Paranormal phenomena

NASA Astrophysics Data System (ADS)

Gaina, Alex

1996-08-01

Critical analysis is given of some paranormal phenomena events (UFO, healers, psychokinesis (telekinesis))reported in Moldova. It is argued that correct analysis of paranormal phenomena should be made in the framework of electromagnetism.

Logic Dynamics for Deductive Inference -- Its Stability and Neural Basis

NASA Astrophysics Data System (ADS)

Tsuda, Ichiro

2014-12-01

We propose a dynamical model that represents a process of deductive inference. We discuss the stability of logic dynamics and a neural basis for the dynamics. We propose a new concept of descriptive stability, thereby enabling a structure of stable descriptions of mathematical models concerning dynamic phenomena to be clarified. The present theory is based on the wider and deeper thoughts of John S. Nicolis. In particular, it is based on our joint paper on the chaos theory of human short-term memories with a magic number of seven plus or minus two.

Period doubling and other nonlinear phenomena in volcanic earthquakes and tremor

USGS Publications Warehouse

Julian, B.R.

2000-01-01

Evidence of subharmonic period-doubling cascades has recently been recognized in seismograms of volcanic tremor from several volcanoes. This phenomenon occurs only in nonlinear systems, and is the commonest route by which such systems change from periodic to chaotic behavior. It is predicted to occur in a model of volcanic tremor excitation by flow-induced vibration, and it might well also occur in other volcano-seismic source process. If the possibility of period doubling is not taken into account in interpreting spectra of tremor and long-period earthquakes, then low-frequency "sub-harmonic" oscillations may be mis-identified as normal modes of a linear acoustic resonator, leading to errors of an order of magnitude or more in inferred magma-body dimensions. This example illustrates the importance of nonlinear phenomena in attempts to understand volcano-seismic phenomena physically. Linear systems are fundamentally incapable of causing earthquakes or exciting tremor, so nonlinearity is essential to any theory of volcano-seismic phenomena. Nonlinear processes are in many respects qualitatively different from linear ones. A few of their characteristics that might be relevant in volcanoes include the possibility: (1) that damping might increase, rather than decrease, oscillation frequencies; and (2) that these frequencies might be functions of the amplitude of oscillation, so that temporal variations in spectral peak frequencies might not be manifestations of changes of conditions within the magmatic system.

G. F. Parrot and the theory of unconscious inferences.

PubMed

Allik, Jüri; Konstabel, Kenn

2005-01-01

In 1839, Georg Friedrich Parrot (1767-1852) published a short note about a peculiar visual phenomenon--the diminishing of the size of external objects situated at a relatively small distance from the window of a fast-moving train. For the explanation of this illusion, Parrot proposed a concept of unconscious inferences, a very rapid syllogistic conclusion from two premises, which anticipated the revival of Alhazen's theory of unconscious inferences by Hermann von Helmholtz, Wilhelm Wundt, and John Stuart Mill. He also advanced the notion that the speed of mental processes is not infinitely high and that it can be measured by means of systematic experimentation. Although Parrot was only partly correct in the description of the movement-induced changes of the perceived size, his general intention to understand basic mechanisms of the human mind was in harmony with the founding ideas of experimental psychology: it is possible to study the phenomena of the mind in the same general way that the physical world is studied, either in terms of mechanical or mathematical laws. 2005 Wiley Periodicals, Inc.

Morality Principles for Risk Modelling: Needs and Links with the Origins of Plausible Inference

NASA Astrophysics Data System (ADS)

Solana-Ortega, Alberto; Solana, Vicente

2009-12-01

In comparison with the foundations of probability calculus, the inescapable and controversial issue of how to assign probabilities has only recently become a matter of formal study. The introduction of information as a technical concept was a milestone, but the most promising entropic assignment methods still face unsolved difficulties, manifesting the incompleteness of plausible inference theory. In this paper we examine the situation faced by risk analysts in the critical field of extreme events modelling, where the former difficulties are especially visible, due to scarcity of observational data, the large impact of these phenomena and the obligation to assume professional responsibilities. To respond to the claim for a sound framework to deal with extremes, we propose a metafoundational approach to inference, based on a canon of extramathematical requirements. We highlight their strong moral content, and show how this emphasis in morality, far from being new, is connected with the historic origins of plausible inference. Special attention is paid to the contributions of Caramuel, a contemporary of Pascal, unfortunately ignored in the usual mathematical accounts of probability.

Ultrafast Phenomena XIV

NASA Astrophysics Data System (ADS)

Kobayashi, Takayoshi; Okada, Tadashi; Kobayashi, Tetsuro; Nelson, Keith A.; de Silvestri, Sandro

Ultrafast Phenomena XIV presents the latest advances in ultrafast science, including ultrafast laser and measurement technology as well as studies of ultrafast phenomena. Pico-, femto-, and atosecond processes relevant in physics, chemistry, biology, and engineering are presented. Ultrafast technology is now having a profound impact within a wide range of applications, among them imaging, material diagnostics, and transformation and high-speed optoelectronics . This book summarizes results presented at the 14th Ultrafast Phenomena Conference and reviews the state of the art in this important and rapidly advancing field.

Children's and adults' evaluation of the certainty of deductive inferences, inductive inferences, and guesses.

PubMed

Pillow, Bradford H

2002-01-01

Two experiments investigated kindergarten through fourth-grade children's and adults' (N = 128) ability to (1) evaluate the certainty of deductive inferences, inductive inferences, and guesses; and (2) explain the origins of inferential knowledge. When judging their own cognitive state, children in first grade and older rated deductive inferences as more certain than guesses; but when judging another person's knowledge, children did not distinguish valid inferences from invalid inferences and guesses until fourth grade. By third grade, children differentiated their own deductive inferences from inductive inferences and guesses, but only adults both differentiated deductive inferences from inductive inferences and differentiated inductive inferences from guesses. Children's recognition of their own inferences may contribute to the development of knowledge about cognitive processes, scientific reasoning, and a constructivist epistemology.

Nursing phenomena in inpatient psychiatry.

PubMed

Frauenfelder, F; Müller-Staub, M; Needham, I; Van Achterberg, T

2011-04-01

Little is known about the question if the nursing diagnosis classification of North American Nursing Association-International (NANDA-I) describes the adult inpatient psychiatric nursing care. The present study aimed to identify nursing phenomena mentioned in journal articles about the psychiatric inpatient nursing care and to compare these phenomena with the labels and the definitions of the nursing diagnoses to elucidate how well this classification covers these phenomena. A search of journal articles took place in the databases MedLine, PsychInfo, Cochrane and CINAHL. A qualitative content analysis approach was used to identify nursing phenomena in the articles. Various phenomena were found in the articles. The study demonstrated that NANDA-I describes essential phenomena for the adult inpatient psychiatry on the level of labels and definitions. However, some apparently important nursing phenomena are not covered by the labels or definitions of NANDA-I. Other phenomena are assigned as defining characteristics or as related factors to construct nursing diagnoses. The further development of the classification NANDA-I will strengthen the application in the daily work of psychiatric nurses and enhance the quality of nursing care in the inpatient setting. © 2010 Blackwell Publishing.

Phenomena Associated With EIT Waves

NASA Technical Reports Server (NTRS)

Thompson, B. J.; Biesecker, D. A.; Gopalswamy, N.

2003-01-01

We discuss phenomena associated with "EIT Wave" transients. These phenomena include coronal mass ejections, flares, EUV/SXR dimmings, chromospheric waves, Moreton waves, solar energetic particle events, energetic electron events, and radio signatures. Although the occurrence of many phenomena correlate with the appearance of EIT waves, it is difficult to mfer which associations are causal. The presentation will include a discussion of correlation surveys of these phenomena.

Sequential Monte Carlo for inference of latent ARMA time-series with innovations correlated in time

NASA Astrophysics Data System (ADS)

Urteaga, Iñigo; Bugallo, Mónica F.; Djurić, Petar M.

2017-12-01

We consider the problem of sequential inference of latent time-series with innovations correlated in time and observed via nonlinear functions. We accommodate time-varying phenomena with diverse properties by means of a flexible mathematical representation of the data. We characterize statistically such time-series by a Bayesian analysis of their densities. The density that describes the transition of the state from time t to the next time instant t+1 is used for implementation of novel sequential Monte Carlo (SMC) methods. We present a set of SMC methods for inference of latent ARMA time-series with innovations correlated in time for different assumptions in knowledge of parameters. The methods operate in a unified and consistent manner for data with diverse memory properties. We show the validity of the proposed approach by comprehensive simulations of the challenging stochastic volatility model.

Transport phenomena in environmental engineering

NASA Astrophysics Data System (ADS)

Sander, Aleksandra; Kardum, Jasna Prlić; Matijašić, Gordana; Žižek, Krunoslav

2018-01-01

A term transport phenomena arises as a second paradigm at the end of 1950s with high awareness that there was a strong need to improve the scoping of chemical engineering science. At that point, engineers became highly aware that it is extremely important to take step forward from pure empirical description and the concept of unit operations only to understand the specific process using phenomenological equations that rely on three elementary physical processes: momentum, energy and mass transport. This conceptual evolution of chemical engineering was first presented with a well-known book of R. Byron Bird, Warren E. Stewart and Edwin N. Lightfoot, Transport Phenomena, published in 1960 [1]. What transport phenomena are included in environmental engineering? It is hard to divide those phenomena through different engineering disciplines. The core is the same but the focus changes. Intention of the authors here is to present the transport phenomena that are omnipresent in treatment of various process streams. The focus in this chapter is made on the transport phenomena that permanently occur in mechanical macroprocesses of sedimentation and filtration for separation in solid-liquid particulate systems and on the phenomena of the flow through a fixed and a fluidized bed of particles that are immanent in separation processes in packed columns and in environmental catalysis. The fundamental phenomena for each thermal and equilibrium separation process technology are presented as well. Understanding and mathematical description of underlying transport phenomena result in scoping the separation processes in a way that ChEs should act worldwide.

Inference or Observation?

ERIC Educational Resources Information Center

Finson, Kevin D.

2010-01-01

Learning about what inferences are, and what a good inference is, will help students become more scientifically literate and better understand the nature of science in inquiry. Students in K-4 should be able to give explanations about what they investigate (NSTA 1997) and that includes doing so through inferring. This article provides some tips…

Surface phenomena revealed by in situ imaging: studies from adhesion, wear and cutting

NASA Astrophysics Data System (ADS)

Viswanathan, Koushik; Mahato, Anirban; Yeung, Ho; Chandrasekar, Srinivasan

2017-03-01

Surface deformation and flow phenomena are ubiquitous in mechanical processes. In this work we present an in situ imaging framework for studying a range of surface mechanical phenomena at high spatial resolution and across a range of time scales. The in situ framework is capable of resolving deformation and flow fields quantitatively in terms of surface displacements, velocities, strains and strain rates. Three case studies are presented demonstrating the power of this framework for studying surface deformation. In the first, the origin of stick-slip motion in adhesive polymer interfaces is investigated, revealing a intimate link between stick-slip and surface wave propagation. Second, the role of flow in mediating formation of surface defects and wear particles in metals is analyzed using a prototypical sliding process. It is shown that conventional post-mortem observation and inference can lead to erroneous conclusions with regard to formation of surface cracks and wear particles. The in situ framework is shown to unambiguously capture delamination wear in sliding. Third, material flow and surface deformation in a typical cutting process is analyzed. It is shown that a long-standing problem in the cutting of annealed metals is resolved by the imaging, with other benefits such as estimation of energy dissipation and power from the flow fields. In closure, guidelines are provided for profitably exploiting in situ observations to study large-strain deformation, flow and friction phenomena at surfaces that display a variety of time-scales.

Data mining and statistical inference in selective laser melting

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

Kamath, Chandrika

Selective laser melting (SLM) is an additive manufacturing process that builds a complex three-dimensional part, layer-by-layer, using a laser beam to fuse fine metal powder together. The design freedom afforded by SLM comes associated with complexity. As the physical phenomena occur over a broad range of length and time scales, the computational cost of modeling the process is high. At the same time, the large number of parameters that control the quality of a part make experiments expensive. In this paper, we describe ways in which we can use data mining and statistical inference techniques to intelligently combine simulations andmore » experiments to build parts with desired properties. We start with a brief summary of prior work in finding process parameters for high-density parts. We then expand on this work to show how we can improve the approach by using feature selection techniques to identify important variables, data-driven surrogate models to reduce computational costs, improved sampling techniques to cover the design space adequately, and uncertainty analysis for statistical inference. Here, our results indicate that techniques from data mining and statistics can complement those from physical modeling to provide greater insight into complex processes such as selective laser melting.« less

Data mining and statistical inference in selective laser melting

DOE PAGES

Kamath, Chandrika

2016-01-11

Selective laser melting (SLM) is an additive manufacturing process that builds a complex three-dimensional part, layer-by-layer, using a laser beam to fuse fine metal powder together. The design freedom afforded by SLM comes associated with complexity. As the physical phenomena occur over a broad range of length and time scales, the computational cost of modeling the process is high. At the same time, the large number of parameters that control the quality of a part make experiments expensive. In this paper, we describe ways in which we can use data mining and statistical inference techniques to intelligently combine simulations andmore » experiments to build parts with desired properties. We start with a brief summary of prior work in finding process parameters for high-density parts. We then expand on this work to show how we can improve the approach by using feature selection techniques to identify important variables, data-driven surrogate models to reduce computational costs, improved sampling techniques to cover the design space adequately, and uncertainty analysis for statistical inference. Here, our results indicate that techniques from data mining and statistics can complement those from physical modeling to provide greater insight into complex processes such as selective laser melting.« less

Entropic Inference

NASA Astrophysics Data System (ADS)

Caticha, Ariel

2011-03-01

In this tutorial we review the essential arguments behing entropic inference. We focus on the epistemological notion of information and its relation to the Bayesian beliefs of rational agents. The problem of updating from a prior to a posterior probability distribution is tackled through an eliminative induction process that singles out the logarithmic relative entropy as the unique tool for inference. The resulting method of Maximum relative Entropy (ME), includes as special cases both MaxEnt and Bayes' rule, and therefore unifies the two themes of these workshops—the Maximum Entropy and the Bayesian methods—into a single general inference scheme.

The Dimensionality of Inference Making: Are Local and Global Inferences Distinguishable?

ERIC Educational Resources Information Center

Muijselaar, Marloes M. L.

2018-01-01

We investigated the dimensionality of inference making in samples of 4- to 9-year-olds (Ns = 416-783) to determine if local and global coherence inferences could be distinguished. In addition, we examined the validity of our experimenter-developed inference measure by comparing with three additional measures of listening comprehension. Multitrait,…

Self-enforcing Private Inference Control

NASA Astrophysics Data System (ADS)

Yang, Yanjiang; Li, Yingjiu; Weng, Jian; Zhou, Jianying; Bao, Feng

Private inference control enables simultaneous enforcement of inference control and protection of users' query privacy. Private inference control is a useful tool for database applications, especially when users are increasingly concerned about individual privacy nowadays. However, protection of query privacy on top of inference control is a double-edged sword: without letting the database server know the content of user queries, users can easily launch DoS attacks. To assuage DoS attacks in private inference control, we propose the concept of self-enforcing private inference control, whose intuition is to force users to only make inference-free queries by enforcing inference control themselves; otherwise, penalty will inflict upon the violating users.

Switching Phenomena

NASA Astrophysics Data System (ADS)

Stanley, H. E.; Buldyrev, S. V.; Franzese, G.; Havlin, S.; Mallamace, F.; Mazza, M. G.; Kumar, P.; Plerou, V.; Preis, T.; Stokely, K.; Xu, L.

One challenge of biology, medicine, and economics is that the systems treated by these serious scientific disciplines can suddenly "switch" from one behavior to another, even though they possess no perfect metronome in time. As if by magic, out of nothing but randomness one finds remarkably fine-tuned processes in time. The past century has, philosophically, been concerned with placing aside the human tendency to see the universe as a fine-tuned machine. Here we will address the challenge of uncovering how, through randomness (albeit, as we shall see, strongly correlated randomness), one can arrive at some of the many temporal patterns in physics, economics, and medicine and even begin to characterize the switching phenomena that enable a system to pass from one state to another. We discuss some applications of correlated randomness to understanding switching phenomena in various fields. Specifically, we present evidence from experiments and from computer simulations supporting the hypothesis that water's anomalies are related to a switching point (which is not unlike the "tipping point" immortalized by Malcolm Gladwell), and that the bubbles in economic phenomena that occur on all scales are not "outliers" (another Gladwell immortalization).

More than one kind of inference: re-examining what's learned in feature inference and classification.

PubMed

Sweller, Naomi; Hayes, Brett K

2010-08-01

Three studies examined how task demands that impact on attention to typical or atypical category features shape the category representations formed through classification learning and inference learning. During training categories were learned via exemplar classification or by inferring missing exemplar features. In the latter condition inferences were made about missing typical features alone (typical feature inference) or about both missing typical and atypical features (mixed feature inference). Classification and mixed feature inference led to the incorporation of typical and atypical features into category representations, with both kinds of features influencing inferences about familiar (Experiments 1 and 2) and novel (Experiment 3) test items. Those in the typical inference condition focused primarily on typical features. Together with formal modelling, these results challenge previous accounts that have characterized inference learning as producing a focus on typical category features. The results show that two different kinds of inference learning are possible and that these are subserved by different kinds of category representations.

Source amplitudes of NTS explosions inferred from Rayleigh waves at Albuquerque and Tucson. Topical report

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

Bache, T.C.; Rodi, W.L.; Mason, B.F.

1978-06-01

Comparing observed and synthetic seismograms, source amplitudes of NTS explosions are inferred from Rayleigh wave recordings from the WWSSN stations at Albuquerque, New Mexico (ALQ) and Tucson, Arizona (TUC). The potential influence of source complexities, particularly surface spallation and related phenomena, is studied in detail. As described in earlier work by Bache, Rodi and Harkrider, the earth model for the synthetic were converted from observations at ALQ and TUC. The agreement of observed and synthetic seismograms is quite good and is sensitive to important features of the source.

Optimal inference with suboptimal models: Addiction and active Bayesian inference

PubMed Central

Schwartenbeck, Philipp; FitzGerald, Thomas H.B.; Mathys, Christoph; Dolan, Ray; Wurst, Friedrich; Kronbichler, Martin; Friston, Karl

2015-01-01

When casting behaviour as active (Bayesian) inference, optimal inference is defined with respect to an agent’s beliefs – based on its generative model of the world. This contrasts with normative accounts of choice behaviour, in which optimal actions are considered in relation to the true structure of the environment – as opposed to the agent’s beliefs about worldly states (or the task). This distinction shifts an understanding of suboptimal or pathological behaviour away from aberrant inference as such, to understanding the prior beliefs of a subject that cause them to behave less ‘optimally’ than our prior beliefs suggest they should behave. Put simply, suboptimal or pathological behaviour does not speak against understanding behaviour in terms of (Bayes optimal) inference, but rather calls for a more refined understanding of the subject’s generative model upon which their (optimal) Bayesian inference is based. Here, we discuss this fundamental distinction and its implications for understanding optimality, bounded rationality and pathological (choice) behaviour. We illustrate our argument using addictive choice behaviour in a recently described ‘limited offer’ task. Our simulations of pathological choices and addictive behaviour also generate some clear hypotheses, which we hope to pursue in ongoing empirical work. PMID:25561321

Ion exchange phenomena

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

Bourg, I.C.; Sposito, G.

Ion exchange phenomena involve the population of readily exchangeable ions, the subset of adsorbed solutes that balance the intrinsic surface charge and can be readily replaced by major background electrolyte ions (Sposito, 2008). These phenomena have occupied a central place in soil chemistry research since Way (1850) first showed that potassium uptake by soils resulted in the release of an equal quantity of moles of charge of calcium and magnesium. Ion exchange phenomena are now routinely modeled in studies of soil formation (White et al., 2005), soil reclamation (Kopittke et al., 2006), soil fertilitization (Agbenin and Yakubu, 2006), colloidal dispersion/flocculationmore » (Charlet and Tournassat, 2005), the mechanics of argillaceous media (Gajo and Loret, 2007), aquitard pore water chemistry (Tournassat et al., 2008), and groundwater (Timms and Hendry, 2007; McNab et al., 2009) and contaminant hydrology (Chatterjee et al., 2008; van Oploo et al., 2008; Serrano et al., 2009).« less

Hox gene duplications correlate with posterior heteronomy in scorpions

PubMed Central

Sharma, Prashant P.; Schwager, Evelyn E.; Extavour, Cassandra G.; Wheeler, Ward C.

2014-01-01

The evolutionary success of the largest animal phylum, Arthropoda, has been attributed to tagmatization, the coordinated evolution of adjacent metameres to form morphologically and functionally distinct segmental regions called tagmata. Specification of regional identity is regulated by the Hox genes, of which 10 are inferred to be present in the ancestor of arthropods. With six different posterior segmental identities divided into two tagmata, the bauplan of scorpions is the most heteronomous within Chelicerata. Expression domains of the anterior eight Hox genes are conserved in previously surveyed chelicerates, but it is unknown how Hox genes regionalize the three tagmata of scorpions. Here, we show that the scorpion Centruroides sculpturatus has two paralogues of all Hox genes except Hox3, suggesting cluster and/or whole genome duplication in this arachnid order. Embryonic anterior expression domain boundaries of each of the last four pairs of Hox genes (two paralogues each of Antp, Ubx, abd-A and Abd-B) are unique and distinguish segmental groups, such as pectines, book lungs and the characteristic tail, while maintaining spatial collinearity. These distinct expression domains suggest neofunctionalization of Hox gene paralogues subsequent to duplication. Our data reconcile previous understanding of Hox gene function across arthropods with the extreme heteronomy of scorpions. PMID:25122224

Teaching optical phenomena with Tracker

NASA Astrophysics Data System (ADS)

Rodrigues, M.; Simeão Carvalho, P.

2014-11-01

Since the invention and dissemination of domestic laser pointers, observing optical phenomena is a relatively easy task. Any student can buy a laser and experience at home, in a qualitative way, the reflection, refraction and even diffraction phenomena of light. However, quantitative experiments need instruments of high precision that have a relatively complex setup. Fortunately, nowadays it is possible to analyse optical phenomena in a simple and quantitative way using the freeware video analysis software ‘Tracker’. In this paper, we show the advantages of video-based experimental activities for teaching concepts in optics. We intend to show: (a) how easy the study of such phenomena can be, even at home, because only simple materials are needed, and Tracker provides the necessary measuring instruments; and (b) how we can use Tracker to improve students’ understanding of some optical concepts. We give examples using video modelling to study the laws of reflection, Snell’s laws, focal distances in lenses and mirrors, and diffraction phenomena, which we hope will motivate teachers to implement it in their own classes and schools.

Aura phenomena during syncope.

PubMed

Benke, T; Hochleitner, M; Bauer, G

1997-01-01

We studied the frequency and clinical characteristics of aura phenomena in 60 patients with cardiac and 40 subjects with vasovagal syncopes. The majority (93%) of all syncope patients recalled having experienced an aura. Aura phenomena were similar in both groups and were mostly compound auras comprising epigastric, vertiginous, visual, or somatosensory experiences, but were more detailed in the noncardiac group. The localizing significance of auras preceding a syncope was generally poor. Although hard to distinguish from epileptic auras from their structure and shape, syncope-related auras lacked symptoms that are commonly reported after epileptic seizures such as tastes, smells, déjà vu phenomena, scenic visual perceptions, and speech impairments. A detailed anamnestic exploration of auras seems worthwhile in unexplained disorders of consciousness.

Stan: Statistical inference

NASA Astrophysics Data System (ADS)

Stan Development Team

2018-01-01

Stan facilitates statistical inference at the frontiers of applied statistics and provides both a modeling language for specifying complex statistical models and a library of statistical algorithms for computing inferences with those models. These components are exposed through interfaces in environments such as R, Python, and the command line.

Is awareness necessary for true inference?

PubMed

Leo, Peter D; Greene, Anthony J

2008-09-01

In transitive inference, participants learn a set of context-dependent discriminations that can be organized into a hierarchy that supports inference. Several studies show that inference occurs with or without task awareness. However, some studies assert that without awareness, performance is attributable to pseudoinference. By this account, inference-like performance is achieved by differential stimulus weighting according to the stimuli's proximity to the end items of the hierarchy. We implement an inference task that cannot be based on differential stimulus weighting. The design itself rules out pseudoinference strategies. Success on the task without evidence of deliberative strategies would therefore suggest that true inference can be achieved implicitly. We found that accurate performance on the inference task was not dependent on explicit awareness. The finding is consistent with a growing body of evidence that indicates that forms of learning and memory supporting inference and flexibility do not necessarily depend on task awareness.

Children's Category-Based Inferences Affect Classification

ERIC Educational Resources Information Center

Ross, Brian H.; Gelman, Susan A.; Rosengren, Karl S.

2005-01-01

Children learn many new categories and make inferences about these categories. Much work has examined how children make inferences on the basis of category knowledge. However, inferences may also affect what is learned about a category. Four experiments examine whether category-based inferences during category learning influence category knowledge…

Teaching Optical Phenomena with Tracker

ERIC Educational Resources Information Center

Rodrigues, M.; Carvalho, P. Simeão

2014-01-01

Since the invention and dissemination of domestic laser pointers, observing optical phenomena is a relatively easy task. Any student can buy a laser and experience at home, in a qualitative way, the reflection, refraction and even diffraction phenomena of light. However, quantitative experiments need instruments of high precision that have a…

Is there a hierarchy of social inferences? The likelihood and speed of inferring intentionality, mind, and personality.

PubMed

Malle, Bertram F; Holbrook, Jess

2012-04-01

People interpret behavior by making inferences about agents' intentionality, mind, and personality. Past research studied such inferences 1 at a time; in real life, people make these inferences simultaneously. The present studies therefore examined whether 4 major inferences (intentionality, desire, belief, and personality), elicited simultaneously in response to an observed behavior, might be ordered in a hierarchy of likelihood and speed. To achieve generalizability, the studies included a wide range of stimulus behaviors, presented them verbally and as dynamic videos, and assessed inferences both in a retrieval paradigm (measuring the likelihood and speed of accessing inferences immediately after they were made) and in an online processing paradigm (measuring the speed of forming inferences during behavior observation). Five studies provide evidence for a hierarchy of social inferences-from intentionality and desire to belief to personality-that is stable across verbal and visual presentations and that parallels the order found in developmental and primate research. (c) 2012 APA, all rights reserved.

Transport Phenomena.

ERIC Educational Resources Information Center

McCready, Mark J.; Leighton, David T.

1987-01-01

Discusses the problems created in graduate chemical engineering programs when students enter with a wide diversity of understandings of transport phenomena. Describes a two-semester graduate transport course sequence at the University of Notre Dame which focuses on fluid mechanics and heat and mass transfer. (TW)

Network inference using informative priors.

PubMed

Mukherjee, Sach; Speed, Terence P

2008-09-23

Recent years have seen much interest in the study of systems characterized by multiple interacting components. A class of statistical models called graphical models, in which graphs are used to represent probabilistic relationships between variables, provides a framework for formal inference regarding such systems. In many settings, the object of inference is the network structure itself. This problem of "network inference" is well known to be a challenging one. However, in scientific settings there is very often existing information regarding network connectivity. A natural idea then is to take account of such information during inference. This article addresses the question of incorporating prior information into network inference. We focus on directed models called Bayesian networks, and use Markov chain Monte Carlo to draw samples from posterior distributions over network structures. We introduce prior distributions on graphs capable of capturing information regarding network features including edges, classes of edges, degree distributions, and sparsity. We illustrate our approach in the context of systems biology, applying our methods to network inference in cancer signaling.

Observation of Celestial Phenomena in Ancient China

NASA Astrophysics Data System (ADS)

Sun, Xiaochun

Because of the need for calendar-making and portent astrology, the Chinese were diligent and meticulous observers of celestial phenomena. China has maintained the longest continuous historical records of celestial phenomena in the world. Extraordinary or abnormal celestial events were particularly noted because of their astrological significance. The historical records cover various types of celestial phenomena, which include solar and lunar eclipses, sunspots, "guest stars" (novae or supernovae as we understand today), comets and meteors, and all kinds of planetary phenomena. These records provide valuable historical data for astronomical studies today.

Bayes factors and multimodel inference

USGS Publications Warehouse

Link, W.A.; Barker, R.J.; Thomson, David L.; Cooch, Evan G.; Conroy, Michael J.

2009-01-01

Multimodel inference has two main themes: model selection, and model averaging. Model averaging is a means of making inference conditional on a model set, rather than on a selected model, allowing formal recognition of the uncertainty associated with model choice. The Bayesian paradigm provides a natural framework for model averaging, and provides a context for evaluation of the commonly used AIC weights. We review Bayesian multimodel inference, noting the importance of Bayes factors. Noting the sensitivity of Bayes factors to the choice of priors on parameters, we define and propose nonpreferential priors as offering a reasonable standard for objective multimodel inference.

Variations on Bayesian Prediction and Inference

DTIC Science & Technology

2016-05-09

inference 2.2.1 Background There are a number of statistical inference problems that are not generally formulated via a full probability model...problem of inference about an unknown parameter, the Bayesian approach requires a full probability 1. REPORT DATE (DD-MM-YYYY) 4. TITLE AND...the problem of inference about an unknown parameter, the Bayesian approach requires a full probability model/likelihood which can be an obstacle

Generic comparison of protein inference engines.

PubMed

Claassen, Manfred; Reiter, Lukas; Hengartner, Michael O; Buhmann, Joachim M; Aebersold, Ruedi

2012-04-01

Protein identifications, instead of peptide-spectrum matches, constitute the biologically relevant result of shotgun proteomics studies. How to appropriately infer and report protein identifications has triggered a still ongoing debate. This debate has so far suffered from the lack of appropriate performance measures that allow us to objectively assess protein inference approaches. This study describes an intuitive, generic and yet formal performance measure and demonstrates how it enables experimentalists to select an optimal protein inference strategy for a given collection of fragment ion spectra. We applied the performance measure to systematically explore the benefit of excluding possibly unreliable protein identifications, such as single-hit wonders. Therefore, we defined a family of protein inference engines by extending a simple inference engine by thousands of pruning variants, each excluding a different specified set of possibly unreliable identifications. We benchmarked these protein inference engines on several data sets representing different proteomes and mass spectrometry platforms. Optimally performing inference engines retained all high confidence spectral evidence, without posterior exclusion of any type of protein identifications. Despite the diversity of studied data sets consistently supporting this rule, other data sets might behave differently. In order to ensure maximal reliable proteome coverage for data sets arising in other studies we advocate abstaining from rigid protein inference rules, such as exclusion of single-hit wonders, and instead consider several protein inference approaches and assess these with respect to the presented performance measure in the specific application context.

Children's and Adults' Evaluation of Their Own Inductive Inferences, Deductive Inferences, and Guesses

ERIC Educational Resources Information Center

Pillow, Bradford H.; Pearson, RaeAnne M.

2009-01-01

Adults' and kindergarten through fourth-grade children's evaluations and explanations of inductive inferences, deductive inferences, and guesses were assessed. Beginning in kindergarten, participants rated deductions as more certain than weak inductions or guesses. Beginning in third grade, deductions were rated as more certain than strong…

Classifying prion and prion-like phenomena.

PubMed

Harbi, Djamel; Harrison, Paul M

2014-01-01

The universe of prion and prion-like phenomena has expanded significantly in the past several years. Here, we overview the challenges in classifying this data informatically, given that terms such as "prion-like", "prion-related" or "prion-forming" do not have a stable meaning in the scientific literature. We examine the spectrum of proteins that have been described in the literature as forming prions, and discuss how "prion" can have a range of meaning, with a strict definition being for demonstration of infection with in vitro-derived recombinant prions. We suggest that although prion/prion-like phenomena can largely be apportioned into a small number of broad groups dependent on the type of transmissibility evidence for them, as new phenomena are discovered in the coming years, a detailed ontological approach might be necessary that allows for subtle definition of different "flavors" of prion / prion-like phenomena.

Spontaneous Trait Inferences on Social Media.

PubMed

Levordashka, Ana; Utz, Sonja

2017-01-01

The present research investigates whether spontaneous trait inferences occur under conditions characteristic of social media and networking sites: nonextreme, ostensibly self-generated content, simultaneous presentation of multiple cues, and self-paced browsing. We used an established measure of trait inferences (false recognition paradigm) and a direct assessment of impressions. Without being asked to do so, participants spontaneously formed impressions of people whose status updates they saw. Our results suggest that trait inferences occurred from nonextreme self-generated content, which is commonly found in social media updates (Experiment 1) and when nine status updates from different people were presented in parallel (Experiment 2). Although inferences did occur during free browsing, the results suggest that participants did not necessarily associate the traits with the corresponding status update authors (Experiment 3). Overall, the findings suggest that spontaneous trait inferences occur on social media. We discuss implications for online communication and research on spontaneous trait inferences.

The inference from a single case: moral versus scientific inferences in implementing new biotechnologies.

PubMed

Hofmann, B

2008-06-01

Are there similarities between scientific and moral inference? This is the key question in this article. It takes as its point of departure an instance of one person's story in the media changing both Norwegian public opinion and a brand-new Norwegian law prohibiting the use of saviour siblings. The case appears to falsify existing norms and to establish new ones. The analysis of this case reveals similarities in the modes of inference in science and morals, inasmuch as (a) a single case functions as a counter-example to an existing rule; (b) there is a common presupposition of stability, similarity and order, which makes it possible to reason from a few cases to a general rule; and (c) this makes it possible to hold things together and retain order. In science, these modes of inference are referred to as falsification, induction and consistency. In morals, they have a variety of other names. Hence, even without abandoning the fact-value divide, there appear to be similarities between inference in science and inference in morals, which may encourage communication across the boundaries between "the two cultures" and which are relevant to medical humanities.

Network inference using informative priors

PubMed Central

Mukherjee, Sach; Speed, Terence P.

2008-01-01

Recent years have seen much interest in the study of systems characterized by multiple interacting components. A class of statistical models called graphical models, in which graphs are used to represent probabilistic relationships between variables, provides a framework for formal inference regarding such systems. In many settings, the object of inference is the network structure itself. This problem of “network inference” is well known to be a challenging one. However, in scientific settings there is very often existing information regarding network connectivity. A natural idea then is to take account of such information during inference. This article addresses the question of incorporating prior information into network inference. We focus on directed models called Bayesian networks, and use Markov chain Monte Carlo to draw samples from posterior distributions over network structures. We introduce prior distributions on graphs capable of capturing information regarding network features including edges, classes of edges, degree distributions, and sparsity. We illustrate our approach in the context of systems biology, applying our methods to network inference in cancer signaling. PMID:18799736

Active inference, communication and hermeneutics☆

PubMed Central

Friston, Karl J.; Frith, Christopher D.

2015-01-01

Hermeneutics refers to interpretation and translation of text (typically ancient scriptures) but also applies to verbal and non-verbal communication. In a psychological setting it nicely frames the problem of inferring the intended content of a communication. In this paper, we offer a solution to the problem of neural hermeneutics based upon active inference. In active inference, action fulfils predictions about how we will behave (e.g., predicting we will speak). Crucially, these predictions can be used to predict both self and others – during speaking and listening respectively. Active inference mandates the suppression of prediction errors by updating an internal model that generates predictions – both at fast timescales (through perceptual inference) and slower timescales (through perceptual learning). If two agents adopt the same model, then – in principle – they can predict each other and minimise their mutual prediction errors. Heuristically, this ensures they are singing from the same hymn sheet. This paper builds upon recent work on active inference and communication to illustrate perceptual learning using simulated birdsongs. Our focus here is the neural hermeneutics implicit in learning, where communication facilitates long-term changes in generative models that are trying to predict each other. In other words, communication induces perceptual learning and enables others to (literally) change our minds and vice versa. PMID:25957007

Active inference, communication and hermeneutics.

PubMed

Friston, Karl J; Frith, Christopher D

2015-07-01

Hermeneutics refers to interpretation and translation of text (typically ancient scriptures) but also applies to verbal and non-verbal communication. In a psychological setting it nicely frames the problem of inferring the intended content of a communication. In this paper, we offer a solution to the problem of neural hermeneutics based upon active inference. In active inference, action fulfils predictions about how we will behave (e.g., predicting we will speak). Crucially, these predictions can be used to predict both self and others--during speaking and listening respectively. Active inference mandates the suppression of prediction errors by updating an internal model that generates predictions--both at fast timescales (through perceptual inference) and slower timescales (through perceptual learning). If two agents adopt the same model, then--in principle--they can predict each other and minimise their mutual prediction errors. Heuristically, this ensures they are singing from the same hymn sheet. This paper builds upon recent work on active inference and communication to illustrate perceptual learning using simulated birdsongs. Our focus here is the neural hermeneutics implicit in learning, where communication facilitates long-term changes in generative models that are trying to predict each other. In other words, communication induces perceptual learning and enables others to (literally) change our minds and vice versa. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Social Dynamics Modeling and Inference

DTIC Science & Technology

2018-03-29

AFRL-AFOSR-JP-TR-2018-0027 Social Dynamics Modeling and Inference Kwang-Cheng Chen NATIONAL TAIWAN UNIVERSITY Final Report 03/29/2018 DISTRIBUTION A...DATES COVERED (From - To)      14 May 2014 to 13 May 2017 4.  TITLE AND SUBTITLE Social Dynamics Modeling and Inference 5a.  CONTRACT NUMBER 5b.  GRANT...behavior in human society, to set up the foundation of future possible inference and even control of social collective behavior. Two primary

The role of familiarity in binary choice inferences.

PubMed

Honda, Hidehito; Abe, Keiga; Matsuka, Toshihiko; Yamagishi, Kimihiko

2011-07-01

In research on the recognition heuristic (Goldstein & Gigerenzer, Psychological Review, 109, 75-90, 2002), knowledge of recognized objects has been categorized as "recognized" or "unrecognized" without regard to the degree of familiarity of the recognized object. In the present article, we propose a new inference model--familiarity-based inference. We hypothesize that when subjective knowledge levels (familiarity) of recognized objects differ, the degree of familiarity of recognized objects will influence inferences. Specifically, people are predicted to infer that the more familiar object in a pair of two objects has a higher criterion value on the to-be-judged dimension. In two experiments, using a binary choice task, we examined inferences about populations in a pair of two cities. Results support predictions of familiarity-based inference. Participants inferred that the more familiar city in a pair was more populous. Statistical modeling showed that individual differences in familiarity-based inference lie in the sensitivity to differences in familiarity. In addition, we found that familiarity-based inference can be generally regarded as an ecologically rational inference. Furthermore, when cue knowledge about the inference criterion was available, participants made inferences based on the cue knowledge about population instead of familiarity. Implications of the role of familiarity in psychological processes are discussed.

Self field electromagnetism and quantum phenomena

NASA Astrophysics Data System (ADS)

Schatten, Kenneth H.

1994-07-01

Quantum Electrodynamics (QED) has been extremely successful inits predictive capability for atomic phenomena. Thus the greatest hope for any alternative view is solely to mimic the predictive capability of quantum mechanics (QM), and perhaps its usefulness will lie in gaining a better understanding of microscopic phenomena. Many ?paradoxes? and problematic situations emerge in QED. To combat the QED problems, the field of Stochastics Electrodynamics (SE) emerged, wherein a random ?zero point radiation? is assumed to fill all of space in an attmept to explain quantum phenomena, without some of the paradoxical concerns. SE, however, has greater failings. One is that the electromagnetic field energy must be infinit eto work. We have examined a deterministic side branch of SE, ?self field? electrodynamics, which may overcome the probelms of SE. Self field electrodynamics (SFE) utilizes the chaotic nature of electromagnetic emissions, as charges lose energy near atomic dimensions, to try to understand and mimic quantum phenomena. These fields and charges can ?interact with themselves? in a non-linear fashion, and may thereby explain many quantum phenomena from a semi-classical viewpoint. Referred to as self fields, they have gone by other names in the literature: ?evanesccent radiation?, ?virtual photons?, and ?vacuum fluctuations?. Using self fields, we discuss the uncertainty principles, the Casimir effects, and the black-body radiation spectrum, diffraction and interference effects, Schrodinger's equation, Planck's constant, and the nature of the electron and how they might be understood in the present framework. No new theory could ever replace QED. The self field view (if correct) would, at best, only serve to provide some understanding of the processes by which strange quantum phenomena occur at the atomic level. We discuss possible areas where experiments might be employed to test SFE, and areas where future work may lie.

Children's and Adults' Judgments of the Certainty of Deductive Inferences, Inductive Inferences, and Guesses

ERIC Educational Resources Information Center

Pillow, Bradford H.; Pearson, RaeAnne M.; Hecht, Mary; Bremer, Amanda

2010-01-01

Children and adults rated their own certainty following inductive inferences, deductive inferences, and guesses. Beginning in kindergarten, participants rated deductions as more certain than weak inductions or guesses. Deductions were rated as more certain than strong inductions beginning in Grade 3, and fourth-grade children and adults…

Light flash phenomena induced by HzE particles

NASA Technical Reports Server (NTRS)

Mcnulty, P. J.; Pease, V. P.

1980-01-01

Astronauts and Apollo and Skylab missions have reported observing a variety of visual phenomena when their eyes are closed and adapted to darkness. These phenomena have been collectively labelled as light flashes. Visual phenomena which are similar in appearance to those observed in space have been demonstrated at the number of accelerator facilities by expressing the eyes of human subjects to beams of various types of radiation. In some laboratory experiments Cerenkov radiation was found to be the basis for the flashes observed while in other experiments Cerenkov radiation could apparently be ruled out. Experiments that differentiate between Cerenkov radiation and other possible mechanisms for inducing visual phenomena was then compared. The phenomena obtained in the presence and absence of Cerenkov radiation were designed and conducted. A new mechanism proposed to explain the visual phenomena observed by Skylab astronauts as they passed through the South Atlantic Anomaly, namely nuclear interactions in and near the sensitive layer of the retina, is covered. Also some studies to search for similar transient effects of space radiation on sensors and microcomputer memories are described.

Luminous Phenomena - A Scientific Investigation of Anomalous Luminous Atmospheric Phenomena

NASA Astrophysics Data System (ADS)

Teodorani, M.

2003-12-01

Anomalous atmospheric luminous phenomena reoccur in several locations of Earth, in the form of multi-color light balls characterized by large dimensions, erratic motion, long duration and a correlated electromagnetic field. The author (an astrophysicist) of this book, which is organized as a selection of some of his technical and popularizing papers and seminars, describes and discusses all the efforts that have been done in 10 years, through several missions and a massive data analysis, in order to obtain some scientific explanation of this kind of anomalies, in particular the Hessdalen anomaly in Norway. The following topics are treated in the book: a) geographic archive of the areas of Earth where such phenomena are known to reoccur most often; b) observational techniques of astrophysical kind that have been used to acquire the data; c) main scientific results obtained so far; d) physical interpretation and natural hypothesis vs. ETV hypothesis; e) historical and chronological issues; f) the importance to brindle new energy sources; g) the importance to keep distance from any kind of "ufology". An unpublished chapter is entirely devoted to a detailed scientific investigation project of light phenomena reoccurring on the Ontario lake; the chosen new-generation multi-wavelength sensing instrumentation that is planned to be used in future missions in that specific area, is described together with scientific rationale and planned procedures. The main results, which were obtained in other areas of the world, such as the Arizona desert, USA and the Sibillini Mountains, Italy, are also briefly mentioned. One chapter is entirely dedicated to the presentation of extensive abstracts of technical papers by the author concerning this specific subject. The book is accompanied with a rich source of bibliographic references.

Forward and backward inference in spatial cognition.

PubMed

Penny, Will D; Zeidman, Peter; Burgess, Neil

2013-01-01

This paper shows that the various computations underlying spatial cognition can be implemented using statistical inference in a single probabilistic model. Inference is implemented using a common set of 'lower-level' computations involving forward and backward inference over time. For example, to estimate where you are in a known environment, forward inference is used to optimally combine location estimates from path integration with those from sensory input. To decide which way to turn to reach a goal, forward inference is used to compute the likelihood of reaching that goal under each option. To work out which environment you are in, forward inference is used to compute the likelihood of sensory observations under the different hypotheses. For reaching sensory goals that require a chaining together of decisions, forward inference can be used to compute a state trajectory that will lead to that goal, and backward inference to refine the route and estimate control signals that produce the required trajectory. We propose that these computations are reflected in recent findings of pattern replay in the mammalian brain. Specifically, that theta sequences reflect decision making, theta flickering reflects model selection, and remote replay reflects route and motor planning. We also propose a mapping of the above computational processes onto lateral and medial entorhinal cortex and hippocampus.

Theories of dynamical phenomena in sunspots

NASA Technical Reports Server (NTRS)

Thomas, J. H.

1981-01-01

Attempts that have been made to understand and explain observed dynamical phenomena in sunspots within the framework of magnetohydrodynamic theory are surveyed. The qualitative aspects of the theory and physical arguments are emphasized, with mathematical details generally avoided. The dynamical phenomena in sunspots are divided into two categories: aperiodic (quasi-steady) and oscillatory. For each phenomenon discussed, the salient observational features that any theory should explain are summarized. The two contending theoretical models that can account for the fine structure of the Evershed motion, namely the convective roll model and the siphon flow model, are described. With regard to oscillatory phenomena, attention is given to overstability and oscillatory convection, umbral oscillations and flashes. penumbral waves, five-minute oscillations in sunspots, and the wave cooling of sunspots.

Time-Variable Phenomena in the Jovian System

NASA Technical Reports Server (NTRS)

Belton, Michael J. S. (Editor); West, Robert A. (Editor); Rahe, Jurgen (Editor); Pereyda, Margarita

1989-01-01

The current state of knowledge of dynamic processes in the Jovian system is assessed and summaries are provided of both theoretical and observational foundations upon which future research might be based. There are three sections: satellite phenomena and rings; magnetospheric phenomena, Io's torus, and aurorae; and atmospheric phenomena. Each chapter discusses time dependent theoretical framework for understanding and interpreting what is observed; others describe the evidence and nature of observed changes or their absence. A few chapters provide historical perspective and attempt to present a comprehensive synthesis of the current state of knowledge.

Reproducibility in Psychological Science: When Do Psychological Phenomena Exist?

PubMed Central

Iso-Ahola, Seppo E.

2017-01-01

Scientific evidence has recently been used to assert that certain psychological phenomena do not exist. Such claims, however, cannot be made because (1) scientific method itself is seriously limited (i.e., it can never prove a negative); (2) non-existence of phenomena would require a complete absence of both logical (theoretical) and empirical support; even if empirical support is weak, logical and theoretical support can be strong; (3) statistical data are only one piece of evidence and cannot be used to reduce psychological phenomena to statistical phenomena; and (4) psychological phenomena vary across time, situations and persons. The human mind is unreproducible from one situation to another. Psychological phenomena are not particles that can decisively be tested and discovered. Therefore, a declaration that a phenomenon is not real is not only theoretically and empirically unjustified but runs counter to the propositional and provisional nature of scientific knowledge. There are only “temporary winners” and no “final truths” in scientific knowledge. Psychology is a science of subtleties in human affect, cognition and behavior. Its phenomena fluctuate with conditions and may sometimes be difficult to detect and reproduce empirically. When strictly applied, reproducibility is an overstated and even questionable concept in psychological science. Furthermore, statistical measures (e.g., effect size) are poor indicators of the theoretical importance and relevance of phenomena (cf. “deliberate practice” vs. “talent” in expert performance), not to mention whether phenomena are real or unreal. To better understand psychological phenomena, their theoretical and empirical properties should be examined via multiple parameters and criteria. Ten such parameters are suggested. PMID:28626435

Feature Inference Learning and Eyetracking

ERIC Educational Resources Information Center

Rehder, Bob; Colner, Robert M.; Hoffman, Aaron B.

2009-01-01

Besides traditional supervised classification learning, people can learn categories by inferring the missing features of category members. It has been proposed that feature inference learning promotes learning a category's internal structure (e.g., its typical features and interfeature correlations) whereas classification promotes the learning of…

Solar Phenomena Associated with "EIT Waves"

NASA Technical Reports Server (NTRS)

Biesecker, D. A.; Myers, D. C.; Thompson, B. J.; Hammer, D. M.; Vourlidas, A.

2002-01-01

In an effort to understand what an 'EIT wave' is and what its causes are, we have looked for correlations between the initiation of EIT waves and the occurrence of other solar phenomena. An EIT wave is a coronal disturbance, typically appearing as a diffuse brightening propagating across the Sun. A catalog of EIT waves, covering the period from 1997 March through 1998 June, was used in this study. For each EIT wave, the catalog gives the heliographic location and a rating for each wave, where the rating is determined by the reliability of the observations. Since EIT waves are transient, coronal phenomena, we have looked for correlations with other transient, coronal phenomena: X-ray flares, coronal mass ejections (CMEs), and metric type II radio bursts. An unambiguous correlation between EIT waves and CMEs has been found. The correlation of EIT waves with flares is significantly weaker, and EIT waves frequently are not accompanied by radio bursts. To search for trends in the data, proxies for each of these transient phenomena are examined. We also use the accumulated data to show the robustness of the catalog and to reveal biases that must be accounted for in this study.

Inferring Microbial Fitness Landscapes

DTIC Science & Technology

2016-02-25

infer from data the determinants of microbial evolution with sufficient resolution that we can quantify 1. REPORT DATE (DD-MM-YYYY) 4. TITLE AND...Distribution Unlimited UU UU UU UU 25-02-2016 1-Oct-2012 30-Sep-2015 Final Report: Inferring Microbial Fitness Landscapes The views, opinions and/or findings...Triangle Park, NC 27709-2211 evolution, fitness landscapes, epistasis, microbial populations REPORT DOCUMENTATION PAGE 11. SPONSOR/MONITOR’S REPORT

Comprehending emergent systems phenomena through direct-manipulation animation

NASA Astrophysics Data System (ADS)

Aguirre, Priscilla Abel

This study seeks to understand the type of interaction mode that best supports learning and comprehension of emergent systems phenomena. Given that the literature has established that students hold robust misconceptions of such phenomena, this study investigates the influence of using three types of interaction; speed-manipulation animation (SMN), post-manipulation animation (PMA) and direct-manipulation animation (DMA) for increasing comprehension and testing transfer of the phenomena, by looking at the effect of simultaneous interaction of haptic and visual channels on long term and working memories when seeking to comprehend emergent phenomena. The questions asked were: (1) Does the teaching of emergent phenomena, with the aid of a dynamic interactive modeling tool (i.e., SMA, PMA or DMA), improve students' mental model construction of systems, thus increasing comprehension of this scientific concept? And (2) does the teaching of emergent phenomena, with the aid of a dynamic interactive modeling tool, give the students the necessary complex cognitive skill which can then be applied to similar (near transfer) and/or novel, but different, (far transfer) scenarios? In an empirical study undergraduate and graduate students were asked to participate in one of three experimental conditions: SMA, PMA, or DMA. The results of the study found that it was the participants of the SMA treatment condition that had the most improvement in post-test scores. Students' understanding of the phenomena increased most when they used a dynamic model with few interactive elements (i.e., start, stop, and speed) that allowed for real time visualization of one's interaction on the phenomena. Furthermore, no indication was found that the learning of emergent phenomena, with the aid of a dynamic interactive modeling tool, gave the students the necessary complex cognitive skill which could then be applied to similar (near transfer) and/or novel, but different, (far transfer) scenarios

Learning to Observe "and" Infer

ERIC Educational Resources Information Center

Hanuscin, Deborah L.; Park Rogers, Meredith A.

2008-01-01

Researchers describe the need for students to have multiple opportunities and social interaction to learn about the differences between observation and inference and their role in developing scientific explanations (Harlen 2001; Simpson 2000). Helping children develop their skills of observation and inference in science while emphasizing the…

Causal Inference in Retrospective Studies.

ERIC Educational Resources Information Center

Holland, Paul W.; Rubin, Donald B.

1988-01-01

The problem of drawing causal inferences from retrospective case-controlled studies is considered. A model for causal inference in prospective studies is applied to retrospective studies. Limitations of case-controlled studies are formulated concerning relevant parameters that can be estimated in such studies. A coffee-drinking/myocardial…

Category Representation for Classification and Feature Inference

ERIC Educational Resources Information Center

Johansen, Mark K.; Kruschke, John K.

2005-01-01

This research's purpose was to contrast the representations resulting from learning of the same categories by either classifying instances or inferring instance features. Prior inference learning research, particularly T. Yamauchi and A. B. Markman (1998), has suggested that feature inference learning fosters prototype representation, whereas…

Forward and Backward Inference in Spatial Cognition

PubMed Central

Penny, Will D.; Zeidman, Peter; Burgess, Neil

2013-01-01

This paper shows that the various computations underlying spatial cognition can be implemented using statistical inference in a single probabilistic model. Inference is implemented using a common set of ‘lower-level’ computations involving forward and backward inference over time. For example, to estimate where you are in a known environment, forward inference is used to optimally combine location estimates from path integration with those from sensory input. To decide which way to turn to reach a goal, forward inference is used to compute the likelihood of reaching that goal under each option. To work out which environment you are in, forward inference is used to compute the likelihood of sensory observations under the different hypotheses. For reaching sensory goals that require a chaining together of decisions, forward inference can be used to compute a state trajectory that will lead to that goal, and backward inference to refine the route and estimate control signals that produce the required trajectory. We propose that these computations are reflected in recent findings of pattern replay in the mammalian brain. Specifically, that theta sequences reflect decision making, theta flickering reflects model selection, and remote replay reflects route and motor planning. We also propose a mapping of the above computational processes onto lateral and medial entorhinal cortex and hippocampus. PMID:24348230

Fair Inference on Outcomes

PubMed Central

Nabi, Razieh; Shpitser, Ilya

2017-01-01

In this paper, we consider the problem of fair statistical inference involving outcome variables. Examples include classification and regression problems, and estimating treatment effects in randomized trials or observational data. The issue of fairness arises in such problems where some covariates or treatments are “sensitive,” in the sense of having potential of creating discrimination. In this paper, we argue that the presence of discrimination can be formalized in a sensible way as the presence of an effect of a sensitive covariate on the outcome along certain causal pathways, a view which generalizes (Pearl 2009). A fair outcome model can then be learned by solving a constrained optimization problem. We discuss a number of complications that arise in classical statistical inference due to this view and provide workarounds based on recent work in causal and semi-parametric inference.

Crystal Melting and Wall Crossing Phenomena

NASA Astrophysics Data System (ADS)

Yamazaki, Masahito

This paper summarizes recent developments in the theory of Bogomol'nyi-Prasad-Sommerfield (BPS) state counting and the wall crossing phenomena, emphasizing in particular the role of the statistical mechanical model of crystal melting. This paper is divided into two parts, which are closely related to each other. In the first part, we discuss the statistical mechanical model of crystal melting counting BPS states. Each of the BPS states contributing to the BPS index is in one-to-one correspondence with a configuration of a molten crystal, and the statistical partition function of the melting crystal gives the BPS partition function. We also show that smooth geometry of the Calabi-Yau manifold emerges in the thermodynamic limit of the crystal. This suggests a remarkable interpretation that an atom in the crystal is a discretization of the classical geometry, giving an important clue as such to the geometry at the Planck scale. In the second part, we discuss the wall crossing phenomena. Wall crossing phenomena states that the BPS index depends on the value of the moduli of the Calabi-Yau manifold, and jumps along real codimension one subspaces in the moduli space. We show that by using type IIA/M-theory duality, we can provide a simple and an intuitive derivation of the wall crossing phenomena, furthermore clarifying the connection with the topological string theory. This derivation is consistent with another derivation from the wall crossing formula, motivated by multicentered BPS extremal black holes. We also explain the representation of the wall crossing phenomena in terms of crystal melting, and the generalization of the counting problem and the wall crossing to the open BPS invariants.

Causal Inference and Developmental Psychology

ERIC Educational Resources Information Center

Foster, E. Michael

2010-01-01

Causal inference is of central importance to developmental psychology. Many key questions in the field revolve around improving the lives of children and their families. These include identifying risk factors that if manipulated in some way would foster child development. Such a task inherently involves causal inference: One wants to know whether…

Bayesian Nonparametric Inference – Why and How

PubMed Central

Müller, Peter; Mitra, Riten

2013-01-01

We review inference under models with nonparametric Bayesian (BNP) priors. The discussion follows a set of examples for some common inference problems. The examples are chosen to highlight problems that are challenging for standard parametric inference. We discuss inference for density estimation, clustering, regression and for mixed effects models with random effects distributions. While we focus on arguing for the need for the flexibility of BNP models, we also review some of the more commonly used BNP models, thus hopefully answering a bit of both questions, why and how to use BNP. PMID:24368932

Ambroise August Liébeault and psychic phenomena.

PubMed

Alvarado, Carlos S

2009-10-01

Some nineteenth-century hypnosis researchers did not limit their interest to the study of the conventional psychological and behavioral aspects of hypnosis, but also studied and wrote about psychic phenomena such as mental suggestion and clairvoyance. One example, and the topic of this paper, was French physician Ambroise August Liébeault (1823-1904), who influenced the Nancy school of hypnosis. Liébeault wrote about mental suggestion, clairvoyance, mediumship, and even so-called poltergeists. Some of his writings provide conventional explanations of the phenomena. Still of interest today, Liébeault's writings about psychic phenomena illustrate the overlap that existed during the nineteenth-century between hypnosis and psychic phenomena--an overlap related to the potentials of the mind and its subconscious activity.

