of these networks provides a rich source of information for inferring functional annotations for genes and proteins. An important challenge has been to develop methods for combining these heterogeneous networks to extract useful protein feature representations for function prediction. Most of the existing approaches for network integration use shallow models that encounter difficulty in capturing complex and highly-nonlinear network structures. Thus, we propose deepNF, a network fusion method based on Multimodal Deep Autoencoders to extract high-level features of proteins from multiple heterogeneous interaction networks. We apply this method to combine STRING networks to construct a common low-dimensional representation containing high-level protein features. We use separate layers for different network types in the early stages of the multimodal autoencoder, later connecting all the layers into a single bottleneck layer from which we extract features to predict protein function. We compare the cross-validation and temporal holdout predictive performance of our method with state-of-the-art methods, including the recently proposed method Mashup. Our results show that our method outperforms previous methods for both human and yeast STRING networks. We also show substantial improvement in the performance of our method in predicting GO terms of varying type and specificity. deepNF is freely available at: https://github.com/VGligorijevic/deepNF. vgligorijevic@flatironinstitute.org, rb133@nyu.edu. Supplementary data are available at Bioinformatics online.

The evolution of cooperation on geographical networks

NASA Astrophysics Data System (ADS)

Li, Yixiao; Wang, Yi; Sheng, Jichuan

2017-11-01

We study evolutionary public goods game on geographical networks, i.e., complex networks which are located on a geographical plane. The geographical feature effects in two ways: In one way, the geographically-induced network structure influences the overall evolutionary dynamics, and, in the other way, the geographical length of an edge influences the cost when the two players at the two ends interact. For the latter effect, we design a new cost function of cooperators, which simply assumes that the longer the distance between two players, the higher cost the cooperator(s) of them have to pay. In this study, network substrates are generated by a previous spatial network model with a cost-benefit parameter controlling the network topology. Our simulations show that the greatest promotion of cooperation is achieved in the intermediate regime of the parameter, in which empirical estimates of various railway networks fall. Further, we investigate how the distribution of edges' geographical costs influences the evolutionary dynamics and consider three patterns of the distribution: an approximately-equal distribution, a diverse distribution, and a polarized distribution. For normal geographical networks which are generated using intermediate values of the cost-benefit parameter, a diverse distribution hinders the evolution of cooperation, whereas a polarized distribution lowers the threshold value of the amplification factor for cooperation in public goods game. These results are helpful for understanding the evolution of cooperation on real-world geographical networks.

Lessons learned from the design of chemical space networks and opportunities for new applications.

PubMed

Vogt, Martin; Stumpfe, Dagmar; Maggiora, Gerald M; Bajorath, Jürgen

2016-03-01

The concept of chemical space is of fundamental relevance in chemical informatics and computer-aided drug discovery. In a series of articles published in the Journal of Computer-Aided Molecular Design, principles of chemical space design were evaluated, molecular networks proposed as an alternative to conventional coordinate-based chemical reference spaces, and different types of chemical space networks (CSNs) constructed and analyzed. Central to the generation of CSNs was the way in which molecular similarity relationships were assessed and a primary focal point was the network-based representation of biologically relevant chemical space. The design and comparison of CSNs based upon alternative similarity measures can be viewed as an evolutionary path with interesting lessons learned along the way. CSN design has matured to the point that such chemical space representations can be used in practice. In this contribution, highlights from the sequence of CSN design efforts are discussed in context, providing a perspective for future practical applications.

Ontology-supported research on vaccine efficacy, safety and integrative biological networks.

PubMed

He, Yongqun

2014-07-01

While vaccine efficacy and safety research has dramatically progressed with the methods of in silico prediction and data mining, many challenges still exist. A formal ontology is a human- and computer-interpretable set of terms and relations that represent entities in a specific domain and how these terms relate to each other. Several community-based ontologies (including Vaccine Ontology, Ontology of Adverse Events and Ontology of Vaccine Adverse Events) have been developed to support vaccine and adverse event representation, classification, data integration, literature mining of host-vaccine interaction networks, and analysis of vaccine adverse events. The author further proposes minimal vaccine information standards and their ontology representations, ontology-based linked open vaccine data and meta-analysis, an integrative One Network ('OneNet') Theory of Life, and ontology-based approaches to study and apply the OneNet theory. In the Big Data era, these proposed strategies provide a novel framework for advanced data integration and analysis of fundamental biological networks including vaccine immune mechanisms.

Lessons learned from the design of chemical space networks and opportunities for new applications

NASA Astrophysics Data System (ADS)

Vogt, Martin; Stumpfe, Dagmar; Maggiora, Gerald M.; Bajorath, Jürgen

2016-03-01

The concept of chemical space is of fundamental relevance in chemical informatics and computer-aided drug discovery. In a series of articles published in the Journal of Computer- Aided Molecular Design, principles of chemical space design were evaluated, molecular networks proposed as an alternative to conventional coordinate-based chemical reference spaces, and different types of chemical space networks (CSNs) constructed and analyzed. Central to the generation of CSNs was the way in which molecular similarity relationships were assessed and a primary focal point was the network-based representation of biologically relevant chemical space. The design and comparison of CSNs based upon alternative similarity measures can be viewed as an evolutionary path with interesting lessons learned along the way. CSN design has matured to the point that such chemical space representations can be used in practice. In this contribution, highlights from the sequence of CSN design efforts are discussed in context, providing a perspective for future practical applications.

Ontology-supported Research on Vaccine Efficacy, Safety, and Integrative Biological Networks

PubMed Central

He, Yongqun

2016-01-01

Summary While vaccine efficacy and safety research has dramatically progressed with the methods of in silico prediction and data mining, many challenges still exist. A formal ontology is a human- and computer-interpretable set of terms and relations that represent entities in a specific domain and how these terms relate to each other. Several community-based ontologies (including the Vaccine Ontology, Ontology of Adverse Events, and Ontology of Vaccine Adverse Events) have been developed to support vaccine and adverse event representation, classification, data integration, literature mining of host-vaccine interaction networks, and analysis of vaccine adverse events. The author further proposes minimal vaccine information standards and their ontology representations, ontology-based linked open vaccine data and meta-analysis, an integrative One Network (“OneNet”) Theory of Life, and ontology-based approaches to study and apply the OneNet theory. In the Big Data era, these proposed strategies provide a novel framework for advanced data integration and analysis of fundamental biological networks including vaccine immune mechanisms. PMID:24909153

Design of chemical space networks on the basis of Tversky similarity

NASA Astrophysics Data System (ADS)

Wu, Mengjun; Vogt, Martin; Maggiora, Gerald M.; Bajorath, Jürgen

2016-01-01

Chemical space networks (CSNs) have been introduced as a coordinate-free representation of chemical space. In CSNs, nodes represent compounds and edges pairwise similarity relationships. These network representations are mostly used to navigate sections of biologically relevant chemical space. Different types of CSNs have been designed on the basis of alternative similarity measures including continuous numerical similarity values or substructure-based similarity criteria. CSNs can be characterized and compared on the basis of statistical concepts from network science. Herein, a new CSN design is introduced that is based upon asymmetric similarity assessment using the Tversky coefficient and termed TV-CSN. Compared to other CSNs, TV-CSNs have unique features. While CSNs typically contain separate compound communities and exhibit small world character, many TV-CSNs are also scale-free in nature and contain hubs, i.e., extensively connected central compounds. Compared to other CSNs, these hubs are a characteristic of TV-CSN topology. Hub-containing compound communities are of particular interest for the exploration of structure-activity relationships.

Nonlinear Dynamics on Interconnected Networks

NASA Astrophysics Data System (ADS)

Arenas, Alex; De Domenico, Manlio

2016-06-01

Networks of dynamical interacting units can represent many complex systems, from the human brain to transportation systems and societies. The study of these complex networks, when accounting for different types of interactions has become a subject of interest in the last few years, especially because its representational power in the description of users' interactions in diverse online social platforms (Facebook, Twitter, Instagram, etc.) [1], or in representing different transportation modes in urban networks [2,3]. The general name coined for these networks is multilayer networks, where each layer accounts for a type of interaction (see Fig. 1).

Ontology Design of Influential People Identification Using Centrality

NASA Astrophysics Data System (ADS)

Maulana Awangga, Rolly; Yusril, Muhammad; Setyawan, Helmi

2018-04-01

Identifying influential people as a node in a graph theory commonly calculated by social network analysis. The social network data has the user as node and edge as relation forming a friend relation graph. This research is conducting different meaning of every nodes relation in the social network. Ontology was perfect match science to describe the social network data as conceptual and domain. Ontology gives essential relationship in a social network more than a current graph. Ontology proposed as a standard for knowledge representation for the semantic web by World Wide Web Consortium. The formal data representation use Resource Description Framework (RDF) and Web Ontology Language (OWL) which is strategic for Open Knowledge-Based website data. Ontology used in the semantic description for a relationship in the social network, it is open to developing semantic based relationship ontology by adding and modifying various and different relationship to have influential people as a conclusion. This research proposes a model using OWL and RDF for influential people identification in the social network. The study use degree centrality, between ness centrality, and closeness centrality measurement for data validation. As a conclusion, influential people identification in Facebook can use proposed Ontology model in the Group, Photos, Photo Tag, Friends, Events and Works data.

Parallel or convergent evolution in human population genomic data revealed by genotype networks.

PubMed

R Vahdati, Ali; Wagner, Andreas

2016-08-02

Genotype networks are representations of genetic variation data that are complementary to phylogenetic trees. A genotype network is a graph whose nodes are genotypes (DNA sequences) with the same broadly defined phenotype. Two nodes are connected if they differ in some minimal way, e.g., in a single nucleotide. We analyze human genome variation data from the 1,000 genomes project, and construct haploid genotype (haplotype) networks for 12,235 protein coding genes. The structure of these networks varies widely among genes, indicating different patterns of variation despite a shared evolutionary history. We focus on those genes whose genotype networks show many cycles, which can indicate homoplasy, i.e., parallel or convergent evolution, on the sequence level. For 42 genes, the observed number of cycles is so large that it cannot be explained by either chance homoplasy or recombination. When analyzing possible explanations, we discovered evidence for positive selection in 21 of these genes and, in addition, a potential role for constrained variation and purifying selection. Balancing selection plays at most a small role. The 42 genes with excess cycles are enriched in functions related to immunity and response to pathogens. Genotype networks are representations of genetic variation data that can help understand unusual patterns of genomic variation.

Efficient probabilistic inference in generic neural networks trained with non-probabilistic feedback.

PubMed

Orhan, A Emin; Ma, Wei Ji

2017-07-26

Animals perform near-optimal probabilistic inference in a wide range of psychophysical tasks. Probabilistic inference requires trial-to-trial representation of the uncertainties associated with task variables and subsequent use of this representation. Previous work has implemented such computations using neural networks with hand-crafted and task-dependent operations. We show that generic neural networks trained with a simple error-based learning rule perform near-optimal probabilistic inference in nine common psychophysical tasks. In a probabilistic categorization task, error-based learning in a generic network simultaneously explains a monkey's learning curve and the evolution of qualitative aspects of its choice behavior. In all tasks, the number of neurons required for a given level of performance grows sublinearly with the input population size, a substantial improvement on previous implementations of probabilistic inference. The trained networks develop a novel sparsity-based probabilistic population code. Our results suggest that probabilistic inference emerges naturally in generic neural networks trained with error-based learning rules.Behavioural tasks often require probability distributions to be inferred about task specific variables. Here, the authors demonstrate that generic neural networks can be trained using a simple error-based learning rule to perform such probabilistic computations efficiently without any need for task specific operations.

Learning-Induced Plasticity in Medial Prefrontal Cortex Predicts Preference Malleability

PubMed Central

Garvert, Mona M.; Moutoussis, Michael; Kurth-Nelson, Zeb; Behrens, Timothy E.J.; Dolan, Raymond J.

2015-01-01

Summary Learning induces plasticity in neuronal networks. As neuronal populations contribute to multiple representations, we reasoned plasticity in one representation might influence others. We used human fMRI repetition suppression to show that plasticity induced by learning another individual’s values impacts upon a value representation for oneself in medial prefrontal cortex (mPFC), a plasticity also evident behaviorally in a preference shift. We show this plasticity is driven by a striatal “prediction error,” signaling the discrepancy between the other’s choice and a subject’s own preferences. Thus, our data highlight that mPFC encodes agent-independent representations of subjective value, such that prediction errors simultaneously update multiple agents’ value representations. As the resulting change in representational similarity predicts interindividual differences in the malleability of subjective preferences, our findings shed mechanistic light on complex human processes such as the powerful influence of social interaction on beliefs and preferences. PMID:25611512

Examination about the Spatial Representation of PM2.5 Obtained from Limited Stations Using a Network Observation

NASA Astrophysics Data System (ADS)

Shi, X.; Zhao, C.

2017-12-01

Haze aerosol pollution has been a focus issue in China, and its characteristics is highly demanded. With limited observation sites, aerosol properties obtained from a single site is frequently used to represent the haze condition over a large domain, such as tens of kilometers. This could result in high uncertainties in the haze characteristics due to their spatial variation. Using a network observation from November 2015 to February 2016 over an urban city in North China with high spatial resolution, this study examines the spatial representation of ground site observations. A method is first developed to determine the representative area of measurements from limited stations. The key idea of this method is to determine the spatial variability of particulate matter with diameters less than 2.5 μm (PM2.5) concentration using a variance function in 2km x 2km grids. Based on the high spatial resolution (0.5km x 0.5km) measurements of PM2.5, the grids in which PM2.5 have high correlations and weak value differences are determined as the representation area of measurements at these grids. Note that the size representation area is not exactly a circle region. It shows that the size representation are for the study region and study period ranges from 0.25 km2 to 16.25 km2. The representation area varies with locations. For the 20 km x 20 km study region, 10 station observations would have a good representation of the PM2.5 observations obtained from current 169 stations at the four-month time scale.

Hierarchical Organization of Auditory and Motor Representations in Speech Perception: Evidence from Searchlight Similarity Analysis.

PubMed

Evans, Samuel; Davis, Matthew H

2015-12-01

How humans extract the identity of speech sounds from highly variable acoustic signals remains unclear. Here, we use searchlight representational similarity analysis (RSA) to localize and characterize neural representations of syllables at different levels of the hierarchically organized temporo-frontal pathways for speech perception. We asked participants to listen to spoken syllables that differed considerably in their surface acoustic form by changing speaker and degrading surface acoustics using noise-vocoding and sine wave synthesis while we recorded neural responses with functional magnetic resonance imaging. We found evidence for a graded hierarchy of abstraction across the brain. At the peak of the hierarchy, neural representations in somatomotor cortex encoded syllable identity but not surface acoustic form, at the base of the hierarchy, primary auditory cortex showed the reverse. In contrast, bilateral temporal cortex exhibited an intermediate response, encoding both syllable identity and the surface acoustic form of speech. Regions of somatomotor cortex associated with encoding syllable identity in perception were also engaged when producing the same syllables in a separate session. These findings are consistent with a hierarchical account of how variable acoustic signals are transformed into abstract representations of the identity of speech sounds. © The Author 2015. Published by Oxford University Press.

Developing a Network of and for Geometric Reasoning

ERIC Educational Resources Information Center

Mamolo, Ami; Ruttenberg-Rozen, Robyn; Whiteley, Walter

2015-01-01

In this article, we develop a theoretical model for restructuring mathematical tasks, usually considered advanced, with a network of spatial visual representations designed to support geometric reasoning for learners of disparate ages, stages, strengths, and preparation. Through our geometric reworking of the well-known "open box…

Carbonate-coordinated metal complexes precede the formation of liquid amorphous mineral emulsions of divalent metal carbonates†

PubMed Central

Wolf, Stephan E.; Müller, Lars; Barrea, Raul; Kampf, Christopher J.; Leiterer, Jork; Panne, Ulrich; Hoffmann, Thorsten

2011-01-01

During the mineralisation of metal carbonates MCO3 (M = Ca, Sr, Ba, Mn, Cd, Pb) liquid-like amorphous intermediates emerge. These intermediates that form via a liquid/liquid phase separation behave like a classical emulsion and are stabilized electrostatically. The occurrence of these intermediates is attributed to the formation of highly hydrated networks whose stability is mainly based on weak interactions and the variability of the metal-containing pre-critical clusters. Their existence and compositional freedom are evidenced by electrospray ionization mass spectrometry (ESI-MS). Liquid intermediates in non-classical crystallisation pathways seem to be more common than assumed. PMID:21218241

A Comparison of Reasoning Processes in a Collaborative Modelling Environment: Learning about genetics problems using virtual chat

NASA Astrophysics Data System (ADS)

Pata, Kai; Sarapuu, Tago

2006-09-01

This study investigated the possible activation of different types of model-based reasoning processes in two learning settings, and the influence of various terms of reasoning on the learners’ problem representation development. Changes in 53 students’ problem representations about genetic issue were analysed while they worked with different modelling tools in a synchronous network-based environment. The discussion log-files were used for the “microgenetic” analysis of reasoning types. For studying the stages of students’ problem representation development, individual pre-essays and post-essays and their utterances during two reasoning phases were used. An approach for mapping problem representations was developed. Characterizing the elements of mental models and their reasoning level enabled the description of five hierarchical categories of problem representations. Learning in exploratory and experimental settings was registered as the shift towards more complex stages of problem representations in genetics. The effect of different types of reasoning could be observed as the divergent development of problem representations within hierarchical categories.

Part-based deep representation for product tagging and search

NASA Astrophysics Data System (ADS)

Chen, Keqing

2017-06-01

Despite previous studies, tagging and indexing the product images remain challenging due to the large inner-class variation of the products. In the traditional methods, the quantized hand-crafted features such as SIFTs are extracted as the representation of the product images, which are not discriminative enough to handle the inner-class variation. For discriminative image representation, this paper firstly presents a novel deep convolutional neural networks (DCNNs) architect true pre-trained on a large-scale general image dataset. Compared to the traditional features, our DCNNs representation is of more discriminative power with fewer dimensions. Moreover, we incorporate the part-based model into the framework to overcome the negative effect of bad alignment and cluttered background and hence the descriptive ability of the deep representation is further enhanced. Finally, we collect and contribute a well-labeled shoe image database, i.e., the TBShoes, on which we apply the part-based deep representation for product image tagging and search, respectively. The experimental results highlight the advantages of the proposed part-based deep representation.

Beyond the Mental Number Line: A Neural Network Model of Number-Space Interactions

ERIC Educational Resources Information Center

Chen, Qi; Verguts, Tom

2010-01-01

It is commonly assumed that there is an interaction between the representations of number and space (e.g., [Dehaene et al., 1993] and [Walsh, 2003]), typically ascribed to a mental number line. The exact nature of this interaction has remained elusive, however. Here we propose that spatial aspects are not inherent to number representations, but…

NetWeaver for EMDS user guide (version 1.1): a knowledge base development system.

Treesearch

Keith M. Reynolds

1999-01-01

The guide describes use of the NetWeaver knowledge base development system. Knowledge representation in NetWeaver is based on object-oriented fuzzy-logic networks that offer several significant advantages over the more traditional rulebased representation. Compared to rule-based knowledge bases, NetWeaver knowledge bases are easier to build, test, and maintain because...

When locomotion is used to interact with the environment: investigation of the link between emotions and the twofold goal-directed locomotion in humans.

