Introduction

NASA Astrophysics Data System (ADS)

Callegati, Franco; Aracil, Javier; López, Víctor

At the present time, optical transmission systems are capable of sending data over hundreds of wavelengths on a single fiber thanks to dense wavelength division multiplexing (DWDM) technologies, reaching bit rates on the order of gigabits per second per wavelength and terabits per second per fiber. In the last decade the availability of such a huge bandwidth caused transport networks to be considered as having infinite capacity. The recent massive deployment of Asymmetric Digital Subscriber Line (ADSL) and broadband wireless access solutions, as well as the outburst of new multimedia network services (such as Skype, YouTube, Joost, etc.) caused a significant increase of end user traffic and bandwidth demands. Therefore, the apparently “infinite” capacity of optical networks appears much more “finite” today, despite the latest developments in photonic transmission.

All optical OFDM transmission for passive optical networks

NASA Astrophysics Data System (ADS)

Kachare, Nitin; Ashik T., J.; Bai, K. Kalyani; Kumar, D. Sriram

2017-06-01

This paper demonstrates the idea of data transmission at a very higher rate (Tbits/s) through optical fibers in a passive optical network using the most efficient data transmission technique widely used in wireless communication that is orthogonal frequency division multiplexing. With an increase in internet users, data traffic has also increased significantly and the current dense wavelength division multiplexing (DWDM) systems may not support the next generation passive optical networks (PONs) requirements. The approach discussed in this paper allows to increase the downstream data rate per user and extend the standard single-mode fiber reach for future long-haul applications. All-optical OFDM is a promising solution for terabit per second capable single wavelength transmission, with high spectral efficiency and high tolerance to chromatic dispersion.

Digital optical feeder links system for broadband geostationary satellite

NASA Astrophysics Data System (ADS)

Poulenard, Sylvain; Mège, Alexandre; Fuchs, Christian; Perlot, Nicolas; Riedi, Jerome; Perdigues, Josep

2017-02-01

An optical link based on a multiplex of wavelengths at 1.55μm is foreseen to be a valuable solution for the feeder link of the next generation of high-throughput geostationary satellite. The main satellite operator specifications for such link are an availability of 99.9% over the year, a capacity around 500Gbit/s and to be bent-pipe. Optical ground station networks connected to Terabit/s terrestrial fibers are proposed. The availability of the optical feeder link is simulated over 5 years based on a state-of-the-art cloud mask data bank and an atmospheric turbulence strength model. Yearly and seasonal optical feeder link availabilities are derived and discussed. On-ground and on-board terminals are designed to be compliant with 10Gbit/s per optical channel data rate taking into account adaptive optic systems to mitigate the impact of atmospheric turbulences on single-mode optical fiber receivers. The forward and return transmission chains, concept and implementation, are described. These are based on a digital transparent on-off keying optical link with digitalization of the DVB-S2 and DVB-RCS signals prior to the transmission, and a forward error correcting code. In addition, the satellite architecture is described taking into account optical and radiofrequency payloads as well as their interfaces.

Holographic optical disc

NASA Astrophysics Data System (ADS)

Zhou, Gan; An, Xin; Pu, Allen; Psaltis, Demetri; Mok, Fai H.

1999-11-01

The holographic disc is a high capacity, disk-based data storage device that can provide the performance for next generation mass data storage needs. With a projected capacity approaching 1 terabit on a single 12 cm platter, the holographic disc has the potential to become a highly efficient storage hardware for data warehousing applications. The high readout rate of holographic disc makes it especially suitable for generating multiple, high bandwidth data streams such as required for network server computers. Multimedia applications such as interactive video and HDTV can also potentially benefit from the high capacity and fast data access of holographic memory.

Hybrid WDM/OCDMA for next generation access network

NASA Astrophysics Data System (ADS)

Wang, Xu; Wada, Naoya; Miyazaki, T.; Cincotti, G.; Kitayama, Ken-ichi

2007-11-01

Hybrid wavelength division multiplexing/optical code division multiple access (WDM/OCDMA) passive optical network (PON), where asynchronous OCDMA traffic transmits over WDM network, can be one potential candidate for gigabit-symmetric fiber-to-the-home (FTTH) services. In a cost-effective WDM/OCDMA network, a large scale multi-port encoder/decoder can be employed in the central office, and a low cost encoder/decoder will be used in optical network unit (ONU). The WDM/OCDMA system could be one promising solution to the symmetric high capacity access network with high spectral efficiency, cost effective, good flexibility and enhanced security. Asynchronous WDM/OCDMA systems have been experimentally demonstrated using superstructured fiber Bragg gratings (SSFBG) and muti-port OCDMA en/decoders. The total throughput has reached above Tera-bit/s with spectral efficiency of about 0.41. The key enabling techniques include ultra-long SSFBG, multi-port E/D with high power contrast ratio, optical thresholding, differential phase shift keying modulation with balanced detection, forward error correction, and etc. Using multi-level modulation formats to carry multi-bit information with single pulse, the total capacity and spectral efficiency could be further enhanced.

Optoelectronic-cache memory system architecture.

PubMed

Chiarulli, D M; Levitan, S P

1996-05-10

We present an investigation of the architecture of an optoelectronic cache that can integrate terabit optical memories with the electronic caches associated with high-performance uniprocessors and multiprocessors. The use of optoelectronic-cache memories enables these terabit technologies to provide transparently low-latency secondary memory with frame sizes comparable with disk pages but with latencies that approach those of electronic secondary-cache memories. This enables the implementation of terabit memories with effective access times comparable with the cycle times of current microprocessors. The cache design is based on the use of a smart-pixel array and combines parallel free-space optical input-output to-and-from optical memory with conventional electronic communication to the processor caches. This cache and the optical memory system to which it will interface provide a large random-access memory space that has a lower overall latency than that of magnetic disks and disk arrays. In addition, as a consequence of the high-bandwidth parallel input-output capabilities of optical memories, fault service times for the optoelectronic cache are substantially less than those currently achievable with any rotational media.

Advanced Silicon Photonic Transceivers - the Case of a Wavelength Division and Polarization Multiplexed Quadrature Phase Shift Keying Receiver for Terabit/s Optical Transmission

DTIC Science & Technology

2017-03-10

formats by the co- integration of a passive 90 degree optical hybrid, highspeed balanced Ge photodetectors and a high-speed two-channel transimpedance...40 Gbaud and can handle advanced modulation formats by the co-integration of a passive 90 degree optical hybrid, high- speed balanced Ge...reached at an OSNR of 12.4 dB. The hard -decision FEC (HD-FEC) threshold (BER of 3.8 × 10-3 for 7% overhead) requires 14 dB OSNR. For 16-QAM this requires

Terabit Wireless Communication Challenges

NASA Technical Reports Server (NTRS)

Hwu, Shian U.

2012-01-01

This presentation briefly discusses a research effort on Terabit Wireless communication systems for possible space applications. Recently, terahertz (THz) technology (300-3000 GHz frequency) has attracted a great deal of interest from academia and industry. This is due to a number of interesting features of THz waves, including the nearly unlimited bandwidths available, and the non-ionizing radiation nature which does not damage human tissues and DNA with minimum health threat. Also, as millimeter-wave communication systems mature, the focus of research is, naturally, moving to the THz range. Many scientists regard THz as the last great frontier of the electromagnetic spectrum, but finding new applications outside the traditional niches of radio astronomy, Earth and planetary remote sensing, and molecular spectroscopy particularly in biomedical imaging and wireless communications has been relatively slow. Radiologists find this area of study so attractive because t-rays are non-ionizing, which suggests no harm is done to tissue or DNA. They also offer the possibility of performing spectroscopic measurements over a very wide frequency range, and can even capture signatures from liquids and solids. According to Shannon theory, the broad bandwidth of the THz frequency bands can be used for terabit-per-second (Tb/s) wireless communication systems. This enables several new applications, such as cell phones with 360 degrees autostereoscopic displays, optic-fiber replacement, and wireless Tb/s file transferring. Although THz technology could satisfy the demand for an extremely high data rate, a number of technical challenges need to be overcome before its development. This presentation provides an overview the state-of-the- art in THz wireless communication and the technical challenges for an emerging application in Terabit wireless systems. The main issue for THz wave propagation is the high atmospheric attenuation, which is dominated by water vapor absorption in the THz frequency band. The technical challenges in design such a system and the techniques to overcome the challenges will be discussed in this presentation.

The ASCI Network for SC '99: A Step on the Path to a 100 Gigabit Per Second Supercomputing Network

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

PRATT,THOMAS J.; TARMAN,THOMAS D.; MARTINEZ,LUIS M.

2000-07-24

This document highlights the Discom{sup 2}'s Distance computing and communication team activities at the 1999 Supercomputing conference in Portland, Oregon. This conference is sponsored by the IEEE and ACM. Sandia, Lawrence Livermore and Los Alamos National laboratories have participated in this conference for eleven years. For the last four years the three laboratories have come together at the conference under the DOE's ASCI, Accelerated Strategic Computing Initiatives rubric. Communication support for the ASCI exhibit is provided by the ASCI DISCOM{sup 2} project. The DISCOM{sup 2} communication team uses this forum to demonstrate and focus communication and networking developments within themore » community. At SC 99, DISCOM built a prototype of the next generation ASCI network demonstrated remote clustering techniques, demonstrated the capabilities of the emerging Terabit Routers products, demonstrated the latest technologies for delivering visualization data to the scientific users, and demonstrated the latest in encryption methods including IP VPN technologies and ATM encryption research. The authors also coordinated the other production networking activities within the booth and between their demonstration partners on the exhibit floor. This paper documents those accomplishments, discusses the details of their implementation, and describes how these demonstrations support Sandia's overall strategies in ASCI networking.« less

Nanoscale CuO solid-electrolyte-based conductive-bridging, random-access memory cell with a TiN liner

NASA Astrophysics Data System (ADS)

Lee, Jong-Sun; Kim, Dong-Won; Kim, Hea-Jee; Jin, Soo-Min; Song, Myung-Jin; Kwon, Ki-Hyun; Park, Jea-Gun; Jalalah, Mohammed; Al-Hajry, Ali

2018-01-01

The Conductive-bridge random-access memory (CBRAM) cell is a promising candidate for a terabit-level non-volatile memory due to its remarkable advantages. We present for the first time TiN as a diffusion barrier in CBRAM cells for enhancing their reliability. CuO solid-electrolyte-based CBRAM cells implemented with a 0.1-nm TiN liner demonstrated better non-volatile memory characteristics such as 106 AC write/erase endurance cycles with 100-μs AC pulse width and a long retention time of 7.4-years at 85 °C. In addition, the analysis of Ag diffusion in the CBRAM cell suggests that the morphology of the Ag filaments in the electrolyte can be effectively controlled by tuning the thickness of the TiN liner. These promising results pave the way for faster commercialization of terabit-level non-volatile memories.

Trade-offs between lens complexity and real estate utilization in a free-space multichip global interconnection module.

PubMed

Milojkovic, Predrag; Christensen, Marc P; Haney, Michael W

2006-07-01

The FAST-Net (Free-space Accelerator for Switching Terabit Networks) concept uses an array of wide-field-of-view imaging lenses to realize a high-density shuffle interconnect pattern across an array of smart-pixel integrated circuits. To simplify the optics we evaluated the efficiency gained in replacing spherical surfaces with aspherical surfaces by exploiting the large disparity between narrow vertical cavity surface emitting laser (VCSEL) beams and the wide field of view of the imaging optics. We then analyzed trade-offs between lens complexity and chip real estate utilization and determined that there exists an optimal numerical aperture for VCSELs that maximizes their area density. The results provide a general framework for the design of wide-field-of-view free-space interconnection systems that incorporate high-density VCSEL arrays.

Terabit optical OFDM superchannel transmission via coherent carriers of a hybrid chip-scale soliton frequency comb

NASA Astrophysics Data System (ADS)

Geng, Yong; Huang, Xiatao; Cui, Wenwen; Ling, Yun; Xu, Bo; Zhang, Jin; Yi, Xingwen; Wu, Baojian; Huang, Shu-Wei; Qiu, Kun; Wong, Chee Wei; Zhou, Heng

2018-05-01

We demonstrate seamless channel multiplexing and high bitrate superchannel transmission of coherent optical orthogonal-frequency-division-multiplexing (CO-OFDM) data signals utilizing a dissipative Kerr soliton (DKS) frequency comb generated in an on-chip microcavity. Aided by comb line multiplication through Nyquist pulse modulation, the high stability and mutual coherence among mode-locked Kerr comb lines are exploited for the first time to eliminate the guard intervals between communication channels and achieve full spectral density bandwidth utilization. Spectral efficiency as high as 2.625 bit/Hz/s is obtained for 180 CO-OFDM bands encoded with 12.75 Gbaud 8-QAM data, adding up to total bitrate of 6.885 Tb/s within 2.295 THz frequency comb bandwidth. Our study confirms that high coherence is the key superiority of Kerr soliton frequency combs over independent laser diodes, as a multi-spectral coherent laser source for high-bandwidth high-spectral-density transmission networks.

Multi terabits/s optical access transport technologies

NASA Astrophysics Data System (ADS)

Binh, Le Nguyen; Wang Tao, Thomas; Livshits, Daniil; Gubenko, Alexey; Karinou, Fotini; Liu Ning, Gordon; Shkolnik, Alexey

2016-02-01

Tremendous efforts have been developed for multi-Tbps over ultra-long distance and metro and access optical networks. With the exponential increase demand on data transmission, storage and serving, especially the 5G wireless access scenarios, the optical Internet networking has evolved to data-center based optical networks pressuring on novel and economical access transmission systems. This paper reports (1) Experimental platforms and transmission techniques employing band-limited optical components operating at 10G for 100G based at 28G baud. Advanced modulation formats such as PAM-4, DMT, duo-binary etc are reported and their advantages and disadvantages are analyzed so as to achieve multi-Tbps optical transmission systems for access inter- and intra- data-centered-based networks; (2) Integrated multi-Tbps combining comb laser sources and micro-ring modulators meeting the required performance for access systems are reported. Ten-sub-carrier quantum dot com lasers are employed in association with wideband optical intensity modulators to demonstrate the feasibility of such sources and integrated micro-ring modulators acting as a combined function of demultiplexing/multiplexing and modulation, hence compactness and economy scale. Under the use of multi-level modulation and direct detection at 56 GBd an aggregate of higher than 2Tbps and even 3Tbps can be achieved by interleaved two comb lasers of 16 sub-carrier lines; (3) Finally the fundamental designs of ultra-compacts flexible filters and switching integrated components based on Si photonics for multi Tera-bps active interconnection are presented. Experimental results on multi-channels transmissions and performances of optical switching matrices and effects on that of data channels are proposed.

The 30/20 GHz fixed communications systems service demand assessment. Volume 2: Main report

NASA Technical Reports Server (NTRS)

Gamble, R. B.; Seltzer, H. R.; Speter, K. M.; Westheimer, M.

1979-01-01

A forecast of demand for telecommunications services through the year 2000 is presented with particular reference to demand for satellite communications. Estimates of demand are provided for voice, video, and data services and for various subcategories of these services. The results are converted to a common digital measure in terms of terabits per year and aggregated to obtain total demand projections.

CMOL: A New Concept for Nanoelectronics

NASA Astrophysics Data System (ADS)

Likharev, Konstantin

2005-03-01

I will review the recent work on devices and architectures for future hybrid semiconductor/molecular integrated circuits, in particular those of ``CMOL'' variety [1]. Such circuits would combine an advanced CMOS subsystem fabricated by the usual lithographic patterning, two layers of parallel metallic nanowires formed, e.g., by nanoimprint, and two-terminal molecular devices self-assembled on the nanowire crosspoints. Estimates show that this powerful combination may allow CMOL circuits to reach an unparalleled density (up to 10^12 functions per cm^2) and ultrahigh rate of information processing (up to 10^20 operations per second on a single chip), at acceptable power dissipation. The main challenges on the way toward practical CMOL technology are: (i) reliable chemically-directed self-assembly of mid-size organic molecules, and (ii) the development of efficient defect-tolerant architectures for CMOL circuits. Our recent work has shown that such architectures may be developed not only for terabit-scale memories and naturally defect-tolerant mixed-signal neuromorphic networks, but (rather unexpectedly) also for FPGA-style digital Boolean circuits. [1] For details, see http://rsfq1.physics.sunysb.edu/˜likharev/nano/Springer04.pdf

Advanced time and wavelength division multiplexing for metropolitan area optical data communication networks

NASA Astrophysics Data System (ADS)

Watford, M.; DeCusatis, C.

2005-09-01

With the advent of new regulations governing the protection and recovery of sensitive business data, including the Sarbanes-Oxley Act, there has been a renewed interest in business continuity and disaster recovery applications for metropolitan area networks. Specifically, there has been a need for more efficient bandwidth utilization and lower cost per channel to facilitate mirroring of multi-terabit data bases. These applications have further blurred the boundary between metropolitan and wide area networks, with synchronous disaster recovery applications running up to 100 km and asynchronous solutions extending to 300 km or more. In this paper, we discuss recent enhancements in the Nortel Optical Metro 5200 Dense Wavelength Division Multiplexing (DWDM) platform, including features recently qualified for data communication applications such as Metro Mirror, Global Mirror, and Geographically Distributed Parallel Sysplex (GDPS). Using a 10 Gigabit/second (Gbit/s) backbone, this solution transports significantly more Fibre Channel protocol traffic with up to five times greater hardware density in the same physical package. This is also among the first platforms to utilize forward error correction (FEC) on the aggregate signals to improve bit error rate (BER) performance beyond industry standards. When combined with encapsulation into wide area network protocols, the use of FEC can compensate for impairments in BER across a service provider infrastructure without impacting application level performance. Design and implementation of these features will be discussed, including results from experimental test beds which validate these solutions for a number of applications. Future extensions of this environment will also be considered, including ways to provide configurable bandwidth on demand, mitigate Fibre Channel buffer credit management issues, and support for other GDPS protocols.