Quantum-Like Representation of Non-Bayesian Inference

NASA Astrophysics Data System (ADS)

Asano, M.; Basieva, I.; Khrennikov, A.; Ohya, M.; Tanaka, Y.

2013-01-01

This research is related to the problem of "irrational decision making or inference" that have been discussed in cognitive psychology. There are some experimental studies, and these statistical data cannot be described by classical probability theory. The process of decision making generating these data cannot be reduced to the classical Bayesian inference. For this problem, a number of quantum-like coginitive models of decision making was proposed. Our previous work represented in a natural way the classical Bayesian inference in the frame work of quantum mechanics. By using this representation, in this paper, we try to discuss the non-Bayesian (irrational) inference that is biased by effects like the quantum interference. Further, we describe "psychological factor" disturbing "rationality" as an "environment" correlating with the "main system" of usual Bayesian inference.

The mechanisms of temporal inference

NASA Technical Reports Server (NTRS)

Fox, B. R.; Green, S. R.

1987-01-01

The properties of a temporal language are determined by its constituent elements: the temporal objects which it can represent, the attributes of those objects, the relationships between them, the axioms which define the default relationships, and the rules which define the statements that can be formulated. The methods of inference which can be applied to a temporal language are derived in part from a small number of axioms which define the meaning of equality and order and how those relationships can be propagated. More complex inferences involve detailed analysis of the stated relationships. Perhaps the most challenging area of temporal inference is reasoning over disjunctive temporal constraints. Simple forms of disjunction do not sufficiently increase the expressive power of a language while unrestricted use of disjunction makes the analysis NP-hard. In many cases a set of disjunctive constraints can be converted to disjunctive normal form and familiar methods of inference can be applied to the conjunctive sub-expressions. This process itself is NP-hard but it is made more tractable by careful expansion of a tree-structured search space.

A Connection between Transport Phenomena and Thermodynamics

ERIC Educational Resources Information Center

Swaney, Ross; Bird, R. Byron

2017-01-01

Although students take courses in transport phenomena and thermodynamics, they probably do not ask whether these two subjects are related. Here we give an answer to that question. Specifically we give relationships between the equations of change for total energy, internal energy, and entropy of transport phenomena and key equations of equilibrium…

Analysis of the GRNs Inference by Using Tsallis Entropy and a Feature Selection Approach

NASA Astrophysics Data System (ADS)

Lopes, Fabrício M.; de Oliveira, Evaldo A.; Cesar, Roberto M.

An important problem in the bioinformatics field is to understand how genes are regulated and interact through gene networks. This knowledge can be helpful for many applications, such as disease treatment design and drugs creation purposes. For this reason, it is very important to uncover the functional relationship among genes and then to construct the gene regulatory network (GRN) from temporal expression data. However, this task usually involves data with a large number of variables and small number of observations. In this way, there is a strong motivation to use pattern recognition and dimensionality reduction approaches. In particular, feature selection is specially important in order to select the most important predictor genes that can explain some phenomena associated with the target genes. This work presents a first study about the sensibility of entropy methods regarding the entropy functional form, applied to the problem of topology recovery of GRNs. The generalized entropy proposed by Tsallis is used to study this sensibility. The inference process is based on a feature selection approach, which is applied to simulated temporal expression data generated by an artificial gene network (AGN) model. The inferred GRNs are validated in terms of global network measures. Some interesting conclusions can be drawn from the experimental results, as reported for the first time in the present paper.

Paramutation phenomena in plants.

PubMed

Pilu, Roberto

2015-08-01

Paramutation is a particular epigenetic phenomenon discovered in Zea mays by Alexander Brink in the 1950s, and then also found in other plants and animals. Brink coined the term paramutation (from the Greek syllable "para" meaning beside, near, beyond, aside) in 1958, with the aim to differentiate paramutation from mutation. The peculiarity of paramutation with respect to other gene silencing phenomena consists in the ability of the silenced allele (named paramutagenic) to silence the other allele (paramutable) present in trans. The newly silenced (paramutated) allele remains stable in the next generations even after segregation from the paramutagenic allele and acquires paramutagenic ability itself. The inheritance behaviour of these epialleles permits a fast diffusion of a particular gene expression level/phenotype in a population even in the absence of other evolutionary influences, thus breaking the Hardy-Weinberg law. As with other gene silencing phenomena such as quelling in the fungus Neurospora crassa, transvection in Drosophila, co-suppression and virus-induced gene silencing (VIGS) described in transgenic plants and RNA interference (RNAi) in the nematode Caenorhabditis elegans, paramutation occurs without changes in the DNA sequence. So far the molecular basis of paramutation remains not fully understood, although many studies point to the involvement of RNA causing changes in DNA methylation and chromatin structure of the silenced genes. In this review I summarize all paramutation phenomena described in plants, focusing on the similarities and differences between them. Copyright © 2015 Elsevier Ltd. All rights reserved.

The making of extraordinary psychological phenomena.

PubMed

Lamont, Peter

2012-01-01

This article considers the extraordinary phenomena that have been central to unorthodox areas of psychological knowledge. It shows how even the agreed facts relating to mesmerism, spiritualism, psychical research, and parapsychology have been framed as evidence both for and against the reality of the phenomena. It argues that these disputes can be seen as a means through which beliefs have been formulated and maintained in the face of potentially challenging evidence. It also shows how these disputes appealed to different forms of expertise, and that both sides appealed to belief in various ways as part of the ongoing dispute about both the facts and expertise. Finally, it shows how, when a formal Psychology of paranormal belief emerged in the twentieth century, it took two different forms, each reflecting one side of the ongoing dispute about the reality of the phenomena. © 2012 Wiley Periodicals, Inc.

Abnormal pressures as hydrodynamic phenomena

USGS Publications Warehouse

Neuzil, C.E.

1995-01-01

So-called abnormal pressures, subsurface fluid pressures significantly higher or lower than hydrostatic, have excited speculation about their origin since subsurface exploration first encountered them. Two distinct conceptual models for abnormal pressures have gained currency among earth scientists. The static model sees abnormal pressures generally as relict features preserved by a virtual absence of fluid flow over geologic time. The hydrodynamic model instead envisions abnormal pressures as phenomena in which flow usually plays an important role. This paper develops the theoretical framework for abnormal pressures as hydrodynamic phenomena, shows that it explains the manifold occurrences of abnormal pressures, and examines the implications of this approach. -from Author

The Role of Family Phenomena in Posttraumatic Stress in Youth

PubMed Central

Deatrick, Janet A.

2010-01-01

Topic Youth face trauma that can cause posttraumatic stress (PTS). Purpose 1). To identify the family phenomena used in youth PTS research; and 2). Critically examine the research findings regarding the relationship between family phenomena and youth PTS. Sources Systematic literature review in PsycInfo, PILOTS, CINAHL, and MEDLINE. Twenty-six empirical articles met inclusion criteria. Conclusion Measurement of family phenomena included family functioning, support, environment, expressiveness, relationships, cohesion, communication, satisfaction, life events related to family, parental style of influence, and parental bonding. Few studies gave clear conceptualization of family or family phenomena. Empirical findings from the 26 studies indicate inconsistent empirical relationships between family phenomena and youth PTS, though a majority of the prospective studies support a relationship between family phenomena and youth PTS. Future directions for leadership by psychiatric nurses in this area of research and practice are recommended. PMID:21344778

Bayesian Inference: with ecological applications

USGS Publications Warehouse

Link, William A.; Barker, Richard J.

2010-01-01

This text provides a mathematically rigorous yet accessible and engaging introduction to Bayesian inference with relevant examples that will be of interest to biologists working in the fields of ecology, wildlife management and environmental studies as well as students in advanced undergraduate statistics.. This text opens the door to Bayesian inference, taking advantage of modern computational efficiencies and easily accessible software to evaluate complex hierarchical models.

"Magnitude-based inference": a statistical review.

PubMed

Welsh, Alan H; Knight, Emma J

2015-04-01

We consider "magnitude-based inference" and its interpretation by examining in detail its use in the problem of comparing two means. We extract from the spreadsheets, which are provided to users of the analysis (http://www.sportsci.org/), a precise description of how "magnitude-based inference" is implemented. We compare the implemented version of the method with general descriptions of it and interpret the method in familiar statistical terms. We show that "magnitude-based inference" is not a progressive improvement on modern statistics. The additional probabilities introduced are not directly related to the confidence interval but, rather, are interpretable either as P values for two different nonstandard tests (for different null hypotheses) or as approximate Bayesian calculations, which also lead to a type of test. We also discuss sample size calculations associated with "magnitude-based inference" and show that the substantial reduction in sample sizes claimed for the method (30% of the sample size obtained from standard frequentist calculations) is not justifiable so the sample size calculations should not be used. Rather than using "magnitude-based inference," a better solution is to be realistic about the limitations of the data and use either confidence intervals or a fully Bayesian analysis.

Inferring Phylogenetic Networks Using PhyloNet.

PubMed

Wen, Dingqiao; Yu, Yun; Zhu, Jiafan; Nakhleh, Luay

2018-07-01

PhyloNet was released in 2008 as a software package for representing and analyzing phylogenetic networks. At the time of its release, the main functionalities in PhyloNet consisted of measures for comparing network topologies and a single heuristic for reconciling gene trees with a species tree. Since then, PhyloNet has grown significantly. The software package now includes a wide array of methods for inferring phylogenetic networks from data sets of unlinked loci while accounting for both reticulation (e.g., hybridization) and incomplete lineage sorting. In particular, PhyloNet now allows for maximum parsimony, maximum likelihood, and Bayesian inference of phylogenetic networks from gene tree estimates. Furthermore, Bayesian inference directly from sequence data (sequence alignments or biallelic markers) is implemented. Maximum parsimony is based on an extension of the "minimizing deep coalescences" criterion to phylogenetic networks, whereas maximum likelihood and Bayesian inference are based on the multispecies network coalescent. All methods allow for multiple individuals per species. As computing the likelihood of a phylogenetic network is computationally hard, PhyloNet allows for evaluation and inference of networks using a pseudolikelihood measure. PhyloNet summarizes the results of the various analyzes and generates phylogenetic networks in the extended Newick format that is readily viewable by existing visualization software.

Reduplication phenomena: body, mind and archetype.

PubMed

Garner, J

2000-09-01

The many biological and few psychodynamic explanations of reduplicative syndromes tend to have paralleled the dualism of the phenomenon with organic theories concentrating on form and dynamic theories emphasising content. This paper extends the contribution of psychoanalytic thinking to an elucidation of the form of the delusion. Literature on clinical and aetiological aspects of reduplicative phenomena is reviewed alongside a brief examination of psychoanalytic models not overtly related to these phenomena. The human experience of doubles as universal archetype is considered. There is an obvious aetiological role for brain lesions in delusional misidentifications, but psychological symptoms in an individual can rarely be reduced to an organic disorder. The splitting and doubling which occurs in the phenomena have resonances in cultural mythology and in theories from different schools of psychodynamic thought. For the individual patient and doctor, it is a diverting but potentially empty debate to endeavour to draw strict divisions between what is physical and what is psychological although both need to be investigated. Nevertheless, in patients in whom there is clear evidence of an organic contribution to aetiology a psychodynamic understanding may serve to illuminate the patient's experience. Organic brain disease or serious functional illness predispose to regression to earlier modes of archetypical and primitive thinking with concretization of the metaphorical and mythological world. Psychoanalytic models have a contribution in describing the form as well as the content of reduplicative phenomena.

Anomalous Light Phenomena vs. Bioelectric Brain Activity

NASA Astrophysics Data System (ADS)

Teodorani, M.; Nobili, G.

We present a research proposal concerning the instrumented investigation of anomalous light phenomena that are apparently correlated with particular mind states, such as prayer, meditation or psi. Previous research by these authors demonstrate that such light phenomena can be monitored and measured quite efficiently in areas of the world where they are reported in a recurrent way. Instruments such as optical equipment for photography and spectroscopy, VLF spectrometers, magnetometers, radar and IR viewers were deployed and used massively in several areas of the world. Results allowed us to develop physical models concerning the structural and time-variable behaviour of light phenomena, and their kinematics. Recent insights and witnesses have suggested to us that a sort of "synchronous connection" seems to exist between plasma-like phenomena and particular mind states of experiencers who seem to trigger a light manifestation which is very similar to the one previously investigated. The main goal of these authors is now aimed at the search for a concrete "entanglement-like effect" between the experiencer's mind and the light phenomena, in such a way that both aspects are intended to be monitored and measured simultaneously using appropriate instrumentation. The goal of this research project is twofold: a) to verify quantitatively the existence of one very particular kind of mind-matter interaction and to study in real time its physical and biophysical manifestations; b) to repeat the same kind of experiment using the same test-subject in different locations and under various conditions of geomagnetic activity.

Active Inference, homeostatic regulation and adaptive behavioural control.

PubMed

Pezzulo, Giovanni; Rigoli, Francesco; Friston, Karl

2015-11-01

We review a theory of homeostatic regulation and adaptive behavioural control within the Active Inference framework. Our aim is to connect two research streams that are usually considered independently; namely, Active Inference and associative learning theories of animal behaviour. The former uses a probabilistic (Bayesian) formulation of perception and action, while the latter calls on multiple (Pavlovian, habitual, goal-directed) processes for homeostatic and behavioural control. We offer a synthesis these classical processes and cast them as successive hierarchical contextualisations of sensorimotor constructs, using the generative models that underpin Active Inference. This dissolves any apparent mechanistic distinction between the optimization processes that mediate classical control or learning. Furthermore, we generalize the scope of Active Inference by emphasizing interoceptive inference and homeostatic regulation. The ensuing homeostatic (or allostatic) perspective provides an intuitive explanation for how priors act as drives or goals to enslave action, and emphasises the embodied nature of inference. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Active Inference, homeostatic regulation and adaptive behavioural control

PubMed Central

Pezzulo, Giovanni; Rigoli, Francesco; Friston, Karl

2015-01-01

We review a theory of homeostatic regulation and adaptive behavioural control within the Active Inference framework. Our aim is to connect two research streams that are usually considered independently; namely, Active Inference and associative learning theories of animal behaviour. The former uses a probabilistic (Bayesian) formulation of perception and action, while the latter calls on multiple (Pavlovian, habitual, goal-directed) processes for homeostatic and behavioural control. We offer a synthesis these classical processes and cast them as successive hierarchical contextualisations of sensorimotor constructs, using the generative models that underpin Active Inference. This dissolves any apparent mechanistic distinction between the optimization processes that mediate classical control or learning. Furthermore, we generalize the scope of Active Inference by emphasizing interoceptive inference and homeostatic regulation. The ensuing homeostatic (or allostatic) perspective provides an intuitive explanation for how priors act as drives or goals to enslave action, and emphasises the embodied nature of inference. PMID:26365173

Generative Inferences Based on Learned Relations

ERIC Educational Resources Information Center

Chen, Dawn; Lu, Hongjing; Holyoak, Keith J.

2017-01-01

A key property of relational representations is their "generativity": From partial descriptions of relations between entities, additional inferences can be drawn about other entities. A major theoretical challenge is to demonstrate how the capacity to make generative inferences could arise as a result of learning relations from…

Incorporating interfacial phenomena in solidification models

NASA Technical Reports Server (NTRS)

Beckermann, Christoph; Wang, Chao Yang

1994-01-01

A general methodology is available for the incorporation of microscopic interfacial phenomena in macroscopic solidification models that include diffusion and convection. The method is derived from a formal averaging procedure and a multiphase approach, and relies on the presence of interfacial integrals in the macroscopic transport equations. In a wider engineering context, these techniques are not new, but their application in the analysis and modeling of solidification processes has largely been overlooked. This article describes the techniques and demonstrates their utility in two examples in which microscopic interfacial phenomena are of great importance.

Inference generation and story comprehension among children with ADHD.

PubMed

Van Neste, Jessica; Hayden, Angela; Lorch, Elizabeth P; Milich, Richard

2015-02-01

Academic difficulties are well-documented among children with ADHD. Exploring these difficulties through story comprehension research has revealed deficits among children with ADHD in making causal connections between events and in using causal structure and thematic importance to guide recall of stories. Important to theories of story comprehension and implied in these deficits is the ability to make inferences. Often, characters' goals are implicit and explanations of events must be inferred. The purpose of the present study was to compare the inferences generated during story comprehension by 23 7- to 11-year-old children with ADHD (16 males) and 35 comparison peers (19 males). Children watched two televised stories, each paused at five points. In the experimental condition, at each pause children told what they were thinking about the story, whereas in the control condition no responses were made during pauses. After viewing, children recalled the story. Several types of inferences and inference plausibility were coded. Children with ADHD generated fewer of the most essential inferences, plausible explanatory inferences, than did comparison children, both during story processing and during story recall. The groups did not differ on production of other types of inferences. Group differences in generating inferences during the think-aloud task significantly mediated group differences in patterns of recall. Both groups recalled more of the most important story information after completing the think-aloud task. Generating fewer explanatory inferences has important implications for story comprehension deficits in children with ADHD.

Reasoning about Causal Relationships: Inferences on Causal Networks

PubMed Central

Rottman, Benjamin Margolin; Hastie, Reid

2013-01-01

Over the last decade, a normative framework for making causal inferences, Bayesian Probabilistic Causal Networks, has come to dominate psychological studies of inference based on causal relationships. The following causal networks—[X→Y→Z, X←Y→Z, X→Y←Z]—supply answers for questions like, “Suppose both X and Y occur, what is the probability Z occurs?” or “Suppose you intervene and make Y occur, what is the probability Z occurs?” In this review, we provide a tutorial for how normatively to calculate these inferences. Then, we systematically detail the results of behavioral studies comparing human qualitative and quantitative judgments to the normative calculations for many network structures and for several types of inferences on those networks. Overall, when the normative calculations imply that an inference should increase, judgments usually go up; when calculations imply a decrease, judgments usually go down. However, two systematic deviations appear. First, people’s inferences violate the Markov assumption. For example, when inferring Z from the structure X→Y→Z, people think that X is relevant even when Y completely mediates the relationship between X and Z. Second, even when people’s inferences are directionally consistent with the normative calculations, they are often not as sensitive to the parameters and the structure of the network as they should be. We conclude with a discussion of productive directions for future research. PMID:23544658

Inferring Learners' Knowledge from Their Actions

ERIC Educational Resources Information Center

Rafferty, Anna N.; LaMar, Michelle M.; Griffiths, Thomas L.

2015-01-01

Watching another person take actions to complete a goal and making inferences about that person's knowledge is a relatively natural task for people. This ability can be especially important in educational settings, where the inferences can be used for assessment, diagnosing misconceptions, and providing informative feedback. In this paper, we…

The Reasoning behind Informal Statistical Inference

ERIC Educational Resources Information Center

Makar, Katie; Bakker, Arthur; Ben-Zvi, Dani

2011-01-01

Informal statistical inference (ISI) has been a frequent focus of recent research in statistics education. Considering the role that context plays in developing ISI calls into question the need to be more explicit about the reasoning that underpins ISI. This paper uses educational literature on informal statistical inference and philosophical…

Application of Transformations in Parametric Inference

ERIC Educational Resources Information Center

Brownstein, Naomi; Pensky, Marianna

2008-01-01

The objective of the present paper is to provide a simple approach to statistical inference using the method of transformations of variables. We demonstrate performance of this powerful tool on examples of constructions of various estimation procedures, hypothesis testing, Bayes analysis and statistical inference for the stress-strength systems.…

Forward Inferences: From Activation to Long-Term Memory.

ERIC Educational Resources Information Center

Klin, Celia M.; Murray, John D.; Levine, William H.; Guzman, Alexandria E.

1999-01-01

Investigates the extent to which forward inferences are activated and encoded during reading, as well as their prevalence and their time course. Finds that inferences were encoded and retained in working memory in both high- and low-predictability conditions, and that high-predictability forward inferences were encoded into long-term memory.…

Spontaneous evaluative inferences and their relationship to spontaneous trait inferences.

PubMed

Schneid, Erica D; Carlston, Donal E; Skowronski, John J

2015-05-01

Three experiments are reported that explore affectively based spontaneous evaluative impressions (SEIs) of stimulus persons. Experiments 1 and 2 used modified versions of the savings in relearning paradigm (Carlston & Skowronski, 1994) to confirm the occurrence of SEIs, indicating that they are equivalent whether participants are instructed to form trait impressions, evaluative impressions, or neither. These experiments also show that SEIs occur independently of explicit recall for the trait implications of the stimuli. Experiment 3 provides a single dissociation test to distinguish SEIs from spontaneous trait inferences (STIs), showing that disrupting cognitive processing interferes with a trait-based prediction task that presumably reflects STIs, but not with an affectively based social approach task that presumably reflects SEIs. Implications of these findings for the potential independence of spontaneous trait and evaluative inferences, as well as limitations and important steps for future study are discussed. (c) 2015 APA, all rights reserved).

Cluster mass inference via random field theory.

PubMed

Zhang, Hui; Nichols, Thomas E; Johnson, Timothy D

2009-01-01

Cluster extent and voxel intensity are two widely used statistics in neuroimaging inference. Cluster extent is sensitive to spatially extended signals while voxel intensity is better for intense but focal signals. In order to leverage strength from both statistics, several nonparametric permutation methods have been proposed to combine the two methods. Simulation studies have shown that of the different cluster permutation methods, the cluster mass statistic is generally the best. However, to date, there is no parametric cluster mass inference available. In this paper, we propose a cluster mass inference method based on random field theory (RFT). We develop this method for Gaussian images, evaluate it on Gaussian and Gaussianized t-statistic images and investigate its statistical properties via simulation studies and real data. Simulation results show that the method is valid under the null hypothesis and demonstrate that it can be more powerful than the cluster extent inference method. Further, analyses with a single subject and a group fMRI dataset demonstrate better power than traditional cluster size inference, and good accuracy relative to a gold-standard permutation test.

Algorithm Optimally Orders Forward-Chaining Inference Rules

NASA Technical Reports Server (NTRS)

James, Mark

2008-01-01

People typically develop knowledge bases in a somewhat ad hoc manner by incrementally adding rules with no specific organization. This often results in a very inefficient execution of those rules since they are so often order sensitive. This is relevant to tasks like Deep Space Network in that it allows the knowledge base to be incrementally developed and have it automatically ordered for efficiency. Although data flow analysis was first developed for use in compilers for producing optimal code sequences, its usefulness is now recognized in many software systems including knowledge-based systems. However, this approach for exhaustively computing data-flow information cannot directly be applied to inference systems because of the ubiquitous execution of the rules. An algorithm is presented that efficiently performs a complete producer/consumer analysis for each antecedent and consequence clause in a knowledge base to optimally order the rules to minimize inference cycles. An algorithm was developed that optimally orders a knowledge base composed of forwarding chaining inference rules such that independent inference cycle executions are minimized, thus, resulting in significantly faster execution. This algorithm was integrated into the JPL tool Spacecraft Health Inference Engine (SHINE) for verification and it resulted in a significant reduction in inference cycles for what was previously considered an ordered knowledge base. For a knowledge base that is completely unordered, then the improvement is much greater.

Geophysical phenomena classification by artificial neural networks

NASA Technical Reports Server (NTRS)

Gough, M. P.; Bruckner, J. R.

1995-01-01

Space science information systems involve accessing vast data bases. There is a need for an automatic process by which properties of the whole data set can be assimilated and presented to the user. Where data are in the form of spectrograms, phenomena can be detected by pattern recognition techniques. Presented are the first results obtained by applying unsupervised Artificial Neural Networks (ANN's) to the classification of magnetospheric wave spectra. The networks used here were a simple unsupervised Hamming network run on a PC and a more sophisticated CALM network run on a Sparc workstation. The ANN's were compared in their geophysical data recognition performance. CALM networks offer such qualities as fast learning, superiority in generalizing, the ability to continuously adapt to changes in the pattern set, and the possibility to modularize the network to allow the inter-relation between phenomena and data sets. This work is the first step toward an information system interface being developed at Sussex, the Whole Information System Expert (WISE). Phenomena in the data are automatically identified and provided to the user in the form of a data occurrence morphology, the Whole Information System Data Occurrence Morphology (WISDOM), along with relationships to other parameters and phenomena.

Antagonistic Phenomena in Network Dynamics

NASA Astrophysics Data System (ADS)

Motter, Adilson E.; Timme, Marc

2018-03-01

Recent research on the network modeling of complex systems has led to a convenient representation of numerous natural, social, and engineered systems that are now recognized as networks of interacting parts. Such systems can exhibit a wealth of phenomena that not only cannot be anticipated from merely examining their parts, as per the textbook definition of complexity, but also challenge intuition even when considered in the context of what is now known in network science. Here, we review the recent literature on two major classes of such phenomena that have far-reaching implications: (a) antagonistic responses to changes of states or parameters and (b) coexistence of seemingly incongruous behaviors or properties - both deriving from the collective and inherently decentralized nature of the dynamics. They include effects as diverse as negative compressibility in engineered materials, rescue interactions in biological networks, negative resistance in fluid networks, and the Braess paradox occurring across transport and supply networks. They also include remote synchronization, chimera states, and the converse of symmetry breaking in brain, power-grid, and oscillator networks as well as remote control in biological and bioinspired systems. By offering a unified view of these various scenarios, we suggest that they are representative of a yet broader class of unprecedented network phenomena that ought to be revealed and explained by future research.

Reasoning about Informal Statistical Inference: One Statistician's View

ERIC Educational Resources Information Center

Rossman, Allan J.

2008-01-01

This paper identifies key concepts and issues associated with the reasoning of informal statistical inference. I focus on key ideas of inference that I think all students should learn, including at secondary level as well as tertiary. I argue that a fundamental component of inference is to go beyond the data at hand, and I propose that statistical…

Flexible retrieval: When true inferences produce false memories.

PubMed

Carpenter, Alexis C; Schacter, Daniel L

2017-03-01

Episodic memory involves flexible retrieval processes that allow us to link together distinct episodes, make novel inferences across overlapping events, and recombine elements of past experiences when imagining future events. However, the same flexible retrieval and recombination processes that underpin these adaptive functions may also leave memory prone to error or distortion, such as source misattributions in which details of one event are mistakenly attributed to another related event. To determine whether the same recombination-related retrieval mechanism supports both successful inference and source memory errors, we developed a modified version of an associative inference paradigm in which participants encoded everyday scenes comprised of people, objects, and other contextual details. These scenes contained overlapping elements (AB, BC) that could later be linked to support novel inferential retrieval regarding elements that had not appeared together previously (AC). Our critical experimental manipulation concerned whether contextual details were probed before or after the associative inference test, thereby allowing us to assess whether (a) false memories increased for successful versus unsuccessful inferences, and (b) any such effects were specific to after compared with before participants received the inference test. In each of 4 experiments that used variants of this paradigm, participants were more susceptible to false memories for contextual details after successful than unsuccessful inferential retrieval, but only when contextual details were probed after the associative inference test. These results suggest that the retrieval-mediated recombination mechanism that underlies associative inference also contributes to source misattributions that result from combining elements of distinct episodes. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Flexible Retrieval: When True Inferences Produce False Memories

PubMed Central

Carpenter, Alexis C.; Schacter, Daniel L.

2016-01-01

Episodic memory involves flexible retrieval processes that allow us to link together distinct episodes, make novel inferences across overlapping events, and recombine elements of past experiences when imagining future events. However, the same flexible retrieval and recombination processes that underpin these adaptive functions may also leave memory prone to error or distortion, such as source misattributions in which details of one event are mistakenly attributed to another related event. To determine whether the same recombination-related retrieval mechanism supports both successful inference and source memory errors, we developed a modified version of an associative inference paradigm in which participants encoded everyday scenes comprised of people, objects, and other contextual details. These scenes contained overlapping elements (AB, BC) that could later be linked to support novel inferential retrieval regarding elements that had not appeared together previously (AC). Our critical experimental manipulation concerned whether contextual details were probed before or after the associative inference test, thereby allowing us to assess whether a) false memories increased for successful versus unsuccessful inferences, and b) any such effects were specific to after as compared to before participants received the inference test. In each of four experiments that used variants of this paradigm, participants were more susceptible to false memories for contextual details after successful than unsuccessful inferential retrieval, but only when contextual details were probed after the associative inference test. These results suggest that the retrieval-mediated recombination mechanism that underlies associative inference also contributes to source misattributions that result from combining elements of distinct episodes. PMID:27918169

Meta-learning framework applied in bioinformatics inference system design.

PubMed

Arredondo, Tomás; Ormazábal, Wladimir

2015-01-01

This paper describes a meta-learner inference system development framework which is applied and tested in the implementation of bioinformatic inference systems. These inference systems are used for the systematic classification of the best candidates for inclusion in bacterial metabolic pathway maps. This meta-learner-based approach utilises a workflow where the user provides feedback with final classification decisions which are stored in conjunction with analysed genetic sequences for periodic inference system training. The inference systems were trained and tested with three different data sets related to the bacterial degradation of aromatic compounds. The analysis of the meta-learner-based framework involved contrasting several different optimisation methods with various different parameters. The obtained inference systems were also contrasted with other standard classification methods with accurate prediction capabilities observed.

Unconscious relational inference recruits the hippocampus.

PubMed

Reber, Thomas P; Luechinger, Roger; Boesiger, Peter; Henke, Katharina

2012-05-02

Relational inference denotes the capacity to encode, flexibly retrieve, and integrate multiple memories to combine past experiences to update knowledge and improve decision-making in new situations. Although relational inference is thought to depend on the hippocampus and consciousness, we now show in young, healthy men that it may occur outside consciousness but still recruits the hippocampus. In temporally distinct and unique subliminal episodes, we presented word pairs that either overlapped ("winter-red", "red-computer") or not. Effects of unconscious relational inference emerged in reaction times recorded during unconscious encoding and in the outcome of decisions made 1 min later at test, when participants judged the semantic relatedness of two supraliminal words. These words were either episodically related through a common word ("winter-computer" related through "red") or unrelated. Hippocampal activity increased during the unconscious encoding of overlapping versus nonoverlapping word pairs and during the unconscious retrieval of episodically related versus unrelated words. Furthermore, hippocampal activity during unconscious encoding predicted the outcome of decisions made at test. Hence, unconscious inference may influence decision-making in new situations.

Common-Sense Rule Inference

NASA Astrophysics Data System (ADS)

Lombardi, Ilaria; Console, Luca

In the paper we show how rule-based inference can be made more flexible by exploiting semantic information associated with the concepts involved in the rules. We introduce flexible forms of common sense reasoning in which whenever no rule applies to a given situation, the inference engine can fire rules that apply to more general or to similar situations. This can be obtained by defining new forms of match between rules and the facts in the working memory and new forms of conflict resolution. We claim that in this way we can overcome some of the brittleness problems that are common in rule-based systems.

Thermodynamic constraints on fluctuation phenomena

NASA Astrophysics Data System (ADS)

Maroney, O. J. E.

2009-12-01

The relationships among reversible Carnot cycles, the absence of perpetual motion machines, and the existence of a nondecreasing globally unique entropy function form the starting point of many textbook presentations of the foundations of thermodynamics. However, the thermal fluctuation phenomena associated with statistical mechanics has been argued to restrict the domain of validity of this basis of the second law of thermodynamics. Here we demonstrate that fluctuation phenomena can be incorporated into the traditional presentation, extending rather than restricting the domain of validity of the phenomenologically motivated second law. Consistency conditions lead to constraints upon the possible spectrum of thermal fluctuations. In a special case this uniquely selects the Gibbs canonical distribution and more generally incorporates the Tsallis distributions. No particular model of microscopic dynamics need be assumed.

Thermodynamic constraints on fluctuation phenomena.

PubMed

Maroney, O J E

2009-12-01

The relationships among reversible Carnot cycles, the absence of perpetual motion machines, and the existence of a nondecreasing globally unique entropy function form the starting point of many textbook presentations of the foundations of thermodynamics. However, the thermal fluctuation phenomena associated with statistical mechanics has been argued to restrict the domain of validity of this basis of the second law of thermodynamics. Here we demonstrate that fluctuation phenomena can be incorporated into the traditional presentation, extending rather than restricting the domain of validity of the phenomenologically motivated second law. Consistency conditions lead to constraints upon the possible spectrum of thermal fluctuations. In a special case this uniquely selects the Gibbs canonical distribution and more generally incorporates the Tsallis distributions. No particular model of microscopic dynamics need be assumed.

Statistical inference and Aristotle's Rhetoric.

PubMed

Macdonald, Ranald R

2004-11-01

Formal logic operates in a closed system where all the information relevant to any conclusion is present, whereas this is not the case when one reasons about events and states of the world. Pollard and Richardson drew attention to the fact that the reasoning behind statistical tests does not lead to logically justifiable conclusions. In this paper statistical inferences are defended not by logic but by the standards of everyday reasoning. Aristotle invented formal logic, but argued that people mostly get at the truth with the aid of enthymemes--incomplete syllogisms which include arguing from examples, analogies and signs. It is proposed that statistical tests work in the same way--in that they are based on examples, invoke the analogy of a model and use the size of the effect under test as a sign that the chance hypothesis is unlikely. Of existing theories of statistical inference only a weak version of Fisher's takes this into account. Aristotle anticipated Fisher by producing an argument of the form that there were too many cases in which an outcome went in a particular direction for that direction to be plausibly attributed to chance. We can therefore conclude that Aristotle would have approved of statistical inference and there is a good reason for calling this form of statistical inference classical.

Children's and adults' judgments of the certainty of deductive inferences, inductive inferences, and guesses.

PubMed

Pillow, Bradford H; Pearson, Raeanne M; Hecht, Mary; Bremer, Amanda

2010-01-01

Children and adults rated their own certainty following inductive inferences, deductive inferences, and guesses. Beginning in kindergarten, participants rated deductions as more certain than weak inductions or guesses. Deductions were rated as more certain than strong inductions beginning in Grade 3, and fourth-grade children and adults differentiated strong inductions, weak inductions, and informed guesses from pure guesses. By Grade 3, participants also gave different types of explanations for their deductions and inductions. These results are discussed in relation to children's concepts of cognitive processes, logical reasoning, and epistemological development.

Collective phenomena in photonic, plasmonic and hybrid structures.

PubMed

Boriskina, Svetlana V; Povinelli, Michelle; Astratov, Vasily N; Zayats, Anatoly V; Podolskiy, Viktor A

2011-10-24

Preface to a focus issue of invited articles that review recent progress in studying the fundamental physics of collective phenomena associated with coupling of confined photonic, plasmonic, electronic and phononic states and in exploiting these phenomena to engineer novel devices for light generation, optical sensing, and information processing. © 2011 Optical Society of America

Annual Rainfall Forecasting by Using Mamdani Fuzzy Inference System

NASA Astrophysics Data System (ADS)

Fallah-Ghalhary, G.-A.; Habibi Nokhandan, M.; Mousavi Baygi, M.

2009-04-01

Long-term rainfall prediction is very important to countries thriving on agro-based economy. In general, climate and rainfall are highly non-linear phenomena in nature giving rise to what is known as "butterfly effect". The parameters that are required to predict the rainfall are enormous even for a short period. Soft computing is an innovative approach to construct computationally intelligent systems that are supposed to possess humanlike expertise within a specific domain, adapt themselves and learn to do better in changing environments, and explain how they make decisions. Unlike conventional artificial intelligence techniques the guiding principle of soft computing is to exploit tolerance for imprecision, uncertainty, robustness, partial truth to achieve tractability, and better rapport with reality. In this paper, 33 years of rainfall data analyzed in khorasan state, the northeastern part of Iran situated at latitude-longitude pairs (31°-38°N, 74°- 80°E). this research attempted to train Fuzzy Inference System (FIS) based prediction models with 33 years of rainfall data. For performance evaluation, the model predicted outputs were compared with the actual rainfall data. Simulation results reveal that soft computing techniques are promising and efficient. The test results using by FIS model showed that the RMSE was obtained 52 millimeter.

Inferring the temperature dependence of population parameters: the effects of experimental design and inference algorithm

PubMed Central

Palamara, Gian Marco; Childs, Dylan Z; Clements, Christopher F; Petchey, Owen L; Plebani, Marco; Smith, Matthew J

2014-01-01

Understanding and quantifying the temperature dependence of population parameters, such as intrinsic growth rate and carrying capacity, is critical for predicting the ecological responses to environmental change. Many studies provide empirical estimates of such temperature dependencies, but a thorough investigation of the methods used to infer them has not been performed yet. We created artificial population time series using a stochastic logistic model parameterized with the Arrhenius equation, so that activation energy drives the temperature dependence of population parameters. We simulated different experimental designs and used different inference methods, varying the likelihood functions and other aspects of the parameter estimation methods. Finally, we applied the best performing inference methods to real data for the species Paramecium caudatum. The relative error of the estimates of activation energy varied between 5% and 30%. The fraction of habitat sampled played the most important role in determining the relative error; sampling at least 1% of the habitat kept it below 50%. We found that methods that simultaneously use all time series data (direct methods) and methods that estimate population parameters separately for each temperature (indirect methods) are complementary. Indirect methods provide a clearer insight into the shape of the functional form describing the temperature dependence of population parameters; direct methods enable a more accurate estimation of the parameters of such functional forms. Using both methods, we found that growth rate and carrying capacity of Paramecium caudatum scale with temperature according to different activation energies. Our study shows how careful choice of experimental design and inference methods can increase the accuracy of the inferred relationships between temperature and population parameters. The comparison of estimation methods provided here can increase the accuracy of model predictions, with important

Deep Learning for Population Genetic Inference.

PubMed

Sheehan, Sara; Song, Yun S

2016-03-01

Given genomic variation data from multiple individuals, computing the likelihood of complex population genetic models is often infeasible. To circumvent this problem, we introduce a novel likelihood-free inference framework by applying deep learning, a powerful modern technique in machine learning. Deep learning makes use of multilayer neural networks to learn a feature-based function from the input (e.g., hundreds of correlated summary statistics of data) to the output (e.g., population genetic parameters of interest). We demonstrate that deep learning can be effectively employed for population genetic inference and learning informative features of data. As a concrete application, we focus on the challenging problem of jointly inferring natural selection and demography (in the form of a population size change history). Our method is able to separate the global nature of demography from the local nature of selection, without sequential steps for these two factors. Studying demography and selection jointly is motivated by Drosophila, where pervasive selection confounds demographic analysis. We apply our method to 197 African Drosophila melanogaster genomes from Zambia to infer both their overall demography, and regions of their genome under selection. We find many regions of the genome that have experienced hard sweeps, and fewer under selection on standing variation (soft sweep) or balancing selection. Interestingly, we find that soft sweeps and balancing selection occur more frequently closer to the centromere of each chromosome. In addition, our demographic inference suggests that previously estimated bottlenecks for African Drosophila melanogaster are too extreme.

Application of Non-Kolmogorovian Probability and Quantum Adaptive Dynamics to Unconscious Inference in Visual Perception Process

NASA Astrophysics Data System (ADS)

Accardi, Luigi; Khrennikov, Andrei; Ohya, Masanori; Tanaka, Yoshiharu; Yamato, Ichiro

2016-07-01

Recently a novel quantum information formalism — quantum adaptive dynamics — was developed and applied to modelling of information processing by bio-systems including cognitive phenomena: from molecular biology (glucose-lactose metabolism for E.coli bacteria, epigenetic evolution) to cognition, psychology. From the foundational point of view quantum adaptive dynamics describes mutual adapting of the information states of two interacting systems (physical or biological) as well as adapting of co-observations performed by the systems. In this paper we apply this formalism to model unconscious inference: the process of transition from sensation to perception. The paper combines theory and experiment. Statistical data collected in an experimental study on recognition of a particular ambiguous figure, the Schröder stairs, support the viability of the quantum(-like) model of unconscious inference including modelling of biases generated by rotation-contexts. From the probabilistic point of view, we study (for concrete experimental data) the problem of contextuality of probability, its dependence on experimental contexts. Mathematically contextuality leads to non-Komogorovness: probability distributions generated by various rotation contexts cannot be treated in the Kolmogorovian framework. At the same time they can be embedded in a “big Kolmogorov space” as conditional probabilities. However, such a Kolmogorov space has too complex structure and the operational quantum formalism in the form of quantum adaptive dynamics simplifies the modelling essentially.

Using Alien Coins to Test Whether Simple Inference Is Bayesian

ERIC Educational Resources Information Center

Cassey, Peter; Hawkins, Guy E.; Donkin, Chris; Brown, Scott D.

2016-01-01

Reasoning and inference are well-studied aspects of basic cognition that have been explained as statistically optimal Bayesian inference. Using a simplified experimental design, we conducted quantitative comparisons between Bayesian inference and human inference at the level of individuals. In 3 experiments, with more than 13,000 participants, we…

Quantum Chess: Making Quantum Phenomena Accessible

NASA Astrophysics Data System (ADS)

Cantwell, Christopher

Quantum phenomena have remained largely inaccessible to the general public. There tends to be a scare factor associated with the word ``Quantum''. This is in large part due to the alien nature of phenomena such as superposition and entanglement. However, Quantum Computing is a very active area of research and one day we will have games that run on those quantum computers. Quantum phenomena such as superposition and entanglement will seem as normal as gravity. Is it possible to create such games today? Can we make games that are built on top of a realistic quantum simulation and introduce players of any background to quantum concepts in a fun and mentally stimulating way? One of the difficulties with any quantum simulation run on a classical computer is that the Hilbert space grows exponentially, making simulations of an appreciable size physically impossible due largely to memory restrictions. Here we will discuss the conception and development of Quantum Chess, and how to overcome some of the difficulties faced. We can then ask the question, ``What's next?'' What are some of the difficulties Quantum Chess still faces, and what is the future of quantum games?

Causal inference from observational data.

PubMed

Listl, Stefan; Jürges, Hendrik; Watt, Richard G

2016-10-01

Randomized controlled trials have long been considered the 'gold standard' for causal inference in clinical research. In the absence of randomized experiments, identification of reliable intervention points to improve oral health is often perceived as a challenge. But other fields of science, such as social science, have always been challenged by ethical constraints to conducting randomized controlled trials. Methods have been established to make causal inference using observational data, and these methods are becoming increasingly relevant in clinical medicine, health policy and public health research. This study provides an overview of state-of-the-art methods specifically designed for causal inference in observational data, including difference-in-differences (DiD) analyses, instrumental variables (IV), regression discontinuity designs (RDD) and fixed-effects panel data analysis. The described methods may be particularly useful in dental research, not least because of the increasing availability of routinely collected administrative data and electronic health records ('big data'). © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Electrocapillary Phenomena at Edible Oil/Saline Interfaces.

PubMed

Nishimura, Satoshi; Ohzono, Takuya; Shoji, Kohei; Yagihara, Shin; Hayashi, Masafumi; Tanaka, Hisao

2017-03-01

Interfacial tension between edible oil and saline was measured under applied electric fields to understand the electrocapillary phenomena at the edible oil/saline interfaces. The electric responses of saline droplets in edible oil were also observed microscopically to examine the relationship between the electrocapillary phenomena and interfacial polarization. When sodium oleate (SO) was added to edible oil (SO-oil), the interfacial tension between SO-oil and saline decreased. However, no decrease was observed for additive-free oil or oleic acid (OA)-added oil (OA-oil). Microscopic observations suggested that the magnitude of interfacial polarization increased in the order of additive-free oil < OA-oil < SO-oil. The difference in electrocapillary phenomena between OA- and SO-oils was closely related to the polarization magnitude. In the case of SO-oil, the decrease in interfacial tension was remarkably larger for saline (pH 5.4~5.6) than that for phosphate-buffered saline (PBS, pH 7.2~7.4). However, no difference was observed between the electric responses of PBS and saline droplets in SO-oil. The difference in electrocapillary phenomena for PBS and saline could not be simply explained in terms of polarization magnitude. The ratio of ionized and non-ionized OA at the interfaces changed with the saline pH, possibly leading to the above difference.

Testing the potential paradoxes in "retrocausal" phenomena

NASA Astrophysics Data System (ADS)

Jolij, Jacob; Bierman, Dick J.

2017-05-01

Discussions with regard to potential paradoxes arising from "retrocausal" phenomena have been purely theoretical because so far no empirical effects had been established that allowed for empirical exploration of these potential paradoxes. In this article we describe three human experiments that showed clear "retrocausal" effects. In these neuropsychological, so-called, face-detection experiments, consisting of hundreds of trials per participant, we use brain signals to predict an upcoming random stimulus. The binary random decision, corresponding to showing a noisy cartoon face or showing only noise on a display with equal probability is taken after the brain signals have been measured. The prediction accuracy ranges from 50.5-56.5% for the 3 experiments where chance performance would be 50%. The prediction algorithm is based on a template constructed out of all the pre-stimulus brain signals obtained in other trials of that particular participant. This approach thus controls for individual difference in brain functioning. Subsequently we describe an experiment based upon these findings where the predictive information is used in part of the trials to determine the stimulus rather than randomly select that stimulus. In those trials we analyze what the brain signals tell us what the future stimulus would be and then we reverse the actual future that is presented on the display. This is a `bilking' condition. We analyze what the consequence of the introduction of this bilking condition is on the accuracy of the remaining (normal) trials and, following a suggestion inferred from Thorne et al, we also check what the effect is on the random decision to either bilk or not bilk the specific trial. The bilking experiment is in progress and the results so far do not allow for conclusions and are presented only as an illustration.

Causal inference in economics and marketing.

PubMed

Varian, Hal R

2016-07-05

This is an elementary introduction to causal inference in economics written for readers familiar with machine learning methods. The critical step in any causal analysis is estimating the counterfactual-a prediction of what would have happened in the absence of the treatment. The powerful techniques used in machine learning may be useful for developing better estimates of the counterfactual, potentially improving causal inference.

Making inference from wildlife collision data: inferring predator absence from prey strikes

PubMed Central

Hosack, Geoffrey R.; Barry, Simon C.

2017-01-01

Wildlife collision data are ubiquitous, though challenging for making ecological inference due to typically irreducible uncertainty relating to the sampling process. We illustrate a new approach that is useful for generating inference from predator data arising from wildlife collisions. By simply conditioning on a second prey species sampled via the same collision process, and by using a biologically realistic numerical response functions, we can produce a coherent numerical response relationship between predator and prey. This relationship can then be used to make inference on the population size of the predator species, including the probability of extinction. The statistical conditioning enables us to account for unmeasured variation in factors influencing the runway strike incidence for individual airports and to enable valid comparisons. A practical application of the approach for testing hypotheses about the distribution and abundance of a predator species is illustrated using the hypothesized red fox incursion into Tasmania, Australia. We estimate that conditional on the numerical response between fox and lagomorph runway strikes on mainland Australia, the predictive probability of observing no runway strikes of foxes in Tasmania after observing 15 lagomorph strikes is 0.001. We conclude there is enough evidence to safely reject the null hypothesis that there is a widespread red fox population in Tasmania at a population density consistent with prey availability. The method is novel and has potential wider application. PMID:28243534

Property transmission: an explanatory account of the role of similarity information in causal inference.

PubMed

White, Peter A

2009-09-01

Many kinds of common and easily observed causal relations exhibit property transmission, which is a tendency for the causal object to impose its own properties on the effect object. It is proposed that property transmission becomes a general and readily available hypothesis used to make interpretations and judgments about causal questions under conditions of uncertainty, in which property transmission functions as a heuristic. The property transmission hypothesis explains why and when similarity information is used in causal inference. It can account for magical contagion beliefs, some cases of illusory correlation, the correspondence bias, overestimation of cross-situational consistency in behavior, nonregressive tendencies in prediction, the belief that acts of will are causes of behavior, and a range of other phenomena. People learn that property transmission is often moderated by other factors, but under conditions of uncertainty in which the operation of relevant other factors is unknown, it tends to exhibit a pervasive influence on thinking about causality. (c) 2009 APA, all rights reserved.

Causal inference in biology networks with integrated belief propagation.

PubMed

Chang, Rui; Karr, Jonathan R; Schadt, Eric E

2015-01-01

Inferring causal relationships among molecular and higher order phenotypes is a critical step in elucidating the complexity of living systems. Here we propose a novel method for inferring causality that is no longer constrained by the conditional dependency arguments that limit the ability of statistical causal inference methods to resolve causal relationships within sets of graphical models that are Markov equivalent. Our method utilizes Bayesian belief propagation to infer the responses of perturbation events on molecular traits given a hypothesized graph structure. A distance measure between the inferred response distribution and the observed data is defined to assess the 'fitness' of the hypothesized causal relationships. To test our algorithm, we infer causal relationships within equivalence classes of gene networks in which the form of the functional interactions that are possible are assumed to be nonlinear, given synthetic microarray and RNA sequencing data. We also apply our method to infer causality in real metabolic network with v-structure and feedback loop. We show that our method can recapitulate the causal structure and recover the feedback loop only from steady-state data which conventional method cannot.

Wisdom of crowds for robust gene network inference

PubMed Central

Marbach, Daniel; Costello, James C.; Küffner, Robert; Vega, Nicci; Prill, Robert J.; Camacho, Diogo M.; Allison, Kyle R.; Kellis, Manolis; Collins, James J.; Stolovitzky, Gustavo

2012-01-01

Reconstructing gene regulatory networks from high-throughput data is a long-standing problem. Through the DREAM project (Dialogue on Reverse Engineering Assessment and Methods), we performed a comprehensive blind assessment of over thirty network inference methods on Escherichia coli, Staphylococcus aureus, Saccharomyces cerevisiae, and in silico microarray data. We characterize performance, data requirements, and inherent biases of different inference approaches offering guidelines for both algorithm application and development. We observe that no single inference method performs optimally across all datasets. In contrast, integration of predictions from multiple inference methods shows robust and high performance across diverse datasets. Thereby, we construct high-confidence networks for E. coli and S. aureus, each comprising ~1700 transcriptional interactions at an estimated precision of 50%. We experimentally test 53 novel interactions in E. coli, of which 23 were supported (43%). Our results establish community-based methods as a powerful and robust tool for the inference of transcriptional gene regulatory networks. PMID:22796662

Experimental evidence for circular inference in schizophrenia.

PubMed

Jardri, Renaud; Duverne, Sandrine; Litvinova, Alexandra S; Denève, Sophie

2017-01-31

Schizophrenia (SCZ) is a complex mental disorder that may result in some combination of hallucinations, delusions and disorganized thinking. Here SCZ patients and healthy controls (CTLs) report their level of confidence on a forced-choice task that manipulated the strength of sensory evidence and prior information. Neither group's responses can be explained by simple Bayesian inference. Rather, individual responses are best captured by a model with different degrees of circular inference. Circular inference refers to a corruption of sensory data by prior information and vice versa, leading us to 'see what we expect' (through descending loops), to 'expect what we see' (through ascending loops) or both. Ascending loops are stronger for SCZ than CTLs and correlate with the severity of positive symptoms. Descending loops correlate with the severity of negative symptoms. Both loops correlate with disorganized symptoms. The findings suggest that circular inference might mediate the clinical manifestations of SCZ.

Experimental evidence for circular inference in schizophrenia

NASA Astrophysics Data System (ADS)

Jardri, Renaud; Duverne, Sandrine; Litvinova, Alexandra S.; Denève, Sophie

2017-01-01

Schizophrenia (SCZ) is a complex mental disorder that may result in some combination of hallucinations, delusions and disorganized thinking. Here SCZ patients and healthy controls (CTLs) report their level of confidence on a forced-choice task that manipulated the strength of sensory evidence and prior information. Neither group's responses can be explained by simple Bayesian inference. Rather, individual responses are best captured by a model with different degrees of circular inference. Circular inference refers to a corruption of sensory data by prior information and vice versa, leading us to `see what we expect' (through descending loops), to `expect what we see' (through ascending loops) or both. Ascending loops are stronger for SCZ than CTLs and correlate with the severity of positive symptoms. Descending loops correlate with the severity of negative symptoms. Both loops correlate with disorganized symptoms. The findings suggest that circular inference might mediate the clinical manifestations of SCZ.

Deep Learning for Population Genetic Inference

PubMed Central

Sheehan, Sara; Song, Yun S.

2016-01-01

Given genomic variation data from multiple individuals, computing the likelihood of complex population genetic models is often infeasible. To circumvent this problem, we introduce a novel likelihood-free inference framework by applying deep learning, a powerful modern technique in machine learning. Deep learning makes use of multilayer neural networks to learn a feature-based function from the input (e.g., hundreds of correlated summary statistics of data) to the output (e.g., population genetic parameters of interest). We demonstrate that deep learning can be effectively employed for population genetic inference and learning informative features of data. As a concrete application, we focus on the challenging problem of jointly inferring natural selection and demography (in the form of a population size change history). Our method is able to separate the global nature of demography from the local nature of selection, without sequential steps for these two factors. Studying demography and selection jointly is motivated by Drosophila, where pervasive selection confounds demographic analysis. We apply our method to 197 African Drosophila melanogaster genomes from Zambia to infer both their overall demography, and regions of their genome under selection. We find many regions of the genome that have experienced hard sweeps, and fewer under selection on standing variation (soft sweep) or balancing selection. Interestingly, we find that soft sweeps and balancing selection occur more frequently closer to the centromere of each chromosome. In addition, our demographic inference suggests that previously estimated bottlenecks for African Drosophila melanogaster are too extreme. PMID:27018908

Causal inference in economics and marketing

PubMed Central

Varian, Hal R.

2016-01-01

This is an elementary introduction to causal inference in economics written for readers familiar with machine learning methods. The critical step in any causal analysis is estimating the counterfactual—a prediction of what would have happened in the absence of the treatment. The powerful techniques used in machine learning may be useful for developing better estimates of the counterfactual, potentially improving causal inference. PMID:27382144

Pendulum Phenomena and the Assessment of Scientific Inquiry Capabilities

ERIC Educational Resources Information Center

Zachos, Paul

2004-01-01

Phenomena associated with the "pendulum" present numerous opportunities for assessing higher order human capabilities related to "scientific inquiry" and the "discovery" of natural law. This paper illustrates how systematic "assessment of scientific inquiry capabilities", using "pendulum" phenomena, can provide a useful tool for classroom teachers…

Realistic generation of natural phenomena based on video synthesis

NASA Astrophysics Data System (ADS)

Wang, Changbo; Quan, Hongyan; Li, Chenhui; Xiao, Zhao; Chen, Xiao; Li, Peng; Shen, Liuwei

2009-10-01

Research on the generation of natural phenomena has many applications in special effects of movie, battlefield simulation and virtual reality, etc. Based on video synthesis technique, a new approach is proposed for the synthesis of natural phenomena, including flowing water and fire flame. From the fire and flow video, the seamless video of arbitrary length is generated. Then, the interaction between wind and fire flame is achieved through the skeleton of flame. Later, the flow is also synthesized by extending the video textures using an edge resample method. Finally, we can integrate the synthesized natural phenomena into a virtual scene.

Chimpanzees know that others make inferences

PubMed Central

Schmelz, Martin; Call, Josep; Tomasello, Michael

2011-01-01

If chimpanzees are faced with two opaque boards on a table, in the context of searching for a single piece of food, they do not choose the board lying flat (because if food was under there it would not be lying flat) but, rather, they choose the slanted one— presumably inferring that some unperceived food underneath is causing the slant. Here we demonstrate that chimpanzees know that other chimpanzees in the same situation will make a similar inference. In a back-and-forth foraging game, when their competitor had chosen before them, chimpanzees tended to avoid the slanted board on the assumption that the competitor had already chosen it. Chimpanzees can determine the inferences that a conspecific is likely to make and then adjust their competitive strategies accordingly. PMID:21282649

Explanatory Preferences Shape Learning and Inference.

PubMed

Lombrozo, Tania

2016-10-01

Explanations play an important role in learning and inference. People often learn by seeking explanations, and they assess the viability of hypotheses by considering how well they explain the data. An emerging body of work reveals that both children and adults have strong and systematic intuitions about what constitutes a good explanation, and that these explanatory preferences have a systematic impact on explanation-based processes. In particular, people favor explanations that are simple and broad, with the consequence that engaging in explanation can shape learning and inference by leading people to seek patterns and favor hypotheses that support broad and simple explanations. Given the prevalence of explanation in everyday cognition, understanding explanation is therefore crucial to understanding learning and inference. Copyright © 2016 Elsevier Ltd. All rights reserved.

The experimental evidence for parapsychological phenomena: A review.

PubMed

Cardeña, Etzel

2018-05-24

This article presents a comprehensive integration of current experimental evidence and theories about so-called parapsychological (psi) phenomena. Throughout history, people have reported events that seem to violate the common sense view of space and time. Some psychologists have been at the forefront of investigating these phenomena with sophisticated research protocols and theory, while others have devoted much of their careers to criticizing the field. Both stances can be explained by psychologists' expertise on relevant processes such as perception, memory, belief, and conscious and nonconscious processes. This article clarifies the domain of psi, summarizes recent theories from physics and psychology that present psi phenomena as at least plausible, and then provides an overview of recent/updated meta-analyses. The evidence provides cumulative support for the reality of psi, which cannot be readily explained away by the quality of the studies, fraud, selective reporting, experimental or analytical incompetence, or other frequent criticisms. The evidence for psi is comparable to that for established phenomena in psychology and other disciplines, although there is no consensual understanding of them. The article concludes with recommendations for further progress in the field including the use of project and data repositories, conducting multidisciplinary studies with enough power, developing further nonconscious measures of psi and falsifiable theories, analyzing the characteristics of successful sessions and participants, improving the ecological validity of studies, testing how to increase effect sizes, recruiting more researchers at least open to the possibility of psi, and situating psi phenomena within larger domains such as the study of consciousness. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Experimental evidence for circular inference in schizophrenia

PubMed Central

Jardri, Renaud; Duverne, Sandrine; Litvinova, Alexandra S; Denève, Sophie

2017-01-01

Schizophrenia (SCZ) is a complex mental disorder that may result in some combination of hallucinations, delusions and disorganized thinking. Here SCZ patients and healthy controls (CTLs) report their level of confidence on a forced-choice task that manipulated the strength of sensory evidence and prior information. Neither group's responses can be explained by simple Bayesian inference. Rather, individual responses are best captured by a model with different degrees of circular inference. Circular inference refers to a corruption of sensory data by prior information and vice versa, leading us to ‘see what we expect' (through descending loops), to ‘expect what we see' (through ascending loops) or both. Ascending loops are stronger for SCZ than CTLs and correlate with the severity of positive symptoms. Descending loops correlate with the severity of negative symptoms. Both loops correlate with disorganized symptoms. The findings suggest that circular inference might mediate the clinical manifestations of SCZ. PMID:28139642

An inference engine for embedded diagnostic systems

NASA Technical Reports Server (NTRS)

Fox, Barry R.; Brewster, Larry T.