PubMed

Vernazza-Martin, S; Longuet, S; Damry, T; Chamot, J M; Dru, V

2015-10-01

Walking as a means to interact with the environment has a twofold goal: body displacement (intermediate goal) and the future action on the environment (final representational goal). This involves different processes that plan, program, and control goal-directed locomotion linked to motivation as an "emotional state," which leads to achieving this twofold goal. The aim of the present study was to determine whether emotional valence associated with the final representational goal influences these processes or whether they depend more on the emotional valence associated with the intermediate goal in young adults. Twenty subjects, aged 18-35 years, were instructed to erase an emotional picture that appeared on a wall as soon as they saw it. They had to press a stop button located 5 m in front of them with their right hand. Their gait was analyzed using a force platform and the Vicon system. The main results suggest that the emotional valence of the intermediate goal has the greatest effect on the processes that organize and modulate goal-directed locomotion. A positive valence facilitates cognitive processes involved in the temporal organization of locomotion. A negative valence disturbs the cognitive processes involved in the spatial organization of the locomotion and online motor control, leading to a deviating trajectory and a final body position that is more distant from the stop button. These results are discussed in line with the motivational direction hypothesis and with the affective meaning of the intended response goal.

Analysis of the process of representing clinical statements for decision-support applications: a comparison of openEHR archetypes and HL7 virtual medical record.

PubMed

González-Ferrer, A; Peleg, M; Marcos, M; Maldonado, J A

2016-07-01

Delivering patient-specific decision-support based on computer-interpretable guidelines (CIGs) requires mapping CIG clinical statements (data items, clinical recommendations) into patients' data. This is most effectively done via intermediate data schemas, which enable querying the data according to the semantics of a shared standard intermediate schema. This study aims to evaluate the use of HL7 virtual medical record (vMR) and openEHR archetypes as intermediate schemas for capturing clinical statements from CIGs that are mappable to electronic health records (EHRs) containing patient data and patient-specific recommendations. Using qualitative research methods, we analyzed the encoding of ten representative clinical statements taken from two CIGs used in real decision-support systems into two health information models (openEHR archetypes and HL7 vMR instances) by four experienced informaticians. Discussion among the modelers about each case study example greatly increased our understanding of the capabilities of these standards, which we share in this educational paper. Differing in content and structure, the openEHR archetypes were found to contain a greater level of representational detail and structure while the vMR representations took fewer steps to complete. The use of openEHR in the encoding of CIG clinical statements could potentially facilitate applications other than decision-support, including intelligent data analysis and integration of additional properties of data items from existing EHRs. On the other hand, due to their smaller size and fewer details, the use of vMR potentially supports quicker mapping of EHR data into clinical statements.

F4 , E6 and G2 exceptional gauge groups in the vacuum domain structure model

NASA Astrophysics Data System (ADS)

Shahlaei, Amir; Rafibakhsh, Shahnoosh

2018-03-01

Using a vacuum domain structure model, we calculate trivial static potentials in various representations of F4 , E6, and G2 exceptional groups by means of the unit center element. Due to the absence of the nontrivial center elements, the potential of every representation is screened at far distances. However, the linear part is observed at intermediate quark separations and is investigated by the decomposition of the exceptional group to its maximal subgroups. Comparing the group factor of the supergroup with the corresponding one obtained from the nontrivial center elements of S U (3 ) subgroup shows that S U (3 ) is not the direct cause of temporary confinement in any of the exceptional groups. However, the trivial potential obtained from the group decomposition into the S U (3 ) subgroup is the same as the potential of the supergroup itself. In addition, any regular or singular decomposition into the S U (2 ) subgroup that produces the Cartan generator with the same elements as h1, in any exceptional group, leads to the linear intermediate potential of the exceptional gauge groups. The other S U (2 ) decompositions with the Cartan generator different from h1 are still able to describe the linear potential if the number of S U (2 ) nontrivial center elements that emerge in the decompositions is the same. As a result, it is the center vortices quantized in terms of nontrivial center elements of the S U (2 ) subgroup that give rise to the intermediate confinement in the static potentials.

Signal Sampling for Efficient Sparse Representation of Resting State FMRI Data

PubMed Central

Ge, Bao; Makkie, Milad; Wang, Jin; Zhao, Shijie; Jiang, Xi; Li, Xiang; Lv, Jinglei; Zhang, Shu; Zhang, Wei; Han, Junwei; Guo, Lei; Liu, Tianming

2015-01-01

As the size of brain imaging data such as fMRI grows explosively, it provides us with unprecedented and abundant information about the brain. How to reduce the size of fMRI data but not lose much information becomes a more and more pressing issue. Recent literature studies tried to deal with it by dictionary learning and sparse representation methods, however, their computation complexities are still high, which hampers the wider application of sparse representation method to large scale fMRI datasets. To effectively address this problem, this work proposes to represent resting state fMRI (rs-fMRI) signals of a whole brain via a statistical sampling based sparse representation. First we sampled the whole brain’s signals via different sampling methods, then the sampled signals were aggregate into an input data matrix to learn a dictionary, finally this dictionary was used to sparsely represent the whole brain’s signals and identify the resting state networks. Comparative experiments demonstrate that the proposed signal sampling framework can speed-up by ten times in reconstructing concurrent brain networks without losing much information. The experiments on the 1000 Functional Connectomes Project further demonstrate its effectiveness and superiority. PMID:26646924

Object migration and authentication. [in computer operating systems design

NASA Technical Reports Server (NTRS)

Gligor, V. D.; Lindsay, B. G.

1979-01-01

The paper presents a mechanism permitting a type manager to fabricate a migrated object representation which can be entrusted to other subsystems or transmitted outside of the control of a local computer system. The migrated object representation is signed by the type manager in such a way that the type manager's signature cannot be forged and the manager is able to authenticate its own signature. Subsequently, the type manager can retrieve the migrated representation and validate its contents before reconstructing the object in its original representation. This facility allows type managers to authenticate the contents of off-line or network storage and solves problems stemming from the hierarchical structure of the system itself.

Representation in dynamical agents.

PubMed

Ward, Ronnie; Ward, Robert

2009-04-01

This paper extends experiments by Beer [Beer, R. D. (1996). Toward the evolution of dynamical neural networks for minimally cognitive behavior. In P. Maes, M. Mataric, J. Meyer, J. Pollack, & S. Wilson (Eds.), From animals to animats 4: Proceedings of the fourth international conference on simulation of adaptive behavior (pp. 421-429). MIT Press; Beer, R. D. (2003). The dynamics of active categorical perception in an evolved model agent (with commentary and response). Adaptive Behavior, 11 (4), 209-243] with an evolved, dynamical agent to further explore the question of representation in cognitive systems. Beer's environmentally-situated visual agent was controlled by a continuous-time recurrent neural network, and evolved to perform a categorical perception task, discriminating circles from diamonds. Despite the agent's high levels of discrimination performance, Beer found no evidence of internal representation in the best-evolved agent's nervous system. Here we examine the generality of this result. We evolved an agent for shape discrimination, and performed extensive behavioral analyses to test for representation. In this case we find that agents developed to discriminate equal-width shapes exhibit what Clark [Clark, A. (1997). The dynamical challenge. Cognitive Science, 21 (4), 461-481] calls "weak-substantive representation". The agent had internal configurations that (1) were understandably related to the object in the environment, and (2) were functionally used in a task relevant way when the target was not visible to the agent.

Multichannel activity propagation across an engineered axon network

NASA Astrophysics Data System (ADS)

Chen, H. Isaac; Wolf, John A.; Smith, Douglas H.

2017-04-01

Objective. Although substantial progress has been made in mapping the connections of the brain, less is known about how this organization translates into brain function. In particular, the massive interconnectivity of the brain has made it difficult to specifically examine data transmission between two nodes of the connectome, a central component of the ‘neural code.’ Here, we investigated the propagation of multiple streams of asynchronous neuronal activity across an isolated in vitro ‘connectome unit.’ Approach. We used the novel technique of axon stretch growth to create a model of a long-range cortico-cortical network, a modular system consisting of paired nodes of cortical neurons connected by axon tracts. Using optical stimulation and multi-electrode array recording techniques, we explored how input patterns are represented by cortical networks, how these representations shift as they are transmitted between cortical nodes and perturbed by external conditions, and how well the downstream node distinguishes different patterns. Main results. Stimulus representations included direct, synaptic, and multiplexed responses that grew in complexity as the distance between the stimulation source and recorded neuron increased. These representations collapsed into patterns with lower information content at higher stimulation frequencies. With internodal activity propagation, a hierarchy of network pathways, including latent circuits, was revealed using glutamatergic blockade. As stimulus channels were added, divergent, non-linear effects were observed in local versus distant network layers. Pairwise difference analysis of neuronal responses suggested that neuronal ensembles generally outperformed individual cells in discriminating input patterns. Significance. Our data illuminate the complexity of spiking activity propagation in cortical networks in vitro, which is characterized by the transformation of an input into myriad outputs over several network layers. These results provide insight into how the brain potentially processes information and generates the neural code and could guide the development of clinical therapies based on multichannel brain stimulation.

Social Networking, Microlending, and Translation in the Spanish Service-Learning Classroom

ERIC Educational Resources Information Center

Faszer-McMahon, Debra

2013-01-01

This small-scale study analyzes the use of service-learning pedagogy via non-profit translation in the intermediate-level language classroom. Forty-three students at the intermediate-high level in three Spanish classes in Greensburg, Pennsylvania served as part of a translation team for the non-profit organization Kiva, which helps to fund…

Effects of Category-Specific Costs on Neural Systems for Perceptual Decision-Making

PubMed Central

Whiteley, Louise; Hulme, Oliver J.; Sahani, Maneesh; Dolan, Raymond J.

2010-01-01

Perceptual judgments are often biased by prospective losses, leading to changes in decision criteria. Little is known about how and where sensory evidence and cost information interact in the brain to influence perceptual categorization. Here we show that prospective losses systematically bias the perception of noisy face-house images. Asymmetries in category-specific cost were associated with enhanced blood-oxygen-level-dependent signal in a frontoparietal network. We observed selective activation of parahippocampal gyrus for changes in category-specific cost in keeping with the hypothesis that loss functions enact a particular task set that is communicated to visual regions. Across subjects, greater shifts in decision criteria were associated with greater activation of the anterior cingulate cortex (ACC). Our results support a hypothesis that costs bias an intermediate representation between perception and action, expressed via general effects on frontal cortex, and selective effects on extrastriate cortex. These findings indicate that asymmetric costs may affect a neural implementation of perceptual decision making in a similar manner to changes in category expectation, constituting a step toward accounting for how prospective losses are flexibly integrated with sensory evidence in the brain. PMID:20357071

Progressive transmission of secured images with authentication using decompositions into monovariate functions

NASA Astrophysics Data System (ADS)

Leni, Pierre-Emmanuel; Fougerolle, Yohan D.; Truchetet, Frédéric

2014-05-01

We propose a progressive transmission approach of an image authenticated using an overlapping subimage that can be removed to restore the original image. Our approach is different from most visible watermarking approaches that allow one to later remove the watermark, because the mark is not directly introduced in the two-dimensional image space. Instead, it is rather applied to an equivalent monovariate representation of the image. Precisely, the approach is based on our progressive transmission approach that relies on a modified Kolmogorov spline network, and therefore inherits its advantages: resilience to packet losses during transmission and support of heterogeneous display environments. The marked image can be accessed at any intermediate resolution, and a key is needed to remove the mark to fully recover the original image without loss. Moreover, the key can be different for every resolution, and the images can be globally restored in case of packet losses during the transmission. Our contributions lie in the proposition of decomposing a mark (an overlapping image) and an image into monovariate functions following the Kolmogorov superposition theorem; and in the combination of these monovariate functions to provide a removable visible "watermarking" of images with the ability to restore the original image using a key.

Link prediction based on local weighted paths for complex networks

NASA Astrophysics Data System (ADS)

Yao, Yabing; Zhang, Ruisheng; Yang, Fan; Yuan, Yongna; Hu, Rongjing; Zhao, Zhili

As a significant problem in complex networks, link prediction aims to find the missing and future links between two unconnected nodes by estimating the existence likelihood of potential links. It plays an important role in understanding the evolution mechanism of networks and has broad applications in practice. In order to improve prediction performance, a variety of structural similarity-based methods that rely on different topological features have been put forward. As one topological feature, the path information between node pairs is utilized to calculate the node similarity. However, many path-dependent methods neglect the different contributions of paths for a pair of nodes. In this paper, a local weighted path (LWP) index is proposed to differentiate the contributions between paths. The LWP index considers the effect of the link degrees of intermediate links and the connectivity influence of intermediate nodes on paths to quantify the path weight in the prediction procedure. The experimental results on 12 real-world networks show that the LWP index outperforms other seven prediction baselines.

Information Network Model Query Processing

NASA Astrophysics Data System (ADS)

Song, Xiaopu

Information Networking Model (INM) [31] is a novel database model for real world objects and relationships management. It naturally and directly supports various kinds of static and dynamic relationships between objects. In INM, objects are networked through various natural and complex relationships. INM Query Language (INM-QL) [30] is designed to explore such information network, retrieve information about schema, instance, their attributes, relationships, and context-dependent information, and process query results in the user specified form. INM database management system has been implemented using Berkeley DB, and it supports INM-QL. This thesis is mainly focused on the implementation of the subsystem that is able to effectively and efficiently process INM-QL. The subsystem provides a lexical and syntactical analyzer of INM-QL, and it is able to choose appropriate evaluation strategies and index mechanism to process queries in INM-QL without the user's intervention. It also uses intermediate result structure to hold intermediate query result and other helping structures to reduce complexity of query processing.

Epithelial structure revealed by chemical dissection and unembedded electron microscopy.

PubMed

Fey, E G; Capco, D G; Krochmalnic, G; Penman, S

1984-07-01

Cytoskeletal structures obtained after extraction of Madin-Darby canine kidney epithelial cell monolayers with Triton X-100 were examined in transmission electron micrographs of cell whole mounts and unembedded thick sections. The cytoskeleton, an ordered structure consisting of a peripheral plasma lamina, a complex network of filaments, and chromatin-containing nuclei, was revealed after extraction of intact cells with a nearly physiological buffer containing Triton X-100. The cytoskeleton was further fractionated by extraction with (NH4)2SO4, which left a structure enriched in intermediate filaments and desmosomes around the nuclei. A further digestion with nuclease and elution with (NH4)2SO4 removed the chromatin. The stable structure that remained after this procedure retained much of the epithelial morphology and contained essentially all of the cytokeratin filaments and desmosomes and the chromatin-depleted nuclear matrices. This structural network may serve as a scaffold for epithelial organization. The cytoskeleton and the underlying nuclear matrix intermediate filament scaffold, when examined in both conventional embedded thin sections and in unembedded whole mounts and thick sections, showed the retention of many of the detailed morphological aspects of the intact cells, which suggests a structural continuum linking the nuclear matrix, the intermediate filament network, and the intercellular desmosomal junctions. Most importantly, the protein composition of each of the four fractions obtained by this sequential procedure was essentially unique. Thus, the proteins constituting the soluble fraction, the cytoskeleton, the chromatin fraction, and the underlying nuclear matrix-intermediate filament scaffold are biochemically distinct.

Epithelial structure revealed by chemical dissection and unembedded electron microscopy

PubMed Central

Fey, E. G.; Capco, D. G.; Krochmalnic, G.; Penman, S.

1984-01-01

Cytoskeletal structures obtained after extraction of Madin-Darby canine kidney epithelial cell monolayers with Triton X-100 were examined in transmission electron micrographs of cell whole mounts and unembedded thick sections. The cytoskeleton, an ordered structure consisting of a peripheral plasma lamina, a complex network of filaments, and chromatin-containing nuclei, was revealed after extraction of intact cells with a nearly physiological buffer containing Triton X-100. The cytoskeleton was further fractionated by extraction with (NH4)2SO4, which left a structure enriched in intermediate filaments and desmosomes around the nuclei. A further digestion with nuclease and elution with (NH4)2SO4 removed the chromatin. The stable structure that remained after this procedure retained much of the epithelial morphology and contained essentially all of the cytokeratin filaments and desmosomes and the chromatin-depleted nuclear matrices. This structural network may serve as a scaffold for epithelial organization. The cytoskeleton and the underlying nuclear matrix intermediate filament scaffold, when examined in both conventional embedded thin sections and in unembedded whole mounts and thick sections, showed the retention of many of the detailed morphological aspects of the intact cells, which suggests a structural continuum linking the nuclear matrix, the intermediate filament network, and the intercellular desmosomal junctions. Most importantly, the protein composition of each of the four fractions obtained by this sequential procedure was essentially unique. Thus, the proteins constituting the soluble fraction, the cytoskeleton, the chromatin fraction, and the underlying nuclear matrix-intermediate filament scaffold are biochemically distinct. PMID:6540264

Sustainable Thorium Nuclear Fuel Cycles: A Comparison of Intermediate and Fast Neutron Spectrum Systems

DOE PAGES

Brown, Nicholas R.; Powers, Jeffrey J.; Feng, B.; ...

2015-05-21

This paper presents analyses of possible reactor representations of a nuclear fuel cycle with continuous recycling of thorium and produced uranium (mostly U-233) with thorium-only feed. The analysis was performed in the context of a U.S. Department of Energy effort to develop a compendium of informative nuclear fuel cycle performance data. The objective of this paper is to determine whether intermediate spectrum systems, having a majority of fission events occurring with incident neutron energies between 1 eV and 10 5 eV, perform as well as fast spectrum systems in this fuel cycle. The intermediate spectrum options analyzed include tight latticemore » heavy or light water-cooled reactors, continuously refueled molten salt reactors, and a sodium-cooled reactor with hydride fuel. All options were modeled in reactor physics codes to calculate their lattice physics, spectrum characteristics, and fuel compositions over time. Based on these results, detailed metrics were calculated to compare the fuel cycle performance. These metrics include waste management and resource utilization, and are binned to accommodate uncertainties. The performance of the intermediate systems for this selfsustaining thorium fuel cycle was similar to a representative fast spectrum system. However, the number of fission neutrons emitted per neutron absorbed limits performance in intermediate spectrum systems.« less

A Common Representational System Governed by Weber's Law: Nonverbal Numerical Similarity Judgments in 6-Year-Olds and Rhesus Macaques

ERIC Educational Resources Information Center

Jordan, Kerry E.; Brannon, Elizabeth M.

2006-01-01

This study compared nonverbal numerical processing in 6-year-olds with that in nonhuman animals using a numerical bisection task. In the study, 16 children were trained on a delayed match-to-sample paradigm to match exemplars of two anchor numerosities. Children were then required to indicate whether a sample intermediate to the anchor values was…

Simulating Network Retrieval of Arithmetic Facts.

ERIC Educational Resources Information Center

Ashcraft, Mark H.

This report describes a simulation of adults' retrieval of arithmetic facts from a network-based memory representation. The goals of the simulation project are to: demonstrate in specific form the nature of a spreading activation model of mental arithmetic; account for three important reaction time effects observed in laboratory investigations;…

OASIS: Prototyping Graphical Interfaces to Networked Information.

ERIC Educational Resources Information Center

Buckland, Michael K.; And Others

1993-01-01

Describes the latest modifications being made to OASIS, a front-end enhancement to the University of California's MELVYL online union catalog. Highlights include the X Windows interface; multiple database searching to act as an information network; Lisp implementation for flexible data representation; and OASIS commands and features to help…

34 CFR 361.23 - Requirements related to the statewide workforce investment system.

Code of Federal Regulations, 2013 CFR

2013-07-01

...) Enter into a memorandum of understanding (MOU) with the Local Workforce Investment Board under section.... (5) Provide representation on the Local Workforce Investment Board under section 117 of the Workforce... electronic networks, including nonvisual electronic networks, and that relate to subjects such as employment...

34 CFR 361.23 - Requirements related to the statewide workforce investment system.

Code of Federal Regulations, 2012 CFR

2012-07-01

...) Enter into a memorandum of understanding (MOU) with the Local Workforce Investment Board under section.... (5) Provide representation on the Local Workforce Investment Board under section 117 of the Workforce... electronic networks, including nonvisual electronic networks, and that relate to subjects such as employment...