Integrated InAs/InP quantum-dot coherence comb lasers (Conference Presentation)

NASA Astrophysics Data System (ADS)

Lu, Zhenguo; Liu, Jiaren; Poole, Philip J.; Song, Chun-Ying; Webber, John; Mao, Linda; Chang, Shoude; Ding, Heping; Barrios, Pedro J.; Poitras, Daniel; Janz, Siegfried

2017-02-01

Current communication networks needs to keep up with the exponential growth of today's internet traffic, and telecommunications industry is looking for radically new integrated photonics components for new generation optical networks. We at National Research Council (NRC) Canada have successfully developed nanostructure InAs/InP quantum dot (QD) coherence comb lasers (CCLs) around 1.55 μm. Unlike uniform semiconductor layers in most telecommunication lasers, in these QD CCLs light is emitted and amplified by millions of semiconductor QDs less than 60 nm in diameter. Each QD acts like an isolated light source acting independently of its neighbours, and each QD emits light at its own unique wavelength. The end result is a QD CCL is more stable and has ultra-low timing jitter. But most importantly, a single QD CCL can simultaneously produce 50 or more separate laser beams at distinct wavelengths over the telecommunications C-band. Utilizing those unique properties we have put considerable effort well to design, grow and fabricate InAs/InP QD gain materials. After our integrated packaging and using electrical feedback-loop control systems, we have successfully demonstrated ultra-low intensity and phase noise, frequency-stabilized integrated QD CCLs with the repetition rates from 10 GHz to 100 GHz and the total output power up to 60 mW at room temperature. We have investigated their relative intensity noises, phase noises, RF beating signals and other performance of both filtered individual channel and the whole CCLs. Those highly phase-coherence comb lasers are the promising candidates for flexible bandwidth terabit coherent optical networks and signal processing applications.

Active holographic interconnects for interfacing volume storage

NASA Astrophysics Data System (ADS)

Domash, Lawrence H.; Schwartz, Jay R.; Nelson, Arthur R.; Levin, Philip S.

1992-04-01

In order to achieve the promise of terabit/cm3 data storage capacity for volume holographic optical memory, two technological challenges must be met. Satisfactory storage materials must be developed and the input/output architectures able to match their capacity with corresponding data access rates must also be designed. To date the materials problem has received more attention than devices and architectures for access and addressing. Two philosophies of parallel data access to 3-D storage have been discussed. The bit-oriented approach, represented by recent work on two-photon memories, attempts to store bits at local sites within a volume without affecting neighboring bits. High speed acousto-optic or electro- optic scanners together with dynamically focused lenses not presently available would be required. The second philosophy is that volume optical storage is essentially holographic in nature, and that each data write or read is to be distributed throughout the material volume on the basis of angle multiplexing or other schemes consistent with the principles of holography. The requirements for free space optical interconnects for digital computers and fiber optic network switching interfaces are also closely related to this class of devices. Interconnects, beamlet generators, angle multiplexers, scanners, fiber optic switches, and dynamic lenses are all devices which may be implemented by holographic or microdiffractive devices of various kinds, which we shall refer to collectively as holographic interconnect devices. At present, holographic interconnect devices are either fixed holograms or spatial light modulators. Optically or computer generated holograms (submicron resolution, 2-D or 3-D, encoding 1013 bits, nearly 100 diffraction efficiency) can implement sophisticated mathematical design principles, but of course once fabricated they cannot be changed. Spatial light modulators offer high speed programmability but have limited resolution (512 X 512 pixels, encoding about 106 bits of data) and limited diffraction efficiency. For any application, one must choose between high diffractive performance and programmability.

Up-to-date state of storage techniques used for large numerical data files

NASA Technical Reports Server (NTRS)

Chlouba, V.

1975-01-01

Methods for data storage and output in data banks and memory files are discussed along with a survey of equipment available for this. Topics discussed include magnetic tapes, magnetic disks, Terabit magnetic tape memory, Unicon 690 laser memory, IBM 1360 photostore, microfilm recording equipment, holographic recording, film readers, optical character readers, digital data storage techniques, and photographic recording. The individual types of equipment are summarized in tables giving the basic technical parameters.

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

Lentine, Anthony L.; DeRose, Christopher T.

In this study, small silicon photonics micro-resonator modulators and filters hold the promise for multi-terabit per-second interconnects at energy consumptions well below 1 pJ/bit. To date, no products exist and little known commercial development is occurring using this technology. Why? In this talk, we review the many challenges that remain to be overcome in bringing this technology from the research labs to the field where they can overcome important commercial, industrial, and national security limitations of existing photonic technologies.

Directed Block Copolymer Assembly versus Electron Beam Lithography for Bit-Patterned Media with Areal Density of 1 Terabit/inch(2) and Beyond.

PubMed

Yang, Xiaomin; Wan, Lei; Xiao, Shuaigang; Xu, Yuan; Weller, Dieter K

2009-07-28

The directed self-assembly of block copolymer (BCP) offers a new route to perfect nanolithographic patterning at sub-50 nm length scale with molecular scale precision. We have explored the feasibility of using the BCP approach versus the conventional electron beam (e-beam) lithography to create highly dense dot patterns for bit-patterned media (BPM) applications. Cylinder-forming poly(styrene-b-methyl methacrylate) (PS-b-PMMA) directly self-assembled on a chemically prepatterned substrate. The nearly perfect hexagonal arrays of perpendicularly oriented cylindrical pores at a density of approximately 1 Terabit per square inch (Tb/in.(2)) are achieved over an arbitrarily large area. Considerable gains in the BCP process are observed relative to the conventional e-beam lithography in terms of the dot size variation, the placement accuracy, the pattern uniformity, and the exposure latitude. The maximum dimensional latitude in the cylinder-forming BCP patterns and the maximum skew angle that the BCP can tolerate have been investigated for the first time. The dimensional latitude restricts the formation of more than one lattice configuration in certain ranges. More defects in BCP patterns are observed when using low molecular weight BCP materials or on non-hexagonal prepatterns due to the dimensional latitude restriction. Finally, the limitations and challenges in the BCP approach that are associated with BPM applications will be briefly discussed.

Optical ground station optimization for future optical geostationary satellite feeder uplinks

NASA Astrophysics Data System (ADS)

Camboulives, A.-R.; Velluet, M.-T.; Poulenard, S.; Saint-Antonin, L.; Michau, V.

2017-02-01

An optical link based on a multiplex of wavelengths at 1:55 μm is foreseen to be a valuable alternative to the conventional radio-frequencies for the feeder link of the next-generation of high throughput geostationary satellite. Considering the limited power of lasers envisioned for feeder links, the beam divergence has to be dramatically reduced. Consequently, the beam pointing becomes a key issue. During its propagation between the ground station and a geostationary satellite, the optical beam is deflected (beam wandering), and possibly distorted (beam spreading), by atmospheric turbulence. It induces strong fluctuations of the detected telecom signal, thus increasing the bit error rate (BER). A steering mirror using a measurement from a beam coming from the satellite is used to pre-compensate the deflection. Because of the point-ahead angle between the downlink and the uplink, the turbulence effects experienced by both beams are slightly different, inducing an error in the correction. This error is characterized as a function of the turbulence characteristics as well as of the terminal characteristics, such as the servo-loop bandwidth or the beam diameter, and is included in the link budget. From this result, it is possible to predict intensity fluctuations detected by the satellite statistically (mean intensity, scintillation index, probability of fade, etc.)). The final objective is to optimize the different parameters of an optical ground station capable of mitigating the impact of atmospheric turbulence on the uplink in order to be compliant with the targeted capacity (1Terabit/s by 2025).

Class network routing

DOEpatents

Bhanot, Gyan [Princeton, NJ; Blumrich, Matthias A [Ridgefield, CT; Chen, Dong [Croton On Hudson, NY; Coteus, Paul W [Yorktown Heights, NY; Gara, Alan G [Mount Kisco, NY; Giampapa, Mark E [Irvington, NY; Heidelberger, Philip [Cortlandt Manor, NY; Steinmacher-Burow, Burkhard D [Mount Kisco, NY; Takken, Todd E [Mount Kisco, NY; Vranas, Pavlos M [Bedford Hills, NY

2009-09-08

Class network routing is implemented in a network such as a computer network comprising a plurality of parallel compute processors at nodes thereof. Class network routing allows a compute processor to broadcast a message to a range (one or more) of other compute processors in the computer network, such as processors in a column or a row. Normally this type of operation requires a separate message to be sent to each processor. With class network routing pursuant to the invention, a single message is sufficient, which generally reduces the total number of messages in the network as well as the latency to do a broadcast. Class network routing is also applied to dense matrix inversion algorithms on distributed memory parallel supercomputers with hardware class function (multicast) capability. This is achieved by exploiting the fact that the communication patterns of dense matrix inversion can be served by hardware class functions, which results in faster execution times.

Challenges in the implementation of dense wavelength division multiplexed (DWDM) optical interconnects using resonant silicon photonics (invited)

DOE PAGES

Lentine, Anthony L.; DeRose, Christopher T.

2016-02-12

In this study, small silicon photonics micro-resonator modulators and filters hold the promise for multi-terabit per-second interconnects at energy consumptions well below 1 pJ/bit. To date, no products exist and little known commercial development is occurring using this technology. Why? In this talk, we review the many challenges that remain to be overcome in bringing this technology from the research labs to the field where they can overcome important commercial, industrial, and national security limitations of existing photonic technologies.

Field-programmable logic devices with optical input-output.

PubMed

Szymanski, T H; Saint-Laurent, M; Tyan, V; Au, A; Supmonchai, B

2000-02-10

A field-programmable logic device (FPLD) with optical I/O is described. FPLD's with optical I/O can have their functionality specified in the field by means of downloading a control-bit stream and can be used in a wide range of applications, such as optical signal processing, optical image processing, and optical interconnects. Our device implements six state-of-the-art dynamically programmable logic arrays (PLA's) on a 2 mm x 2 mm die. The devices were fabricated through the Lucent Technologies-Advanced Research Projects Agency-Consortium for Optical and Optoelectronic Technologies in Computing (Lucent/ARPA/COOP) workshop by use of 0.5-microm complementary metal-oxide semiconductor-self-electro-optic device technology and were delivered in 1998. All devices are fully functional: The electronic data paths have been verified at 200 MHz, and optical tests are pending. The device has been programmed to implement a two-stage optical switching network with six 4 x 4 crossbar switches, which can realize more than 190 x 10(6) unique programmable input-output permutations. The same device scaled to a 2 cm x 2 cm substrate could support as many as 4000 optical I/O and 1 Tbit/s of optical I/O bandwidth and offer fully programmable digital functionality with approximately 110,000 programmable logic gates. The proposed optoelectronic FPLD is also ideally suited to realizing dense, statically reconfigurable crossbar switches. We describe an attractive application area for such devices: a rearrangeable three-stage optical switch for a wide-area-network backbone, switching 1000 traffic streams at the OC-48 data rate and supporting several terabits of traffic.

THESEUS: A wavelength division multiplexed/microwave subcarrier multiplexed optical network, its ATM switch applications and device requirements

NASA Astrophysics Data System (ADS)

Xin, Wei

1997-10-01

A Terabit Hybrid Electro-optical /underline[Se]lf- routing Ultrafast Switch (THESEUS) has been proposed. It is a self-routing wavelength division multiplexed (WDM) / microwave subcarrier multiplexed (SCM) asynchronous transfer mode (ATM) switch for the multirate ATM networks. It has potential to be extended to a large ATM switch as 1000 x 1000 without internal blocking. Among the advantages of the hybrid implementation are flexibility in service upgrade, relaxed tolerances on optical filtering, protocol simplification and less processing overhead. For a small ATM switch, the subcarrier can be used as output buffers to solve output contention. A mathematical analysis was conducted to evaluate different buffer configurations. A testbed has been successfully constructed. Multirate binary data streams have been switched through the testbed and error free reception ([<]10-9 bit error rate) has been achieved. A simple, intuitive theoretical model has been developed to describe the heterodyne optical beat interference. A new concept of interference time and interference length has been introduced. An experimental confirmation has been conducted. The experimental results match the model very well. It shows that a large portion of optical bandwidth is wasted due to the beat interference. Based on the model, several improvement approaches have been proposed. The photo-generated carrier lifetime of silicon germanium has been measured using time-resolved reflectivity measurement. Via oxygen ion implantation, the carrier lifetime has been reduced to as short as 1 ps, corresponding to 1 THz of photodetector bandwidth. It has also been shown that copper dopants act as recombination centers in the silicon germanium.

High Data Rate Satellite Communications for Environmental Remote Sensing

NASA Astrophysics Data System (ADS)

Jackson, J. M.; Munger, J.; Emch, P. G.; Sen, B.; Gu, D.

2014-12-01

Satellite to ground communication bandwidth limitations place constraints on current earth remote sensing instruments which limit the spatial and spectral resolution of data transmitted to the ground for processing. Instruments such as VIIRS, CrIS and OMPS on the Soumi-NPP spacecraft must aggregate data both spatially and spectrally in order to fit inside current data rate constraints limiting the optimal use of the as-built sensors. Future planned missions such as HyspIRI, SLI, PACE, and NISAR will have to trade spatial and spectral resolution if increased communication band width is not made available. A number of high-impact, environmental remote sensing disciplines such as hurricane observation, mega-city air quality, wild fire detection and monitoring, and monitoring of coastal oceans would benefit dramatically from enabling the downlinking of sensor data at higher spatial and spectral resolutions. The enabling technologies of multi-Gbps Ka-Band communication, flexible high speed on-board processing, and multi-Terabit SSRs are currently available with high technological maturity enabling high data volume mission requirements to be met with minimal mission constraints while utilizing a limited set of ground sites from NASA's Near Earth Network (NEN) or TDRSS. These enabling technologies will be described in detail with emphasis on benefits to future remote sensing missions currently under consideration by government agencies.

A heuristic method for consumable resource allocation in multi-class dynamic PERT networks

NASA Astrophysics Data System (ADS)

Yaghoubi, Saeed; Noori, Siamak; Mazdeh, Mohammad Mahdavi

2013-06-01

This investigation presents a heuristic method for consumable resource allocation problem in multi-class dynamic Project Evaluation and Review Technique (PERT) networks, where new projects from different classes (types) arrive to system according to independent Poisson processes with different arrival rates. Each activity of any project is operated at a devoted service station located in a node of the network with exponential distribution according to its class. Indeed, each project arrives to the first service station and continues its routing according to precedence network of its class. Such system can be represented as a queuing network, while the discipline of queues is first come, first served. On the basis of presented method, a multi-class system is decomposed into several single-class dynamic PERT networks, whereas each class is considered separately as a minisystem. In modeling of single-class dynamic PERT network, we use Markov process and a multi-objective model investigated by Azaron and Tavakkoli-Moghaddam in 2007. Then, after obtaining the resources allocated to service stations in every minisystem, the final resources allocated to activities are calculated by the proposed method.

47 CFR 36.353 - Network operations expenses-Account 6530 (Class B telephone companies); Accounts 6531, 6532, 6533...

Code of Federal Regulations, 2010 CFR

2010-10-01

... 47 Telecommunication 2 2010-10-01 2010-10-01 false Network operations expenses-Account 6530 (Class... Expenses and Taxes Network Operations Expenses § 36.353 Network operations expenses—Account 6530 (Class B... account includes the expenses associated with the provisions of power, network administration, testing...

47 CFR 36.353 - Network operations expenses-Account 6530 (Class B telephone companies); Accounts 6531, 6532, 6533...

Code of Federal Regulations, 2011 CFR

2011-10-01

... 47 Telecommunication 2 2011-10-01 2011-10-01 false Network operations expenses-Account 6530 (Class... Expenses and Taxes Network Operations Expenses § 36.353 Network operations expenses—Account 6530 (Class B... account includes the expenses associated with the provisions of power, network administration, testing...

The Dynamics of the Atmospheric Radiation Environment at Aviation Altitudes

NASA Technical Reports Server (NTRS)

Stassinopoulos, Epaminondas G.

2004-01-01

Single Event Effects vulnerability of on-board computers that regulate the: navigational, flight control, communication, and life support systems has become an issue in advanced modern aircraft, especially those that may be equipped with new technology devices in terabit memory banks (low voltage, nanometer feature size, gigabit integration). To address this concern, radiation spectrometers need to fly continually on a multitude of carriers over long periods of time so as to accumulate sufficient information that will broaden our understanding of the very dynamic and complex nature of the atmospheric radiation environment regarding: composition, spectral distribution, intensity, temporal variation, and spatial variation.

Ultra-High-Density Ferroelectric Memories

NASA Technical Reports Server (NTRS)

Thakoor, Sarita

1995-01-01

Features include fast input and output via optical fibers. Memory devices of proposed type include thin ferroelectric films in which data stored in form of electric polarization. Assuming one datum stored in region as small as polarization domain, sizes of such domains impose upper limits on achievable storage densities. Limits approach 1 terabit/cm(Sup2) in all-optical versions of these ferroelectric memories and exceeds 1 gigabit/cm(Sup2) in optoelectronic versions. Memories expected to exhibit operational lives of about 10 years, input/output times of about 10 ns, and fatigue lives of about 10(Sup13) cycles.