1987-01-01

The implementation of an inference engine for embedded diagnostic systems is described. The system consists of two distinct parts. The first is an off-line compiler which accepts a propositional logical statement of the relationship between facts and conclusions and produces data structures required by the on-line inference engine. The second part consists of the inference engine and interface routines which accept assertions of fact and return the conclusions which necessarily follow. Given a set of assertions, it will generate exactly the conclusions which logically follow. At the same time, it will detect any inconsistencies which may propagate from an inconsistent set of assertions or a poorly formulated set of rules. The memory requirements are fixed and the worst case execution times are bounded at compile time. The data structures and inference algorithms are very simple and well understood. The data structures and algorithms are described in detail. The system has been implemented on Lisp, Pascal, and Modula-2.

BagReg: Protein inference through machine learning.

PubMed

Zhao, Can; Liu, Dao; Teng, Ben; He, Zengyou

2015-08-01

Protein inference from the identified peptides is of primary importance in the shotgun proteomics. The target of protein inference is to identify whether each candidate protein is truly present in the sample. To date, many computational methods have been proposed to solve this problem. However, there is still no method that can fully utilize the information hidden in the input data. In this article, we propose a learning-based method named BagReg for protein inference. The method firstly artificially extracts five features from the input data, and then chooses each feature as the class feature to separately build models to predict the presence probabilities of proteins. Finally, the weak results from five prediction models are aggregated to obtain the final result. We test our method on six public available data sets. The experimental results show that our method is superior to the state-of-the-art protein inference algorithms. Copyright © 2015 Elsevier Ltd. All rights reserved.

Metacognitive inferences from other people's memory performance.

PubMed

Smith, Robert W; Schwarz, Norbert

2016-09-01

Three studies show that people draw metacognitive inferences about events from how well others remember the event. Given that memory fades over time, detailed accounts of distant events suggest that the event must have been particularly memorable, for example, because it was extreme. Accordingly, participants inferred that a physical assault (Study 1) or a poor restaurant experience (Studies 2-3) were more extreme when they were well remembered one year rather than one week later. These inferences influence behavioral intentions. For example, participants recommended a more severe punishment for a well-remembered distant rather than recent assault (Study 1). These metacognitive inferences are eliminated when people attribute the reporter's good memory to an irrelevant cause (e.g., photographic memory), thus undermining the informational value of memory performance (Study 3). These studies illuminate how people use lay theories of memory to learn from others' memory performance about characteristics of the world. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Thermal transport phenomena in nanoparticle suspensions

NASA Astrophysics Data System (ADS)

Cardellini, Annalisa; Fasano, Matteo; Bozorg Bigdeli, Masoud; Chiavazzo, Eliodoro; Asinari, Pietro

2016-12-01

Nanoparticle suspensions in liquids have received great attention, as they may offer an approach to enhance thermophysical properties of base fluids. A good variety of applications in engineering and biomedicine has been investigated with the aim of exploiting the above potential. However, the multiscale nature of nanosuspensions raises several issues in defining a comprehensive modelling framework, incorporating relevant molecular details and much larger scale phenomena, such as particle aggregation and their dynamics. The objectives of the present topical review is to report and discuss the main heat and mass transport phenomena ruling macroscopic behaviour of nanosuspensions, arising from molecular details. Relevant experimental results are included and properly put in the context of recent observations and theoretical studies, which solved long-standing debates about thermophysical properties enhancement. Major transport phenomena are discussed and in-depth analysis is carried out for highlighting the role of geometrical (nanoparticle shape, size, aggregation, concentration), chemical (pH, surfactants, functionalization) and physical parameters (temperature, density). We finally overview several computational techniques available at different scales with the aim of drawing the attention on the need for truly multiscale predictive models. This may help the development of next-generation nanoparticle suspensions and their rational use in thermal applications.

Ensemble stacking mitigates biases in inference of synaptic connectivity.

PubMed

Chambers, Brendan; Levy, Maayan; Dechery, Joseph B; MacLean, Jason N

2018-01-01

A promising alternative to directly measuring the anatomical connections in a neuronal population is inferring the connections from the activity. We employ simulated spiking neuronal networks to compare and contrast commonly used inference methods that identify likely excitatory synaptic connections using statistical regularities in spike timing. We find that simple adjustments to standard algorithms improve inference accuracy: A signing procedure improves the power of unsigned mutual-information-based approaches and a correction that accounts for differences in mean and variance of background timing relationships, such as those expected to be induced by heterogeneous firing rates, increases the sensitivity of frequency-based methods. We also find that different inference methods reveal distinct subsets of the synaptic network and each method exhibits different biases in the accurate detection of reciprocity and local clustering. To correct for errors and biases specific to single inference algorithms, we combine methods into an ensemble. Ensemble predictions, generated as a linear combination of multiple inference algorithms, are more sensitive than the best individual measures alone, and are more faithful to ground-truth statistics of connectivity, mitigating biases specific to single inference methods. These weightings generalize across simulated datasets, emphasizing the potential for the broad utility of ensemble-based approaches.

A new asymptotic method for jump phenomena

NASA Technical Reports Server (NTRS)

Reiss, E. L.

1980-01-01

Physical phenomena involving rapid and sudden transitions, such as snap buckling of elastic shells, explosions, and earthquakes, are characterized mathematically as a small disturbance causing a large-amplitude response. Because of this, standard asymptotic and perturbation methods are ill-suited to these problems. In the present paper, a new method of analyzing jump phenomena is proposed. The principal feature of the method is the representation of the response in terms of rational functions. For illustration, the method is applied to the snap buckling of an elastic arch and to a simple combustion problem.

Light-induced phenomena in one-component gas: The transport phenomena

NASA Astrophysics Data System (ADS)

Chermyaninov, I. V.; Chernyak, V. G.

2016-09-01

The article presents the theory of transport processes in a one-component gas located in the capillary under the action of resonant laser radiation and the temperature and pressure gradients. The expressions for the kinetic coefficients determining heat and mass transport in the gas are obtained on the basis of the modified Boltzmann equations for the excited and unexcited particles. The Onsager reciprocal relations for cross kinetic coefficients are proven for all Knudsen numbers and for any law interaction of gas particles with each other and boundary surface. Light-induced phenomena associated with the possible non-equilibrium stationary states of system are analyzed.

Enhancing Transparency and Control When Drawing Data-Driven Inferences About Individuals.

PubMed

Chen, Daizhuo; Fraiberger, Samuel P; Moakler, Robert; Provost, Foster

2017-09-01

Recent studies show the remarkable power of fine-grained information disclosed by users on social network sites to infer users' personal characteristics via predictive modeling. Similar fine-grained data are being used successfully in other commercial applications. In response, attention is turning increasingly to the transparency that organizations provide to users as to what inferences are drawn and why, as well as to what sort of control users can be given over inferences that are drawn about them. In this article, we focus on inferences about personal characteristics based on information disclosed by users' online actions. As a use case, we explore personal inferences that are made possible from "Likes" on Facebook. We first present a means for providing transparency into the information responsible for inferences drawn by data-driven models. We then introduce the "cloaking device"-a mechanism for users to inhibit the use of particular pieces of information in inference. Using these analytical tools we ask two main questions: (1) How much information must users cloak to significantly affect inferences about their personal traits? We find that usually users must cloak only a small portion of their actions to inhibit inference. We also find that, encouragingly, false-positive inferences are significantly easier to cloak than true-positive inferences. (2) Can firms change their modeling behavior to make cloaking more difficult? The answer is a definitive yes. We demonstrate a simple modeling change that requires users to cloak substantially more information to affect the inferences drawn. The upshot is that organizations can provide transparency and control even into complicated, predictive model-driven inferences, but they also can make control easier or harder for their users.

Seasonality of alcohol-related phenomena in Estonia.

PubMed

Silm, Siiri; Ahas, Rein

2005-03-01

We studied alcohol consumption and its consequences as a seasonal phenomenon in Estonia and analysed the social and environmental factors that may cause its seasonal rhythm. There are two important questions when researching the seasonality of human activities: (1) whether it is caused by natural or social factors, and (2) whether the impact of the factors is direct or indirect. Often the seasonality of social phenomena is caused by social factors, but the triggering mechanisms are related to environmental factors like temperature, precipitation, and radiation via the circannual calendar. The indicators of alcohol consumption in the current paper are grouped as: (1) pre-consumption phenomena, i.e. production, tax and excise, sales (beer, wine and vodka are analysed separately), and (2) post-consumption phenomena, i.e. alcohol-related crime and traffic accidents and the number of people detained in lockups and admitted to alcohol treatment clinics. In addition, seasonal variability in the amount of alcohol advertising has been studied, and a survey has been carried out among 87 students of Tartu University. The analysis shows that different phenomena related to alcohol have a clear seasonal rhythm in Estonia. The peak period of phenomena related to beer is in the summer, from June to August and the low point is during the first months of the year. Beer consumption correlates well with air temperature. The consumption of vodka increases sharply at the end of the year and in June; the production of vodka does not have a significant correlation with negative temperatures. The consumption of wine increases during summer and in December. The consequences of alcohol consumption, expressed as the rate of traffic accidents or the frequency of medical treatment, also show seasonal variability. Seasonal variability of alcohol consumption in Estonia is influenced by natural factors (temperature, humidity, etc.) and by social factors (celebrations, vacations, etc.). However

Seasonality of alcohol-related phenomena in Estonia

NASA Astrophysics Data System (ADS)

Silm, Siiri; Ahas, Rein

2005-03-01

We studied alcohol consumption and its consequences as a seasonal phenomenon in Estonia and analysed the social and environmental factors that may cause its seasonal rhythm. There are two important questions when researching the seasonality of human activities: (1) whether it is caused by natural or social factors, and (2) whether the impact of the factors is direct or indirect. Often the seasonality of social phenomena is caused by social factors, but the triggering mechanisms are related to environmental factors like temperature, precipitation, and radiation via the circannual calendar. The indicators of alcohol consumption in the current paper are grouped as: (1) pre-consumption phenomena, i.e. production, tax and excise, sales (beer, wine and vodka are analysed separately), and (2) post-consumption phenomena, i.e. alcohol-related crime and traffic accidents and the number of people detained in lockups and admitted to alcohol treatment clinics. In addition, seasonal variability in the amount of alcohol advertising has been studied, and a survey has been carried out among 87 students of Tartu University. The analysis shows that different phenomena related to alcohol have a clear seasonal rhythm in Estonia. The peak period of phenomena related to beer is in the summer, from June to August and the low point is during the first months of the year. Beer consumption correlates well with air temperature. The consumption of vodka increases sharply at the end of the year and in June; the production of vodka does not have a significant correlation with negative temperatures. The consumption of wine increases during summer and in December. The consequences of alcohol consumption, expressed as the rate of traffic accidents or the frequency of medical treatment, also show seasonal variability. Seasonal variability of alcohol consumption in Estonia is influenced by natural factors (temperature, humidity, etc.) and by social factors (celebrations, vacations, etc.). However

Development of L2 Word-Meaning Inference while Reading

ERIC Educational Resources Information Center

Hamada, Megumi

2009-01-01

Ability to infer the meaning of unknown words encountered while reading plays an important role in learners' L2 word-knowledge development. Despite numerous findings reported on word-meaning inference, how learners develop this ability is still unclear. In order to provide a developmental inquiry into L2 word-meaning inference while reading, this…

Department of Energy Natural Phenomena Hazards Mitigation Program

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

Murray, R.C.

1993-09-01

This paper will present a summary of past and present accomplishments of the Natural Phenomena Hazards Program that has been ongoing at Lawrence Livermore National Laboratory since 1975. The Natural Phenomena covered includes earthquake; winds, hurricanes, and tornadoes; flooding and precipitation; lightning; and volcanic events. The work is organized into four major areas (1) Policy, requirements, standards, and guidance (2) Technical support, research development, (3) Technology transfer, and (4) Oversight.

HIERARCHICAL PROBABILISTIC INFERENCE OF COSMIC SHEAR

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

Schneider, Michael D.; Dawson, William A.; Hogg, David W.

2015-07-01

Point estimators for the shearing of galaxy images induced by gravitational lensing involve a complex inverse problem in the presence of noise, pixelization, and model uncertainties. We present a probabilistic forward modeling approach to gravitational lensing inference that has the potential to mitigate the biased inferences in most common point estimators and is practical for upcoming lensing surveys. The first part of our statistical framework requires specification of a likelihood function for the pixel data in an imaging survey given parameterized models for the galaxies in the images. We derive the lensing shear posterior by marginalizing over all intrinsic galaxymore » properties that contribute to the pixel data (i.e., not limited to galaxy ellipticities) and learn the distributions for the intrinsic galaxy properties via hierarchical inference with a suitably flexible conditional probabilitiy distribution specification. We use importance sampling to separate the modeling of small imaging areas from the global shear inference, thereby rendering our algorithm computationally tractable for large surveys. With simple numerical examples we demonstrate the improvements in accuracy from our importance sampling approach, as well as the significance of the conditional distribution specification for the intrinsic galaxy properties when the data are generated from an unknown number of distinct galaxy populations with different morphological characteristics.« less

Bayesian structural inference for hidden processes.

PubMed

Strelioff, Christopher C; Crutchfield, James P

2014-04-01

We introduce a Bayesian approach to discovering patterns in structurally complex processes. The proposed method of Bayesian structural inference (BSI) relies on a set of candidate unifilar hidden Markov model (uHMM) topologies for inference of process structure from a data series. We employ a recently developed exact enumeration of topological ε-machines. (A sequel then removes the topological restriction.) This subset of the uHMM topologies has the added benefit that inferred models are guaranteed to be ε-machines, irrespective of estimated transition probabilities. Properties of ε-machines and uHMMs allow for the derivation of analytic expressions for estimating transition probabilities, inferring start states, and comparing the posterior probability of candidate model topologies, despite process internal structure being only indirectly present in data. We demonstrate BSI's effectiveness in estimating a process's randomness, as reflected by the Shannon entropy rate, and its structure, as quantified by the statistical complexity. We also compare using the posterior distribution over candidate models and the single, maximum a posteriori model for point estimation and show that the former more accurately reflects uncertainty in estimated values. We apply BSI to in-class examples of finite- and infinite-order Markov processes, as well to an out-of-class, infinite-state hidden process.

Bayesian structural inference for hidden processes

NASA Astrophysics Data System (ADS)

Strelioff, Christopher C.; Crutchfield, James P.

2014-04-01

We introduce a Bayesian approach to discovering patterns in structurally complex processes. The proposed method of Bayesian structural inference (BSI) relies on a set of candidate unifilar hidden Markov model (uHMM) topologies for inference of process structure from a data series. We employ a recently developed exact enumeration of topological ɛ-machines. (A sequel then removes the topological restriction.) This subset of the uHMM topologies has the added benefit that inferred models are guaranteed to be ɛ-machines, irrespective of estimated transition probabilities. Properties of ɛ-machines and uHMMs allow for the derivation of analytic expressions for estimating transition probabilities, inferring start states, and comparing the posterior probability of candidate model topologies, despite process internal structure being only indirectly present in data. We demonstrate BSI's effectiveness in estimating a process's randomness, as reflected by the Shannon entropy rate, and its structure, as quantified by the statistical complexity. We also compare using the posterior distribution over candidate models and the single, maximum a posteriori model for point estimation and show that the former more accurately reflects uncertainty in estimated values. We apply BSI to in-class examples of finite- and infinite-order Markov processes, as well to an out-of-class, infinite-state hidden process.

Hierarchical Probabilistic Inference of Cosmic Shear

NASA Astrophysics Data System (ADS)

Schneider, Michael D.; Hogg, David W.; Marshall, Philip J.; Dawson, William A.; Meyers, Joshua; Bard, Deborah J.; Lang, Dustin

2015-07-01

Point estimators for the shearing of galaxy images induced by gravitational lensing involve a complex inverse problem in the presence of noise, pixelization, and model uncertainties. We present a probabilistic forward modeling approach to gravitational lensing inference that has the potential to mitigate the biased inferences in most common point estimators and is practical for upcoming lensing surveys. The first part of our statistical framework requires specification of a likelihood function for the pixel data in an imaging survey given parameterized models for the galaxies in the images. We derive the lensing shear posterior by marginalizing over all intrinsic galaxy properties that contribute to the pixel data (i.e., not limited to galaxy ellipticities) and learn the distributions for the intrinsic galaxy properties via hierarchical inference with a suitably flexible conditional probabilitiy distribution specification. We use importance sampling to separate the modeling of small imaging areas from the global shear inference, thereby rendering our algorithm computationally tractable for large surveys. With simple numerical examples we demonstrate the improvements in accuracy from our importance sampling approach, as well as the significance of the conditional distribution specification for the intrinsic galaxy properties when the data are generated from an unknown number of distinct galaxy populations with different morphological characteristics.

The Impact of Disablers on Predictive Inference

ERIC Educational Resources Information Center

Cummins, Denise Dellarosa

2014-01-01

People consider alternative causes when deciding whether a cause is responsible for an effect (diagnostic inference) but appear to neglect them when deciding whether an effect will occur (predictive inference). Five experiments were conducted to test a 2-part explanation of this phenomenon: namely, (a) that people interpret standard predictive…

Automatic physical inference with information maximizing neural networks

NASA Astrophysics Data System (ADS)

Charnock, Tom; Lavaux, Guilhem; Wandelt, Benjamin D.

2018-04-01

Compressing large data sets to a manageable number of summaries that are informative about the underlying parameters vastly simplifies both frequentist and Bayesian inference. When only simulations are available, these summaries are typically chosen heuristically, so they may inadvertently miss important information. We introduce a simulation-based machine learning technique that trains artificial neural networks to find nonlinear functionals of data that maximize Fisher information: information maximizing neural networks (IMNNs). In test cases where the posterior can be derived exactly, likelihood-free inference based on automatically derived IMNN summaries produces nearly exact posteriors, showing that these summaries are good approximations to sufficient statistics. In a series of numerical examples of increasing complexity and astrophysical relevance we show that IMNNs are robustly capable of automatically finding optimal, nonlinear summaries of the data even in cases where linear compression fails: inferring the variance of Gaussian signal in the presence of noise, inferring cosmological parameters from mock simulations of the Lyman-α forest in quasar spectra, and inferring frequency-domain parameters from LISA-like detections of gravitational waveforms. In this final case, the IMNN summary outperforms linear data compression by avoiding the introduction of spurious likelihood maxima. We anticipate that the automatic physical inference method described in this paper will be essential to obtain both accurate and precise cosmological parameter estimates from complex and large astronomical data sets, including those from LSST and Euclid.

Genetic network inference as a series of discrimination tasks.

PubMed

Kimura, Shuhei; Nakayama, Satoshi; Hatakeyama, Mariko

2009-04-01

Genetic network inference methods based on sets of differential equations generally require a great deal of time, as the equations must be solved many times. To reduce the computational cost, researchers have proposed other methods for inferring genetic networks by solving sets of differential equations only a few times, or even without solving them at all. When we try to obtain reasonable network models using these methods, however, we must estimate the time derivatives of the gene expression levels with great precision. In this study, we propose a new method to overcome the drawbacks of inference methods based on sets of differential equations. Our method infers genetic networks by obtaining classifiers capable of predicting the signs of the derivatives of the gene expression levels. For this purpose, we defined a genetic network inference problem as a series of discrimination tasks, then solved the defined series of discrimination tasks with a linear programming machine. Our experimental results demonstrated that the proposed method is capable of correctly inferring genetic networks, and doing so more than 500 times faster than the other inference methods based on sets of differential equations. Next, we applied our method to actual expression data of the bacterial SOS DNA repair system. And finally, we demonstrated that our approach relates to the inference method based on the S-system model. Though our method provides no estimation of the kinetic parameters, it should be useful for researchers interested only in the network structure of a target system. Supplementary data are available at Bioinformatics online.

Inference as Prediction

ERIC Educational Resources Information Center

Watson, Jane

2007-01-01

Inference, or decision making, is seen in curriculum documents as the final step in a statistical investigation. For a formal statistical enquiry this may be associated with sophisticated tests involving probability distributions. For young students without the mathematical background to perform such tests, it is still possible to draw informal…

A Course on Surface Phenomena.

ERIC Educational Resources Information Center

Woods, Donald R.

1983-01-01

Describes a graduate or senior elective course combining fundamentals of surface phenomena with practical problem-solving structured around a series of case problems. Discusses topics covered and their development through acquiring new knowledge applied to the case problem, practical calculations of solutions, and applications to additional…

Enhancing Transparency and Control When Drawing Data-Driven Inferences About Individuals

PubMed Central

Chen, Daizhuo; Fraiberger, Samuel P.; Moakler, Robert; Provost, Foster

2017-01-01

Abstract Recent studies show the remarkable power of fine-grained information disclosed by users on social network sites to infer users' personal characteristics via predictive modeling. Similar fine-grained data are being used successfully in other commercial applications. In response, attention is turning increasingly to the transparency that organizations provide to users as to what inferences are drawn and why, as well as to what sort of control users can be given over inferences that are drawn about them. In this article, we focus on inferences about personal characteristics based on information disclosed by users' online actions. As a use case, we explore personal inferences that are made possible from “Likes” on Facebook. We first present a means for providing transparency into the information responsible for inferences drawn by data-driven models. We then introduce the “cloaking device”—a mechanism for users to inhibit the use of particular pieces of information in inference. Using these analytical tools we ask two main questions: (1) How much information must users cloak to significantly affect inferences about their personal traits? We find that usually users must cloak only a small portion of their actions to inhibit inference. We also find that, encouragingly, false-positive inferences are significantly easier to cloak than true-positive inferences. (2) Can firms change their modeling behavior to make cloaking more difficult? The answer is a definitive yes. We demonstrate a simple modeling change that requires users to cloak substantially more information to affect the inferences drawn. The upshot is that organizations can provide transparency and control even into complicated, predictive model-driven inferences, but they also can make control easier or harder for their users. PMID:28933942

Contextual Information and Verifying Inferences from Conversations.

ERIC Educational Resources Information Center

Dubitsky, Tony

Research was conducted to investigate the effects of contextual information on the speed and accuracy with which two general classes of inferences were verified by readers. These types of inferences were based on information in conversations that were or were not topically ambiguous, depending upon the amount of available contextual information.…

A review of causal inference for biomedical informatics

PubMed Central

Kleinberg, Samantha; Hripcsak, George

2011-01-01

Causality is an important concept throughout the health sciences and is particularly vital for informatics work such as finding adverse drug events or risk factors for disease using electronic health records. While philosophers and scientists working for centuries on formalizing what makes something a cause have not reached a consensus, new methods for inference show that we can make progress in this area in many practical cases. This article reviews core concepts in understanding and identifying causality and then reviews current computational methods for inference and explanation, focusing on inference from large-scale observational data. While the problem is not fully solved, we show that graphical models and Granger causality provide useful frameworks for inference and that a more recent approach based on temporal logic addresses some of the limitations of these methods. PMID:21782035

Inconsistencies in spontaneous and intentional trait inferences.

PubMed

Ma, Ning; Vandekerckhove, Marie; Baetens, Kris; Van Overwalle, Frank; Seurinck, Ruth; Fias, Wim

2012-11-01

This study explores the fMRI correlates of observers making trait inferences about other people under conflicting social cues. Participants were presented with several behavioral descriptions involving an agent that implied a particular trait. The last behavior was either consistent or inconsistent with the previously implied trait. This was done under instructions that elicited either spontaneous trait inferences ('read carefully') or intentional trait inferences ('infer a trait'). The results revealed that when the behavioral descriptions violated earlier trait implications, regardless of instruction, the medial prefrontal cortex (mPFC) was more strongly recruited as well as the domain-general conflict network including the posterior medial frontal cortex (pmFC) and the right prefrontal cortex (rPFC). These latter two areas were more strongly activated under intentional than spontaneous instructions. These findings suggest that when trait-relevant behavioral information is inconsistent, not only is activity increased in the mentalizing network responsible for trait processing, but control is also passed to a higher level conflict monitoring network in order to detect and resolve the contradiction.

Consumer psychology: categorization, inferences, affect, and persuasion.

PubMed

Loken, Barbara

2006-01-01

This chapter reviews research on consumer psychology with emphasis on the topics of categorization, inferences, affect, and persuasion. The chapter reviews theory-based empirical research during the period 1994-2004. Research on categorization includes empirical research on brand categories, goals as organizing frameworks and motivational bases for judgments, and self-based processing. Research on inferences includes numerous types of inferences that are cognitively and/or experienced based. Research on affect includes the effects of mood on processing and cognitive and noncognitive bases for attitudes and intentions. Research on persuasion focuses heavily on the moderating role of elaboration and dual-process models, and includes research on attitude strength responses, advertising responses, and negative versus positive evaluative dimensions.

Bayesian multimodel inference for dose-response studies

USGS Publications Warehouse

Link, W.A.; Albers, P.H.

2007-01-01

Statistical inference in dose?response studies is model-based: The analyst posits a mathematical model of the relation between exposure and response, estimates parameters of the model, and reports conclusions conditional on the model. Such analyses rarely include any accounting for the uncertainties associated with model selection. The Bayesian inferential system provides a convenient framework for model selection and multimodel inference. In this paper we briefly describe the Bayesian paradigm and Bayesian multimodel inference. We then present a family of models for multinomial dose?response data and apply Bayesian multimodel inferential methods to the analysis of data on the reproductive success of American kestrels (Falco sparveriuss) exposed to various sublethal dietary concentrations of methylmercury.

An algebra-based method for inferring gene regulatory networks

PubMed Central

2014-01-01

Background The inference of gene regulatory networks (GRNs) from experimental observations is at the heart of systems biology. This includes the inference of both the network topology and its dynamics. While there are many algorithms available to infer the network topology from experimental data, less emphasis has been placed on methods that infer network dynamics. Furthermore, since the network inference problem is typically underdetermined, it is essential to have the option of incorporating into the inference process, prior knowledge about the network, along with an effective description of the search space of dynamic models. Finally, it is also important to have an understanding of how a given inference method is affected by experimental and other noise in the data used. Results This paper contains a novel inference algorithm using the algebraic framework of Boolean polynomial dynamical systems (BPDS), meeting all these requirements. The algorithm takes as input time series data, including those from network perturbations, such as knock-out mutant strains and RNAi experiments. It allows for the incorporation of prior biological knowledge while being robust to significant levels of noise in the data used for inference. It uses an evolutionary algorithm for local optimization with an encoding of the mathematical models as BPDS. The BPDS framework allows an effective representation of the search space for algebraic dynamic models that improves computational performance. The algorithm is validated with both simulated and experimental microarray expression profile data. Robustness to noise is tested using a published mathematical model of the segment polarity gene network in Drosophila melanogaster. Benchmarking of the algorithm is done by comparison with a spectrum of state-of-the-art network inference methods on data from the synthetic IRMA network to demonstrate that our method has good precision and recall for the network reconstruction task, while also

An algebra-based method for inferring gene regulatory networks.

PubMed

Vera-Licona, Paola; Jarrah, Abdul; Garcia-Puente, Luis David; McGee, John; Laubenbacher, Reinhard

2014-03-26

The inference of gene regulatory networks (GRNs) from experimental observations is at the heart of systems biology. This includes the inference of both the network topology and its dynamics. While there are many algorithms available to infer the network topology from experimental data, less emphasis has been placed on methods that infer network dynamics. Furthermore, since the network inference problem is typically underdetermined, it is essential to have the option of incorporating into the inference process, prior knowledge about the network, along with an effective description of the search space of dynamic models. Finally, it is also important to have an understanding of how a given inference method is affected by experimental and other noise in the data used. This paper contains a novel inference algorithm using the algebraic framework of Boolean polynomial dynamical systems (BPDS), meeting all these requirements. The algorithm takes as input time series data, including those from network perturbations, such as knock-out mutant strains and RNAi experiments. It allows for the incorporation of prior biological knowledge while being robust to significant levels of noise in the data used for inference. It uses an evolutionary algorithm for local optimization with an encoding of the mathematical models as BPDS. The BPDS framework allows an effective representation of the search space for algebraic dynamic models that improves computational performance. The algorithm is validated with both simulated and experimental microarray expression profile data. Robustness to noise is tested using a published mathematical model of the segment polarity gene network in Drosophila melanogaster. Benchmarking of the algorithm is done by comparison with a spectrum of state-of-the-art network inference methods on data from the synthetic IRMA network to demonstrate that our method has good precision and recall for the network reconstruction task, while also predicting several of the

Correlated randomness and switching phenomena

NASA Astrophysics Data System (ADS)

Stanley, H. E.; Buldyrev, S. V.; Franzese, G.; Havlin, S.; Mallamace, F.; Kumar, P.; Plerou, V.; Preis, T.

2010-08-01

One challenge of biology, medicine, and economics is that the systems treated by these serious scientific disciplines have no perfect metronome in time and no perfect spatial architecture-crystalline or otherwise. Nonetheless, as if by magic, out of nothing but randomness one finds remarkably fine-tuned processes in time and remarkably fine-tuned structures in space. Further, many of these processes and structures have the remarkable feature of “switching” from one behavior to another as if by magic. The past century has, philosophically, been concerned with placing aside the human tendency to see the universe as a fine-tuned machine. Here we will address the challenge of uncovering how, through randomness (albeit, as we shall see, strongly correlated randomness), one can arrive at some of the many spatial and temporal patterns in biology, medicine, and economics and even begin to characterize the switching phenomena that enables a system to pass from one state to another. Inspired by principles developed by A. Nihat Berker and scores of other statistical physicists in recent years, we discuss some applications of correlated randomness to understand switching phenomena in various fields. Specifically, we present evidence from experiments and from computer simulations supporting the hypothesis that water’s anomalies are related to a switching point (which is not unlike the “tipping point” immortalized by Malcolm Gladwell), and that the bubbles in economic phenomena that occur on all scales are not “outliers” (another Gladwell immortalization). Though more speculative, we support the idea of disease as arising from some kind of yet-to-be-understood complex switching phenomenon, by discussing data on selected examples, including heart disease and Alzheimer disease.

Identification and Inference for Econometric Models

NASA Astrophysics Data System (ADS)

Andrews, Donald W. K.; Stock, James H.

2005-07-01

This volume contains the papers presented in honor of the lifelong achievements of Thomas J. Rothenberg on the occasion of his retirement. The authors of the chapters include many of the leading econometricians of our day, and the chapters address topics of current research significance in econometric theory. The chapters cover four themes: identification and efficient estimation in econometrics, asymptotic approximations to the distributions of econometric estimators and tests, inference involving potentially nonstationary time series, such as processes that might have a unit autoregressive root, and nonparametric and semiparametric inference. Several of the chapters provide overviews and treatments of basic conceptual issues, while others advance our understanding of the properties of existing econometric procedures and/or propose new ones. Specific topics include identification in nonlinear models, inference with weak instruments, tests for nonstationary in time series and panel data, generalized empirical likelihood estimation, and the bootstrap.

Single board system for fuzzy inference

NASA Technical Reports Server (NTRS)

Symon, James R.; Watanabe, Hiroyuki

1991-01-01

The very large scale integration (VLSI) implementation of a fuzzy logic inference mechanism allows the use of rule-based control and decision making in demanding real-time applications. Researchers designed a full custom VLSI inference engine. The chip was fabricated using CMOS technology. The chip consists of 688,000 transistors of which 476,000 are used for RAM memory. The fuzzy logic inference engine board system incorporates the custom designed integrated circuit into a standard VMEbus environment. The Fuzzy Logic system uses Transistor-Transistor Logic (TTL) parts to provide the interface between the Fuzzy chip and a standard, double height VMEbus backplane, allowing the chip to perform application process control through the VMEbus host. High level C language functions hide details of the hardware system interface from the applications level programmer. The first version of the board was installed on a robot at Oak Ridge National Laboratory in January of 1990.

Bion and Tustin: the autistic phenomena.

PubMed

Korbivcher, Celia Fix

2013-08-01

This article examines the implications of the proposal of autistic transformations within the general context of Bion's theory of Transformations. The aim is to confirm the coherence of this proposal of autistic transformations within the overall structure of Bion's theory of Transformations. She examines the relation between emotional links and their negatives, particularly -K. She questions in which of the dimensions of the mind the autistic phenomena are located, the relation between autistic phenomena and beta elements, and where to place them in the Grid. The author tries to form metapsychological support for the incorporation of the autistic area in Bion's theory of Transformations. She argues that, despite the incongruence and imprecision of this incorporation, such autistic phenomena cannot be excluded from the complexus of the human mind and should therefore be accounted for in Bion's transformations. She discusses the idea that the theory of transformations includes the field of the neurosis and psychosis and deals with emotions, whereas the autistic area is dominated by sensations. The author asks how to add the autistic area to Bion's theory. Clinical material of a child for whom the non-psychotic part of the personality predominates and who presents autistic nuclei provides material for the discussion. Copyright © 2013 Institute of Psychoanalysis.

Bayesian Inference and Online Learning in Poisson Neuronal Networks.

PubMed

Huang, Yanping; Rao, Rajesh P N

2016-08-01

Motivated by the growing evidence for Bayesian computation in the brain, we show how a two-layer recurrent network of Poisson neurons can perform both approximate Bayesian inference and learning for any hidden Markov model. The lower-layer sensory neurons receive noisy measurements of hidden world states. The higher-layer neurons infer a posterior distribution over world states via Bayesian inference from inputs generated by sensory neurons. We demonstrate how such a neuronal network with synaptic plasticity can implement a form of Bayesian inference similar to Monte Carlo methods such as particle filtering. Each spike in a higher-layer neuron represents a sample of a particular hidden world state. The spiking activity across the neural population approximates the posterior distribution over hidden states. In this model, variability in spiking is regarded not as a nuisance but as an integral feature that provides the variability necessary for sampling during inference. We demonstrate how the network can learn the likelihood model, as well as the transition probabilities underlying the dynamics, using a Hebbian learning rule. We present results illustrating the ability of the network to perform inference and learning for arbitrary hidden Markov models.

Human Inferences about Sequences: A Minimal Transition Probability Model

PubMed Central

2016-01-01

The brain constantly infers the causes of the inputs it receives and uses these inferences to generate statistical expectations about future observations. Experimental evidence for these expectations and their violations include explicit reports, sequential effects on reaction times, and mismatch or surprise signals recorded in electrophysiology and functional MRI. Here, we explore the hypothesis that the brain acts as a near-optimal inference device that constantly attempts to infer the time-varying matrix of transition probabilities between the stimuli it receives, even when those stimuli are in fact fully unpredictable. This parsimonious Bayesian model, with a single free parameter, accounts for a broad range of findings on surprise signals, sequential effects and the perception of randomness. Notably, it explains the pervasive asymmetry between repetitions and alternations encountered in those studies. Our analysis suggests that a neural machinery for inferring transition probabilities lies at the core of human sequence knowledge. PMID:28030543

Inferring Passenger Denial Behavior of Taxi Drivers from Large-Scale Taxi Traces.

PubMed

Zhang, Sihai; Wang, Zhiyang

2016-01-01

How to understand individual human actions is a fundamental question to modern science, which drives and incurs many social, technological, racial, religious and economic phenomena. Human dynamics tries to reveal the temporal pattern and internal mechanism of human actions in letter or electronic communications, from the perspective of continuous interactions among friends or acquaintances. For interactions between stranger to stranger, taxi industry provide fruitful phenomina and evidence to investigate the action decisions. In fact, one striking disturbing events commonly reported in taxi industry is passenger refusing or denial, whose reasons vary, including skin color, blind passenger, being a foreigner or too close destination, religion reasons and anti specific nationality, so that complaints about taxi passenger refusing have to be concerned and processed carefully by local governments. But more universal factors for this phenomena are of great significance, which might be fulfilled by big data research to obtain novel insights in this question. In this paper, we demonstrate the big data analytics application in revealing novel insights from massive taxi trace data, which, for the first time, validates the passengers denial in taxi industry and estimates the denial ratio in Beijing city. We first quantify the income differentiation facts among taxi drivers. Then we find out that choosing the drop-off places also contributes to the high income for taxi drivers, compared to the previous explanation of mobility intelligence. Moreover, we propose the pick-up, drop-off and grid diversity concepts and related diversity analysis suggest that, high income taxi drivers will deny passengers in some situations, so as to choose the passengers' destination they prefer. Finally we design an estimation method for denial ratio and infer that high income taxi drivers will deny passengers with 8.52% likelihood in Beijing. Our work exhibits the power of big data analysis in

Inferring Passenger Denial Behavior of Taxi Drivers from Large-Scale Taxi Traces

PubMed Central

Zhang, Sihai; Wang, Zhiyang

2016-01-01

How to understand individual human actions is a fundamental question to modern science, which drives and incurs many social, technological, racial, religious and economic phenomena. Human dynamics tries to reveal the temporal pattern and internal mechanism of human actions in letter or electronic communications, from the perspective of continuous interactions among friends or acquaintances. For interactions between stranger to stranger, taxi industry provide fruitful phenomina and evidence to investigate the action decisions. In fact, one striking disturbing events commonly reported in taxi industry is passenger refusing or denial, whose reasons vary, including skin color, blind passenger, being a foreigner or too close destination, religion reasons and anti specific nationality, so that complaints about taxi passenger refusing have to be concerned and processed carefully by local governments. But more universal factors for this phenomena are of great significance, which might be fulfilled by big data research to obtain novel insights in this question. In this paper, we demonstrate the big data analytics application in revealing novel insights from massive taxi trace data, which, for the first time, validates the passengers denial in taxi industry and estimates the denial ratio in Beijing city. We first quantify the income differentiation facts among taxi drivers. Then we find out that choosing the drop-off places also contributes to the high income for taxi drivers, compared to the previous explanation of mobility intelligence. Moreover, we propose the pick-up, drop-off and grid diversity concepts and related diversity analysis suggest that, high income taxi drivers will deny passengers in some situations, so as to choose the passengers’ destination they prefer. Finally we design an estimation method for denial ratio and infer that high income taxi drivers will deny passengers with 8.52% likelihood in Beijing. Our work exhibits the power of big data analysis in

Data-driven inference for the spatial scan statistic.

PubMed

Almeida, Alexandre C L; Duarte, Anderson R; Duczmal, Luiz H; Oliveira, Fernando L P; Takahashi, Ricardo H C

2011-08-02

Kulldorff's spatial scan statistic for aggregated area maps searches for clusters of cases without specifying their size (number of areas) or geographic location in advance. Their statistical significance is tested while adjusting for the multiple testing inherent in such a procedure. However, as is shown in this work, this adjustment is not done in an even manner for all possible cluster sizes. A modification is proposed to the usual inference test of the spatial scan statistic, incorporating additional information about the size of the most likely cluster found. A new interpretation of the results of the spatial scan statistic is done, posing a modified inference question: what is the probability that the null hypothesis is rejected for the original observed cases map with a most likely cluster of size k, taking into account only those most likely clusters of size k found under null hypothesis for comparison? This question is especially important when the p-value computed by the usual inference process is near the alpha significance level, regarding the correctness of the decision based in this inference. A practical procedure is provided to make more accurate inferences about the most likely cluster found by the spatial scan statistic.

Reinforcement and inference in cross-situational word learning.

PubMed

Tilles, Paulo F C; Fontanari, José F

2013-01-01

Cross-situational word learning is based on the notion that a learner can determine the referent of a word by finding something in common across many observed uses of that word. Here we propose an adaptive learning algorithm that contains a parameter that controls the strength of the reinforcement applied to associations between concurrent words and referents, and a parameter that regulates inference, which includes built-in biases, such as mutual exclusivity, and information of past learning events. By adjusting these parameters so that the model predictions agree with data from representative experiments on cross-situational word learning, we were able to explain the learning strategies adopted by the participants of those experiments in terms of a trade-off between reinforcement and inference. These strategies can vary wildly depending on the conditions of the experiments. For instance, for fast mapping experiments (i.e., the correct referent could, in principle, be inferred in a single observation) inference is prevalent, whereas for segregated contextual diversity experiments (i.e., the referents are separated in groups and are exhibited with members of their groups only) reinforcement is predominant. Other experiments are explained with more balanced doses of reinforcement and inference.

Research into Surface Wave Phenomena in Sedimentary Basins.

DTIC Science & Technology

1981-12-31

150 km of the southerly extension of the Overthrust Belt, 350 km of the Green River Basin paralleling the Uinta Mountains and 150 km across the Front...WEIDLINGER ASSOCIATES O300 SAND HiLL ROAD BUILDING 4, SUITE 245 MENLO PARK, CALIFORNIA 9462 RESEARCH INTO SURFACE WAVE PHENOMENA IN SEDIMENTARY BASINS BY...PARK, CALIFORNIA 94025 ! I RESEARCH INTO SURFACE WAVE PHENOMENA IN SEDIMENTARY BASINS I Dy G.L. Wojcik J. Isenberg F. Ma E. Richardson Prepared for

Visualizing Chemical Phenomena in Microdroplets

ERIC Educational Resources Information Center

Lee, Sunghee; Wiener, Joseph

2011-01-01

Phenomena that occur in microdroplets are described to the undergraduate chemistry community. Droplets having a diameter in the micrometer range can have unique and interesting properties, which arise because of their small size and, especially, their high surface area-to-volume ratio. Students are generally unfamiliar with the characteristics of…

Double jeopardy in inferring cognitive processes

PubMed Central

Fific, Mario

2014-01-01

Inferences we make about underlying cognitive processes can be jeopardized in two ways due to problematic forms of aggregation. First, averaging across individuals is typically considered a very useful tool for removing random variability. The threat is that averaging across subjects leads to averaging across different cognitive strategies, thus harming our inferences. The second threat comes from the construction of inadequate research designs possessing a low diagnostic accuracy of cognitive processes. For that reason we introduced the systems factorial technology (SFT), which has primarily been designed to make inferences about underlying processing order (serial, parallel, coactive), stopping rule (terminating, exhaustive), and process dependency. SFT proposes that the minimal research design complexity to learn about n number of cognitive processes should be equal to 2n. In addition, SFT proposes that (a) each cognitive process should be controlled by a separate experimental factor, and (b) The saliency levels of all factors should be combined in a full factorial design. In the current study, the author cross combined the levels of jeopardies in a 2 × 2 analysis, leading to four different analysis conditions. The results indicate a decline in the diagnostic accuracy of inferences made about cognitive processes due to the presence of each jeopardy in isolation and when combined. The results warrant the development of more individual subject analyses and the utilization of full-factorial (SFT) experimental designs. PMID:25374545

Nanoflares, Spicules, and Other Small-Scale Dynamic Phenomena on the Sun

NASA Technical Reports Server (NTRS)

Klimchuk, James

2010-01-01

There is abundant evidence of highly dynamic phenomena occurring on very small scales in the solar atmosphere. For example, the observed pr operties of many coronal loops can only be explained if the loops are bundles of unresolved strands that are heated impulsively by nanoflares. Type II spicules recently discovered by Hinode are an example of small-scale impulsive events occurring in the chromosphere. The exist ence of these and other small-scale phenomena is not surprising given the highly structured nature of the magnetic field that is revealed by photospheric observations. Dynamic phenomena also occur on much lar ger scales, including coronal jets, flares, and CMEs. It is tempting to suggest that these different phenomena are all closely related and represent a continuous distribution of sizes and energies. However, this is a dangerous over simplification in my opinion. While it is tru e that the phenomena all involve "magnetic reconnection" (the changin g of field line connectivity) in some form, how this occurs depends s trongly on the magnetic geometry. A nanoflare resulting from the interaction of tangled magnetic strands within a confined coronal loop is much different from a major flare occurring at the current sheet form ed when a CME rips open an active region. I will review the evidence for ubiquitous small-scale dynamic phenomena on the Sun and discuss wh y different phenomena are not all fundamentally the same.

A quantum probability account of order effects in inference.

PubMed

Trueblood, Jennifer S; Busemeyer, Jerome R

2011-01-01

Order of information plays a crucial role in the process of updating beliefs across time. In fact, the presence of order effects makes a classical or Bayesian approach to inference difficult. As a result, the existing models of inference, such as the belief-adjustment model, merely provide an ad hoc explanation for these effects. We postulate a quantum inference model for order effects based on the axiomatic principles of quantum probability theory. The quantum inference model explains order effects by transforming a state vector with different sequences of operators for different orderings of information. We demonstrate this process by fitting the quantum model to data collected in a medical diagnostic task and a jury decision-making task. To further test the quantum inference model, a new jury decision-making experiment is developed. Using the results of this experiment, we compare the quantum inference model with two versions of the belief-adjustment model, the adding model and the averaging model. We show that both the quantum model and the adding model provide good fits to the data. To distinguish the quantum model from the adding model, we develop a new experiment involving extreme evidence. The results from this new experiment suggest that the adding model faces limitations when accounting for tasks involving extreme evidence, whereas the quantum inference model does not. Ultimately, we argue that the quantum model provides a more coherent account for order effects that was not possible before. Copyright © 2011 Cognitive Science Society, Inc.

Vector analysis of postcardiotomy behavioral phenomena.

PubMed

Caston, J C; Miller, W C; Felber, W J

1975-04-01

The classification of postcardiotomy behavioral phenomena in Figure 1 is proposed for use as a clinical instrument to analyze etiological determinants. The utilization of a vector analysis analogy inherently denies absolutism. Classifications A-P are presented as prototypes of certain ratio imbalances of the metabolic, hemodynamic, environmental, and psychic vectors. Such a system allows for change from one type to another according to the individuality of the patient and the highly specific changes in his clinical presentation. A vector analysis also allows for infinite intermediary ratio imbalances between classification types as a function of time. Thus, postcardiotomy behavioral phenomena could be viewed as the vector summation of hemodynamic, metabolic, environmental, and psychic processes at a given point in time. Elaboration of unknown determinants in this complex syndrome appears to be task for the future.

Wolf-Rayet phenomena

NASA Technical Reports Server (NTRS)

Conti, P. S.

1982-01-01

The properties of stars showing Wolf-Rayet phenomena are outlined along with the direction of future work. Emphasis is placed on the characteristics of W-R spectra. Specifically the following topics are covered: the absolute visual magnitudes; the heterogeneity of WN spectra; the existence of transition type spectra and compositions the mass loss rates; and the existence of very luminous and possibly very massive W-R stars. Also, a brief overview of current understanding of the theoretical aspects of stellar evolution and stellar winds and the various scenarios that have been proposed to understand W-R spectra are included.

Training for Fostering Knowledge Co-Construction from Collaborative Inference-Drawing

ERIC Educational Resources Information Center

Deiglmayr, Anne; Spada, Hans

2011-01-01

Groups typically have difficulties drawing inferences that integrate individuals' unique information (collaborative inferences) and thus yield a true assembly bonus. An experiment with 36 dyads of university-level students in four training conditions showed, particularly in untrained dyads, that collaborative inferences were less likely to be…

Limited utility of residue masking for positive-selection inference.

PubMed

Spielman, Stephanie J; Dawson, Eric T; Wilke, Claus O

2014-09-01

Errors in multiple sequence alignments (MSAs) can reduce accuracy in positive-selection inference. Therefore, it has been suggested to filter MSAs before conducting further analyses. One widely used filter, Guidance, allows users to remove MSA positions aligned with low confidence. However, Guidance's utility in positive-selection inference has been disputed in the literature. We have conducted an extensive simulation-based study to characterize fully how Guidance impacts positive-selection inference, specifically for protein-coding sequences of realistic divergence levels. We also investigated whether novel scoring algorithms, which phylogenetically corrected confidence scores, and a new gap-penalization score-normalization scheme improved Guidance's performance. We found that no filter, including original Guidance, consistently benefitted positive-selection inferences. Moreover, all improvements detected were exceedingly minimal, and in certain circumstances, Guidance-based filters worsened inferences. © The Author 2014. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Neural correlates of species-typical illogical cognitive bias in human inference.

PubMed

Ogawa, Akitoshi; Yamazaki, Yumiko; Ueno, Kenichi; Cheng, Kang; Iriki, Atsushi

2010-09-01

The ability to think logically is a hallmark of human intelligence, yet our innate inferential abilities are marked by implicit biases that often lead to illogical inference. For example, given AB ("if A then B"), people frequently but fallaciously infer the inverse, BA. This mode of inference, called symmetry, is logically invalid because, although it may be true, it is not necessarily true. Given pairs of conditional relations, such as AB and BC, humans reflexively perform two additional modes of inference: transitivity, whereby one (validly) infers AC; and equivalence, whereby one (invalidly) infers CA. In sharp contrast, nonhuman animals can handle transitivity but can rarely be made to acquire symmetry or equivalence. In the present study, human subjects performed logical and illogical inferences about the relations between abstract, visually presented figures while their brain activation was monitored with fMRI. The prefrontal, medial frontal, and intraparietal cortices were activated during all modes of inference. Additional activation in the precuneus and posterior parietal cortex was observed during transitivity and equivalence, which may reflect the need to retrieve the intermediate stimulus (B) from memory. Surprisingly, the patterns of brain activation in illogical and logical inference were very similar. We conclude that the observed inference-related fronto-parietal network is adapted for processing categorical, but not logical, structures of association among stimuli. Humans might prefer categorization over the memorization of logical structures in order to minimize the cognitive working memory load when processing large volumes of information.

Statistical Inference at Work: Statistical Process Control as an Example

ERIC Educational Resources Information Center

Bakker, Arthur; Kent, Phillip; Derry, Jan; Noss, Richard; Hoyles, Celia

2008-01-01

To characterise statistical inference in the workplace this paper compares a prototypical type of statistical inference at work, statistical process control (SPC), with a type of statistical inference that is better known in educational settings, hypothesis testing. Although there are some similarities between the reasoning structure involved in…

Inferring Demographic History Using Two-Locus Statistics.

PubMed

Ragsdale, Aaron P; Gutenkunst, Ryan N

2017-06-01

Population demographic history may be learned from contemporary genetic variation data. Methods based on aggregating the statistics of many single loci into an allele frequency spectrum (AFS) have proven powerful, but such methods ignore potentially informative patterns of linkage disequilibrium (LD) between neighboring loci. To leverage such patterns, we developed a composite-likelihood framework for inferring demographic history from aggregated statistics of pairs of loci. Using this framework, we show that two-locus statistics are more sensitive to demographic history than single-locus statistics such as the AFS. In particular, two-locus statistics escape the notorious confounding of depth and duration of a bottleneck, and they provide a means to estimate effective population size based on the recombination rather than mutation rate. We applied our approach to a Zambian population of Drosophila melanogaster Notably, using both single- and two-locus statistics, we inferred a substantially lower ancestral effective population size than previous works and did not infer a bottleneck history. Together, our results demonstrate the broad potential for two-locus statistics to enable powerful population genetic inference. Copyright © 2017 by the Genetics Society of America.

Transport phenomena in porous media

NASA Astrophysics Data System (ADS)

Bear, Jacob; Corapcioglu, M. Yavuz

The Advanced Study Institute on Fundamentals of Transport Phenomena in Porous Media, held July 14-23, 1985 in Newark, Del. and directed by Jacob Bear (Israel Institute of Technology, Haifa) and M. Yavuz Corapcioglu (City College of New York), under the auspices of NATO, was a sequel to the NATO Advanced Study Institute (ASI) held in 1982 (proceedings published as Fundamentals of Transport Phenomena in Porous Media, J. Bear, and M.Y. Corapcioglu (Ed.), Martinus Nijhoff, Dordrecht, the Netherlands, 1984). The meeting was attended by 106 participants and lecturers from 21 countries.As in the first NATO/ASI, the objective of this meeting—which was a combination of a conference of experts and a teaching institute— was to present and discuss selected topics of transport in porous media. In selecting topics and lecturers, an attempt was made to bridge the gap that sometimes exists between research and practice. An effort was also made to demonstrate the unified approach to the transport of mass of a fluid phase, components of a fluid phase, momentum, and heat in a porous medium domain. The void space may be occupied by a single fluid phase or by a number of such phases; each fluid may constitute a multicomponent system; the solid matrix may be deformable; and the whole process of transport in the system may take place under nonisothermal conditions, with or without phase changes. Such phenomena are encountered in a variety of disciplines, e.g., petroleum engineering, civil engineering (in connection with groundwater flow and contamination), soil mechanics, and chemical engineering. One of the goals of the 1985 NATO/ASI, as in the 1982 institute, was to bring together experts from all these disciplines and enhance communication among them.

Brain Imaging, Forward Inference, and Theories of Reasoning

PubMed Central

Heit, Evan

2015-01-01

This review focuses on the issue of how neuroimaging studies address theoretical accounts of reasoning, through the lens of the method of forward inference (Henson, 2005, 2006). After theories of deductive and inductive reasoning are briefly presented, the method of forward inference for distinguishing between psychological theories based on brain imaging evidence is critically reviewed. Brain imaging studies of reasoning, comparing deductive and inductive arguments, comparing meaningful versus non-meaningful material, investigating hemispheric localization, and comparing conditional and relational arguments, are assessed in light of the method of forward inference. Finally, conclusions are drawn with regard to future research opportunities. PMID:25620926

Brain imaging, forward inference, and theories of reasoning.

PubMed

Heit, Evan

2014-01-01

This review focuses on the issue of how neuroimaging studies address theoretical accounts of reasoning, through the lens of the method of forward inference (Henson, 2005, 2006). After theories of deductive and inductive reasoning are briefly presented, the method of forward inference for distinguishing between psychological theories based on brain imaging evidence is critically reviewed. Brain imaging studies of reasoning, comparing deductive and inductive arguments, comparing meaningful versus non-meaningful material, investigating hemispheric localization, and comparing conditional and relational arguments, are assessed in light of the method of forward inference. Finally, conclusions are drawn with regard to future research opportunities.

A prior-based integrative framework for functional transcriptional regulatory network inference

PubMed Central

Siahpirani, Alireza F.

2017-01-01

Abstract Transcriptional regulatory networks specify regulatory proteins controlling the context-specific expression levels of genes. Inference of genome-wide regulatory networks is central to understanding gene regulation, but remains an open challenge. Expression-based network inference is among the most popular methods to infer regulatory networks, however, networks inferred from such methods have low overlap with experimentally derived (e.g. ChIP-chip and transcription factor (TF) knockouts) networks. Currently we have a limited understanding of this discrepancy. To address this gap, we first develop a regulatory network inference algorithm, based on probabilistic graphical models, to integrate expression with auxiliary datasets supporting a regulatory edge. Second, we comprehensively analyze our and other state-of-the-art methods on different expression perturbation datasets. Networks inferred by integrating sequence-specific motifs with expression have substantially greater agreement with experimentally derived networks, while remaining more predictive of expression than motif-based networks. Our analysis suggests natural genetic variation as the most informative perturbation for network inference, and, identifies core TFs whose targets are predictable from expression. Multiple reasons make the identification of targets of other TFs difficult, including network architecture and insufficient variation of TF mRNA level. Finally, we demonstrate the utility of our inference algorithm to infer stress-specific regulatory networks and for regulator prioritization. PMID:27794550

Computational statistics using the Bayesian Inference Engine

NASA Astrophysics Data System (ADS)

Weinberg, Martin D.

2013-09-01

This paper introduces the Bayesian Inference Engine (BIE), a general parallel, optimized software package for parameter inference and model selection. This package is motivated by the analysis needs of modern astronomical surveys and the need to organize and reuse expensive derived data. The BIE is the first platform for computational statistics designed explicitly to enable Bayesian update and model comparison for astronomical problems. Bayesian update is based on the representation of high-dimensional posterior distributions using metric-ball-tree based kernel density estimation. Among its algorithmic offerings, the BIE emphasizes hybrid tempered Markov chain Monte Carlo schemes that robustly sample multimodal posterior distributions in high-dimensional parameter spaces. Moreover, the BIE implements a full persistence or serialization system that stores the full byte-level image of the running inference and previously characterized posterior distributions for later use. Two new algorithms to compute the marginal likelihood from the posterior distribution, developed for and implemented in the BIE, enable model comparison for complex models and data sets. Finally, the BIE was designed to be a collaborative platform for applying Bayesian methodology to astronomy. It includes an extensible object-oriented and easily extended framework that implements every aspect of the Bayesian inference. By providing a variety of statistical algorithms for all phases of the inference problem, a scientist may explore a variety of approaches with a single model and data implementation. Additional technical details and download details are available from http://www.astro.umass.edu/bie. The BIE is distributed under the GNU General Public License.