34 CFR 361.23 - Requirements related to the statewide workforce investment system.

Code of Federal Regulations, 2014 CFR

2014-07-01

...) Enter into a memorandum of understanding (MOU) with the Local Workforce Investment Board under section.... (5) Provide representation on the Local Workforce Investment Board under section 117 of the Workforce... electronic networks, including nonvisual electronic networks, and that relate to subjects such as employment...

Causal Networks or Causal Islands? The Representation of Mechanisms and the Transitivity of Causal Judgment

ERIC Educational Resources Information Center

Johnson, Samuel G. B.; Ahn, Woo-kyoung

2015-01-01

Knowledge of mechanisms is critical for causal reasoning. We contrasted two possible organizations of causal knowledge--an interconnected causal "network," where events are causally connected without any boundaries delineating discrete mechanisms; or a set of disparate mechanisms--causal "islands"--such that events in different…

Connectionist Learning Procedures.

ERIC Educational Resources Information Center

Hinton, Geoffrey E.

A major goal of research on networks of neuron-like processing units is to discover efficient learning procedures that allow these networks to construct complex internal representations of their environment. The learning procedures must be capable of modifying the connection strengths in such a way that internal units which are not part of the…

Graph Representations of Flow and Transport in Fracture Networks using Machine Learning

NASA Astrophysics Data System (ADS)

Srinivasan, G.; Viswanathan, H. S.; Karra, S.; O'Malley, D.; Godinez, H. C.; Hagberg, A.; Osthus, D.; Mohd-Yusof, J.

2017-12-01

Flow and transport of fluids through fractured systems is governed by the properties and interactions at the micro-scale. Retaining information about the micro-structure such as fracture length, orientation, aperture and connectivity in mesh-based computational models results in solving for millions to billions of degrees of freedom and quickly renders the problem computationally intractable. Our approach depicts fracture networks graphically, by mapping fractures to nodes and intersections to edges, thereby greatly reducing computational burden. Additionally, we use machine learning techniques to build simulators on the graph representation, trained on data from the mesh-based high fidelity simulations to speed up computation by orders of magnitude. We demonstrate our methodology on ensembles of discrete fracture networks, dividing up the data into training and validation sets. Our machine learned graph-based solvers result in over 3 orders of magnitude speedup without any significant sacrifice in accuracy.

Randomizing world trade. II. A weighted network analysis

NASA Astrophysics Data System (ADS)

Squartini, Tiziano; Fagiolo, Giorgio; Garlaschelli, Diego

2011-10-01

Based on the misleading expectation that weighted network properties always offer a more complete description than purely topological ones, current economic models of the International Trade Network (ITN) generally aim at explaining local weighted properties, not local binary ones. Here we complement our analysis of the binary projections of the ITN by considering its weighted representations. We show that, unlike the binary case, all possible weighted representations of the ITN (directed and undirected, aggregated and disaggregated) cannot be traced back to local country-specific properties, which are therefore of limited informativeness. Our two papers show that traditional macroeconomic approaches systematically fail to capture the key properties of the ITN. In the binary case, they do not focus on the degree sequence and hence cannot characterize or replicate higher-order properties. In the weighted case, they generally focus on the strength sequence, but the knowledge of the latter is not enough in order to understand or reproduce indirect effects.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jones, J.P.; Bangs, A.L.; Butler, P.L.

Hetero Helix is a programming environment which simulates shared memory on a heterogeneous network of distributed-memory computers. The machines in the network may vary with respect to their native operating systems and internal representation of numbers. Hetero Helix presents a simple programming model to developers, and also considers the needs of designers, system integrators, and maintainers. The key software technology underlying Hetero Helix is the use of a compiler'' which analyzes the data structures in shared memory and automatically generates code which translates data representations from the format native to each machine into a common format, and vice versa. Themore » design of Hetero Helix was motivated in particular by the requirements of robotics applications. Hetero Helix has been used successfully in an integration effort involving 27 CPUs in a heterogeneous network and a body of software totaling roughly 100,00 lines of code. 25 refs., 6 figs.« less

A novel neural network for the synthesis of antennas and microwave devices.

PubMed

Delgado, Heriberto Jose; Thursby, Michael H; Ham, Fredric M

2005-11-01

A novel artificial neural network (SYNTHESIS-ANN) is presented, which has been designed for computationally intensive problems and applied to the optimization of antennas and microwave devices. The antenna example presented is optimized with respect to voltage standing-wave ratio, bandwidth, and frequency of operation. A simple microstrip transmission line problem is used to further describe the ANN effectiveness, in which microstrip line width is optimized with respect to line impedance. The ANNs exploit a unique number representation of input and output data in conjunction with a more standard neural network architecture. An ANN consisting of a heteroassociative memory provided a very efficient method of computing necessary geometrical values for the antenna when used in conjunction with a new randomization process. The number representation used provides significant insight into this new method of fault-tolerant computing. Further work is needed to evaluate the potential of this new paradigm.

Strong systematicity through sensorimotor conceptual grounding: an unsupervised, developmental approach to connectionist sentence processing

NASA Astrophysics Data System (ADS)

Jansen, Peter A.; Watter, Scott

2012-03-01

Connectionist language modelling typically has difficulty with syntactic systematicity, or the ability to generalise language learning to untrained sentences. This work develops an unsupervised connectionist model of infant grammar learning. Following the semantic boostrapping hypothesis, the network distils word category using a developmentally plausible infant-scale database of grounded sensorimotor conceptual representations, as well as a biologically plausible semantic co-occurrence activation function. The network then uses this knowledge to acquire an early benchmark clausal grammar using correlational learning, and further acquires separate conceptual and grammatical category representations. The network displays strongly systematic behaviour indicative of the general acquisition of the combinatorial systematicity present in the grounded infant-scale language stream, outperforms previous contemporary models that contain primarily noun and verb word categories, and successfully generalises broadly to novel untrained sensorimotor grounded sentences composed of unfamiliar nouns and verbs. Limitations as well as implications to later grammar learning are discussed.

Super-resolution using a light inception layer in convolutional neural network

NASA Astrophysics Data System (ADS)

Mou, Qinyang; Guo, Jun

2018-04-01

Recently, several models based on CNN architecture have achieved great result on Single Image Super-Resolution (SISR) problem. In this paper, we propose an image super-resolution method (SR) using a light inception layer in convolutional network (LICN). Due to the strong representation ability of our well-designed inception layer that can learn richer representation with less parameters, we can build our model with shallow architecture that can reduce the effect of vanishing gradients problem and save computational costs. Our model strike a balance between computational speed and the quality of the result. Compared with state-of-the-art result, we produce comparable or better results with faster computational speed.

Optimal spatiotemporal representation of multichannel EEG for recognition of brain states associated with distinct visual stimulus

NASA Astrophysics Data System (ADS)

Hramov, Alexander; Musatov, Vyacheslav Yu.; Runnova, Anastasija E.; Efremova, Tatiana Yu.; Koronovskii, Alexey A.; Pisarchik, Alexander N.

2018-04-01

In the paper we propose an approach based on artificial neural networks for recognition of different human brain states associated with distinct visual stimulus. Based on the developed numerical technique and the analysis of obtained experimental multichannel EEG data, we optimize the spatiotemporal representation of multichannel EEG to provide close to 97% accuracy in recognition of the EEG brain states during visual perception. Different interpretations of an ambiguous image produce different oscillatory patterns in the human EEG with similar features for every interpretation. Since these features are inherent to all subjects, a single artificial network can classify with high quality the associated brain states of other subjects.

Fine-grained visual marine vessel classification for coastal surveillance and defense applications

NASA Astrophysics Data System (ADS)

Solmaz, Berkan; Gundogdu, Erhan; Karaman, Kaan; Yücesoy, Veysel; Koç, Aykut

2017-10-01

The need for capabilities of automated visual content analysis has substantially increased due to presence of large number of images captured by surveillance cameras. With a focus on development of practical methods for extracting effective visual data representations, deep neural network based representations have received great attention due to their success in visual categorization of generic images. For fine-grained image categorization, a closely related yet a more challenging research problem compared to generic image categorization due to high visual similarities within subgroups, diverse applications were developed such as classifying images of vehicles, birds, food and plants. Here, we propose the use of deep neural network based representations for categorizing and identifying marine vessels for defense and security applications. First, we gather a large number of marine vessel images via online sources grouping them into four coarse categories; naval, civil, commercial and service vessels. Next, we subgroup naval vessels into fine categories such as corvettes, frigates and submarines. For distinguishing images, we extract state-of-the-art deep visual representations and train support-vector-machines. Furthermore, we fine tune deep representations for marine vessel images. Experiments address two scenarios, classification and verification of naval marine vessels. Classification experiment aims coarse categorization, as well as learning models of fine categories. Verification experiment embroils identification of specific naval vessels by revealing if a pair of images belongs to identical marine vessels by the help of learnt deep representations. Obtaining promising performance, we believe these presented capabilities would be essential components of future coastal and on-board surveillance systems.

CellNetVis: a web tool for visualization of biological networks using force-directed layout constrained by cellular components.

PubMed

Heberle, Henry; Carazzolle, Marcelo Falsarella; Telles, Guilherme P; Meirelles, Gabriela Vaz; Minghim, Rosane

2017-09-13

The advent of "omics" science has brought new perspectives in contemporary biology through the high-throughput analyses of molecular interactions, providing new clues in protein/gene function and in the organization of biological pathways. Biomolecular interaction networks, or graphs, are simple abstract representations where the components of a cell (e.g. proteins, metabolites etc.) are represented by nodes and their interactions are represented by edges. An appropriate visualization of data is crucial for understanding such networks, since pathways are related to functions that occur in specific regions of the cell. The force-directed layout is an important and widely used technique to draw networks according to their topologies. Placing the networks into cellular compartments helps to quickly identify where network elements are located and, more specifically, concentrated. Currently, only a few tools provide the capability of visually organizing networks by cellular compartments. Most of them cannot handle large and dense networks. Even for small networks with hundreds of nodes the available tools are not able to reposition the network while the user is interacting, limiting the visual exploration capability. Here we propose CellNetVis, a web tool to easily display biological networks in a cell diagram employing a constrained force-directed layout algorithm. The tool is freely available and open-source. It was originally designed for networks generated by the Integrated Interactome System and can be used with networks from others databases, like InnateDB. CellNetVis has demonstrated to be applicable for dynamic investigation of complex networks over a consistent representation of a cell on the Web, with capabilities not matched elsewhere.

Process-in-Network: A Comprehensive Network Processing Approach

PubMed Central

Urzaiz, Gabriel; Villa, David; Villanueva, Felix; Lopez, Juan Carlos

2012-01-01

A solid and versatile communications platform is very important in modern Ambient Intelligence (AmI) applications, which usually require the transmission of large amounts of multimedia information over a highly heterogeneous network. This article focuses on the concept of Process-in-Network (PIN), which is defined as the possibility that the network processes information as it is being transmitted, and introduces a more comprehensive approach than current network processing technologies. PIN can take advantage of waiting times in queues of routers, idle processing capacity in intermediate nodes, and the information that passes through the network. PMID:22969390

Relating brain signal variability to knowledge representation.

PubMed

Heisz, Jennifer J; Shedden, Judith M; McIntosh, Anthony R

2012-11-15

We assessed the hypothesis that brain signal variability is a reflection of functional network reconfiguration during memory processing. In the present experiments, we use multiscale entropy to capture the variability of human electroencephalogram (EEG) while manipulating the knowledge representation associated with faces stored in memory. Across two experiments, we observed increased variability as a function of greater knowledge representation. In Experiment 1, individuals with greater familiarity for a group of famous faces displayed more brain signal variability. In Experiment 2, brain signal variability increased with learning after multiple experimental exposures to previously unfamiliar faces. The results demonstrate that variability increases with face familiarity; cognitive processes during the perception of familiar stimuli may engage a broader network of regions, which manifests as higher complexity/variability in spatial and temporal domains. In addition, effects of repetition suppression on brain signal variability were observed, and the pattern of results is consistent with a selectivity model of neural adaptation. Crown Copyright © 2012. Published by Elsevier Inc. All rights reserved.

Classification of time-series images using deep convolutional neural networks

NASA Astrophysics Data System (ADS)

Hatami, Nima; Gavet, Yann; Debayle, Johan

2018-04-01

Convolutional Neural Networks (CNN) has achieved a great success in image recognition task by automatically learning a hierarchical feature representation from raw data. While the majority of Time-Series Classification (TSC) literature is focused on 1D signals, this paper uses Recurrence Plots (RP) to transform time-series into 2D texture images and then take advantage of the deep CNN classifier. Image representation of time-series introduces different feature types that are not available for 1D signals, and therefore TSC can be treated as texture image recognition task. CNN model also allows learning different levels of representations together with a classifier, jointly and automatically. Therefore, using RP and CNN in a unified framework is expected to boost the recognition rate of TSC. Experimental results on the UCR time-series classification archive demonstrate competitive accuracy of the proposed approach, compared not only to the existing deep architectures, but also to the state-of-the art TSC algorithms.

MARTA GANs: Unsupervised Representation Learning for Remote Sensing Image Classification

NASA Astrophysics Data System (ADS)

Lin, Daoyu; Fu, Kun; Wang, Yang; Xu, Guangluan; Sun, Xian

2017-11-01

With the development of deep learning, supervised learning has frequently been adopted to classify remotely sensed images using convolutional networks (CNNs). However, due to the limited amount of labeled data available, supervised learning is often difficult to carry out. Therefore, we proposed an unsupervised model called multiple-layer feature-matching generative adversarial networks (MARTA GANs) to learn a representation using only unlabeled data. MARTA GANs consists of both a generative model $G$ and a discriminative model $D$. We treat $D$ as a feature extractor. To fit the complex properties of remote sensing data, we use a fusion layer to merge the mid-level and global features. $G$ can produce numerous images that are similar to the training data; therefore, $D$ can learn better representations of remotely sensed images using the training data provided by $G$. The classification results on two widely used remote sensing image databases show that the proposed method significantly improves the classification performance compared with other state-of-the-art methods.

Unrealistic representations of "the self": A cognitive neuroscience assessment of anosognosia for memory deficit.

PubMed

Berlingeri, Manuela; Ravasio, Alessandra; Cranna, Silvia; Basilico, Stefania; Sberna, Maurizio; Bottini, Gabriella; Paulesu, Eraldo

2015-12-01

Three cognitive components may play a crucial role in both memory awareness and in anosognosia for memory deficit (AMD): (1) a personal data base (PDB), i.e., a memory store that contains "semantic" representations about the self, (2) monitoring processes (MPs) and (3) an explicit evaluation system (EES), or comparator, that assesses and binds the representations stored in the PDB with information obtained from the environment. We compared both the behavior and the functional connectivity (as assessed by resting-state fMRI) of AMD patients with aware patients and healthy controls. We found that AMD is associated with an impoverished PDB, while MPs are necessary to successfully update the PDB. AMD was associated with reduced functional connectivity within both the default-mode network and in a network that includes the left lateral temporal cortex, the hippocampus and the insula. The reduced connectivity between the hippocampus and the insular cortex was correlated with AMD severity. Copyright © 2015 Elsevier Inc. All rights reserved.

Continous Representation Learning via User Feedback

DOE Office of Scientific and Technical Information (OSTI.GOV)

Representation learning is a deep-learning based technique for extracting features from data for the purpose of machine learning. This requires a large amount of data, on order tens of thousands to millions of samples, to properly teach the deep neural network. This a system for continuous representation learning, where the system may be improved with a small number of additional samples (order 10-100). The unique characteristics of this invention include a human-computer feedback component, where assess the quality of the current representation and then provides a better representation to the system. The system then mixes the new data with oldmore » training examples to avoid overfitting and improve overall performance of the system. The model can be exported and shared with other users, and it may be applied to additional images the system hasn't seen before.« less

Structure and Charge Hopping Dynamics in Green Rust

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wander, Matthew C; Rosso, Kevin M; Schoonen, Martin A

Green rust is a family of mixed-valent iron phases formed by a number of abiotic and biotic processes under alkaline suboxic conditions. Due to its high Fe 2+ content, green rust is a potentially important phase for pollution remediation by serving as a powerful electron donor for reductive transformation. However, mechanisms of oxidation of this material are poorly understood. An essential component of the green rust structure is a mixed-valent brucite-like Fe(OH) 2 sheet comprised of a two dimensional network of edge-sharing iron octahedra. Room temperature Mössbauer spectra show a characteristic signature for intermediate valence on the iron atoms inmore » this sheet, indicative of a Fe 2+-Fe 3+ valence interchange reaction faster than approximately 10 7 s -1. Using Fe(OH) 2 as structural analogue for reduced green rust, we performed Hartree-Fock calculations on periodic slab models and cluster representations to determine the structure and hopping mobility of Fe 3+ hole polarons in this material, providing a first principles assessment of the Fe 2+-Fe 3+ valence interchange reaction rate. The calculations show that among three possible symmetry unique iron-to-iron hops within a sheet, a hop to next-nearest neighbors at an intermediate distance of 5.6 Å is the fastest. The predicted rate is on the order of 10 12 s -1 consistent the Mössbauer-based constraint. All other possibilities, including hopping across interlayer spaces, are predicted to be slower than 10 7 s -1. Collectively, the findings suggest the possibility of hole self-diffusion along sheets as a mechanism for regeneration of lattice Fe 2+ sites, consistent with previous experimental observations of edge-inward progressive oxidation of green rust.« less

On Complex Networks Representation and Computation of Hydrologycal Quantities

NASA Astrophysics Data System (ADS)

Serafin, F.; Bancheri, M.; David, O.; Rigon, R.

2017-12-01

Water is our blue gold. Despite results of discovery-based science keep warning public opinion about the looming worldwide water crisis, water is still treated as a not worth taking resource. Could a different multi-scale perspective affect environmental decision-making more deeply? Can also a further pairing to a new graphical representation of processes interaction sway decision-making more effectively and public opinion consequently?This abstract introduces a complex networks driven way to represent catchments eco-hydrology and related flexible informatics to manage it. The representation is built upon mathematical category. A category is an algebraic structure that comprises "objects" linked by "arrows". It is an evolution of Petri Nets said Time Continuous Petri Nets (TCPN). It aims to display (water) budgets processes and catchment interactions using explicative and self-contained symbolism. The result improves readability of physical processes compared to current descriptions. The IT perspective hinges on the Object Modeling System (OMS) v3. The latter is a non-invasive flexible environmental modeling framework designed to support component-based model development. The implementation of a Directed Acyclic Graph (DAG) data structure, named Net3, has recently enhanced its flexibility. Net3 represents interacting systems as complex networks: vertices match up with any sort of time evolving quantity; edges correspond to their data (fluxes) interchange. It currently hosts JGrass-NewAge components, and those implementing travel time analysis of fluxes. Further bio-physical or management oriented components can be easily added.This talk introduces both graphical representation and related informatics exercising actual applications and examples.

Deep Neural Networks as a Computational Model for Human Shape Sensitivity

PubMed Central

Op de Beeck, Hans P.