Towards Terabit Memories

NASA Astrophysics Data System (ADS)

Hoefflinger, Bernd

Memories have been the major yardstick for the continuing validity of Moore's law. In single-transistor-per-Bit dynamic random-access memories (DRAM), the number of bits per chip pretty much gives us the number of transistors. For decades, DRAM's have offered the largest storage capacity per chip. However, DRAM does not scale any longer, both in density and voltage, severely limiting its power efficiency to 10 fJ/b. A differential DRAM would gain four-times in density and eight-times in energy. Static CMOS RAM (SRAM) with its six transistors/cell is gaining in reputation because it scales well in cell size and operating voltage so that its fundamental advantage of speed, non-destructive read-out and low-power standby could lead to just 2.5 electrons/bit in standby and to a dynamic power efficiency of 2aJ/b. With a projected 2020 density of 16 Gb/cm², the SRAM would be as dense as normal DRAM and vastly better in power efficiency, which would mean a major change in the architecture and market scenario for DRAM versus SRAM. Non-volatile Flash memory have seen two quantum jumps in density well beyond the roadmap: Multi-Bit storage per transistor and high-density TSV (through-silicon via) technology. The number of electrons required per Bit on the storage gate has been reduced since their first realization in 1996 by more than an order of magnitude to 400 electrons/Bit in 2010 for a complexity of 32Gbit per chip at the 32 nm node. Chip stacking of eight chips with TSV has produced a 32GByte solid-state drive (SSD). A stack of 32 chips with 2 b/cell at the 16 nm node will reach a density of 2.5 Terabit/cm². Non-volatile memory with a density of 10 × 10 nm²/Bit is the target for widespread development. Phase-change memory (PCM) and resistive memory (RRAM) lead in cell density, and they will reach 20 Gb/cm² in 2D and higher with 3D chip stacking. This is still almost an order-of-magnitude less than Flash. However, their read-out speed is ~10-times faster, with as yet little data on their energy/b. As a read-out memory with unparalleled retention and lifetime, the ROM with electron-beam direct-write-lithography (Chap. 8) should be considered for its projected 2D density of 250 Gb/cm², a very small read energy of 0.1 μW/Gb/s. The lithography write-speed 10 ms/Terabit makes this ROM a serious contentender for the optimum in non-volatile, tamper-proof storage.

Linear matrix inequality approach to exponential synchronization of a class of chaotic neural networks with time-varying delays

NASA Astrophysics Data System (ADS)

Wu, Wei; Cui, Bao-Tong

2007-07-01

In this paper, a synchronization scheme for a class of chaotic neural networks with time-varying delays is presented. This class of chaotic neural networks covers several well-known neural networks, such as Hopfield neural networks, cellular neural networks, and bidirectional associative memory networks. The obtained criteria are expressed in terms of linear matrix inequalities, thus they can be efficiently verified. A comparison between our results and the previous results shows that our results are less restrictive.

Machine Learning-Assisted Network Inference Approach to Identify a New Class of Genes that Coordinate the Functionality of Cancer Networks.

PubMed

Ghanat Bari, Mehrab; Ung, Choong Yong; Zhang, Cheng; Zhu, Shizhen; Li, Hu

2017-08-01

Emerging evidence indicates the existence of a new class of cancer genes that act as "signal linkers" coordinating oncogenic signals between mutated and differentially expressed genes. While frequently mutated oncogenes and differentially expressed genes, which we term Class I cancer genes, are readily detected by most analytical tools, the new class of cancer-related genes, i.e., Class II, escape detection because they are neither mutated nor differentially expressed. Given this hypothesis, we developed a Machine Learning-Assisted Network Inference (MALANI) algorithm, which assesses all genes regardless of expression or mutational status in the context of cancer etiology. We used 8807 expression arrays, corresponding to 9 cancer types, to build more than 2 × 10 8 Support Vector Machine (SVM) models for reconstructing a cancer network. We found that ~3% of ~19,000 not differentially expressed genes are Class II cancer gene candidates. Some Class II genes that we found, such as SLC19A1 and ATAD3B, have been recently reported to associate with cancer outcomes. To our knowledge, this is the first study that utilizes both machine learning and network biology approaches to uncover Class II cancer genes in coordinating functionality in cancer networks and will illuminate our understanding of how genes are modulated in a tissue-specific network contribute to tumorigenesis and therapy development.

A Comparison of Geographic Information Systems, Complex Networks, and Other Models for Analyzing Transportation Network Topologies

NASA Technical Reports Server (NTRS)

Alexandrov, Natalia (Technical Monitor); Kuby, Michael; Tierney, Sean; Roberts, Tyler; Upchurch, Christopher

2005-01-01

This report reviews six classes of models that are used for studying transportation network topologies. The report is motivated by two main questions. First, what can the "new science" of complex networks (scale-free, small-world networks) contribute to our understanding of transport network structure, compared to more traditional methods? Second, how can geographic information systems (GIS) contribute to studying transport networks? The report defines terms that can be used to classify different kinds of models by their function, composition, mechanism, spatial and temporal dimensions, certainty, linearity, and resolution. Six broad classes of models for analyzing transport network topologies are then explored: GIS; static graph theory; complex networks; mathematical programming; simulation; and agent-based modeling. Each class of models is defined and classified according to the attributes introduced earlier. The paper identifies some typical types of research questions about network structure that have been addressed by each class of model in the literature.

Neural network recognition of chemical class information in mobility spectra obtained at high temperatures

NASA Technical Reports Server (NTRS)

Bell, S.; Nazarov, E.; Wang, Y. F.; Rodriguez, J. E.; Eiceman, G. A.

2000-01-01

A minimal neural network was applied to a large library of high-temperature mobility spectra drawn from 16 chemical classes including 154 substances with 2000 spectra at various concentrations. A genetic algorithm was used to create a representative subset of points from the mobility spectrum as input to a cascade-type back-propagation network. This network demonstrated that significant information specific to chemical class was located in the spectral region near the reactant ions. This network failed to generalize the solution to unfamiliar compounds necessitating the use of complete spectra in network processing. An extended back-propagation network classified unfamiliar chemicals by functional group with a mean for average values of 0.83 without sulfides and 0.79 with sulfides. Further experiments confirmed that chemical class information was resident in the spectral region near the reactant ions. Deconvolution of spectra demonstrated the presence of ions, merged with the reactant ion peaks that originated from introduced samples. The ability of the neural network to generalize the solution to unfamiliar compounds suggests that these ions are distinct and class specific.

Peer Support Networks in a Large Introductory Psychology Class.

ERIC Educational Resources Information Center

Slotnick, Robert S.; And Others

Networks have emerged as a major topic of interest in the behavioral sciences, and network concepts have recently been extended by community psychologists to higher education. To examine the effectiveness of peer networks within an introductory psychology class, networks of four students each met weekly in place of a lecture to review material and…

47 CFR 36.311 - Network Support/General Support Expenses-Accounts 6110 and 6120 (Class B Telephone Companies...

Code of Federal Regulations, 2011 CFR

2011-10-01

... 47 Telecommunication 2 2011-10-01 2011-10-01 false Network Support/General Support Expenses... Operating Expenses and Taxes Network Support/general Support Expenses § 36.311 Network Support/General..., 6122, 6123, and 6124 (Class A Telephone Companies). (a) Network Support Expenses are expenses...

47 CFR 36.311 - Network Support/General Support Expenses-Accounts 6110 and 6120 (Class B Telephone Companies...

Code of Federal Regulations, 2010 CFR

2010-10-01

... 47 Telecommunication 2 2010-10-01 2010-10-01 false Network Support/General Support Expenses... Operating Expenses and Taxes Network Support/general Support Expenses § 36.311 Network Support/General..., 6122, 6123, and 6124 (Class A Telephone Companies). (a) Network Support Expenses are expenses...

Social class shapes the form and function of relationships and selves.

PubMed

Carey, Rebecca M; Markus, Hazel Rose

2017-12-01

Social class shapes relational realities, which in turn situate and structure different selves and their associated psychological tendencies. We first briefly review how higher class contexts tend to foster independent models of self and lower class contexts tend to foster interdependent models of self. We then consider how these independent and interdependent models of self are situated in and adapted to different social class-driven relational realities. We review research demonstrating that in lower social class contexts, social networks tend to be small, dense, homogenous and strongly connected. Ties in these networks provide the bonding capital that is key for survival and that promotes the interdependence between self and other(s). In higher social class contexts, social networks tend to be large, far-reaching, diverse and loosely connected. Ties in these networks provide the bridging capital that is key for achieving personal goals and that promotes an independence of self from other. We conclude that understanding and addressing issues tied to social class and inequality requires understanding the form and function of relationships across class contexts. Copyright © 2017 Elsevier Ltd. All rights reserved.

NASA's Evolution to K(sub a)- Band Space Communications for Near-Earth Spacecraft

NASA Technical Reports Server (NTRS)

McCarthy, Kevin P.; Stocklin, Frank J.; Geldzahler, Barry J.; Friedman, Daniel E.; Celeste, Peter B.

2010-01-01

Over the next several years, NASA plans to launch multiple earth-science missions which will send data from low-Earth orbits to ground stations at 1-3 Gbps, to achieve data throughputs of 5-40 terabits per day. These transmission rates exceed the capabilities of S-band and X-band frequency allocations used for science probe downlinks in the past. Accordingly, NASA is exploring enhancements to its space communication capabilities to provide the Agency's first Ka-band architecture solution for next generation missions in the near-earth regime. This paper describes the proposed Ka-band solution's drivers and concept, constraints and analyses which shaped that concept, and expansibility for future needs

Classification of asteroid spectra using a neural network

NASA Technical Reports Server (NTRS)

Howell, E. S.; Merenyi, E.; Lebofsky, L. A.

1994-01-01

The 52-color asteroid survey (Bell et al., 1988) together with the 8-color asteroid survey (Zellner et al., 1985) provide a data set of asteroid spectra spanning 0.3-2.5 micrometers. An artificial neural network clusters these asteroid spectra based on their similarity to each other. We have also trained the neural network with a categorization learning output layer in a supervised mode to associate the established clusters with taxonomic classes. Results of our classification agree with Tholen's classification based on the 8-color data alone. When extending the spectral range using the 52-color survey data, we find that some modification of the Tholen classes is indicated to produce a cleaner, self-consistent set of taxonomic classes. After supervised training using our modified classes, the network correctly classifies both the training examples, and additional spectra into the correct class with an average of 90% accuracy. Our classification supports the separation of the K class from the S class, as suggested by Bell et al. (1987), based on the near-infrared spectrum. We define two end-member subclasses which seem to have compositional significance within the S class: the So class, which is olivine-rich and red, and the Sp class, which is pyroxene-rich and less red. The remaining S-class asteroids have intermediate compositions of both olivine and pyroxene and moderately red continua. The network clustering suggests some additional structure within the E-, M-, and P-class asteroids, even in the absence of albedo information, which is the only discriminant between these in the Tholen classification. New relationships are seen between the C class and related G, B, and F classes. However, in both cases, the number of spectra is too small to interpret or determine the significance of these separations.

Modified-hybrid optical neural network filter for multiple object recognition within cluttered scenes

NASA Astrophysics Data System (ADS)

Kypraios, Ioannis; Young, Rupert C. D.; Chatwin, Chris R.

2009-08-01

Motivated by the non-linear interpolation and generalization abilities of the hybrid optical neural network filter between the reference and non-reference images of the true-class object we designed the modifiedhybrid optical neural network filter. We applied an optical mask to the hybrid optical neural network's filter input. The mask was built with the constant weight connections of a randomly chosen image included in the training set. The resulted design of the modified-hybrid optical neural network filter is optimized for performing best in cluttered scenes of the true-class object. Due to the shift invariance properties inherited by its correlator unit the filter can accommodate multiple objects of the same class to be detected within an input cluttered image. Additionally, the architecture of the neural network unit of the general hybrid optical neural network filter allows the recognition of multiple objects of different classes within the input cluttered image by modifying the output layer of the unit. We test the modified-hybrid optical neural network filter for multiple objects of the same and of different classes' recognition within cluttered input images and video sequences of cluttered scenes. The filter is shown to exhibit with a single pass over the input data simultaneously out-of-plane rotation, shift invariance and good clutter tolerance. It is able to successfully detect and classify correctly the true-class objects within background clutter for which there has been no previous training.

To Enhance Collaborative Learning and Practice Network Knowledge with a Virtualization Laboratory and Online Synchronous Discussion

ERIC Educational Resources Information Center

Hwang, Wu-Yuin; Kongcharoen, Chaknarin; Ghinea, Gheorghita

2014-01-01

Recently, various computer networking courses have included additional laboratory classes in order to enhance students' learning achievement. However, these classes need to establish a suitable laboratory where each student can connect network devices to configure and test functions within different network topologies. In this case, the Linux…

Results from Two Years of Ka-Band Propagation Characterization at Svalbard, Norway

NASA Technical Reports Server (NTRS)

Nessel, James A.; Morse, Jacquelynne Rose; Zemba, Michael

2014-01-01

Over the several years, NASA plans to launch several earth science missions which are expected to achieve data throughputs of 5-40 terabits per day transmitted from low earth orbiting spacecraft to ground stations. The current S-band and X-band frequency allocations in use by NASA, however, are incapable of supporting the data rates required to meet this demand. As such, NASA is in the planning stages to upgrade its existing Near Earth Network (NEN) Polar ground stations to support Ka-band (25.5-27 GHz) operations. Consequently, it becomes imperative that characterization of propagation effects at these NEN sites is conducted to determine expected system performance, particularly at low elevation angles ((is) less than 10 deg) where spacecraft signal acquisition typically occurs. Since May 2011, NASA Glenn Research Center has installed and operated a Ka-band radiometer at the NEN site located in Svalbard, Norway. The Ka-band radiometer monitors the water vapor line, as well as 6 frequencies around 26.5 GHz at multiple elevation angles: 45 deg, 20 deg, and 10 deg. Two year data collection results indicate comparable performance to previously characterized northern latitude sites in the United States, i.e., Fairbanks, Alaska. It is observed that cloud cover at the Svalbard site remains the dominant loss mechanism for Ka-band links, resulting in a margin requirement of 4.1 dB to maintain link availability of 99% at 10 deg elevation.

Percolation and epidemics in random clustered networks

NASA Astrophysics Data System (ADS)

Miller, Joel C.

2009-08-01

The social networks that infectious diseases spread along are typically clustered. Because of the close relation between percolation and epidemic spread, the behavior of percolation in such networks gives insight into infectious disease dynamics. A number of authors have studied percolation or epidemics in clustered networks, but the networks often contain preferential contacts in high degree nodes. We introduce a class of random clustered networks and a class of random unclustered networks with the same preferential mixing. Percolation in the clustered networks reduces the component sizes and increases the epidemic threshold compared to the unclustered networks.

Systematic Hybrid Network Scheduling for Multiple Traffic Classes with Host Timing and Phase Constraints

NASA Technical Reports Server (NTRS)

Hall, Brendan (Inventor); Bonk, Ted (Inventor); Varadarajan, Srivatsan (Inventor); Smithgall, William Todd (Inventor); DeLay, Benjamin F. (Inventor)

2017-01-01

Systems and methods for systematic hybrid network scheduling for multiple traffic classes with host timing and phase constraints are provided. In certain embodiments, a method of scheduling communications in a network comprises scheduling transmission of virtual links pertaining to a first traffic class on a global schedule to coordinate transmission of the virtual links pertaining to the first traffic class across all transmitting end stations on the global schedule; and scheduling transmission of each virtual link pertaining to a second traffic class on a local schedule of the respective transmitting end station from which each respective virtual link pertaining to the second traffic class is transmitted such that transmission of each virtual link pertaining to the second traffic class is coordinated only at the respective end station from which each respective virtual link pertaining to the second traffic class is transmitted.

A Network Implementation Class Exercise: BusinessQuest Business Incubator, LLC

ERIC Educational Resources Information Center

Arling, Priscilla A.

2009-01-01

One way to bring concepts to life in an introductory data networks course is for students to physically build a network that addresses a real business problem. However it can be challenging to find a suitable business problem, particularly if the network can exist only during the class period. This case presents a realistic business scenario and…

Network coding based joint signaling and dynamic bandwidth allocation scheme for inter optical network unit communication in passive optical networks

NASA Astrophysics Data System (ADS)

Wei, Pei; Gu, Rentao; Ji, Yuefeng

2014-06-01

As an innovative and promising technology, network coding has been introduced to passive optical networks (PON) in recent years to support inter optical network unit (ONU) communication, yet the signaling process and dynamic bandwidth allocation (DBA) in PON with network coding (NC-PON) still need further study. Thus, we propose a joint signaling and DBA scheme for efficiently supporting differentiated services of inter ONU communication in NC-PON. In the proposed joint scheme, the signaling process lays the foundation to fulfill network coding in PON, and it can not only avoid the potential threat to downstream security in previous schemes but also be suitable for the proposed hybrid dynamic bandwidth allocation (HDBA) scheme. In HDBA, a DBA cycle is divided into two sub-cycles for applying different coding, scheduling and bandwidth allocation strategies to differentiated classes of services. Besides, as network traffic load varies, the entire upstream transmission window for all REPORT messages slides accordingly, leaving the transmission time of one or two sub-cycles to overlap with the bandwidth allocation calculation time at the optical line terminal (the OLT), so that the upstream idle time can be efficiently eliminated. Performance evaluation results validate that compared with the existing two DBA algorithms deployed in NC-PON, HDBA demonstrates the best quality of service (QoS) support in terms of delay for all classes of services, especially guarantees the end-to-end delay bound of high class services. Specifically, HDBA can eliminate queuing delay and scheduling delay of high class services, reduce those of lower class services by at least 20%, and reduce the average end-to-end delay of all services over 50%. Moreover, HDBA also achieves the maximum delay fairness between coded and uncoded lower class services, and medium delay fairness for high class services.