Inferring explicit weighted consensus networks to represent alternative evolutionary histories

PubMed Central

2013-01-01

Background The advent of molecular biology techniques and constant increase in availability of genetic material have triggered the development of many phylogenetic tree inference methods. However, several reticulate evolution processes, such as horizontal gene transfer and hybridization, have been shown to blur the species evolutionary history by causing discordance among phylogenies inferred from different genes. Methods To tackle this problem, we hereby describe a new method for inferring and representing alternative (reticulate) evolutionary histories of species as an explicit weighted consensus network which can be constructed from a collection of gene trees with or without prior knowledge of the species phylogeny. Results We provide a way of building a weighted phylogenetic network for each of the following reticulation mechanisms: diploid hybridization, intragenic recombination and complete or partial horizontal gene transfer. We successfully tested our method on some synthetic and real datasets to infer the above-mentioned evolutionary events which may have influenced the evolution of many species. Conclusions Our weighted consensus network inference method allows one to infer, visualize and validate statistically major conflicting signals induced by the mechanisms of reticulate evolution. The results provided by the new method can be used to represent the inferred conflicting signals by means of explicit and easy-to-interpret phylogenetic networks. PMID:24359207

Membrane Transport Phenomena (MTP)

NASA Technical Reports Server (NTRS)

Mason, Larry W.

1997-01-01

The third semi-annual period of the MTP project has been involved with performing experiments using the Membrane Transport Apparatus (MTA), development of analysis techniques for the experiment results, analytical modeling of the osmotic transport phenomena, and completion of a DC-9 microgravity flight to test candidate fluid cell geometries. Preparations were also made for the MTP Science Concept Review (SCR), held on 13 June 1997 at Lockheed Martin Astronautics in Denver. These activities are detailed in the report.

Statistical learning and selective inference.

PubMed

Taylor, Jonathan; Tibshirani, Robert J

2015-06-23

We describe the problem of "selective inference." This addresses the following challenge: Having mined a set of data to find potential associations, how do we properly assess the strength of these associations? The fact that we have "cherry-picked"--searched for the strongest associations--means that we must set a higher bar for declaring significant the associations that we see. This challenge becomes more important in the era of big data and complex statistical modeling. The cherry tree (dataset) can be very large and the tools for cherry picking (statistical learning methods) are now very sophisticated. We describe some recent new developments in selective inference and illustrate their use in forward stepwise regression, the lasso, and principal components analysis.

Eighty phenomena about the self: representation, evaluation, regulation, and change

PubMed Central

Thagard, Paul; Wood, Joanne V.

2015-01-01

We propose a new approach for examining self-related aspects and phenomena. The approach includes (1) a taxonomy and (2) an emphasis on multiple levels of mechanisms. The taxonomy categorizes approximately eighty self-related phenomena according to three primary functions involving the self: representing, effecting, and changing. The representing self encompasses the ways in which people depict themselves, either to themselves or to others (e.g., self-concepts, self-presentation). The effecting self concerns ways in which people facilitate or limit their own traits and behaviors (e.g., self-enhancement, self-regulation). The changing self is less time-limited than the effecting self; it concerns phenomena that involve lasting alterations in how people represent and control themselves (e.g., self-expansion, self-development). Each self-related phenomenon within these three categories may be examined at four levels of interacting mechanisms (social, individual, neural, and molecular). We illustrate our approach by focusing on seven self-related phenomena. PMID:25870574

Inference of effective river properties from remotely sensed observations of water surface

NASA Astrophysics Data System (ADS)

Garambois, Pierre-André; Monnier, Jérôme

2015-05-01

The future SWOT mission (Surface Water and Ocean Topography) will provide cartographic measurements of inland water surfaces (elevation, widths and slope) at an unprecedented spatial and temporal resolution. Given synthetic SWOT like data, forward flow models of hierarchical-complexity are revisited and few inverse formulations are derived and assessed for retrieving the river low flow bathymetry, roughness and discharge (A0, K, Q) . The concept of an effective low flow bathymetry A0 (the real one being never observed) and roughness K , hence an effective river dynamics description, is introduced. The few inverse models elaborated for inferring (A0, K, Q) are analyzed in two contexts: (1) only remotely sensed observations of the water surface (surface elevation, width and slope) are available; (2) one additional water depth measurement (or estimate) is available. The inverse models elaborated are independent of data acquisition dynamics; they are assessed on 91 synthetic test cases sampling a wide range of steady-state river flows (the Froude number varying between 0.05 and 0.5 for 1 km reaches) and in the case of a flood on the Garonne River (France) characterized by large spatio-temporal variabilities. It is demonstrated that the most complete shallow-water like model allowing to separate the roughness and bathymetry terms is the so-called low Froude model. In Case (1), the resulting RMSE on infered discharges are on the order of 15% for first guess errors larger than 50%. An important feature of the present inverse methods is the fairly good accuracy of the discharge Q obtained, while the identified roughness coefficient K includes the measurement errors and the misfit of physics between the real flow and the hypothesis on which the inverse models rely; the later neglecting the unobserved temporal variations of the flow and the inertia effects. A compensation phenomena between the indentifiedvalues of K and the unobserved bathymetry A0 is highlighted, while the

CONFERENCES AND SYMPOSIA: Long-lived light phenomena in the atmosphere

NASA Astrophysics Data System (ADS)

Smirnov, Boris M.

1994-05-01

The state of knowledge of long-lived light phenomena in the atmosphere is reviewed in the light of contributions to the International Interdisciplinary Congress on Unsolved Problems of Atmospheric Electricity, September 1993, Salzburg, Austria; and the First International Workshop on Unidentified Atmospheric Light Phenomena, March 1994, Hessdalen, Norway.

About recent star formation rates inferences

NASA Astrophysics Data System (ADS)

Cerviño, M.; Bongiovanni, A.; Hidalgo, S.

2017-03-01

Star Formation Rate (SFR) inferences are based in the so-called constant SFR approximation, where synthesis models are require to provide a calibration; we aims to study the key points of such approximation to produce accurate SFR inferences. We use the intrinsic algebra used in synthesis models, and we explore how SFR can be inferred from the integrated light without any assumption about the underling Star Formation history (SFH). We show that the constant SFR approximation is actually a simplified expression of more deeper characteristics of synthesis models: It is a characterization of the evolution of single stellar populations (SSPs), acting the SSPs as sensitivity curve over different measures of the SFH can be obtained. As results, we find that (1) the best age to calibrate SFR indices is the age of the observed system (i.e. about 13 Gyr for z = 0 systems); (2) constant SFR and steady-state luminosities are not requirements to calibrate the SFR ; (3) it is not possible to define a SFR single time scale over which the recent SFH is averaged, and we suggest to use typical SFR indices (ionizing flux, UV fluxes) together with no typical ones (optical/IR fluxes) to correct the SFR from the contribution of the old component of the SFH, we show how to use galaxy colors to quote age ranges where the recent component of the SFH is stronger/softer than the older component. Particular values of SFR calibrations are (almost) not affect by this work, but the meaning of what is obtained by SFR inferences does. In our framework, results as the correlation of SFR time scales with galaxy colors, or the sensitivity of different SFR indices to sort and long scale variations in the SFH, fit naturally. In addition, the present framework provides a theoretical guideline to optimize the available information from data/numerical experiments to improve the accuracy of SFR inferences. More info en Cerviño, Bongiovanni & Hidalgo A&A 588, 108C (2016)

Investigation of Hypervelocity Impact Phenomena Using Real-Time Concurrent Diagnostics

NASA Astrophysics Data System (ADS)

Mihaly, Jonathan Michael

Hypervelocity impact of meteoroids and orbital debris poses a serious and growing threat to spacecraft. To study hypervelocity impact phenomena, a comprehensive ensemble of real-time concurrently operated diagnostics has been developed and implemented in the Small Particle Hypervelocity Impact Range (SPHIR) facility. This suite of simultaneously operated instrumentation provides multiple complementary measurements that facilitate the characterization of many impact phenomena in a single experiment. The investigation of hypervelocity impact phenomena described in this work focuses on normal impacts of 1.8 mm nylon 6/6 cylinder projectiles and variable thickness aluminum targets. The SPHIR facility two-stage light-gas gun is capable of routinely launching 5.5 mg nylon impactors to speeds of 5 to 7 km/s. Refinement of legacy SPHIR operation procedures and the investigation of first-stage pressure have improved the velocity performance of the facility, resulting in an increase in average impact velocity of at least 0.57 km/s. Results for the perforation area indicate the considered range of target thicknesses represent multiple regimes describing the non-monotonic scaling of target perforation with decreasing target thickness. The laser side-lighting (LSL) system has been developed to provide ultra-high-speed shadowgraph images of the impact event. This novel optical technique is demonstrated to characterize the propagation velocity and two-dimensional optical density of impact-generated debris clouds. Additionally, a debris capture system is located behind the target during every experiment to provide complementary information regarding the trajectory distribution and penetration depth of individual debris particles. The utilization of a coherent, collimated illumination source in the LSL system facilitates the simultaneous measurement of impact phenomena with near-IR and UV-vis spectrograph systems. Comparison of LSL images to concurrent IR results indicates two

The probabilistic convolution tree: efficient exact Bayesian inference for faster LC-MS/MS protein inference.

PubMed

Serang, Oliver

2014-01-01

Exact Bayesian inference can sometimes be performed efficiently for special cases where a function has commutative and associative symmetry of its inputs (called "causal independence"). For this reason, it is desirable to exploit such symmetry on big data sets. Here we present a method to exploit a general form of this symmetry on probabilistic adder nodes by transforming those probabilistic adder nodes into a probabilistic convolution tree with which dynamic programming computes exact probabilities. A substantial speedup is demonstrated using an illustration example that can arise when identifying splice forms with bottom-up mass spectrometry-based proteomics. On this example, even state-of-the-art exact inference algorithms require a runtime more than exponential in the number of splice forms considered. By using the probabilistic convolution tree, we reduce the runtime to O(k log(k)2) and the space to O(k log(k)) where k is the number of variables joined by an additive or cardinal operator. This approach, which can also be used with junction tree inference, is applicable to graphs with arbitrary dependency on counting variables or cardinalities and can be used on diverse problems and fields like forward error correcting codes, elemental decomposition, and spectral demixing. The approach also trivially generalizes to multiple dimensions.

The Probabilistic Convolution Tree: Efficient Exact Bayesian Inference for Faster LC-MS/MS Protein Inference

PubMed Central

Serang, Oliver

2014-01-01

Exact Bayesian inference can sometimes be performed efficiently for special cases where a function has commutative and associative symmetry of its inputs (called “causal independence”). For this reason, it is desirable to exploit such symmetry on big data sets. Here we present a method to exploit a general form of this symmetry on probabilistic adder nodes by transforming those probabilistic adder nodes into a probabilistic convolution tree with which dynamic programming computes exact probabilities. A substantial speedup is demonstrated using an illustration example that can arise when identifying splice forms with bottom-up mass spectrometry-based proteomics. On this example, even state-of-the-art exact inference algorithms require a runtime more than exponential in the number of splice forms considered. By using the probabilistic convolution tree, we reduce the runtime to and the space to where is the number of variables joined by an additive or cardinal operator. This approach, which can also be used with junction tree inference, is applicable to graphs with arbitrary dependency on counting variables or cardinalities and can be used on diverse problems and fields like forward error correcting codes, elemental decomposition, and spectral demixing. The approach also trivially generalizes to multiple dimensions. PMID:24626234

Memory-Based Simple Heuristics as Attribute Substitution: Competitive Tests of Binary Choice Inference Models.

PubMed

Honda, Hidehito; Matsuka, Toshihiko; Ueda, Kazuhiro

2017-05-01

Some researchers on binary choice inference have argued that people make inferences based on simple heuristics, such as recognition, fluency, or familiarity. Others have argued that people make inferences based on available knowledge. To examine the boundary between heuristic and knowledge usage, we examine binary choice inference processes in terms of attribute substitution in heuristic use (Kahneman & Frederick, 2005). In this framework, it is predicted that people will rely on heuristic or knowledge-based inference depending on the subjective difficulty of the inference task. We conducted competitive tests of binary choice inference models representing simple heuristics (fluency and familiarity heuristics) and knowledge-based inference models. We found that a simple heuristic model (especially a familiarity heuristic model) explained inference patterns for subjectively difficult inference tasks, and that a knowledge-based inference model explained subjectively easy inference tasks. These results were consistent with the predictions of the attribute substitution framework. Issues on usage of simple heuristics and psychological processes are discussed. Copyright © 2016 Cognitive Science Society, Inc.

Reward inference by primate prefrontal and striatal neurons.

PubMed

Pan, Xiaochuan; Fan, Hongwei; Sawa, Kosuke; Tsuda, Ichiro; Tsukada, Minoru; Sakagami, Masamichi

2014-01-22

The brain contains multiple yet distinct systems involved in reward prediction. To understand the nature of these processes, we recorded single-unit activity from the lateral prefrontal cortex (LPFC) and the striatum in monkeys performing a reward inference task using an asymmetric reward schedule. We found that neurons both in the LPFC and in the striatum predicted reward values for stimuli that had been previously well experienced with set reward quantities in the asymmetric reward task. Importantly, these LPFC neurons could predict the reward value of a stimulus using transitive inference even when the monkeys had not yet learned the stimulus-reward association directly; whereas these striatal neurons did not show such an ability. Nevertheless, because there were two set amounts of reward (large and small), the selected striatal neurons were able to exclusively infer the reward value (e.g., large) of one novel stimulus from a pair after directly experiencing the alternative stimulus with the other reward value (e.g., small). Our results suggest that although neurons that predict reward value for old stimuli in the LPFC could also do so for new stimuli via transitive inference, those in the striatum could only predict reward for new stimuli via exclusive inference. Moreover, the striatum showed more complex functions than was surmised previously for model-free learning.

Reward Inference by Primate Prefrontal and Striatal Neurons

PubMed Central

Pan, Xiaochuan; Fan, Hongwei; Sawa, Kosuke; Tsuda, Ichiro; Tsukada, Minoru

2014-01-01

The brain contains multiple yet distinct systems involved in reward prediction. To understand the nature of these processes, we recorded single-unit activity from the lateral prefrontal cortex (LPFC) and the striatum in monkeys performing a reward inference task using an asymmetric reward schedule. We found that neurons both in the LPFC and in the striatum predicted reward values for stimuli that had been previously well experienced with set reward quantities in the asymmetric reward task. Importantly, these LPFC neurons could predict the reward value of a stimulus using transitive inference even when the monkeys had not yet learned the stimulus–reward association directly; whereas these striatal neurons did not show such an ability. Nevertheless, because there were two set amounts of reward (large and small), the selected striatal neurons were able to exclusively infer the reward value (e.g., large) of one novel stimulus from a pair after directly experiencing the alternative stimulus with the other reward value (e.g., small). Our results suggest that although neurons that predict reward value for old stimuli in the LPFC could also do so for new stimuli via transitive inference, those in the striatum could only predict reward for new stimuli via exclusive inference. Moreover, the striatum showed more complex functions than was surmised previously for model-free learning. PMID:24453328

Optical Inference Machines

DTIC Science & Technology

1988-06-27

de olf nessse end Id e ;-tl Sb ieeI smleo) ,Optical Artificial Intellegence ; Optical inference engines; Optical logic; Optical informationprocessing...common. They arise in areas such as expert systems and other artificial intelligence systems. In recent years, the computer science language PROLOG has...cal processors should in principle be well suited for : I artificial intelligence applications. In recent years, symbolic logic processing. , the

Evolutionary inference via the Poisson Indel Process.

PubMed

Bouchard-Côté, Alexandre; Jordan, Michael I

2013-01-22

We address the problem of the joint statistical inference of phylogenetic trees and multiple sequence alignments from unaligned molecular sequences. This problem is generally formulated in terms of string-valued evolutionary processes along the branches of a phylogenetic tree. The classic evolutionary process, the TKF91 model [Thorne JL, Kishino H, Felsenstein J (1991) J Mol Evol 33(2):114-124] is a continuous-time Markov chain model composed of insertion, deletion, and substitution events. Unfortunately, this model gives rise to an intractable computational problem: The computation of the marginal likelihood under the TKF91 model is exponential in the number of taxa. In this work, we present a stochastic process, the Poisson Indel Process (PIP), in which the complexity of this computation is reduced to linear. The Poisson Indel Process is closely related to the TKF91 model, differing only in its treatment of insertions, but it has a global characterization as a Poisson process on the phylogeny. Standard results for Poisson processes allow key computations to be decoupled, which yields the favorable computational profile of inference under the PIP model. We present illustrative experiments in which Bayesian inference under the PIP model is compared with separate inference of phylogenies and alignments.

Evolutionary inference via the Poisson Indel Process

PubMed Central

Bouchard-Côté, Alexandre; Jordan, Michael I.

2013-01-01

We address the problem of the joint statistical inference of phylogenetic trees and multiple sequence alignments from unaligned molecular sequences. This problem is generally formulated in terms of string-valued evolutionary processes along the branches of a phylogenetic tree. The classic evolutionary process, the TKF91 model [Thorne JL, Kishino H, Felsenstein J (1991) J Mol Evol 33(2):114–124] is a continuous-time Markov chain model composed of insertion, deletion, and substitution events. Unfortunately, this model gives rise to an intractable computational problem: The computation of the marginal likelihood under the TKF91 model is exponential in the number of taxa. In this work, we present a stochastic process, the Poisson Indel Process (PIP), in which the complexity of this computation is reduced to linear. The Poisson Indel Process is closely related to the TKF91 model, differing only in its treatment of insertions, but it has a global characterization as a Poisson process on the phylogeny. Standard results for Poisson processes allow key computations to be decoupled, which yields the favorable computational profile of inference under the PIP model. We present illustrative experiments in which Bayesian inference under the PIP model is compared with separate inference of phylogenies and alignments. PMID:23275296

Active interoceptive inference and the emotional brain

PubMed Central

Friston, Karl J.

2016-01-01

We review a recent shift in conceptions of interoception and its relationship to hierarchical inference in the brain. The notion of interoceptive inference means that bodily states are regulated by autonomic reflexes that are enslaved by descending predictions from deep generative models of our internal and external milieu. This re-conceptualization illuminates several issues in cognitive and clinical neuroscience with implications for experiences of selfhood and emotion. We first contextualize interoception in terms of active (Bayesian) inference in the brain, highlighting its enactivist (embodied) aspects. We then consider the key role of uncertainty or precision and how this might translate into neuromodulation. We next examine the implications for understanding the functional anatomy of the emotional brain, surveying recent observations on agranular cortex. Finally, we turn to theoretical issues, namely, the role of interoception in shaping a sense of embodied self and feelings. We will draw links between physiological homoeostasis and allostasis, early cybernetic ideas of predictive control and hierarchical generative models in predictive processing. The explanatory scope of interoceptive inference ranges from explanations for autism and depression, through to consciousness. We offer a brief survey of these exciting developments. This article is part of the themed issue ‘Interoception beyond homeostasis: affect, cognition and mental health’. PMID:28080966

Permutation inference for the general linear model

PubMed Central

Winkler, Anderson M.; Ridgway, Gerard R.; Webster, Matthew A.; Smith, Stephen M.; Nichols, Thomas E.

2014-01-01

Permutation methods can provide exact control of false positives and allow the use of non-standard statistics, making only weak assumptions about the data. With the availability of fast and inexpensive computing, their main limitation would be some lack of flexibility to work with arbitrary experimental designs. In this paper we report on results on approximate permutation methods that are more flexible with respect to the experimental design and nuisance variables, and conduct detailed simulations to identify the best method for settings that are typical for imaging research scenarios. We present a generic framework for permutation inference for complex general linear models (glms) when the errors are exchangeable and/or have a symmetric distribution, and show that, even in the presence of nuisance effects, these permutation inferences are powerful while providing excellent control of false positives in a wide range of common and relevant imaging research scenarios. We also demonstrate how the inference on glm parameters, originally intended for independent data, can be used in certain special but useful cases in which independence is violated. Detailed examples of common neuroimaging applications are provided, as well as a complete algorithm – the “randomise” algorithm – for permutation inference with the glm. PMID:24530839

Investigation of Statistical Inference Methodologies Through Scale Model Propagation Experiments

DTIC Science & Technology

2015-09-30

statistical inference methodologies for ocean- acoustic problems by investigating and applying statistical methods to data collected from scale-model...to begin planning experiments for statistical inference applications. APPROACH In the ocean acoustics community over the past two decades...solutions for waveguide parameters. With the introduction of statistical inference to the field of ocean acoustics came the desire to interpret marginal

Observational data needs for plasma phenomena

NASA Technical Reports Server (NTRS)

Niedner, M. B., Jr.

1981-01-01

Bright comets display a rich variety of interesting plasma phenomena which occur over an enormous range of spatial scales, and which require different observational techniques to be studied effectively. Wide-angle photography of high time resolution is probably the best method of studying the phenomenon of largest known scale: the plasma tail disconnection event (DE), which has been attributed to magnetic reconnection at interplanetary sector boundary crossings. These structures usually accelerate as they recede from the head region and observed velocities are typically in the range 50 V km/s. They are often visible for several days following the time of disconnection, and are sometimes seen out past 0.2 AU from the cometary head. The following areas pertaining to plasma phenomena in the ionoshere are addressed: the existence, size, and heliocentric distance variations of the contact surface, and the observational signatures of magnetic reconnection at sector boundary crossings.

Resistive switching phenomena: A review of statistical physics approaches

DOE PAGES

Lee, Jae Sung; Lee, Shinbuhm; Noh, Tae Won

2015-08-31

Here we report that resistive switching (RS) phenomena are reversible changes in the metastable resistance state induced by external electric fields. After discovery ~50 years ago, RS phenomena have attracted great attention due to their potential application in next-generation electrical devices. Considerable research has been performed to understand the physical mechanisms of RS and explore the feasibility and limits of such devices. There have also been several reviews on RS that attempt to explain the microscopic origins of how regions that were originally insulators can change into conductors. However, little attention has been paid to the most important factor inmore » determining resistance: how conducting local regions are interconnected. Here, we provide an overview of the underlying physics behind connectivity changes in highly conductive regions under an electric field. We first classify RS phenomena according to their characteristic current–voltage curves: unipolar, bipolar, and threshold switchings. Second, we outline the microscopic origins of RS in oxides, focusing on the roles of oxygen vacancies: the effect of concentration, the mechanisms of channel formation and rupture, and the driving forces of oxygen vacancies. Third, we review RS studies from the perspective of statistical physics to understand connectivity change in RS phenomena. We discuss percolation model approaches and the theory for the scaling behaviors of numerous transport properties observed in RS. Fourth, we review various switching-type conversion phenomena in RS: bipolar-unipolar, memory-threshold, figure-of-eight, and counter-figure-of-eight conversions. Finally, we review several related technological issues, such as improvement in high resistance fluctuations, sneak-path problems, and multilevel switching problems.« less

Exploring Beginning Inference with Novice Grade 7 Students

ERIC Educational Resources Information Center

Watson, Jane M.

2008-01-01

This study documented efforts to facilitate ideas of beginning inference in novice grade 7 students. A design experiment allowed modified teaching opportunities in light of observation of components of a framework adapted from that developed by Pfannkuch for teaching informal inference with box plots. Box plots were replaced by hat plots, a…

Inferring Centrality from Network Snapshots

NASA Astrophysics Data System (ADS)

Shao, Haibin; Mesbahi, Mehran; Li, Dewei; Xi, Yugeng

2017-01-01

The topology and dynamics of a complex network shape its functionality. However, the topologies of many large-scale networks are either unavailable or incomplete. Without the explicit knowledge of network topology, we show how the data generated from the network dynamics can be utilised to infer the tempo centrality, which is proposed to quantify the influence of nodes in a consensus network. We show that the tempo centrality can be used to construct an accurate estimate of both the propagation rate of influence exerted on consensus networks and the Kirchhoff index of the underlying graph. Moreover, the tempo centrality also encodes the disturbance rejection of nodes in a consensus network. Our findings provide an approach to infer the performance of a consensus network from its temporal data.

System Support for Forensic Inference

NASA Astrophysics Data System (ADS)

Gehani, Ashish; Kirchner, Florent; Shankar, Natarajan

Digital evidence is playing an increasingly important role in prosecuting crimes. The reasons are manifold: financially lucrative targets are now connected online, systems are so complex that vulnerabilities abound and strong digital identities are being adopted, making audit trails more useful. If the discoveries of forensic analysts are to hold up to scrutiny in court, they must meet the standard for scientific evidence. Software systems are currently developed without consideration of this fact. This paper argues for the development of a formal framework for constructing “digital artifacts” that can serve as proxies for physical evidence; a system so imbued would facilitate sound digital forensic inference. A case study involving a filesystem augmentation that provides transparent support for forensic inference is described.

Diffusion phenomena of cells and biomolecules in microfluidic devices.

PubMed

Yildiz-Ozturk, Ece; Yesil-Celiktas, Ozlem

2015-09-01

Biomicrofluidics is an emerging field at the cross roads of microfluidics and life sciences which requires intensive research efforts in terms of introducing appropriate designs, production techniques, and analysis. The ultimate goal is to deliver innovative and cost-effective microfluidic devices to biotech, biomedical, and pharmaceutical industries. Therefore, creating an in-depth understanding of the transport phenomena of cells and biomolecules becomes vital and concurrently poses significant challenges. The present article outlines the recent advancements in diffusion phenomena of cells and biomolecules by highlighting transport principles from an engineering perspective, cell responses in microfluidic devices with emphases on diffusion- and flow-based microfluidic gradient platforms, macroscopic and microscopic approaches for investigating the diffusion phenomena of biomolecules, microfluidic platforms for the delivery of these molecules, as well as the state of the art in biological applications of mammalian cell responses and diffusion of biomolecules.

Design-based and model-based inference in surveys of freshwater mollusks

USGS Publications Warehouse

Dorazio, R.M.

1999-01-01

Well-known concepts in statistical inference and sampling theory are used to develop recommendations for planning and analyzing the results of quantitative surveys of freshwater mollusks. Two methods of inference commonly used in survey sampling (design-based and model-based) are described and illustrated using examples relevant in surveys of freshwater mollusks. The particular objectives of a survey and the type of information observed in each unit of sampling can be used to help select the sampling design and the method of inference. For example, the mean density of a sparsely distributed population of mollusks can be estimated with higher precision by using model-based inference or by using design-based inference with adaptive cluster sampling than by using design-based inference with conventional sampling. More experience with quantitative surveys of natural assemblages of freshwater mollusks is needed to determine the actual benefits of different sampling designs and inferential procedures.

Maximum caliber inference of nonequilibrium processes

NASA Astrophysics Data System (ADS)

Otten, Moritz; Stock, Gerhard

2010-07-01

Thirty years ago, Jaynes suggested a general theoretical approach to nonequilibrium statistical mechanics, called maximum caliber (MaxCal) [Annu. Rev. Phys. Chem. 31, 579 (1980)]. MaxCal is a variational principle for dynamics in the same spirit that maximum entropy is a variational principle for equilibrium statistical mechanics. Motivated by the success of maximum entropy inference methods for equilibrium problems, in this work the MaxCal formulation is applied to the inference of nonequilibrium processes. That is, given some time-dependent observables of a dynamical process, one constructs a model that reproduces these input data and moreover, predicts the underlying dynamics of the system. For example, the observables could be some time-resolved measurements of the folding of a protein, which are described by a few-state model of the free energy landscape of the system. MaxCal then calculates the probabilities of an ensemble of trajectories such that on average the data are reproduced. From this probability distribution, any dynamical quantity of the system can be calculated, including population probabilities, fluxes, or waiting time distributions. After briefly reviewing the formalism, the practical numerical implementation of MaxCal in the case of an inference problem is discussed. Adopting various few-state models of increasing complexity, it is demonstrated that the MaxCal principle indeed works as a practical method of inference: The scheme is fairly robust and yields correct results as long as the input data are sufficient. As the method is unbiased and general, it can deal with any kind of time dependency such as oscillatory transients and multitime decays.

Bayesian Inference for Functional Dynamics Exploring in fMRI Data.

PubMed

Guo, Xuan; Liu, Bing; Chen, Le; Chen, Guantao; Pan, Yi; Zhang, Jing

2016-01-01

This paper aims to review state-of-the-art Bayesian-inference-based methods applied to functional magnetic resonance imaging (fMRI) data. Particularly, we focus on one specific long-standing challenge in the computational modeling of fMRI datasets: how to effectively explore typical functional interactions from fMRI time series and the corresponding boundaries of temporal segments. Bayesian inference is a method of statistical inference which has been shown to be a powerful tool to encode dependence relationships among the variables with uncertainty. Here we provide an introduction to a group of Bayesian-inference-based methods for fMRI data analysis, which were designed to detect magnitude or functional connectivity change points and to infer their functional interaction patterns based on corresponding temporal boundaries. We also provide a comparison of three popular Bayesian models, that is, Bayesian Magnitude Change Point Model (BMCPM), Bayesian Connectivity Change Point Model (BCCPM), and Dynamic Bayesian Variable Partition Model (DBVPM), and give a summary of their applications. We envision that more delicate Bayesian inference models will be emerging and play increasingly important roles in modeling brain functions in the years to come.

Gene-network inference by message passing

NASA Astrophysics Data System (ADS)

Braunstein, A.; Pagnani, A.; Weigt, M.; Zecchina, R.

2008-01-01

The inference of gene-regulatory processes from gene-expression data belongs to the major challenges of computational systems biology. Here we address the problem from a statistical-physics perspective and develop a message-passing algorithm which is able to infer sparse, directed and combinatorial regulatory mechanisms. Using the replica technique, the algorithmic performance can be characterized analytically for artificially generated data. The algorithm is applied to genome-wide expression data of baker's yeast under various environmental conditions. We find clear cases of combinatorial control, and enrichment in common functional annotations of regulated genes and their regulators.

Ancestral inference from haplotypes and mutations.

PubMed

Griffiths, Robert C; Tavaré, Simon

2018-04-25

We consider inference about the history of a sample of DNA sequences, conditional upon the haplotype counts and the number of segregating sites observed at the present time. After deriving some theoretical results in the coalescent setting, we implement rejection sampling and importance sampling schemes to perform the inference. The importance sampling scheme addresses an extension of the Ewens Sampling Formula for a configuration of haplotypes and the number of segregating sites in the sample. The implementations include both constant and variable population size models. The methods are illustrated by two human Y chromosome datasets. Copyright © 2018. Published by Elsevier Inc.

New Phenomena in NC Field Theory and Emergent Spacetime Geometry

NASA Astrophysics Data System (ADS)

Ydri, Badis

2010-10-01

We give a brief review of two nonperturbative phenomena typical of noncommutative field theory which are known to lead to the perturbative instability known as the UV-IR mixing. The first phenomena concerns the emergence/evaporation of spacetime geometry in matrix models which describe perturbative noncommutative gauge theory on fuzzy backgrounds. In particular we show that the transition from a geometrical background to a matrix phase makes the description of noncommutative gauge theory in terms of fields via the Weyl map only valid below a critical value g*. The second phenomena concerns the appearance of a nonuniform ordered phase in noncommutative scalar φ4 field theory and the spontaneous symmetry breaking of translational/rotational invariance which happens even in two dimensions. We argue that this phenomena also originates in the underlying matrix degrees of freedom of the noncommutative field theory. Furthermore it is conjectured that in addition to the usual WF fixed point at θ = 0 there must exist a novel fixed point at θ = ∞ corresponding to the quartic hermitian matrix model.

Critical phenomena on k -booklets

NASA Astrophysics Data System (ADS)

Grassberger, Peter

2017-01-01

We define a "k -booklet" to be a set of k semi-infinite planes with -∞ phenomena: self-avoiding random walks, the Ising model, and percolation. For k =2 , a booklet is equivalent to a single infinite lattice, and for k =1 to a semi-infinite lattice. In both these cases the systems show standard critical phenomena. This is not so for k ≥3 . Self-avoiding walks starting at y =0 show a first-order transition at a shifted critical point, with no power-behaved scaling laws. The Ising model and percolation show hybrid transitions, i.e., the scaling laws of the standard models coexist with discontinuities of the order parameter at y ≈0 , and the critical points are not shifted. In the case of the Ising model, ergodicity is already broken at T =Tc , and not only for T

Algorithm of OMA for large-scale orthology inference

PubMed Central

Roth, Alexander CJ; Gonnet, Gaston H; Dessimoz, Christophe

2008-01-01

Background OMA is a project that aims to identify orthologs within publicly available, complete genomes. With 657 genomes analyzed to date, OMA is one of the largest projects of its kind. Results The algorithm of OMA improves upon standard bidirectional best-hit approach in several respects: it uses evolutionary distances instead of scores, considers distance inference uncertainty, includes many-to-many orthologous relations, and accounts for differential gene losses. Herein, we describe in detail the algorithm for inference of orthology and provide the rationale for parameter selection through multiple tests. Conclusion OMA contains several novel improvement ideas for orthology inference and provides a unique dataset of large-scale orthology assignments. PMID:19055798

[Causal inference in medicine--a historical view in epidemiology].

PubMed

Tsuda, T; Babazono, A; Mino, Y; Matsuoka, H; Yamamoto, E

1996-07-01

Changes of causal inference concepts in medicine, especially those having to do with chronic diseases, were reviewed. The review is divided into five sections. First, several articles on the increased academic acceptance of observational research are cited. Second, the definitions of confounder and effect modifier concepts are explained. Third, the debate over the so-called "criteria for causal inference" was discussed. Many articles have pointed out various problems related to the lack of logical bases for standard criteria, however, such criteria continue to be misapplied in Japan. Fourth, the Popperian and verificationist concepts of causal inference are summarized. Lastly, a recent controversy on meta-analysis is explained. Causal inference plays an important role in epidemiologic theory and medicine. However, because this concept has not been well-introduced in Japan, there has been much misuse of the concept, especially when used for conventional criteria.

Calcium contained tap water phenomena: students misconception patterns of acids-bases concept

NASA Astrophysics Data System (ADS)

Liliasari, S.; Albaiti, A.; Wahyudi, A.

2018-05-01

Acids and bases concept is very important and fundamental concept in learning chemistry. It is one of the chemistry subjects considered as an abstract and difficult concept to understand. The aim of this research was to explore student’s misconception pattern about acids and bases phenomena in daily life, such as calcium contained tap water. This was a qualitative research with descriptive methods. Participants were 546 undergraduate students of chemistry education and chemistry program, and graduate students of chemistry education in West Java, Indonesia. The test to explore students’ misconception about this phenomena was essay test. The results showed that there were five patterns of students’ misconception in explaining the phenomena of calcium carbonate precipitation on heating tap water. Students used irrelevant concepts in explaining this phenomena, i.e. temporary hardness, coagulation, density, and phase concepts. No students had right answer in explaining this phenomena. This research contributes to design meaningful learning and to achieve better understanding.

Knowledge representation for fuzzy inference aided medical image interpretation.

PubMed

Gal, Norbert; Stoicu-Tivadar, Vasile

2012-01-01

Knowledge defines how an automated system transforms data into information. This paper suggests a representation method of medical imaging knowledge using fuzzy inference systems coded in XML files. The imaging knowledge incorporates features of the investigated objects in linguistic form and inference rules that can transform the linguistic data into information about a possible diagnosis. A fuzzy inference system is used to model the vagueness of the linguistic medical imaging terms. XML files are used to facilitate easy manipulation and deployment of the knowledge into the imaging software. Preliminary results are presented.

Working memory supports inference learning just like classification learning.

PubMed

Craig, Stewart; Lewandowsky, Stephan

2013-08-01

Recent research has found a positive relationship between people's working memory capacity (WMC) and their speed of category learning. To date, only classification-learning tasks have been considered, in which people learn to assign category labels to objects. It is unknown whether learning to make inferences about category features might also be related to WMC. We report data from a study in which 119 participants undertook classification learning and inference learning, and completed a series of WMC tasks. Working memory capacity was positively related to people's classification and inference learning performance.

INFERENCE BUILDING BLOCKS

DTIC Science & Technology

2018-02-15

address the problem that probabilistic inference algorithms are diÿcult and tedious to implement, by expressing them in terms of a small number of...building blocks, which are automatic transformations on probabilistic programs. On one hand, our curation of these building blocks reflects the way human...reasoning with low-level computational optimization, so the speed and accuracy of the generated solvers are competitive with state-of-the-art systems. 15

Foot morphometric phenomena.

PubMed

Agić, Ante

2007-06-01

Knowledge of the foot morphometry is important for proper foot structure and function. Foot structure as a vital part of human body is important for many reasons. The foot anthropometric and morphology phenomena are analyzed together with hidden biomechanical descriptors in order to fully characterize foot functionality. For Croatian student population the scatter data of the individual foot variables were interpolated by multivariate statistics. Foot morphometric descriptors are influenced by many factors, such as life style, climate, and things of great importance in human society. Dominant descriptors related to fit and comfort are determined by the use 3D foot shape and advanced foot biomechanics. Some practical recommendations and conclusions for medical, sportswear and footwear practice are highlighted.

Thermal Wave Phenomena

NASA Technical Reports Server (NTRS)

1999-01-01

This map from the MGS Horizon Sensor Assembly (HORSE) shows middle atmospheric temperatures near the 1 mbar level of Mars between Ls 170 to 175 (approx. July 14 - 23, 1999). Local Mars times between 1:30 and 4:30 AM are included. Infrared radiation measured by the Mars Horizon Sensor Assembly was used to make the map. That device continuously views the 'limb' of Mars in four directions, to help orient the spacecraft instruments to the nadir: straight down.

The map shows thermal wave phenomena that are caused by the large topographic variety of Mars' surface, as well the latitudinally symmetric behavior expected at this time of year near the equinox.

Deduced Inference in the Analysis of Experimental Data

ERIC Educational Resources Information Center

Bird, Kevin D.

2011-01-01

Any set of confidence interval inferences on J - 1 linearly independent contrasts on J means, such as the two comparisons [mu][subscript 1] - [mu][subscript 2] and [mu][subscript 2] - [mu][subscript 3] on 3 means, provides a basis for the deduction of interval inferences on all other contrasts, such as the redundant comparison [mu][subscript 1] -…

Visualization of solidification front phenomena

NASA Technical Reports Server (NTRS)

Workman, Gary L.; Smith, Guy A.

1993-01-01

Directional solidification experiments have been utilized throughout the Materials Processing in Space Program to provide an experimental platform which minimizes variables in solidification experiments. Because of the wide-spread use of this experimental technique in space-based research, it has become apparent that a better understanding of all the phenomena occurring during solidification can be better understood if direct visualization of the solidification interface were possible.

The Role of Visuospatial Resources in Generating Predictive and Bridging Inferences

ERIC Educational Resources Information Center

Fincher-Kiefer, Rebecca; D'Agostino, Paul R.

2004-01-01

It has been suggested that predictive and bridging inferences are generated at different levels of text representation: predictive inferences at a reader's situation model and bridging inferences at a reader's propositional textbase (Fincher-Kiefer, 1993, 1996; McDaniel, Schmalhofer, & Keefe, 2001; Schmalhofer, McDaniel, & Keefe, 2002). Recently,…

Universal Darwinism As a Process of Bayesian Inference.

PubMed

Campbell, John O

2016-01-01

Many of the mathematical frameworks describing natural selection are equivalent to Bayes' Theorem, also known as Bayesian updating. By definition, a process of Bayesian Inference is one which involves a Bayesian update, so we may conclude that these frameworks describe natural selection as a process of Bayesian inference. Thus, natural selection serves as a counter example to a widely-held interpretation that restricts Bayesian Inference to human mental processes (including the endeavors of statisticians). As Bayesian inference can always be cast in terms of (variational) free energy minimization, natural selection can be viewed as comprising two components: a generative model of an "experiment" in the external world environment, and the results of that "experiment" or the "surprise" entailed by predicted and actual outcomes of the "experiment." Minimization of free energy implies that the implicit measure of "surprise" experienced serves to update the generative model in a Bayesian manner. This description closely accords with the mechanisms of generalized Darwinian process proposed both by Dawkins, in terms of replicators and vehicles, and Campbell, in terms of inferential systems. Bayesian inference is an algorithm for the accumulation of evidence-based knowledge. This algorithm is now seen to operate over a wide range of evolutionary processes, including natural selection, the evolution of mental models and cultural evolutionary processes, notably including science itself. The variational principle of free energy minimization may thus serve as a unifying mathematical framework for universal Darwinism, the study of evolutionary processes operating throughout nature.

The anatomy of choice: active inference and agency

PubMed Central

Friston, Karl; Schwartenbeck, Philipp; FitzGerald, Thomas; Moutoussis, Michael; Behrens, Timothy; Dolan, Raymond J.

2013-01-01

This paper considers agency in the setting of embodied or active inference. In brief, we associate a sense of agency with prior beliefs about action and ask what sorts of beliefs underlie optimal behavior. In particular, we consider prior beliefs that action minimizes the Kullback–Leibler (KL) divergence between desired states and attainable states in the future. This allows one to formulate bounded rationality as approximate Bayesian inference that optimizes a free energy bound on model evidence. We show that constructs like expected utility, exploration bonuses, softmax choice rules and optimism bias emerge as natural consequences of this formulation. Previous accounts of active inference have focused on predictive coding and Bayesian filtering schemes for minimizing free energy. Here, we consider variational Bayes as an alternative scheme that provides formal constraints on the computational anatomy of inference and action—constraints that are remarkably consistent with neuroanatomy. Furthermore, this scheme contextualizes optimal decision theory and economic (utilitarian) formulations as pure inference problems. For example, expected utility theory emerges as a special case of free energy minimization, where the sensitivity or inverse temperature (of softmax functions and quantal response equilibria) has a unique and Bayes-optimal solution—that minimizes free energy. This sensitivity corresponds to the precision of beliefs about behavior, such that attainable goals are afforded a higher precision or confidence. In turn, this means that optimal behavior entails a representation of confidence about outcomes that are under an agent's control. PMID:24093015

The anatomy of choice: active inference and agency.

PubMed

Friston, Karl; Schwartenbeck, Philipp; Fitzgerald, Thomas; Moutoussis, Michael; Behrens, Timothy; Dolan, Raymond J

2013-01-01

This paper considers agency in the setting of embodied or active inference. In brief, we associate a sense of agency with prior beliefs about action and ask what sorts of beliefs underlie optimal behavior. In particular, we consider prior beliefs that action minimizes the Kullback-Leibler (KL) divergence between desired states and attainable states in the future. This allows one to formulate bounded rationality as approximate Bayesian inference that optimizes a free energy bound on model evidence. We show that constructs like expected utility, exploration bonuses, softmax choice rules and optimism bias emerge as natural consequences of this formulation. Previous accounts of active inference have focused on predictive coding and Bayesian filtering schemes for minimizing free energy. Here, we consider variational Bayes as an alternative scheme that provides formal constraints on the computational anatomy of inference and action-constraints that are remarkably consistent with neuroanatomy. Furthermore, this scheme contextualizes optimal decision theory and economic (utilitarian) formulations as pure inference problems. For example, expected utility theory emerges as a special case of free energy minimization, where the sensitivity or inverse temperature (of softmax functions and quantal response equilibria) has a unique and Bayes-optimal solution-that minimizes free energy. This sensitivity corresponds to the precision of beliefs about behavior, such that attainable goals are afforded a higher precision or confidence. In turn, this means that optimal behavior entails a representation of confidence about outcomes that are under an agent's control.

Diffusion phenomena of cells and biomolecules in microfluidic devices

PubMed Central

Yildiz-Ozturk, Ece; Yesil-Celiktas, Ozlem

2015-01-01

Biomicrofluidics is an emerging field at the cross roads of microfluidics and life sciences which requires intensive research efforts in terms of introducing appropriate designs, production techniques, and analysis. The ultimate goal is to deliver innovative and cost-effective microfluidic devices to biotech, biomedical, and pharmaceutical industries. Therefore, creating an in-depth understanding of the transport phenomena of cells and biomolecules becomes vital and concurrently poses significant challenges. The present article outlines the recent advancements in diffusion phenomena of cells and biomolecules by highlighting transport principles from an engineering perspective, cell responses in microfluidic devices with emphases on diffusion- and flow-based microfluidic gradient platforms, macroscopic and microscopic approaches for investigating the diffusion phenomena of biomolecules, microfluidic platforms for the delivery of these molecules, as well as the state of the art in biological applications of mammalian cell responses and diffusion of biomolecules. PMID:26180576

Training Inference Making Skills Using a Situation Model Approach Improves Reading Comprehension

PubMed Central

Bos, Lisanne T.; De Koning, Bjorn B.; Wassenburg, Stephanie I.; van der Schoot, Menno

2016-01-01

This study aimed to enhance third and fourth graders’ text comprehension at the situation model level. Therefore, we tested a reading strategy training developed to target inference making skills, which are widely considered to be pivotal to situation model construction. The training was grounded in contemporary literature on situation model-based inference making and addressed the source (text-based versus knowledge-based), type (necessary versus unnecessary for (re-)establishing coherence), and depth of an inference (making single lexical inferences versus combining multiple lexical inferences), as well as the type of searching strategy (forward versus backward). Results indicated that, compared to a control group (n = 51), children who followed the experimental training (n = 67) improved their inference making skills supportive to situation model construction. Importantly, our training also resulted in increased levels of general reading comprehension and motivation. In sum, this study showed that a ‘level of text representation’-approach can provide a useful framework to teach inference making skills to third and fourth graders. PMID:26913014

Using genetic data to strengthen causal inference in observational research.

PubMed

Pingault, Jean-Baptiste; O'Reilly, Paul F; Schoeler, Tabea; Ploubidis, George B; Rijsdijk, Frühling; Dudbridge, Frank

2018-06-05

Causal inference is essential across the biomedical, behavioural and social sciences.By progressing from confounded statistical associations to evidence of causal relationships, causal inference can reveal complex pathways underlying traits and diseases and help to prioritize targets for intervention. Recent progress in genetic epidemiology - including statistical innovation, massive genotyped data sets and novel computational tools for deep data mining - has fostered the intense development of methods exploiting genetic data and relatedness to strengthen causal inference in observational research. In this Review, we describe how such genetically informed methods differ in their rationale, applicability and inherent limitations and outline how they should be integrated in the future to offer a rich causal inference toolbox.

Inferring Centrality from Network Snapshots

PubMed Central

Shao, Haibin; Mesbahi, Mehran; Li, Dewei; Xi, Yugeng

2017-01-01

The topology and dynamics of a complex network shape its functionality. However, the topologies of many large-scale networks are either unavailable or incomplete. Without the explicit knowledge of network topology, we show how the data generated from the network dynamics can be utilised to infer the tempo centrality, which is proposed to quantify the influence of nodes in a consensus network. We show that the tempo centrality can be used to construct an accurate estimate of both the propagation rate of influence exerted on consensus networks and the Kirchhoff index of the underlying graph. Moreover, the tempo centrality also encodes the disturbance rejection of nodes in a consensus network. Our findings provide an approach to infer the performance of a consensus network from its temporal data. PMID:28098166

Differentiable cortical networks for inferences concerning people's intentions versus physical causality.

PubMed

Mason, Robert A; Just, Marcel Adam

2011-02-01

Cortical activity associated with generating an inference was measured using fMRI. Participants read three-sentence passages that differed in whether or not an inference needed to be drawn to understand them. The inference was based on either a protagonist's intention or a physical consequence of a character's action. Activation was expected in Theory of Mind brain regions for the passages based on protagonists' intentions but not for the physical consequence passages. The activation measured in the right temporo-parietal junction was greater in the intentional passages than in the consequence passages, consistent with predictions from a Theory of Mind perspective. In contrast, there was increased occipital activation in the physical inference passages. For both types of passage, the cortical activity related to the reading of the critical inference sentence demonstrated a recruitment of a common inference cortical network. This general inference-related activation appeared bilaterally in the language processing areas (the inferior frontal gyrus, the temporal gyrus, and the angular gyrus), as well as in the medial to superior frontal gyrus, which has been found to be active in Theory of Mind tasks. These findings are consistent with the hypothesis that component areas of the discourse processing network are recruited as needed based on the nature of the inference. A Protagonist monitoring and synthesis network is proposed as a more accurate account for Theory of Mind activation during narrative comprehension. Copyright © 2010 Wiley-Liss, Inc.

Classification versus inference learning contrasted with real-world categories.

PubMed

Jones, Erin L; Ross, Brian H

2011-07-01

Categories are learned and used in a variety of ways, but the research focus has been on classification learning. Recent work contrasting classification with inference learning of categories found important later differences in category performance. However, theoretical accounts differ on whether this is due to an inherent difference between the tasks or to the implementation decisions. The inherent-difference explanation argues that inference learners focus on the internal structure of the categories--what each category is like--while classification learners focus on diagnostic information to predict category membership. In two experiments, using real-world categories and controlling for earlier methodological differences, inference learners learned more about what each category was like than did classification learners, as evidenced by higher performance on a novel classification test. These results suggest that there is an inherent difference between learning new categories by classifying an item versus inferring a feature.

Aging and Predicting Inferences: A Diffusion Model Analysis

ERIC Educational Resources Information Center

McKoon, Gail; Ratcliff, Roger

2013-01-01

In the domain of discourse processing, it has been claimed that older adults (60-0-year-olds) are less likely to encode and remember some kinds of information from texts than young adults. The experiment described here shows that they do make a particular kind of inference to the same extent that college-age adults do. The inferences examined were…

Comparison of the light-flash phenomena observed in space and in laboratory experiments.

PubMed

McNulty, P J; Pease, V P; Bond, V P

1977-01-01

Astronauts on Apollo and Skylab missions have reported observing a variety of visual phenomena when their eyes were closed and adapted to darkness. These observations were studied under controlled conditions during a number of sessions on board Apollo and Skylab spacecraft and the data available to date on these so-called light flashes are in the form of descriptions of the phenomena and frequency of occurrence. Similar visual phenomena have been demonstrated in a number of laboratories by exposing the eyes of human subjects to beams of neutrons, alpha particles, pions and protons. More than one physical mechanism is involved in the laboratory and space phenomena. No direct comparison of the laboratory and space observations has been made by observers who have experienced both. However, the range of visual phenomena observed in the laboratory is consistent with the Apollo and Skylab observations. Measured detection efficiencies can be used to estimate the frequencies with which various phenomena would be observed if that subject was exposed to cosmic rays in space.

Wave phenomena in sunspots

NASA Astrophysics Data System (ADS)

Löhner-Böttcher, Johannes

2016-03-01

Context: The dynamic atmosphere of the Sun exhibits a wealth of magnetohydrodynamic (MHD) waves. In the presence of strong magnetic fields, most spectacular and powerful waves evolve in the sunspot atmosphere. Allover the sunspot area, continuously propagating waves generate strong oscillations in spectral intensity and velocity. The most prominent and fascinating phenomena are the 'umbral flashes' and 'running penumbral waves' as seen in the sunspot chromosphere. Their nature and relation have been under intense discussion in the last decades. Aims: Waves are suggested to propagate upward along the magnetic field lines of sunspots. An observational study is performed to prove or disprove the field-guided nature and coupling of the prevalent umbral and penumbral waves. Comprehensive spectroscopic observations at high resolution shall provide new insights into the wave characteristics and distribution across the sunspot atmosphere. Methods: Two prime sunspot observations were carried out with the Dunn Solar Telescope at the National Solar Observatory in New Mexico and with the Vacuum Tower Telescope at the Teide Observatory on Tenerife. The two-dimensional spectroscopic observations were performed with the interferometric spectrometers IBIS and TESOS. Multiple spectral lines are scanned co-temporally to sample the dynamics at the photospheric and chromospheric layers. The time series (1 - 2.5 h) taken at high spatial and temporal resolution are analyzed according to their evolution in spectral intensities and Doppler velocities. A wavelet analysis was used to obtain the wave power and dominating wave periods. A reconstruction of the magnetic field inclination based on sunspot oscillations was developed. Results and conclusions: Sunspot oscillations occur continuously in spectral intensity and velocity. The obtained wave characteristics of umbral flashes and running penumbral waves strongly support the scenario of slow-mode magnetoacoustic wave propagation along the

IMAGINE: Interstellar MAGnetic field INference Engine

NASA Astrophysics Data System (ADS)

Steininger, Theo

2018-03-01

IMAGINE (Interstellar MAGnetic field INference Engine) performs inference on generic parametric models of the Galaxy. The modular open source framework uses highly optimized tools and technology such as the MultiNest sampler (ascl:1109.006) and the information field theory framework NIFTy (ascl:1302.013) to create an instance of the Milky Way based on a set of parameters for physical observables, using Bayesian statistics to judge the mismatch between measured data and model prediction. The flexibility of the IMAGINE framework allows for simple refitting for newly available data sets and makes state-of-the-art Bayesian methods easily accessible particularly for random components of the Galactic magnetic field.

Stokes phenomena in discrete Painlevé II.

PubMed

Joshi, N; Lustri, C J; Luu, S

2017-02-01

We consider the asymptotic behaviour of the second discrete Painlevé equation in the limit as the independent variable becomes large. Using asymptotic power series, we find solutions that are asymptotically pole-free within some region of the complex plane. These asymptotic solutions exhibit Stokes phenomena, which is typically invisible to classical power series methods. We subsequently apply exponential asymptotic techniques to investigate such phenomena, and obtain mathematical descriptions of the rapid switching behaviour associated with Stokes curves. Through this analysis, we determine the regions of the complex plane in which the asymptotic behaviour is described by a power series expression, and find that the behaviour of these asymptotic solutions shares a number of features with the tronquée and tri-tronquée solutions of the second continuous Painlevé equation.

Stokes phenomena in discrete Painlevé II

PubMed Central

Joshi, N.

2017-01-01

We consider the asymptotic behaviour of the second discrete Painlevé equation in the limit as the independent variable becomes large. Using asymptotic power series, we find solutions that are asymptotically pole-free within some region of the complex plane. These asymptotic solutions exhibit Stokes phenomena, which is typically invisible to classical power series methods. We subsequently apply exponential asymptotic techniques to investigate such phenomena, and obtain mathematical descriptions of the rapid switching behaviour associated with Stokes curves. Through this analysis, we determine the regions of the complex plane in which the asymptotic behaviour is described by a power series expression, and find that the behaviour of these asymptotic solutions shares a number of features with the tronquée and tri-tronquée solutions of the second continuous Painlevé equation. PMID:28293132

Evaluation of Second-Level Inference in fMRI Analysis

PubMed Central

Roels, Sanne P.; Loeys, Tom; Moerkerke, Beatrijs

2016-01-01

We investigate the impact of decisions in the second-level (i.e., over subjects) inferential process in functional magnetic resonance imaging on (1) the balance between false positives and false negatives and on (2) the data-analytical stability, both proxies for the reproducibility of results. Second-level analysis based on a mass univariate approach typically consists of 3 phases. First, one proceeds via a general linear model for a test image that consists of pooled information from different subjects. We evaluate models that take into account first-level (within-subjects) variability and models that do not take into account this variability. Second, one proceeds via inference based on parametrical assumptions or via permutation-based inference. Third, we evaluate 3 commonly used procedures to address the multiple testing problem: familywise error rate correction, False Discovery Rate (FDR) correction, and a two-step procedure with minimal cluster size. Based on a simulation study and real data we find that the two-step procedure with minimal cluster size results in most stable results, followed by the familywise error rate correction. The FDR results in most variable results, for both permutation-based inference and parametrical inference. Modeling the subject-specific variability yields a better balance between false positives and false negatives when using parametric inference. PMID:26819578

Quality of Computationally Inferred Gene Ontology Annotations

PubMed Central

Škunca, Nives; Altenhoff, Adrian; Dessimoz, Christophe

2012-01-01

Gene Ontology (GO) has established itself as the undisputed standard for protein function annotation. Most annotations are inferred electronically, i.e. without individual curator supervision, but they are widely considered unreliable. At the same time, we crucially depend on those automated annotations, as most newly sequenced genomes are non-model organisms. Here, we introduce a methodology to systematically and quantitatively evaluate electronic annotations. By exploiting changes in successive releases of the UniProt Gene Ontology Annotation database, we assessed the quality of electronic annotations in terms of specificity, reliability, and coverage. Overall, we not only found that electronic annotations have significantly improved in recent years, but also that their reliability now rivals that of annotations inferred by curators when they use evidence other than experiments from primary literature. This work provides the means to identify the subset of electronic annotations that can be relied upon—an important outcome given that >98% of all annotations are inferred without direct curation. PMID:22693439

Inferring subunit stoichiometry from single molecule photobleaching

PubMed Central

2013-01-01

Single molecule photobleaching is a powerful tool for determining the stoichiometry of protein complexes. By attaching fluorophores to proteins of interest, the number of associated subunits in a complex can be deduced by imaging single molecules and counting fluorophore photobleaching steps. Because some bleaching steps might be unobserved, the ensemble of steps will be binomially distributed. In this work, it is shown that inferring the true composition of a complex from such data is nontrivial because binomially distributed observations present an ill-posed inference problem. That is, a unique and optimal estimate of the relevant parameters cannot be extracted from the observations. Because of this, a method has not been firmly established to quantify confidence when using this technique. This paper presents a general inference model for interpreting such data and provides methods for accurately estimating parameter confidence. The formalization and methods presented here provide a rigorous analytical basis for this pervasive experimental tool. PMID:23712552

Mathematical Modeling of Diverse Phenomena

NASA Technical Reports Server (NTRS)

Howard, J. C.

1979-01-01

Tensor calculus is applied to the formulation of mathematical models of diverse phenomena. Aeronautics, fluid dynamics, and cosmology are among the areas of application. The feasibility of combining tensor methods and computer capability to formulate problems is demonstrated. The techniques described are an attempt to simplify the formulation of mathematical models by reducing the modeling process to a series of routine operations, which can be performed either manually or by computer.