2016-01-01

Theories of object recognition agree that shape is of primordial importance, but there is no consensus about how shape might be represented, and so far attempts to implement a model of shape perception that would work with realistic stimuli have largely failed. Recent studies suggest that state-of-the-art convolutional ‘deep’ neural networks (DNNs) capture important aspects of human object perception. We hypothesized that these successes might be partially related to a human-like representation of object shape. Here we demonstrate that sensitivity for shape features, characteristic to human and primate vision, emerges in DNNs when trained for generic object recognition from natural photographs. We show that these models explain human shape judgments for several benchmark behavioral and neural stimulus sets on which earlier models mostly failed. In particular, although never explicitly trained for such stimuli, DNNs develop acute sensitivity to minute variations in shape and to non-accidental properties that have long been implicated to form the basis for object recognition. Even more strikingly, when tested with a challenging stimulus set in which shape and category membership are dissociated, the most complex model architectures capture human shape sensitivity as well as some aspects of the category structure that emerges from human judgments. As a whole, these results indicate that convolutional neural networks not only learn physically correct representations of object categories but also develop perceptually accurate representational spaces of shapes. An even more complete model of human object representations might be in sight by training deep architectures for multiple tasks, which is so characteristic in human development. PMID:27124699

A Novel Method of Case Representation and Retrieval in CBR for E-Learning

ERIC Educational Resources Information Center

Khamparia, Aditya; Pandey, Babita

2017-01-01

In this paper we have discussed a novel method which has been developed for representation and retrieval of cases in case based reasoning (CBR) as a part of e-learning system which is based on various student features. In this approach we have integrated Artificial Neural Network (ANN) with Data mining (DM) and CBR. ANN is used to find the…

Self-teaching neural network learns difficult reactor control problem

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jouse, W.C.

1989-01-01

A self-teaching neural network used as an adaptive controller quickly learns to control an unstable reactor configuration. The network models the behavior of a human operator. It is trained by allowing it to operate the reactivity control impulsively. It is punished whenever either the power or fuel temperature stray outside technical limits. Using a simple paradigm, the network constructs an internal representation of the punishment and of the reactor system. The reactor is constrained to small power orbits.

Event-Driven Random Back-Propagation: Enabling Neuromorphic Deep Learning Machines

PubMed Central

Neftci, Emre O.; Augustine, Charles; Paul, Somnath; Detorakis, Georgios

2017-01-01

An ongoing challenge in neuromorphic computing is to devise general and computationally efficient models of inference and learning which are compatible with the spatial and temporal constraints of the brain. One increasingly popular and successful approach is to take inspiration from inference and learning algorithms used in deep neural networks. However, the workhorse of deep learning, the gradient descent Gradient Back Propagation (BP) rule, often relies on the immediate availability of network-wide information stored with high-precision memory during learning, and precise operations that are difficult to realize in neuromorphic hardware. Remarkably, recent work showed that exact backpropagated gradients are not essential for learning deep representations. Building on these results, we demonstrate an event-driven random BP (eRBP) rule that uses an error-modulated synaptic plasticity for learning deep representations. Using a two-compartment Leaky Integrate & Fire (I&F) neuron, the rule requires only one addition and two comparisons for each synaptic weight, making it very suitable for implementation in digital or mixed-signal neuromorphic hardware. Our results show that using eRBP, deep representations are rapidly learned, achieving classification accuracies on permutation invariant datasets comparable to those obtained in artificial neural network simulations on GPUs, while being robust to neural and synaptic state quantizations during learning. PMID:28680387

How does the body representation system develop in the human brain?

PubMed

Fontan, Aurelie; Cignetti, Fabien; Nazarian, Bruno; Anton, Jean-Luc; Vaugoyeau, Marianne; Assaiante, Christine

2017-04-01

Exploration of the body representation system (BRS) from kinaesthetic illusions in fMRI has revealed a complex network composed of sensorimotor and frontoparietal components. Here, we evaluated the degree of maturity of this network in children aged 7-11 years, and the extent to which structural factors account for network differences with adults. Brain activation following tendon vibration at 100Hz ('illusion') and 30Hz ('no illusion') were analysed using the two-stage random effects model, with or without white and grey matter covariates. The BRS was already well established in children as revealed by the contrast 'illusion' vs 'no illusion', although still immature in some aspects. This included a lower level of activation in primary somatosensory and posterior parietal regions, and the exclusive activation of the frontopolar cortex (FPC) in children compared to adults. The former differences were related to structure, while the latter difference reflected a functional strategy where the FPC may serve as the 'top' in top-down modulation of the activity of the other BRS regions to facilitate the establishment of body representations. Hence, the development of the BRS not only relies on structural maturation, but also involves the disengagement of an executive region not classically involved in body processing. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Event-Driven Random Back-Propagation: Enabling Neuromorphic Deep Learning Machines.

PubMed

Neftci, Emre O; Augustine, Charles; Paul, Somnath; Detorakis, Georgios

2017-01-01

An ongoing challenge in neuromorphic computing is to devise general and computationally efficient models of inference and learning which are compatible with the spatial and temporal constraints of the brain. One increasingly popular and successful approach is to take inspiration from inference and learning algorithms used in deep neural networks. However, the workhorse of deep learning, the gradient descent Gradient Back Propagation (BP) rule, often relies on the immediate availability of network-wide information stored with high-precision memory during learning, and precise operations that are difficult to realize in neuromorphic hardware. Remarkably, recent work showed that exact backpropagated gradients are not essential for learning deep representations. Building on these results, we demonstrate an event-driven random BP (eRBP) rule that uses an error-modulated synaptic plasticity for learning deep representations. Using a two-compartment Leaky Integrate & Fire (I&F) neuron, the rule requires only one addition and two comparisons for each synaptic weight, making it very suitable for implementation in digital or mixed-signal neuromorphic hardware. Our results show that using eRBP, deep representations are rapidly learned, achieving classification accuracies on permutation invariant datasets comparable to those obtained in artificial neural network simulations on GPUs, while being robust to neural and synaptic state quantizations during learning.

Mitigating TCP Degradation over Intermittent Link Failures using Intermediate Buffers

DTIC Science & Technology

2006-06-01

signal strength [10]. The Preemptive routing in ad hoc networks [10] attempts to predict that a route will fail by looking at the signal power of the...when the error rate is high there are non -optimal back offs in the Retransmission Timeout. And third, in the high error situation the slow start...network storage follows. In Beck et. al. [3], Logistical Networking is outlined as a means of storing data throughout the network. End to end

Discrete-state phasor neural networks

NASA Astrophysics Data System (ADS)

Noest, André J.

1988-08-01

An associative memory network with local variables assuming one of q equidistant positions on the unit circle (q-state phasors) is introduced, and its recall behavior is solved exactly for any q when the interactions are sparse and asymmetric. Such models can describe natural or artifical networks of (neuro-)biological, chemical, or electronic limit-cycle oscillators with q-fold instead of circular symmetry, or similar optical computing devices using a phase-encoded data representation.

Empirical Evaluation of Different Feature Representations for Social Circles Detection

DTIC Science & Technology

2015-06-16

study and compare the performance on the available labelled Facebook data from the Kaggle competition on learning social circles in networks . We...Kaggle competition on learning social circles in networks [5]. The data consist of hand- labelled friendship egonets from Facebook and a set of 57...16. SECURITY CLASSIFICATION OF: Social circles detection is a special case of community detection in social network that is currently attracting a

Shear Stress Induced Reorganization of the Keratin Intermediate Filament Network Requires Phosphorylation by Protein Kinase C ζ

PubMed Central

Sivaramakrishnan, Sivaraj; Schneider, Jaime L.; Sitikov, Albert; Goldman, Robert D.

2009-01-01

Keratin intermediate filaments (KIFs) form a fibrous polymer network that helps epithelial cells withstand external mechanical forces. Recently, we established a correlation between the structure of the KIF network and its local mechanical properties in alveolar epithelial cells. Shear stress applied across the cell surface resulted in the structural remodeling of KIF and a substantial increase in the elastic modulus of the network. This study examines the mechanosignaling that regulates the structural remodeling of the KIF network. We report that the shear stress–mediated remodeling of the KIF network is facilitated by a twofold increase in the dynamic exchange rate of KIF subunits, which is regulated in a PKC ζ and 14-3-3–dependent manner. PKC ζ phosphorylates K18pSer33, and this is required for the structural reorganization because the KIF network in A549 cells transfected with a dominant negative PKC ζ, or expressing the K18Ser33Ala mutation, is unchanged. Blocking the shear stress–mediated reorganization results in reduced cellular viability and increased apoptotic levels. These data suggest that shear stress mediates the phosphorylation of K18pSer33, which is required for the reorganization of the KIF network, resulting in changes in mechanical properties of the cell that help maintain the integrity of alveolar epithelial cells. PMID:19357195

Discriminative Relational Topic Models.

PubMed

Chen, Ning; Zhu, Jun; Xia, Fei; Zhang, Bo

2015-05-01

Relational topic models (RTMs) provide a probabilistic generative process to describe both the link structure and document contents for document networks, and they have shown promise on predicting network structures and discovering latent topic representations. However, existing RTMs have limitations in both the restricted model expressiveness and incapability of dealing with imbalanced network data. To expand the scope and improve the inference accuracy of RTMs, this paper presents three extensions: 1) unlike the common link likelihood with a diagonal weight matrix that allows the-same-topic interactions only, we generalize it to use a full weight matrix that captures all pairwise topic interactions and is applicable to asymmetric networks; 2) instead of doing standard Bayesian inference, we perform regularized Bayesian inference (RegBayes) with a regularization parameter to deal with the imbalanced link structure issue in real networks and improve the discriminative ability of learned latent representations; and 3) instead of doing variational approximation with strict mean-field assumptions, we present collapsed Gibbs sampling algorithms for the generalized relational topic models by exploring data augmentation without making restricting assumptions. Under the generic RegBayes framework, we carefully investigate two popular discriminative loss functions, namely, the logistic log-loss and the max-margin hinge loss. Experimental results on several real network datasets demonstrate the significance of these extensions on improving prediction performance.

Transductive multi-view zero-shot learning.

PubMed

Fu, Yanwei; Hospedales, Timothy M; Xiang, Tao; Gong, Shaogang

2015-11-01

Most existing zero-shot learning approaches exploit transfer learning via an intermediate semantic representation shared between an annotated auxiliary dataset and a target dataset with different classes and no annotation. A projection from a low-level feature space to the semantic representation space is learned from the auxiliary dataset and applied without adaptation to the target dataset. In this paper we identify two inherent limitations with these approaches. First, due to having disjoint and potentially unrelated classes, the projection functions learned from the auxiliary dataset/domain are biased when applied directly to the target dataset/domain. We call this problem the projection domain shift problem and propose a novel framework, transductive multi-view embedding, to solve it. The second limitation is the prototype sparsity problem which refers to the fact that for each target class, only a single prototype is available for zero-shot learning given a semantic representation. To overcome this problem, a novel heterogeneous multi-view hypergraph label propagation method is formulated for zero-shot learning in the transductive embedding space. It effectively exploits the complementary information offered by different semantic representations and takes advantage of the manifold structures of multiple representation spaces in a coherent manner. We demonstrate through extensive experiments that the proposed approach (1) rectifies the projection shift between the auxiliary and target domains, (2) exploits the complementarity of multiple semantic representations, (3) significantly outperforms existing methods for both zero-shot and N-shot recognition on three image and video benchmark datasets, and (4) enables novel cross-view annotation tasks.

Organizational routines, innovation, and flexibility: the application of narrative networks to dynamic workflow.

PubMed

Hayes, Gillian R; Lee, Charlotte P; Dourish, Paul

2011-08-01

The purpose of this paper is to demonstrate how current visual representations of organizational and technological processes do not fully account for the variability present in everyday practices. We further demonstrate how narrative networks can augment these representations to indicate potential areas for successful or problematic adoption of new technologies and potential needs for additional training. We conducted a qualitative study of the processes and routines at a major academic medical center slated to be supported by the development and installation of a new comprehensive HIT system. We used qualitative data collection techniques including observations of the activities to be supported by the new system and interviews with department heads, researchers, and both clinical and non-clinical staff. We conducted a narrative network analysis of these data by choosing exemplar processes to be modeled, selecting and analyzing narrative fragments, and developing visual representations of the interconnection of these narratives. Narrative networks enable us to view the variety of ways work has been and can be performed in practice, influencing our ability to design for innovation in use. Narrative networks are a means for analyzing and visualizing organizational routines in concert with more traditional requirements engineering, workflow modeling, and quality improvement outcome measurement. This type of analysis can support a deeper and more nuanced understanding of how and why certain routines continue to exist, change, or stop entirely. At the same time, it can illuminate areas in which adoption may be slow, more training or communication may be needed, and routines preferred by the leadership are subverted by routines preferred by the staff. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Hebbian learning of hand-centred representations in a hierarchical neural network model of the primate visual system.

PubMed

Born, Jannis; Galeazzi, Juan M; Stringer, Simon M

2017-01-01

A subset of neurons in the posterior parietal and premotor areas of the primate brain respond to the locations of visual targets in a hand-centred frame of reference. Such hand-centred visual representations are thought to play an important role in visually-guided reaching to target locations in space. In this paper we show how a biologically plausible, Hebbian learning mechanism may account for the development of localized hand-centred representations in a hierarchical neural network model of the primate visual system, VisNet. The hand-centered neurons developed in the model use an invariance learning mechanism known as continuous transformation (CT) learning. In contrast to previous theoretical proposals for the development of hand-centered visual representations, CT learning does not need a memory trace of recent neuronal activity to be incorporated in the synaptic learning rule. Instead, CT learning relies solely on a Hebbian learning rule, which is able to exploit the spatial overlap that naturally occurs between successive images of a hand-object configuration as it is shifted across different retinal locations due to saccades. Our simulations show how individual neurons in the network model can learn to respond selectively to target objects in particular locations with respect to the hand, irrespective of where the hand-object configuration occurs on the retina. The response properties of these hand-centred neurons further generalise to localised receptive fields in the hand-centred space when tested on novel hand-object configurations that have not been explored during training. Indeed, even when the network is trained with target objects presented across a near continuum of locations around the hand during training, the model continues to develop hand-centred neurons with localised receptive fields in hand-centred space. With the help of principal component analysis, we provide the first theoretical framework that explains the behavior of Hebbian learning in VisNet.

Hebbian learning of hand-centred representations in a hierarchical neural network model of the primate visual system

PubMed Central

Born, Jannis; Stringer, Simon M.

2017-01-01

A subset of neurons in the posterior parietal and premotor areas of the primate brain respond to the locations of visual targets in a hand-centred frame of reference. Such hand-centred visual representations are thought to play an important role in visually-guided reaching to target locations in space. In this paper we show how a biologically plausible, Hebbian learning mechanism may account for the development of localized hand-centred representations in a hierarchical neural network model of the primate visual system, VisNet. The hand-centered neurons developed in the model use an invariance learning mechanism known as continuous transformation (CT) learning. In contrast to previous theoretical proposals for the development of hand-centered visual representations, CT learning does not need a memory trace of recent neuronal activity to be incorporated in the synaptic learning rule. Instead, CT learning relies solely on a Hebbian learning rule, which is able to exploit the spatial overlap that naturally occurs between successive images of a hand-object configuration as it is shifted across different retinal locations due to saccades. Our simulations show how individual neurons in the network model can learn to respond selectively to target objects in particular locations with respect to the hand, irrespective of where the hand-object configuration occurs on the retina. The response properties of these hand-centred neurons further generalise to localised receptive fields in the hand-centred space when tested on novel hand-object configurations that have not been explored during training. Indeed, even when the network is trained with target objects presented across a near continuum of locations around the hand during training, the model continues to develop hand-centred neurons with localised receptive fields in hand-centred space. With the help of principal component analysis, we provide the first theoretical framework that explains the behavior of Hebbian learning in VisNet. PMID:28562618

Why the Brain Knows More than We Do: Non-Conscious Representations and Their Role in the Construction of Conscious Experience

PubMed Central

Dresp-Langley, Birgitta

2011-01-01

Scientific studies have shown that non-conscious stimuli and representations influence information processing during conscious experience. In the light of such evidence, questions about potential functional links between non-conscious brain representations and conscious experience arise. This article discusses neural model capable of explaining how statistical learning mechanisms in dedicated resonant circuits could generate specific temporal activity traces of non-conscious representations in the brain. How reentrant signaling, top-down matching, and statistical coincidence of such activity traces may lead to the progressive consolidation of temporal patterns that constitute the neural signatures of conscious experience in networks extending across large distances beyond functionally specialized brain regions is then explained. PMID:24962683

Neural representations of social status hierarchy in human inferior parietal cortex.

PubMed

Chiao, Joan Y; Harada, Tokiko; Oby, Emily R; Li, Zhang; Parrish, Todd; Bridge, Donna J

2009-01-01

Mental representations of social status hierarchy share properties with that of numbers. Previous neuroimaging studies have shown that the neural representation of numerical magnitude lies within a network of regions within inferior parietal cortex. However the neural basis of social status hierarchy remains unknown. Using fMRI, we studied subjects while they compared social status magnitude of people, objects and symbols, as well as numerical magnitude. Both social status and number comparisons recruited bilateral intraparietal sulci. We also observed a semantic distance effect whereby neural activity within bilateral intraparietal sulci increased for semantically close relative to far numerical and social status comparisons. These results demonstrate that social status and number comparisons recruit distinct and overlapping neuronal representations within human inferior parietal cortex.

Object detection approach using generative sparse, hierarchical networks with top-down and lateral connections for combining texture/color detection and shape/contour detection

DOEpatents

Paiton, Dylan M.; Kenyon, Garrett T.; Brumby, Steven P.; Schultz, Peter F.; George, John S.

2015-07-28

An approach to detecting objects in an image dataset may combine texture/color detection, shape/contour detection, and/or motion detection using sparse, generative, hierarchical models with lateral and top-down connections. A first independent representation of objects in an image dataset may be produced using a color/texture detection algorithm. A second independent representation of objects in the image dataset may be produced using a shape/contour detection algorithm. A third independent representation of objects in the image dataset may be produced using a motion detection algorithm. The first, second, and third independent representations may then be combined into a single coherent output using a combinatorial algorithm.

An Algebraic Approach to Guarantee Harmonic Balance Method Using Gröbner Base

NASA Astrophysics Data System (ADS)

Yagi, Masakazu; Hisakado, Takashi; Okumura, Kohshi

Harmonic balance (HB) method is well known principle for analyzing periodic oscillations on nonlinear networks and systems. Because the HB method has a truncation error, approximated solutions have been guaranteed by error bounds. However, its numerical computation is very time-consuming compared with solving the HB equation. This paper proposes an algebraic representation of the error bound using Gröbner base. The algebraic representation enables to decrease the computational cost of the error bound considerably. Moreover, using singular points of the algebraic representation, we can obtain accurate break points of the error bound by collisions.

Attention doesn’t slide: spatiotopic updating after eye movements instantiates a new, discrete attentional locus

PubMed Central

Marino, Alexandria C.; Chun, Marvin M.

2011-01-01

During natural vision, eye movements can drastically alter the retinotopic (eye-centered) coordinates of locations and objects, yet the spatiotopic (world-centered) percept remains stable. Maintaining visuospatial attention in spatiotopic coordinates requires updating of attentional representations following each eye movement. However, this updating is not instantaneous; attentional facilitation temporarily lingers at the previous retinotopic location after a saccade, a phenomenon known as the retinotopic attentional trace. At various times after a saccade, we probed attention at an intermediate location between the retinotopic and spatiotopic locations to determine whether a single locus of attentional facilitation slides progressively from the previous retinotopic location to the appropriate spatiotopic location, or whether retinotopic facilitation decays while a new, independent spatiotopic locus concurrently becomes active. Facilitation at the intermediate location was not significant at any time, suggesting that top-down attention can result in enhancement of discrete retinotopic and spatiotopic locations without passing through intermediate locations. PMID:21258903

Quantum delocalization of protons in the hydrogen-bond network of an enzyme active site.