PRED-CLASS: cascading neural networks for generalized protein classification and genome-wide applications.

PubMed

Pasquier, C; Promponas, V J; Hamodrakas, S J

2001-08-15

A cascading system of hierarchical, artificial neural networks (named PRED-CLASS) is presented for the generalized classification of proteins into four distinct classes-transmembrane, fibrous, globular, and mixed-from information solely encoded in their amino acid sequences. The architecture of the individual component networks is kept very simple, reducing the number of free parameters (network synaptic weights) for faster training, improved generalization, and the avoidance of data overfitting. Capturing information from as few as 50 protein sequences spread among the four target classes (6 transmembrane, 10 fibrous, 13 globular, and 17 mixed), PRED-CLASS was able to obtain 371 correct predictions out of a set of 387 proteins (success rate approximately 96%) unambiguously assigned into one of the target classes. The application of PRED-CLASS to several test sets and complete proteomes of several organisms demonstrates that such a method could serve as a valuable tool in the annotation of genomic open reading frames with no functional assignment or as a preliminary step in fold recognition and ab initio structure prediction methods. Detailed results obtained for various data sets and completed genomes, along with a web sever running the PRED-CLASS algorithm, can be accessed over the World Wide Web at http://o2.biol.uoa.gr/PRED-CLASS.

Providing end-to-end QoS for multimedia applications in 3G wireless networks

NASA Astrophysics Data System (ADS)

Guo, Katherine; Rangarajan, Samapth; Siddiqui, M. A.; Paul, Sanjoy

2003-11-01

As the usage of wireless packet data services increases, wireless carriers today are faced with the challenge of offering multimedia applications with QoS requirements within current 3G data networks. End-to-end QoS requires support at the application, network, link and medium access control (MAC) layers. We discuss existing CDMA2000 network architecture and show its shortcomings that prevent supporting multiple classes of traffic at the Radio Access Network (RAN). We then propose changes in RAN within the standards framework that enable support for multiple traffic classes. In addition, we discuss how Session Initiation Protocol (SIP) can be augmented with QoS signaling for supporting end-to-end QoS. We also review state of the art scheduling algorithms at the base station and provide possible extensions to these algorithms to support different classes of traffic as well as different classes of users.

Link Analysis of High Throughput Spacecraft Communication Systems for Future Science Missions

NASA Technical Reports Server (NTRS)

Simons, Rainee N.

2015-01-01

NASA's plan to launch several spacecrafts into low Earth Orbit (LEO) to support science missions in the next ten years and beyond requires down link throughput on the order of several terabits per day. The ability to handle such a large volume of data far exceeds the capabilities of current systems. This paper proposes two solutions, first, a high data rate link between the LEO spacecraft and ground via relay satellites in geostationary orbit (GEO). Second, a high data rate direct to ground link from LEO. Next, the paper presents results from computer simulations carried out for both types of links taking into consideration spacecraft transmitter frequency, EIRP, and waveform; elevation angle dependent path loss through Earths atmosphere, and ground station receiver GT.

Link Analysis of High Throughput Spacecraft Communication Systems for Future Science Missions

NASA Technical Reports Server (NTRS)

Simons, Rainee N.

2015-01-01

NASA's plan to launch several spacecraft into low Earth Orbit (LEO) to support science missions in the next ten years and beyond requires down link throughput on the order of several terabits per day. The ability to handle such a large volume of data far exceeds the capabilities of current systems. This paper proposes two solutions, first, a high data rate link between the LEO spacecraft and ground via relay satellites in geostationary orbit (GEO). Second, a high data rate direct to ground link from LEO. Next, the paper presents results from computer simulations carried out for both types of links taking into consideration spacecraft transmitter frequency, EIRP, and waveform; elevation angle dependent path loss through Earths atmosphere, and ground station receiver GT.

Ultra-Sensitive Photoreceiver Boosts Data Transmission

NASA Technical Reports Server (NTRS)

2007-01-01

NASA depends on advanced, ultra-sensitive photoreceivers and photodetectors to provide high-data communications and pinpoint image-detection and -recognition capabilities from great distances. In 2003, Epitaxial Technologies LLC was awarded a Small Business Innovation Research (SBIR) contract from Goddard Space Flight Center to address needs for advanced sensor components. Epitaxial developed a photoreciever capable of single proton sensitivity that is also smaller, lighter, and requires less power than its predecessor. This receiver operates in several wavelength ranges; will allow data rate transmissions in the terabit range; and will enhance Earth-based missions for remote sensing of crops and other natural resources, including applications for fluorescence and phosphorescence detection. Widespread military and civilian applications are anticipated, especially through enhancing fiber optic communications, laser imaging, and laser communications.

Innovation network

PubMed Central

Acemoglu, Daron; Akcigit, Ufuk; Kerr, William R.

2016-01-01

Technological progress builds upon itself, with the expansion of invention in one domain propelling future work in linked fields. Our analysis uses 1.8 million US patents and their citation properties to map the innovation network and its strength. Past innovation network structures are calculated using citation patterns across technology classes during 1975–1994. The interaction of this preexisting network structure with patent growth in upstream technology fields has strong predictive power on future innovation after 1995. This pattern is consistent with the idea that when there is more past upstream innovation for a particular technology class to build on, then that technology class innovates more. PMID:27681628

Using multi-class queuing network to solve performance models of e-business sites.

PubMed

Zheng, Xiao-ying; Chen, De-ren

2004-01-01

Due to e-business's variety of customers with different navigational patterns and demands, multi-class queuing network is a natural performance model for it. The open multi-class queuing network(QN) models are based on the assumption that no service center is saturated as a result of the combined loads of all the classes. Several formulas are used to calculate performance measures, including throughput, residence time, queue length, response time and the average number of requests. The solution technique of closed multi-class QN models is an approximate mean value analysis algorithm (MVA) based on three key equations, because the exact algorithm needs huge time and space requirement. As mixed multi-class QN models, include some open and some closed classes, the open classes should be eliminated to create a closed multi-class QN so that the closed model algorithm can be applied. Some corresponding examples are given to show how to apply the algorithms mentioned in this article. These examples indicate that multi-class QN is a reasonably accurate model of e-business and can be solved efficiently.

Concordant Chemical Reaction Networks

PubMed Central

Shinar, Guy; Feinberg, Martin

2015-01-01

We describe a large class of chemical reaction networks, those endowed with a subtle structural property called concordance. We show that the class of concordant networks coincides precisely with the class of networks which, when taken with any weakly monotonic kinetics, invariably give rise to kinetic systems that are injective — a quality that, among other things, precludes the possibility of switch-like transitions between distinct positive steady states. We also provide persistence characteristics of concordant networks, instability implications of discordance, and consequences of stronger variants of concordance. Some of our results are in the spirit of recent ones by Banaji and Craciun, but here we do not require that every species suffer a degradation reaction. This is especially important in studying biochemical networks, for which it is rare to have all species degrade. PMID:22659063

Do Convolutional Neural Networks Learn Class Hierarchy?

PubMed

Bilal, Alsallakh; Jourabloo, Amin; Ye, Mao; Liu, Xiaoming; Ren, Liu

2018-01-01

Convolutional Neural Networks (CNNs) currently achieve state-of-the-art accuracy in image classification. With a growing number of classes, the accuracy usually drops as the possibilities of confusion increase. Interestingly, the class confusion patterns follow a hierarchical structure over the classes. We present visual-analytics methods to reveal and analyze this hierarchy of similar classes in relation with CNN-internal data. We found that this hierarchy not only dictates the confusion patterns between the classes, it furthermore dictates the learning behavior of CNNs. In particular, the early layers in these networks develop feature detectors that can separate high-level groups of classes quite well, even after a few training epochs. In contrast, the latter layers require substantially more epochs to develop specialized feature detectors that can separate individual classes. We demonstrate how these insights are key to significant improvement in accuracy by designing hierarchy-aware CNNs that accelerate model convergence and alleviate overfitting. We further demonstrate how our methods help in identifying various quality issues in the training data.

Relationships between probabilistic Boolean networks and dynamic Bayesian networks as models of gene regulatory networks

PubMed Central

Lähdesmäki, Harri; Hautaniemi, Sampsa; Shmulevich, Ilya; Yli-Harja, Olli

2006-01-01

A significant amount of attention has recently been focused on modeling of gene regulatory networks. Two frequently used large-scale modeling frameworks are Bayesian networks (BNs) and Boolean networks, the latter one being a special case of its recent stochastic extension, probabilistic Boolean networks (PBNs). PBN is a promising model class that generalizes the standard rule-based interactions of Boolean networks into the stochastic setting. Dynamic Bayesian networks (DBNs) is a general and versatile model class that is able to represent complex temporal stochastic processes and has also been proposed as a model for gene regulatory systems. In this paper, we concentrate on these two model classes and demonstrate that PBNs and a certain subclass of DBNs can represent the same joint probability distribution over their common variables. The major benefit of introducing the relationships between the models is that it opens up the possibility of applying the standard tools of DBNs to PBNs and vice versa. Hence, the standard learning tools of DBNs can be applied in the context of PBNs, and the inference methods give a natural way of handling the missing values in PBNs which are often present in gene expression measurements. Conversely, the tools for controlling the stationary behavior of the networks, tools for projecting networks onto sub-networks, and efficient learning schemes can be used for DBNs. In other words, the introduced relationships between the models extend the collection of analysis tools for both model classes. PMID:17415411

Networking for English Literature Class: Cooperative Learning in Chinese Context

ERIC Educational Resources Information Center

Li, Huiyin

2017-01-01

This action research was conducted to investigate the efficacy of networking, an adjusted cooperative learning method employed in an English literature class for non-English majors in China. Questionnaire was administered online anonymously to college students after a 14-week cooperative learning in literature class in a Chinese university, aiming…

A neural networks application for the study of the influence of transport conditions on the working performance

NASA Astrophysics Data System (ADS)

Anghel, D.-C.; Ene, A.; Ştirbu, C.; Sicoe, G.

2017-10-01

This paper presents a study about the factors that influence the working performances of workers in the automotive industry. These factors regard mainly the transportations conditions, taking into account the fact that a large number of workers live in places that are far away of the enterprise. The quantitative data obtained from this study will be generalized by using a neural network, software simulated. The neural network is able to estimate the performance of workers even for the combinations of input factors that had been not recorded by the study. The experimental data obtained from the study will be divided in two classes. The first class that contains approximately 80% of data will be used by the Java software for the training of the neural network. The weights resulted from the training process will be saved in a text file. The other class that contains the rest of the 20% of experimental data will be used to validate the neural network. The training and the validation of the networks are performed in a Java software (TrainAndValidate java class). We designed another java class, Test.java that will be used with new input data, for new situations. The experimental data collected from the study. The software that simulated the neural network. The software that estimates the working performance, when new situations are met. This application is useful for human resources department of an enterprise. The output results are not quantitative. They are qualitative (from low performance to high performance, divided in five classes).

Co-Ethnic Network, Social Class, and Heritage Language Maintenance among Chinese Immigrant Families

ERIC Educational Resources Information Center

Zhang, Donghui

2012-01-01

This ethnographic study investigated heritage language maintenance among two distinct groups of Chinese immigrant families (Mandarin and Fujianese) from the social network perspective. The results indicated that a co-ethnic network could be a double-edged sword, which works differently on children from different social classes. While the Mandarin…

Prototype-Incorporated Emotional Neural Network.

PubMed

Oyedotun, Oyebade K; Khashman, Adnan

2017-08-15

Artificial neural networks (ANNs) aim to simulate the biological neural activities. Interestingly, many ''engineering'' prospects in ANN have relied on motivations from cognition and psychology studies. So far, two important learning theories that have been subject of active research are the prototype and adaptive learning theories. The learning rules employed for ANNs can be related to adaptive learning theory, where several examples of the different classes in a task are supplied to the network for adjusting internal parameters. Conversely, the prototype-learning theory uses prototypes (representative examples); usually, one prototype per class of the different classes contained in the task. These prototypes are supplied for systematic matching with new examples so that class association can be achieved. In this paper, we propose and implement a novel neural network algorithm based on modifying the emotional neural network (EmNN) model to unify the prototype- and adaptive-learning theories. We refer to our new model as ``prototype-incorporated EmNN''. Furthermore, we apply the proposed model to two real-life challenging tasks, namely, static hand-gesture recognition and face recognition, and compare the result to those obtained using the popular back-propagation neural network (BPNN), emotional BPNN (EmNN), deep networks, an exemplar classification model, and k-nearest neighbor.

A social network-informed latent class analysis of patterns of substance use, sexual behavior, and mental health: Social Network Study III, Winnipeg, Manitoba, Canada.

PubMed

Hopfer, Suellen; Tan, Xianming; Wylie, John L

2014-05-01

We assessed whether a meaningful set of latent risk profiles could be identified in an inner-city population through individual and network characteristics of substance use, sexual behaviors, and mental health status. Data came from 600 participants in Social Network Study III, conducted in 2009 in Winnipeg, Manitoba, Canada. We used latent class analysis (LCA) to identify risk profiles and, with covariates, to identify predictors of class. A 4-class model of risk profiles fit the data best: (1) solitary users reported polydrug use at the individual level, but low probabilities of substance use or concurrent sexual partners with network members; (2) social-all-substance users reported polydrug use at the individual and network levels; (3) social-noninjection drug users reported less likelihood of injection drug and solvent use; (4) low-risk users reported low probabilities across substances. Unstable housing, preadolescent substance use, age, and hepatitis C status predicted risk profiles. Incorporation of social network variables into LCA can distinguish important subgroups with varying patterns of risk behaviors that can lead to sexually transmitted and bloodborne infections.

On the number of different dynamics in Boolean networks with deterministic update schedules.

PubMed

Aracena, J; Demongeot, J; Fanchon, E; Montalva, M

2013-04-01

Deterministic Boolean networks are a type of discrete dynamical systems widely used in the modeling of genetic networks. The dynamics of such systems is characterized by the local activation functions and the update schedule, i.e., the order in which the nodes are updated. In this paper, we address the problem of knowing the different dynamics of a Boolean network when the update schedule is changed. We begin by proving that the problem of the existence of a pair of update schedules with different dynamics is NP-complete. However, we show that certain structural properties of the interaction diagraph are sufficient for guaranteeing distinct dynamics of a network. In [1] the authors define equivalence classes which have the property that all the update schedules of a given class yield the same dynamics. In order to determine the dynamics associated to a network, we develop an algorithm to efficiently enumerate the above equivalence classes by selecting a representative update schedule for each class with a minimum number of blocks. Finally, we run this algorithm on the well known Arabidopsis thaliana network to determine the full spectrum of its different dynamics. Copyright © 2013 Elsevier Inc. All rights reserved.

Object class segmentation of RGB-D video using recurrent convolutional neural networks.

PubMed

Pavel, Mircea Serban; Schulz, Hannes; Behnke, Sven

2017-04-01

Object class segmentation is a computer vision task which requires labeling each pixel of an image with the class of the object it belongs to. Deep convolutional neural networks (DNN) are able to learn and take advantage of local spatial correlations required for this task. They are, however, restricted by their small, fixed-sized filters, which limits their ability to learn long-range dependencies. Recurrent Neural Networks (RNN), on the other hand, do not suffer from this restriction. Their iterative interpretation allows them to model long-range dependencies by propagating activity. This property is especially useful when labeling video sequences, where both spatial and temporal long-range dependencies occur. In this work, a novel RNN architecture for object class segmentation is presented. We investigate several ways to train such a network. We evaluate our models on the challenging NYU Depth v2 dataset for object class segmentation and obtain competitive results. Copyright © 2017 Elsevier Ltd. All rights reserved.

Real-Time Network Management

DTIC Science & Technology

1998-07-01

Report No. WH97JR00-A002 Sponsored by REAL-TIME NETWORK MANAGEMENT FINAL TECHNICAL REPORT K CD July 1998 CO CO O W O Defense Advanced...Approved for public release; distribution unlimited. t^GquALmmsPEami Report No. WH97JR00-A002 REAL-TIME NETWORK MANAGEMENT Synectics Corporation...2.1.2.1 WAN-class Networks 12 2.1.2.2 IEEE 802.3-class Networks 13 2.2 Task 2 - Object Modeling for Architecture 14 2.2.1 Managed Objects 14 2.2.2

A class of convergent neural network dynamics

NASA Astrophysics Data System (ADS)

Fiedler, Bernold; Gedeon, Tomáš

1998-01-01

We consider a class of systems of differential equations in Rn which exhibits convergent dynamics. We find a Lyapunov function and show that every bounded trajectory converges to the set of equilibria. Our result generalizes the results of Cohen and Grossberg (1983) for convergent neural networks. It replaces the symmetry assumption on the matrix of weights by the assumption on the structure of the connections in the neural network. We prove the convergence result also for a large class of Lotka-Volterra systems. These are naturally defined on the closed positive orthant. We show that there are no heteroclinic cycles on the boundary of the positive orthant for the systems in this class.

Embedding global and collective in a torus network with message class map based tree path selection

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

Chen, Dong; Coteus, Paul W.; Eisley, Noel A.