Optimal design of gene knockout experiments for gene regulatory network inference

PubMed Central

Ud-Dean, S. M. Minhaz; Gunawan, Rudiyanto

2016-01-01

Motivation: We addressed the problem of inferring gene regulatory network (GRN) from gene expression data of knockout (KO) experiments. This inference is known to be underdetermined and the GRN is not identifiable from data. Past studies have shown that suboptimal design of experiments (DOE) contributes significantly to the identifiability issue of biological networks, including GRNs. However, optimizing DOE has received much less attention than developing methods for GRN inference. Results: We developed REDuction of UnCertain Edges (REDUCE) algorithm for finding the optimal gene KO experiment for inferring directed graphs (digraphs) of GRNs. REDUCE employed ensemble inference to define uncertain gene interactions that could not be verified by prior data. The optimal experiment corresponds to the maximum number of uncertain interactions that could be verified by the resulting data. For this purpose, we introduced the concept of edge separatoid which gave a list of nodes (genes) that upon their removal would allow the verification of a particular gene interaction. Finally, we proposed a procedure that iterates over performing KO experiments, ensemble update and optimal DOE. The case studies including the inference of Escherichia coli GRN and DREAM 4 100-gene GRNs, demonstrated the efficacy of the iterative GRN inference. In comparison to systematic KOs, REDUCE could provide much higher information return per gene KO experiment and consequently more accurate GRN estimates. Conclusions: REDUCE represents an enabling tool for tackling the underdetermined GRN inference. Along with advances in gene deletion and automation technology, the iterative procedure brings an efficient and fully automated GRN inference closer to reality. Availability and implementation: MATLAB and Python scripts of REDUCE are available on www.cabsel.ethz.ch/tools/REDUCE. Contact: rudi.gunawan@chem.ethz.ch Supplementary information: Supplementary data are available at Bioinformatics online. PMID

When Some Is Actually All: Scalar Inferences in Face-Threatening Contexts

ERIC Educational Resources Information Center

Bonnefon, Jean-Francois; Feeney, Aidan; Villejoubert, Gaelle

2009-01-01

Accounts of the scalar inference from "some X-ed" to "not all X-ed" are central to the debate between contemporary theories of conversational pragmatics. An important contribution to this debate is to identify contexts that decrease the endorsement rate of the inference. We suggest that the inference is endorsed less often in face-threatening…

Neural Correlates of Species-Typical Illogical Cognitive Bias in Human Inference

ERIC Educational Resources Information Center

Ogawa, Akitoshi; Yamazaki, Yumiko; Ueno, Kenichi; Cheng, Kang; Iriki, Atsushi

2010-01-01

The ability to think logically is a hallmark of human intelligence, yet our innate inferential abilities are marked by implicit biases that often lead to illogical inference. For example, given AB ("if A then B"), people frequently but fallaciously infer the inverse, BA. This mode of inference, called symmetry, is logically invalid because,…

Novel collective phenomena in high-energy proton–proton and proton–nucleus collisions

DOE PAGES

Dusling, Kevin; Li, Wei; Schenke, Björn

2016-01-22

The observation of long-range rapidity correlations among particles in high-multiplicity p–p and p–Pb collisions created new opportunities for investigating novel high-density QCD phenomena in small colliding systems. We also review experimental results related to the study of collective phenomena in small systems at RHIC and the LHC along with the related developments in theory and phenomenology. Finally, perspectives on possible future directions for research are discussed with the aim of exploring emergent QCD phenomena.

Some Phenomena on Negative Inversion Constructions

ERIC Educational Resources Information Center

Sung, Tae-Soo

2013-01-01

We examine the characteristics of NDI (negative degree inversion) and its relation with other inversion phenomena such as SVI (subject-verb inversion) and SAI (subject-auxiliary inversion). The negative element in the NDI construction may be" not," a negative adverbial, or a negative verb. In this respect, NDI has similar licensing…

Bayesian Inference of High-Dimensional Dynamical Ocean Models

NASA Astrophysics Data System (ADS)

Lin, J.; Lermusiaux, P. F. J.; Lolla, S. V. T.; Gupta, A.; Haley, P. J., Jr.

2015-12-01

This presentation addresses a holistic set of challenges in high-dimension ocean Bayesian nonlinear estimation: i) predict the probability distribution functions (pdfs) of large nonlinear dynamical systems using stochastic partial differential equations (PDEs); ii) assimilate data using Bayes' law with these pdfs; iii) predict the future data that optimally reduce uncertainties; and (iv) rank the known and learn the new model formulations themselves. Overall, we allow the joint inference of the state, equations, geometry, boundary conditions and initial conditions of dynamical models. Examples are provided for time-dependent fluid and ocean flows, including cavity, double-gyre and Strait flows with jets and eddies. The Bayesian model inference, based on limited observations, is illustrated first by the estimation of obstacle shapes and positions in fluid flows. Next, the Bayesian inference of biogeochemical reaction equations and of their states and parameters is presented, illustrating how PDE-based machine learning can rigorously guide the selection and discovery of complex ecosystem models. Finally, the inference of multiscale bottom gravity current dynamics is illustrated, motivated in part by classic overflows and dense water formation sites and their relevance to climate monitoring and dynamics. This is joint work with our MSEAS group at MIT.

In defence of model-based inference in phylogeography

PubMed Central

Beaumont, Mark A.; Nielsen, Rasmus; Robert, Christian; Hey, Jody; Gaggiotti, Oscar; Knowles, Lacey; Estoup, Arnaud; Panchal, Mahesh; Corander, Jukka; Hickerson, Mike; Sisson, Scott A.; Fagundes, Nelson; Chikhi, Lounès; Beerli, Peter; Vitalis, Renaud; Cornuet, Jean-Marie; Huelsenbeck, John; Foll, Matthieu; Yang, Ziheng; Rousset, Francois; Balding, David; Excoffier, Laurent

2017-01-01

Recent papers have promoted the view that model-based methods in general, and those based on Approximate Bayesian Computation (ABC) in particular, are flawed in a number of ways, and are therefore inappropriate for the analysis of phylogeographic data. These papers further argue that Nested Clade Phylogeographic Analysis (NCPA) offers the best approach in statistical phylogeography. In order to remove the confusion and misconceptions introduced by these papers, we justify and explain the reasoning behind model-based inference. We argue that ABC is a statistically valid approach, alongside other computational statistical techniques that have been successfully used to infer parameters and compare models in population genetics. We also examine the NCPA method and highlight numerous deficiencies, either when used with single or multiple loci. We further show that the ages of clades are carelessly used to infer ages of demographic events, that these ages are estimated under a simple model of panmixia and population stationarity but are then used under different and unspecified models to test hypotheses, a usage the invalidates these testing procedures. We conclude by encouraging researchers to study and use model-based inference in population genetics. PMID:29284924

Organic Bistable Memory Switching Phenomena in Squarylium-Dye Langmuir-Blodgett Films

NASA Astrophysics Data System (ADS)

Kushida, Masahito; Inomata, Hisao; Miyata, Hiroshi; Harada, Kieko; Saito, Kyoichi; Sugita, Kazuyuki

2003-06-01

We have investigated the relationship between the switching phenomena and H-like aggregates in squarylium-dye Langmuir-Blodgett (SQ LB) films. The current-voltage characteristics of SQ LB films sandwiched between the top gold electrode and the bottom aluminum electrode indicated conductance switching phenomena below the temperature of 100°C but not at 140°C. Current densities suddenly increased at switching voltages between 2 and 4 V. The switching voltage increased as the temperature increased between room temperature and 100°C. Current densities were 50-100 μA/cm2 in a low-impedance state (ON state). A high-impedance state (OFF state) can be recovered by applying a reverse bias, and therefore, these bistable devices are ideal for memory applications. The dependence of conductance switching phenomena and ultraviolet-visible absorption spectra on annealing temperatures was studied. The results revealed that conductance switching phenomena were caused by the presence of H-like aggregates in the SQ LB films.

Effects of Inferred Motive on Evaluations of Nonaccommodative Communication

ERIC Educational Resources Information Center

Gasiorek, Jessica; Giles, Howard

2012-01-01

In two studies, we propose, refine, and test a new model of inferred motive predicting of individuals' reactions to nonaccommodation, defined as communicative behavior that is inappropriately adjusted for participants in an interaction. Inferring a negative motive for others' problematic behavior resulted in significantly less positive evaluations…

Hybrid Optical Inference Machines

DTIC Science & Technology

1991-09-27

with labels. Now, events. a set of facts cal be generated in the dyadic form "u, R 1,2" Eichmann and Caulfield (19] consider the same type of and can...these enceding-schemes. These architectures are-based pri- 19. G. Eichmann and H. J. Caulfield, "Optical Learning (Inference)marily on optical inner

Analogical and category-based inference: a theoretical integration with Bayesian causal models.

PubMed

Holyoak, Keith J; Lee, Hee Seung; Lu, Hongjing

2010-11-01

A fundamental issue for theories of human induction is to specify constraints on potential inferences. For inferences based on shared category membership, an analogy, and/or a relational schema, it appears that the basic goal of induction is to make accurate and goal-relevant inferences that are sensitive to uncertainty. People can use source information at various levels of abstraction (including both specific instances and more general categories), coupled with prior causal knowledge, to build a causal model for a target situation, which in turn constrains inferences about the target. We propose a computational theory in the framework of Bayesian inference and test its predictions (parameter-free for the cases we consider) in a series of experiments in which people were asked to assess the probabilities of various causal predictions and attributions about a target on the basis of source knowledge about generative and preventive causes. The theory proved successful in accounting for systematic patterns of judgments about interrelated types of causal inferences, including evidence that analogical inferences are partially dissociable from overall mapping quality.

Universal Darwinism As a Process of Bayesian Inference

PubMed Central

Campbell, John O.

2016-01-01

Many of the mathematical frameworks describing natural selection are equivalent to Bayes' Theorem, also known as Bayesian updating. By definition, a process of Bayesian Inference is one which involves a Bayesian update, so we may conclude that these frameworks describe natural selection as a process of Bayesian inference. Thus, natural selection serves as a counter example to a widely-held interpretation that restricts Bayesian Inference to human mental processes (including the endeavors of statisticians). As Bayesian inference can always be cast in terms of (variational) free energy minimization, natural selection can be viewed as comprising two components: a generative model of an “experiment” in the external world environment, and the results of that “experiment” or the “surprise” entailed by predicted and actual outcomes of the “experiment.” Minimization of free energy implies that the implicit measure of “surprise” experienced serves to update the generative model in a Bayesian manner. This description closely accords with the mechanisms of generalized Darwinian process proposed both by Dawkins, in terms of replicators and vehicles, and Campbell, in terms of inferential systems. Bayesian inference is an algorithm for the accumulation of evidence-based knowledge. This algorithm is now seen to operate over a wide range of evolutionary processes, including natural selection, the evolution of mental models and cultural evolutionary processes, notably including science itself. The variational principle of free energy minimization may thus serve as a unifying mathematical framework for universal Darwinism, the study of evolutionary processes operating throughout nature. PMID:27375438

Learning and retention through predictive inference and classification.

PubMed

Sakamoto, Yasuaki; Love, Bradley C

2010-12-01

Work in category learning addresses how humans acquire knowledge and, thus, should inform classroom practices. In two experiments, we apply and evaluate intuitions garnered from laboratory-based research in category learning to learning tasks situated in an educational context. In Experiment 1, learning through predictive inference and classification were compared for fifth-grade students using class-related materials. Making inferences about properties of category members and receiving feedback led to the acquisition of both queried (i.e., tested) properties and nonqueried properties that were correlated with a queried property (e.g., even if not queried, students learned about a species' habitat because it correlated with a queried property, like the species' size). In contrast, classifying items according to their species and receiving feedback led to knowledge of only the property most diagnostic of category membership. After multiple-day delay, the fifth-graders who learned through inference selectively retained information about the queried properties, and the fifth-graders who learned through classification retained information about the diagnostic property, indicating a role for explicit evaluation in establishing memories. Overall, inference learning resulted in fewer errors, better retention, and more liking of the categories than did classification learning. Experiment 2 revealed that querying a property only a few times was enough to manifest the full benefits of inference learning in undergraduate students. These results suggest that classroom teaching should emphasize reasoning from the category to multiple properties rather than from a set of properties to the category. (PsycINFO Database Record (c) 2010 APA, all rights reserved).

Genealogical and evolutionary inference with the human Y chromosome.

PubMed

Stumpf, M P; Goldstein, D B

2001-03-02

Population genetics has emerged as a powerful tool for unraveling human history. In addition to the study of mitochondrial and autosomal DNA, attention has recently focused on Y-chromosome variation. Ambiguities and inaccuracies in data analysis, however, pose an important obstacle to further development of the field. Here we review the methods available for genealogical inference using Y-chromosome data. Approaches can be divided into those that do and those that do not use an explicit population model in genealogical inference. We describe the strengths and weaknesses of these model-based and model-free approaches, as well as difficulties associated with the mutation process that affect both methods. In the case of genealogical inference using microsatellite loci, we use coalescent simulations to show that relatively simple generalizations of the mutation process can greatly increase the accuracy of genealogical inference. Because model-free and model-based approaches have different biases and limitations, we conclude that there is considerable benefit in the continued use of both types of approaches.

Functional networks inference from rule-based machine learning models.

PubMed

Lazzarini, Nicola; Widera, Paweł; Williamson, Stuart; Heer, Rakesh; Krasnogor, Natalio; Bacardit, Jaume

2016-01-01

Functional networks play an important role in the analysis of biological processes and systems. The inference of these networks from high-throughput (-omics) data is an area of intense research. So far, the similarity-based inference paradigm (e.g. gene co-expression) has been the most popular approach. It assumes a functional relationship between genes which are expressed at similar levels across different samples. An alternative to this paradigm is the inference of relationships from the structure of machine learning models. These models are able to capture complex relationships between variables, that often are different/complementary to the similarity-based methods. We propose a protocol to infer functional networks from machine learning models, called FuNeL. It assumes, that genes used together within a rule-based machine learning model to classify the samples, might also be functionally related at a biological level. The protocol is first tested on synthetic datasets and then evaluated on a test suite of 8 real-world datasets related to human cancer. The networks inferred from the real-world data are compared against gene co-expression networks of equal size, generated with 3 different methods. The comparison is performed from two different points of view. We analyse the enriched biological terms in the set of network nodes and the relationships between known disease-associated genes in a context of the network topology. The comparison confirms both the biological relevance and the complementary character of the knowledge captured by the FuNeL networks in relation to similarity-based methods and demonstrates its potential to identify known disease associations as core elements of the network. Finally, using a prostate cancer dataset as a case study, we confirm that the biological knowledge captured by our method is relevant to the disease and consistent with the specialised literature and with an independent dataset not used in the inference process. The

Introgression Makes Waves in Inferred Histories of Effective Population Size.

PubMed

Hawks, John

2017-01-01

Human populations have a complex history of introgression and of changing population size. Human genetic variation has been affected by both these processes, so inference of past population size depends upon the pattern of gene flow and introgression among past populations. One remarkable aspect of human population history as inferred from genetics is a consistent "wave" of larger effective population sizes, found in both African and non-African populations, that appears to reflect events prior to the last 100,000 years. I carried out a series of simulations to investigate how introgression and gene flow from genetically divergent ancestral populations affect the inference of ancestral effective population size. Both introgression and gene flow from an extinct, genetically divergent population consistently produce a wave in the history of inferred effective population size. The time and amplitude of the wave reflect the time of origin of the genetically divergent ancestral populations and the strength of introgression or gene flow. These results demonstrate that even small fractions of introgression or gene flow from ancient populations may have visible effects on the inference of effective population size.

Light Phenomena from the Nothing

NASA Astrophysics Data System (ADS)

Teodorani, M.

2004-11-01

The most recent results of scientific investigations on anomalous light phenomena, which were carried out in Hessdalen, Norway, are described and discussed. The data acquired so far show that the phenomenon is a plasma form triggered by piezoelectricity and maintained by electrochemical effects that become effective when a plasma concentration interacts with an atmosphere rich of water vapor and aerosols. It is also shown that the electrochemical mechanism able to permit an efficient confinement of the plasma can explain some peculiar kinematic and structural characteristics too, being the light phenomenon formed by clusters of small light balls, which are occasionally ejected from the core. The mechanism, with which amplitude time-variations of pulsating light phenomena occur, is also described. It is finally shown how, however, some peculiar aspects of the phenomenon, in particular the occurrence of some transiently geometric shapes, cannot be explained using a geophysical standard model. One hypothesis concerning quantum fluctuations of the zero point energy, including a possible interaction with a form of "electromagnetic intelligence", is discussed as a possible speculation, which is ventured in order to suggest to all physical scientists working in this field to carry out a more in-depth study of the light phenomenon in its entirety.

Review of Natural Phenomena Hazard (NPH) Assessments for the DOE Hanford Site

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

Snow, Robert L.; Ross, Steven B.

2011-09-15

The purpose of this review is to assess the need for updating Natural Phenomena Hazard (NPH) assessments for the DOE's Hanford Site, as required by DOE Order 420.1B Chapter IV, Natural Phenomena Hazards Mitigation, based on significant changes in state-of-the-art NPH assessment methodology or site-specific information. This review is an update and expansion to the September 2010 review of PNNL-19751, Review of Natural Phenomena Hazard (NPH) Assessments for the Hanford 200 Areas (Non-Seismic).

Implementing and analyzing the multi-threaded LP-inference

NASA Astrophysics Data System (ADS)

Bolotova, S. Yu; Trofimenko, E. V.; Leschinskaya, M. V.

2018-03-01

The logical production equations provide new possibilities for the backward inference optimization in intelligent production-type systems. The strategy of a relevant backward inference is aimed at minimization of a number of queries to external information source (either to a database or an interactive user). The idea of the method is based on the computing of initial preimages set and searching for the true preimage. The execution of each stage can be organized independently and in parallel and the actual work at a given stage can also be distributed between parallel computers. This paper is devoted to the parallel algorithms of the relevant inference based on the advanced scheme of the parallel computations “pipeline” which allows to increase the degree of parallelism. The author also provides some details of the LP-structures implementation.

Microgravity Transport Phenomena Experiment (MTPE) Overview

NASA Technical Reports Server (NTRS)

Mason, Larry W.

1999-01-01

The Microgravity Transport Phenomena Experiment (MTPE) is a fluids experiment supported by the Fundamentals in Biotechnology program in association with the Human Exploration and Development of Space (BEDS) initiative. The MTP Experiment will investigate fluid transport phenomena both in ground based experiments and in the microgravity environment. Many fluid transport processes are affected by gravity. Osmotic flux kinetics in planar membrane systems have been shown to be influenced by gravimetric orientation, either through convective mixing caused by unstably stratified fluid layers, or through a stable fluid boundary layer structure that forms in association with the membrane. Coupled transport phenomena also show gravity related effects. Coefficients associated with coupled transport processes are defined in terms of a steady state condition. Buoyancy (gravity) driven convection interferes with the attainment of steady state, and the measurement of coupled processes. The MTP Experiment measures the kinetics of molecular migration that occurs in fluids, in response to the application of various driving potentials. Three separate driving potentials may be applied to the MTP Experiment fluids, either singly or in combination. The driving potentials include chemical potential, thermal potential, and electrical potential. Two separate fluid arrangements are used to study membrane mediated and bulk fluid transport phenomena. Transport processes of interest in membrane mediated systems include diffusion, osmosis, and streaming potential. Bulk fluid processes of interest include coupled phenomena such as the Soret Effect, Dufour Effect, Donnan Effect, and thermal diffusion potential. MTP Experiments are performed in the Microgravity Transport Apparatus (MTA), an instrument that has been developed specifically for precision measurement of transport processes. Experiment fluids are contained within the MTA fluid cells, designed to create a one dimensional flow geometry

[Dissociative phenomena in a sample of outpatients].

PubMed

Cantone, Daniela; Sperandeo, Raffaele; Maldonato, Mauro Nelson; Cozzolino, Pasquale; Perris, Francesco

2012-01-01

The study describes the frequency and the quality of dissociative phenomena and their relationship with axis I disorders and the psychopathological severity in outpatients. The sample (N=383) was subjected to MINI diagnostic interview and self-assessment scales DES and SCL-90. The data were analysed using SPSS. The 11,0% of subjects has a score ≥20 on DES. The 5,2% has no dissociative symptoms. The absorption images is the most frequent dissociative phenomenon, the less common is the dissociation amnesia. A relationship between dissociative phenomena and conditions unemployment, marital separation and single parties and an inverse relationship with age founded. Dissociative phenomena are more frequent in participants who have been diagnosed at least one axis I disorder and their severity is positively correlated with the number of diagnosed diseases and scores to the General Symptomatic Index. Our results point towards the existence of three types of dissociative experiences. The first type, represented by the factor absorption/imaginative involvement, is expressed along a continuum from normal to pathological; a second type, represented by the factor depersonalization/derealization, occurs in a significantly more intense and specific among subjects with axis I disorders; the latest manifestation dissociative, described by the dissociation amnesia, seems to have a predominantly typological feature that qualifies it as an experience not commonly distributed in the general population. The identifying of dissociative symptoms is necessary for the psychopathologic evaluation and to improve the effectiveness of treatment programs.

Accurate HLA type inference using a weighted similarity graph.

PubMed

Xie, Minzhu; Li, Jing; Jiang, Tao

2010-12-14

The human leukocyte antigen system (HLA) contains many highly variable genes. HLA genes play an important role in the human immune system, and HLA gene matching is crucial for the success of human organ transplantations. Numerous studies have demonstrated that variation in HLA genes is associated with many autoimmune, inflammatory and infectious diseases. However, typing HLA genes by serology or PCR is time consuming and expensive, which limits large-scale studies involving HLA genes. Since it is much easier and cheaper to obtain single nucleotide polymorphism (SNP) genotype data, accurate computational algorithms to infer HLA gene types from SNP genotype data are in need. To infer HLA types from SNP genotypes, the first step is to infer SNP haplotypes from genotypes. However, for the same SNP genotype data set, the haplotype configurations inferred by different methods are usually inconsistent, and it is often difficult to decide which one is true. In this paper, we design an accurate HLA gene type inference algorithm by utilizing SNP genotype data from pedigrees, known HLA gene types of some individuals and the relationship between inferred SNP haplotypes and HLA gene types. Given a set of haplotypes inferred from the genotypes of a population consisting of many pedigrees, the algorithm first constructs a weighted similarity graph based on a new haplotype similarity measure and derives constraint edges from known HLA gene types. Based on the principle that different HLA gene alleles should have different background haplotypes, the algorithm searches for an optimal labeling of all the haplotypes with unknown HLA gene types such that the total weight among the same HLA gene types is maximized. To deal with ambiguous haplotype solutions, we use a genetic algorithm to select haplotype configurations that tend to maximize the same optimization criterion. Our experiments on a previously typed subset of the HapMap data show that the algorithm is highly accurate

Shock Tunnel Studies of Scramjet Phenomena

NASA Technical Reports Server (NTRS)

Stalker, R. J.

1996-01-01

Work focussed on a large number of preliminary studies of supersonic combustion in a simple combustion duct - thrust nozzle combination, investigating effects of Mach number, equivalence ratio, combustor divergence, fuel injecting angle and other parameters with an influence on the combustion process. This phase lasted for some three or four years, during which strongest emphasis was placed on responding to the request for preliminary experimental information on high enthalpy effects, to support the technology maturation activities of the NASP program. As the need for preliminary data became less urgent, it was possible to conduct more systematic studies of high enthalpy combustion phenomena, and to initiate other projects aimed at improving the facilities and instrumentation used for studying scramjet phenomena at high enthalpies. The combustion studies were particularly directed towards hypersonic combustion, and to the effects of injecting fuel along the combustion chamber wall. A substantial effort was directed towards a study of the effect of scale on the supersonic combustion process. The influence of wave phenomena (both compression waves and expansion waves) on the realization of thrust from a supersonic combustion process was also investigated. The effect of chemical kinetics was looked into, particularly as it affected the composition of the test flow provided by a ground facility. The effect of injection of the fuel through wall orifices was compared with injection from a strut spanning the stream, and the effect of heating the fuel prior to injection was investigated. Studies of fuel-air mixing by shock impingement were also done, as well as mass spectrometer surveys of a combustion wake. The use of hypersonic nozzles with an expansion tube was investigated. A new method was developed for measuring the forces acting of a model in less than one millisecond. Also included in this report are listings of published journal papers and conference presentations.

Transport Phenomena During Equiaxed Solidification of Alloys

NASA Technical Reports Server (NTRS)

Beckermann, C.; deGroh, H. C., III

1997-01-01

Recent progress in modeling of transport phenomena during dendritic alloy solidification is reviewed. Starting from the basic theorems of volume averaging, a general multiphase modeling framework is outlined. This framework allows for the incorporation of a variety of microscale phenomena in the macroscopic transport equations. For the case of diffusion dominated solidification, a simplified set of model equations is examined in detail and validated through comparisons with numerous experimental data for both columnar and equiaxed dendritic growth. This provides a critical assessment of the various model assumptions. Models that include melt flow and solid phase transport are also discussed, although their validation is still at an early stage. Several numerical results are presented that illustrate some of the profound effects of convective transport on the final compositional and structural characteristics of a solidified part. Important issues that deserve continuing attention are identified.

The Strong-Inference Protocol: Not Just for Grant Proposals

ERIC Educational Resources Information Center

Hiebert, Sara M.

2007-01-01

The strong-inference protocol puts into action the important concepts in Platt's often-assigned, classic paper on the strong-inference method (10). Yet, perhaps because students are frequently performing experiments with known outcomes, the protocols they write as undergraduates are usually little more than step-by-step instructions for performing…

Computational Natural Language Inference: Robust and Interpretable Question Answering

ERIC Educational Resources Information Center

Sharp, Rebecca Reynolds

2017-01-01

We address the challenging task of "computational natural language inference," by which we mean bridging two or more natural language texts while also providing an explanation of how they are connected. In the context of question answering (i.e., finding short answers to natural language questions), this inference connects the question…

Time-Frequency and Non-Laplacian Phenomena at Radio Frequencies

DTIC Science & Technology

2017-01-22

Unlimited UU UU UU UU 22-01-2017 30-Sep-2012 30-Sep-2016 Final Report: Time -Frequency and Non-Laplacian Phenomena at Radio Frequencies The views...average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data... Time ‐Frequency and Non‐Laplacian Phenomena at Radio Frequencies  U.S. Army Research Office grant W911NF‐12‐1‐0526  Michael B. Steer  Department of

Electrophysiological time course and brain areas of spontaneous and intentional trait inferences

PubMed Central

Van Duynslaeger, Marijke; Verstraeten, Edwin

2007-01-01

This study measured event-related potentials during spontaneous and intentional trait inferences. Participants read sentences describing the behavior of a target person from which a strong moral trait could be inferred. The last word of each sentence determined the consistency with the trait induced during an introductory paragraph. In comparison with behaviors that were consistent with the implied trait, a P300 waveform was obtained when the behaviors were evaluative inconsistent with that trait. This dependency on behavioral consistency indicates that trait inferences were made previously while reading the preceding behaviors, irrespective of the participants’ spontaneous or intentional goals. Overall, the P300 shows considerable parallels between spontaneous and intentional inferences, indicating that the type and timing of the inconsistency process is very similar. In contrast, source localization (LORETA) of the event-related potentials suggest that spontaneous inferences show greater activation in the temporo-parietal junction compared to intentional inferences following an inconsistency. Memory measures taken after the presentation of the stimulus material involved sentence completion and trait-cued recall, and supported the occurrence of trait inferences associated with the actor. They also showed significant correlations with the neural components (i.e. P300 and its current density at the temporo-parietal junction) predominantly following spontaneous instructions, indicating that these components are valid neural indices of spontaneous inferences. PMID:18985139

Application of AI techniques to infer vegetation characteristics from directional reflectance(s)

NASA Technical Reports Server (NTRS)

Kimes, D. S.; Smith, J. A.; Harrison, P. A.; Harrison, P. R.

1994-01-01

Traditionally, the remote sensing community has relied totally on spectral knowledge to extract vegetation characteristics. However, there are other knowledge bases (KB's) that can be used to significantly improve the accuracy and robustness of inference techniques. Using AI (artificial intelligence) techniques a KB system (VEG) was developed that integrates input spectral measurements with diverse KB's. These KB's consist of data sets of directional reflectance measurements, knowledge from literature, and knowledge from experts which are combined into an intelligent and efficient system for making vegetation inferences. VEG accepts spectral data of an unknown target as input, determines the best techniques for inferring the desired vegetation characteristic(s), applies the techniques to the target data, and provides a rigorous estimate of the accuracy of the inference. VEG was developed to: infer spectral hemispherical reflectance from any combination of nadir and/or off-nadir view angles; infer percent ground cover from any combination of nadir and/or off-nadir view angles; infer unknown view angle(s) from known view angle(s) (known as view angle extension); and discriminate between user defined vegetation classes using spectral and directional reflectance relationships developed from an automated learning algorithm. The errors for these techniques were generally very good ranging between 2 to 15% (proportional root mean square). The system is designed to aid scientists in developing, testing, and applying new inference techniques using directional reflectance data.

Hemispheric processing of predictive inferences during reading: The influence of negatively emotional valenced stimuli.

PubMed

Virtue, Sandra; Schutzenhofer, Michael; Tomkins, Blaine

2017-07-01

Although a left hemisphere advantage is usually evident during language processing, the right hemisphere is highly involved during the processing of weakly constrained inferences. However, currently little is known about how the emotional valence of environmental stimuli influences the hemispheric processing of these inferences. In the current study, participants read texts promoting either strongly or weakly constrained predictive inferences and performed a lexical decision task to inference-related targets presented to the left visual field-right hemisphere or the right visual field-left hemisphere. While reading these texts, participants either listened to dissonant music (i.e., the music condition) or did not listen to music (i.e., the no music condition). In the no music condition, the left hemisphere showed an advantage for strongly constrained inferences compared to weakly constrained inferences, whereas the right hemisphere showed high facilitation for both strongly and weakly constrained inferences. In the music condition, both hemispheres showed greater facilitation for strongly constrained inferences than for weakly constrained inferences. These results suggest that negatively valenced stimuli (such as dissonant music) selectively influences the right hemisphere's processing of weakly constrained inferences during reading.

Making Inferences in Adulthood: Falling Leaves Mean It's Fall.

ERIC Educational Resources Information Center

Zandi, Taher; Gregory, Monica E.

1988-01-01

Assessed age differences in making inferences from prose. Older adults correctly answered mean of 10 questions related to implicit information and 8 related to explicit information. Young adults answered mean of 7 implicit and 12 explicit information questions. In spite of poorer recall of factual details, older subjects made inferences to greater…

Inference Strategies in Reading Comprehension. Technical Report No. 410.

ERIC Educational Resources Information Center

Phillips, Linda M.

A study investigated the inference strategies used by sixth grade students reading narratives, and the results were compared with the inference strategies identified as those used by skilled adult readers. Subjects, 80 sixth grade students from two Canadian urban centers were divided into two groups: 40 high-proficiency readers and 40…

Causal Inference and Language Comprehension: Event-Related Potential Investigations

ERIC Educational Resources Information Center

Davenport, Tristan S.

2014-01-01

The most important information conveyed by language is often contained not in the utterance itself, but in the interaction between the utterance and the comprehender's knowledge of the world and the current situation. This dissertation uses psycholinguistic methods to explore the effects of a common type of inference--causal inference--on language…

Maxwell Prize Talk: Scaling Laws for the Dynamical Plasma Phenomena

NASA Astrophysics Data System (ADS)

Ryutov, Livermore, Ca 94550, Usa, D. D.

2017-10-01

The scaling and similarity technique is a powerful tool for developing and testing reduced models of complex phenomena, including plasma phenomena. The technique has been successfully used in identifying appropriate simplified models of transport in quasistationary plasmas. In this talk, the similarity and scaling arguments will be applied to highly dynamical systems, in which temporal evolution of the plasma leads to a significant change of plasma dimensions, shapes, densities, and other parameters with respect to initial state. The scaling and similarity techniques for dynamical plasma systems will be presented as a set of case studies of problems from various domains of the plasma physics, beginning with collisonless plasmas, through intermediate collisionalities, to highly collisional plasmas describable by the single-fluid MHD. Basic concepts of the similarity theory will be introduced along the way. Among the results discussed are: self-similarity of Langmuir turbulence driven by a hot electron cloud expanding into a cold background plasma; generation of particle beams in disrupting pinches; interference between collisionless and collisional phenomena in the shock physics; similarity for liner-imploded plasmas; MHD similarities with an emphasis on the effect of small-scale (turbulent) structures on global dynamics. Relations between astrophysical phenomena and scaled laboratory experiments will be discussed.

Improving Explanatory Inferences from Assessments

ERIC Educational Resources Information Center

Diakow, Ronli Phyllis

2013-01-01

This dissertation comprises three papers that propose, discuss, and illustrate models to make improved inferences about research questions regarding student achievement in education. Addressing the types of questions common in educational research today requires three different "extensions" to traditional educational assessment: (1)…

Action starring narratives and events: Structure and inference in visual narrative comprehension.

PubMed

Cohn, Neil; Wittenberg, Eva

Studies of discourse have long placed focus on the inference generated by information that is not overtly expressed, and theories of visual narrative comprehension similarly focused on the inference generated between juxtaposed panels. Within the visual language of comics, star-shaped "flashes" commonly signify impacts, but can be enlarged to the size of a whole panel that can omit all other representational information. These "action star" panels depict a narrative culmination (a "Peak"), but have content which readers must infer, thereby posing a challenge to theories of inference generation in visual narratives that focus only on the semantic changes between juxtaposed images. This paper shows that action stars demand more inference than depicted events, and that they are more coherent in narrative sequences than scrambled sequences (Experiment 1). In addition, action stars play a felicitous narrative role in the sequence (Experiment 2). Together, these results suggest that visual narratives use conventionalized depictions that demand the generation of inferences while retaining narrative coherence of a visual sequence.

Intracranial EEG correlates of implicit relational inference within the hippocampus.

PubMed

Reber, T P; Do Lam, A T A; Axmacher, N; Elger, C E; Helmstaedter, C; Henke, K; Fell, J

2016-01-01

Drawing inferences from past experiences enables adaptive behavior in future situations. Inference has been shown to depend on hippocampal processes. Usually, inference is considered a deliberate and effortful mental act which happens during retrieval, and requires the focus of our awareness. Recent fMRI studies hint at the possibility that some forms of hippocampus-dependent inference can also occur during encoding and possibly also outside of awareness. Here, we sought to further explore the feasibility of hippocampal implicit inference, and specifically address the temporal evolution of implicit inference using intracranial EEG. Presurgical epilepsy patients with hippocampal depth electrodes viewed a sequence of word pairs, and judged the semantic fit between two words in each pair. Some of the word pairs entailed a common word (e.g., "winter-red," "red-cat") such that an indirect relation was established in following word pairs (e.g., "winter-cat"). The behavioral results suggested that drawing inference implicitly from past experience is feasible because indirect relations seemed to foster "fit" judgments while the absence of indirect relations fostered "do not fit" judgments, even though the participants were unaware of the indirect relations. A event-related potential (ERP) difference emerging 400 ms post-stimulus was evident in the hippocampus during encoding, suggesting that indirect relations were already established automatically during encoding of the overlapping word pairs. Further ERP differences emerged later post-stimulus (1,500 ms), were modulated by the participants' responses and were evident during encoding and test. Furthermore, response-locked ERP effects were evident at test. These ERP effects could hence be a correlate of the interaction of implicit memory with decision-making. Together, the data map out a time-course in which the hippocampus automatically integrates memories from discrete but related episodes to implicitly influence future

Feature inference with uncertain categorization: Re-assessing Anderson's rational model.

PubMed

Konovalova, Elizaveta; Le Mens, Gaël

2017-09-18

A key function of categories is to help predictions about unobserved features of objects. At the same time, humans are often in situations where the categories of the objects they perceive are uncertain. In an influential paper, Anderson (Psychological Review, 98(3), 409-429, 1991) proposed a rational model for feature inferences with uncertain categorization. A crucial feature of this model is the conditional independence assumption-it assumes that the within category feature correlation is zero. In prior research, this model has been found to provide a poor fit to participants' inferences. This evidence is restricted to task environments inconsistent with the conditional independence assumption. Currently available evidence thus provides little information about how this model would fit participants' inferences in a setting with conditional independence. In four experiments based on a novel paradigm and one experiment based on an existing paradigm, we assess the performance of Anderson's model under conditional independence. We find that this model predicts participants' inferences better than competing models. One model assumes that inferences are based on just the most likely category. The second model is insensitive to categories but sensitive to overall feature correlation. The performance of Anderson's model is evidence that inferences were influenced not only by the more likely category but also by the other candidate category. Our findings suggest that a version of Anderson's model which relaxes the conditional independence assumption will likely perform well in environments characterized by within-category feature correlation.

Estimating mountain basin-mean precipitation from streamflow using Bayesian inference

NASA Astrophysics Data System (ADS)

Henn, Brian; Clark, Martyn P.; Kavetski, Dmitri; Lundquist, Jessica D.

2015-10-01

Estimating basin-mean precipitation in complex terrain is difficult due to uncertainty in the topographical representativeness of precipitation gauges relative to the basin. To address this issue, we use Bayesian methodology coupled with a multimodel framework to infer basin-mean precipitation from streamflow observations, and we apply this approach to snow-dominated basins in the Sierra Nevada of California. Using streamflow observations, forcing data from lower-elevation stations, the Bayesian Total Error Analysis (BATEA) methodology and the Framework for Understanding Structural Errors (FUSE), we infer basin-mean precipitation, and compare it to basin-mean precipitation estimated using topographically informed interpolation from gauges (PRISM, the Parameter-elevation Regression on Independent Slopes Model). The BATEA-inferred spatial patterns of precipitation show agreement with PRISM in terms of the rank of basins from wet to dry but differ in absolute values. In some of the basins, these differences may reflect biases in PRISM, because some implied PRISM runoff ratios may be inconsistent with the regional climate. We also infer annual time series of basin precipitation using a two-step calibration approach. Assessment of the precision and robustness of the BATEA approach suggests that uncertainty in the BATEA-inferred precipitation is primarily related to uncertainties in hydrologic model structure. Despite these limitations, time series of inferred annual precipitation under different model and parameter assumptions are strongly correlated with one another, suggesting that this approach is capable of resolving year-to-year variability in basin-mean precipitation.

Interoceptive inference: From computational neuroscience to clinic.

PubMed

Owens, Andrew P; Allen, Micah; Ondobaka, Sasha; Friston, Karl J

2018-04-22

The central and autonomic nervous systems can be defined by their anatomical, functional and neurochemical characteristics, but neither functions in isolation. For example, fundamental components of autonomically mediated homeostatic processes are afferent interoceptive signals reporting the internal state of the body and efferent signals acting on interoceptive feedback assimilated by the brain. Recent predictive coding (interoceptive inference) models formulate interoception in terms of embodied predictive processes that support emotion and selfhood. We propose interoception may serve as a way to investigate holistic nervous system function and dysfunction in disorders of brain, body and behaviour. We appeal to predictive coding and (active) interoceptive inference, to describe the homeostatic functions of the central and autonomic nervous systems. We do so by (i) reviewing the active inference formulation of interoceptive and autonomic function, (ii) survey clinical applications of this formulation and (iii) describe how it offers an integrative approach to human physiology; particularly, interactions between the central and peripheral nervous systems in health and disease. Crown Copyright © 2018. Published by Elsevier Ltd. All rights reserved.

MultiNest: Efficient and Robust Bayesian Inference

NASA Astrophysics Data System (ADS)

Feroz, F.; Hobson, M. P.; Bridges, M.

2011-09-01

We present further development and the first public release of our multimodal nested sampling algorithm, called MultiNest. This Bayesian inference tool calculates the evidence, with an associated error estimate, and produces posterior samples from distributions that may contain multiple modes and pronounced (curving) degeneracies in high dimensions. The developments presented here lead to further substantial improvements in sampling efficiency and robustness, as compared to the original algorithm presented in Feroz & Hobson (2008), which itself significantly outperformed existing MCMC techniques in a wide range of astrophysical inference problems. The accuracy and economy of the MultiNest algorithm is demonstrated by application to two toy problems and to a cosmological inference problem focusing on the extension of the vanilla LambdaCDM model to include spatial curvature and a varying equation of state for dark energy. The MultiNest software is fully parallelized using MPI and includes an interface to CosmoMC. It will also be released as part of the SuperBayeS package, for the analysis of supersymmetric theories of particle physics, at this http URL.

Real Rules of Inference

DTIC Science & Technology

1986-01-01

the AAAI Workshop on Uncertainty and Probability in Artificial Intelligence , 1985. [McC771 McCarthy, J. "Epistemological Problems of Aritificial ...NUMBER OF PAGES Artificial Intelligence , Data Fusion, Inference, Probability, 30 Philosophy, Inheritance Hierachies, Default Reasoning ia.PRCECODE I...prominent philosophers Glymour and Thomason even applaud the uninhibited steps: Artificial Intelligence has done us the service not only of reminding us

Aerothermodynamic flow phenomena of the airframe-integrated supersonic combustion ramjet

NASA Technical Reports Server (NTRS)

Walton, James T.

1992-01-01

The unique component flow phenomena is discussed of the airframe-integrated supersonic combustion ramjet (scramjet) in a format geared towards new players in the arena of hypersonic propulsion. After giving an overview of the scramjet aerothermodynamic cycle, the characteristics are then covered individually of the vehicle forebody, inlet, combustor, and vehicle afterbody/nozzle. Attention is given to phenomena such as inlet speeding, inlet starting, inlet spillage, fuel injection, thermal choking, and combustor-inlet interaction.

Reliability of dose volume constraint inference from clinical data.

PubMed

Lutz, C M; Møller, D S; Hoffmann, L; Knap, M M; Alber, M

2017-04-21

Dose volume histogram points (DVHPs) frequently serve as dose constraints in radiotherapy treatment planning. An experiment was designed to investigate the reliability of DVHP inference from clinical data for multiple cohort sizes and complication incidence rates. The experimental background was radiation pneumonitis in non-small cell lung cancer and the DVHP inference method was based on logistic regression. From 102 NSCLC real-life dose distributions and a postulated DVHP model, an 'ideal' cohort was generated where the most predictive model was equal to the postulated model. A bootstrap and a Cohort Replication Monte Carlo (CoRepMC) approach were applied to create 1000 equally sized populations each. The cohorts were then analyzed to establish inference frequency distributions. This was applied to nine scenarios for cohort sizes of 102 (1), 500 (2) to 2000 (3) patients (by sampling with replacement) and three postulated DVHP models. The Bootstrap was repeated for a 'non-ideal' cohort, where the most predictive model did not coincide with the postulated model. The Bootstrap produced chaotic results for all models of cohort size 1 for both the ideal and non-ideal cohorts. For cohort size 2 and 3, the distributions for all populations were more concentrated around the postulated DVHP. For the CoRepMC, the inference frequency increased with cohort size and incidence rate. Correct inference rates  >[Formula: see text] were only achieved by cohorts with more than 500 patients. Both Bootstrap and CoRepMC indicate that inference of the correct or approximate DVHP for typical cohort sizes is highly uncertain. CoRepMC results were less spurious than Bootstrap results, demonstrating the large influence that randomness in dose-response has on the statistical analysis.

Reliability of dose volume constraint inference from clinical data

NASA Astrophysics Data System (ADS)

Lutz, C. M.; Møller, D. S.; Hoffmann, L.; Knap, M. M.; Alber, M.

2017-04-01

Dose volume histogram points (DVHPs) frequently serve as dose constraints in radiotherapy treatment planning. An experiment was designed to investigate the reliability of DVHP inference from clinical data for multiple cohort sizes and complication incidence rates. The experimental background was radiation pneumonitis in non-small cell lung cancer and the DVHP inference method was based on logistic regression. From 102 NSCLC real-life dose distributions and a postulated DVHP model, an ‘ideal’ cohort was generated where the most predictive model was equal to the postulated model. A bootstrap and a Cohort Replication Monte Carlo (CoRepMC) approach were applied to create 1000 equally sized populations each. The cohorts were then analyzed to establish inference frequency distributions. This was applied to nine scenarios for cohort sizes of 102 (1), 500 (2) to 2000 (3) patients (by sampling with replacement) and three postulated DVHP models. The Bootstrap was repeated for a ‘non-ideal’ cohort, where the most predictive model did not coincide with the postulated model. The Bootstrap produced chaotic results for all models of cohort size 1 for both the ideal and non-ideal cohorts. For cohort size 2 and 3, the distributions for all populations were more concentrated around the postulated DVHP. For the CoRepMC, the inference frequency increased with cohort size and incidence rate. Correct inference rates  >85 % were only achieved by cohorts with more than 500 patients. Both Bootstrap and CoRepMC indicate that inference of the correct or approximate DVHP for typical cohort sizes is highly uncertain. CoRepMC results were less spurious than Bootstrap results, demonstrating the large influence that randomness in dose-response has on the statistical analysis.

When Anaphor Resolution Fails: Partial Encoding of Anaphoric Inferences

ERIC Educational Resources Information Center

Klin, Celia M.; Guzman, Alexandria E.; Weingartner, Kristin M.; Ralano, Angela S.

2006-01-01

Klin et al., 2004 and Levine et al., 2000 concluded that readers fail to resolve noun phrase anaphors when the antecedent is difficult to retrieve from memory and the inference is not necessary for comprehension. In four experiments we investigated the hypothesis that these inferences were actually partially encoded. Although the results of a…

Inference of beliefs and emotions in patients with Alzheimer's disease.

PubMed

Zaitchik, Deborah; Koff, Elissa; Brownell, Hiram; Winner, Ellen; Albert, Marilyn

2006-01-01

The present study compared 20 patients with mild to moderate Alzheimer's disease with 20 older controls (ages 69-94 years) on their ability to make inferences about emotions and beliefs in others. Six tasks tested their ability to make 1st-order and 2nd-order inferences as well as to offer explanations and moral evaluations of human action by appeal to emotions and beliefs. Results showed that the ability to infer emotions and beliefs in 1st-order tasks remains largely intact in patients with mild to moderate Alzheimer's. Patients were able to use mental states in the prediction, explanation, and moral evaluation of behavior. Impairment on 2nd-order tasks involving inference of mental states was equivalent to impairment on control tasks, suggesting that patients' difficulty is secondary to their cognitive impairments. ((c) 2006 APA, all rights reserved).

Computational Neuropsychology and Bayesian Inference.

PubMed

Parr, Thomas; Rees, Geraint; Friston, Karl J

2018-01-01

Computational theories of brain function have become very influential in neuroscience. They have facilitated the growth of formal approaches to disease, particularly in psychiatric research. In this paper, we provide a narrative review of the body of computational research addressing neuropsychological syndromes, and focus on those that employ Bayesian frameworks. Bayesian approaches to understanding brain function formulate perception and action as inferential processes. These inferences combine 'prior' beliefs with a generative (predictive) model to explain the causes of sensations. Under this view, neuropsychological deficits can be thought of as false inferences that arise due to aberrant prior beliefs (that are poor fits to the real world). This draws upon the notion of a Bayes optimal pathology - optimal inference with suboptimal priors - and provides a means for computational phenotyping. In principle, any given neuropsychological disorder could be characterized by the set of prior beliefs that would make a patient's behavior appear Bayes optimal. We start with an overview of some key theoretical constructs and use these to motivate a form of computational neuropsychology that relates anatomical structures in the brain to the computations they perform. Throughout, we draw upon computational accounts of neuropsychological syndromes. These are selected to emphasize the key features of a Bayesian approach, and the possible types of pathological prior that may be present. They range from visual neglect through hallucinations to autism. Through these illustrative examples, we review the use of Bayesian approaches to understand the link between biology and computation that is at the heart of neuropsychology.

Computational Neuropsychology and Bayesian Inference

PubMed Central

Parr, Thomas; Rees, Geraint; Friston, Karl J.

2018-01-01

Computational theories of brain function have become very influential in neuroscience. They have facilitated the growth of formal approaches to disease, particularly in psychiatric research. In this paper, we provide a narrative review of the body of computational research addressing neuropsychological syndromes, and focus on those that employ Bayesian frameworks. Bayesian approaches to understanding brain function formulate perception and action as inferential processes. These inferences combine ‘prior’ beliefs with a generative (predictive) model to explain the causes of sensations. Under this view, neuropsychological deficits can be thought of as false inferences that arise due to aberrant prior beliefs (that are poor fits to the real world). This draws upon the notion of a Bayes optimal pathology – optimal inference with suboptimal priors – and provides a means for computational phenotyping. In principle, any given neuropsychological disorder could be characterized by the set of prior beliefs that would make a patient’s behavior appear Bayes optimal. We start with an overview of some key theoretical constructs and use these to motivate a form of computational neuropsychology that relates anatomical structures in the brain to the computations they perform. Throughout, we draw upon computational accounts of neuropsychological syndromes. These are selected to emphasize the key features of a Bayesian approach, and the possible types of pathological prior that may be present. They range from visual neglect through hallucinations to autism. Through these illustrative examples, we review the use of Bayesian approaches to understand the link between biology and computation that is at the heart of neuropsychology. PMID:29527157

Generative inference for cultural evolution.

PubMed

Kandler, Anne; Powell, Adam

2018-04-05

One of the major challenges in cultural evolution is to understand why and how various forms of social learning are used in human populations, both now and in the past. To date, much of the theoretical work on social learning has been done in isolation of data, and consequently many insights focus on revealing the learning processes or the distributions of cultural variants that are expected to have evolved in human populations. In population genetics, recent methodological advances have allowed a greater understanding of the explicit demographic and/or selection mechanisms that underlie observed allele frequency distributions across the globe, and their change through time. In particular, generative frameworks-often using coalescent-based simulation coupled with approximate Bayesian computation (ABC)-have provided robust inferences on the human past, with no reliance on a priori assumptions of equilibrium. Here, we demonstrate the applicability and utility of generative inference approaches to the field of cultural evolution. The framework advocated here uses observed population-level frequency data directly to establish the likely presence or absence of particular hypothesized learning strategies. In this context, we discuss the problem of equifinality and argue that, in the light of sparse cultural data and the multiplicity of possible social learning processes, the exclusion of those processes inconsistent with the observed data might be the most instructive outcome. Finally, we summarize the findings of generative inference approaches applied to a number of case studies.This article is part of the theme issue 'Bridging cultural gaps: interdisciplinary studies in human cultural evolution'. © 2018 The Author(s).

Moon-based Earth Observation for Large Scale Geoscience Phenomena

NASA Astrophysics Data System (ADS)

Guo, Huadong; Liu, Guang; Ding, Yixing

2016-07-01

The capability of Earth observation for large-global-scale natural phenomena needs to be improved and new observing platform are expected. We have studied the concept of Moon as an Earth observation in these years. Comparing with manmade satellite platform, Moon-based Earth observation can obtain multi-spherical, full-band, active and passive information,which is of following advantages: large observation range, variable view angle, long-term continuous observation, extra-long life cycle, with the characteristics of longevity ,consistency, integrity, stability and uniqueness. Moon-based Earth observation is suitable for monitoring the large scale geoscience phenomena including large scale atmosphere change, large scale ocean change,large scale land surface dynamic change,solid earth dynamic change,etc. For the purpose of establishing a Moon-based Earth observation platform, we already have a plan to study the five aspects as follows: mechanism and models of moon-based observing earth sciences macroscopic phenomena; sensors' parameters optimization and methods of moon-based Earth observation; site selection and environment of moon-based Earth observation; Moon-based Earth observation platform; and Moon-based Earth observation fundamental scientific framework.

Doubly robust nonparametric inference on the average treatment effect.

PubMed

Benkeser, D; Carone, M; Laan, M J Van Der; Gilbert, P B

2017-12-01

Doubly robust estimators are widely used to draw inference about the average effect of a treatment. Such estimators are consistent for the effect of interest if either one of two nuisance parameters is consistently estimated. However, if flexible, data-adaptive estimators of these nuisance parameters are used, double robustness does not readily extend to inference. We present a general theoretical study of the behaviour of doubly robust estimators of an average treatment effect when one of the nuisance parameters is inconsistently estimated. We contrast different methods for constructing such estimators and investigate the extent to which they may be modified to also allow doubly robust inference. We find that while targeted minimum loss-based estimation can be used to solve this problem very naturally, common alternative frameworks appear to be inappropriate for this purpose. We provide a theoretical study and a numerical evaluation of the alternatives considered. Our simulations highlight the need for and usefulness of these approaches in practice, while our theoretical developments have broad implications for the construction of estimators that permit doubly robust inference in other problems.

Inhomogeneous Poisson process rate function inference from dead-time limited observations.

PubMed

Verma, Gunjan; Drost, Robert J

2017-05-01

The estimation of an inhomogeneous Poisson process (IHPP) rate function from a set of process observations is an important problem arising in optical communications and a variety of other applications. However, because of practical limitations of detector technology, one is often only able to observe a corrupted version of the original process. In this paper, we consider how inference of the rate function is affected by dead time, a period of time after the detection of an event during which a sensor is insensitive to subsequent IHPP events. We propose a flexible nonparametric Bayesian approach to infer an IHPP rate function given dead-time limited process realizations. Simulation results illustrate the effectiveness of our inference approach and suggest its ability to extend the utility of existing sensor technology by permitting more accurate inference on signals whose observations are dead-time limited. We apply our inference algorithm to experimentally collected optical communications data, demonstrating the practical utility of our approach in the context of channel modeling and validation.

Multi-Agent Inference in Social Networks: A Finite Population Learning Approach.

PubMed

Fan, Jianqing; Tong, Xin; Zeng, Yao

When people in a society want to make inference about some parameter, each person may want to use data collected by other people. Information (data) exchange in social networks is usually costly, so to make reliable statistical decisions, people need to trade off the benefits and costs of information acquisition. Conflicts of interests and coordination problems will arise in the process. Classical statistics does not consider people's incentives and interactions in the data collection process. To address this imperfection, this work explores multi-agent Bayesian inference problems with a game theoretic social network model. Motivated by our interest in aggregate inference at the societal level, we propose a new concept, finite population learning , to address whether with high probability, a large fraction of people in a given finite population network can make "good" inference. Serving as a foundation, this concept enables us to study the long run trend of aggregate inference quality as population grows.

Multi-Agent Inference in Social Networks: A Finite Population Learning Approach

PubMed Central

Tong, Xin; Zeng, Yao

2016-01-01

When people in a society want to make inference about some parameter, each person may want to use data collected by other people. Information (data) exchange in social networks is usually costly, so to make reliable statistical decisions, people need to trade off the benefits and costs of information acquisition. Conflicts of interests and coordination problems will arise in the process. Classical statistics does not consider people’s incentives and interactions in the data collection process. To address this imperfection, this work explores multi-agent Bayesian inference problems with a game theoretic social network model. Motivated by our interest in aggregate inference at the societal level, we propose a new concept, finite population learning, to address whether with high probability, a large fraction of people in a given finite population network can make “good” inference. Serving as a foundation, this concept enables us to study the long run trend of aggregate inference quality as population grows. PMID:27076691

Role of Utility and Inference in the Evolution of Functional Information

PubMed Central

Sharov, Alexei A.

2009-01-01

Functional information means an encoded network of functions in living organisms from molecular signaling pathways to an organism’s behavior. It is represented by two components: code and an interpretation system, which together form a self-sustaining semantic closure. Semantic closure allows some freedom between components because small variations of the code are still interpretable. The interpretation system consists of inference rules that control the correspondence between the code and the function (phenotype) and determines the shape of the fitness landscape. The utility factor operates at multiple time scales: short-term selection drives evolution towards higher survival and reproduction rate within a given fitness landscape, and long-term selection favors those fitness landscapes that support adaptability and lead to evolutionary expansion of certain lineages. Inference rules make short-term selection possible by shaping the fitness landscape and defining possible directions of evolution, but they are under control of the long-term selection of lineages. Communication normally occurs within a set of agents with compatible interpretation systems, which I call communication system. Functional information cannot be directly transferred between communication systems with incompatible inference rules. Each biological species is a genetic communication system that carries unique functional information together with inference rules that determine evolutionary directions and constraints. This view of the relation between utility and inference can resolve the conflict between realism/positivism and pragmatism. Realism overemphasizes the role of inference in evolution of human knowledge because it assumes that logic is embedded in reality. Pragmatism substitutes usefulness for truth and therefore ignores the advantage of inference. The proposed concept of evolutionary pragmatism rejects the idea that logic is embedded in reality; instead, inference rules are

Scalable Probabilistic Inference for Global Seismic Monitoring

NASA Astrophysics Data System (ADS)

Arora, N. S.; Dear, T.; Russell, S.

2011-12-01

We describe a probabilistic generative model for seismic events, their transmission through the earth, and their detection (or mis-detection) at seismic stations. We also describe an inference algorithm that constructs the most probable event bulletin explaining the observed set of detections. The model and inference are called NET-VISA (network processing vertically integrated seismic analysis) and is designed to replace the current automated network processing at the IDC, the SEL3 bulletin. Our results (attached table) demonstrate that NET-VISA significantly outperforms SEL3 by reducing the missed events from 30.3% down to 12.5%. The difference is even more dramatic for smaller magnitude events. NET-VISA has no difficulty in locating nuclear explosions as well. The attached figure demonstrates the location predicted by NET-VISA versus other bulletins for the second DPRK event. Further evaluation on dense regional networks demonstrates that NET-VISA finds many events missed in the LEB bulletin, which is produced by the human analysts. Large aftershock sequences, as produced by the 2004 December Sumatra earthquake and the 2011 March Tohoku earthquake, can pose a significant load for automated processing, often delaying the IDC bulletins by weeks or months. Indeed these sequences can overload the serial NET-VISA inference as well. We describe an enhancement to NET-VISA to make it multi-threaded, and hence take full advantage of the processing power of multi-core and -cpu machines. Our experiments show that the new inference algorithm is able to achieve 80% efficiency in parallel speedup.