PubMed

Wang, Lu; Fried, Stephen D; Boxer, Steven G; Markland, Thomas E

2014-12-30

Enzymes use protein architectures to create highly specialized structural motifs that can greatly enhance the rates of complex chemical transformations. Here, we use experiments, combined with ab initio simulations that exactly include nuclear quantum effects, to show that a triad of strongly hydrogen-bonded tyrosine residues within the active site of the enzyme ketosteroid isomerase (KSI) facilitates quantum proton delocalization. This delocalization dramatically stabilizes the deprotonation of an active-site tyrosine residue, resulting in a very large isotope effect on its acidity. When an intermediate analog is docked, it is incorporated into the hydrogen-bond network, giving rise to extended quantum proton delocalization in the active site. These results shed light on the role of nuclear quantum effects in the hydrogen-bond network that stabilizes the reactive intermediate of KSI, and the behavior of protons in biological systems containing strong hydrogen bonds.

Quantum delocalization of protons in the hydrogen-bond network of an enzyme active site

PubMed Central

Wang, Lu; Fried, Stephen D.; Boxer, Steven G.; Markland, Thomas E.

2014-01-01

Enzymes use protein architectures to create highly specialized structural motifs that can greatly enhance the rates of complex chemical transformations. Here, we use experiments, combined with ab initio simulations that exactly include nuclear quantum effects, to show that a triad of strongly hydrogen-bonded tyrosine residues within the active site of the enzyme ketosteroid isomerase (KSI) facilitates quantum proton delocalization. This delocalization dramatically stabilizes the deprotonation of an active-site tyrosine residue, resulting in a very large isotope effect on its acidity. When an intermediate analog is docked, it is incorporated into the hydrogen-bond network, giving rise to extended quantum proton delocalization in the active site. These results shed light on the role of nuclear quantum effects in the hydrogen-bond network that stabilizes the reactive intermediate of KSI, and the behavior of protons in biological systems containing strong hydrogen bonds. PMID:25503367

Window Dressing on the Set: An Update.

ERIC Educational Resources Information Center

Commission on Civil Rights, Washington, DC.

Analyzed in this report are the portrayals of minorities and women in television drama from 1975-1977 and the representation of minorities and women in the network news of 1977. Also analyzed are 1977 employment patterns at local and network television stations and television's effects on viewers and the first amendment. Data presented show that…

Linking Transnational Logics: A Feminist Rhetorical Analysis of Public Policy Networks

ERIC Educational Resources Information Center

Dingo, Rebecca

2008-01-01

In this article, the author investigates the circulation and appropriation of representations of women in public policy. The author effectively mobilizes the metaphor of the network to examine the discursive intersections and transnational links between U.S. welfare programs and the World Bank gender mainstreaming policies. Her analysis reveals…

Using regional bird density distribution models to evaluate protected area networks and inform conservation planning

Treesearch

John D. Alexander; Jaime L. Stephens; Sam Veloz; Leo Salas; Josée S. Rousseau; C. John Ralph; Daniel A. Sarr

2017-01-01

As data about populations of indicator species become available, proactive strategies that improve representation of biological diversity within protected area networks should consider finer-scaled evaluations, especially in regions identified as important through course-scale analyses. We use density distribution models derived from a robust regional bird...

Use of Networked Collaborative Concept Mapping To Measure Team Processes and Team Outcomes.

ERIC Educational Resources Information Center

Chung, Gregory K. W. K.; O'Neil, Harold F., Jr.; Herl, Howard E.; Dennis, Robert A.

The feasibility of using a computer-based networked collaborative concept mapping system to measure teamwork skills was studied. A concept map is a node-link-node representation of content, where the nodes represent concepts and links represent relationships between connected concepts. Teamwork processes were examined for a group concept mapping…

Assessment of Matrix Multiplication Learning with a Rule-Based Analytical Model--"A Bayesian Network Representation"

ERIC Educational Resources Information Center

Zhang, Zhidong

2016-01-01

This study explored an alternative assessment procedure to examine learning trajectories of matrix multiplication. It took rule-based analytical and cognitive task analysis methods specifically to break down operation rules for a given matrix multiplication. Based on the analysis results, a hierarchical Bayesian network, an assessment model,…

Mapping the Terrain: Teach for America, Charter School Reform, and Corporate Sponsorship

ERIC Educational Resources Information Center

Kretchmar, Kerry; Sondel, Beth; Ferrare, Joseph J.

2014-01-01

In this paper we illustrate the relationships between Teach For America (TFA) and federal charter school reform to interrogate how policy decisions are shaped by networks of individuals, organizations, and private corporations. We use policy network analysis to create a visual representation of TFA's key role in developing and connecting…

Network representations of immune system complexity

PubMed Central

Subramanian, Naeha; Torabi-Parizi, Parizad; Gottschalk, Rachel A.; Germain, Ronald N.; Dutta, Bhaskar

2015-01-01

The mammalian immune system is a dynamic multi-scale system composed of a hierarchically organized set of molecular, cellular and organismal networks that act in concert to promote effective host defense. These networks range from those involving gene regulatory and protein-protein interactions underlying intracellular signaling pathways and single cell responses to increasingly complex networks of in vivo cellular interaction, positioning and migration that determine the overall immune response of an organism. Immunity is thus not the product of simple signaling events but rather non-linear behaviors arising from dynamic, feedback-regulated interactions among many components. One of the major goals of systems immunology is to quantitatively measure these complex multi-scale spatial and temporal interactions, permitting development of computational models that can be used to predict responses to perturbation. Recent technological advances permit collection of comprehensive datasets at multiple molecular and cellular levels while advances in network biology support representation of the relationships of components at each level as physical or functional interaction networks. The latter facilitate effective visualization of patterns and recognition of emergent properties arising from the many interactions of genes, molecules, and cells of the immune system. We illustrate the power of integrating ‘omics’ and network modeling approaches for unbiased reconstruction of signaling and transcriptional networks with a focus on applications involving the innate immune system. We further discuss future possibilities for reconstruction of increasingly complex cellular and organism-level networks and development of sophisticated computational tools for prediction of emergent immune behavior arising from the concerted action of these networks. PMID:25625853

dfnWorks: A discrete fracture network framework for modeling subsurface flow and transport

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hyman, Jeffrey D.; Karra, Satish; Makedonska, Nataliia

DFNWORKS is a parallelized computational suite to generate three-dimensional discrete fracture networks (DFN) and simulate flow and transport. Developed at Los Alamos National Laboratory over the past five years, it has been used to study flow and transport in fractured media at scales ranging from millimeters to kilometers. The networks are created and meshed using DFNGEN, which combines FRAM (the feature rejection algorithm for meshing) methodology to stochastically generate three-dimensional DFNs with the LaGriT meshing toolbox to create a high-quality computational mesh representation. The representation produces a conforming Delaunay triangulation suitable for high performance computing finite volume solvers in anmore » intrinsically parallel fashion. Flow through the network is simulated in dfnFlow, which utilizes the massively parallel subsurface flow and reactive transport finite volume code PFLOTRAN. A Lagrangian approach to simulating transport through the DFN is adopted within DFNTRANS to determine pathlines and solute transport through the DFN. Example applications of this suite in the areas of nuclear waste repository science, hydraulic fracturing and CO 2 sequestration are also included.« less

Systems Biology Graphical Notation: Entity Relationship language Level 1 Version 2.

PubMed

Sorokin, Anatoly; Le Novère, Nicolas; Luna, Augustin; Czauderna, Tobias; Demir, Emek; Haw, Robin; Mi, Huaiyu; Moodie, Stuart; Schreiber, Falk; Villéger, Alice

2015-09-04

The Systems Biological Graphical Notation (SBGN) is an international community effort for standardized graphical representations of biological pathways and networks. The goal of SBGN is to provide unambiguous pathway and network maps for readers with different scientific backgrounds as well as to support efficient and accurate exchange of biological knowledge between different research communities, industry, and other players in systems biology. Three SBGN languages, Process Description (PD), Entity Relationship (ER) and Activity Flow (AF), allow for the representation of different aspects of biological and biochemical systems at different levels of detail. The SBGN Entity Relationship language (ER) represents biological entities and their interactions and relationships within a network. SBGN ER focuses on all potential relationships between entities without considering temporal aspects. The nodes (elements) describe biological entities, such as proteins and complexes. The edges (connections) provide descriptions of interactions and relationships (or influences), e.g., complex formation, stimulation and inhibition. Among all three languages of SBGN, ER is the closest to protein interaction networks in biological literature and textbooks, but its well-defined semantics offer a superior precision in expressing biological knowledge.

TopologyNet: Topology based deep convolutional and multi-task neural networks for biomolecular property predictions

PubMed Central

2017-01-01

Although deep learning approaches have had tremendous success in image, video and audio processing, computer vision, and speech recognition, their applications to three-dimensional (3D) biomolecular structural data sets have been hindered by the geometric and biological complexity. To address this problem we introduce the element-specific persistent homology (ESPH) method. ESPH represents 3D complex geometry by one-dimensional (1D) topological invariants and retains important biological information via a multichannel image-like representation. This representation reveals hidden structure-function relationships in biomolecules. We further integrate ESPH and deep convolutional neural networks to construct a multichannel topological neural network (TopologyNet) for the predictions of protein-ligand binding affinities and protein stability changes upon mutation. To overcome the deep learning limitations from small and noisy training sets, we propose a multi-task multichannel topological convolutional neural network (MM-TCNN). We demonstrate that TopologyNet outperforms the latest methods in the prediction of protein-ligand binding affinities, mutation induced globular protein folding free energy changes, and mutation induced membrane protein folding free energy changes. Availability: weilab.math.msu.edu/TDL/ PMID:28749969

dfnWorks: A discrete fracture network framework for modeling subsurface flow and transport

DOE PAGES

Hyman, Jeffrey D.; Karra, Satish; Makedonska, Nataliia; ...

2015-11-01

DFNWORKS is a parallelized computational suite to generate three-dimensional discrete fracture networks (DFN) and simulate flow and transport. Developed at Los Alamos National Laboratory over the past five years, it has been used to study flow and transport in fractured media at scales ranging from millimeters to kilometers. The networks are created and meshed using DFNGEN, which combines FRAM (the feature rejection algorithm for meshing) methodology to stochastically generate three-dimensional DFNs with the LaGriT meshing toolbox to create a high-quality computational mesh representation. The representation produces a conforming Delaunay triangulation suitable for high performance computing finite volume solvers in anmore » intrinsically parallel fashion. Flow through the network is simulated in dfnFlow, which utilizes the massively parallel subsurface flow and reactive transport finite volume code PFLOTRAN. A Lagrangian approach to simulating transport through the DFN is adopted within DFNTRANS to determine pathlines and solute transport through the DFN. Example applications of this suite in the areas of nuclear waste repository science, hydraulic fracturing and CO 2 sequestration are also included.« less

End-to-End ASR-Free Keyword Search From Speech

NASA Astrophysics Data System (ADS)

Audhkhasi, Kartik; Rosenberg, Andrew; Sethy, Abhinav; Ramabhadran, Bhuvana; Kingsbury, Brian

2017-12-01

End-to-end (E2E) systems have achieved competitive results compared to conventional hybrid hidden Markov model (HMM)-deep neural network based automatic speech recognition (ASR) systems. Such E2E systems are attractive due to the lack of dependence on alignments between input acoustic and output grapheme or HMM state sequence during training. This paper explores the design of an ASR-free end-to-end system for text query-based keyword search (KWS) from speech trained with minimal supervision. Our E2E KWS system consists of three sub-systems. The first sub-system is a recurrent neural network (RNN)-based acoustic auto-encoder trained to reconstruct the audio through a finite-dimensional representation. The second sub-system is a character-level RNN language model using embeddings learned from a convolutional neural network. Since the acoustic and text query embeddings occupy different representation spaces, they are input to a third feed-forward neural network that predicts whether the query occurs in the acoustic utterance or not. This E2E ASR-free KWS system performs respectably despite lacking a conventional ASR system and trains much faster.

Feature-Selective Attentional Modulations in Human Frontoparietal Cortex.

PubMed

Ester, Edward F; Sutterer, David W; Serences, John T; Awh, Edward

2016-08-03

Control over visual selection has long been framed in terms of a dichotomy between "source" and "site," where top-down feedback signals originating in frontoparietal cortical areas modulate or bias sensory processing in posterior visual areas. This distinction is motivated in part by observations that frontoparietal cortical areas encode task-level variables (e.g., what stimulus is currently relevant or what motor outputs are appropriate), while posterior sensory areas encode continuous or analog feature representations. Here, we present evidence that challenges this distinction. We used fMRI, a roving searchlight analysis, and an inverted encoding model to examine representations of an elementary feature property (orientation) across the entire human cortical sheet while participants attended either the orientation or luminance of a peripheral grating. Orientation-selective representations were present in a multitude of visual, parietal, and prefrontal cortical areas, including portions of the medial occipital cortex, the lateral parietal cortex, and the superior precentral sulcus (thought to contain the human homolog of the macaque frontal eye fields). Additionally, representations in many-but not all-of these regions were stronger when participants were instructed to attend orientation relative to luminance. Collectively, these findings challenge models that posit a strict segregation between sources and sites of attentional control on the basis of representational properties by demonstrating that simple feature values are encoded by cortical regions throughout the visual processing hierarchy, and that representations in many of these areas are modulated by attention. Influential models of visual attention posit a distinction between top-down control and bottom-up sensory processing networks. These models are motivated in part by demonstrations showing that frontoparietal cortical areas associated with top-down control represent abstract or categorical stimulus information, while visual areas encode parametric feature information. Here, we show that multivariate activity in human visual, parietal, and frontal cortical areas encode representations of a simple feature property (orientation). Moreover, representations in several (though not all) of these areas were modulated by feature-based attention in a similar fashion. These results provide an important challenge to models that posit dissociable top-down control and sensory processing networks on the basis of representational properties. Copyright © 2016 the authors 0270-6474/16/368188-12$15.00/0.

Representational analysis of extended disorder in atomistic ensembles derived from total scattering data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Neilson, James R.; McQueen, Tyrel M.

With the increased availability of high-intensity time-of-flight neutron and synchrotron X-ray scattering sources that can access wide ranges of momentum transfer, the pair distribution function method has become a standard analysis technique for studying disorder of local coordination spheres and at intermediate atomic separations. In some cases, rational modeling of the total scattering data (Bragg and diffuse) becomes intractable with least-squares approaches, necessitating reverse Monte Carlo simulations using large atomistic ensembles. However, the extraction of meaningful information from the resulting atomistic ensembles is challenging, especially at intermediate length scales. Representational analysis is used here to describe the displacements of atomsmore » in reverse Monte Carlo ensembles from an ideal crystallographic structure in an approach analogous to tight-binding methods. Rewriting the displacements in terms of a local basis that is descriptive of the ideal crystallographic symmetry provides a robust approach to characterizing medium-range order (and disorder) and symmetry breaking in complex and disordered crystalline materials. Lastly, this method enables the extraction of statistically relevant displacement modes (orientation, amplitude and distribution) of the crystalline disorder and provides directly meaningful information in a locally symmetry-adapted basis set that is most descriptive of the crystal chemistry and physics.« less

Representational analysis of extended disorder in atomistic ensembles derived from total scattering data

DOE PAGES

Neilson, James R.; McQueen, Tyrel M.

2015-09-20

With the increased availability of high-intensity time-of-flight neutron and synchrotron X-ray scattering sources that can access wide ranges of momentum transfer, the pair distribution function method has become a standard analysis technique for studying disorder of local coordination spheres and at intermediate atomic separations. In some cases, rational modeling of the total scattering data (Bragg and diffuse) becomes intractable with least-squares approaches, necessitating reverse Monte Carlo simulations using large atomistic ensembles. However, the extraction of meaningful information from the resulting atomistic ensembles is challenging, especially at intermediate length scales. Representational analysis is used here to describe the displacements of atomsmore » in reverse Monte Carlo ensembles from an ideal crystallographic structure in an approach analogous to tight-binding methods. Rewriting the displacements in terms of a local basis that is descriptive of the ideal crystallographic symmetry provides a robust approach to characterizing medium-range order (and disorder) and symmetry breaking in complex and disordered crystalline materials. Lastly, this method enables the extraction of statistically relevant displacement modes (orientation, amplitude and distribution) of the crystalline disorder and provides directly meaningful information in a locally symmetry-adapted basis set that is most descriptive of the crystal chemistry and physics.« less

Spatial frequency supports the emergence of categorical representations in visual cortex during natural scene perception.

PubMed

Dima, Diana C; Perry, Gavin; Singh, Krish D

2018-06-11

In navigating our environment, we rapidly process and extract meaning from visual cues. However, the relationship between visual features and categorical representations in natural scene perception is still not well understood. Here, we used natural scene stimuli from different categories and filtered at different spatial frequencies to address this question in a passive viewing paradigm. Using representational similarity analysis (RSA) and cross-decoding of magnetoencephalography (MEG) data, we show that categorical representations emerge in human visual cortex at ∼180 ms and are linked to spatial frequency processing. Furthermore, dorsal and ventral stream areas reveal temporally and spatially overlapping representations of low and high-level layer activations extracted from a feedforward neural network. Our results suggest that neural patterns from extrastriate visual cortex switch from low-level to categorical representations within 200 ms, highlighting the rapid cascade of processing stages essential in human visual perception. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

Endogenous network of firms and systemic risk

NASA Astrophysics Data System (ADS)

Ma, Qianting; He, Jianmin; Li, Shouwei

2018-02-01

We construct an endogenous network characterized by commercial credit relationships connecting the upstream and downstream firms. Simulation results indicate that the endogenous network model displays a scale-free property which exists in real-world firm systems. In terms of the network structure, with the expansion of the scale of network nodes, the systemic risk increases significantly, while the heterogeneities of network nodes have no effect on systemic risk. As for firm micro-behaviors, including the selection range of trading partners, actual output, labor requirement, price of intermediate products and employee salaries, increase of all these parameters will lead to higher systemic risk.

Learning of Multimodal Representations With Random Walks on the Click Graph.

PubMed

Wu, Fei; Lu, Xinyan; Song, Jun; Yan, Shuicheng; Zhang, Zhongfei Mark; Rui, Yong; Zhuang, Yueting

2016-02-01

In multimedia information retrieval, most classic approaches tend to represent different modalities of media in the same feature space. With the click data collected from the users' searching behavior, existing approaches take either one-to-one paired data (text-image pairs) or ranking examples (text-query-image and/or image-query-text ranking lists) as training examples, which do not make full use of the click data, particularly the implicit connections among the data objects. In this paper, we treat the click data as a large click graph, in which vertices are images/text queries and edges indicate the clicks between an image and a query. We consider learning a multimodal representation from the perspective of encoding the explicit/implicit relevance relationship between the vertices in the click graph. By minimizing both the truncated random walk loss as well as the distance between the learned representation of vertices and their corresponding deep neural network output, the proposed model which is named multimodal random walk neural network (MRW-NN) can be applied to not only learn robust representation of the existing multimodal data in the click graph, but also deal with the unseen queries and images to support cross-modal retrieval. We evaluate the latent representation learned by MRW-NN on a public large-scale click log data set Clickture and further show that MRW-NN achieves much better cross-modal retrieval performance on the unseen queries/images than the other state-of-the-art methods.

MINER: exploratory analysis of gene interaction networks by machine learning from expression data.

PubMed

Kadupitige, Sidath Randeni; Leung, Kin Chun; Sellmeier, Julia; Sivieng, Jane; Catchpoole, Daniel R; Bain, Michael E; Gaëta, Bruno A

2009-12-03

The reconstruction of gene regulatory networks from high-throughput "omics" data has become a major goal in the modelling of living systems. Numerous approaches have been proposed, most of which attempt only "one-shot" reconstruction of the whole network with no intervention from the user, or offer only simple correlation analysis to infer gene dependencies. We have developed MINER (Microarray Interactive Network Exploration and Representation), an application that combines multivariate non-linear tree learning of individual gene regulatory dependencies, visualisation of these dependencies as both trees and networks, and representation of known biological relationships based on common Gene Ontology annotations. MINER allows biologists to explore the dependencies influencing the expression of individual genes in a gene expression data set in the form of decision, model or regression trees, using their domain knowledge to guide the exploration and formulate hypotheses. Multiple trees can then be summarised in the form of a gene network diagram. MINER is being adopted by several of our collaborators and has already led to the discovery of a new significant regulatory relationship with subsequent experimental validation. Unlike most gene regulatory network inference methods, MINER allows the user to start from genes of interest and build the network gene-by-gene, incorporating domain expertise in the process. This approach has been used successfully with RNA microarray data but is applicable to other quantitative data produced by high-throughput technologies such as proteomics and "next generation" DNA sequencing.