Embodiments of the invention provide a method, system and computer program product for embedding a global barrier and global interrupt network in a parallel computer system organized as a torus network. The computer system includes a multitude of nodes. In one embodiment, the method comprises taking inputs from a set of receivers of the nodes, dividing the inputs from the receivers into a plurality of classes, combining the inputs of each of the classes to obtain a result, and sending said result to a set of senders of the nodes. Embodiments of the invention provide a method, system and computermore » program product for embedding a collective network in a parallel computer system organized as a torus network. In one embodiment, the method comprises adding to a torus network a central collective logic to route messages among at least a group of nodes in a tree structure.« less

Anatomy and histology as socially networked learning environments: some preliminary findings.

PubMed

Hafferty, Frederic W; Castellani, Brian; Hafferty, Philip K; Pawlina, Wojciech

2013-09-01

An exploratory study to better understand the "networked" life of the medical school as a learning environment. In a recent academic year, the authors gathered data during two six-week blocks of a sequential histology and anatomy course at a U.S. medical college. An eight-item questionnaire captured different dimensions of student interactions. The student cohort/network was 48 first-year medical students. Using social network analysis (SNA), the authors focused on (1) the initial structure and the evolution of informal class networks over time, (2) how informal class networks compare to formal in-class small-group assignments in influencing student information gathering, and (3) how peer assignment of professionalism role model status is shaped more by informal than formal ties. In examining these latter two issues, the authors explored not only how formal group assignment persisted over time but also how it functioned to prevent the tendency for groupings based on gender or ethnicity. The study revealed an evolving dynamic between the formal small-group learning structure of the course blocks and the emergence of informal student networks. For example, whereas formal group membership did influence in-class questions and did prevent formation of groups of like gender and ethnicity, outside-class questions and professionalism were influenced more by informal group ties where gender and, to a much lesser extent, ethnicity influence student information gathering. The richness of these preliminary findings suggests that SNA may be a useful tool in examining an array of medical student learning encounters.

Hidden Connectivity in Networks with Vulnerable Classes of Nodes

NASA Astrophysics Data System (ADS)

Krause, Sebastian M.; Danziger, Michael M.; Zlatić, Vinko

2016-10-01

In many complex systems representable as networks, nodes can be separated into different classes. Often these classes can be linked to a mutually shared vulnerability. Shared vulnerabilities may be due to a shared eavesdropper or correlated failures. In this paper, we show the impact of shared vulnerabilities on robust connectivity and how the heterogeneity of node classes can be exploited to maintain functionality by utilizing multiple paths. Percolation is the field of statistical physics that is generally used to analyze connectivity in complex networks, but in its existing forms, it cannot treat the heterogeneity of multiple vulnerable classes. To analyze the connectivity under these constraints, we describe each class as a color and develop a "color-avoiding" percolation. We present an analytic theory for random networks and a numerical algorithm for all networks, with which we can determine which nodes are color-avoiding connected and whether the maximal set percolates in the system. We find that the interaction of topology and color distribution implies a rich critical behavior, with critical values and critical exponents depending both on the topology and on the color distribution. Applying our physics-based theory to the Internet, we show how color-avoiding percolation can be used as the basis for new topologically aware secure communication protocols. Beyond applications to cybersecurity, our framework reveals a new layer of hidden structure in a wide range of natural and technological systems.

Spacecraft optical disk recorder memory buffer control

NASA Technical Reports Server (NTRS)

Hodson, Robert F.

1993-01-01

This paper discusses the research completed under the NASA-ASEE summer faculty fellowship program. The project involves development of an Application Specific Integrated Circuit (ASIC) to be used as a Memory Buffer Controller (MBC) in the Spacecraft Optical Disk System (SODR). The SODR system has demanding capacity and data rate specifications requiring specialized electronics to meet processing demands. The system is being designed to support Gigabit transfer rates with Terabit storage capability. The complete SODR system is designed to exceed the capability of all existing mass storage systems today. The ASIC development for SODR consist of developing a 144 pin CMOS device to perform format conversion and data buffering. The final simulations of the MBC were completed during this summer's NASA-ASEE fellowship along with design preparations for fabrication to be performed by an ASIC manufacturer.

Classes of real-world 'small-world' networks: From the neural network of C. Elegans to the web of human sexual contacts

NASA Astrophysics Data System (ADS)

Nunes Amaral, Luis A.

2002-03-01

We study the statistical properties of a variety of diverse real-world networks including the neural network of C. Elegans, food webs for seven distinct environments, transportation and technological networks, and a number of distinct social networks [1-5]. We present evidence of the occurrence of three classes of small-world networks [2]: (a) scale-free networks, characterized by a vertex connectivity distribution that decays as a power law; (b) broad-scale networks, characterized by a connectivity distribution that has a power-law regime followed by a sharp cut-off; (c) single-scale networks, characterized by a connectivity distribution with a fast decaying tail. Moreover, we note for the classes of broad-scale and single-scale networks that there are constraints limiting the addition of new links. Our results suggest that the nature of such constraints may be the controlling factor for the emergence of different classes of networks. [See http://polymer.bu.edu/ amaral/Networks.html for details and htpp://polymer.bu.edu/ amaral/Professional.html for access to PDF files of articles.] 1. M. Barthélémy, L. A. N. Amaral, Phys. Rev. Lett. 82, 3180-3183 (1999). 2. L. A. N. Amaral, A. Scala, M. Barthélémy, H. E. Stanley, Proc. Nat. Acad. Sci. USA 97, 11149-11152 (2000). 3. F. Liljeros, C. R. Edling, L. A. N. Amaral, H. E. Stanley, and Y. Åberg, Nature 411, 907-908 (2001). 4. J. Camacho, R. Guimera, L.A.N. Amaral, Phys. Rev. E RC (to appear). 5. S. Mossa, M. Barthelemy, H.E. Stanley, L.A.N. Amaral (submitted).

A new technique in the global reliability of cyclic communications network

NASA Technical Reports Server (NTRS)

Sjogren, Jon A.

1989-01-01

The global reliability of a communications network is the probability that given any pair of nodes, there exists a viable path between them. A characterization of connectivity, for a given class of networks, can enable one to find this reliability. Such a characterization is described for a useful class of undirected networks called daisy-chained or braided networks. This leads to a new method of quickly computing the global reliability of these networks. Asymptotic behavior in terms of component reliability is related to geometric properties of the given graph. Generalization of the technique is discussed.

Dissociable intrinsic functional networks support noun-object and verb-action processing.

PubMed

Yang, Huichao; Lin, Qixiang; Han, Zaizhu; Li, Hongyu; Song, Luping; Chen, Lingjuan; He, Yong; Bi, Yanchao

2017-12-01

The processing mechanism of verbs-actions and nouns-objects is a central topic of language research, with robust evidence for behavioral dissociation. The neural basis for these two major word and/or conceptual classes, however, remains controversial. Two experiments were conducted to study this question from the network perspective. Experiment 1 found that nodes of the same class, obtained through task-evoked brain imaging meta-analyses, were more strongly connected with each other than nodes of different classes during resting-state, forming segregated network modules. Experiment 2 examined the behavioral relevance of these intrinsic networks using data from 88 brain-damaged patients, finding that across patients the relative strength of functional connectivity of the two networks significantly correlated with the noun-object vs. verb-action relative behavioral performances. In summary, we found that verbs-actions and nouns-objects are supported by separable intrinsic functional networks and that the integrity of such networks accounts for the relative noun-object- and verb-action-selective deficits. Copyright © 2017 Elsevier Inc. All rights reserved.

Classification of ion mobility spectra by functional groups using neural networks

NASA Technical Reports Server (NTRS)

Bell, S.; Nazarov, E.; Wang, Y. F.; Eiceman, G. A.

1999-01-01

Neural networks were trained using whole ion mobility spectra from a standardized database of 3137 spectra for 204 chemicals at various concentrations. Performance of the network was measured by the success of classification into ten chemical classes. Eleven stages for evaluation of spectra and of spectral pre-processing were employed and minimums established for response thresholds and spectral purity. After optimization of the database, network, and pre-processing routines, the fraction of successful classifications by functional group was 0.91 throughout a range of concentrations. Network classification relied on a combination of features, including drift times, number of peaks, relative intensities, and other factors apparently including peak shape. The network was opportunistic, exploiting different features within different chemical classes. Application of neural networks in a two-tier design where chemicals were first identified by class and then individually eliminated all but one false positive out of 161 test spectra. These findings establish that ion mobility spectra, even with low resolution instrumentation, contain sufficient detail to permit the development of automated identification systems.

NeAT: a toolbox for the analysis of biological networks, clusters, classes and pathways.

PubMed

Brohée, Sylvain; Faust, Karoline; Lima-Mendez, Gipsi; Sand, Olivier; Janky, Rekin's; Vanderstocken, Gilles; Deville, Yves; van Helden, Jacques

2008-07-01

The network analysis tools (NeAT) (http://rsat.ulb.ac.be/neat/) provide a user-friendly web access to a collection of modular tools for the analysis of networks (graphs) and clusters (e.g. microarray clusters, functional classes, etc.). A first set of tools supports basic operations on graphs (comparison between two graphs, neighborhood of a set of input nodes, path finding and graph randomization). Another set of programs makes the connection between networks and clusters (graph-based clustering, cliques discovery and mapping of clusters onto a network). The toolbox also includes programs for detecting significant intersections between clusters/classes (e.g. clusters of co-expression versus functional classes of genes). NeAT are designed to cope with large datasets and provide a flexible toolbox for analyzing biological networks stored in various databases (protein interactions, regulation and metabolism) or obtained from high-throughput experiments (two-hybrid, mass-spectrometry and microarrays). The web interface interconnects the programs in predefined analysis flows, enabling to address a series of questions about networks of interest. Each tool can also be used separately by entering custom data for a specific analysis. NeAT can also be used as web services (SOAP/WSDL interface), in order to design programmatic workflows and integrate them with other available resources.

Kin investment in wage-labor economies : Effects on child and marriage market outcomes.

PubMed

Shenk, Mary K

2005-03-01

Various human groups, from food foragers to inner-city urban Americans, have used widespread sharing of resources through kin networks as a means of buffering themselves against fluctuations in resource availability in their environments. This paper addresses the effects of progressive incorporation into a wage-labor economy on the benefits of traditional kin networks for two social classes in urban South India. Predictions regarding the effects of kin network wealth, education, and size on child and spouse characteristics and methods of financing marriages are tested using various regression techniques. Despite the rapid growth of participation in a wage-labor economy, it is found that kin network characteristics still have an important impact on investment behavior among families in Bangalore in both social classes. Network wealth is found to have a positive effect on child and spouse characteristics, and large networks are found to act as significant drains on family resources. However, the results for education are broadly consistent with an interpretation of increasing family autonomy as parents' education has a far stronger influence on child and spouse characteristics across categories than network education does. Finally, professional-class parents are found to prefer financing marriages using formal mechanisms such as savings and bank loans while working-class parents preferentially finance marriages using credit from relatives and friends.

Educational commitment and social networking: The power of informal networks

NASA Astrophysics Data System (ADS)

Zwolak, Justyna P.; Zwolak, Michael; Brewe, Eric

2018-06-01

The lack of an engaging pedagogy and the highly competitive atmosphere in introductory science courses tend to discourage students from pursuing science, technology, engineering, and mathematics (STEM) majors. Once in a STEM field, academic and social integration has been long thought to be important for students' persistence. Yet, it is rarely investigated. In particular, the relative impact of in-class and out-of-class interactions remains an open issue. Here, we demonstrate that, surprisingly, for students whose grades fall in the "middle of the pack," the out-of-class network is the most significant predictor of persistence. To do so, we use logistic regression combined with Akaike's information criterion to assess in- and out-of-class networks, grades, and other factors. For students with grades at the very top (and bottom), final grade, unsurprisingly, is the best predictor of persistence—these students are likely already committed (or simply restricted from continuing) so they persist (or drop out). For intermediate grades, though, only out-of-class closeness—a measure of one's immersion in the network—helps predict persistence. This does not negate the need for in-class ties. However, it suggests that, in this cohort, only students that get past the convenient in-class interactions and start forming strong bonds outside of class are or become committed to their studies. Since many students are lost through attrition, our results suggest practical routes for increasing students' persistence in STEM majors.

Deep neural network-based domain adaptation for classification of remote sensing images

NASA Astrophysics Data System (ADS)

Ma, Li; Song, Jiazhen

2017-10-01

We investigate the effectiveness of deep neural network for cross-domain classification of remote sensing images in this paper. In the network, class centroid alignment is utilized as a domain adaptation strategy, making the network able to transfer knowledge from the source domain to target domain on a per-class basis. Since predicted labels of target data should be used to estimate the centroid of each class, we use overall centroid alignment as a coarse domain adaptation method to improve the estimation accuracy. In addition, rectified linear unit is used as the activation function to produce sparse features, which may improve the separation capability. The proposed network can provide both aligned features and an adaptive classifier, as well as obtain label-free classification of target domain data. The experimental results using Hyperion, NCALM, and WorldView-2 remote sensing images demonstrated the effectiveness of the proposed approach.

Embedding global barrier and collective in torus network with each node combining input from receivers according to class map for output to senders

DOEpatents

Chen, Dong; Coteus, Paul W; Eisley, Noel A; Gara, Alan; Heidelberger, Philip; Senger, Robert M; Salapura, Valentina; Steinmacher-Burow, Burkhard; Sugawara, Yutaka; Takken, Todd E

2013-08-27

Embodiments of the invention provide a method, system and computer program product for embedding a global barrier and global interrupt network in a parallel computer system organized as a torus network. The computer system includes a multitude of nodes. In one embodiment, the method comprises taking inputs from a set of receivers of the nodes, dividing the inputs from the receivers into a plurality of classes, combining the inputs of each of the classes to obtain a result, and sending said result to a set of senders of the nodes. Embodiments of the invention provide a method, system and computer program product for embedding a collective network in a parallel computer system organized as a torus network. In one embodiment, the method comprises adding to a torus network a central collective logic to route messages among at least a group of nodes in a tree structure.

Protograph LDPC Codes Over Burst Erasure Channels

NASA Technical Reports Server (NTRS)

Divsalar, Dariush; Dolinar, Sam; Jones, Christopher

2006-01-01

In this paper we design high rate protograph based LDPC codes suitable for binary erasure channels. To simplify the encoder and decoder implementation for high data rate transmission, the structure of codes are based on protographs and circulants. These LDPC codes can improve data link and network layer protocols in support of communication networks. Two classes of codes were designed. One class is designed for large block sizes with an iterative decoding threshold that approaches capacity of binary erasure channels. The other class is designed for short block sizes based on maximizing minimum stopping set size. For high code rates and short blocks the second class outperforms the first class.

Bifurcations of large networks of two-dimensional integrate and fire neurons.

PubMed

Nicola, Wilten; Campbell, Sue Ann

2013-08-01

Recently, a class of two-dimensional integrate and fire models has been used to faithfully model spiking neurons. This class includes the Izhikevich model, the adaptive exponential integrate and fire model, and the quartic integrate and fire model. The bifurcation types for the individual neurons have been thoroughly analyzed by Touboul (SIAM J Appl Math 68(4):1045-1079, 2008). However, when the models are coupled together to form networks, the networks can display bifurcations that an uncoupled oscillator cannot. For example, the networks can transition from firing with a constant rate to burst firing. This paper introduces a technique to reduce a full network of this class of neurons to a mean field model, in the form of a system of switching ordinary differential equations. The reduction uses population density methods and a quasi-steady state approximation to arrive at the mean field system. Reduced models are derived for networks with different topologies and different model neurons with biologically derived parameters. The mean field equations are able to qualitatively and quantitatively describe the bifurcations that the full networks display. Extensions and higher order approximations are discussed.

Inferring anatomical therapeutic chemical (ATC) class of drugs using shortest path and random walk with restart algorithms.

PubMed

Chen, Lei; Liu, Tao; Zhao, Xian

2018-06-01

The anatomical therapeutic chemical (ATC) classification system is a widely accepted drug classification scheme. This system comprises five levels and includes several classes in each level. Drugs are classified into classes according to their therapeutic effects and characteristics. The first level includes 14 main classes. In this study, we proposed two network-based models to infer novel potential chemicals deemed to belong in the first level of ATC classification. To build these models, two large chemical networks were constructed using the chemical-chemical interaction information retrieved from the Search Tool for Interactions of Chemicals (STITCH). Two classic network algorithms, shortest path (SP) and random walk with restart (RWR) algorithms, were executed on the corresponding network to mine novel chemicals for each ATC class using the validated drugs in a class as seed nodes. Then, the obtained chemicals yielded by these two algorithms were further evaluated by a permutation test and an association test. The former can exclude chemicals produced by the structure of the network, i.e., false positive discoveries. By contrast, the latter identifies the most important chemicals that have strong associations with the ATC class. Comparisons indicated that the two models can provide quite dissimilar results, suggesting that the results yielded by one model can be essential supplements for those obtained by the other model. In addition, several representative inferred chemicals were analyzed to confirm the reliability of the results generated by the two models. This article is part of a Special Issue entitled: Accelerating Precision Medicine through Genetic and Genomic Big Data Analysis edited by Yudong Cai & Tao Huang. Copyright © 2017 Elsevier B.V. All rights reserved.

Implementability of two-qubit unitary operations over the butterfly network and the ladder network with free classical communication

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

Akibue, Seiseki; Murao, Mio

2014-12-04

We investigate distributed implementation of two-qubit unitary operations over two primitive networks, the butterfly network and the ladder network, as a first step to apply network coding for quantum computation. By classifying two-qubit unitary operations in terms of the Kraus-Cirac number, the number of non-zero parameters describing the global part of two-qubit unitary operations, we analyze which class of two-qubit unitary operations is implementable over these networks with free classical communication. For the butterfly network, we show that two classes of two-qubit unitary operations, which contain all Clifford, controlled-unitary and matchgate operations, are implementable over the network. For the laddermore » network, we show that two-qubit unitary operations are implementable over the network if and only if their Kraus-Cirac number do not exceed the number of the bridges of the ladder.« less

Assortativeness and information in scale-free networks

NASA Astrophysics Data System (ADS)

Piraveenan, M.; Prokopenko, M.; Zomaya, A. Y.