Differentiable cortical networks for inferences concerning people’s intentions versus physical causality

PubMed Central

Mason, Robert A.; Just, Marcel Adam

2010-01-01

Cortical activity associated with generating an inference was measured using fMRI. Participants read three-sentence passages that differed in whether or not an inference needed to be drawn to understand them. The inference was based on either a protagonist’s intention or a physical consequence of a character’s action. Activation was expected in Theory of Mind brain regions for the passages based on protagonists’ intentions but not for the physical consequence passages. The activation measured in the right temporo-parietal junction was greater in the intentional passages than in the consequence passages, consistent with predictions from a Theory of Mind perspective. In contrast, there was increased occipital activation in the physical inference passages. For both types of passage, the cortical activity related to the reading of the critical inference sentence demonstrated a recruitment of a common inference cortical network. This general inference-related activation appeared bilaterally in the language processing areas (the inferior frontal gyrus, the temporal gyrus, and the angular gyrus), as well as in the medial to superior frontal gyrus, which has been found to be active in Theory of Mind tasks. These findings are consistent with the hypothesis that component areas of the discourse processing network are recruited as needed based on the nature of the inference. A Protagonist monitoring and synthesis network is proposed as a more accurate account for Theory of Mind activation during narrative comprehension. PMID:21229617

Spectral likelihood expansions for Bayesian inference

NASA Astrophysics Data System (ADS)

Nagel, Joseph B.; Sudret, Bruno

2016-03-01

A spectral approach to Bayesian inference is presented. It pursues the emulation of the posterior probability density. The starting point is a series expansion of the likelihood function in terms of orthogonal polynomials. From this spectral likelihood expansion all statistical quantities of interest can be calculated semi-analytically. The posterior is formally represented as the product of a reference density and a linear combination of polynomial basis functions. Both the model evidence and the posterior moments are related to the expansion coefficients. This formulation avoids Markov chain Monte Carlo simulation and allows one to make use of linear least squares instead. The pros and cons of spectral Bayesian inference are discussed and demonstrated on the basis of simple applications from classical statistics and inverse modeling.

Parametric inference for biological sequence analysis.

PubMed

Pachter, Lior; Sturmfels, Bernd

2004-11-16

One of the major successes in computational biology has been the unification, by using the graphical model formalism, of a multitude of algorithms for annotating and comparing biological sequences. Graphical models that have been applied to these problems include hidden Markov models for annotation, tree models for phylogenetics, and pair hidden Markov models for alignment. A single algorithm, the sum-product algorithm, solves many of the inference problems that are associated with different statistical models. This article introduces the polytope propagation algorithm for computing the Newton polytope of an observation from a graphical model. This algorithm is a geometric version of the sum-product algorithm and is used to analyze the parametric behavior of maximum a posteriori inference calculations for graphical models.

Multipoint observations of plasma phenomena made in space by Cluster

NASA Astrophysics Data System (ADS)

Goldstein, M. L.; Escoubet, P.; Hwang, K.-Joo; Wendel, D. E.; Viñas, A.-F.; Fung, S. F.; Perri, S.; Servidio, S.; Pickett, J. S.; Parks, G. K.; Sahraoui, F.; Gurgiolo, C.; Matthaeus, W.; Weygand, J. M.

2015-06-01

Plasmas are ubiquitous in nature, surround our local geospace environment, and permeate the universe. Plasma phenomena in space give rise to energetic particles, the aurora, solar flares and coronal mass ejections, as well as many energetic phenomena in interstellar space. Although plasmas can be studied in laboratory settings, it is often difficult, if not impossible, to replicate the conditions (density, temperature, magnetic and electric fields, etc.) of space. Single-point space missions too numerous to list have described many properties of near-Earth and heliospheric plasmas as measured both in situ and remotely (see http://www.nasa.gov/missions/#.U1mcVmeweRY for a list of NASA-related missions). However, a full description of our plasma environment requires three-dimensional spatial measurements. Cluster is the first, and until data begin flowing from the Magnetospheric Multiscale Mission (MMS), the only mission designed to describe the three-dimensional spatial structure of plasma phenomena in geospace. In this paper, we concentrate on some of the many plasma phenomena that have been studied using data from Cluster. To date, there have been more than 2000 refereed papers published using Cluster data but in this paper we will, of necessity, refer to only a small fraction of the published work. We have focused on a few basic plasma phenomena, but, for example, have not dealt with most of the vast body of work describing dynamical phenomena in Earth's magnetosphere, including the dynamics of current sheets in Earth's magnetotail and the morphology of the dayside high latitude cusp. Several review articles and special publications are available that describe aspects of that research in detail and interested readers are referred to them (see for example, Escoubet et al. 2005 Multiscale Coupling of Sun-Earth Processes, p. 459, Keith et al. 2005 Sur. Geophys. 26, 307-339, Paschmann et al. 2005 Outer Magnetospheric Boundaries: Cluster Results, Space Sciences Series

Inference of neuronal network spike dynamics and topology from calcium imaging data

PubMed Central

Lütcke, Henry; Gerhard, Felipe; Zenke, Friedemann; Gerstner, Wulfram; Helmchen, Fritjof

2013-01-01

Two-photon calcium imaging enables functional analysis of neuronal circuits by inferring action potential (AP) occurrence (“spike trains”) from cellular fluorescence signals. It remains unclear how experimental parameters such as signal-to-noise ratio (SNR) and acquisition rate affect spike inference and whether additional information about network structure can be extracted. Here we present a simulation framework for quantitatively assessing how well spike dynamics and network topology can be inferred from noisy calcium imaging data. For simulated AP-evoked calcium transients in neocortical pyramidal cells, we analyzed the quality of spike inference as a function of SNR and data acquisition rate using a recently introduced peeling algorithm. Given experimentally attainable values of SNR and acquisition rate, neural spike trains could be reconstructed accurately and with up to millisecond precision. We then applied statistical neuronal network models to explore how remaining uncertainties in spike inference affect estimates of network connectivity and topological features of network organization. We define the experimental conditions suitable for inferring whether the network has a scale-free structure and determine how well hub neurons can be identified. Our findings provide a benchmark for future calcium imaging studies that aim to reliably infer neuronal network properties. PMID:24399936

Feature-to-Feature Inference Under Conditions of Cue Restriction and Dimensional Correlation.

PubMed

Lancaster, Matthew E; Homa, Donald

2017-01-01

The present study explored feature-to-feature and label-to-feature inference in a category task for different category structures. In the correlated condition, each of the 4 dimensions comprising the category was positively correlated to each other and to the category label. In the uncorrelated condition, no correlation existed between the 4 dimensions comprising the category, although the dimension to category label correlation matched that of the correlated condition. After learning, participants made inference judgments of a missing feature, given 1, 2, or 3 feature cues; on half the trials, the category label was also included as a cue. The results showed superior inference of features following training on the correlated structure, with accurate inference when only a single feature was presented. In contrast, a single-feature cue resulted in chance levels of inference for the uncorrelated structure. Feature inference systematically improved with number of cues after training on the correlated structure. Surprisingly, a similar outcome was obtained for the uncorrelated structure, an outcome that must have reflected mediation via the category label. A descriptive model is briefly introduced to explain the results, with a suggestion that this paradigm might be profitably extended to hierarchical structures where the levels of feature-to-feature inference might vary with the depth of the hierarchy.

Cortical Hierarchies Perform Bayesian Causal Inference in Multisensory Perception

PubMed Central

Rohe, Tim; Noppeney, Uta

2015-01-01

To form a veridical percept of the environment, the brain needs to integrate sensory signals from a common source but segregate those from independent sources. Thus, perception inherently relies on solving the “causal inference problem.” Behaviorally, humans solve this problem optimally as predicted by Bayesian Causal Inference; yet, the underlying neural mechanisms are unexplored. Combining psychophysics, Bayesian modeling, functional magnetic resonance imaging (fMRI), and multivariate decoding in an audiovisual spatial localization task, we demonstrate that Bayesian Causal Inference is performed by a hierarchy of multisensory processes in the human brain. At the bottom of the hierarchy, in auditory and visual areas, location is represented on the basis that the two signals are generated by independent sources (= segregation). At the next stage, in posterior intraparietal sulcus, location is estimated under the assumption that the two signals are from a common source (= forced fusion). Only at the top of the hierarchy, in anterior intraparietal sulcus, the uncertainty about the causal structure of the world is taken into account and sensory signals are combined as predicted by Bayesian Causal Inference. Characterizing the computational operations of signal interactions reveals the hierarchical nature of multisensory perception in human neocortex. It unravels how the brain accomplishes Bayesian Causal Inference, a statistical computation fundamental for perception and cognition. Our results demonstrate how the brain combines information in the face of uncertainty about the underlying causal structure of the world. PMID:25710328

ARL Support of NRL Rocket Experiments to Investigate Ionospheric Phenomena

DTIC Science & Technology

2010-08-31

reallocate the funds to support NRL’s ongoing efforts to develop an ionospheric tomography network in South America to support the C/NOFS satellite...of NRL Rocket Experiments to Investigate Ionospheric Phenomena 5a. CONTRACT NUMBER N00173-09-1-G036 5b. GRANT NUMBER N00173-09-1-G036 5c...1-G036 ARL Support of NRL Rocket Experiments to Investigate Ionospheric Phenomena BY DR. TREVOR W. GARNER APPLIED RESEARCH LABORATORIES THE

Displaying Computer Simulations Of Physical Phenomena

NASA Technical Reports Server (NTRS)

Watson, Val

1991-01-01

Paper discusses computer simulation as means of experiencing and learning to understand physical phenomena. Covers both present simulation capabilities and major advances expected in near future. Visual, aural, tactile, and kinesthetic effects used to teach such physical sciences as dynamics of fluids. Recommends classrooms in universities, government, and industry be linked to advanced computing centers so computer simulations integrated into education process.

Natural phenomena exhibited by forest fires

Treesearch

J. S. Barrows

1961-01-01

Forest fire phenomena are presented through a series of motion pictures and 35 mm slides. These films have been taken by the staffs of the Southeastern, Pacific Southwest, and Intermountain Forest and Range Experiment Stations of the U. S. Forest Service and by Dr. Vincent J. Schaefer during the course of fire research activities. Both regular speed and time-lapse...

Memory-Based Simple Heuristics as Attribute Substitution: Competitive Tests of Binary Choice Inference Models

ERIC Educational Resources Information Center

Honda, Hidehito; Matsuka, Toshihiko; Ueda, Kazuhiro

2017-01-01

Some researchers on binary choice inference have argued that people make inferences based on simple heuristics, such as recognition, fluency, or familiarity. Others have argued that people make inferences based on available knowledge. To examine the boundary between heuristic and knowledge usage, we examine binary choice inference processes in…

A Study of Inference in Standardized Reading Test Items and Its Relationship to Difficulty.

ERIC Educational Resources Information Center

Marzano, Robert J.

To study the relationship between inferences made on standardized reading tests and item difficulty, 50 items on the reading comprehension section of the Metropolitan Achievement Test were analyzed independently in this study by two raters using four general categories of inferences: (1) reference inferences, (2) between proposition inferences,…

Hierarchical Active Inference: A Theory of Motivated Control.

PubMed

Pezzulo, Giovanni; Rigoli, Francesco; Friston, Karl J

2018-04-01

Motivated control refers to the coordination of behaviour to achieve affectively valenced outcomes or goals. The study of motivated control traditionally assumes a distinction between control and motivational processes, which map to distinct (dorsolateral versus ventromedial) brain systems. However, the respective roles and interactions between these processes remain controversial. We offer a novel perspective that casts control and motivational processes as complementary aspects - goal propagation and prioritization, respectively - of active inference and hierarchical goal processing under deep generative models. We propose that the control hierarchy propagates prior preferences or goals, but their precision is informed by the motivational context, inferred at different levels of the motivational hierarchy. The ensuing integration of control and motivational processes underwrites action and policy selection and, ultimately, motivated behaviour, by enabling deep inference to prioritize goals in a context-sensitive way. Copyright © 2018 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Exact Bayesian Inference for Phylogenetic Birth-Death Models.

PubMed

Parag, K V; Pybus, O G

2018-04-26

Inferring the rates of change of a population from a reconstructed phylogeny of genetic sequences is a central problem in macro-evolutionary biology, epidemiology, and many other disciplines. A popular solution involves estimating the parameters of a birth-death process (BDP), which links the shape of the phylogeny to its birth and death rates. Modern BDP estimators rely on random Markov chain Monte Carlo (MCMC) sampling to infer these rates. Such methods, while powerful and scalable, cannot be guaranteed to converge, leading to results that may be hard to replicate or difficult to validate. We present a conceptually and computationally different parametric BDP inference approach using flexible and easy to implement Snyder filter (SF) algorithms. This method is deterministic so its results are provable, guaranteed, and reproducible. We validate the SF on constant rate BDPs and find that it solves BDP likelihoods known to produce robust estimates. We then examine more complex BDPs with time-varying rates. Our estimates compare well with a recently developed parametric MCMC inference method. Lastly, we performmodel selection on an empirical Agamid species phylogeny, obtaining results consistent with the literature. The SF makes no approximations, beyond those required for parameter quantisation and numerical integration, and directly computes the posterior distribution of model parameters. It is a promising alternative inference algorithm that may serve either as a standalone Bayesian estimator or as a useful diagnostic reference for validating more involved MCMC strategies. The Snyder filter is implemented in Matlab and the time-varying BDP models are simulated in R. The source code and data are freely available at https://github.com/kpzoo/snyder-birth-death-code. kris.parag@zoo.ox.ac.uk. Supplementary material is available at Bioinformatics online.

Efficient Exact Inference With Loss Augmented Objective in Structured Learning.

PubMed

Bauer, Alexander; Nakajima, Shinichi; Muller, Klaus-Robert

2016-08-19

Structural support vector machine (SVM) is an elegant approach for building complex and accurate models with structured outputs. However, its applicability relies on the availability of efficient inference algorithms--the state-of-the-art training algorithms repeatedly perform inference to compute a subgradient or to find the most violating configuration. In this paper, we propose an exact inference algorithm for maximizing nondecomposable objectives due to special type of a high-order potential having a decomposable internal structure. As an important application, our method covers the loss augmented inference, which enables the slack and margin scaling formulations of structural SVM with a variety of dissimilarity measures, e.g., Hamming loss, precision and recall, Fβ-loss, intersection over union, and many other functions that can be efficiently computed from the contingency table. We demonstrate the advantages of our approach in natural language parsing and sequence segmentation applications.

A linear programming model for protein inference problem in shotgun proteomics.

PubMed

Huang, Ting; He, Zengyou

2012-11-15

Assembling peptides identified from tandem mass spectra into a list of proteins, referred to as protein inference, is an important issue in shotgun proteomics. The objective of protein inference is to find a subset of proteins that are truly present in the sample. Although many methods have been proposed for protein inference, several issues such as peptide degeneracy still remain unsolved. In this article, we present a linear programming model for protein inference. In this model, we use a transformation of the joint probability that each peptide/protein pair is present in the sample as the variable. Then, both the peptide probability and protein probability can be expressed as a formula in terms of the linear combination of these variables. Based on this simple fact, the protein inference problem is formulated as an optimization problem: minimize the number of proteins with non-zero probabilities under the constraint that the difference between the calculated peptide probability and the peptide probability generated from peptide identification algorithms should be less than some threshold. This model addresses the peptide degeneracy issue by forcing some joint probability variables involving degenerate peptides to be zero in a rigorous manner. The corresponding inference algorithm is named as ProteinLP. We test the performance of ProteinLP on six datasets. Experimental results show that our method is competitive with the state-of-the-art protein inference algorithms. The source code of our algorithm is available at: https://sourceforge.net/projects/prolp/. zyhe@dlut.edu.cn. Supplementary data are available at Bioinformatics Online.

Analogical and Category-Based Inference: A Theoretical Integration with Bayesian Causal Models

ERIC Educational Resources Information Center

Holyoak, Keith J.; Lee, Hee Seung; Lu, Hongjing

2010-01-01

A fundamental issue for theories of human induction is to specify constraints on potential inferences. For inferences based on shared category membership, an analogy, and/or a relational schema, it appears that the basic goal of induction is to make accurate and goal-relevant inferences that are sensitive to uncertainty. People can use source…

Bayesian Inference on Proportional Elections

PubMed Central

Brunello, Gabriel Hideki Vatanabe; Nakano, Eduardo Yoshio

2015-01-01

Polls for majoritarian voting systems usually show estimates of the percentage of votes for each candidate. However, proportional vote systems do not necessarily guarantee the candidate with the most percentage of votes will be elected. Thus, traditional methods used in majoritarian elections cannot be applied on proportional elections. In this context, the purpose of this paper was to perform a Bayesian inference on proportional elections considering the Brazilian system of seats distribution. More specifically, a methodology to answer the probability that a given party will have representation on the chamber of deputies was developed. Inferences were made on a Bayesian scenario using the Monte Carlo simulation technique, and the developed methodology was applied on data from the Brazilian elections for Members of the Legislative Assembly and Federal Chamber of Deputies in 2010. A performance rate was also presented to evaluate the efficiency of the methodology. Calculations and simulations were carried out using the free R statistical software. PMID:25786259

Bayesian inference on proportional elections.

PubMed

Brunello, Gabriel Hideki Vatanabe; Nakano, Eduardo Yoshio

2015-01-01

Polls for majoritarian voting systems usually show estimates of the percentage of votes for each candidate. However, proportional vote systems do not necessarily guarantee the candidate with the most percentage of votes will be elected. Thus, traditional methods used in majoritarian elections cannot be applied on proportional elections. In this context, the purpose of this paper was to perform a Bayesian inference on proportional elections considering the Brazilian system of seats distribution. More specifically, a methodology to answer the probability that a given party will have representation on the chamber of deputies was developed. Inferences were made on a Bayesian scenario using the Monte Carlo simulation technique, and the developed methodology was applied on data from the Brazilian elections for Members of the Legislative Assembly and Federal Chamber of Deputies in 2010. A performance rate was also presented to evaluate the efficiency of the methodology. Calculations and simulations were carried out using the free R statistical software.

Action starring narratives and events: Structure and inference in visual narrative comprehension

PubMed Central

Cohn, Neil; Wittenberg, Eva

2015-01-01

Studies of discourse have long placed focus on the inference generated by information that is not overtly expressed, and theories of visual narrative comprehension similarly focused on the inference generated between juxtaposed panels. Within the visual language of comics, star-shaped “flashes” commonly signify impacts, but can be enlarged to the size of a whole panel that can omit all other representational information. These “action star” panels depict a narrative culmination (a “Peak”), but have content which readers must infer, thereby posing a challenge to theories of inference generation in visual narratives that focus only on the semantic changes between juxtaposed images. This paper shows that action stars demand more inference than depicted events, and that they are more coherent in narrative sequences than scrambled sequences (Experiment 1). In addition, action stars play a felicitous narrative role in the sequence (Experiment 2). Together, these results suggest that visual narratives use conventionalized depictions that demand the generation of inferences while retaining narrative coherence of a visual sequence. PMID:26709362

Functional neuroanatomy of intuitive physical inference

PubMed Central

Mikhael, John G.; Tenenbaum, Joshua B.; Kanwisher, Nancy

2016-01-01

To engage with the world—to understand the scene in front of us, plan actions, and predict what will happen next—we must have an intuitive grasp of the world’s physical structure and dynamics. How do the objects in front of us rest on and support each other, how much force would be required to move them, and how will they behave when they fall, roll, or collide? Despite the centrality of physical inferences in daily life, little is known about the brain mechanisms recruited to interpret the physical structure of a scene and predict how physical events will unfold. Here, in a series of fMRI experiments, we identified a set of cortical regions that are selectively engaged when people watch and predict the unfolding of physical events—a “physics engine” in the brain. These brain regions are selective to physical inferences relative to nonphysical but otherwise highly similar scenes and tasks. However, these regions are not exclusively engaged in physical inferences per se or, indeed, even in scene understanding; they overlap with the domain-general “multiple demand” system, especially the parts of that system involved in action planning and tool use, pointing to a close relationship between the cognitive and neural mechanisms involved in parsing the physical content of a scene and preparing an appropriate action. PMID:27503892

Functional neuroanatomy of intuitive physical inference.

PubMed

Fischer, Jason; Mikhael, John G; Tenenbaum, Joshua B; Kanwisher, Nancy

2016-08-23

To engage with the world-to understand the scene in front of us, plan actions, and predict what will happen next-we must have an intuitive grasp of the world's physical structure and dynamics. How do the objects in front of us rest on and support each other, how much force would be required to move them, and how will they behave when they fall, roll, or collide? Despite the centrality of physical inferences in daily life, little is known about the brain mechanisms recruited to interpret the physical structure of a scene and predict how physical events will unfold. Here, in a series of fMRI experiments, we identified a set of cortical regions that are selectively engaged when people watch and predict the unfolding of physical events-a "physics engine" in the brain. These brain regions are selective to physical inferences relative to nonphysical but otherwise highly similar scenes and tasks. However, these regions are not exclusively engaged in physical inferences per se or, indeed, even in scene understanding; they overlap with the domain-general "multiple demand" system, especially the parts of that system involved in action planning and tool use, pointing to a close relationship between the cognitive and neural mechanisms involved in parsing the physical content of a scene and preparing an appropriate action.

Viral quasispecies inference from 454 pyrosequencing

PubMed Central

2013-01-01

Background Many potentially life-threatening infectious viruses are highly mutable in nature. Characterizing the fittest variants within a quasispecies from infected patients is expected to allow unprecedented opportunities to investigate the relationship between quasispecies diversity and disease epidemiology. The advent of next-generation sequencing technologies has allowed the study of virus diversity with high-throughput sequencing, although these methods come with higher rates of errors which can artificially increase diversity. Results Here we introduce a novel computational approach that incorporates base quality scores from next-generation sequencers for reconstructing viral genome sequences that simultaneously infers the number of variants within a quasispecies that are present. Comparisons on simulated and clinical data on dengue virus suggest that the novel approach provides a more accurate inference of the underlying number of variants within the quasispecies, which is vital for clinical efforts in mapping the within-host viral diversity. Sequence alignments generated by our approach are also found to exhibit lower rates of error. Conclusions The ability to infer the viral quasispecies colony that is present within a human host provides the potential for a more accurate classification of the viral phenotype. Understanding the genomics of viruses will be relevant not just to studying how to control or even eradicate these viral infectious diseases, but also in learning about the innate protection in the human host against the viruses. PMID:24308284

Selection of species and sampling areas: The importance of inference

Treesearch

Paul Stephen Corn

2009-01-01

Inductive inference, the process of drawing general conclusions from specific observations, is fundamental to the scientific method. Platt (1964) termed conclusions obtained through rigorous application of the scientific method as "strong inference" and noted the following basic steps: generating alternative hypotheses; devising experiments, the...

Causal inference in survival analysis using pseudo-observations.

PubMed

Andersen, Per K; Syriopoulou, Elisavet; Parner, Erik T

2017-07-30

Causal inference for non-censored response variables, such as binary or quantitative outcomes, is often based on either (1) direct standardization ('G-formula') or (2) inverse probability of treatment assignment weights ('propensity score'). To do causal inference in survival analysis, one needs to address right-censoring, and often, special techniques are required for that purpose. We will show how censoring can be dealt with 'once and for all' by means of so-called pseudo-observations when doing causal inference in survival analysis. The pseudo-observations can be used as a replacement of the outcomes without censoring when applying 'standard' causal inference methods, such as (1) or (2) earlier. We study this idea for estimating the average causal effect of a binary treatment on the survival probability, the restricted mean lifetime, and the cumulative incidence in a competing risks situation. The methods will be illustrated in a small simulation study and via a study of patients with acute myeloid leukemia who received either myeloablative or non-myeloablative conditioning before allogeneic hematopoetic cell transplantation. We will estimate the average causal effect of the conditioning regime on outcomes such as the 3-year overall survival probability and the 3-year risk of chronic graft-versus-host disease. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Emergent Phenomena at Oxide Interfaces

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

Hwang, H.Y.

2012-02-16

Transition metal oxides (TMOs) are an ideal arena for the study of electronic correlations because the s-electrons of the transition metal ions are removed and transferred to oxygen ions, and hence the strongly correlated d-electrons determine their physical properties such as electrical transport, magnetism, optical response, thermal conductivity, and superconductivity. These electron correlations prohibit the double occupancy of metal sites and induce a local entanglement of charge, spin, and orbital degrees of freedom. This gives rise to a variety of phenomena, e.g., Mott insulators, various charge/spin/orbital orderings, metal-insulator transitions, multiferroics, and superconductivity. In recent years, there has been a burstmore » of activity to manipulate these phenomena, as well as create new ones, using oxide heterostructures. Most fundamental to understanding the physical properties of TMOs is the concept of symmetry of the order parameter. As Landau recognized, the essence of phase transitions is the change of the symmetry. For example, ferromagnetic ordering breaks the rotational symmetry in spin space, i.e., the ordered phase has lower symmetry than the Hamiltonian of the system. There are three most important symmetries to be considered here. (i) Spatial inversion (I), defined as r {yields} -r. In the case of an insulator, breaking this symmetry can lead to spontaneous electric polarization, i.e. ferroelectricity, or pyroelectricity once the point group belongs to polar group symmetry. (ii) Time-reversal symmetry (T) defined as t {yields} -t. In quantum mechanics, the time-evolution of the wave-function {Psi} is given by the phase factor e{sup -iEt/{h_bar}} with E being the energy, and hence time-reversal basically corresponds to taking the complex conjugate of the wave-function. Also the spin, which is induced by the 'spinning' of the particle, is reversed by time-reversal. Broken T-symmetry is most naturally associated with magnetism, since the

Uncertainties in cylindrical anode current inferences on pulsed power drivers

NASA Astrophysics Data System (ADS)

Porwitzky, Andrew; Brown, Justin

2018-06-01

For over a decade, velocimetry based techniques have been used to infer the electrical current delivered to dynamic materials properties experiments on pulsed power drivers such as the Z Machine. Though originally developed for planar load geometries, in recent years, inferring the current delivered to cylindrical coaxial loads has become a valuable diagnostic tool for numerous platforms. Presented is a summary of uncertainties that can propagate through the current inference technique when applied to expanding cylindrical anodes. An equation representing quantitative uncertainty is developed which shows the unfold method to be accurate to a few percent above 10 MA of load current.

Mathematical methods of studying physical phenomena

NASA Astrophysics Data System (ADS)

Man'ko, Margarita A.

2013-03-01

In recent decades, substantial theoretical and experimental progress was achieved in understanding the quantum nature of physical phenomena that serves as the foundation of present and future quantum technologies. Quantum correlations like the entanglement of the states of composite systems, the phenomenon of quantum discord, which captures other aspects of quantum correlations, quantum contextuality and, connected with these phenomena, uncertainty relations for conjugate variables and entropies, like Shannon and Rényi entropies, and the inequalities for spin states, like Bell inequalities, reflect the recently understood quantum properties of micro and macro systems. The mathematical methods needed to describe all quantum phenomena mentioned above were also the subject of intense studies in the end of the last, and beginning of the new, century. In this section of CAMOP 'Mathematical Methods of Studying Physical Phenomena' new results and new trends in the rapidly developing domain of quantum (and classical) physics are presented. Among the particular topics under discussion there are some reviews on the problems of dynamical invariants and their relations with symmetries of the physical systems. In fact, this is a very old problem of both classical and quantum systems, e.g. the systems of parametric oscillators with time-dependent parameters, like Ermakov systems, which have specific constants of motion depending linearly or quadratically on the oscillator positions and momenta. Such dynamical invariants play an important role in studying the dynamical Casimir effect, the essence of the effect being the creation of photons from the vacuum in a cavity with moving boundaries due to the presence of purely quantum fluctuations of the electromagnetic field in the vacuum. It is remarkable that this effect was recently observed experimentally. The other new direction in developing the mathematical approach in physics is quantum tomography that provides a new vision of

The dynamics of information-driven coordination phenomena: A transfer entropy analysis

PubMed Central

Borge-Holthoefer, Javier; Perra, Nicola; Gonçalves, Bruno; González-Bailón, Sandra; Arenas, Alex; Moreno, Yamir; Vespignani, Alessandro

2016-01-01

Data from social media provide unprecedented opportunities to investigate the processes that govern the dynamics of collective social phenomena. We consider an information theoretical approach to define and measure the temporal and structural signatures typical of collective social events as they arise and gain prominence. We use the symbolic transfer entropy analysis of microblogging time series to extract directed networks of influence among geolocalized subunits in social systems. This methodology captures the emergence of system-level dynamics close to the onset of socially relevant collective phenomena. The framework is validated against a detailed empirical analysis of five case studies. In particular, we identify a change in the characteristic time scale of the information transfer that flags the onset of information-driven collective phenomena. Furthermore, our approach identifies an order-disorder transition in the directed network of influence between social subunits. In the absence of clear exogenous driving, social collective phenomena can be represented as endogenously driven structural transitions of the information transfer network. This study provides results that can help define models and predictive algorithms for the analysis of societal events based on open source data. PMID:27051875

The dynamics of information-driven coordination phenomena: A transfer entropy analysis.

PubMed

Borge-Holthoefer, Javier; Perra, Nicola; Gonçalves, Bruno; González-Bailón, Sandra; Arenas, Alex; Moreno, Yamir; Vespignani, Alessandro

2016-04-01

Data from social media provide unprecedented opportunities to investigate the processes that govern the dynamics of collective social phenomena. We consider an information theoretical approach to define and measure the temporal and structural signatures typical of collective social events as they arise and gain prominence. We use the symbolic transfer entropy analysis of microblogging time series to extract directed networks of influence among geolocalized subunits in social systems. This methodology captures the emergence of system-level dynamics close to the onset of socially relevant collective phenomena. The framework is validated against a detailed empirical analysis of five case studies. In particular, we identify a change in the characteristic time scale of the information transfer that flags the onset of information-driven collective phenomena. Furthermore, our approach identifies an order-disorder transition in the directed network of influence between social subunits. In the absence of clear exogenous driving, social collective phenomena can be represented as endogenously driven structural transitions of the information transfer network. This study provides results that can help define models and predictive algorithms for the analysis of societal events based on open source data.

Crystallization phenomena in slags

NASA Astrophysics Data System (ADS)

Orrling, Carl Folke

2000-09-01

The crystallization of the mold slag affects both the heat transfer and the lubrication between the mold and the strand in continuous casting of steel. In order for mold slag design to become an engineering science rather than an empirical exercise, a fundamental understanding of the melting and solidification behavior of a slag must be developed. Thus it is necessary to be able to quantify the phenomena that occur under the thermal conditions that are found in the mold of a continuous caster. The double hot thermocouple technique (DHTT) and the Confocal Laser Scanning Microscope used in this study are two novel techniques for investigating melting and solidification phenomena of transparent slags. Results from these techniques are useful in defining the phenomena that occur when the slag film infiltrates between the mold and the shell of the casting. TTT diagrams were obtained for various slags and indicated that the onset of crystallization is a function of cooling rate and slag chemistry. Crystal morphology was found to be dependent upon the experimental temperature and four different morphologies were classified based upon the degree of melt undercooling. Continuous cooling experiments were carried out to develop CCT diagrams and it was found that the amount and appearance of the crystalline fraction greatly depends on the cooling conditions. The DHTT can also be used to mimic the cooling profile encountered by the slag in the mold of a continuous caster. In this differential cooling mode (DCT), it was found that the details of the cooling rate determine the actual response of the slag to a thermal gradient and small changes can lead to significantly different results. Crystal growth rates were measured and found to be in the range between 0.11 mum/s to 11.73 mum/s depending on temperature and slag chemistry. Alumina particles were found to be effective innoculants in oxide melts reducing the incubation time for the onset of crystallization and also extending

Ventromedial Prefrontal Cortex Is Necessary for Normal Associative Inference and Memory Integration.

PubMed

Spalding, Kelsey N; Schlichting, Margaret L; Zeithamova, Dagmar; Preston, Alison R; Tranel, Daniel; Duff, Melissa C; Warren, David E

2018-04-11

The ability to flexibly combine existing knowledge in response to novel circumstances is highly adaptive. However, the neural correlates of flexible associative inference are not well characterized. Laboratory tests of associative inference have measured memory for overlapping pairs of studied items (e.g., AB, BC) and for nonstudied pairs with common associates (i.e., AC). Findings from functional neuroimaging and neuropsychology suggest the ventromedial prefrontal cortex (vmPFC) may be necessary for associative inference. Here, we used a neuropsychological approach to test the necessity of vmPFC for successful memory-guided associative inference in humans using an overlapping pairs associative memory task. We predicted that individuals with focal vmPFC damage ( n = 5; 3F, 2M) would show impaired inferential memory but intact non-inferential memory. Performance was compared with normal comparison participants ( n = 10; 6F, 4M). Participants studied pairs of visually presented objects including overlapping pairs (AB, BC) and nonoverlapping pairs (XY). Participants later completed a three-alternative forced-choice recognition task for studied pairs (AB, BC, XY) and inference pairs (AC). As predicted, the vmPFC group had intact memory for studied pairs but significantly impaired memory for inferential pairs. These results are consistent with the perspective that the vmPFC is necessary for memory-guided associative inference, indicating that the vmPFC is critical for adaptive abilities that require application of existing knowledge to novel circumstances. Additionally, vmPFC damage was associated with unexpectedly reduced memory for AB pairs post-inference, which could potentially reflect retroactive interference. Together, these results reinforce an emerging understanding of a role for the vmPFC in brain networks supporting associative memory processes. SIGNIFICANCE STATEMENT We live in a constantly changing environment, so the ability to adapt our knowledge to

Inferring causal molecular networks: empirical assessment through a community-based effort

PubMed Central

Hill, Steven M.; Heiser, Laura M.; Cokelaer, Thomas; Unger, Michael; Nesser, Nicole K.; Carlin, Daniel E.; Zhang, Yang; Sokolov, Artem; Paull, Evan O.; Wong, Chris K.; Graim, Kiley; Bivol, Adrian; Wang, Haizhou; Zhu, Fan; Afsari, Bahman; Danilova, Ludmila V.; Favorov, Alexander V.; Lee, Wai Shing; Taylor, Dane; Hu, Chenyue W.; Long, Byron L.; Noren, David P.; Bisberg, Alexander J.; Mills, Gordon B.; Gray, Joe W.; Kellen, Michael; Norman, Thea; Friend, Stephen; Qutub, Amina A.; Fertig, Elana J.; Guan, Yuanfang; Song, Mingzhou; Stuart, Joshua M.; Spellman, Paul T.; Koeppl, Heinz; Stolovitzky, Gustavo; Saez-Rodriguez, Julio; Mukherjee, Sach

2016-01-01

Inferring molecular networks is a central challenge in computational biology. However, it has remained unclear whether causal, rather than merely correlational, relationships can be effectively inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge that focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results constitute the most comprehensive assessment of causal network inference in a mammalian setting carried out to date and suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess the causal validity of inferred molecular networks. PMID:26901648

Contemporary Quantitative Methods and "Slow" Causal Inference: Response to Palinkas

ERIC Educational Resources Information Center

Stone, Susan

2014-01-01

This response considers together simultaneously occurring discussions about causal inference in social work and allied health and social science disciplines. It places emphasis on scholarship that integrates the potential outcomes model with directed acyclic graphing techniques to extract core steps in causal inference. Although this scholarship…

Uncertainty and inferred reserve estimates; the 1995 National Assessment

USGS Publications Warehouse

Attanasi, E.D.; Coburn, Timothy C.

2003-01-01

Inferred reserves are expected additions to proved reserves of oil and gas fields discovered as of a certain date. Inferred reserves accounted for 65 percent of the total oil and 34 percent of the total gas assessed in the U.S. Geological Survey's 1995 National Assessment of oil and gas in onshore and State offshore areas. The assessment predicted that over the 80-year period from 1992 through 2071, the sizes of pre-1992 discoveries in the lower 48 onshore and State offshore areas will increase by 48 billion barrels of oil (BBO) and 313 trillion cubic feet of wet gas (TCF). At that time, only point estimates were reported. This study presents a scheme to compute confidence intervals for these estimates. The recentered 90 percent confidence interval for the estimated inferred oil of 48 BBO is 25 BBO and 82 BBO. Similarly, the endpoints of the confidence interval about inferred reserve estimate of 313 TCF are 227 TCF and 439 TCF. The range of the estimates provides a basis for development of scenarios for projecting reserve additions and ultimately oil and gas production, information important to energy policy analysis.

Sowing the seeds of stereotypes: Spontaneous inferences about groups.

PubMed

Hamilton, David L; Chen, Jacqueline M; Ko, Deborah M; Winczewski, Lauren; Banerji, Ishani; Thurston, Joel A

2015-10-01

Although dispositional inferences may be consciously drawn from the trait implications of observed behavior, abundant research has shown that people also spontaneously infer trait dispositions simply in the process of comprehending behavior. These spontaneous trait inferences (STIs) can occur without intention or awareness. All research on STIs has studied STIs based on behaviors of individual persons. Yet important aspects of social life occur in groups, and people regularly perceive groups engaging in coordinated action. We propose that perceivers make spontaneous trait inferences about groups (STIGs), parallel to the STIs formed about individuals. In 5 experiments we showed that (a) perceivers made STIGs comparable with STIs about individuals (based on the same behaviors), (b) a cognitive load manipulation did not affect the occurrence of STIGs, (c) STIGs occurred for groups varying in entitativity, (d) STIGs influenced perceivers' impression ratings of those groups, and (e) STIG-based group impressions generalized to new group members. These experiments provide the first evidence for STIGs, a process that may contribute to the formation of spontaneous group impressions. Implications for stereotype formation are discussed. (c) 2015 APA, all rights reserved).

Inferring ontology graph structures using OWL reasoning.

PubMed

Rodríguez-García, Miguel Ángel; Hoehndorf, Robert

2018-01-05

Ontologies are representations of a conceptualization of a domain. Traditionally, ontologies in biology were represented as directed acyclic graphs (DAG) which represent the backbone taxonomy and additional relations between classes. These graphs are widely exploited for data analysis in the form of ontology enrichment or computation of semantic similarity. More recently, ontologies are developed in a formal language such as the Web Ontology Language (OWL) and consist of a set of axioms through which classes are defined or constrained. While the taxonomy of an ontology can be inferred directly from the axioms of an ontology as one of the standard OWL reasoning tasks, creating general graph structures from OWL ontologies that exploit the ontologies' semantic content remains a challenge. We developed a method to transform ontologies into graphs using an automated reasoner while taking into account all relations between classes. Searching for (existential) patterns in the deductive closure of ontologies, we can identify relations between classes that are implied but not asserted and generate graph structures that encode for a large part of the ontologies' semantic content. We demonstrate the advantages of our method by applying it to inference of protein-protein interactions through semantic similarity over the Gene Ontology and demonstrate that performance is increased when graph structures are inferred using deductive inference according to our method. Our software and experiment results are available at http://github.com/bio-ontology-research-group/Onto2Graph . Onto2Graph is a method to generate graph structures from OWL ontologies using automated reasoning. The resulting graphs can be used for improved ontology visualization and ontology-based data analysis.

Brain functional integration: an epidemiologic study on stress-producing dissociative phenomena.

PubMed

Sperandeo, Raffaele; Monda, Vincenzo; Messina, Giovanni; Carotenuto, Marco; Maldonato, Nelson Mauro; Moretto, Enrico; Leone, Elena; De Luca, Vincenzo; Monda, Marcellino; Messina, Antonietta

2018-01-01

Dissociative phenomena are common among psychiatric patients; the presence of these symptoms can worsen the prognosis, increasing the severity of their clinical conditions and exposing them to increased risk of suicidal behavior. Personality disorders as long duration stressful experiences may support the development of dissociative phenomena. In 933 psychiatric outpatients consecutively recruited, presence of dissociative phenomena was identified with the Dissociative Experience Scale (DES). Dissociative phenomena were significantly more severe in the group of people with mental disorders and/or personality disorders. All psychopathologic traits detected with the symptom checklist-90-revised had a significant correlation with the total score on the DES. Using total DES score as the dependent variable, a linear regression model was constructed. Mental and personality disorders which were associated with greater severity of dissociative phenomena on analysis of variance were included as predictors; scores from the nine scales of symptom checklist-90-revised, significantly correlated to total DES score, were used as covariates. The model consisted of seven explanatory variables (four factors and three covariates) explaining 82% of variance. The four significant factors were the presence of borderline and narcissistic personality disorder, substance abuse disorders and psychotic disorders. Significant covariates were psychopathologic traits of anger, psychoticism and obsessiveness. This study, confirming Janet's theory, explains that, mental disorders and psychopathologic experiences of patients can configure the chronic stress condition that produces functional damage to the adaptive executive system. The symptoms of dissociative depersonalization/derealization and dissociative amnesia can be explained, in large part, through their current and previous psychopathologic experiences.

Brain functional integration: an epidemiologic study on stress-producing dissociative phenomena

PubMed Central

Messina, Giovanni; Carotenuto, Marco; Maldonato, Nelson Mauro; Moretto, Enrico; Leone, Elena; De Luca, Vincenzo; Monda, Marcellino; Messina, Antonietta

2018-01-01

Dissociative phenomena are common among psychiatric patients; the presence of these symptoms can worsen the prognosis, increasing the severity of their clinical conditions and exposing them to increased risk of suicidal behavior. Personality disorders as long duration stressful experiences may support the development of dissociative phenomena. In 933 psychiatric outpatients consecutively recruited, presence of dissociative phenomena was identified with the Dissociative Experience Scale (DES). Dissociative phenomena were significantly more severe in the group of people with mental disorders and/or personality disorders. All psychopathologic traits detected with the symptom checklist-90-revised had a significant correlation with the total score on the DES. Using total DES score as the dependent variable, a linear regression model was constructed. Mental and personality disorders which were associated with greater severity of dissociative phenomena on analysis of variance were included as predictors; scores from the nine scales of symptom checklist-90-revised, significantly correlated to total DES score, were used as covariates. The model consisted of seven explanatory variables (four factors and three covariates) explaining 82% of variance. The four significant factors were the presence of borderline and narcissistic personality disorder, substance abuse disorders and psychotic disorders. Significant covariates were psychopathologic traits of anger, psychoticism and obsessiveness. This study, confirming Janet’s theory, explains that, mental disorders and psychopathologic experiences of patients can configure the chronic stress condition that produces functional damage to the adaptive executive system. The symptoms of dissociative depersonalization/derealization and dissociative amnesia can be explained, in large part, through their current and previous psychopathologic experiences. PMID:29296086

Learning about the internal structure of categories through classification and feature inference.

PubMed

Jee, Benjamin D; Wiley, Jennifer

2014-01-01

Previous research on category learning has found that classification tasks produce representations that are skewed toward diagnostic feature dimensions, whereas feature inference tasks lead to richer representations of within-category structure. Yet, prior studies often measure category knowledge through tasks that involve identifying only the typical features of a category. This neglects an important aspect of a category's internal structure: how typical and atypical features are distributed within a category. The present experiments tested the hypothesis that inference learning results in richer knowledge of internal category structure than classification learning. We introduced several new measures to probe learners' representations of within-category structure. Experiment 1 found that participants in the inference condition learned and used a wider range of feature dimensions than classification learners. Classification learners, however, were more sensitive to the presence of atypical features within categories. Experiment 2 provided converging evidence that classification learners were more likely to incorporate atypical features into their representations. Inference learners were less likely to encode atypical category features, even in a "partial inference" condition that focused learners' attention on the feature dimensions relevant to classification. Overall, these results are contrary to the hypothesis that inference learning produces superior knowledge of within-category structure. Although inference learning promoted representations that included a broad range of category-typical features, classification learning promoted greater sensitivity to the distribution of typical and atypical features within categories.

seXY: a tool for sex inference from genotype arrays.

PubMed

Qian, David C; Busam, Jonathan A; Xiao, Xiangjun; O'Mara, Tracy A; Eeles, Rosalind A; Schumacher, Frederick R; Phelan, Catherine M; Amos, Christopher I

2017-02-15

Checking concordance between reported sex and genotype-inferred sex is a crucial quality control measure in genome-wide association studies (GWAS). However, limited insights exist regarding the true accuracy of software that infer sex from genotype array data. We present seXY, a logistic regression model trained on both X chromosome heterozygosity and Y chromosome missingness, that consistently demonstrated >99.5% sex inference accuracy in cross-validation for 889 males and 5,361 females enrolled in prostate cancer and ovarian cancer GWAS. Compared to PLINK, one of the most popular tools for sex inference in GWAS that assesses only X chromosome heterozygosity, seXY achieved marginally better male classification and 3% more accurate female classification. https://github.com/Christopher-Amos-Lab/seXY. Christopher.I.Amos@dartmouth.edu. Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Laboratory Measurements of Cometary Photochemical Phenomena.

DTIC Science & Technology

1981-12-04

PROGFIAM ELEMENT.PROJECT TASK Laser .Chemistry Division AREA & WORK UNIT NUMaZRS Department of Chemistry - Howard University NR.051-733 Wash’ ngtQn, D. C...William M. Jackson Laser Chemistry Division Department of Chemistry Howard University .Washington, D. C. 20059 / Published by Jet Propulsion Laboratory...MEASUREMENTS OF COMETARY PHOTOCHEMICAL PHENOMENA William M. Jackson Howard University Washington, DC 20059 Abstract Laboratory experiments are described

Phenomena and Diosignes of Aratous

NASA Astrophysics Data System (ADS)

Avgoloupis, S. I.

2013-01-01

Aratous (305-240B.C.) was a singular intellectual, writer and poet which engage himself to compose a very interesting astronomical poet, using the "Dactylous sixstage' style, the formal style of the ancient Greek Epic poetry. This astronomic poem of Aratous "Phenomena and Diosignes" became very favorite reading during the Alexandrine, the Romman and the Byzandin eras as well and had received many praises from significant poets and particularly from Hipparchous and from Theonas from Alexandria, an astronomer of 4rth century A.C.(in Greeks)

Quantum phenomena in gravitational field

NASA Astrophysics Data System (ADS)

Bourdel, Th.; Doser, M.; Ernest, A. D.; Voronin, A. Yu.; Voronin, V. V.

2011-10-01

The subjects presented here are very different. Their common feature is that they all involve quantum phenomena in a gravitational field: gravitational quantum states of ultracold antihydrogen above a material surface and measuring a gravitational interaction of antihydrogen in AEGIS, a quantum trampoline for ultracold atoms, and a hypothesis on naturally occurring gravitational quantum states, an Eötvös-type experiment with cold neutrons and others. Considering them together, however, we could learn that they have many common points both in physics and in methodology.

Inferring mass in complex scenes by mental simulation.

PubMed

Hamrick, Jessica B; Battaglia, Peter W; Griffiths, Thomas L; Tenenbaum, Joshua B

2016-12-01

After observing a collision between two boxes, you can immediately tell which is empty and which is full of books based on how the boxes moved. People form rich perceptions about the physical properties of objects from their interactions, an ability that plays a crucial role in learning about the physical world through our experiences. Here, we present three experiments that demonstrate people's capacity to reason about the relative masses of objects in naturalistic 3D scenes. We find that people make accurate inferences, and that they continue to fine-tune their beliefs over time. To explain our results, we propose a cognitive model that combines Bayesian inference with approximate knowledge of Newtonian physics by estimating probabilities from noisy physical simulations. We find that this model accurately predicts judgments from our experiments, suggesting that the same simulation mechanism underlies both peoples' predictions and inferences about the physical world around them. Copyright © 2016 Elsevier B.V. All rights reserved.

Release phenomena and iterative activities in psychiatric geriatric patients

PubMed Central

Villeneuve, A.; Turcotte, J.; Bouchard, M.; Côté, J. M.; Jus, A.

1974-01-01

This survey was undertaken to assess the frequency of some of the so-called release phenomena and iterative activities in an aged psychiatric population. Three groups of geriatric psychiatric patients with diagnoses of (I) organic brain syndrome, including senile dementia (56), (II) functional psychoses, predominantly schizophrenia (51) and (III) chronic schizophrenia never treated by neuroleptics or other biologic agents (16), were compared with (IV) a control group of 32 elderly people in good physical and mental health. In general, for the manifestations studied, the geriatric psychiatric patients suffering from an organic brain syndrome and treated with neuroleptics differed notably from the control group. This latter group, although older, had few neurological signs of senescence and the spontaneous oral movements usually associated with the use of neuroleptics were absent. Release phenomena such as the grasp and pouting reflexes, as well as the stereotyped activities, were encountered significantly more frequently in patients with an organic brain syndrome than in the two other groups of patients. Our survey has yielded limited results with regard to the possible influence of type of illness and neuroleptic treatment on the incidence of release phenomena and iterative activities. PMID:4810188

Nonparametric Bayesian inference of the microcanonical stochastic block model

NASA Astrophysics Data System (ADS)

Peixoto, Tiago P.

2017-01-01

A principled approach to characterize the hidden modular structure of networks is to formulate generative models and then infer their parameters from data. When the desired structure is composed of modules or "communities," a suitable choice for this task is the stochastic block model (SBM), where nodes are divided into groups, and the placement of edges is conditioned on the group memberships. Here, we present a nonparametric Bayesian method to infer the modular structure of empirical networks, including the number of modules and their hierarchical organization. We focus on a microcanonical variant of the SBM, where the structure is imposed via hard constraints, i.e., the generated networks are not allowed to violate the patterns imposed by the model. We show how this simple model variation allows simultaneously for two important improvements over more traditional inference approaches: (1) deeper Bayesian hierarchies, with noninformative priors replaced by sequences of priors and hyperpriors, which not only remove limitations that seriously degrade the inference on large networks but also reveal structures at multiple scales; (2) a very efficient inference algorithm that scales well not only for networks with a large number of nodes and edges but also with an unlimited number of modules. We show also how this approach can be used to sample modular hierarchies from the posterior distribution, as well as to perform model selection. We discuss and analyze the differences between sampling from the posterior and simply finding the single parameter estimate that maximizes it. Furthermore, we expose a direct equivalence between our microcanonical approach and alternative derivations based on the canonical SBM.

Gene network inference by fusing data from diverse distributions

PubMed Central

Žitnik, Marinka; Zupan, Blaž

2015-01-01

Motivation: Markov networks are undirected graphical models that are widely used to infer relations between genes from experimental data. Their state-of-the-art inference procedures assume the data arise from a Gaussian distribution. High-throughput omics data, such as that from next generation sequencing, often violates this assumption. Furthermore, when collected data arise from multiple related but otherwise nonidentical distributions, their underlying networks are likely to have common features. New principled statistical approaches are needed that can deal with different data distributions and jointly consider collections of datasets. Results: We present FuseNet, a Markov network formulation that infers networks from a collection of nonidentically distributed datasets. Our approach is computationally efficient and general: given any number of distributions from an exponential family, FuseNet represents model parameters through shared latent factors that define neighborhoods of network nodes. In a simulation study, we demonstrate good predictive performance of FuseNet in comparison to several popular graphical models. We show its effectiveness in an application to breast cancer RNA-sequencing and somatic mutation data, a novel application of graphical models. Fusion of datasets offers substantial gains relative to inference of separate networks for each dataset. Our results demonstrate that network inference methods for non-Gaussian data can help in accurate modeling of the data generated by emergent high-throughput technologies. Availability and implementation: Source code is at https://github.com/marinkaz/fusenet. Contact: blaz.zupan@fri.uni-lj.si Supplementary information: Supplementary information is available at Bioinformatics online. PMID:26072487

Different states of the transient luminous phenomena in Hessdalen valley, Norway.

NASA Astrophysics Data System (ADS)

Hauge, B. G.; Montebugnoli, S.

2012-04-01

The transient luminous phenomena's in Hessdalen valley has at least been observed for 200 years, since 1811, when the priest Jacob T. Krogh did the first written documentation. The valley is located in the middle of Norway, isolated and with sub arctic climate. The former mining district has no more than 140 inhabitants, and the deep mines are closed and filled with water. The valley has been under scientific surveillance since 1998 when the first automated and remote controlled observatory was put into action. Today a Norwegian, Italian and French collaboration runs 3 different research stations inside the valley. Each year a scientific field campaign establishes 4 temporary bases in the mountains, and up to 100 students and researchers man these bases for up to 14 days in september when the moon is down. The Hessdalen phenomena is not easy to detect, and approximately only 20 observations is done each year. The work done the last 14 years suggests that the phenomenon has different states, at least 6 detected so far. The states are so different that to se a coupling between them is difficult. New work done into dusty plasma physics suggest that the different phenomena's may be of the same origin, since the ionized grains of dusty plasma can change states from weakly coupled (gaseous) to crystalline, altering shape/formation and leading to different phenomena. Optical spectrometry from 2007 suggested that the luminous phenomena consisted of burning air and dust from the valley. Work done by G.S Paiva and C.A Taft suggests that radon decay from closed mines may be the mechanism that ionizes dust and triggers this phenomena. The 6 different main states of the Hessdalen phenomena, Doublet, Fireball, Plasma ray, Dust cloud, Flash and Invisible state is described and discussed. Investigation of the atmosphere inside the Hessdalen valley with low frequency directional RADAR, reveals large areas of ionized matter, giving a reflecting area big enough to saturate the input

Strategic Processing and Predictive Inference Generation in L2 Reading

ERIC Educational Resources Information Center

Nahatame, Shingo

2014-01-01

Predictive inference is the anticipation of the likely consequences of events described in a text. This study investigated predictive inference generation during second language (L2) reading, with a focus on the effects of strategy instructions. In this experiment, Japanese university students read several short narrative passages designed to…

Fast Particle Methods for Multiscale Phenomena Simulations

NASA Technical Reports Server (NTRS)

Koumoutsakos, P.; Wray, A.; Shariff, K.; Pohorille, Andrew

2000-01-01

We are developing particle methods oriented at improving computational modeling capabilities of multiscale physical phenomena in : (i) high Reynolds number unsteady vortical flows, (ii) particle laden and interfacial flows, (iii)molecular dynamics studies of nanoscale droplets and studies of the structure, functions, and evolution of the earliest living cell. The unifying computational approach involves particle methods implemented in parallel computer architectures. The inherent adaptivity, robustness and efficiency of particle methods makes them a multidisciplinary computational tool capable of bridging the gap of micro-scale and continuum flow simulations. Using efficient tree data structures, multipole expansion algorithms, and improved particle-grid interpolation, particle methods allow for simulations using millions of computational elements, making possible the resolution of a wide range of length and time scales of these important physical phenomena.The current challenges in these simulations are in : [i] the proper formulation of particle methods in the molecular and continuous level for the discretization of the governing equations [ii] the resolution of the wide range of time and length scales governing the phenomena under investigation. [iii] the minimization of numerical artifacts that may interfere with the physics of the systems under consideration. [iv] the parallelization of processes such as tree traversal and grid-particle interpolations We are conducting simulations using vortex methods, molecular dynamics and smooth particle hydrodynamics, exploiting their unifying concepts such as : the solution of the N-body problem in parallel computers, highly accurate particle-particle and grid-particle interpolations, parallel FFT's and the formulation of processes such as diffusion in the context of particle methods. This approach enables us to transcend among seemingly unrelated areas of research.

The Role of Perspective Taking in How Children Connect Reference Frames When Explaining Astronomical Phenomena

ERIC Educational Resources Information Center

Plummer, Julia D.; Bower, Corinne A.; Liben, Lynn S.

2016-01-01

This study investigates the role of perspective-taking skills in how children explain spatially complex astronomical phenomena. Explaining many astronomical phenomena, especially those studied in elementary and middle school, requires shifting between an Earth-based description of the phenomena and a space-based reference frame. We studied 7- to…

OncoNEM: inferring tumor evolution from single-cell sequencing data.

PubMed

Ross, Edith M; Markowetz, Florian

2016-04-15

Single-cell sequencing promises a high-resolution view of genetic heterogeneity and clonal evolution in cancer. However, methods to infer tumor evolution from single-cell sequencing data lag behind methods developed for bulk-sequencing data. Here, we present OncoNEM, a probabilistic method for inferring intra-tumor evolutionary lineage trees from somatic single nucleotide variants of single cells. OncoNEM identifies homogeneous cellular subpopulations and infers their genotypes as well as a tree describing their evolutionary relationships. In simulation studies, we assess OncoNEM's robustness and benchmark its performance against competing methods. Finally, we show its applicability in case studies of muscle-invasive bladder cancer and essential thrombocythemia.

Illusory inferences from a disjunction of conditionals: a new mental models account.

PubMed

Barrouillet, P; Lecas, J F

2000-08-14

(Johnson-Laird, P.N., & Savary, F. (1999, Illusory inferences: a novel class of erroneous deductions. Cognition, 71, 191-229.) have recently presented a mental models account, based on the so-called principle of truth, for the occurrence of inferences that are compelling but invalid. This article presents an alternative account of the illusory inferences resulting from a disjunction of conditionals. In accordance with our modified theory of mental models of the conditional, we show that the way individuals represent conditionals leads them to misinterpret the locus of the disjunction and prevents them from drawing conclusions from a false conditional, thus accounting for the compelling character of the illusory inference.

Children's and Adults' Abilities To Use Episodic and Semantic Information To Derive Inferences.