Connectivity Strength-Weighted Sparse Group Representation-Based Brain Network Construction for MCI Classification

PubMed Central

Yu, Renping; Zhang, Han; An, Le; Chen, Xiaobo; Wei, Zhihui; Shen, Dinggang

2017-01-01

Brain functional network analysis has shown great potential in understanding brain functions and also in identifying biomarkers for brain diseases, such as Alzheimer's disease (AD) and its early stage, mild cognitive impairment (MCI). In these applications, accurate construction of biologically meaningful brain network is critical. Sparse learning has been widely used for brain network construction; however, its l1-norm penalty simply penalizes each edge of a brain network equally, without considering the original connectivity strength which is one of the most important inherent linkwise characters. Besides, based on the similarity of the linkwise connectivity, brain network shows prominent group structure (i.e., a set of edges sharing similar attributes). In this article, we propose a novel brain functional network modeling framework with a “connectivity strength-weighted sparse group constraint.” In particular, the network modeling can be optimized by considering both raw connectivity strength and its group structure, without losing the merit of sparsity. Our proposed method is applied to MCI classification, a challenging task for early AD diagnosis. Experimental results based on the resting-state functional MRI, from 50 MCI patients and 49 healthy controls, show that our proposed method is more effective (i.e., achieving a significantly higher classification accuracy, 84.8%) than other competing methods (e.g., sparse representation, accuracy = 65.6%). Post hoc inspection of the informative features further shows more biologically meaningful brain functional connectivities obtained by our proposed method. PMID:28150897

Prediction and validation of protein intermediate states from structurally rich ensembles and coarse-grained simulations

NASA Astrophysics Data System (ADS)

Orellana, Laura; Yoluk, Ozge; Carrillo, Oliver; Orozco, Modesto; Lindahl, Erik

2016-08-01

Protein conformational changes are at the heart of cell functions, from signalling to ion transport. However, the transient nature of the intermediates along transition pathways hampers their experimental detection, making the underlying mechanisms elusive. Here we retrieve dynamic information on the actual transition routes from principal component analysis (PCA) of structurally-rich ensembles and, in combination with coarse-grained simulations, explore the conformational landscapes of five well-studied proteins. Modelling them as elastic networks in a hybrid elastic-network Brownian dynamics simulation (eBDIMS), we generate trajectories connecting stable end-states that spontaneously sample the crystallographic motions, predicting the structures of known intermediates along the paths. We also show that the explored non-linear routes can delimit the lowest energy passages between end-states sampled by atomistic molecular dynamics. The integrative methodology presented here provides a powerful framework to extract and expand dynamic pathway information from the Protein Data Bank, as well as to validate sampling methods in general.

Representations of people with dementia - subaltern, person, citizen.

PubMed

Gilmour, Jean A; Brannelly, Tula

2010-09-01

This study traces shifts in health professional representations of people with dementia. The concepts of subaltern, personhood and citizenship are used to draw attention to issues around visibility, voice and inclusion. Professional discourses and practices draw upon, and are shaped by historical and contemporary representations. Until recently, people with dementia were subaltern in nursing and medical discourses; marginalised and silenced. The incorporation of contemporary representations foregrounding personhood and citizenship into health professional accounts provide space for transformative styles of care. Privileging personhood centralises the person with dementia in social networks, focusing on their experiences and relationships. Respecting citizenship involves challenging discrimination and stigma: nursing from a rights-based approach necessitates listening and being responsive to the needs of the person with dementia. Incorporating contemporary representations in health professional practice requires the discarding of the historically dominant elite and authoritarian accounts of dementia still apparent in some nursing texts along with, perhaps, the historically burdened term of dementia itself.

Sharpening of Hierarchical Visual Feature Representations of Blurred Images.

PubMed

Abdelhack, Mohamed; Kamitani, Yukiyasu

2018-01-01

The robustness of the visual system lies in its ability to perceive degraded images. This is achieved through interacting bottom-up, recurrent, and top-down pathways that process the visual input in concordance with stored prior information. The interaction mechanism by which they integrate visual input and prior information is still enigmatic. We present a new approach using deep neural network (DNN) representation to reveal the effects of such integration on degraded visual inputs. We transformed measured human brain activity resulting from viewing blurred images to the hierarchical representation space derived from a feedforward DNN. Transformed representations were found to veer toward the original nonblurred image and away from the blurred stimulus image. This indicated deblurring or sharpening in the neural representation, and possibly in our perception. We anticipate these results will help unravel the interplay mechanism between bottom-up, recurrent, and top-down pathways, leading to more comprehensive models of vision.

Perceptually relevant grouping of image tokens on the basis of constraint propagation from local binary patterns

NASA Astrophysics Data System (ADS)

Behlim, Sadaf Iqbal; Syed, Tahir Qasim; Malik, Muhammad Yameen; Vigneron, Vincent

2016-11-01

Grouping image tokens is an intermediate step needed to arrive at meaningful image representation and summarization. Usually, perceptual cues, for instance, gestalt properties inform token grouping. However, they do not take into account structural continuities that could be derived from other tokens belonging to similar structures irrespective of their location. We propose an image representation that encodes structural constraints emerging from local binary patterns (LBP), which provides a long-distance measure of similarity but in a structurally connected way. Our representation provides a grouping of pixels or larger image tokens that is free of numeric similarity measures and could therefore be extended to nonmetric spaces. The representation lends itself nicely to ubiquitous image processing applications such as connected component labeling and segmentation. We test our proposed representation on the perceptual grouping or segmentation task on the popular Berkeley segmentation dataset (BSD500) that with respect to human segmented images achieves an average F-measure of 0.559. Our algorithm achieves a high average recall of 0.787 and is therefore well-suited to other applications such as object retrieval and category-independent object recognition. The proposed merging heuristic based on levels of singular tree component has shown promising results on the BSD500 dataset and currently ranks 12th among all benchmarked algorithms, but contrary to the others, it requires no data-driven training or specialized preprocessing.

A Non-Cognitive Formal Approach to Knowledge Representation in Artificial Intelligence.

DTIC Science & Technology

1986-06-01

example, Duda and others translated production rules into a partitioned semantic network (73). Representations were also translated into production...153. Berlin: Springer-Verlag, 1982. 38. Blikle, Andrzej . "Equational Languages," Information and Control, 21: 134-147 (September 1972). 285 39. Ezawa...Conference on Artificial Intelligence, IJCAI-75. 115-121. William Kaufmann, Inc., Los Altos CA, 1975. 73. Duda , Richard 0. and others. "Semantic

Intelligent Agents as a Basis for Natural Language Interfaces

DTIC Science & Technology

1988-01-01

language analysis component of UC, which produces a semantic representa tion of the input. This representation is in the form of a KODIAK network (see...Appendix A). Next, UC’s Concretion Mechanism performs concretion inferences ([Wilensky, 1983] and [Norvig, 1983]) based on the semantic network...The first step in UC’s processing is done by UC’s parser/understander component which produces a KODIAK semantic network representa tion of

Topological Ordering and Viscosity in the Glassy Ge-Se System: The Search for a Structural or Dynamical Signature of the Intermediate Phase

NASA Astrophysics Data System (ADS)

Zeidler, Anita; Salmon, Philip S.; Whittaker, Dean A. J.; Pizzey, Keiron J.; Hannon, Alex C.

2017-11-01

The topological ordering of the network structure in vitreous Ge_xSe_{1-x} was investigated across most of the glass-forming region (0 ≤ x ≤ 0.4) by using high-resolution neutron diffraction to measure the Bhatia-Thornton number-number partial structure factor. This approach gives access to the composition dependence of the mean coordination number \\bar{n} and correlation lengths associated with the network ordering. The thermal properties of the samples were also measured by using temperature-modulated differential scanning calorimetry. The results do not point to a structural origin of the so-called intermediate phase, which in our work is indicated for the composition range 0.175(8) ≤ x ≤ 0.235(8) by a vanishingly-small non-reversing enthalpy near the glass transition. The midpoint of this range coincides with the mean-field expectation of a floppy-to-rigid transition at x = 0.20. The composition dependence of the liquid viscosity, as taken from the literature, was also investigated to look for a dynamical origin of the intermediate phase, using the Mauro-Yue-Ellison-Gupta-Allan (MYEGA) model to estimate the viscosity at the liquidus temperature. The evidence points to a maximum in the viscosity at the liquidus temperature, and a minimum in the fragility index, for the range 0.20 ≤ x ≤ 0.22. The utility of the intermediate phase as a predictor of the material properties in network glass-forming systems is discussed.

The intermediate filament network protein, vimentin, is required for parvoviral infection

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fay, Nikta; Panté, Nelly, E-mail: pante@zoology.ubc.ca

Intermediate filaments (IFs) have recently been shown to serve novel roles during infection by many viruses. Here we have begun to study the role of IFs during the early steps of infection by the parvovirus minute virus of mice (MVM). We found that during early infection with MVM, after endosomal escape, the vimentin IF network was considerably altered, yielding collapsed immunofluorescence staining near the nuclear periphery. Furthermore, we found that vimentin plays an important role in the life cycle of MVM. The number of cells, which successfully replicated MVM, was reduced in infected cells in which the vimentin network wasmore » genetically or pharmacologically modified; viral endocytosis, however, remained unaltered. Perinuclear accumulation of MVM-containing vesicles was reduced in cells lacking vimentin. Our data suggests that vimentin is required for the MVM life cycle, presenting possibly a dual role: (1) following MVM escape from endosomes and (2) during endosomal trafficking of MVM. - Highlights: • MVM infection changes the distribution of the vimentin network to perinuclear regions. • Disrupting the vimentin network with acrylamide decreases MVM replication. • MVM replication is significantly reduced in vimentin-null cells. • Distribution of MVM-containing vesicles is affected in MVM infected vimentin-null cells.« less

Collective stochastic coherence in recurrent neuronal networks

NASA Astrophysics Data System (ADS)

Sancristóbal, Belén; Rebollo, Beatriz; Boada, Pol; Sanchez-Vives, Maria V.; Garcia-Ojalvo, Jordi

2016-09-01

Recurrent networks of dynamic elements frequently exhibit emergent collective oscillations, which can show substantial regularity even when the individual elements are considerably noisy. How noise-induced dynamics at the local level coexists with regular oscillations at the global level is still unclear. Here we show that a combination of stochastic recurrence-based initiation with deterministic refractoriness in an excitable network can reconcile these two features, leading to maximum collective coherence for an intermediate noise level. We report this behaviour in the slow oscillation regime exhibited by a cerebral cortex network under dynamical conditions resembling slow-wave sleep and anaesthesia. Computational analysis of a biologically realistic network model reveals that an intermediate level of background noise leads to quasi-regular dynamics. We verify this prediction experimentally in cortical slices subject to varying amounts of extracellular potassium, which modulates neuronal excitability and thus synaptic noise. The model also predicts that this effectively regular state should exhibit noise-induced memory of the spatial propagation profile of the collective oscillations, which is also verified experimentally. Taken together, these results allow us to construe the high regularity observed experimentally in the brain as an instance of collective stochastic coherence.

Predictive modelling of flow in a two-dimensional intermediate-scale, heterogeneous porous media

USGS Publications Warehouse

Barth, Gilbert R.; Hill, M.C.; Illangasekare, T.H.; Rajaram, H.

2000-01-01

To better understand the role of sedimentary structures in flow through porous media, and to determine how small-scale laboratory-measured values of hydraulic conductivity relate to in situ values this work deterministically examines flow through simple, artificial structures constructed for a series of intermediate-scale (10 m long), two-dimensional, heterogeneous, laboratory experiments. Nonlinear regression was used to determine optimal values of in situ hydraulic conductivity, which were compared to laboratory-measured values. Despite explicit numerical representation of the heterogeneity, the optimized values were generally greater than the laboratory-measured values. Discrepancies between measured and optimal values varied depending on the sand sieve size, but their contribution to error in the predicted flow was fairly consistent for all sands. Results indicate that, even under these controlled circumstances, laboratory-measured values of hydraulic conductivity need to be applied to models cautiously.To better understand the role of sedimentary structures in flow through porous media, and to determine how small-scale laboratory-measured values of hydraulic conductivity relate to in situ values this work deterministically examines flow through simple, artificial structures constructed for a series of intermediate-scale (10 m long), two-dimensional, heterogeneous, laboratory experiments. Nonlinear regression was used to determine optimal values of in situ hydraulic conductivity, which were compared to laboratory-measured values. Despite explicit numerical representation of the heterogeneity, the optimized values were generally greater than the laboratory-measured values. Discrepancies between measured and optimal values varied depending on the sand sieve size, but their contribution to error in the predicted flow was fairly consistent for all sands. Results indicate that, even under these controlled circumstances, laboratory-measured values of hydraulic conductivity need to be applied to models cautiously.

Hierarchical Representation Learning for Kinship Verification.

PubMed

Kohli, Naman; Vatsa, Mayank; Singh, Richa; Noore, Afzel; Majumdar, Angshul

2017-01-01

Kinship verification has a number of applications such as organizing large collections of images and recognizing resemblances among humans. In this paper, first, a human study is conducted to understand the capabilities of human mind and to identify the discriminatory areas of a face that facilitate kinship-cues. The visual stimuli presented to the participants determine their ability to recognize kin relationship using the whole face as well as specific facial regions. The effect of participant gender and age and kin-relation pair of the stimulus is analyzed using quantitative measures such as accuracy, discriminability index d' , and perceptual information entropy. Utilizing the information obtained from the human study, a hierarchical kinship verification via representation learning (KVRL) framework is utilized to learn the representation of different face regions in an unsupervised manner. We propose a novel approach for feature representation termed as filtered contractive deep belief networks (fcDBN). The proposed feature representation encodes relational information present in images using filters and contractive regularization penalty. A compact representation of facial images of kin is extracted as an output from the learned model and a multi-layer neural network is utilized to verify the kin accurately. A new WVU kinship database is created, which consists of multiple images per subject to facilitate kinship verification. The results show that the proposed deep learning framework (KVRL-fcDBN) yields the state-of-the-art kinship verification accuracy on the WVU kinship database and on four existing benchmark data sets. Furthermore, kinship information is used as a soft biometric modality to boost the performance of face verification via product of likelihood ratio and support vector machine based approaches. Using the proposed KVRL-fcDBN framework, an improvement of over 20% is observed in the performance of face verification.

Psychology of knowledge representation.

PubMed

Grimm, Lisa R

2014-05-01

Every cognitive enterprise involves some form of knowledge representation. Humans represent information about the external world and internal mental states, like beliefs and desires, and use this information to meet goals (e.g., classification or problem solving). Unfortunately, researchers do not have direct access to mental representations. Instead, cognitive scientists design experiments and implement computational models to develop theories about the mental representations present during task performance. There are several main types of mental representation and corresponding processes that have been posited: spatial, feature, network, and structured. Each type has a particular structure and a set of processes that are capable of accessing and manipulating information within the representation. The structure and processes determine what information can be used during task performance and what information has not been represented at all. As such, the different types of representation are likely used to solve different kinds of tasks. For example, structured representations are more complex and computationally demanding, but are good at representing relational information. Researchers interested in human psychology would benefit from considering how knowledge is represented in their domain of inquiry. For further resources related to this article, please visit the WIREs website. The author has declared no conflicts of interest for this article. © 2014 John Wiley & Sons, Ltd.

Framework for experimenting with QoS for multimedia services

NASA Astrophysics Data System (ADS)

Chen, Deming; Colwell, Regis; Gelman, Herschel; Chrysanthis, Panos K.; Mosse, Daniel

1996-03-01

It has been recognized that an effective support for multimedia applications must provide Quality of Service (QoS) guarantees. Current methods propose to provide such QoS guarantees through coordinated network resource reservations. In our approach, we extend this idea providing system-wide QoS guarantees that consider the data manipulation and transformations needed in the intermediate and end sites of the network. Given a user's QoS requirements, multisegment virtual channels are established with the necessary communication and computation resources reserved for the timely, synchronized, and reliable delivery of the different datatypes. Such data originate in several distributed data repositories, are transformed at intermediate service stations into suitable formats for transportation and presentation, and are delivered to a viewing unit. In this paper, we first review NETWORLD, an architecture that provides such QoS guarantees and an interface for the specification and negotiation of user-level QoS requirements. Our user interface supports both expert and non- expert modes. We then describe how to map user-level QoS requirements into low-level system parameters, leading into a contract between the application and the network. The mapping considers various characteristics of the architectures (such as the hardware and software available at each source, destination, or intermediate site) as well as cost constraints.

Monoclonal antibodies against trophectoderm-specific markers during mouse blastocyst formation.

PubMed Central

Brûlet, P; Babinet, C; Kemler, R; Jacob, F

1980-01-01

Two-dimensional gel electrophoresis has allowed the detection of proteins characteristic of inner cell mass and trophectoderm in mouse blastocyst. Certain of the proteins characterizing trophectoderm copurify with intermediate filaments from trophectoderm and a trophoblastoma cell line. A monoclonal antibody prepared against proteins of these intermediate filaments labels a filament network in trophectoderm but not in inner cell mass cells. Images PMID:6933460

Reputation-based collaborative network biology.

PubMed

Binder, Jean; Boue, Stephanie; Di Fabio, Anselmo; Fields, R Brett; Hayes, William; Hoeng, Julia; Park, Jennifer S; Peitsch, Manuel C

2015-01-01

A pilot reputation-based collaborative network biology platform, Bionet, was developed for use in the sbv IMPROVER Network Verification Challenge to verify and enhance previously developed networks describing key aspects of lung biology. Bionet was successful in capturing a more comprehensive view of the biology associated with each network using the collective intelligence and knowledge of the crowd. One key learning point from the pilot was that using a standardized biological knowledge representation language such as BEL is critical to the success of a collaborative network biology platform. Overall, Bionet demonstrated that this approach to collaborative network biology is highly viable. Improving this platform for de novo creation of biological networks and network curation with the suggested enhancements for scalability will serve both academic and industry systems biology communities.

Relating ASD symptoms to well-being: moving across different construct levels.

PubMed

Deserno, M K; Borsboom, D; Begeer, S; Geurts, H M

2018-05-01

Little is known about the specific factors that contribute to the well-being (WB) of individuals with autism spectrum disorder (ASD). A plausible hypothesis is that ASD symptomatology has a direct negative effect on WB. In the current study, the emerging tools of network analysis allow to explore the functional interdependencies between specific symptoms of ASD and domains of WB in a multivariate framework. We illustrate how studying both higher-order (total score) and lower-order (subscale) representations of ASD symptomatology can clarify the interrelations of factors relevant for domains of WB. We estimated network structures on three different construct levels for ASD symptomatology, as assessed with the Adult Social Behavior Questionnaire (item, subscale, total score), relating them to daily functioning (DF) and subjective WB in 323 adult individuals with clinically identified ASD (aged 17-70 years). For these networks, we assessed the importance of specific factors in the network structure. When focusing on the highest representation level of ASD symptomatology (i.e. a total score), we found a negative connection between ASD symptom severity and domains of WB. However, zooming in on lower representation levels of ASD symptomatology revealed that this connection was mainly funnelled by ASD symptoms related to insistence on sameness and experiencing reduced contact and that those symptom scales, in turn, impact different domains of WB. Zooming in across construct levels of ASD symptom severity into subscales of ASD symptoms can provide us with important insights into how specific domains of ASD symptoms relate to specific domains of DF and WB.