2009-02-01

We analyze Shannon information of scale-free networks in terms of their assortativeness, and identify classes of networks according to the dependency of the joint remaining degree distribution on the assortativeness. We conjecture that these classes comprise minimalistic and maximalistic networks in terms of Shannon information. For the studied classes, the information is shown to depend non-linearly on the absolute value of the assortativeness, with the dominant term of the relationship being a power-law. We exemplify this dependency using a range of real-world networks. Optimization of scale-free networks according to information they contain depends on the landscape of parameters’ search-space, and we identify two regions of interest: a slope region and a stability region. In the slope region, there is more freedom to generate and evaluate candidate networks since the information content can be changed easily by modifying only the assortativeness, while even a small change in the power-law’s scaling exponent brings a reward in a higher rate of information change. This feature may explain why the exponents of real-world scale-free networks are within a certain range, defined by the slope and stability regions.

Following the Leader? Network Models of "World-Class" Universities on Twitter

ERIC Educational Resources Information Center

Shields, Robin

2016-01-01

Much research on higher education has discussed the positional competition induced by global rankings and the complementary concept of "world-class" universities. This paper investigates the network of social media communication between globally ranked universities. Specifically, it examines whether universities seek to preserve and…

Bidirectional selection between two classes in complex social networks.

PubMed

Zhou, Bin; He, Zhe; Jiang, Luo-Luo; Wang, Nian-Xin; Wang, Bing-Hong

2014-12-19

The bidirectional selection between two classes widely emerges in various social lives, such as commercial trading and mate choosing. Until now, the discussions on bidirectional selection in structured human society are quite limited. We demonstrated theoretically that the rate of successfully matching is affected greatly by individuals' neighborhoods in social networks, regardless of the type of networks. Furthermore, it is found that the high average degree of networks contributes to increasing rates of successful matches. The matching performance in different types of networks has been quantitatively investigated, revealing that the small-world networks reinforces the matching rate more than scale-free networks at given average degree. In addition, our analysis is consistent with the modeling result, which provides the theoretical understanding of underlying mechanisms of matching in complex networks.

Friendship networks of inner-city adults: a latent class analysis and multi-level regression of supporter types and the association of supporter latent class membership with supporter and recipient drug use.

PubMed

Bohnert, Amy S B; German, Danielle; Knowlton, Amy R; Latkin, Carl A

2010-03-01

Social support is a multi-dimensional construct that is important to drug use cessation. The present study identified types of supportive friends among the social network members in a community-based sample and examined the relationship of supporter-type classes with supporter, recipient, and supporter-recipient relationship characteristics. We hypothesized that the most supportive network members and their support recipients would be less likely to be current heroin/cocaine users. Participants (n=1453) were recruited from low-income neighborhoods with a high prevalence of drug use. Participants identified their friends via a network inventory, and all nominated friends were included in a latent class analysis and grouped based on their probability of providing seven types of support. These latent classes were included as the dependent variable in a multi-level regression of supporter drug use, recipient drug use, and other characteristics. The best-fitting latent class model identified five support patterns: friends who provided Little/No Support, Low/Moderate Support, High Support, Socialization Support, and Financial Support. In bivariate models, friends in the High, Low/Moderate, and Financial Support were less likely to use heroin or cocaine and had less conflict with and were more trusted by the support recipient than friends in the Low/No Support class. Individuals with supporters in those same support classes compared to the Low/No Support class were less likely to use heroin or cocaine, or to be homeless or female. Multivariable models suggested similar trends. Those with current heroin/cocaine use were less likely to provide or receive comprehensive support from friends. Published by Elsevier Ireland Ltd.

An ASIC memory buffer controller for a high speed disk system

NASA Technical Reports Server (NTRS)

Hodson, Robert F.; Campbell, Steve

1993-01-01

The need for large capacity, high speed mass memory storage devices has become increasingly evident at NASA during the past decade. High performance mass storage systems are crucial to present and future NASA systems. Spaceborne data storage system requirements have grown in response to the increasing amounts of data generated and processed by orbiting scientific experiments. Predictions indicate increases in the volume of data by orders of magnitude during the next decade. Current predictions are for storage capacities on the order of terabits (Tb), with data rates exceeding one gigabit per second (Gbps). As part of the design effort for a state of the art mass storage system, NASA Langley has designed a 144 CMOS ASIC to support high speed data transfers. This paper discusses the system architecture, ASIC design and some of the lessons learned in the development process.

Simultaneous profiling of activity patterns in multiple neuronal subclasses.

PubMed

Parrish, R Ryley; Grady, John; Codadu, Neela K; Trevelyan, Andrew J; Racca, Claudia

2018-06-01

Neuronal networks typically comprise heterogeneous populations of neurons. A core objective when seeking to understand such networks, therefore, is to identify what roles these different neuronal classes play. Acquiring single cell electrophysiology data for multiple cell classes can prove to be a large and daunting task. Alternatively, Ca 2+ network imaging provides activity profiles of large numbers of neurons simultaneously, but without distinguishing between cell classes. We therefore developed a strategy for combining cellular electrophysiology, Ca 2+ network imaging, and immunohistochemistry to provide activity profiles for multiple cell classes at once. This involves cross-referencing easily identifiable landmarks between imaging of the live and fixed tissue, and then using custom MATLAB functions to realign the two imaging data sets, to correct for distortions of the tissue introduced by the fixation or immunohistochemical processing. We illustrate the methodology for analyses of activity profiles during epileptiform events recorded in mouse brain slices. We further demonstrate the activity profile of a population of parvalbumin-positive interneurons prior, during, and following a seizure-like event. Current approaches to Ca 2+ network imaging analyses are severely limited in their ability to subclassify neurons, and often rely on transgenic approaches to identify cell classes. In contrast, our methodology is a generic, affordable, and flexible technique to characterize neuronal behaviour with respect to classification based on morphological and neurochemical identity. We present a new approach for analysing Ca 2+ network imaging datasets, and use this to explore the parvalbumin-positive interneuron activity during epileptiform events. Copyright © 2018 Elsevier B.V. All rights reserved.

Sea ice classification using fast learning neural networks

NASA Technical Reports Server (NTRS)

Dawson, M. S.; Fung, A. K.; Manry, M. T.

1992-01-01

A first learning neural network approach to the classification of sea ice is presented. The fast learning (FL) neural network and a multilayer perceptron (MLP) trained with backpropagation learning (BP network) were tested on simulated data sets based on the known dominant scattering characteristics of the target class. Four classes were used in the data simulation: open water, thick lossy saline ice, thin saline ice, and multiyear ice. The BP network was unable to consistently converge to less than 25 percent error while the FL method yielded an average error of approximately 1 percent on the first iteration of training. The fast learning method presented can significantly reduce the CPU time necessary to train a neural network as well as consistently yield higher classification accuracy than BP networks.

Fourier transform for fermionic systems and the spectral tensor network.

PubMed

Ferris, Andrew J

2014-07-04

Leveraging the decomposability of the fast Fourier transform, I propose a new class of tensor network that is efficiently contractible and able to represent many-body systems with local entanglement that is greater than the area law. Translationally invariant systems of free fermions in arbitrary dimensions as well as 1D systems solved by the Jordan-Wigner transformation are shown to be exactly represented in this class. Further, it is proposed that these tensor networks be used as generic structures to variationally describe more complicated systems, such as interacting fermions. This class shares some similarities with the Evenbly-Vidal branching multiscale entanglement renormalization ansatz, but with some important differences and greatly reduced computational demands.

Nonbinary Tree-Based Phylogenetic Networks.

PubMed

Jetten, Laura; van Iersel, Leo

2018-01-01

Rooted phylogenetic networks are used to describe evolutionary histories that contain non-treelike evolutionary events such as hybridization and horizontal gene transfer. In some cases, such histories can be described by a phylogenetic base-tree with additional linking arcs, which can, for example, represent gene transfer events. Such phylogenetic networks are called tree-based. Here, we consider two possible generalizations of this concept to nonbinary networks, which we call tree-based and strictly-tree-based nonbinary phylogenetic networks. We give simple graph-theoretic characterizations of tree-based and strictly-tree-based nonbinary phylogenetic networks. Moreover, we show for each of these two classes that it can be decided in polynomial time whether a given network is contained in the class. Our approach also provides a new view on tree-based binary phylogenetic networks. Finally, we discuss two examples of nonbinary phylogenetic networks in biology and show how our results can be applied to them.

New MPLS network management techniques based on adaptive learning.

PubMed

Anjali, Tricha; Scoglio, Caterina; de Oliveira, Jaudelice Cavalcante

2005-09-01

The combined use of the differentiated services (DiffServ) and multiprotocol label switching (MPLS) technologies is envisioned to provide guaranteed quality of service (QoS) for multimedia traffic in IP networks, while effectively using network resources. These networks need to be managed adaptively to cope with the changing network conditions and provide satisfactory QoS. An efficient strategy is to map the traffic from different DiffServ classes of service on separate label switched paths (LSPs), which leads to distinct layers of MPLS networks corresponding to each DiffServ class. In this paper, three aspects of the management of such a layered MPLS network are discussed. In particular, an optimal technique for the setup of LSPs, capacity allocation of the LSPs and LSP routing are presented. The presented techniques are based on measurement of the network state to adapt the network configuration to changing traffic conditions.

Providing nearest neighbor point-to-point communications among compute nodes of an operational group in a global combining network of a parallel computer

DOEpatents

Archer, Charles J.; Faraj, Ahmad A.; Inglett, Todd A.; Ratterman, Joseph D.

2012-10-23

Methods, apparatus, and products are disclosed for providing nearest neighbor point-to-point communications among compute nodes of an operational group in a global combining network of a parallel computer, each compute node connected to each adjacent compute node in the global combining network through a link, that include: identifying each link in the global combining network for each compute node of the operational group; designating one of a plurality of point-to-point class routing identifiers for each link such that no compute node in the operational group is connected to two adjacent compute nodes in the operational group with links designated for the same class routing identifiers; and configuring each compute node of the operational group for point-to-point communications with each adjacent compute node in the global combining network through the link between that compute node and that adjacent compute node using that link's designated class routing identifier.

Collective network routing

DOEpatents

Hoenicke, Dirk

2014-12-02

Disclosed are a unified method and apparatus to classify, route, and process injected data packets into a network so as to belong to a plurality of logical networks, each implementing a specific flow of data on top of a common physical network. The method allows to locally identify collectives of packets for local processing, such as the computation of the sum, difference, maximum, minimum, or other logical operations among the identified packet collective. Packets are injected together with a class-attribute and an opcode attribute. Network routers, employing the described method, use the packet attributes to look-up the class-specific route information from a local route table, which contains the local incoming and outgoing directions as part of the specifically implemented global data flow of the particular virtual network.

Porous Networks Through Colloidal Templates

NASA Astrophysics Data System (ADS)

Li, Qin; Retsch, Markus; Wang, Jianjun; Knoll, Wolfgang; Jonas, Ulrich

Porous networks represent a class of materials with interconnected voids with specific properties concerning adsorption, mass and heat transport, and spatial confinement, which lead to a wide range of applications ranging from oil recovery and water purification to tissue engineering. Porous networks with well-defined, highly ordered structure and periodicities around the wavelength of light can furthermore show very sophisticated optical properties. Such networks can be fabricated from a very large range of materials by infiltration of a sacrificial colloidal crystal template and subsequent removal of the template. The preparation procedures reported in the literature are discussed in this review and the resulting porous networks are presented with respect to the underlying material class. Furthermore, methods for hierarchical superstructure formation and functionalization of the network walls are discussed.

Transient Analysis Generator /TAG/ simulates behavior of large class of electrical networks

NASA Technical Reports Server (NTRS)

Thomas, W. J.

1967-01-01

Transient Analysis Generator program simulates both transient and dc steady-state behavior of a large class of electrical networks. It generates a special analysis program for each circuit described in an easily understood and manipulated programming language. A generator or preprocessor and a simulation system make up the TAG system.

Race, Class, and Religious Differences in the Social Networks of Children and Their Parents

ERIC Educational Resources Information Center

Hunter, Andrea G.; Friend, Christian A.; Williams-Wheeler, Meeshay; Fletcher, Anne C.

2012-01-01

The study is a qualitative investigation of mothers' perspectives about and their role in negotiating and developing intergenerational closure across race, class, and religious differences and their management of children's diverse friendships. Black and White mothers (n = 25) of third graders were interviewed about social networks, children's…

Optoelectronics in TESLA, LHC, and pi-of-the-sky experiments

NASA Astrophysics Data System (ADS)

Romaniuk, Ryszard S.; Pozniak, Krzysztof T.; Wrochna, Grzegorz; Simrock, Stefan

2004-09-01

Optical and optoelectronics technologies are more and more widely used in the biggest world experiments of high energy and nuclear physics, as well as in the astronomy. The paper is a kind of a broad digest describing the usage of optoelectronics is such experiments and information about some of the involved teams. The described experiments include: TESLA linear accelerator and FEL, Compact Muon Solenoid at LHC and recently started π-of-the-sky global gamma ray bursts (with asociated optical flashes) observation experiment. Optoelectornics and photonics offer several key features which are either extending the technical parameters of existing solutions or adding quite new practical application possibilities. Some of these favorable features of photonic systems are: high selectivity of optical sensors, immunity to some kinds of noise processes, extremely broad bandwidth exchangeable for either terabit rate transmission or ultrashort pulse generation, parallel image processing capability, etc. The following groups of photonic components and systems were described: (1) discrete components applications like: LED, PD, LD, CCD and CMOS cameras, active optical crystals and optical fibers in radiation dosimetry, astronomical image processing and for building of more complex photonic systems; (2) optical fiber networks serving as very stable phase distribution, clock signal distribution, distributed dosimeters, distributed gigabit transmission for control, diagnostics and data acquisition/processing; (3) fast and stable coherent femtosecond laser systems with active optical components for electro-optical sampling and photocathode excitation in the RF electron gun for linac; The parameters of some of these systems were quoted and discussed. A number of the debated solutions seems to be competitive against the classical ones. Several future fields seem to emerge involving direct coupling between the ultrafast photonic and the VLSI FPGA based technologies.

Neural network modeling of associative memory: Beyond the Hopfield model

NASA Astrophysics Data System (ADS)

Dasgupta, Chandan

1992-07-01

A number of neural network models, in which fixed-point and limit-cycle attractors of the underlying dynamics are used to store and associatively recall information, are described. In the first class of models, a hierarchical structure is used to store an exponentially large number of strongly correlated memories. The second class of models uses limit cycles to store and retrieve individual memories. A neurobiologically plausible network that generates low-amplitude periodic variations of activity, similar to the oscillations observed in electroencephalographic recordings, is also described. Results obtained from analytic and numerical studies of the properties of these networks are discussed.

X-Windows Socket Widget Class

NASA Technical Reports Server (NTRS)

Barry, Matthew R.

2006-01-01

The X-Windows Socket Widget Class ("Class" is used here in the object-oriented-programming sense of the word) was devised to simplify the task of implementing network connections for graphical-user-interface (GUI) computer programs. UNIX Transmission Control Protocol/Internet Protocol (TCP/IP) socket programming libraries require many method calls to configure, operate, and destroy sockets. Most X Windows GUI programs use widget sets or toolkits to facilitate management of complex objects. The widget standards facilitate construction of toolkits and application programs. The X-Windows Socket Widget Class encapsulates UNIX TCP/IP socket-management tasks within the framework of an X Windows widget. Using the widget framework, X Windows GUI programs can treat one or more network socket instances in the same manner as that of other graphical widgets, making it easier to program sockets. Wrapping ISP socket programming libraries inside a widget framework enables a programmer to treat a network interface as though it were a GUI.

Membership generation using multilayer neural network

NASA Technical Reports Server (NTRS)

Kim, Jaeseok

1992-01-01

There has been intensive research in neural network applications to pattern recognition problems. Particularly, the back-propagation network has attracted many researchers because of its outstanding performance in pattern recognition applications. In this section, we describe a new method to generate membership functions from training data using a multilayer neural network. The basic idea behind the approach is as follows. The output values of a sigmoid activation function of a neuron bear remarkable resemblance to membership values. Therefore, we can regard the sigmoid activation values as the membership values in fuzzy set theory. Thus, in order to generate class membership values, we first train a suitable multilayer network using a training algorithm such as the back-propagation algorithm. After the training procedure converges, the resulting network can be treated as a membership generation network, where the inputs are feature values and the outputs are membership values in the different classes. This method allows fairly complex membership functions to be generated because the network is highly nonlinear in general. Also, it is to be noted that the membership functions are generated from a classification point of view. For pattern recognition applications, this is highly desirable, although the membership values may not be indicative of the degree of typicality of a feature value in a particular class.

Transitions in Smokers’ Social Networks After Quit Attempts: A Latent Transition Analysis

PubMed Central

Smith, Rachel A.; Piper, Megan E.; Roberts, Linda J.; Baker, Timothy B.