ERIC Educational Resources Information Center

Bourg, Tammy M.; And Others

A study investigated children's and adults' abilities to derive inferences requiring the integration of two episodic premises (episodic inferences) and inferences requiring the integration of one episodic premise with extra-stimulus, semantic knowledge. Subjects, 95 kindergarten, third grade, seventh grade, and college students, watched either an…

Statistical Inference and Patterns of Inequality in the Global North

ERIC Educational Resources Information Center

Moran, Timothy Patrick

2006-01-01

Cross-national inequality trends have historically been a crucial field of inquiry across the social sciences, and new methodological techniques of statistical inference have recently improved the ability to analyze these trends over time. This paper applies Monte Carlo, bootstrap inference methods to the income surveys of the Luxembourg Income…

Wave Phenomena in an Acoustic Resonant Chamber

ERIC Educational Resources Information Center

Smith, Mary E.; And Others

1974-01-01

Discusses the design and operation of a high Q acoustical resonant chamber which can be used to demonstrate wave phenomena such as three-dimensional normal modes, Q values, densities of states, changes in the speed of sound, Fourier decomposition, damped harmonic oscillations, sound-absorbing properties, and perturbation and scattering problems.…

Fluid Physical and Transport Phenomena Studies aboard the International Space Station: Planned Experiments

NASA Technical Reports Server (NTRS)

Singh, Bhim S.

1999-01-01

This paper provides an overview of the microgravity fluid physics and transport phenomena experiments planned for the International Spare Station. NASA's Office of Life and Microgravity Science and Applications has established a world-class research program in fluid physics and transport phenomena. This program combines the vast expertise of the world research community with NASA's unique microgravity facilities with the objectives of gaining new insight into fluid phenomena by removing the confounding effect of gravity. Due to its criticality to many terrestrial and space-based processes and phenomena, fluid physics and transport phenomena play a central role in the NASA's Microgravity Program. Through widely publicized research announcement and well established peer-reviews, the program has been able to attract a number of world-class researchers and acquired a critical mass of investigations that is now adding rapidly to this field. Currently there arc a total of 106 ground-based and 20 candidate flight principal investigators conducting research in four major thrust areas in the program: complex flows, multiphase flow and phase change, interfacial phenomena, and dynamics and instabilities. The International Space Station (ISS) to be launched in 1998, provides the microgravity research community with a unprecedented opportunity to conduct long-duration microgravity experiments which can be controlled and operated from the Principal Investigators' own laboratory. Frequent planned shuttle flights to the Station will provide opportunities to conduct many more experiments than were previously possible. NASA Lewis Research Center is in the process of designing a Fluids and Combustion Facility (FCF) to be located in the Laboratory Module of the ISS that will not only accommodate multiple users but, allow a broad range of fluid physics and transport phenomena experiments to be conducted in a cost effective manner.

Science Shorts: Observation versus Inference

ERIC Educational Resources Information Center

Leager, Craig R.

2008-01-01

When you observe something, how do you know for sure what you are seeing, feeling, smelling, or hearing? Asking students to think critically about their encounters with the natural world will help to strengthen their understanding and application of the science-process skills of observation and inference. In the following lesson, students make…

Sample Size and Correlational Inference

ERIC Educational Resources Information Center

Anderson, Richard B.; Doherty, Michael E.; Friedrich, Jeff C.

2008-01-01

In 4 studies, the authors examined the hypothesis that the structure of the informational environment makes small samples more informative than large ones for drawing inferences about population correlations. The specific purpose of the studies was to test predictions arising from the signal detection simulations of R. B. Anderson, M. E. Doherty,…

Word Learning as Bayesian Inference

ERIC Educational Resources Information Center

Xu, Fei; Tenenbaum, Joshua B.

2007-01-01

The authors present a Bayesian framework for understanding how adults and children learn the meanings of words. The theory explains how learners can generalize meaningfully from just one or a few positive examples of a novel word's referents, by making rational inductive inferences that integrate prior knowledge about plausible word meanings with…

Perceptual Inference and Autistic Traits

ERIC Educational Resources Information Center

Skewes, Joshua C; Jegindø, Else-Marie; Gebauer, Line

2015-01-01

Autistic people are better at perceiving details. Major theories explain this in terms of bottom-up sensory mechanisms or in terms of top-down cognitive biases. Recently, it has become possible to link these theories within a common framework. This framework assumes that perception is implicit neural inference, combining sensory evidence with…

Improved orthologous databases to ease protozoan targets inference.

PubMed

Kotowski, Nelson; Jardim, Rodrigo; Dávila, Alberto M R

2015-09-29

Homology inference helps on identifying similarities, as well as differences among organisms, which provides a better insight on how closely related one might be to another. In addition, comparative genomics pipelines are widely adopted tools designed using different bioinformatics applications and algorithms. In this article, we propose a methodology to build improved orthologous databases with the potential to aid on protozoan target identification, one of the many tasks which benefit from comparative genomics tools. Our analyses are based on OrthoSearch, a comparative genomics pipeline originally designed to infer orthologs through protein-profile comparison, supported by an HMM, reciprocal best hits based approach. Our methodology allows OrthoSearch to confront two orthologous databases and to generate an improved new one. Such can be later used to infer potential protozoan targets through a similarity analysis against the human genome. The protein sequences of Cryptosporidium hominis, Entamoeba histolytica and Leishmania infantum genomes were comparatively analyzed against three orthologous databases: (i) EggNOG KOG, (ii) ProtozoaDB and (iii) Kegg Orthology (KO). That allowed us to create two new orthologous databases, "KO + EggNOG KOG" and "KO + EggNOG KOG + ProtozoaDB", with 16,938 and 27,701 orthologous groups, respectively. Such new orthologous databases were used for a regular OrthoSearch run. By confronting "KO + EggNOG KOG" and "KO + EggNOG KOG + ProtozoaDB" databases and protozoan species we were able to detect the following total of orthologous groups and coverage (relation between the inferred orthologous groups and the species total number of proteins): Cryptosporidium hominis: 1,821 (11 %) and 3,254 (12 %); Entamoeba histolytica: 2,245 (13 %) and 5,305 (19 %); Leishmania infantum: 2,702 (16 %) and 4,760 (17 %). Using our HMM-based methodology and the largest created orthologous database, it was possible to infer 13

Efficient fuzzy Bayesian inference algorithms for incorporating expert knowledge in parameter estimation

NASA Astrophysics Data System (ADS)

Rajabi, Mohammad Mahdi; Ataie-Ashtiani, Behzad

2016-05-01

Bayesian inference has traditionally been conceived as the proper framework for the formal incorporation of expert knowledge in parameter estimation of groundwater models. However, conventional Bayesian inference is incapable of taking into account the imprecision essentially embedded in expert provided information. In order to solve this problem, a number of extensions to conventional Bayesian inference have been introduced in recent years. One of these extensions is 'fuzzy Bayesian inference' which is the result of integrating fuzzy techniques into Bayesian statistics. Fuzzy Bayesian inference has a number of desirable features which makes it an attractive approach for incorporating expert knowledge in the parameter estimation process of groundwater models: (1) it is well adapted to the nature of expert provided information, (2) it allows to distinguishably model both uncertainty and imprecision, and (3) it presents a framework for fusing expert provided information regarding the various inputs of the Bayesian inference algorithm. However an important obstacle in employing fuzzy Bayesian inference in groundwater numerical modeling applications is the computational burden, as the required number of numerical model simulations often becomes extremely exhaustive and often computationally infeasible. In this paper, a novel approach of accelerating the fuzzy Bayesian inference algorithm is proposed which is based on using approximate posterior distributions derived from surrogate modeling, as a screening tool in the computations. The proposed approach is first applied to a synthetic test case of seawater intrusion (SWI) in a coastal aquifer. It is shown that for this synthetic test case, the proposed approach decreases the number of required numerical simulations by an order of magnitude. Then the proposed approach is applied to a real-world test case involving three-dimensional numerical modeling of SWI in Kish Island, located in the Persian Gulf. An expert

On the Inference of Functional Circadian Networks Using Granger Causality

PubMed Central

Pourzanjani, Arya; Herzog, Erik D.; Petzold, Linda R.

2015-01-01

Being able to infer one way direct connections in an oscillatory network such as the suprachiastmatic nucleus (SCN) of the mammalian brain using time series data is difficult but crucial to understanding network dynamics. Although techniques have been developed for inferring networks from time series data, there have been no attempts to adapt these techniques to infer directional connections in oscillatory time series, while accurately distinguishing between direct and indirect connections. In this paper an adaptation of Granger Causality is proposed that allows for inference of circadian networks and oscillatory networks in general called Adaptive Frequency Granger Causality (AFGC). Additionally, an extension of this method is proposed to infer networks with large numbers of cells called LASSO AFGC. The method was validated using simulated data from several different networks. For the smaller networks the method was able to identify all one way direct connections without identifying connections that were not present. For larger networks of up to twenty cells the method shows excellent performance in identifying true and false connections; this is quantified by an area-under-the-curve (AUC) 96.88%. We note that this method like other Granger Causality-based methods, is based on the detection of high frequency signals propagating between cell traces. Thus it requires a relatively high sampling rate and a network that can propagate high frequency signals. PMID:26413748

Foot anthropometry and morphology phenomena.

PubMed

Agić, Ante; Nikolić, Vasilije; Mijović, Budimir

2006-12-01

Foot structure description is important for many reasons. The foot anthropometric morphology phenomena are analyzed together with hidden biomechanical functionality in order to fully characterize foot structure and function. For younger Croatian population the scatter data of the individual foot variables were interpolated by multivariate statistics. Foot structure descriptors are influenced by many factors, as a style of life, race, climate, and things of the great importance in human society. Dominant descriptors are determined by principal component analysis. Some practical recommendation and conclusion for medical, sportswear and footwear practice are highlighted.

The Effects of an Inference-Making Strategy Taught with and without Goal Setting

ERIC Educational Resources Information Center

Reed, Deborah K.; Lynn, Devon

2016-01-01

This quasi-experimental study examined the effects of a strategy for making text-dependent inferences--with and without embedded self-regulation skills--on the reading comprehension of 24 middle-grade students with disabilities. Classes were randomly assigned to receive the inference intervention only (IO), inference + individual goal setting…

Assessing school-aged children's inference-making: the effect of story test format in listening comprehension.

PubMed

Freed, Jenny; Cain, Kate

2017-01-01

Comprehension is critical for classroom learning and educational success. Inferences are integral to good comprehension: successful comprehension requires the listener to generate local coherence inferences, which involve integrating information between clauses, and global coherence inferences, which involve integrating textual information with background knowledge to infer motivations, themes, etc. A central priority for the diagnosis of comprehension difficulties and our understanding of why these difficulties arise is the development of valid assessment instruments. We explored typically developing children's ability to make local and global coherence inferences using a novel assessment of listening comprehension. The aims were to determine whether children were more likely to make the target inferences when these were asked during story presentation versus after presentation of the story, and whether there were any age differences between conditions. Children in Years 3 (n = 29) and 5 (n = 31) listened to short stories presented either in a segmented format, in which questions to assess local and global coherence inferences were asked at specific points during story presentation, or in a whole format, when all the questions were asked after the story had been presented. There was developmental progression between age groups for both types of inference question. Children also scored higher on the global coherence inference questions than the local coherence inference questions. There was a benefit of the segmented format for younger children, particularly for the local inference questions. The results suggest that children are more likely to make target inferences if prompted during presentation of the story, and that this format is particularly facilitative for younger children and for local coherence inferences. This has implications for the design of comprehension assessments as well as for supporting children with comprehension difficulties in the classroom

Numerical simulation of anomalous wave phenomena in hot nuclear matter

NASA Astrophysics Data System (ADS)

Konyukhov, A. V.; Likhachev, A. P.

2015-11-01

The collective dynamic phenomena accompanying the collision of high-energy heavy ions are suggested to be approximately described in the framework of ideal relativistic hydrodynamics. If the transition from hadron state to quark-gluon plasma is the first-order phase transition (presently this view is prevailing), the hydrodynamic description of the nuclear matter must demonstrate several anomalous wave phenomena—such as the shock splitting and the formation of rarefaction shock and composite waves, which may be indicative of this transition. The present work is devoted to numerical study of these phenomena.

Image-Data Compression Using Edge-Optimizing Algorithm for WFA Inference.

ERIC Educational Resources Information Center

Culik, Karel II; Kari, Jarkko

1994-01-01

Presents an inference algorithm that produces a weighted finite automata (WFA), in particular, the grayness functions of graytone images. Image-data compression results based on the new inference algorithm produces a WFA with a relatively small number of edges. Image-data compression results alone and in combination with wavelets are discussed.…

The Importance of Statistical Modeling in Data Analysis and Inference

ERIC Educational Resources Information Center

Rollins, Derrick, Sr.

2017-01-01

Statistical inference simply means to draw a conclusion based on information that comes from data. Error bars are the most commonly used tool for data analysis and inference in chemical engineering data studies. This work demonstrates, using common types of data collection studies, the importance of specifying the statistical model for sound…

Deontic Introduction: A Theory of Inference from Is to Ought

ERIC Educational Resources Information Center

Elqayam, Shira; Thompson, Valerie A.; Wilkinson, Meredith R.; Evans, Jonathan St. B. T.; Over, David E.

2015-01-01

Humans have a unique ability to generate novel norms. Faced with the knowledge that there are hungry children in Somalia, we easily and naturally infer that we ought to donate to famine relief charities. Although a contentious and lively issue in metaethics, such inference from "is" to "ought" has not been systematically…

Sensory phenomena related to tics, obsessive-compulsive symptoms, and global functioning in Tourette syndrome.

PubMed

Kano, Yukiko; Matsuda, Natsumi; Nonaka, Maiko; Fujio, Miyuki; Kuwabara, Hitoshi; Kono, Toshiaki

2015-10-01

Sensory phenomena, including premonitory urges, are experienced by patients with Tourette syndrome (TS) and obsessive-compulsive disorder (OCD). The goal of the present study was to investigate such phenomena related to tics, obsessive-compulsive symptoms (OCS), and global functioning in Japanese patients with TS. Forty-one patients with TS were assessed using the University of São Paulo Sensory Phenomena Scale (USP-SPS), the Premonitory Urge for Tics Scale (PUTS), the Yale Global Tic Severity Scale (YGTSS), the Dimensional Yale-Brown Obsessive-Compulsive Scale (DY-BOCS), and the Global Assessment of Functioning (GAF) Scale. USP-SPS and PUTS total scores were significantly correlated with YGTSS total and vocal tics scores. Additionally, both sensory phenomena severity scores were significantly correlated with DY-BOCS total OCS scores. Of the six dimensional OCS scores, the USP-SPS scores were significantly correlated with measures of aggression and sexual/religious dimensions. Finally, the PUTS total scores were significantly and negatively correlated with GAF scores. By assessing premonitory urges and broader sensory phenomena, and by viewing OCS from a dimensional approach, this study provides significant insight into sensory phenomena related to tics, OCS, and global functioning in patients with TS. Copyright © 2015 Elsevier Inc. All rights reserved.

Causal learning and inference as a rational process: the new synthesis.

PubMed

Holyoak, Keith J; Cheng, Patricia W

2011-01-01

Over the past decade, an active line of research within the field of human causal learning and inference has converged on a general representational framework: causal models integrated with bayesian probabilistic inference. We describe this new synthesis, which views causal learning and inference as a fundamentally rational process, and review a sample of the empirical findings that support the causal framework over associative alternatives. Causal events, like all events in the distal world as opposed to our proximal perceptual input, are inherently unobservable. A central assumption of the causal approach is that humans (and potentially nonhuman animals) have been designed in such a way as to infer the most invariant causal relations for achieving their goals based on observed events. In contrast, the associative approach assumes that learners only acquire associations among important observed events, omitting the representation of the distal relations. By incorporating bayesian inference over distributions of causal strength and causal structures, along with noisy-logical (i.e., causal) functions for integrating the influences of multiple causes on a single effect, human judgments about causal strength and structure can be predicted accurately for relatively simple causal structures. Dynamic models of learning based on the causal framework can explain patterns of acquisition observed with serial presentation of contingency data and are consistent with available neuroimaging data. The approach has been extended to a diverse range of inductive tasks, including category-based and analogical inferences.

Learning and Inductive Inference

DTIC Science & Technology

1982-07-01

a set of graph grammars to describe visual scenes . Other researchers have applied graph grammars to the pattern recognition of handwritten characters...345 1. Issues / 345 2. Mostows’ operationalizer / 350 0. Learning from ezamples / 360 1. Issues / 3t60 2. Learning in control and pattern recognition ...art.icleis on rote learntinig and ailvice- tAik g. K(ennieth Clarkson contributed Ltte article on grmvit atical inference, anid Geoff’ lroiney wrote

Utilitarian Moral Judgment Exclusively Coheres with Inference from Is to Ought

PubMed Central

Elqayam, Shira; Wilkinson, Meredith R.; Thompson, Valerie A.; Over, David E.; Evans, Jonathan St. B. T.

2017-01-01

Faced with moral choice, people either judge according to pre-existing obligations (deontological judgment), or by taking into account the consequences of their actions (utilitarian judgment). We propose that the latter coheres with a more general cognitive mechanism – deontic introduction, the tendency to infer normative (‘deontic’) conclusions from descriptive premises (is-ought inference). Participants were presented with vignettes that allowed either deontological or utilitarian choice, and asked to draw a range of deontic conclusions, as well as judge the overall moral rightness of each choice separately. We predicted and found a selective defeasibility pattern, in which manipulations that suppressed deontic introduction also suppressed utilitarian moral judgment, but had little effect on deontological moral judgment. Thus, deontic introduction coheres with utilitarian moral judgment almost exclusively. We suggest a family of norm-generating informal inferences, in which normative conclusions are drawn from descriptive (although value-laden) premises. This family includes deontic introduction and utilitarian moral judgment as well as other informal inferences. We conclude with a call for greater integration of research in moral judgment and research into deontic reasoning and informal inference. PMID:28690572

Context recognition for a hyperintensional inference machine

NASA Astrophysics Data System (ADS)

Duží, Marie; Fait, Michal; Menšík, Marek

2017-07-01

The goal of this paper is to introduce the algorithm of context recognition in the functional programming language TIL-Script, which is a necessary condition for the implementation of the TIL-Script inference machine. The TIL-Script language is an operationally isomorphic syntactic variant of Tichý's Transparent Intensional Logic (TIL). From the formal point of view, TIL is a hyperintensional, partial, typed λ-calculus with procedural semantics. Hyperintensional, because TIL λ-terms denote procedures (defined as TIL constructions) producing set-theoretic functions rather than the functions themselves; partial, because TIL is a logic of partial functions; and typed, because all the entities of TIL ontology, including constructions, receive a type within a ramified hierarchy of types. These features make it possible to distinguish three levels of abstraction at which TIL constructions operate. At the highest hyperintensional level the object to operate on is a construction (though a higher-order construction is needed to present this lower-order construction as an object of predication). At the middle intensional level the object to operate on is the function presented, or constructed, by a construction, while at the lowest extensional level the object to operate on is the value (if any) of the presented function. Thus a necessary condition for the development of an inference machine for the TIL-Script language is recognizing a context in which a construction occurs, namely extensional, intensional and hyperintensional context, in order to determine the type of an argument at which a given inference rule can be properly applied. As a result, our logic does not flout logical rules of extensional logic, which makes it possible to develop a hyperintensional inference machine for the TIL-Script language.

Temporal Phenomena in the Korean Conjunctive Constructions

ERIC Educational Resources Information Center

Kim, Dongmin

2015-01-01

The goal of this study is to characterize the temporal phenomena in the Korean conjunctive constructions. These constructions consist of three components: a verbal stem, a clause medial temporal suffix, and a clause terminal suffix. This study focuses on both the temporality of the terminal connective suffixes and the grammatical meanings of the…

Large-scale phenomena, chapter 3, part D

NASA Technical Reports Server (NTRS)

1975-01-01

Oceanic phenomena with horizontal scales from approximately 100 km up to the widths of the oceans themselves are examined. Data include: shape of geoid, quasi-stationary anomalies due to spatial variations in sea density and steady current systems, and the time dependent variations due to tidal and meteorological forces and to varying currents.

RENT+: an improved method for inferring local genealogical trees from haplotypes with recombination

PubMed Central

Mirzaei, Sajad; Wu, Yufeng

2017-01-01

Abstract Motivation: Haplotypes from one or multiple related populations share a common genealogical history. If this shared genealogy can be inferred from haplotypes, it can be very useful for many population genetics problems. However, with the presence of recombination, the genealogical history of haplotypes is complex and cannot be represented by a single genealogical tree. Therefore, inference of genealogical history with recombination is much more challenging than the case of no recombination. Results: In this paper, we present a new approach called RENT+ for the inference of local genealogical trees from haplotypes with the presence of recombination. RENT+ builds on a previous genealogy inference approach called RENT, which infers a set of related genealogical trees at different genomic positions. RENT+ represents a significant improvement over RENT in the sense that it is more effective in extracting information contained in the haplotype data about the underlying genealogy than RENT. The key components of RENT+ are several greatly enhanced genealogy inference rules. Through simulation, we show that RENT+ is more efficient and accurate than several existing genealogy inference methods. As an application, we apply RENT+ in the inference of population demographic history from haplotypes, which outperforms several existing methods. Availability and Implementation: RENT+ is implemented in Java, and is freely available for download from: https://github.com/SajadMirzaei/RentPlus. Contacts: sajad@engr.uconn.edu or ywu@engr.uconn.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:28065901

Modeling the Perception of Audiovisual Distance: Bayesian Causal Inference and Other Models

PubMed Central

2016-01-01

Studies of audiovisual perception of distance are rare. Here, visual and auditory cue interactions in distance are tested against several multisensory models, including a modified causal inference model. In this causal inference model predictions of estimate distributions are included. In our study, the audiovisual perception of distance was overall better explained by Bayesian causal inference than by other traditional models, such as sensory dominance and mandatory integration, and no interaction. Causal inference resolved with probability matching yielded the best fit to the data. Finally, we propose that sensory weights can also be estimated from causal inference. The analysis of the sensory weights allows us to obtain windows within which there is an interaction between the audiovisual stimuli. We find that the visual stimulus always contributes by more than 80% to the perception of visual distance. The visual stimulus also contributes by more than 50% to the perception of auditory distance, but only within a mobile window of interaction, which ranges from 1 to 4 m. PMID:27959919

Computational Precision of Mental Inference as Critical Source of Human Choice Suboptimality.

PubMed

Drugowitsch, Jan; Wyart, Valentin; Devauchelle, Anne-Dominique; Koechlin, Etienne

2016-12-21

Making decisions in uncertain environments often requires combining multiple pieces of ambiguous information from external cues. In such conditions, human choices resemble optimal Bayesian inference, but typically show a large suboptimal variability whose origin remains poorly understood. In particular, this choice suboptimality might arise from imperfections in mental inference rather than in peripheral stages, such as sensory processing and response selection. Here, we dissociate these three sources of suboptimality in human choices based on combining multiple ambiguous cues. Using a novel quantitative approach for identifying the origin and structure of choice variability, we show that imperfections in inference alone cause a dominant fraction of suboptimal choices. Furthermore, two-thirds of this suboptimality appear to derive from the limited precision of neural computations implementing inference rather than from systematic deviations from Bayes-optimal inference. These findings set an upper bound on the accuracy and ultimate predictability of human choices in uncertain environments. Copyright © 2016 Elsevier Inc. All rights reserved.

New developments of a knowledge based system (VEG) for inferring vegetation characteristics

NASA Technical Reports Server (NTRS)

Kimes, D. S.; Harrison, P. A.; Harrison, P. R.

1992-01-01

An extraction technique for inferring physical and biological surface properties of vegetation using nadir and/or directional reflectance data as input has been developed. A knowledge-based system (VEG) accepts spectral data of an unknown target as input, determines the best strategy for inferring the desired vegetation characteristic, applies the strategy to the target data, and provides a rigorous estimate of the accuracy of the inference. Progress in developing the system is presented. VEG combines methods from remote sensing and artificial intelligence, and integrates input spectral measurements with diverse knowledge bases. VEG has been developed to (1) infer spectral hemispherical reflectance from any combination of nadir and/or off-nadir view angles; (2) test and develop new extraction techniques on an internal spectral database; (3) browse, plot, or analyze directional reflectance data in the system's spectral database; (4) discriminate between user-defined vegetation classes using spectral and directional reflectance relationships; and (5) infer unknown view angles from known view angles (known as view angle extension).

"Did You Climax or Are You Just Laughing at Me?" Rare Phenomena Associated With Orgasm.

PubMed

Reinert, Anna E; Simon, James A

2017-07-01

The study of the human orgasm has shown a core set of physiologic and psychological symptoms experienced by most individuals. The study of normal sheds light on the abnormal and has spotlighted rare physical and psychological symptoms experienced by some individuals in association with orgasm. These phenomena are rare and, as is typical of rare phenomena, their documentation in the medical literature is largely confined to case studies. To identify peri-orgasmic phenomena, defined as unusual physical or psychological symptoms subjectively experienced by some individuals as part of the orgasm response, distinct from the usual or normal orgasm response. A list of peri-orgasmic phenomena was made with help from sexual health colleagues and, using this list as a foundation, a literature search was performed of articles published in English. Publications included in this review report on physical or psychological phenomena at the time of orgasm that are distinct from psychological, whole-body, and genito-pelvic sensations commonly experienced at the time of orgasm. Cases of physical symptoms related to the physiology of sexual intercourse and not specifically to orgasm were excluded. Case studies of peri-orgasmic phenomena were reviewed, including cases describing cataplexy (weakness), crying, dysorgasmia, dysphoria, facial and/or ear pain, foot pain, headache, pruritus, laughter, panic attack, post-orgasm illness syndrome, seizures, and sneezing. The literature review confirms the existence of diverse and frequently replicated peri-orgasmic phenomena. The value of case studies is in the collection and recording of observations so that hypotheses can be formed about the observed phenomena. Accordingly, this review could inspire further research on the neurophysiologic mechanisms of orgasm. Reinert AE, Simon JA. "Did You Climax or Are You Just Laughing at Me?" Rare Phenomena Associated With Orgasm. Sex Med Rev 2017;5:275-281. Copyright © 2017 International Society for

Scaling phenomena in the Internet: Critically examining criticality

PubMed Central

Willinger, Walter; Govindan, Ramesh; Jamin, Sugih; Paxson, Vern; Shenker, Scott

2002-01-01

Recent Internet measurements have found pervasive evidence of some surprising scaling properties. The two we focus on in this paper are self-similar scaling in the burst patterns of Internet traffic and, in some contexts, scale-free structure in the network's interconnection topology. These findings have led to a number of proposed models or “explanations” of such “emergent” phenomena. Many of these explanations invoke concepts such as fractals, chaos, or self-organized criticality, mainly because these concepts are closely associated with scale invariance and power laws. We examine these criticality-based explanations of self-similar scaling behavior—of both traffic flows through the Internet and the Internet's topology—to see whether they indeed explain the observed phenomena. To do so, we bring to bear a simple validation framework that aims at testing whether a proposed model is merely evocative, in that it can reproduce the phenomenon of interest but does not necessarily capture and incorporate the true underlying cause, or indeed explanatory, in that it also captures the causal mechanisms (why and how, in addition to what). We argue that the framework can provide a basis for developing a useful, consistent, and verifiable theory of large networks such as the Internet. Applying the framework, we find that, whereas the proposed criticality-based models are able to produce the observed “emergent” phenomena, they unfortunately fail as sound explanations of why such scaling behavior arises in the Internet. PMID:11875212

Assessment of precursory information in seismo-electromagnetic phenomena

NASA Astrophysics Data System (ADS)

Han, P.; Hattori, K.; Zhuang, J.

2017-12-01

Previous statistical studies showed that there were correlations between seismo-electromagnetic phenomena and sizeable earthquakes in Japan. In this study, utilizing Molchan's error diagram, we evaluate whether these phenomena contain precursory information and discuss how they can be used in short-term forecasting of large earthquake events. In practice, for given series of precursory signals and related earthquake events, each prediction strategy is characterized by the leading time of alarms, the length of alarm window, the alarm radius (area) and magnitude. The leading time is the time length between a detected anomaly and its following alarm, and the alarm window is the duration that an alarm lasts. The alarm radius and magnitude are maximum predictable distance and minimum predictable magnitude of earthquake events, respectively. We introduce the modified probability gain (PG') and the probability difference (D') to quantify the forecasting performance and to explore the optimal prediction parameters for a given electromagnetic observation. The above methodology is firstly applied to ULF magnetic data and GPS-TEC data. The results show that the earthquake predictions based on electromagnetic anomalies are significantly better than random guesses, indicating the data contain potential useful precursory information. Meanwhile, we reveal the optimal prediction parameters for both observations. The methodology proposed in this study could be also applied to other pre-earthquake phenomena to find out whether there is precursory information, and then on this base explore the optimal alarm parameters in practical short-term forecast.

Preface: cardiac control pathways: signaling and transport phenomena.

PubMed

Sideman, Samuel

2008-03-01

Signaling is part of a complex system of communication that governs basic cellular functions and coordinates cellular activity. Transfer of ions and signaling molecules and their interactions with appropriate receptors, transmembrane transport, and the consequent intracellular interactions and functional cellular response represent a complex system of interwoven phenomena of transport, signaling, conformational changes, chemical activation, and/or genetic expression. The well-being of the cell thus depends on a harmonic orchestration of all these events and the existence of control mechanisms that assure the normal behavior of the various parameters involved and their orderly expression. The ability of cells to sustain life by perceiving and responding correctly to their microenvironment is the basis for development, tissue repair, and immunity, as well as normal tissue homeostasis. Natural deviations, or human-induced interference in the signaling pathways and/or inter- and intracellular transport and information transfer, are responsible for the generation, modulation, and control of diseases. The present overview aims to highlight some major topics of the highly complex cellular information transfer processes and their control mechanisms. Our goal is to contribute to the understanding of the normal and pathophysiological phenomena associated with cardiac functions so that more efficient therapeutic modalities can be developed. Our objective in this volume is to identify and enhance the study of some basic passive and active physical and chemical transport phenomena, physiological signaling pathways, and their biological consequences.

Efficient inference for genetic association studies with multiple outcomes.

PubMed

Ruffieux, Helene; Davison, Anthony C; Hager, Jorg; Irincheeva, Irina

2017-10-01

Combined inference for heterogeneous high-dimensional data is critical in modern biology, where clinical and various kinds of molecular data may be available from a single study. Classical genetic association studies regress a single clinical outcome on many genetic variants one by one, but there is an increasing demand for joint analysis of many molecular outcomes and genetic variants in order to unravel functional interactions. Unfortunately, most existing approaches to joint modeling are either too simplistic to be powerful or are impracticable for computational reasons. Inspired by Richardson and others (2010, Bayesian Statistics 9), we consider a sparse multivariate regression model that allows simultaneous selection of predictors and associated responses. As Markov chain Monte Carlo (MCMC) inference on such models can be prohibitively slow when the number of genetic variants exceeds a few thousand, we propose a variational inference approach which produces posterior information very close to that of MCMC inference, at a much reduced computational cost. Extensive numerical experiments show that our approach outperforms popular variable selection methods and tailored Bayesian procedures, dealing within hours with problems involving hundreds of thousands of genetic variants and tens to hundreds of clinical or molecular outcomes. © The Author 2017. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Visual Inference Programming

NASA Technical Reports Server (NTRS)

Wheeler, Kevin; Timucin, Dogan; Rabbette, Maura; Curry, Charles; Allan, Mark; Lvov, Nikolay; Clanton, Sam; Pilewskie, Peter

2002-01-01

The goal of visual inference programming is to develop a software framework data analysis and to provide machine learning algorithms for inter-active data exploration and visualization. The topics include: 1) Intelligent Data Understanding (IDU) framework; 2) Challenge problems; 3) What's new here; 4) Framework features; 5) Wiring diagram; 6) Generated script; 7) Results of script; 8) Initial algorithms; 9) Independent Component Analysis for instrument diagnosis; 10) Output sensory mapping virtual joystick; 11) Output sensory mapping typing; 12) Closed-loop feedback mu-rhythm control; 13) Closed-loop training; 14) Data sources; and 15) Algorithms. This paper is in viewgraph form.

The Effect of Gender on the Construction of Backward Inferences

ERIC Educational Resources Information Center

Cakir, Ozler

2008-01-01

The main objective in the present study is to examine the effect of gender on primary school students' construction of elaborative backward inferences during text processing. A total of 333 children, aged 10-11 years (n = 158 girls and 175 boys) participated in the study. Each participant completed a backward inference test. The results indicate…

Network Model-Assisted Inference from Respondent-Driven Sampling Data.

PubMed

Gile, Krista J; Handcock, Mark S

2015-06-01

Respondent-Driven Sampling is a widely-used method for sampling hard-to-reach human populations by link-tracing over their social networks. Inference from such data requires specialized techniques because the sampling process is both partially beyond the control of the researcher, and partially implicitly defined. Therefore, it is not generally possible to directly compute the sampling weights for traditional design-based inference, and likelihood inference requires modeling the complex sampling process. As an alternative, we introduce a model-assisted approach, resulting in a design-based estimator leveraging a working network model. We derive a new class of estimators for population means and a corresponding bootstrap standard error estimator. We demonstrate improved performance compared to existing estimators, including adjustment for an initial convenience sample. We also apply the method and an extension to the estimation of HIV prevalence in a high-risk population.

Inferring Time-Varying Network Topologies from Gene Expression Data

PubMed Central

2007-01-01

Most current methods for gene regulatory network identification lead to the inference of steady-state networks, that is, networks prevalent over all times, a hypothesis which has been challenged. There has been a need to infer and represent networks in a dynamic, that is, time-varying fashion, in order to account for different cellular states affecting the interactions amongst genes. In this work, we present an approach, regime-SSM, to understand gene regulatory networks within such a dynamic setting. The approach uses a clustering method based on these underlying dynamics, followed by system identification using a state-space model for each learnt cluster—to infer a network adjacency matrix. We finally indicate our results on the mouse embryonic kidney dataset as well as the T-cell activation-based expression dataset and demonstrate conformity with reported experimental evidence. PMID:18309363

Inferring time-varying network topologies from gene expression data.

PubMed

Rao, Arvind; Hero, Alfred O; States, David J; Engel, James Douglas

2007-01-01

Most current methods for gene regulatory network identification lead to the inference of steady-state networks, that is, networks prevalent over all times, a hypothesis which has been challenged. There has been a need to infer and represent networks in a dynamic, that is, time-varying fashion, in order to account for different cellular states affecting the interactions amongst genes. In this work, we present an approach, regime-SSM, to understand gene regulatory networks within such a dynamic setting. The approach uses a clustering method based on these underlying dynamics, followed by system identification using a state-space model for each learnt cluster--to infer a network adjacency matrix. We finally indicate our results on the mouse embryonic kidney dataset as well as the T-cell activation-based expression dataset and demonstrate conformity with reported experimental evidence.

Context Inference for Mobile Applications in the UPCASE Project

NASA Astrophysics Data System (ADS)

Santos, André C.; Tarrataca, Luís; Cardoso, João M. P.; Ferreira, Diogo R.; Diniz, Pedro C.; Chainho, Paulo

The growing processing capabilities of mobile devices coupled with portable and wearable sensors have enabled the development of context-aware services tailored to the user environment and its daily activities. The problem of determining the user context at each particular point in time is one of the main challenges in this area. In this paper, we describe the approach pursued in the UPCASE project, which makes use of sensors available in the mobile device as well as sensors externally connected via Bluetooth. We describe the system architecture from raw data acquisition to feature extraction and context inference. As a proof of concept, the inference of contexts is based on a decision tree to learn and identify contexts automatically and dynamically at runtime. Preliminary results suggest that this is a promising approach for context inference in several application scenarios.

Adaptive inference for distinguishing credible from incredible patterns in nature

USGS Publications Warehouse

Holling, Crawford S.; Allen, Craig R.

2002-01-01

Strong inference is a powerful and rapid tool that can be used to identify and explain patterns in molecular biology, cell biology, and physiology. It is effective where causes are single and separable and where discrimination between pairwise alternative hypotheses can be determined experimentally by a simple yes or no answer. But causes in ecological systems are multiple and overlapping and are not entirely separable. Frequently, competing hypotheses cannot be distinguished by a single unambiguous test, but only by a suite of tests of different kinds, that produce a body of evidence to support one line of argument and not others. We call this process "adaptive inference". Instead of pitting each member of a pair of hypotheses against each other, adaptive inference relies on the exuberant invention of multiple, competing hypotheses, after which carefully structured comparative data are used to explore the logical consequences of each. Herein we present an example that demonstrates the attributes of adaptive inference that have developed out of a 30-year study of the resilience of ecosystems.

Inference Processing in Adolescents with Asperger Syndrome: Relationship with Theory of Mind Abilities

ERIC Educational Resources Information Center

Le Sourn-Bissaoui, Sandrine; Caillies, Stephanie; Gierski, Fabien; Motte, Jacques

2009-01-01

The aim of this study was to investigate the role of theory of mind competence in inference processing in adolescents with Asperger syndrome (AS). We sought to pinpoint the level at which AS individuals experience difficulty drawing inferences and identify the factors that account for their inference-drawing problems. We hypothesized that this…

Understanding the coherence of the severity effect and optimism phenomena: Lessons from attention.

PubMed

Harris, Adam J L

2017-04-01

Claims that optimism is a near-universal characteristic of human judgment seem to be at odds with recent results from the judgment and decision making literature suggesting that the likelihood of negative outcomes are overestimated relative to neutral outcomes. In an attempt to reconcile these seemingly contrasting phenomena, inspiration is drawn from the attention literature in which there is evidence that both positive and negative stimuli can have attentional privilege relative to neutral stimuli. This result provides a framework within which I consider three example phenomena that purport to demonstrate that people's likelihood estimates are optimistic: Wishful thinking; Unrealistic comparative optimism and Asymmetric belief updating. The framework clarifies the relationships between these phenomena and stimulates future research questions. Generally, whilst results from the first two phenomena appear reconcilable in this conceptualisation, further research is required in reconciling the third. Copyright © 2016 Elsevier Inc. All rights reserved.

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.

Inferring Genetic Ancestry: Opportunities, Challenges, and Implications

PubMed Central

Royal, Charmaine D.; Novembre, John; Fullerton, Stephanie M.; Goldstein, David B.; Long, Jeffrey C.; Bamshad, Michael J.; Clark, Andrew G.

2010-01-01

Increasing public interest in direct-to-consumer (DTC) genetic ancestry testing has been accompanied by growing concern about issues ranging from the personal and societal implications of the testing to the scientific validity of ancestry inference. The very concept of “ancestry” is subject to misunderstanding in both the general and scientific communities. What do we mean by ancestry? How exactly is ancestry measured? How far back can such ancestry be defined and by which genetic tools? How do we validate inferences about ancestry in genetic research? What are the data that demonstrate our ability to do this correctly? What can we say and what can we not say from our research findings and the test results that we generate? This white paper from the American Society of Human Genetics (ASHG) Ancestry and Ancestry Testing Task Force builds upon the 2008 ASHG Ancestry Testing Summary Statement in providing a more in-depth analysis of key scientific and non-scientific aspects of genetic ancestry inference in academia and industry. It culminates with recommendations for advancing the current debate and facilitating the development of scientifically based, ethically sound, and socially attentive guidelines concerning the use of these continually evolving technologies. PMID:20466090

Theoretical Studies of Nonlinear Phenomena in Plasmas.

DTIC Science & Technology

1984-02-14

AD-Ai38 762 THEORETICAL STUDIES OF NONLINEAR PHENOMENA IN PLASMAS i/i, (U) MARYLAND UNIV COLLEUE FHRK LAB FOR PLASMA AND FUSION ENERGY STUDIES H H...Tnvestiga-tor: H. Chen 4 j DTIC $ELECTE ~ ~.UNIVERSITY OF MARYLAND * S 4ABORATORY FOR PLASMA AND FUSION ENERGY STUDIES " ’ ..COLLEGE PARK, MARYLAND 4k

Physics education students’ cognitive and affective domains toward ecological phenomena

NASA Astrophysics Data System (ADS)

Napitupulu, N. D.; Munandar, A.; Redjeki, S.; Tjasyono, B.

2018-05-01

Environmental education is become prominent in dealing with natural phenomena that occur nowadays. Studying environmental physics will lead students to have conceptual understanding which are importent in enhancing attitudes toward ecological phenomena that link directry to cognitive and affective domains. This research focused on the the relationship of cognitive and affective domains toward ecological phenomena. Thirty-seven Physics Education students participated in this study and validated sources of data were collected to eksplore students’ conceptual understanding as cognitive domain and to investigate students’ attitudes as affective domain. The percentage of cognitive outcome and affective outcome are explore. The features of such approaches to environmental learning are discussion through analysis of contribution of cognitive to develop the attitude ecological as affective outcome. The result shows that cognitive domains do not contribute significantly to affective domain toward ecological henomena as an issue trend in Central Sulawesi although students had passed Environmental Physics instruction for two semester. In fact, inferior knowledge in a way actually contributes to the attitude domain caused by the prior knowledge that students have as ombo as a Kaili local wisdom.

Complex (dusty) plasmas-kinetic studies of strong coupling phenomena

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

Morfill, Gregor E.; Ivlev, Alexei V.; Thomas, Hubertus M.

2012-05-15

'Dusty plasmas' can be found almost everywhere-in the interstellar medium, in star and planet formation, in the solar system in the Earth's atmosphere, and in the laboratory. In astrophysical plasmas, the dust component accounts for only about 1% of the mass, nevertheless this component has a profound influence on the thermodynamics, the chemistry, and the dynamics. Important physical processes are charging, sputtering, cooling, light absorption, and radiation pressure, connecting electromagnetic forces to gravity. Surface chemistry is another important aspect. In the laboratory, there is great interest in industrial processes (e.g., etching, vapor deposition) and-at the fundamental level-in the physics ofmore » strong coupling phenomena. Here, the dust (or microparticles) are the dominant component of the multi-species plasma. The particles can be observed in real time and space, individually resolved at all relevant length and time scales. This provides an unprecedented means for studying self-organisation processes in many-particle systems, including the onset of cooperative phenomena. Due to the comparatively large mass of the microparticles (10{sup -12}to10{sup -9}g), precision experiments are performed on the ISS. The following topics will be discussed: Phase transitions, phase separation, electrorheology, flow phenomena including the onset of turbulence at the kinetic level.« less

Evaluation of artificial time series microarray data for dynamic gene regulatory network inference.

PubMed

Xenitidis, P; Seimenis, I; Kakolyris, S; Adamopoulos, A

2017-08-07

High-throughput technology like microarrays is widely used in the inference of gene regulatory networks (GRNs). We focused on time series data since we are interested in the dynamics of GRNs and the identification of dynamic networks. We evaluated the amount of information that exists in artificial time series microarray data and the ability of an inference process to produce accurate models based on them. We used dynamic artificial gene regulatory networks in order to create artificial microarray data. Key features that characterize microarray data such as the time separation of directly triggered genes, the percentage of directly triggered genes and the triggering function type were altered in order to reveal the limits that are imposed by the nature of microarray data on the inference process. We examined the effect of various factors on the inference performance such as the network size, the presence of noise in microarray data, and the network sparseness. We used a system theory approach and examined the relationship between the pole placement of the inferred system and the inference performance. We examined the relationship between the inference performance in the time domain and the true system parameter identification. Simulation results indicated that time separation and the percentage of directly triggered genes are crucial factors. Also, network sparseness, the triggering function type and noise in input data affect the inference performance. When two factors were simultaneously varied, it was found that variation of one parameter significantly affects the dynamic response of the other. Crucial factors were also examined using a real GRN and acquired results confirmed simulation findings with artificial data. Different initial conditions were also used as an alternative triggering approach. Relevant results confirmed that the number of datasets constitutes the most significant parameter with regard to the inference performance. Copyright © 2017 Elsevier

Inferences in the Comprehension of and Memory for Text. Technical Report No. 49.

ERIC Educational Resources Information Center

Goetz, Ernest T.

Two studies investigated whether variations in the importance of inferences and the salience of premises within a text would affect the probability that the inference would be made. Six stories of about 500 words were used, with eight variations of each story. The target inference, and its plausibility, was constant across all versions. Inference…

Computational approaches to protein inference in shotgun proteomics

PubMed Central

2012-01-01

Shotgun proteomics has recently emerged as a powerful approach to characterizing proteomes in biological samples. Its overall objective is to identify the form and quantity of each protein in a high-throughput manner by coupling liquid chromatography with tandem mass spectrometry. As a consequence of its high throughput nature, shotgun proteomics faces challenges with respect to the analysis and interpretation of experimental data. Among such challenges, the identification of proteins present in a sample has been recognized as an important computational task. This task generally consists of (1) assigning experimental tandem mass spectra to peptides derived from a protein database, and (2) mapping assigned peptides to proteins and quantifying the confidence of identified proteins. Protein identification is fundamentally a statistical inference problem with a number of methods proposed to address its challenges. In this review we categorize current approaches into rule-based, combinatorial optimization and probabilistic inference techniques, and present them using integer programing and Bayesian inference frameworks. We also discuss the main challenges of protein identification and propose potential solutions with the goal of spurring innovative research in this area. PMID:23176300

Inference of epidemiological parameters from household stratified data

PubMed Central

Walker, James N.; Ross, Joshua V.

2017-01-01

We consider a continuous-time Markov chain model of SIR disease dynamics with two levels of mixing. For this so-called stochastic households model, we provide two methods for inferring the model parameters—governing within-household transmission, recovery, and between-household transmission—from data of the day upon which each individual became infectious and the household in which each infection occurred, as might be available from First Few Hundred studies. Each method is a form of Bayesian Markov Chain Monte Carlo that allows us to calculate a joint posterior distribution for all parameters and hence the household reproduction number and the early growth rate of the epidemic. The first method performs exact Bayesian inference using a standard data-augmentation approach; the second performs approximate Bayesian inference based on a likelihood approximation derived from branching processes. These methods are compared for computational efficiency and posteriors from each are compared. The branching process is shown to be a good approximation and remains computationally efficient as the amount of data is increased. PMID:29045456

Confidence set inference with a prior quadratic bound

NASA Technical Reports Server (NTRS)

Backus, George E.

1989-01-01

In the uniqueness part of a geophysical inverse problem, the observer wants to predict all likely values of P unknown numerical properties z=(z sub 1,...,z sub p) of the earth from measurement of D other numerical properties y (sup 0) = (y (sub 1) (sup 0), ..., y (sub D (sup 0)), using full or partial knowledge of the statistical distribution of the random errors in y (sup 0). The data space Y containing y(sup 0) is D-dimensional, so when the model space X is infinite-dimensional the linear uniqueness problem usually is insoluble without prior information about the correct earth model x. If that information is a quadratic bound on x, Bayesian inference (BI) and stochastic inversion (SI) inject spurious structure into x, implied by neither the data nor the quadratic bound. Confidence set inference (CSI) provides an alternative inversion technique free of this objection. Confidence set inference is illustrated in the problem of estimating the geomagnetic field B at the core-mantle boundary (CMB) from components of B measured on or above the earth's surface.

Thermo-Chemical Phenomena Simulation for Ablation

DTIC Science & Technology

2011-02-21

DATES COVERED (1/01/08-30/11/10) 4. TITLE AND SUBTITLE Thermo- Chemical Phenomena Simulation for Ablation 5a. CONTRACT NUMBER...First, a physic based chemical kinetic model for high-temperature gas is developed and verified by comparing with data from the RAM-C-II probe and the...found to be negligible and the energy exchange is dominated by the chemical process for conductive-convective heat transfer. A simplified and more

Ontological Constraints in Children's Inductive Inferences: Evidence From a Comparison of Inferences Within Animals and Vehicles.

PubMed

Tarlowski, Andrzej

2018-01-01

There is a lively debate concerning the role of conceptual and perceptual information in young children's inductive inferences. While most studies focus on the role of basic level categories in induction the present research contributes to the debate by asking whether children's inductions are guided by ontological constraints. Two studies use a novel inductive paradigm to test whether young children have an expectation that all animals share internal commonalities that do not extend to perceptually similar inanimates. The results show that children make category-consistent responses when asked to project an internal feature from an animal to either a dissimilar animal or a similar toy replica. However, the children do not have a universal preference for category-consistent responses in an analogous task involving vehicles and vehicle toy replicas. The results also show the role of context and individual factors in inferences. Children's early reliance on ontological commitments in induction cannot be explained by perceptual similarity or by children's sensitivity to the authenticity of objects.

Ontological Constraints in Children's Inductive Inferences: Evidence From a Comparison of Inferences Within Animals and Vehicles

PubMed Central

Tarlowski, Andrzej

2018-01-01

There is a lively debate concerning the role of conceptual and perceptual information in young children's inductive inferences. While most studies focus on the role of basic level categories in induction the present research contributes to the debate by asking whether children's inductions are guided by ontological constraints. Two studies use a novel inductive paradigm to test whether young children have an expectation that all animals share internal commonalities that do not extend to perceptually similar inanimates. The results show that children make category-consistent responses when asked to project an internal feature from an animal to either a dissimilar animal or a similar toy replica. However, the children do not have a universal preference for category-consistent responses in an analogous task involving vehicles and vehicle toy replicas. The results also show the role of context and individual factors in inferences. Children's early reliance on ontological commitments in induction cannot be explained by perceptual similarity or by children's sensitivity to the authenticity of objects. PMID:29760669

Inference of gene regulatory networks from time series by Tsallis entropy

PubMed Central

2011-01-01

Background The inference of gene regulatory networks (GRNs) from large-scale expression profiles is one of the most challenging problems of Systems Biology nowadays. Many techniques and models have been proposed for this task. However, it is not generally possible to recover the original topology with great accuracy, mainly due to the short time series data in face of the high complexity of the networks and the intrinsic noise of the expression measurements. In order to improve the accuracy of GRNs inference methods based on entropy (mutual information), a new criterion function is here proposed. Results In this paper we introduce the use of generalized entropy proposed by Tsallis, for the inference of GRNs from time series expression profiles. The inference process is based on a feature selection approach and the conditional entropy is applied as criterion function. In order to assess the proposed methodology, the algorithm is applied to recover the network topology from temporal expressions generated by an artificial gene network (AGN) model as well as from the DREAM challenge. The adopted AGN is based on theoretical models of complex networks and its gene transference function is obtained from random drawing on the set of possible Boolean functions, thus creating its dynamics. On the other hand, DREAM time series data presents variation of network size and its topologies are based on real networks. The dynamics are generated by continuous differential equations with noise and perturbation. By adopting both data sources, it is possible to estimate the average quality of the inference with respect to different network topologies, transfer functions and network sizes. Conclusions A remarkable improvement of accuracy was observed in the experimental results by reducing the number of false connections in the inferred topology by the non-Shannon entropy. The obtained best free parameter of the Tsallis entropy was on average in the range 2.5 ≤ q ≤ 3.5 (hence

Probing Cytological and Reproductive Phenomena by Means of Bryophytes.

ERIC Educational Resources Information Center

Newton, M. E.

1985-01-01

Describes procedures (recommended for both secondary and college levels) to study mitosis, Giemsa C-banding, reproductive phenomena (including alternation of generations), and phototropism in mosses and liverworts. (JN)

Autoimmune phenomena following prostatectomy.

PubMed

Tweezer-Zaks, Nurit; Marai, Ibrahim; Livneh, Avi; Bank, Ilan; Langevitz, Pnina

2005-09-01

Benign prostatic hypertrophy is the most common benign tumor in males, resulting in prostatectomy in 20-30% of men who live to the age of 80. There are no data on the association of prostatectomy with autoimmune phenomena in the English-language medical literature. To report our experience with three patients who developed autoimmune disease following prostatectomy. Three patients presented awith autoimmune phenomenon soon after a prostectomy for BPH or prostatic carcinoma: one had clinically diagnosed temporal arteritis, one had leukocytoclastic vasculitis, and the third patient developed sensory Guillian-Barré syndrome following prostatectomy. In view of the temporal association between the removal of the prostate gland andthe autoimmune process, combined with previously known immunohistologic features of BPH, a cause-effect relationship probably exists.

RENT+: an improved method for inferring local genealogical trees from haplotypes with recombination.

PubMed

Mirzaei, Sajad; Wu, Yufeng

2017-04-01

: Haplotypes from one or multiple related populations share a common genealogical history. If this shared genealogy can be inferred from haplotypes, it can be very useful for many population genetics problems. However, with the presence of recombination, the genealogical history of haplotypes is complex and cannot be represented by a single genealogical tree. Therefore, inference of genealogical history with recombination is much more challenging than the case of no recombination. : In this paper, we present a new approach called RENT+  for the inference of local genealogical trees from haplotypes with the presence of recombination. RENT+  builds on a previous genealogy inference approach called RENT , which infers a set of related genealogical trees at different genomic positions. RENT+  represents a significant improvement over RENT in the sense that it is more effective in extracting information contained in the haplotype data about the underlying genealogy than RENT . The key components of RENT+  are several greatly enhanced genealogy inference rules. Through simulation, we show that RENT+  is more efficient and accurate than several existing genealogy inference methods. As an application, we apply RENT+  in the inference of population demographic history from haplotypes, which outperforms several existing methods. : RENT+  is implemented in Java, and is freely available for download from: https://github.com/SajadMirzaei/RentPlus . : sajad@engr.uconn.edu or ywu@engr.uconn.edu. : Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Quasi-Experimental Designs for Causal Inference

ERIC Educational Resources Information Center

Kim, Yongnam; Steiner, Peter

2016-01-01

When randomized experiments are infeasible, quasi-experimental designs can be exploited to evaluate causal treatment effects. The strongest quasi-experimental designs for causal inference are regression discontinuity designs, instrumental variable designs, matching and propensity score designs, and comparative interrupted time series designs. This…

Inferring probabilistic stellar rotation periods using Gaussian processes

NASA Astrophysics Data System (ADS)

Angus, Ruth; Morton, Timothy; Aigrain, Suzanne; Foreman-Mackey, Daniel; Rajpaul, Vinesh

2018-02-01

Variability in the light curves of spotted, rotating stars is often non-sinusoidal and quasi-periodic - spots move on the stellar surface and have finite lifetimes, causing stellar flux variations to slowly shift in phase. A strictly periodic sinusoid therefore cannot accurately model a rotationally modulated stellar light curve. Physical models of stellar surfaces have many drawbacks preventing effective inference, such as highly degenerate or high-dimensional parameter spaces. In this work, we test an appropriate effective model: a Gaussian Process with a quasi-periodic covariance kernel function. This highly flexible model allows sampling of the posterior probability density function of the periodic parameter, marginalizing over the other kernel hyperparameters using a Markov Chain Monte Carlo approach. To test the effectiveness of this method, we infer rotation periods from 333 simulated stellar light curves, demonstrating that the Gaussian process method produces periods that are more accurate than both a sine-fitting periodogram and an autocorrelation function method. We also demonstrate that it works well on real data, by inferring rotation periods for 275 Kepler stars with previously measured periods. We provide a table of rotation periods for these and many more, altogether 1102 Kepler objects of interest, and their posterior probability density function samples. Because this method delivers posterior probability density functions, it will enable hierarchical studies involving stellar rotation, particularly those involving population modelling, such as inferring stellar ages, obliquities in exoplanet systems, or characterizing star-planet interactions. The code used to implement this method is available online.

Sundance Fire: an analysis of fire phenomena

Treesearch

Hal E. Anderson

1968-01-01

The Sundance Fire on September 1, 1967, made a spectacular run of 16 miles in 9 hours and destroyed more than 50,000 acres. This run became the subject of a detailed research analysis of the environmental, topographic, and vegetation variables aimed at reconstructing and describing fire phenomena. This report details the fire's progress; discusses the fire's...

Dopamine, reward learning, and active inference.

PubMed

FitzGerald, Thomas H B; Dolan, Raymond J; Friston, Karl

2015-01-01

Temporal difference learning models propose phasic dopamine signaling encodes reward prediction errors that drive learning. This is supported by studies where optogenetic stimulation of dopamine neurons can stand in lieu of actual reward. Nevertheless, a large body of data also shows that dopamine is not necessary for learning, and that dopamine depletion primarily affects task performance. We offer a resolution to this paradox based on an hypothesis that dopamine encodes the precision of beliefs about alternative actions, and thus controls the outcome-sensitivity of behavior. We extend an active inference scheme for solving Markov decision processes to include learning, and show that simulated dopamine dynamics strongly resemble those actually observed during instrumental conditioning. Furthermore, simulated dopamine depletion impairs performance but spares learning, while simulated excitation of dopamine neurons drives reward learning, through aberrant inference about outcome states. Our formal approach provides a novel and parsimonious reconciliation of apparently divergent experimental findings.

Inferring Network Controls from Topology Using the Chomp Database

DTIC Science & Technology

2015-12-03

AFRL-AFOSR-VA-TR-2016-0033 INFERRING NETWORK CONTROLS FROM TOPOLOGY USING THE CHOMP DATABASE John Harer DUKE UNIVERSITY Final Report 12/03/2015...INFERRING NETWORK CONTROLS FROM TOPOLOGY USING THE CHOMP DATABASE 5a. CONTRACT NUMBER 5b. GRANT NUMBER FA9550-10-1-0436 5c. PROGRAM ELEMENT NUMBER 6...area of Topological Data Analysis (TDA) and it’s application to dynamical systems. The role of this work in the Complex Networks program is based on

The Role of Probability-Based Inference in an Intelligent Tutoring System.

ERIC Educational Resources Information Center

Mislevy, Robert J.; Gitomer, Drew H.