Parallel representation of stimulus identity and intensity in a dual pathway model inspired by the olfactory system of the honeybee.

PubMed

Schmuker, Michael; Yamagata, Nobuhiro; Nawrot, Martin Paul; Menzel, Randolf

2011-01-01

The honeybee Apis mellifera has a remarkable ability to detect and locate food sources during foraging, and to associate odor cues with food rewards. In the honeybee's olfactory system, sensory input is first processed in the antennal lobe (AL) network. Uniglomerular projection neurons (PNs) convey the sensory code from the AL to higher brain regions via two parallel but anatomically distinct pathways, the lateral and the medial antenno-cerebral tract (l- and m-ACT). Neurons innervating either tract show characteristic differences in odor selectivity, concentration dependence, and representation of mixtures. It is still unknown how this differential stimulus representation is achieved within the AL network. In this contribution, we use a computational network model to demonstrate that the experimentally observed features of odor coding in PNs can be reproduced by varying lateral inhibition and gain control in an otherwise unchanged AL network. We show that odor coding in the l-ACT supports detection and accurate identification of weak odor traces at the expense of concentration sensitivity, while odor coding in the m-ACT provides the basis for the computation and following of concentration gradients but provides weaker discrimination power. Both coding strategies are mutually exclusive, which creates a tradeoff between detection accuracy and sensitivity. The development of two parallel systems may thus reflect an evolutionary solution to this problem that enables honeybees to achieve both tasks during bee foraging in their natural environment, and which could inspire the development of artificial chemosensory devices for odor-guided navigation in robots.

The Interaction between Semantic Representation and Episodic Memory.

PubMed

Fang, Jing; Rüther, Naima; Bellebaum, Christian; Wiskott, Laurenz; Cheng, Sen

2018-02-01

The experimental evidence on the interrelation between episodic memory and semantic memory is inconclusive. Are they independent systems, different aspects of a single system, or separate but strongly interacting systems? Here, we propose a computational role for the interaction between the semantic and episodic systems that might help resolve this debate. We hypothesize that episodic memories are represented as sequences of activation patterns. These patterns are the output of a semantic representational network that compresses the high-dimensional sensory input. We show quantitatively that the accuracy of episodic memory crucially depends on the quality of the semantic representation. We compare two types of semantic representations: appropriate representations, which means that the representation is used to store input sequences that are of the same type as those that it was trained on, and inappropriate representations, which means that stored inputs differ from the training data. Retrieval accuracy is higher for appropriate representations because the encoded sequences are less divergent than those encoded with inappropriate representations. Consistent with our model prediction, we found that human subjects remember some aspects of episodes significantly more accurately if they had previously been familiarized with the objects occurring in the episode, as compared to episodes involving unfamiliar objects. We thus conclude that the interaction with the semantic system plays an important role for episodic memory.

Japanese Language Proficiency, Social Networking, and Language Use during Study Abroad: Learners' Perspectives

ERIC Educational Resources Information Center

Dewey, Dan P.; Bown, Jennifer; Eggett, Dennis

2012-01-01

This study examines the self-perceived speaking proficiency development of 204 learners of Japanese who studied abroad in Japan and analyzes connections between self-reported social network development, language use, and speaking development. Learners perceived that they gained the most in areas associated with the intermediate and advanced levels…

Virtual Social Network Communities: An Investigation of Language Learners' Development of Sociopragmatic Awareness and Multiliteracy Skills

ERIC Educational Resources Information Center

Blattner, Geraldine; Fiori, Melissa

2011-01-01

Although often neglected in language textbooks and classrooms, sociopragmatic and multiliteracy skills are crucial elements in language learning that language educators should not disregard. This article investigates whether a social networking community (SNC) website such as Facebook can be exploited in the context of an intermediate foreign…

Networks, Netwar, and Information-Age Terrorism

DTIC Science & Technology

1999-01-01

intermediate nodes. • The star, hub, or wheel network, as in a franchise or a cartel structure where a set of actors is tied to a central node or actor...Aviv and Jerusalem. On March 21, a Hamas satchel bomb exploded at a Tel Aviv cafe , killing three persons and injuring 48; on July 30, two Hamas

Global cosmological dynamics for the scalar field representation of the modified Chaplygin gas

NASA Astrophysics Data System (ADS)

Uggla, Claes

2013-09-01

In this paper we investigate the global dynamics for the minimally coupled scalar field representation of the modified Chaplygin gas in the context of flat Friedmann-Lemaître-Robertson Walker cosmology. The tool for doing this is a new set of bounded variables that lead to a regular dynamical system. It is shown that the exact modified Chaplygin gas perfect fluid solution appears as a straight line in the associated phase plane. It is also shown that no other solutions stay close to this solution during their entire temporal evolution, but that there exists an open subset of solutions that stay arbitrarily close during an intermediate time interval, and into the future in the case when the scalar field potential exhibits a global minimum.

Connectivity, biodiversity conservation and the design of marine reserve networks for coral reefs

NASA Astrophysics Data System (ADS)

Almany, G. R.; Connolly, S. R.; Heath, D. D.; Hogan, J. D.; Jones, G. P.; McCook, L. J.; Mills, M.; Pressey, R. L.; Williamson, D. H.

2009-06-01

Networks of no-take reserves are important for protecting coral reef biodiversity from climate change and other human impacts. Ensuring that reserve populations are connected to each other and non-reserve populations by larval dispersal allows for recovery from disturbance and is a key aspect of resilience. In general, connectivity between reserves should increase as the distance between them decreases. However, enhancing connectivity may often tradeoff against a network’s ability to representatively sample the system’s natural variability. This “representation” objective is typically measured in terms of species richness or diversity of habitats, but has other important elements (e.g., minimizing the risk that multiple reserves will be impacted by catastrophic events). Such representation objectives tend to be better achieved as reserves become more widely spaced. Thus, optimizing the location, size and spacing of reserves requires both an understanding of larval dispersal and explicit consideration of how well the network represents the broader system; indeed the lack of an integrated theory for optimizing tradeoffs between connectivity and representation objectives has inhibited the incorporation of connectivity into reserve selection algorithms. This article addresses these issues by (1) updating general recommendations for the location, size and spacing of reserves based on emerging data on larval dispersal in corals and reef fishes, and on considerations for maintaining genetic diversity; (2) using a spatial analysis of the Great Barrier Reef Marine Park to examine potential tradeoffs between connectivity and representation of biodiversity and (3) describing a framework for incorporating environmental fluctuations into the conceptualization of the tradeoff between connectivity and representation, and that expresses both in a common, demographically meaningful currency, thus making optimization possible.

Evaluation of multisectional and two-section particulate matter photochemical grid models in the Western United States.

PubMed

Morris, Ralph; Koo, Bonyoung; Yarwood, Greg

2005-11-01

Version 4.10s of the comprehensive air-quality model with extensions (CAMx) photochemical grid model has been developed, which includes two options for representing particulate matter (PM) size distribution: (1) a two-section representation that consists of fine (PM2.5) and coarse (PM2.5-10) modes that has no interactions between the sections and assumes all of the secondary PM is fine; and (2) a multisectional representation that divides the PM size distribution into N sections (e.g., N = 10) and simulates the mass transfer between sections because of coagulation, accumulation, evaporation, and other processes. The model was applied to Southern California using the two-section and multisection representation of PM size distribution, and we found that allowing secondary PM to grow into the coarse mode had a substantial effect on PM concentration estimates. CAMx was then applied to the Western United States for the 1996 annual period with a 36-km grid resolution using both the two-section and multisection PM representation. The Community Multiscale Air Quality (CMAQ) and Regional Modeling for Aerosol and Deposition (REMSAD) models were also applied to the 1996 annual period. Similar model performance was exhibited by the four models across the Interagency Monitoring of Protected Visual Environments (IMPROVE) and Clean Air Status and Trends Network monitoring networks. All four of the models exhibited fairly low annual bias for secondary PM sulfate and nitrate but with a winter overestimation and summer underestimation bias. The CAMx multisectional model estimated that coarse mode secondary sulfate and nitrate typically contribute <10% of the total sulfate and nitrate when averaged across the more rural IMPROVE monitoring network.

BioNSi: A Discrete Biological Network Simulator Tool.

PubMed

Rubinstein, Amir; Bracha, Noga; Rudner, Liat; Zucker, Noga; Sloin, Hadas E; Chor, Benny

2016-08-05

Modeling and simulation of biological networks is an effective and widely used research methodology. The Biological Network Simulator (BioNSi) is a tool for modeling biological networks and simulating their discrete-time dynamics, implemented as a Cytoscape App. BioNSi includes a visual representation of the network that enables researchers to construct, set the parameters, and observe network behavior under various conditions. To construct a network instance in BioNSi, only partial, qualitative biological data suffices. The tool is aimed for use by experimental biologists and requires no prior computational or mathematical expertise. BioNSi is freely available at http://bionsi.wix.com/bionsi , where a complete user guide and a step-by-step manual can also be found.

Research in computer science

NASA Technical Reports Server (NTRS)

Ortega, J. M.

1984-01-01

The research efforts of University of Virginia students under a NASA sponsored program are summarized and the status of the program is reported. The research includes: testing method evaluations for N version programming; a representation scheme for modeling three dimensional objects; fault tolerant protocols for real time local area networks; performance investigation of Cyber network; XFEM implementation; and vectorizing incomplete Cholesky conjugate gradients.

I See What You Mean: Theta Power Increases Are Involved in the Retrieval of Lexical Semantic Information

ERIC Educational Resources Information Center

Bastiaansen, Marcel C. M.; Oostenveld, Robert; Jensen, Ole; Hagoort, Peter

2008-01-01

An influential hypothesis regarding the neural basis of the mental lexicon is that semantic representations are neurally implemented as distributed networks carrying sensory, motor and/or more abstract functional information. This work investigates whether the semantic properties of words partly determine the topography of such networks. Subjects…

Gender Representation in an Electronic City Hall: Female Adoption of Santa Monica's PEN System.

ERIC Educational Resources Information Center

Collins-Jarvis, Lori A.

1993-01-01

Discussion of the use of electronic networking systems by women focuses on a study of their use of the Public Electronic Network (PEN) in Santa Monica (California). Characteristics of PEN that contributed to female adoption are described; gender, political participation, and motivation are examined; and future research is suggested. (46…

The CA3 Network as a Memory Store for Spatial Representations

ERIC Educational Resources Information Center

Papp, Gergely; Witter, Menno P.; Treves, Alessandro

2007-01-01

Comparative neuroanatomy suggests that the CA3 region of the mammalian hippocampus is directly homologous with the medio-dorsal pallium in birds and reptiles, with which it largely shares the basic organization of primitive cortex. Autoassociative memory models, which are generically applicable to cortical networks, then help assess how well CA3…

Object detection approach using generative sparse, hierarchical networks with top-down and lateral connections for combining texture/color detection and shape/contour detection

DOE Office of Scientific and Technical Information (OSTI.GOV)

Paiton, Dylan M.; Kenyon, Garrett T.; Brumby, Steven P.

An approach to detecting objects in an image dataset may combine texture/color detection, shape/contour detection, and/or motion detection using sparse, generative, hierarchical models with lateral and top-down connections. A first independent representation of objects in an image dataset may be produced using a color/texture detection algorithm. A second independent representation of objects in the image dataset may be produced using a shape/contour detection algorithm. A third independent representation of objects in the image dataset may be produced using a motion detection algorithm. The first, second, and third independent representations may then be combined into a single coherent output using amore » combinatorial algorithm.« less

Neural networks for link prediction in realistic biomedical graphs: a multi-dimensional evaluation of graph embedding-based approaches.

PubMed

Crichton, Gamal; Guo, Yufan; Pyysalo, Sampo; Korhonen, Anna

2018-05-21

Link prediction in biomedical graphs has several important applications including predicting Drug-Target Interactions (DTI), Protein-Protein Interaction (PPI) prediction and Literature-Based Discovery (LBD). It can be done using a classifier to output the probability of link formation between nodes. Recently several works have used neural networks to create node representations which allow rich inputs to neural classifiers. Preliminary works were done on this and report promising results. However they did not use realistic settings like time-slicing, evaluate performances with comprehensive metrics or explain when or why neural network methods outperform. We investigated how inputs from four node representation algorithms affect performance of a neural link predictor on random- and time-sliced biomedical graphs of real-world sizes (∼ 6 million edges) containing information relevant to DTI, PPI and LBD. We compared the performance of the neural link predictor to those of established baselines and report performance across five metrics. In random- and time-sliced experiments when the neural network methods were able to learn good node representations and there was a negligible amount of disconnected nodes, those approaches outperformed the baselines. In the smallest graph (∼ 15,000 edges) and in larger graphs with approximately 14% disconnected nodes, baselines such as Common Neighbours proved a justifiable choice for link prediction. At low recall levels (∼ 0.3) the approaches were mostly equal, but at higher recall levels across all nodes and average performance at individual nodes, neural network approaches were superior. Analysis showed that neural network methods performed well on links between nodes with no previous common neighbours; potentially the most interesting links. Additionally, while neural network methods benefit from large amounts of data, they require considerable amounts of computational resources to utilise them. Our results indicate that when there is enough data for the neural network methods to use and there are a negligible amount of disconnected nodes, those approaches outperform the baselines. At low recall levels the approaches are mostly equal but at higher recall levels and average performance at individual nodes, neural network approaches are superior. Performance at nodes without common neighbours which indicate more unexpected and perhaps more useful links account for this.

Measure-valued solutions to nonlocal transport equations on networks

NASA Astrophysics Data System (ADS)

Camilli, Fabio; De Maio, Raul; Tosin, Andrea

2018-06-01

Aiming to describe traffic flow on road networks with long-range driver interactions, we study a nonlinear transport equation defined on an oriented network where the velocity field depends not only on the state variable but also on the distribution of the population. We prove existence, uniqueness and continuous dependence results of the solution intended in a suitable measure-theoretic sense. We also provide a representation formula in terms of the push-forward of the initial and boundary data along the network and discuss an explicit example of nonlocal velocity field fitting our framework.

Representation of the Characteristics of Piezoelectric Fiber Composites with Neural Networks

NASA Astrophysics Data System (ADS)

Yapici, A.; Bickraj, K.; Yenilmez, A.; Li, M.; Tansel, I. N.; Martin, S. A.; Pereira, C. M.; Roth, L. E.

2007-03-01

Ideal sensors for the future should be economical, efficient, highly intelligent, and capable of obtaining their operation power from the environment. The use of piezoelectric fiber composites coupled with a low power microprocessor and backpropagation type neural networks is proposed for the development of a simple sensor to estimate the characteristics of harmonic forces. Three neural networks were used for the estimation of amplitude, gain and variation of the load in the time domain. The average estimation errors of the neural networks were less than 8% in all of the studied cases.

Application of neural networks to autonomous rendezvous and docking of space vehicles

NASA Technical Reports Server (NTRS)

Dabney, Richard W.

1992-01-01

NASA-Marshall has investigated the feasibility of numerous autonomous rendezvous and docking (ARD) candidate techniques. Neural networks have been studied as a viable basis for such systems' implementation, due to their intrinsic representation of such nonlinear functions as those for which analytical solutions are either difficult or nonexistent. Neural networks are also able to recognize and adapt to changes in their dynamic environment, thereby enhancing redundancy and fault tolerance. Outstanding performance has been obtained from ARD azimuth, elevation, and roll networks of this type.

Modular analysis of biological networks.

PubMed

Kaltenbach, Hans-Michael; Stelling, Jörg

2012-01-01

The analysis of complex biological networks has traditionally relied on decomposition into smaller, semi-autonomous units such as individual signaling pathways. With the increased scope of systems biology (models), rational approaches to modularization have become an important topic. With increasing acceptance of de facto modularity in biology, widely different definitions of what constitutes a module have sparked controversies. Here, we therefore review prominent classes of modular approaches based on formal network representations. Despite some promising research directions, several important theoretical challenges remain open on the way to formal, function-centered modular decompositions for dynamic biological networks.

VRML metabolic network visualizer.

PubMed

Rojdestvenski, Igor

2003-03-01

A successful date collection visualization should satisfy a set of many requirements: unification of diverse data formats, support for serendipity research, support of hierarchical structures, algorithmizability, vast information density, Internet-readiness, and other. Recently, virtual reality has made significant progress in engineering, architectural design, entertainment and communication. We experiment with the possibility of using the immersive abstract three-dimensional visualizations of the metabolic networks. We present the trial Metabolic Network Visualizer software, which produces graphical representation of a metabolic network as a VRML world from a formal description written in a simple SGML-type scripting language.

Uniform magnetic fields in density-functional theory

NASA Astrophysics Data System (ADS)

Tellgren, Erik I.; Laestadius, Andre; Helgaker, Trygve; Kvaal, Simen; Teale, Andrew M.

2018-01-01

We construct a density-functional formalism adapted to uniform external magnetic fields that is intermediate between conventional density functional theory and Current-Density Functional Theory (CDFT). In the intermediate theory, which we term linear vector potential-DFT (LDFT), the basic variables are the density, the canonical momentum, and the paramagnetic contribution to the magnetic moment. Both a constrained-search formulation and a convex formulation in terms of Legendre-Fenchel transformations are constructed. Many theoretical issues in CDFT find simplified analogs in LDFT. We prove results concerning N-representability, Hohenberg-Kohn-like mappings, existence of minimizers in the constrained-search expression, and a restricted analog to gauge invariance. The issue of additivity of the energy over non-interacting subsystems, which is qualitatively different in LDFT and CDFT, is also discussed.

Asymmetric dark matter models in SO(10)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nagata, Natsumi; Olive, Keith A.; Zheng, Jiaming, E-mail: natsumi@hep-th.phys.s.u-tokyo.ac.jp, E-mail: olive@physics.umn.edu, E-mail: zheng@physics.umn.edu

2017-02-01

We systematically study the possibilities for asymmetric dark matter in the context of non-supersymmetric SO(10) models of grand unification. Dark matter stability in SO(10) is guaranteed by a remnant Z{sub 2} symmetry which is preserved when the intermediate scale gauge subgroup of SO(10) is broken by a (\\bf 126) dimensional representation. The asymmetry in the dark matter states is directly generated through the out-of-equilibrium decay of particles around the intermediate scale, or transferred from the baryon/lepton asymmetry generated in the Standard Model sector by leptogenesis. We systematically classify possible asymmetric dark matter candidates in terms of their quantum numbers, andmore » derive the conditions for each case that the observed dark matter density is (mostly) explained by the asymmetry of dark matter particles.« less

Uniform magnetic fields in density-functional theory.

PubMed

Tellgren, Erik I; Laestadius, Andre; Helgaker, Trygve; Kvaal, Simen; Teale, Andrew M

2018-01-14

We construct a density-functional formalism adapted to uniform external magnetic fields that is intermediate between conventional density functional theory and Current-Density Functional Theory (CDFT). In the intermediate theory, which we term linear vector potential-DFT (LDFT), the basic variables are the density, the canonical momentum, and the paramagnetic contribution to the magnetic moment. Both a constrained-search formulation and a convex formulation in terms of Legendre-Fenchel transformations are constructed. Many theoretical issues in CDFT find simplified analogs in LDFT. We prove results concerning N-representability, Hohenberg-Kohn-like mappings, existence of minimizers in the constrained-search expression, and a restricted analog to gauge invariance. The issue of additivity of the energy over non-interacting subsystems, which is qualitatively different in LDFT and CDFT, is also discussed.