2016-01-01

Introduction: Smokers’ social networks vary in size, composition, and amount of exposure to smoking. The extent to which smokers’ social networks change after a quit attempt is unknown, as is the relation between quitting success and later network changes. Methods: Unique types of social networks for 691 smokers enrolled in a smoking-cessation trial were identified based on network size, new network members, members’ smoking habits, within network smoking, smoking buddies, and romantic partners’ smoking. Latent transition analysis was used to identify the network classes and to predict transitions in class membership across 3 years from biochemically assessed smoking abstinence. Results: Five network classes were identified: Immersed (large network, extensive smoking exposure including smoking buddies), Low Smoking Exposure (large network, minimal smoking exposure), Smoking Partner (small network, smoking exposure primarily from partner), Isolated (small network, minimal smoking exposure), and Distant Smoking Exposure (small network, considerable nonpartner smoking exposure). Abstinence at years 1 and 2 was associated with shifts in participants’ social networks to less contact with smokers and larger networks in years 2 and 3. Conclusions: In the years following a smoking-cessation attempt, smokers’ social networks changed, and abstinence status predicted these changes. Networks defined by high levels of exposure to smokers were especially associated with continued smoking. Abstinence, however, predicted transitions to larger social networks comprising less smoking exposure. These results support treatments that aim to reduce exposure to smoking cues and smokers, including partners who smoke. Implications: Prior research has shown that social network features predict the likelihood of subsequent smoking cessation. The current research illustrates how successful quitting predicts social network change over 3 years following a quit attempt. Specifically, abstinence predicts transitions to networks that are larger and afford less exposure to smokers. This suggests that quitting smoking may expand a person’s social milieu rather than narrow it. This effect, plus reduced exposure to smokers, may help sustain abstinence. PMID:27613925

Anti-synchronization control of BAM memristive neural networks with multiple proportional delays and stochastic perturbations

NASA Astrophysics Data System (ADS)

Wang, Weiping; Yuan, Manman; Luo, Xiong; Liu, Linlin; Zhang, Yao

2018-01-01

Proportional delay is a class of unbounded time-varying delay. A class of bidirectional associative memory (BAM) memristive neural networks with multiple proportional delays is concerned in this paper. First, we propose the model of BAM memristive neural networks with multiple proportional delays and stochastic perturbations. Furthermore, by choosing suitable nonlinear variable transformations, the BAM memristive neural networks with multiple proportional delays can be transformed into the BAM memristive neural networks with constant delays. Based on the drive-response system concept, differential inclusions theory and Lyapunov stability theory, some anti-synchronization criteria are obtained. Finally, the effectiveness of proposed criteria are demonstrated through numerical examples.

Network-based stochastic semisupervised learning.

PubMed

Silva, Thiago Christiano; Zhao, Liang

2012-03-01

Semisupervised learning is a machine learning approach that is able to employ both labeled and unlabeled samples in the training process. In this paper, we propose a semisupervised data classification model based on a combined random-preferential walk of particles in a network (graph) constructed from the input dataset. The particles of the same class cooperate among themselves, while the particles of different classes compete with each other to propagate class labels to the whole network. A rigorous model definition is provided via a nonlinear stochastic dynamical system and a mathematical analysis of its behavior is carried out. A numerical validation presented in this paper confirms the theoretical predictions. An interesting feature brought by the competitive-cooperative mechanism is that the proposed model can achieve good classification rates while exhibiting low computational complexity order in comparison to other network-based semisupervised algorithms. Computer simulations conducted on synthetic and real-world datasets reveal the effectiveness of the model.

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

PubMed

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

2018-02-01

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

Examining the Distribution, Modularity, and Community Structure in Article Networks for Systematic Reviews.

PubMed

Ji, Xiaonan; Machiraju, Raghu; Ritter, Alan; Yen, Po-Yin

2015-01-01

Systematic reviews (SRs) provide high quality evidence for clinical practice, but the article screening process is time and labor intensive. As SRs aim to identify relevant articles with a specific scope, we propose that a pre-defined article relationship, using similarity metrics, could accelerate this process. In this study, we established the article relationship using MEDLINE element similarities and visualized the article network with the Force Atlas layout. We also analyzed the article networks with graph diameter, closeness centrality, and module classes. The results revealed the distribution of articles and found that included articles tended to aggregate together in some module classes, providing further evidence of the existence of strong relationships among included articles. This approach can be utilized to facilitate the articles selection process through early identification of these dominant module classes. We are optimistic that the use of article network visualization can help better SR work prioritization.

Examining the Distribution, Modularity, and Community Structure in Article Networks for Systematic Reviews

PubMed Central

Ji, Xiaonan; Machiraju, Raghu; Ritter, Alan; Yen, Po-Yin

2015-01-01

Systematic reviews (SRs) provide high quality evidence for clinical practice, but the article screening process is time and labor intensive. As SRs aim to identify relevant articles with a specific scope, we propose that a pre-defined article relationship, using similarity metrics, could accelerate this process. In this study, we established the article relationship using MEDLINE element similarities and visualized the article network with the Force Atlas layout. We also analyzed the article networks with graph diameter, closeness centrality, and module classes. The results revealed the distribution of articles and found that included articles tended to aggregate together in some module classes, providing further evidence of the existence of strong relationships among included articles. This approach can be utilized to facilitate the articles selection process through early identification of these dominant module classes. We are optimistic that the use of article network visualization can help better SR work prioritization. PMID:26958292

Snoopy--a unifying Petri net framework to investigate biomolecular networks.

PubMed

Rohr, Christian; Marwan, Wolfgang; Heiner, Monika

2010-04-01

To investigate biomolecular networks, Snoopy provides a unifying Petri net framework comprising a family of related Petri net classes. Models can be hierarchically structured, allowing for the mastering of larger networks. To move easily between the qualitative, stochastic and continuous modelling paradigms, models can be converted into each other. We get models sharing structure, but specialized by their kinetic information. The analysis and iterative reverse engineering of biomolecular networks is supported by the simultaneous use of several Petri net classes, while the graphical user interface adapts dynamically to the active one. Built-in animation and simulation are complemented by exports to various analysis tools. Snoopy facilitates the addition of new Petri net classes thanks to its generic design. Our tool with Petri net samples is available free of charge for non-commercial use at http://www-dssz.informatik.tu-cottbus.de/snoopy.html; supported operating systems: Mac OS X, Windows and Linux (selected distributions).

Influence of homology and node age on the growth of protein-protein interaction networks

NASA Astrophysics Data System (ADS)

Bottinelli, Arianna; Bassetti, Bruno; Lagomarsino, Marco Cosentino; Gherardi, Marco

2012-10-01

Proteins participating in a protein-protein interaction network can be grouped into homology classes following their common ancestry. Proteins added to the network correspond to genes added to the classes, so the dynamics of the two objects are intrinsically linked. Here we first introduce a statistical model describing the joint growth of the network and the partitioning of nodes into classes, which is studied through a combined mean-field and simulation approach. We then employ this unified framework to address the specific issue of the age dependence of protein interactions through the definition of three different node wiring or divergence schemes. A comparison with empirical data indicates that an age-dependent divergence move is necessary in order to reproduce the basic topological observables together with the age correlation between interacting nodes visible in empirical data. We also discuss the possibility of nontrivial joint partition and topology observables.

Using Networks To Understand Medical Data: The Case of Class III Malocclusions

PubMed Central

Scala, Antonio; Auconi, Pietro; Scazzocchio, Marco; Caldarelli, Guido; McNamara, James A.; Franchi, Lorenzo

2012-01-01

A system of elements that interact or regulate each other can be represented by a mathematical object called a network. While network analysis has been successfully applied to high-throughput biological systems, less has been done regarding their application in more applied fields of medicine; here we show an application based on standard medical diagnostic data. We apply network analysis to Class III malocclusion, one of the most difficult to understand and treat orofacial anomaly. We hypothesize that different interactions of the skeletal components can contribute to pathological disequilibrium; in order to test this hypothesis, we apply network analysis to 532 Class III young female patients. The topology of the Class III malocclusion obtained by network analysis shows a strong co-occurrence of abnormal skeletal features. The pattern of these occurrences influences the vertical and horizontal balance of disharmony in skeletal form and position. Patients with more unbalanced orthodontic phenotypes show preponderance of the pathological skeletal nodes and minor relevance of adaptive dentoalveolar equilibrating nodes. Furthermore, by applying Power Graphs analysis we identify some functional modules among orthodontic nodes. These modules correspond to groups of tightly inter-related features and presumably constitute the key regulators of plasticity and the sites of unbalance of the growing dentofacial Class III system. The data of the present study show that, in their most basic abstraction level, the orofacial characteristics can be represented as graphs using nodes to represent orthodontic characteristics, and edges to represent their various types of interactions. The applications of this mathematical model could improve the interpretation of the quantitative, patient-specific information, and help to better targeting therapy. Last but not least, the methodology we have applied in analyzing orthodontic features can be applied easily to other fields of the medical science. PMID:23028552

Mathematics and the Internet: A Source of Enormous Confusion and Great Potential

DTIC Science & Technology

2009-05-01

free Internet Myth The story recounted below of the scale-free nature of the Internet seems convincing, sound, and al- most too good to be true ...models. In fact, much of the initial excitement in the nascent field of network science can be attributed to an ear- ly and appealingly simple class...this new class of networks, com- monly referred to as scale-free networks. The term scale-free derives from the simple observation that power-law node

A training rule which guarantees finite-region stability for a class of closed-loop neural-network control systems.

PubMed

Kuntanapreeda, S; Fullmer, R R

1996-01-01

A training method for a class of neural network controllers is presented which guarantees closed-loop system stability. The controllers are assumed to be nonlinear, feedforward, sampled-data, full-state regulators implemented as single hidden-layer neural networks. The controlled systems must be locally hermitian and observable. Stability of the closed-loop system is demonstrated by determining a Lyapunov function, which can be used to identify a finite stability region about the regulator point.

Communities and classes in symmetric fractals

NASA Astrophysics Data System (ADS)

Krawczyk, Małgorzata J.

2015-07-01

Two aspects of fractal networks are considered: the community structure and the class structure, where classes of nodes appear as a consequence of a local symmetry of nodes. The analyzed systems are the networks constructed for two selected symmetric fractals: the Sierpinski triangle and the Koch curve. Communities are searched for by means of a set of differential equations. Overlapping nodes which belong to two different communities are identified by adding some noise to the initial connectivity matrix. Then, a node can be characterized by a spectrum of probabilities of belonging to different communities. Our main goal is that the overlapping nodes with the same spectra belong to the same class.

Coherence analysis of a class of weighted networks

NASA Astrophysics Data System (ADS)

Dai, Meifeng; He, Jiaojiao; Zong, Yue; Ju, Tingting; Sun, Yu; Su, Weiyi

2018-04-01

This paper investigates consensus dynamics in a dynamical system with additive stochastic disturbances that is characterized as network coherence by using the Laplacian spectrum. We introduce a class of weighted networks based on a complete graph and investigate the first- and second-order network coherence quantifying as the sum and square sum of reciprocals of all nonzero Laplacian eigenvalues. First, the recursive relationship of its eigenvalues at two successive generations of Laplacian matrix is deduced. Then, we compute the sum and square sum of reciprocal of all nonzero Laplacian eigenvalues. The obtained results show that the scalings of first- and second-order coherence with network size obey four and five laws, respectively, along with the range of the weight factor. Finally, it indicates that the scalings of our studied networks are smaller than other studied networks when 1/√{d }

High-speed network for delivery of education-on-demand

NASA Astrophysics Data System (ADS)

Cordero, Carlos; Harris, Dale; Hsieh, Jeff

1996-03-01

A project to investigate the feasibility of delivering on-demand distance education to the desktop, known as the Asynchronous Distance Education ProjecT (ADEPT), is presently being carried out. A set of Stanford engineering classes is digitized on PC, Macintosh, and UNIX platforms, and is made available on servers. Students on campus and in industry may then access class material on these servers via local and metropolitan area networks. Students can download class video and audio, encoded in QuickTimeTM and Show-Me TVTM formats, via file-transfer protocol or the World Wide Web. Alternatively, they may stream a vector-quantized version of the class directly from a server for real-time playback. Students may also download PostscriptTM and Adobe AcrobatTM versions of class notes. Off-campus students may connect to ADEPT servers via the internet, the Silicon Valley Test Track (SVTT), or the Bay-Area Gigabit Network (BAGNet). The SVTT and BAGNet are high-speed metropolitan-area networks, spanning the Bay Area, which provide IP access over asynchronous transfer mode (ATM). Student interaction is encouraged through news groups, electronic mailing lists, and an ADEPT home page. Issues related to having multiple platforms and interoperability are examined in this paper. The ramifications of providing a reliable service are discussed. System performance and the parameters that affect it are then described. Finally, future work on expanding ATM access, real-time delivery of classes, and enhanced student interaction is described.

Finding Sales Promotion and Making Decision for New Product Based on Group Analysis of Edge-Enhanced Product Networks

NASA Astrophysics Data System (ADS)

Huang, Yi; Tan, Jianbin; Wu, Bin

A novel method is proposed in this paper to find the promotive relationship of products from a network point of view. Firstly, a product network is built based on the dataset of handsets’ sale information collected from all outlets of a telecom operator of one province of China, with a period from Jan. 2006 to Jul. 2008. Then the edge enhanced model is applied on product network to divide all the products into several groups, according to which each outlet is assigned to class A or class B for a certain handset. Class A is defined as the outlet which sell the certain handset and contains all of handsets of its group, while other situation for class B which sell the certain handset too. It’s shown from the result of analysis on these two kinds of outlets that many handsets are sold better in outlets of class A than that of class B, even though the sales revenue of all these outlets in the time period is close. That is to say the handsets within a group would promote the sale for each other. Furthermore, a method proposed in this paper gives a way to find out the important attributes of the handsets which lead them to br divided into the same group, and it also explains how to add a new handset to an existing group and where would the new handset be sold best.

Improved classification of drainage networks using junction angles and secondary tributary lengths

NASA Astrophysics Data System (ADS)

Jung, Kichul; Marpu, Prashanth R.; Ouarda, Taha B. M. J.

2015-06-01

River networks in different regions have distinct characteristics generated by geological processes. These differences enable classification of drainage networks using several measures with many features of the networks. In this study, we propose a new approach that only uses the junction angles with secondary tributary lengths to directly classify different network types. This methodology is based on observations on 50 predefined channel networks. The cumulative distributions of secondary tributary lengths for different ranges of junction angles are used to obtain the descriptive values that are defined using a power-law representation. The averages of the values for the known networks are used to represent the classes, and any unclassified network can be classified based on the similarity of the representative values to those of the known classes. The methodology is applied to 10 networks in the United Arab Emirates and Oman and five networks in the USA, and the results are validated using the classification obtained with other methods.

Covering #SAE: A Mobile Reporting Class's Changing Patterns of Interaction on Twitter over Time

ERIC Educational Resources Information Center

Jones, Julie

2015-01-01

This study examined the social network that emerged on Twitter surrounding a mobile reporting class as they covered a national breaking news event. The work introduces pedagogical strategies that enhance students' learning opportunities. Through NodeXL and social network cluster analysis, six groups emerged from the Twitter interactions tied to…

The Educational Use of Facebook as a Social Networking Site in Animal Physiology Classes

ERIC Educational Resources Information Center

Köseoglu, Pinar; Mercan, Gamze

2016-01-01

This study aims at performing a sample application of the educational use of Facebook as a social networking site in Animal Physiology classes, and to determine student's' views on the application. The research sample was composed of 29 third year undergraduate students attending the Biology Education Department of Hacettepe University. The…

Marketing Career Speed Networking: A Classroom Event to Foster Career Awareness

ERIC Educational Resources Information Center

Buff, Cheryl L.; O'Connor, Suzanne

2012-01-01

This paper describes the design, implementation, and evaluation of a marketing career speed networking event held during class time in two sections of the consumer behavior class. The event was coordinated through a partnering effort with marketing faculty and the college's Career Center. A total of 57 students participated in the event, providing…

Providing Hearing-Impaired Students with Learning Care after Classes through Smart Phones and the GPRS Network

ERIC Educational Resources Information Center

Liu, Chen-Chung; Hong, Yi-Ching

2007-01-01

Although computers and network technology have been widely utilised to assist students learn, few technical supports have been developed to help hearing-impaired students learn in Taiwan. A significant challenge for teachers is to provide after-class learning care and assistance to hearing-impaired students that sustain their motivation to…

Birth of an Abstraction: A Dynamical Systems Account of the Discovery of an Elsewhere Principle in a Category Learning Task

ERIC Educational Resources Information Center

Tabor, Whitney; Cho, Pyeong W.; Dankowicz, Harry

2013-01-01

Human participants and recurrent ("connectionist") neural networks were both trained on a categorization system abstractly similar to natural language systems involving irregular ("strong") classes and a default class. Both the humans and the networks exhibited staged learning and a generalization pattern reminiscent of the…

The Double-Stranded DNA Virosphere as a Modular Hierarchical Network of Gene Sharing

PubMed Central

Iranzo, Jaime

2016-01-01

ABSTRACT Virus genomes are prone to extensive gene loss, gain, and exchange and share no universal genes. Therefore, in a broad-scale study of virus evolution, gene and genome network analyses can complement traditional phylogenetics. We performed an exhaustive comparative analysis of the genomes of double-stranded DNA (dsDNA) viruses by using the bipartite network approach and found a robust hierarchical modularity in the dsDNA virosphere. Bipartite networks consist of two classes of nodes, with nodes in one class, in this case genomes, being connected via nodes of the second class, in this case genes. Such a network can be partitioned into modules that combine nodes from both classes. The bipartite network of dsDNA viruses includes 19 modules that form 5 major and 3 minor supermodules. Of these modules, 11 include tailed bacteriophages, reflecting the diversity of this largest group of viruses. The module analysis quantitatively validates and refines previously proposed nontrivial evolutionary relationships. An expansive supermodule combines the large and giant viruses of the putative order “Megavirales” with diverse moderate-sized viruses and related mobile elements. All viruses in this supermodule share a distinct morphogenetic tool kit with a double jelly roll major capsid protein. Herpesviruses and tailed bacteriophages comprise another supermodule, held together by a distinct set of morphogenetic proteins centered on the HK97-like major capsid protein. Together, these two supermodules cover the great majority of currently known dsDNA viruses. We formally identify a set of 14 viral hallmark genes that comprise the hubs of the network and account for most of the intermodule connections. PMID:27486193

On the Structure of Cortical Microcircuits Inferred from Small Sample Sizes.