Probability-based inference in complex networks of interdependent variables is an active topic in statistical research, spurred by such diverse applications as forecasting, pedigree analysis, troubleshooting, and medical diagnosis. This paper concerns the role of Bayesian inference networks for updating student models in intelligent tutoring…

Network Model-Assisted Inference from Respondent-Driven Sampling Data

PubMed Central

Gile, Krista J.; Handcock, Mark S.

2015-01-01

Summary Respondent-Driven Sampling is a widely-used method for sampling hard-to-reach human populations by link-tracing over their social networks. Inference from such data requires specialized techniques because the sampling process is both partially beyond the control of the researcher, and partially implicitly defined. Therefore, it is not generally possible to directly compute the sampling weights for traditional design-based inference, and likelihood inference requires modeling the complex sampling process. As an alternative, we introduce a model-assisted approach, resulting in a design-based estimator leveraging a working network model. We derive a new class of estimators for population means and a corresponding bootstrap standard error estimator. We demonstrate improved performance compared to existing estimators, including adjustment for an initial convenience sample. We also apply the method and an extension to the estimation of HIV prevalence in a high-risk population. PMID:26640328

Nonlinear phenomena in general relativity

NASA Astrophysics Data System (ADS)

Allahyari, Alireza; Firouzjaee, Javad T.; Mansouri, Reza

2018-04-01

The perturbation theory plays an important role in studying structure formation in cosmology and post-Newtonian physics, but not all phenomena can be described by the linear perturbation theory. Thus, it is necessary to study exact solutions or higher-order perturbations. Specifically, we study black hole (apparent) horizons and the cosmological event horizon formation in the perturbation theory. We emphasize that in the perturbative regime of the gravitational potential these horizons cannot form in the lower order. Studying the infinite plane metric, we show that, to capture the cosmological constant effect, we need at least a second-order expansion.

Backward renormalization-group inference of cortical dipole sources and neural connectivity efficacy

NASA Astrophysics Data System (ADS)

Amaral, Selene da Rocha; Baccalá, Luiz A.; Barbosa, Leonardo S.; Caticha, Nestor

2017-06-01

Proper neural connectivity inference has become essential for understanding cognitive processes associated with human brain function. Its efficacy is often hampered by the curse of dimensionality. In the electroencephalogram case, which is a noninvasive electrophysiological monitoring technique to record electrical activity of the brain, a possible way around this is to replace multichannel electrode information with dipole reconstructed data. We use a method based on maximum entropy and the renormalization group to infer the position of the sources, whose success hinges on transmitting information from low- to high-resolution representations of the cortex. The performance of this method compares favorably to other available source inference algorithms, which are ranked here in terms of their performance with respect to directed connectivity inference by using artificially generated dynamic data. We examine some representative scenarios comprising different numbers of dynamically connected dipoles over distinct cortical surface positions and under different sensor noise impairment levels. The overall conclusion is that inverse problem solutions do not affect the correct inference of the direction of the flow of information as long as the equivalent dipole sources are correctly found.

Massive optimal data compression and density estimation for scalable, likelihood-free inference in cosmology

NASA Astrophysics Data System (ADS)

Alsing, Justin; Wandelt, Benjamin; Feeney, Stephen

2018-07-01

Many statistical models in cosmology can be simulated forwards but have intractable likelihood functions. Likelihood-free inference methods allow us to perform Bayesian inference from these models using only forward simulations, free from any likelihood assumptions or approximations. Likelihood-free inference generically involves simulating mock data and comparing to the observed data; this comparison in data space suffers from the curse of dimensionality and requires compression of the data to a small number of summary statistics to be tractable. In this paper, we use massive asymptotically optimal data compression to reduce the dimensionality of the data space to just one number per parameter, providing a natural and optimal framework for summary statistic choice for likelihood-free inference. Secondly, we present the first cosmological application of Density Estimation Likelihood-Free Inference (DELFI), which learns a parametrized model for joint distribution of data and parameters, yielding both the parameter posterior and the model evidence. This approach is conceptually simple, requires less tuning than traditional Approximate Bayesian Computation approaches to likelihood-free inference and can give high-fidelity posteriors from orders of magnitude fewer forward simulations. As an additional bonus, it enables parameter inference and Bayesian model comparison simultaneously. We demonstrate DELFI with massive data compression on an analysis of the joint light-curve analysis supernova data, as a simple validation case study. We show that high-fidelity posterior inference is possible for full-scale cosmological data analyses with as few as ˜104 simulations, with substantial scope for further improvement, demonstrating the scalability of likelihood-free inference to large and complex cosmological data sets.

Emissions of methane in Europe inferred by total column measurements

NASA Astrophysics Data System (ADS)

Wunch, D.; Deutscher, N. M.; Hase, F.; Notholt, J.; Sussmann, R.; Toon, G. C.; Warneke, T.

2017-12-01

Atmospheric total column measurements have been used to infer emissions of methane in urban centres around the world. These measurements have been shown to be useful for verifying city-scale bottom-up inventories, and they can provide both timely and sub-annual emission information. We will present our analysis of atmospheric total column measurements of methane and carbon monoxide to infer annual and seasonal regional emissions of methane within Europe using five long-running atmospheric observatories. These observatories are part of the Total Carbon Column Observing Network, part of a global network that has been carefully designed to measure these gases on a consistent scale. Our inferred emissions will then be used to evaluate gridded emissions inventories in the region.

Mental health assessment: Inference, explanation, and coherence.

PubMed

Thagard, Paul; Larocque, Laurette

2018-06-01

Mental health professionals such as psychiatrists and psychotherapists assess their patients by identifying disorders that explain their symptoms. This assessment requires an inference to the best explanation that compares different disorders with respect to how well they explain the available evidence. Such comparisons are captured by the theory of explanatory coherence that states 7 principles for evaluating competing hypotheses in the light of evidence. The computational model ECHO shows how explanatory coherence can be efficiently computed. We show the applicability of explanatory coherence to mental health assessment by modelling a case of psychiatric interviewing and a case of psychotherapeutic evaluation. We argue that this approach is more plausible than Bayesian inference and hermeneutic interpretation. © 2018 John Wiley & Sons, Ltd.

Some problems in coupling solar activity to meteorological phenomena

NASA Technical Reports Server (NTRS)

Dessler, A. J.

1975-01-01

The development of a theory of coupling of solar activity to meteorological phenomena is hindered by the difficulties of devising a mechanism that can modify the behavior of the troposphere while employing only a negligible amount of energy compared with the energy necessary to drive the normal meteorological system, and determining how such a mechanism can effectively couple some relevant magnetospheric process into the troposphere in such a way as to influence the weather. A clue to the nature of the interaction between the weather and solar activity might be provided by the fact that most solar activity undergoes a definite 11-yr cycle, and meteorological phenomena undergo either no closely correlated variation, an 11-yr variation, or a 22-yr variation.

Show Me the Pragmatic Contribution: A Developmental Investigation of Contrastive Inference

ERIC Educational Resources Information Center

Kronmuller, Edmundo; Morisseau, Tiffany; Noveck, Ira A.

2014-01-01

An utterance such as "Show me the large rabbit" potentially generates a "contrastive inference," i.e., the article "the" and the adjective "large" allow listeners to pragmatically infer the existence of other entities having the same noun (e.g. a "small" rabbit). The primary way to measure…

Metabolic PathFinding: inferring relevant pathways in biochemical networks.

PubMed

Croes, Didier; Couche, Fabian; Wodak, Shoshana J; van Helden, Jacques

2005-07-01

Our knowledge of metabolism can be represented as a network comprising several thousands of nodes (compounds and reactions). Several groups applied graph theory to analyse the topological properties of this network and to infer metabolic pathways by path finding. This is, however, not straightforward, with a major problem caused by traversing irrelevant shortcuts through highly connected nodes, which correspond to pool metabolites and co-factors (e.g. H2O, NADP and H+). In this study, we present a web server implementing two simple approaches, which circumvent this problem, thereby improving the relevance of the inferred pathways. In the simplest approach, the shortest path is computed, while filtering out the selection of highly connected compounds. In the second approach, the shortest path is computed on the weighted metabolic graph where each compound is assigned a weight equal to its connectivity in the network. This approach significantly increases the accuracy of the inferred pathways, enabling the correct inference of relatively long pathways (e.g. with as many as eight intermediate reactions). Available options include the calculation of the k-shortest paths between two specified seed nodes (either compounds or reactions). Multiple requests can be submitted in a queue. Results are returned by email, in textual as well as graphical formats (available in http://www.scmbb.ulb.ac.be/pathfinding/).

Inference of directional selection and mutation parameters assuming equilibrium.

PubMed

Vogl, Claus; Bergman, Juraj

2015-12-01

In a classical study, Wright (1931) proposed a model for the evolution of a biallelic locus under the influence of mutation, directional selection and drift. He derived the equilibrium distribution of the allelic proportion conditional on the scaled mutation rate, the mutation bias and the scaled strength of directional selection. The equilibrium distribution can be used for inference of these parameters with genome-wide datasets of "site frequency spectra" (SFS). Assuming that the scaled mutation rate is low, Wright's model can be approximated by a boundary-mutation model, where mutations are introduced into the population exclusively from sites fixed for the preferred or unpreferred allelic states. With the boundary-mutation model, inference can be partitioned: (i) the shape of the SFS distribution within the polymorphic region is determined by random drift and directional selection, but not by the mutation parameters, such that inference of the selection parameter relies exclusively on the polymorphic sites in the SFS; (ii) the mutation parameters can be inferred from the amount of polymorphic and monomorphic preferred and unpreferred alleles, conditional on the selection parameter. Herein, we derive maximum likelihood estimators for the mutation and selection parameters in equilibrium and apply the method to simulated SFS data as well as empirical data from a Madagascar population of Drosophila simulans. Copyright © 2015 Elsevier Inc. All rights reserved.

Multispectral observations complementary to the study of high-energy solar phenomena

NASA Technical Reports Server (NTRS)

Walker, Arthur B. C., Jr.

1988-01-01

Multispectral observations of phenomena associated with nonthermal events on the sun and characterized by the transient acceleration of electrons and ions to energies ranging from several keV to tens of GeV are discussed. It is shown that observations of the thermal and quasi-thermal phenomena preceeding, coinciding with, and following the impulsive acceleration and heating event itself can be used to study the evolution of magnetic structures in the solar convection zone and atmosphere. Observational techniques are discussed in detail.

Terminal-Area Aircraft Intent Inference Approach Based on Online Trajectory Clustering.

PubMed

Yang, Yang; Zhang, Jun; Cai, Kai-quan

2015-01-01

Terminal-area aircraft intent inference (T-AII) is a prerequisite to detect and avoid potential aircraft conflict in the terminal airspace. T-AII challenges the state-of-the-art AII approaches due to the uncertainties of air traffic situation, in particular due to the undefined flight routes and frequent maneuvers. In this paper, a novel T-AII approach is introduced to address the limitations by solving the problem with two steps that are intent modeling and intent inference. In the modeling step, an online trajectory clustering procedure is designed for recognizing the real-time available routes in replacing of the missed plan routes. In the inference step, we then present a probabilistic T-AII approach based on the multiple flight attributes to improve the inference performance in maneuvering scenarios. The proposed approach is validated with real radar trajectory and flight attributes data of 34 days collected from Chengdu terminal area in China. Preliminary results show the efficacy of the presented approach.

Processing inferences at the semantics/pragmatics frontier: disjunctions and free choice.

PubMed

Chemla, Emmanuel; Bott, Lewis

2014-03-01

Linguistic inferences have traditionally been studied and categorized in several categories, such as entailments, implicatures or presuppositions. This typology is mostly based on traditional linguistic means, such as introspective judgments about phrases occurring in different constructions, in different conversational contexts. More recently, the processing properties of these inferences have also been studied (see, e.g., recent work showing that scalar implicatures is a costly phenomenon). Our focus is on free choice permission, a phenomenon by which conjunctive inferences are unexpectedly added to disjunctive sentences. For instance, a sentence such as "Mary is allowed to eat an ice-cream or a cake" is normally understood as granting permission both for eating an ice-cream and for eating a cake. We provide data from four processing studies, which show that, contrary to arguments coming from the theoretical literature, free choice inferences are different from scalar implicatures. Copyright © 2013 Elsevier B.V. All rights reserved.

The influence of category coherence on inference about cross-classified entities.

PubMed

Patalano, Andrea L; Wengrovitz, Steven M; Sharpes, Kirsten M

2009-01-01

A critical function of categories is their use in property inference (Heit, 2000). However, one challenge to using categories in inference is that most entities in the world belong to multiple categories (e.g., Fido could be a dog, a pet, a mammal, or a security system). Building on Patalano, Chin-Parker, and Ross (2006), we tested the hypothesis that category coherence (the extent to which category features go together in light of prior knowledge) influences the selection of categories for use in property inference about cross-classified entities. In two experiments, we directly contrasted coherent and incoherent categories, both of which included cross-classified entities as members, and we found that the coherent categories were used more readily as the source of both property transfer and property extension. We conclude that category coherence, which has been found to be a potent influence on strength of inference for singly classified entities (Rehder & Hastie, 2004), is also central to category use in reasoning about novel cross-classified ones.

Grammatical gender and inferences about biological properties in german-speaking children.

PubMed

Saalbach, Henrik; Imai, Mutsumi; Schalk, Lennart

2012-01-01

In German, nouns are assigned to one of the three gender classes. For most animal names, however, the assignment is independent of the referent's biological sex. We examined whether German-speaking children understand this independence of grammar from semantics or whether they assume that grammatical gender is mapped onto biological sex when drawing inferences about sex-specific biological properties of animals. Two cross-linguistic studies comparing German-speaking and Japanese-speaking preschoolers were conducted. The results suggest that German-speaking children utilize grammatical gender as a cue for inferences about sex-specific properties of animals. Further, we found that Japanese- and German-speaking children recruit different resources when drawing inferences about sex-specific properties: Whereas Japanese children paralleled their pattern of inference about properties common to all animals, German children relied on the grammatical gender class of the animal. Implications of these findings for studying the relation between language and thought are discussed. Copyright © 2012 Cognitive Science Society, Inc.

When some is actually all: scalar inferences in face-threatening contexts.

PubMed

Bonnefon, Jean-François; Feeney, Aidan; Villejoubert, Gaëlle

2009-08-01

Accounts of the scalar inference from 'some X-ed' to 'not all X-ed' are central to the debate between contemporary theories of conversational pragmatics. An important contribution to this debate is to identify contexts that decrease the endorsement rate of the inference. We suggest that the inference is endorsed less often in face-threatening contexts, i.e., when X implies a loss of face for the listener. This claim is successfully tested in Experiment 1. Experiment 2 rules out a possible confound between face-threatening contexts and lower-bound contexts. Experiment 3 shows that whilst saying 'some X-ed' when one knew for a fact that all X-ed is always perceived as an underinformative utterance, it is also seen as a nice and polite thing to do when X threatens the face of the listener. These findings are considered from the perspective of Relevance Theory as well as that of the Generalized Conversational Inference approach.

Inferring Facts From Fiction: Reading Correct and Incorrect Information Affects Memory for Related Information

PubMed Central

Butler, Andrew C.; Dennis, Nancy A.; Marsh, Elizabeth J.

2012-01-01

People can acquire both true and false knowledge about the world from fictional stories (Marsh & Fazio, 2007). The present study explored whether the benefits and costs of learning about the world from fictional stories extend beyond memory for directly stated pieces of information. Of interest was whether readers would use correct and incorrect story references to make deductive inferences about related information in the story, and then integrate those inferences into their knowledge bases. Subjects read stories containing correct, neutral, and misleading references to facts about the world; each reference could be combined with another reference that occurred in a later sentence to make a deductive inference. Later, they answered general knowledge questions that tested for these deductive inferences. The results showed that subjects generated and retained the deductive inferences regardless of whether the inferences were consistent or inconsistent with world knowledge, and irrespective of whether the references were placed consecutively in the text or separated by many sentences. Readers learn more than what is directly stated in stories; they use references to the real world to make both correct and incorrect inferences that are integrated into their knowledge bases. PMID:22640369

Inferring genetic interactions via a nonlinear model and an optimization algorithm.

PubMed

Chen, Chung-Ming; Lee, Chih; Chuang, Cheng-Long; Wang, Chia-Chang; Shieh, Grace S

2010-02-26

Biochemical pathways are gradually becoming recognized as central to complex human diseases and recently genetic/transcriptional interactions have been shown to be able to predict partial pathways. With the abundant information made available by microarray gene expression data (MGED), nonlinear modeling of these interactions is now feasible. Two of the latest advances in nonlinear modeling used sigmoid models to depict transcriptional interaction of a transcription factor (TF) for a target gene, but do not model cooperative or competitive interactions of several TFs for a target. An S-shape model and an optimization algorithm (GASA) were developed to infer genetic interactions/transcriptional regulation of several genes simultaneously using MGED. GASA consists of a genetic algorithm (GA) and a simulated annealing (SA) algorithm, which is enhanced by a steepest gradient descent algorithm to avoid being trapped in local minimum. Using simulated data with various degrees of noise, we studied how GASA with two model selection criteria and two search spaces performed. Furthermore, GASA was shown to outperform network component analysis, the time series network inference algorithm (TSNI), GA with regular GA (GAGA) and GA with regular SA. Two applications are demonstrated. First, GASA is applied to infer a subnetwork of human T-cell apoptosis. Several of the predicted interactions are supported by the literature. Second, GASA was applied to infer the transcriptional factors of 34 cell cycle regulated targets in S. cerevisiae, and GASA performed better than one of the latest advances in nonlinear modeling, GAGA and TSNI. Moreover, GASA is able to predict multiple transcription factors for certain targets, and these results coincide with experiments confirmed data in YEASTRACT. GASA is shown to infer both genetic interactions and transcriptional regulatory interactions well. In particular, GASA seems able to characterize the nonlinear mechanism of transcriptional regulatory

Intelligent machines in the twenty-first century: foundations of inference and inquiry.

PubMed

Knuth, Kevin H

2003-12-15

The last century saw the application of Boolean algebra to the construction of computing machines, which work by applying logical transformations to information contained in their memory. The development of information theory and the generalization of Boolean algebra to Bayesian inference have enabled these computing machines, in the last quarter of the twentieth century, to be endowed with the ability to learn by making inferences from data. This revolution is just beginning as new computational techniques continue to make difficult problems more accessible. Recent advances in our understanding of the foundations of probability theory have revealed implications for areas other than logic. Of relevance to intelligent machines, we recently identified the algebra of questions as the free distributive algebra, which will now allow us to work with questions in a way analogous to that which Boolean algebra enables us to work with logical statements. In this paper, we examine the foundations of inference and inquiry. We begin with a history of inferential reasoning, highlighting key concepts that have led to the automation of inference in modern machine-learning systems. We then discuss the foundations of inference in more detail using a modern viewpoint that relies on the mathematics of partially ordered sets and the scaffolding of lattice theory. This new viewpoint allows us to develop the logic of inquiry and introduce a measure describing the relevance of a proposed question to an unresolved issue. Last, we will demonstrate the automation of inference, and discuss how this new logic of inquiry will enable intelligent machines to ask questions. Automation of both inference and inquiry promises to allow robots to perform science in the far reaches of our solar system and in other star systems by enabling them not only to make inferences from data, but also to decide which question to ask, which experiment to perform, or which measurement to take given what they have

Intelligent machines in the twenty-first century: foundations of inference and inquiry

NASA Technical Reports Server (NTRS)

Knuth, Kevin H.

2003-01-01

The last century saw the application of Boolean algebra to the construction of computing machines, which work by applying logical transformations to information contained in their memory. The development of information theory and the generalization of Boolean algebra to Bayesian inference have enabled these computing machines, in the last quarter of the twentieth century, to be endowed with the ability to learn by making inferences from data. This revolution is just beginning as new computational techniques continue to make difficult problems more accessible. Recent advances in our understanding of the foundations of probability theory have revealed implications for areas other than logic. Of relevance to intelligent machines, we recently identified the algebra of questions as the free distributive algebra, which will now allow us to work with questions in a way analogous to that which Boolean algebra enables us to work with logical statements. In this paper, we examine the foundations of inference and inquiry. We begin with a history of inferential reasoning, highlighting key concepts that have led to the automation of inference in modern machine-learning systems. We then discuss the foundations of inference in more detail using a modern viewpoint that relies on the mathematics of partially ordered sets and the scaffolding of lattice theory. This new viewpoint allows us to develop the logic of inquiry and introduce a measure describing the relevance of a proposed question to an unresolved issue. Last, we will demonstrate the automation of inference, and discuss how this new logic of inquiry will enable intelligent machines to ask questions. Automation of both inference and inquiry promises to allow robots to perform science in the far reaches of our solar system and in other star systems by enabling them not only to make inferences from data, but also to decide which question to ask, which experiment to perform, or which measurement to take given what they have

Degree of Ice Particle Surface Roughness Inferred from Polarimetric Observations

NASA Technical Reports Server (NTRS)

Hioki, Souichiro; Yang, Ping; Baum, Bryan A.; Platnick, Steven; Meyer, Kerry G.; King, Michael D.; Riedi, Jerome

2016-01-01

The degree of surface roughness of ice particles within thick, cold ice clouds is inferred from multidirectional, multi-spectral satellite polarimetric observations over oceans, assuming a column-aggregate particle habit. An improved roughness inference scheme is employed that provides a more noise-resilient roughness estimate than the conventional best-fit approach. The improvements include the introduction of a quantitative roughness parameter based on empirical orthogonal function analysis and proper treatment of polarization due to atmospheric scattering above clouds. A global 1-month data sample supports the use of a severely roughened ice habit to simulate the polarized reflectivity associated with ice clouds over ocean. The density distribution of the roughness parameter inferred from the global 1- month data sample and further analyses of a few case studies demonstrate the significant variability of ice cloud single-scattering properties. However, the present theoretical results do not agree with observations in the tropics. In the extra-tropics, the roughness parameter is inferred but 74% of the sample is out of the expected parameter range. Potential improvements are discussed to enhance the depiction of the natural variability on a global scale.

Inferring difficulty: Flexibility in the real-time processing of disfluency

PubMed Central

Heller, Daphna; Arnold, Jennifer E.; Klein, Natalie M.; Tanenhaus, Michael K.

2015-01-01

Upon hearing a disfluent referring expression, listeners expect the speaker to refer to an object that is previously-unmentioned, an object that does not have a straightforward label, or an object that requires a longer description. Two visual-world eye-tracking experiments examined whether listeners directly associate disfluency with these properties of objects, or whether disfluency attribution is more flexible and involves situation-specific inferences. Since in natural situations reference to objects that do not have a straightforward label or that require a longer description is correlated with both production difficulty and with disfluency, we used a mini artificial lexicon to dissociate difficulty from these properties, building on the fact that recently-learned names take longer to produce than existing words in one’s mental lexicon. The results demonstrate that disfluency attribution involves situation-specific inferences; we propose that in new situations listeners spontaneously infer what may cause production difficulty. However, the results show that these situation-specific inferences are limited in scope: listeners assessed difficulty relative to their own experience with the artificial names, and did not adapt to the assumed knowledge of the speaker. PMID:26677642

Implicit Value Updating Explains Transitive Inference Performance: The Betasort Model

PubMed Central

Jensen, Greg; Muñoz, Fabian; Alkan, Yelda; Ferrera, Vincent P.; Terrace, Herbert S.

2015-01-01

Transitive inference (the ability to infer that B > D given that B > C and C > D) is a widespread characteristic of serial learning, observed in dozens of species. Despite these robust behavioral effects, reinforcement learning models reliant on reward prediction error or associative strength routinely fail to perform these inferences. We propose an algorithm called betasort, inspired by cognitive processes, which performs transitive inference at low computational cost. This is accomplished by (1) representing stimulus positions along a unit span using beta distributions, (2) treating positive and negative feedback asymmetrically, and (3) updating the position of every stimulus during every trial, whether that stimulus was visible or not. Performance was compared for rhesus macaques, humans, and the betasort algorithm, as well as Q-learning, an established reward-prediction error (RPE) model. Of these, only Q-learning failed to respond above chance during critical test trials. Betasort’s success (when compared to RPE models) and its computational efficiency (when compared to full Markov decision process implementations) suggests that the study of reinforcement learning in organisms will be best served by a feature-driven approach to comparing formal models. PMID:26407227

Implicit Value Updating Explains Transitive Inference Performance: The Betasort Model.

PubMed

Jensen, Greg; Muñoz, Fabian; Alkan, Yelda; Ferrera, Vincent P; Terrace, Herbert S

2015-01-01

Transitive inference (the ability to infer that B > D given that B > C and C > D) is a widespread characteristic of serial learning, observed in dozens of species. Despite these robust behavioral effects, reinforcement learning models reliant on reward prediction error or associative strength routinely fail to perform these inferences. We propose an algorithm called betasort, inspired by cognitive processes, which performs transitive inference at low computational cost. This is accomplished by (1) representing stimulus positions along a unit span using beta distributions, (2) treating positive and negative feedback asymmetrically, and (3) updating the position of every stimulus during every trial, whether that stimulus was visible or not. Performance was compared for rhesus macaques, humans, and the betasort algorithm, as well as Q-learning, an established reward-prediction error (RPE) model. Of these, only Q-learning failed to respond above chance during critical test trials. Betasort's success (when compared to RPE models) and its computational efficiency (when compared to full Markov decision process implementations) suggests that the study of reinforcement learning in organisms will be best served by a feature-driven approach to comparing formal models.

High-dimensional inference with the generalized Hopfield model: principal component analysis and corrections.

PubMed

Cocco, S; Monasson, R; Sessak, V

2011-05-01

We consider the problem of inferring the interactions between a set of N binary variables from the knowledge of their frequencies and pairwise correlations. The inference framework is based on the Hopfield model, a special case of the Ising model where the interaction matrix is defined through a set of patterns in the variable space, and is of rank much smaller than N. We show that maximum likelihood inference is deeply related to principal component analysis when the amplitude of the pattern components ξ is negligible compared to √N. Using techniques from statistical mechanics, we calculate the corrections to the patterns to the first order in ξ/√N. We stress the need to generalize the Hopfield model and include both attractive and repulsive patterns in order to correctly infer networks with sparse and strong interactions. We present a simple geometrical criterion to decide how many attractive and repulsive patterns should be considered as a function of the sampling noise. We moreover discuss how many sampled configurations are required for a good inference, as a function of the system size N and of the amplitude ξ. The inference approach is illustrated on synthetic and biological data.

Drug target inference through pathway analysis of genomics data

PubMed Central

Ma, Haisu; Zhao, Hongyu

2013-01-01

Statistical modeling coupled with bioinformatics is commonly used for drug discovery. Although there exist many approaches for single target based drug design and target inference, recent years have seen a paradigm shift to system-level pharmacological research. Pathway analysis of genomics data represents one promising direction for computational inference of drug targets. This article aims at providing a comprehensive review on the evolving issues is this field, covering methodological developments, their pros and cons, as well as future research directions. PMID:23369829

Recent Applications of Higher-Order Spectral Analysis to Nonlinear Aeroelastic Phenomena

NASA Technical Reports Server (NTRS)

Silva, Walter A.; Hajj, Muhammad R.; Dunn, Shane; Strganac, Thomas W.; Powers, Edward J.; Stearman, Ronald

2005-01-01

Recent applications of higher-order spectral (HOS) methods to nonlinear aeroelastic phenomena are presented. Applications include the analysis of data from a simulated nonlinear pitch and plunge apparatus and from F-18 flight flutter tests. A MATLAB model of the Texas A&MUniversity s Nonlinear Aeroelastic Testbed Apparatus (NATA) is used to generate aeroelastic transients at various conditions including limit cycle oscillations (LCO). The Gaussian or non-Gaussian nature of the transients is investigated, related to HOS methods, and used to identify levels of increasing nonlinear aeroelastic response. Royal Australian Air Force (RAAF) F/A-18 flight flutter test data is presented and analyzed. The data includes high-quality measurements of forced responses and LCO phenomena. Standard power spectral density (PSD) techniques and HOS methods are applied to the data and presented. The goal of this research is to develop methods that can identify the onset of nonlinear aeroelastic phenomena, such as LCO, during flutter testing.

Proceedings of the Fourth Microgravity Fluid Physics and Transport Phenomena Conference

NASA Technical Reports Server (NTRS)

Singh, Bhim S. (Editor)

1999-01-01

This conference presents information to the scientific community on research results, future directions, and research opportunities in microgravity fluid physics and transport phenomena within NASA's microgravity research program. The conference theme is "The International Space Station." Plenary sessions provide an overview of the Microgravity Fluid Physics Program, the International Space Station and the opportunities ISS presents to fluid physics and transport phenomena researchers, and the process by which researchers may become involved in NASA's program, including information about the NASA Research Announcement in this area. Two plenary lectures present promising areas of research in electrohydrodynamics/electrokinetics in the movement of particles and in micro- and meso-scale effects on macroscopic fluid dynamics. Featured speakers in plenary sessions present results of recent flight experiments not heretofore presented. The conference publication consists of this book of abstracts and the full Proceedings of the 4th Microgravity Fluid Physics and Transport Phenomena Conference on CD-ROM, containing full papers presented at the conference (NASA/CP-1999-208526/SUPPL1).

Social Inferences from Faces: Ambient Images Generate a Three-Dimensional Model

ERIC Educational Resources Information Center

Sutherland, Clare A. M.; Oldmeadow, Julian A.; Santos, Isabel M.; Towler, John; Burt, D. Michael; Young, Andrew W.

2013-01-01

Three experiments are presented that investigate the two-dimensional valence/trustworthiness by dominance model of social inferences from faces (Oosterhof & Todorov, 2008). Experiment 1 used image averaging and morphing techniques to demonstrate that consistent facial cues subserve a range of social inferences, even in a highly variable sample of…

Activation of Background Knowledge for Inference Making: Effects on Reading Comprehension

ERIC Educational Resources Information Center

Elbro, Carsten; Buch-Iversen, Ida

2013-01-01

Failure to "activate" relevant, existing background knowledge may be a cause of poor reading comprehension. This failure may cause particular problems with inferences that depend heavily on prior knowledge. Conversely, teaching how to use background knowledge in the context of gap-filling inferences could improve reading comprehension in…

Efficient Bayesian inference for natural time series using ARFIMA processes

NASA Astrophysics Data System (ADS)

Graves, Timothy; Gramacy, Robert; Franzke, Christian; Watkins, Nicholas

2016-04-01

Many geophysical quantities, such as atmospheric temperature, water levels in rivers, and wind speeds, have shown evidence of long memory (LM). LM implies that these quantities experience non-trivial temporal memory, which potentially not only enhances their predictability, but also hampers the detection of externally forced trends. Thus, it is important to reliably identify whether or not a system exhibits LM. We present a modern and systematic approach to the inference of LM. We use the flexible autoregressive fractional integrated moving average (ARFIMA) model, which is widely used in time series analysis, and of increasing interest in climate science. Unlike most previous work on the inference of LM, which is frequentist in nature, we provide a systematic treatment of Bayesian inference. In particular, we provide a new approximate likelihood for efficient parameter inference, and show how nuisance parameters (e.g., short-memory effects) can be integrated over in order to focus on long-memory parameters and hypothesis testing more directly. We illustrate our new methodology on the Nile water level data and the central England temperature (CET) time series, with favorable comparison to the standard estimators [1]. In addition we show how the method can be used to perform joint inference of the stability exponent and the memory parameter when ARFIMA is extended to allow for alpha-stable innovations. Such models can be used to study systems where heavy tails and long range memory coexist. [1] Graves et al, Nonlin. Processes Geophys., 22, 679-700, 2015; doi:10.5194/npg-22-679-2015.

An inference method from multi-layered structure of biomedical data.

PubMed

Kim, Myungjun; Nam, Yonghyun; Shin, Hyunjung

2017-05-18

Biological system is a multi-layered structure of omics with genome, epigenome, transcriptome, metabolome, proteome, etc., and can be further stretched to clinical/medical layers such as diseasome, drugs, and symptoms. One advantage of omics is that we can figure out an unknown component or its trait by inferring from known omics components. The component can be inferred by the ones in the same level of omics or the ones in different levels. To implement the inference process, an algorithm that can be applied to the multi-layered complex system is required. In this study, we develop a semi-supervised learning algorithm that can be applied to the multi-layered complex system. In order to verify the validity of the inference, it was applied to the prediction problem of disease co-occurrence with a two-layered network composed of symptom-layer and disease-layer. The symptom-disease layered network obtained a fairly high value of AUC, 0.74, which is regarded as noticeable improvement when comparing 0.59 AUC of single-layered disease network. If further stretched to whole layered structure of omics, the proposed method is expected to produce more promising results. This research has novelty in that it is a new integrative algorithm that incorporates the vertical structure of omics data, on contrary to other existing methods that integrate the data in parallel fashion. The results can provide enhanced guideline for disease co-occurrence prediction, thereby serve as a valuable tool for inference process of multi-layered biological system.

Free will in Bayesian and inverse Bayesian inference-driven endo-consciousness.

PubMed

Gunji, Yukio-Pegio; Minoura, Mai; Kojima, Kei; Horry, Yoichi

2017-12-01

How can we link challenging issues related to consciousness and/or qualia with natural science? The introduction of endo-perspective, instead of exo-perspective, as proposed by Matsuno, Rössler, and Gunji, is considered one of the most promising candidate approaches. Here, we distinguish the endo-from the exo-perspective in terms of whether the external is or is not directly operated. In the endo-perspective, the external can be neither perceived nor recognized directly; rather, one can only indirectly summon something outside of the perspective, which can be illustrated by a causation-reversal pair. On one hand, causation logically proceeds from the cause to the effect. On the other hand, a reversal from the effect to the cause is non-logical and is equipped with a metaphorical structure. We argue that the differences in exo- and endo-perspectives result not from the difference between Western and Eastern cultures, but from differences between modernism and animism. Here, a causation-reversal pair described using a pair of upward (from premise to consequence) and downward (from consequence to premise) causation and a pair of Bayesian and inverse Bayesian inference (BIB inference). Accordingly, the notion of endo-consciousness is proposed as an agent equipped with BIB inference. We also argue that BIB inference can yield both highly efficient computations through Bayesian interference and robust computations through inverse Bayesian inference. By adapting a logical model of the free will theorem to the BIB inference, we show that endo-consciousness can explain free will as a regression of the controllability of voluntary action. Copyright © 2017. Published by Elsevier Ltd.

Assessing children's inference generation: what do tests of reading comprehension measure?

PubMed

Bowyer-Crane, Claudine; Snowling, Margaret J

2005-06-01

Previous research suggests that children with specific comprehension difficulties have problems with the generation of inferences. This raises important questions as to whether poor comprehenders have poor comprehension skills generally, or whether their problems are confined to specific inference types. The main aims of the study were (a) using two commonly used tests of reading comprehension to classify the questions requiring the generation of inferences, and (b) to investigate the relative performance of skilled and less-skilled comprehenders on questions tapping different inference types. The performance of 10 poor comprehenders (mean age 110.06 months) was compared with the performance of 10 normal readers (mean age 112.78 months) on two tests of reading comprehension. A qualitative analysis of the NARA II (form 1) and the WORD comprehension subtest was carried out. Participants were then administered the NARA II, WORD comprehension subtest and a test of non-word reading. The NARA II was heavily reliant on the generation of knowledge-based inferences, while the WORD comprehension subtest was biased towards the retention of literal information. Children identified by the NARA II as having comprehension difficulties performed in the normal range on the WORD comprehension subtests. Further, children with comprehension difficulties performed poorly on questions requiring the generation of knowledge-based and elaborative inferences. However, they were able to answer questions requiring attention to literal information or use of cohesive devices at a level comparable to normal readers. Different reading tests tap different types of inferencing skills. Lessskilled comprehenders have particular difficulty applying real-world knowledge to a text during reading, and this has implications for the formulation of effective intervention strategies.

Statistical Inference and Reverse Engineering of Gene Regulatory Networks from Observational Expression Data

PubMed Central

Emmert-Streib, Frank; Glazko, Galina V.; Altay, Gökmen; de Matos Simoes, Ricardo

2012-01-01

In this paper, we present a systematic and conceptual overview of methods for inferring gene regulatory networks from observational gene expression data. Further, we discuss two classic approaches to infer causal structures and compare them with contemporary methods by providing a conceptual categorization thereof. We complement the above by surveying global and local evaluation measures for assessing the performance of inference algorithms. PMID:22408642

Neutronics Phenomena Important in Modeling and Simulation of Liquid-Fuel Molten Salt Reactors

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

Diamond, David J.

This paper discusses liquid-fuel molten salt reactors, how they will operate under normal, transient, and accident conditions, and the results of an expert elicitation to determine the corresponding neutronic phenomena important to understanding their behavior. Identifying these phenomena will enable the U.S. Nuclear Regulatory Commission (NRC) to develop or identify modeling functionalities and tools required to carry out confirmatory analyses that examine the validity and accuracy of applicants’ calculations and help determine the margin of safety in plant design. NRC frequently does an expert elicitation using a Phenomena Identification and Ranking Table (PIRT) to identify and evaluate the state ofmore » knowledge of important modeling phenomena. However, few details about the design of these reactors and the sequence of events during accidents are known, so the process used was considered a preliminary PIRT. A panel met to define phenomena that would need to be modeled and considered the impact/importance of each phenomenon with respect to specific figures-of-merit (FoMs) (e.g., power distribution, fluence, kinetics parameters and reactivity). Each FoM reflected a potential impact on radionuclide release or loss of a barrier to release. The panel considered what the path forward might be with respect to being able to model the phenomenon in a simulation code. Results are explained for both thermal and fast spectrum designs.« less

Physical phenomena and the microgravity response

NASA Technical Reports Server (NTRS)

Todd, Paul

1989-01-01

The living biological cell is not a sack of Newtonian fluid containing systems of chemical reactions at equilibrium. It is a kinetically driven system, not a thermodynamically driven system. While the cell as a whole might be considered isothermal, at the scale of individual macromolecular events there is heat generated, and presumably sharp thermal gradients exist at the submicron level. Basic physical phenomena to be considered when exploring the cell's response to inertial acceleration include particle sedimentation, solutal convection, motility electrokinetics, cytoskeletal work, and hydrostatic pressure. Protein crystal growth experiments, for example, illustrate the profound effects of convection currents on macromolecular assembly. Reaction kinetics in the cell vary all the way from diffusion-limited to life-time limited. Transport processes vary from free diffusion, to facilitated and active transmembrane transport, to contractile-protein-driven motility, to crystalline immobilization. At least four physical states of matter exist in the cell: aqueous, non-aqueous, immiscible-aqueous, and solid. Levels of order vary from crystalline to free solution. The relative volumes of these states profoundly influence the cell's response to inertial acceleration. Such subcellular phenomena as stretch-receptor activation, microtubule re-assembly, synaptic junction formation, chemotactic receptor activation, and statolith sedimentation were studied recently with respect to both their basic mechanisms and their responsiveness to inertial acceleration. From such studies a widespread role of cytoskeletal organization is becoming apparent.

Quantum Inference on Bayesian Networks

NASA Astrophysics Data System (ADS)

Yoder, Theodore; Low, Guang Hao; Chuang, Isaac

2014-03-01

Because quantum physics is naturally probabilistic, it seems reasonable to expect physical systems to describe probabilities and their evolution in a natural fashion. Here, we use quantum computation to speedup sampling from a graphical probability model, the Bayesian network. A specialization of this sampling problem is approximate Bayesian inference, where the distribution on query variables is sampled given the values e of evidence variables. Inference is a key part of modern machine learning and artificial intelligence tasks, but is known to be NP-hard. Classically, a single unbiased sample is obtained from a Bayesian network on n variables with at most m parents per node in time (nmP(e) - 1 / 2) , depending critically on P(e) , the probability the evidence might occur in the first place. However, by implementing a quantum version of rejection sampling, we obtain a square-root speedup, taking (n2m P(e) -1/2) time per sample. The speedup is the result of amplitude amplification, which is proving to be broadly applicable in sampling and machine learning tasks. In particular, we provide an explicit and efficient circuit construction that implements the algorithm without the need for oracle access.

Relative evolutionary rate inference in HyPhy with LEISR.

PubMed

Spielman, Stephanie J; Kosakovsky Pond, Sergei L

2018-01-01

We introduce LEISR (Likehood Estimation of Individual Site Rates, pronounced "laser"), a tool to infer relative evolutionary rates from protein and nucleotide data, implemented in HyPhy. LEISR is based on the popular Rate4Site (Pupko et al., 2002) approach for inferring relative site-wise evolutionary rates, primarily from protein data. We extend the original method for more general use in several key ways: (i) we increase the support for nucleotide data with additional models, (ii) we allow for datasets of arbitrary size, (iii) we support analysis of site-partitioned datasets to correct for the presence of recombination breakpoints, (iv) we produce rate estimates at all sites rather than at just a subset of sites, and (v) we implemented LEISR as MPI-enabled to support rapid, high-throughput analysis. LEISR is available in HyPhy starting with version 2.3.8, and it is accessible as an option in the HyPhy analysis menu ("Relative evolutionary rate inference"), which calls the HyPhy batchfile LEISR.bf.

Evidence Accumulation and Change Rate Inference in Dynamic Environments.

PubMed

Radillo, Adrian E; Veliz-Cuba, Alan; Josić, Krešimir; Kilpatrick, Zachary P

2017-06-01

In a constantly changing world, animals must account for environmental volatility when making decisions. To appropriately discount older, irrelevant information, they need to learn the rate at which the environment changes. We develop an ideal observer model capable of inferring the present state of the environment along with its rate of change. Key to this computation is an update of the posterior probability of all possible change point counts. This computation can be challenging, as the number of possibilities grows rapidly with time. However, we show how the computations can be simplified in the continuum limit by a moment closure approximation. The resulting low-dimensional system can be used to infer the environmental state and change rate with accuracy comparable to the ideal observer. The approximate computations can be performed by a neural network model via a rate-correlation-based plasticity rule. We thus show how optimal observers accumulate evidence in changing environments and map this computation to reduced models that perform inference using plausible neural mechanisms.

Remembrance of inferences past: Amortization in human hypothesis generation.

PubMed

Dasgupta, Ishita; Schulz, Eric; Goodman, Noah D; Gershman, Samuel J

2018-05-21

Bayesian models of cognition assume that people compute probability distributions over hypotheses. However, the required computations are frequently intractable or prohibitively expensive. Since people often encounter many closely related distributions, selective reuse of computations (amortized inference) is a computationally efficient use of the brain's limited resources. We present three experiments that provide evidence for amortization in human probabilistic reasoning. When sequentially answering two related queries about natural scenes, participants' responses to the second query systematically depend on the structure of the first query. This influence is sensitive to the content of the queries, only appearing when the queries are related. Using a cognitive load manipulation, we find evidence that people amortize summary statistics of previous inferences, rather than storing the entire distribution. These findings support the view that the brain trades off accuracy and computational cost, to make efficient use of its limited cognitive resources to approximate probabilistic inference. Copyright © 2018 Elsevier B.V. All rights reserved.

Dopamine, reward learning, and active inference

PubMed Central

FitzGerald, Thomas H. B.; Dolan, Raymond J.; Friston, Karl

2015-01-01

Temporal difference learning models propose phasic dopamine signaling encodes reward prediction errors that drive learning. This is supported by studies where optogenetic stimulation of dopamine neurons can stand in lieu of actual reward. Nevertheless, a large body of data also shows that dopamine is not necessary for learning, and that dopamine depletion primarily affects task performance. We offer a resolution to this paradox based on an hypothesis that dopamine encodes the precision of beliefs about alternative actions, and thus controls the outcome-sensitivity of behavior. We extend an active inference scheme for solving Markov decision processes to include learning, and show that simulated dopamine dynamics strongly resemble those actually observed during instrumental conditioning. Furthermore, simulated dopamine depletion impairs performance but spares learning, while simulated excitation of dopamine neurons drives reward learning, through aberrant inference about outcome states. Our formal approach provides a novel and parsimonious reconciliation of apparently divergent experimental findings. PMID:26581305

Assessing Decreased Sensation and Increased Sensory Phenomena in Diabetic Polyneuropathies

PubMed Central

Herrmann, David N.; Staff, Nathan P.; Dyck, P. James B.

2013-01-01

Loss of sensation and increased sensory phenomena are major expressions of varieties of diabetic polyneuropathies needing improved assessments for clinical and research purposes. We provide a neurobiological explanation for the apparent paradox between decreased sensation and increased sensory phenomena. Strongly endorsed is the use of the 10-g monofilaments for screening of feet to detect sensation loss, with the goal of improving diabetic management and prevention of foot ulcers and neurogenic arthropathy. We describe improved methods to assess for the kind, severity, and distribution of both large- and small-fiber sensory loss and which approaches and techniques may be useful for conducting therapeutic trials. The abnormality of attributes of nerve conduction may be used to validate the dysfunction of large sensory fibers. The abnormality of epidermal nerve fibers/1 mm may be used as a surrogate measure of small-fiber sensory loss but appear not to correlate closely with severity of pain. Increased sensory phenomena are recognized by the characteristic words patients use to describe them and by the severity and persistence of these symptoms. Tests of tactile and thermal hyperalgesia are additional markers of neural hyperactivity that are useful for diagnosis and disease management. PMID:24158999

Fast soft x-ray images of magnetohydrodynamic phenomena in NSTX.

PubMed

Bush, C E; Stratton, B C; Robinson, J; Zakharov, L E; Fredrickson, E D; Stutman, D; Tritz, K

2008-10-01

A variety of magnetohydrodynamic (MHD) phenomena have been observed on NSTX. Many of these affect fast particle losses, which are of major concern for future burning plasma experiments. Usual diagnostics for studying these phenomena are arrays of Mirnov coils for magnetic oscillations and p-i-n diode arrays for soft x-ray emission from the plasma core. Data reported here are from a unique fast soft x-ray imaging camera (FSXIC) with a wide-angle (pinhole) tangential view of the entire plasma minor cross section. The camera provides a 64x64 pixel image, on a charge coupled device chip, of light resulting from conversion of soft x rays incident on a phosphor to the visible. We have acquired plasma images at frame rates of 1-500 kHz (300 frames/shot) and have observed a variety of MHD phenomena: disruptions, sawteeth, fishbones, tearing modes, and edge localized modes (ELMs). New data including modes with frequency >90 kHz are also presented. Data analysis and modeling techniques used to interpret the FSXIC data are described and compared, and FSXIC results are compared to Mirnov and p-i-n diode array results.

Regional Phenomena of Vertical Deformation in Southern Part of Indonesia

NASA Astrophysics Data System (ADS)

Sarsito, D. A.; Susilo; Andreas, H.; Pradipta, D.; Gumilar, I.

2018-02-01

Distribution of present-day horizontal and vertical deformation across the Southern Part of Indonesia at Java, Bali and Nusa Tenggara now days can be determined from continuous and campaign types of GNSS GPS data monitoring. For vertical deformation in this case we use the continuous types since they are give better quality of data consistency compare to campaign type. Continuous Global Positioning System (CGPS) are maintaining by Geospatial Information Agency for more than a decade. The vertical displacements or velocity rates are estimated from time series analysis after multi-baseline GPS processing using GAMIT-GLOBK software with respect to the latest International Terrestrial Reference Frame. The result shows some interesting phenomena where the northern part of research area majority have negative value that may indicate land subsidence with or without tectonic subsidence combination. In the middle part, the uplift phenomena are clearly shown and in the southern part show combine pattern between uplift and subsidence. The impacts of those phenomena would be discuss also in this paper since many population and infrastructure are located in the areas that will need more protection planning to reduce the negative impact such as earthquake and flooding.

Kernel learning at the first level of inference.

PubMed

Cawley, Gavin C; Talbot, Nicola L C

2014-05-01

Kernel learning methods, whether Bayesian or frequentist, typically involve multiple levels of inference, with the coefficients of the kernel expansion being determined at the first level and the kernel and regularisation parameters carefully tuned at the second level, a process known as model selection. Model selection for kernel machines is commonly performed via optimisation of a suitable model selection criterion, often based on cross-validation or theoretical performance bounds. However, if there are a large number of kernel parameters, as for instance in the case of automatic relevance determination (ARD), there is a substantial risk of over-fitting the model selection criterion, resulting in poor generalisation performance. In this paper we investigate the possibility of learning the kernel, for the Least-Squares Support Vector Machine (LS-SVM) classifier, at the first level of inference, i.e. parameter optimisation. The kernel parameters and the coefficients of the kernel expansion are jointly optimised at the first level of inference, minimising a training criterion with an additional regularisation term acting on the kernel parameters. The key advantage of this approach is that the values of only two regularisation parameters need be determined in model selection, substantially alleviating the problem of over-fitting the model selection criterion. The benefits of this approach are demonstrated using a suite of synthetic and real-world binary classification benchmark problems, where kernel learning at the first level of inference is shown to be statistically superior to the conventional approach, improves on our previous work (Cawley and Talbot, 2007) and is competitive with Multiple Kernel Learning approaches, but with reduced computational expense. Copyright © 2014 Elsevier Ltd. All rights reserved.

PhySIC_IST: cleaning source trees to infer more informative supertrees

PubMed Central

Scornavacca, Celine; Berry, Vincent; Lefort, Vincent; Douzery, Emmanuel JP; Ranwez, Vincent

2008-01-01

Background Supertree methods combine phylogenies with overlapping sets of taxa into a larger one. Topological conflicts frequently arise among source trees for methodological or biological reasons, such as long branch attraction, lateral gene transfers, gene duplication/loss or deep gene coalescence. When topological conflicts occur among source trees, liberal methods infer supertrees containing the most frequent alternative, while veto methods infer supertrees not contradicting any source tree, i.e. discard all conflicting resolutions. When the source trees host a significant number of topological conflicts or have a small taxon overlap, supertree methods of both kinds can propose poorly resolved, hence uninformative, supertrees. Results To overcome this problem, we propose to infer non-plenary supertrees, i.e. supertrees that do not necessarily contain all the taxa present in the source trees, discarding those whose position greatly differs among source trees or for which insufficient information is provided. We detail a variant of the PhySIC veto method called PhySIC_IST that can infer non-plenary supertrees. PhySIC_IST aims at inferring supertrees that satisfy the same appealing theoretical properties as with PhySIC, while being as informative as possible under this constraint. The informativeness of a supertree is estimated using a variation of the CIC (Cladistic Information Content) criterion, that takes into account both the presence of multifurcations and the absence of some taxa. Additionally, we propose a statistical preprocessing step called STC (Source Trees Correction) to correct the source trees prior to the supertree inference. STC is a liberal step that removes the parts of each source tree that significantly conflict with other source trees. Combining STC with a veto method allows an explicit trade-off between veto and liberal approaches, tuned by a single parameter. Performing large-scale simulations, we observe that STC+PhySIC_IST infers much

PhySIC_IST: cleaning source trees to infer more informative supertrees.

PubMed

Scornavacca, Celine; Berry, Vincent; Lefort, Vincent; Douzery, Emmanuel J P; Ranwez, Vincent

2008-10-04

Supertree methods combine phylogenies with overlapping sets of taxa into a larger one. Topological conflicts frequently arise among source trees for methodological or biological reasons, such as long branch attraction, lateral gene transfers, gene duplication/loss or deep gene coalescence. When topological conflicts occur among source trees, liberal methods infer supertrees containing the most frequent alternative, while veto methods infer supertrees not contradicting any source tree, i.e. discard all conflicting resolutions. When the source trees host a significant number of topological conflicts or have a small taxon overlap, supertree methods of both kinds can propose poorly resolved, hence uninformative, supertrees. To overcome this problem, we propose to infer non-plenary supertrees, i.e. supertrees that do not necessarily contain all the taxa present in the source trees, discarding those whose position greatly differs among source trees or for which insufficient information is provided. We detail a variant of the PhySIC veto method called PhySIC_IST that can infer non-plenary supertrees. PhySIC_IST aims at inferring supertrees that satisfy the same appealing theoretical properties as with PhySIC, while being as informative as possible under this constraint. The informativeness of a supertree is estimated using a variation of the CIC (Cladistic Information Content) criterion, that takes into account both the presence of multifurcations and the absence of some taxa. Additionally, we propose a statistical preprocessing step called STC (Source Trees Correction) to correct the source trees prior to the supertree inference. STC is a liberal step that removes the parts of each source tree that significantly conflict with other source trees. Combining STC with a veto method allows an explicit trade-off between veto and liberal approaches, tuned by a single parameter.Performing large-scale simulations, we observe that STC+PhySIC_IST infers much more informative

Using speakers' referential intentions to model early cross-situational word learning.

PubMed

Frank, Michael C; Goodman, Noah D; Tenenbaum, Joshua B

2009-05-01

Word learning is a "chicken and egg" problem. If a child could understand speakers' utterances, it would be easy to learn the meanings of individual words, and once a child knows what many words mean, it is easy to infer speakers' intended meanings. To the beginning learner, however, both individual word meanings and speakers' intentions are unknown. We describe a computational model of word learning that solves these two inference problems in parallel, rather than relying exclusively on either the inferred meanings of utterances or cross-situational word-meaning associations. We tested our model using annotated corpus data and found that it inferred pairings between words and object concepts with higher precision than comparison models. Moreover, as the result of making probabilistic inferences about speakers' intentions, our model explains a variety of behavioral phenomena described in the word-learning literature. These phenomena include mutual exclusivity, one-trial learning, cross-situational learning, the role of words in object individuation, and the use of inferred intentions to disambiguate reference.

Review of Natural Phenomena Hazard (NPH) Assessments for the Hanford 200 Areas (Non-Seismic)

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

Snow, Robert L.; Ross, Steven B.; Sullivan, Robin S.

2010-09-24

The purpose of this review is to assess the need for updating Natural Phenomena Hazard (NPH) assessments for the Hanford 200 Areas, as required by DOE Order 420.1B Chapter IV, Natural Phenomena Hazards Mitigation, based on significant changes in state-of-the-art NPH assessment methodology or site-specific information. The review includes all natural phenomena hazards with the exception of seismic/earthquake hazards, which are being addressed under a separate effort. It was determined that existing non-seismic NPH assessments are consistent with current design methodology and site specific data.

Exploitation of rapid acidification phenomena of food waste in reducing the hydraulic retention time (HRT) of high rate anaerobic digester without conceding on biogas yield.

PubMed

Kuruti, Kranti; Begum, Sameena; Ahuja, Shruti; Anupoju, Gangagni Rao; Juntupally, Sudharshan; Gandu, Bharath; Ahuja, Devender Kumar

2017-02-01

The aim of the present work was to study and infer a full scale experience on co-digestion of 1000kg of FW (400kg cooked food waste and 600kg uncooked food waste) and 2000L of rice gruel (RG) on daily basis based on a high rate biomethanation technology called "Anaerobic gas lift reactor" (AGR). The pH of raw substrate was low (5.2-5.5) that resulted in rapid acidification phenomena with in 12h in the feed preparation tank that facilitated to obtain a lower hydraulic residence time (HRT) of 10days. At full load, AGR was fed with 245kg of total solids, 205kg of volatile solids (167kg of organic matter in terms of chemical oxygen demand) which resulted in the generation of biogas and bio manure of 140m 3 /day and 110kg/day respectively. The produced biogas replaced 60-70kg of LPG per day. Copyright © 2016 Elsevier Ltd. All rights reserved.

Prefrontal Cortex: Role in Acquisition of Overlapping Associations and Transitive Inference

ERIC Educational Resources Information Center

DeVito, Loren M.; Lykken, Christine; Kanter, Benjamin R.; Eichenbaum, Howard

2010-01-01

"Transitive inference" refers to the ability to judge from memory the relationships between indirectly related items that compose a hierarchically organized series, and this capacity is considered a fundamental feature of relational memory. Here we explored the role of the prefrontal cortex in transitive inference by examining the performance of…

F-OWL: An Inference Engine for Semantic Web

NASA Technical Reports Server (NTRS)

Zou, Youyong; Finin, Tim; Chen, Harry

2004-01-01

Understanding and using the data and knowledge encoded in semantic web documents requires an inference engine. F-OWL is an inference engine for the semantic web language OWL language based on F-logic, an approach to defining frame-based systems in logic. F-OWL is implemented using XSB and Flora-2 and takes full advantage of their features. We describe how F-OWL computes ontology entailment and compare it with other description logic based approaches. We also describe TAGA, a trading agent environment that we have used as a test bed for F-OWL and to explore how multiagent systems can use semantic web concepts and technology.

On the inherent competition between valid and spurious inductive inferences in Boolean data

NASA Astrophysics Data System (ADS)

Andrecut, M.

Inductive inference is the process of extracting general rules from specific observations. This problem also arises in the analysis of biological networks, such as genetic regulatory networks, where the interactions are complex and the observations are incomplete. A typical task in these problems is to extract general interaction rules as combinations of Boolean covariates, that explain a measured response variable. The inductive inference process can be considered as an incompletely specified Boolean function synthesis problem. This incompleteness of the problem will also generate spurious inferences, which are a serious threat to valid inductive inference rules. Using random Boolean data as a null model, here we attempt to measure the competition between valid and spurious inductive inference rules from a given data set. We formulate two greedy search algorithms, which synthesize a given Boolean response variable in a sparse disjunct normal form, and respectively a sparse generalized algebraic normal form of the variables from the observation data, and we evaluate numerically their performance.

Inference and Children's Comprehension of Pronouns.

ERIC Educational Resources Information Center

Wykes, Til

1981-01-01

Investigated five-year-olds' abilities to determine the reference of anaphoric pronouns. Children had difficulty when a sentence contained more than one pronoun, especially when assigning the reference of a pronoun requiring an inference. The children's difficulties stemmed from forgetting premise information and from having problems in carrying…