A survey of visual preprocessing and shape representation techniques

NASA Technical Reports Server (NTRS)

Olshausen, Bruno A.

1988-01-01

Many recent theories and methods proposed for visual preprocessing and shape representation are summarized. The survey brings together research from the fields of biology, psychology, computer science, electrical engineering, and most recently, neural networks. It was motivated by the need to preprocess images for a sparse distributed memory (SDM), but the techniques presented may also prove useful for applying other associative memories to visual pattern recognition. The material of this survey is divided into three sections: an overview of biological visual processing; methods of preprocessing (extracting parts of shape, texture, motion, and depth); and shape representation and recognition (form invariance, primitives and structural descriptions, and theories of attention).

Digital and optical shape representation and pattern recognition; Proceedings of the Meeting, Orlando, FL, Apr. 4-6, 1988

NASA Technical Reports Server (NTRS)

Juday, Richard D. (Editor)

1988-01-01

The present conference discusses topics in pattern-recognition correlator architectures, digital stereo systems, geometric image transformations and their applications, topics in pattern recognition, filter algorithms, object detection and classification, shape representation techniques, and model-based object recognition methods. Attention is given to edge-enhancement preprocessing using liquid crystal TVs, massively-parallel optical data base management, three-dimensional sensing with polar exponential sensor arrays, the optical processing of imaging spectrometer data, hybrid associative memories and metric data models, the representation of shape primitives in neural networks, and the Monte Carlo estimation of moment invariants for pattern recognition.

Character-level neural network for biomedical named entity recognition.

PubMed

Gridach, Mourad

2017-06-01

Biomedical named entity recognition (BNER), which extracts important named entities such as genes and proteins, is a challenging task in automated systems that mine knowledge in biomedical texts. The previous state-of-the-art systems required large amounts of task-specific knowledge in the form of feature engineering, lexicons and data pre-processing to achieve high performance. In this paper, we introduce a novel neural network architecture that benefits from both word- and character-level representations automatically, by using a combination of bidirectional long short-term memory (LSTM) and conditional random field (CRF) eliminating the need for most feature engineering tasks. We evaluate our system on two datasets: JNLPBA corpus and the BioCreAtIvE II Gene Mention (GM) corpus. We obtained state-of-the-art performance by outperforming the previous systems. To the best of our knowledge, we are the first to investigate the combination of deep neural networks, CRF, word embeddings and character-level representation in recognizing biomedical named entities. Copyright © 2017 Elsevier Inc. All rights reserved.

Modeling language and cognition with deep unsupervised learning: a tutorial overview

PubMed Central

Zorzi, Marco; Testolin, Alberto; Stoianov, Ivilin P.

2013-01-01

Deep unsupervised learning in stochastic recurrent neural networks with many layers of hidden units is a recent breakthrough in neural computation research. These networks build a hierarchy of progressively more complex distributed representations of the sensory data by fitting a hierarchical generative model. In this article we discuss the theoretical foundations of this approach and we review key issues related to training, testing and analysis of deep networks for modeling language and cognitive processing. The classic letter and word perception problem of McClelland and Rumelhart (1981) is used as a tutorial example to illustrate how structured and abstract representations may emerge from deep generative learning. We argue that the focus on deep architectures and generative (rather than discriminative) learning represents a crucial step forward for the connectionist modeling enterprise, because it offers a more plausible model of cortical learning as well as a way to bridge the gap between emergentist connectionist models and structured Bayesian models of cognition. PMID:23970869

Towards representation of a perceptual color manifold using associative memory for color constancy.

PubMed

Seow, Ming-Jung; Asari, Vijayan K

2009-01-01

In this paper, we propose the concept of a manifold of color perception through empirical observation that the center-surround properties of images in a perceptually similar environment define a manifold in the high dimensional space. Such a manifold representation can be learned using a novel recurrent neural network based learning algorithm. Unlike the conventional recurrent neural network model in which the memory is stored in an attractive fixed point at discrete locations in the state space, the dynamics of the proposed learning algorithm represent memory as a nonlinear line of attraction. The region of convergence around the nonlinear line is defined by the statistical characteristics of the training data. This learned manifold can then be used as a basis for color correction of the images having different color perception to the learned color perception. Experimental results show that the proposed recurrent neural network learning algorithm is capable of color balance the lighting variations in images captured in different environments successfully.

Modeling language and cognition with deep unsupervised learning: a tutorial overview.

PubMed

Zorzi, Marco; Testolin, Alberto; Stoianov, Ivilin P

2013-01-01

Deep unsupervised learning in stochastic recurrent neural networks with many layers of hidden units is a recent breakthrough in neural computation research. These networks build a hierarchy of progressively more complex distributed representations of the sensory data by fitting a hierarchical generative model. In this article we discuss the theoretical foundations of this approach and we review key issues related to training, testing and analysis of deep networks for modeling language and cognitive processing. The classic letter and word perception problem of McClelland and Rumelhart (1981) is used as a tutorial example to illustrate how structured and abstract representations may emerge from deep generative learning. We argue that the focus on deep architectures and generative (rather than discriminative) learning represents a crucial step forward for the connectionist modeling enterprise, because it offers a more plausible model of cortical learning as well as a way to bridge the gap between emergentist connectionist models and structured Bayesian models of cognition.

Spreading Activation in an Attractor Network with Latching Dynamics: Automatic Semantic Priming Revisited

PubMed Central

Lerner, Itamar; Bentin, Shlomo; Shriki, Oren

2012-01-01

Localist models of spreading activation (SA) and models assuming distributed-representations offer very different takes on semantic priming, a widely investigated paradigm in word recognition and semantic memory research. In the present study we implemented SA in an attractor neural network model with distributed representations and created a unified framework for the two approaches. Our models assumes a synaptic depression mechanism leading to autonomous transitions between encoded memory patterns (latching dynamics), which account for the major characteristics of automatic semantic priming in humans. Using computer simulations we demonstrated how findings that challenged attractor-based networks in the past, such as mediated and asymmetric priming, are a natural consequence of our present model’s dynamics. Puzzling results regarding backward priming were also given a straightforward explanation. In addition, the current model addresses some of the differences between semantic and associative relatedness and explains how these differences interact with stimulus onset asynchrony in priming experiments. PMID:23094718

Toward domain-specific design environments: Some representation ideas from the telecommunications domain

NASA Technical Reports Server (NTRS)

Greenspan, Sol; Feblowitz, Mark

1992-01-01

ACME is an experimental environment for investigating new approaches to modeling and analysis of system requirements and designs. ACME is built on and extends object-oriented conceptual modeling techniques and knowledge representation and reasoning (KRR) tools. The most immediate intended use for ACME is to help represent, understand, and communicate system designs during the early stages of system planning and requirements engineering. While our research is ostensibly aimed at software systems in general, we are particularly motivated to make an impact in the telecommunications domain, especially in the area referred to as Intelligent Networks (IN's). IN systems contain the software to provide services to users of a telecommunications network (e.g., call processing services, information services, etc.) as well as the software that provides the internal infrastructure for providing the services (e.g., resource management, billing, etc.). The software includes not only systems developed by the network proprietors but also by a growing group of independent service software providers.

Synchronous beta rhythms of frontoparietal networks support only behaviorally relevant representations

PubMed Central

Antzoulatos, Evan G; Miller, Earl K

2016-01-01

Categorization has been associated with distributed networks of the primate brain, including the prefrontal cortex (PFC) and posterior parietal cortex (PPC). Although category-selective spiking in PFC and PPC has been established, the frequency-dependent dynamic interactions of frontoparietal networks are largely unexplored. We trained monkeys to perform a delayed-match-to-spatial-category task while recording spikes and local field potentials from the PFC and PPC with multiple electrodes. We found category-selective beta- and delta-band synchrony between and within the areas. However, in addition to the categories, delta synchrony and spiking activity also reflected irrelevant stimulus dimensions. By contrast, beta synchrony only conveyed information about the task-relevant categories. Further, category-selective PFC neurons were synchronized with PPC beta oscillations, while neurons that carried irrelevant information were not. These results suggest that long-range beta-band synchrony could act as a filter that only supports neural representations of the variables relevant to the task at hand. DOI: http://dx.doi.org/10.7554/eLife.17822.001 PMID:27841747

Random Deep Belief Networks for Recognizing Emotions from Speech Signals.

PubMed

Wen, Guihua; Li, Huihui; Huang, Jubing; Li, Danyang; Xun, Eryang

2017-01-01

Now the human emotions can be recognized from speech signals using machine learning methods; however, they are challenged by the lower recognition accuracies in real applications due to lack of the rich representation ability. Deep belief networks (DBN) can automatically discover the multiple levels of representations in speech signals. To make full of its advantages, this paper presents an ensemble of random deep belief networks (RDBN) method for speech emotion recognition. It firstly extracts the low level features of the input speech signal and then applies them to construct lots of random subspaces. Each random subspace is then provided for DBN to yield the higher level features as the input of the classifier to output an emotion label. All outputted emotion labels are then fused through the majority voting to decide the final emotion label for the input speech signal. The conducted experimental results on benchmark speech emotion databases show that RDBN has better accuracy than the compared methods for speech emotion recognition.

Random Deep Belief Networks for Recognizing Emotions from Speech Signals

PubMed Central

Li, Huihui; Huang, Jubing; Li, Danyang; Xun, Eryang

2017-01-01

Now the human emotions can be recognized from speech signals using machine learning methods; however, they are challenged by the lower recognition accuracies in real applications due to lack of the rich representation ability. Deep belief networks (DBN) can automatically discover the multiple levels of representations in speech signals. To make full of its advantages, this paper presents an ensemble of random deep belief networks (RDBN) method for speech emotion recognition. It firstly extracts the low level features of the input speech signal and then applies them to construct lots of random subspaces. Each random subspace is then provided for DBN to yield the higher level features as the input of the classifier to output an emotion label. All outputted emotion labels are then fused through the majority voting to decide the final emotion label for the input speech signal. The conducted experimental results on benchmark speech emotion databases show that RDBN has better accuracy than the compared methods for speech emotion recognition. PMID:28356908

Analyzing Distributional Learning of Phonemic Categories in Unsupervised Deep Neural Networks

PubMed Central

Räsänen, Okko; Nagamine, Tasha; Mesgarani, Nima

2017-01-01

Infants’ speech perception adapts to the phonemic categories of their native language, a process assumed to be driven by the distributional properties of speech. This study investigates whether deep neural networks (DNNs), the current state-of-the-art in distributional feature learning, are capable of learning phoneme-like representations of speech in an unsupervised manner. We trained DNNs with unlabeled and labeled speech and analyzed the activations of each layer with respect to the phones in the input segments. The analyses reveal that the emergence of phonemic invariance in DNNs is dependent on the availability of phonemic labeling of the input during the training. No increased phonemic selectivity of the hidden layers was observed in the purely unsupervised networks despite successful learning of low-dimensional representations for speech. This suggests that additional learning constraints or more sophisticated models are needed to account for the emergence of phone-like categories in distributional learning operating on natural speech. PMID:29359204

Graphs for information security control in software defined networks

NASA Astrophysics Data System (ADS)

Grusho, Alexander A.; Abaev, Pavel O.; Shorgin, Sergey Ya.; Timonina, Elena E.

2017-07-01

Information security control in software defined networks (SDN) is connected with execution of the security policy rules regulating information accesses and protection against distribution of the malicious code and harmful influences. The paper offers a representation of a security policy in the form of hierarchical structure which in case of distribution of resources for the solution of tasks defines graphs of admissible interactions in a networks. These graphs define commutation tables of switches via the SDN controller.

Prefrontal Cortex Networks Shift from External to Internal Modes during Learning.

PubMed

Brincat, Scott L; Miller, Earl K

2016-09-14

As we learn about items in our environment, their neural representations become increasingly enriched with our acquired knowledge. But there is little understanding of how network dynamics and neural processing related to external information changes as it becomes laden with "internal" memories. We sampled spiking and local field potential activity simultaneously from multiple sites in the lateral prefrontal cortex (PFC) and the hippocampus (HPC)-regions critical for sensory associations-of monkeys performing an object paired-associate learning task. We found that in the PFC, evoked potentials to, and neural information about, external sensory stimulation decreased while induced beta-band (∼11-27 Hz) oscillatory power and synchrony associated with "top-down" or internal processing increased. By contrast, the HPC showed little evidence of learning-related changes in either spiking activity or network dynamics. The results suggest that during associative learning, PFC networks shift their resources from external to internal processing. As we learn about items in our environment, their representations in our brain become increasingly enriched with our acquired "top-down" knowledge. We found that in the prefrontal cortex, but not the hippocampus, processing of external sensory inputs decreased while internal network dynamics related to top-down processing increased. The results suggest that during learning, prefrontal cortex networks shift their resources from external (sensory) to internal (memory) processing. Copyright © 2016 the authors 0270-6474/16/369739-16$15.00/0.

Prefrontal Cortex Networks Shift from External to Internal Modes during Learning

PubMed Central

Brincat, Scott L.

2016-01-01

As we learn about items in our environment, their neural representations become increasingly enriched with our acquired knowledge. But there is little understanding of how network dynamics and neural processing related to external information changes as it becomes laden with “internal” memories. We sampled spiking and local field potential activity simultaneously from multiple sites in the lateral prefrontal cortex (PFC) and the hippocampus (HPC)—regions critical for sensory associations—of monkeys performing an object paired-associate learning task. We found that in the PFC, evoked potentials to, and neural information about, external sensory stimulation decreased while induced beta-band (∼11–27 Hz) oscillatory power and synchrony associated with “top-down” or internal processing increased. By contrast, the HPC showed little evidence of learning-related changes in either spiking activity or network dynamics. The results suggest that during associative learning, PFC networks shift their resources from external to internal processing. SIGNIFICANCE STATEMENT As we learn about items in our environment, their representations in our brain become increasingly enriched with our acquired “top-down” knowledge. We found that in the prefrontal cortex, but not the hippocampus, processing of external sensory inputs decreased while internal network dynamics related to top-down processing increased. The results suggest that during learning, prefrontal cortex networks shift their resources from external (sensory) to internal (memory) processing. PMID:27629722

Progress with modeling activity landscapes in drug discovery.

PubMed

Vogt, Martin

2018-04-19

Activity landscapes (ALs) are representations and models of compound data sets annotated with a target-specific activity. In contrast to quantitative structure-activity relationship (QSAR) models, ALs aim at characterizing structure-activity relationships (SARs) on a large-scale level encompassing all active compounds for specific targets. The popularity of AL modeling has grown substantially with the public availability of large activity-annotated compound data sets. AL modeling crucially depends on molecular representations and similarity metrics used to assess structural similarity. Areas covered: The concepts of AL modeling are introduced and its basis in quantitatively assessing molecular similarity is discussed. The different types of AL modeling approaches are introduced. AL designs can broadly be divided into three categories: compound-pair based, dimensionality reduction, and network approaches. Recent developments for each of these categories are discussed focusing on the application of mathematical, statistical, and machine learning tools for AL modeling. AL modeling using chemical space networks is covered in more detail. Expert opinion: AL modeling has remained a largely descriptive approach for the analysis of SARs. Beyond mere visualization, the application of analytical tools from statistics, machine learning and network theory has aided in the sophistication of AL designs and provides a step forward in transforming ALs from descriptive to predictive tools. To this end, optimizing representations that encode activity relevant features of molecules might prove to be a crucial step.

A cortical network that marks the moment when conscious representations are updated.

PubMed

Stöttinger, Elisabeth; Filipowicz, Alex; Valadao, Derick; Culham, Jody C; Goodale, Melvyn A; Anderson, Britt; Danckert, James

2015-12-01

In order to survive in a complex, noisy and constantly changing environment we need to categorize the world (e.g., Is this food edible or poisonous?) and we need to update our interpretations when things change. How does our brain update when object categories change from one to the next? We investigated the neural correlates associated with this updating process. We used event-related fMRI while people viewed a sequence of images that morphed from one object (e.g., a plane) to another (e.g., a shark). All participants were naïve as to the identity of the second object. The point at which participants 'saw' the second object was unpredictable and uncontaminated by any dramatic or salient change to the images themselves. The moment when subjective perceptual representations changed activated a circumscribed network including the anterior insula, medial and inferior frontal regions and inferior parietal cortex. In a setting where neither the timing nor nature of the visual transition was predictable, this restricted cortical network signals the time of updating a perceptual representation. The anterior insula and mid-frontal regions (including the ACC) were activated not only at the actual time when change was reported, but also immediately before, suggesting that these areas are also involved in processing alternative options after a mismatch has been detected. Copyright © 2015 Elsevier Ltd. All rights reserved.

New Mexico Community Health Councils: Documenting Contributions to Systems Changes.

PubMed

Sánchez, Victoria; Andrews, Mark L; Carrillo, Christina; Hale, Ron

2015-01-01

Coalition research has shifted from delineating structures and processes to identifying intermediate, systems changes (e.g., changes in policies) that contribute to longterm community health improvement. The University of New Mexico, the New Mexico Department of Health, and community health councils entered a multiyear participatory evaluation process to answer: What actions did health councils take that led to improving health through intermediate, systems changes? The evaluation system was created over several phases through an iterative, participatory process. Data were collected for councils' health priority areas (e.g., substance abuse) from 2009 to 2011. Twenty-three community health councils participated. Intermediate systems changes were measured: 1) networking and partnering, 2) joint planning of strategies, programs, and services, 3) leveraging resources, and 4) policy initiatives. Health councils reported data for each intermediate outcome by health priority area. Data showed councils identified local public health priorities and addressed those priorities through strengthening networks and partnerships, which lead to the creation and enhancement of strategies, services, and programs. Data also showed councils influenced policies in several ways (e.g., developing policy, identifying new policy, or sponsoring informational forums). Additionally, data showed councils leveraged $1.10 for every dollar invested by the state. When funding was suspended in July 2010, data showed dramatic decreases in activity levels from 2010 to 2011. The data demonstrate the feasibility and utility of an Internet-based system designed to gather intermediate systems changes evaluation data. This process is a model for similar efforts to capture common outcomes across diverse coalitions and partnerships.

Analyzing milestoning networks for molecular kinetics: definitions, algorithms, and examples.

PubMed

Viswanath, Shruthi; Kreuzer, Steven M; Cardenas, Alfredo E; Elber, Ron

2013-11-07

Network representations are becoming increasingly popular for analyzing kinetic data from techniques like Milestoning, Markov State Models, and Transition Path Theory. Mapping continuous phase space trajectories into a relatively small number of discrete states helps in visualization of the data and in dissecting complex dynamics to concrete mechanisms. However, not only are molecular networks derived from molecular dynamics simulations growing in number, they are also getting increasingly complex, owing partly to the growth in computer power that allows us to generate longer and better converged trajectories. The increased complexity of the networks makes simple interpretation and qualitative insight of the molecular systems more difficult to achieve. In this paper, we focus on various network representations of kinetic data and algorithms to identify important edges and pathways in these networks. The kinetic data can be local and partial (such as the value of rate coefficients between states) or an exact solution to kinetic equations for the entire system (such as the stationary flux between vertices). In particular, we focus on the Milestoning method that provides fluxes as the main output. We proposed Global Maximum Weight Pathways as a useful tool for analyzing molecular mechanism in Milestoning networks. A closely related definition was made in the context of Transition Path Theory. We consider three algorithms to find Global Maximum Weight Pathways: Recursive Dijkstra's, Edge-Elimination, and Edge-List Bisection. The asymptotic efficiency of the algorithms is analyzed and numerical tests on finite networks show that Edge-List Bisection and Recursive Dijkstra's algorithms are most efficient for sparse and dense networks, respectively. Pathways are illustrated for two examples: helix unfolding and membrane permeation. Finally, we illustrate that networks based on local kinetic information can lead to incorrect interpretation of molecular mechanisms.