PubMed

Vegué, Marina; Perin, Rodrigo; Roxin, Alex

2017-08-30

The structure in cortical microcircuits deviates from what would be expected in a purely random network, which has been seen as evidence of clustering. To address this issue, we sought to reproduce the nonrandom features of cortical circuits by considering several distinct classes of network topology, including clustered networks, networks with distance-dependent connectivity, and those with broad degree distributions. To our surprise, we found that all of these qualitatively distinct topologies could account equally well for all reported nonrandom features despite being easily distinguishable from one another at the network level. This apparent paradox was a consequence of estimating network properties given only small sample sizes. In other words, networks that differ markedly in their global structure can look quite similar locally. This makes inferring network structure from small sample sizes, a necessity given the technical difficulty inherent in simultaneous intracellular recordings, problematic. We found that a network statistic called the sample degree correlation (SDC) overcomes this difficulty. The SDC depends only on parameters that can be estimated reliably given small sample sizes and is an accurate fingerprint of every topological family. We applied the SDC criterion to data from rat visual and somatosensory cortex and discovered that the connectivity was not consistent with any of these main topological classes. However, we were able to fit the experimental data with a more general network class, of which all previous topologies were special cases. The resulting network topology could be interpreted as a combination of physical spatial dependence and nonspatial, hierarchical clustering. SIGNIFICANCE STATEMENT The connectivity of cortical microcircuits exhibits features that are inconsistent with a simple random network. Here, we show that several classes of network models can account for this nonrandom structure despite qualitative differences in their global properties. This apparent paradox is a consequence of the small numbers of simultaneously recorded neurons in experiment: when inferred via small sample sizes, many networks may be indistinguishable despite being globally distinct. We develop a connectivity measure that successfully classifies networks even when estimated locally with a few neurons at a time. We show that data from rat cortex is consistent with a network in which the likelihood of a connection between neurons depends on spatial distance and on nonspatial, asymmetric clustering. Copyright © 2017 the authors 0270-6474/17/378498-13$15.00/0.

A kilobyte rewritable atomic memory

NASA Astrophysics Data System (ADS)

Kalff, Floris; Rebergen, Marnix; Fahrenfort, Nora; Girovsky, Jan; Toskovic, Ranko; Lado, Jose; FernáNdez-Rossier, JoaquíN.; Otte, Sander

The ability to manipulate individual atoms by means of scanning tunneling microscopy (STM) opens op opportunities for storage of digital data on the atomic scale. Recent achievements in this direction include data storage based on bits encoded in the charge state, the magnetic state, or the local presence of single atoms or atomic assemblies. However, a key challenge at this stage is the extension of such technologies into large-scale rewritable bit arrays. We demonstrate a digital atomic-scale memory of up to 1 kilobyte (8000 bits) using an array of individual surface vacancies in a chlorine terminated Cu(100) surface. The chlorine vacancies are found to be stable at temperatures up to 77 K. The memory, crafted using scanning tunneling microscopy at low temperature, can be read and re-written automatically by means of atomic-scale markers, and offers an areal density of 502 Terabits per square inch, outperforming state-of-the-art hard disk drives by three orders of magnitude.

Terabit bandwidth-adaptive transmission using low-complexity format-transparent digital signal processing.

PubMed

Zhuge, Qunbi; Morsy-Osman, Mohamed; Chagnon, Mathieu; Xu, Xian; Qiu, Meng; Plant, David V

2014-02-10

In this paper, we propose a low-complexity format-transparent digital signal processing (DSP) scheme for next generation flexible and energy-efficient transceiver. It employs QPSK symbols as the training and pilot symbols for the initialization and tracking stage of the receiver-side DSP, respectively, for various modulation formats. The performance is numerically and experimentally evaluated in a dual polarization (DP) 11 Gbaud 64QAM system. Employing the proposed DSP scheme, we conduct a system-level study of Tb/s bandwidth-adaptive superchannel transmissions with flexible modulation formats including QPSK, 8QAM and 16QAM. The spectrum bandwidth allocation is realized in the digital domain instead of turning on/off sub-channels, which improves the performance of higher order QAM. Various transmission distances ranging from 240 km to 6240 km are demonstrated with a colorless detection for hardware complexity reduction.

Design and fabrication of hybrid SPP waveguides for ultrahigh-bandwidth low-penalty terabit-scale data transmission.

PubMed

Du, Jing; Wang, Jian

2017-11-27

Here we design and fabricate a hybrid surface plasmon polarities (SPP) waveguide on the silicon-on-insulator (SOI) photonics platform. The designed hybrid SPP waveguide is composed of a metal ridge, an air gap, and a silicon ridge. We simulate the mode characteristics in the structure and design the waveguide with a wide air gap that can simplify the fabrication process and maintain the advantages of the hybrid SPP mode. The performance of ultrahigh-bandwidth data transmission through the proposed waveguide is then investigated using 161 wavelength-division multiplexing (WDM) channels, each carrying a 11.2-Gbit/s orthogonal frequency-division multiplexing (OFDM) 16-ary quadrature amplitude modulation (16-QAM) signal. The bit-error rates (BERs) of all 161 channels are less than 1e-3. The favorable results show the prospect of on-chip optical interconnection using the proposed hybrid SPP waveguide.

Nanoelectronics from the bottom up.

PubMed

Lu, Wei; Lieber, Charles M

2007-11-01

Electronics obtained through the bottom-up approach of molecular-level control of material composition and structure may lead to devices and fabrication strategies not possible with top-down methods. This review presents a brief summary of bottom-up and hybrid bottom-up/top-down strategies for nanoelectronics with an emphasis on memories based on the crossbar motif. First, we will discuss representative electromechanical and resistance-change memory devices based on carbon nanotube and core-shell nanowire structures, respectively. These device structures show robust switching, promising performance metrics and the potential for terabit-scale density. Second, we will review architectures being developed for circuit-level integration, hybrid crossbar/CMOS circuits and array-based systems, including experimental demonstrations of key concepts such lithography-independent, chemically coded stochastic demultipluxers. Finally, bottom-up fabrication approaches, including the opportunity for assembly of three-dimensional, vertically integrated multifunctional circuits, will be critically discussed.

Methodology for designing and implementing a class for service for the transmission of medical images over a common network

NASA Astrophysics Data System (ADS)

Dimond, David A.; Burgess, Robert; Barrios, Nolan; Johnson, Neil D.

2000-05-01

Traditionally, to guarantee the network performance of medical image data transmission, imaging traffic was isolated on a separate network. Organizations are depending on a new generation of multi-purpose networks to transport both normal information and image traffic as they expand access to images throughout the enterprise. These organi want to leverage their existing infrastructure for imaging traffic, but are not willing to accept degradations in overall network performance. To guarantee 'on demand' network performance for image transmissions anywhere at any time, networks need to be designed with the ability to 'carve out' bandwidth for specific applications and to minimize the chances of network failures. This paper will present the methodology Cincinnati Children's Hospital Medical Center (CHMC) used to enhance the physical and logical network design of the existing hospital network to guarantee a class of service for imaging traffic. PACS network designs should utilize the existing enterprise local area network i.e. (LAN) infrastructure where appropriate. Logical separation or segmentation provides the application independence from other clinical and administrative applications as required, ensuring bandwidth and service availability.

MONET: a MOnitoring NEtwork of Telescopes

NASA Astrophysics Data System (ADS)

Hessman, F. V.; Beuermann, K.

2002-01-01

MONET is a planned network of two 1m-class robotic telescopes which will be used for various photometric monitoring projects -- variable stars, planet searches, AGN's, GRB's -- as well as by school children in Germany and over the world. The two host partners, the Univ. of Texas' McDonald Observatory and the South African Astronomical Observatory, will operate the telescopes in exchange for observing time on the network. MONET will be one of the first robotic telescope networks offering 1-m class telescopes, complete coverage of the sky, good longitude coverage for long observing sequences on objects near the celestial equator, and a heavy educational emphasis.

Reducing Neuronal Networks to Discrete Dynamics

PubMed Central

Terman, David; Ahn, Sungwoo; Wang, Xueying; Just, Winfried

2008-01-01

We consider a general class of purely inhibitory and excitatory-inhibitory neuronal networks, with a general class of network architectures, and characterize the complex firing patterns that emerge. Our strategy for studying these networks is to first reduce them to a discrete model. In the discrete model, each neuron is represented as a finite number of states and there are rules for how a neuron transitions from one state to another. In this paper, we rigorously demonstrate that the continuous neuronal model can be reduced to the discrete model if the intrinsic and synaptic properties of the cells are chosen appropriately. In a companion paper [1], we analyze the discrete model. PMID:18443649

Generalised Sandpile Dynamics on Artificial and Real-World Directed Networks

PubMed Central

Zachariou, Nicky; Expert, Paul; Takayasu, Misako; Christensen, Kim

2015-01-01

The main finding of this paper is a novel avalanche-size exponent τ ≈ 1.87 when the generalised sandpile dynamics evolves on the real-world Japanese inter-firm network. The topology of this network is non-layered and directed, displaying the typical bow tie structure found in real-world directed networks, with cycles and triangles. We show that one can move from a strictly layered regular lattice to a more fluid structure of the inter-firm network in a few simple steps. Relaxing the regular lattice structure by introducing an interlayer distribution for the interactions, forces the scaling exponent of the avalanche-size probability density function τ out of the two-dimensional directed sandpile universality class τ = 4/3, into the mean field universality class τ = 3/2. Numerical investigation shows that these two classes are the only that exist on the directed sandpile, regardless of the underlying topology, as long as it is strictly layered. Randomly adding a small proportion of links connecting non adjacent layers in an otherwise layered network takes the system out of the mean field regime to produce non-trivial avalanche-size probability density function. Although these do not display proper scaling, they closely reproduce the behaviour observed on the Japanese inter-firm network. PMID:26606143

Generalised Sandpile Dynamics on Artificial and Real-World Directed Networks.

PubMed

Zachariou, Nicky; Expert, Paul; Takayasu, Misako; Christensen, Kim

2015-01-01

The main finding of this paper is a novel avalanche-size exponent τ ≈ 1.87 when the generalised sandpile dynamics evolves on the real-world Japanese inter-firm network. The topology of this network is non-layered and directed, displaying the typical bow tie structure found in real-world directed networks, with cycles and triangles. We show that one can move from a strictly layered regular lattice to a more fluid structure of the inter-firm network in a few simple steps. Relaxing the regular lattice structure by introducing an interlayer distribution for the interactions, forces the scaling exponent of the avalanche-size probability density function τ out of the two-dimensional directed sandpile universality class τ = 4/3, into the mean field universality class τ = 3/2. Numerical investigation shows that these two classes are the only that exist on the directed sandpile, regardless of the underlying topology, as long as it is strictly layered. Randomly adding a small proportion of links connecting non adjacent layers in an otherwise layered network takes the system out of the mean field regime to produce non-trivial avalanche-size probability density function. Although these do not display proper scaling, they closely reproduce the behaviour observed on the Japanese inter-firm network.

Google in a Quantum Network

PubMed Central

Paparo, G. D.; Martin-Delgado, M. A.

2012-01-01

We introduce the characterization of a class of quantum PageRank algorithms in a scenario in which some kind of quantum network is realizable out of the current classical internet web, but no quantum computer is yet available. This class represents a quantization of the PageRank protocol currently employed to list web pages according to their importance. We have found an instance of this class of quantum protocols that outperforms its classical counterpart and may break the classical hierarchy of web pages depending on the topology of the web. PMID:22685626

Childhood Trauma, Social Networks, and the Mental Health of Adult Survivors.

PubMed

Schneider, F David; Loveland Cook, Cynthia A; Salas, Joanne; Scherrer, Jeffrey; Cleveland, Ivy N; Burge, Sandra K

2017-03-01

The purpose of this study was to investigate the relationship of childhood trauma to the quality of social networks and health outcomes later in adulthood. Data were obtained from a convenience sample of 254 adults seen in one of 10 primary care clinics in the state of Texas. Standardized measures of adverse childhood experiences (ACEs), stressful and supportive social relationships, medical conditions, anxiety, depression, and health-related quality of life were administered. Using latent class analysis, subjects were assigned to one of four ACE classes: (a) minimal childhood abuse (56%), (b) physical/verbal abuse of both child and mother with household alcohol abuse (13%), (c) verbal and physical abuse of child with household mental illness (12%), and (d) verbal abuse only (19%). Statistically significant differences across the four ACE classes were found for mental health outcomes in adulthood. Although respondents who were physically and verbally abused as children reported compromised mental health, this was particularly true for those who witnessed physical abuse of their mother. A similar relationship between ACE class and physical health was not found. The quality of adult social networks partly accounted for the relationship between ACE classes and mental health outcomes. Respondents exposed to ACEs with more supportive social networks as adults had diminished odds of reporting poor mental health. Conversely, increasing numbers of stressful social relationships contributed to adverse mental health outcomes. Although efforts to prevent childhood trauma remain a critical priority, the treatment of adult survivors needs to expand its focus on both strengthening social networks and decreasing the negative effects of stressful ones.

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

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.

A class of finite-time dual neural networks for solving quadratic programming problems and its k-winners-take-all application.

PubMed

Li, Shuai; Li, Yangming; Wang, Zheng

2013-03-01

This paper presents a class of recurrent neural networks to solve quadratic programming problems. Different from most existing recurrent neural networks for solving quadratic programming problems, the proposed neural network model converges in finite time and the activation function is not required to be a hard-limiting function for finite convergence time. The stability, finite-time convergence property and the optimality of the proposed neural network for solving the original quadratic programming problem are proven in theory. Extensive simulations are performed to evaluate the performance of the neural network with different parameters. In addition, the proposed neural network is applied to solving the k-winner-take-all (k-WTA) problem. Both theoretical analysis and numerical simulations validate the effectiveness of our method for solving the k-WTA problem. Copyright © 2012 Elsevier Ltd. All rights reserved.

Logarithmic r-θ mapping for hybrid optical neural network filter for multiple objects recognition within cluttered scenes

NASA Astrophysics Data System (ADS)

Kypraios, Ioannis; Young, Rupert C. D.; Chatwin, Chris R.; Birch, Phil M.

2009-04-01

θThe window unit in the design of the complex logarithmic r-θ mapping for hybrid optical neural network filter can allow multiple objects of the same class to be detected within the input image. Additionally, the architecture of the neural network unit of the complex logarithmic r-θ mapping for hybrid optical neural network filter becomes attractive for accommodating the recognition of multiple objects of different classes within the input image by modifying the output layer of the unit. We test the overall filter for multiple objects of the same and of different classes' recognition within cluttered input images and video sequences of cluttered scenes. Logarithmic r-θ mapping for hybrid optical neural network filter is shown to exhibit with a single pass over the input data simultaneously in-plane rotation, out-of-plane rotation, scale, log r-θ map translation and shift invariance, and good clutter tolerance by recognizing correctly the different objects within the cluttered scenes. We record in our results additional extracted information from the cluttered scenes about the objects' relative position, scale and in-plane rotation.

Using Social Networking Environments to Support Collaborative Learning in a Chinese University Class: Interaction Pattern and Influencing Factors

ERIC Educational Resources Information Center

Lu, Jie; Churchill, Daniel

2014-01-01

This paper reports a study that investigated the social interaction pattern of collaborative learning and the factors affecting the effectiveness of collaborative learning in a social networking environment (SNE). A class of 55 undergraduate students enrolled in an elective course at a Chinese university was recruited for the study. The…

Sending Learning Pills to Mobile Devices in Class to Enhance Student Performance and Motivation in Network Services Configuration Courses

ERIC Educational Resources Information Center

Munoz-Organero, M.; Munoz-Merino, P. J.; Kloos, C. D.

2012-01-01

Teaching electrical and computer software engineers how to configure network services normally requires the detailed presentation of many configuration commands and their numerous parameters. Students tend to find it difficult to maintain acceptable levels of motivation. In many cases, this results in their not attending classes and not dedicating…

Bounded-Degree Approximations of Stochastic Networks

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

Quinn, Christopher J.; Pinar, Ali; Kiyavash, Negar

2017-06-01

We propose algorithms to approximate directed information graphs. Directed information graphs are probabilistic graphical models that depict causal dependencies between stochastic processes in a network. The proposed algorithms identify optimal and near-optimal approximations in terms of Kullback-Leibler divergence. The user-chosen sparsity trades off the quality of the approximation against visual conciseness and computational tractability. One class of approximations contains graphs with speci ed in-degrees. Another class additionally requires that the graph is connected. For both classes, we propose algorithms to identify the optimal approximations and also near-optimal approximations, using a novel relaxation of submodularity. We also propose algorithms to identifymore » the r-best approximations among these classes, enabling robust decision making.« less

Folding and unfolding phylogenetic trees and networks.

PubMed

Huber, Katharina T; Moulton, Vincent; Steel, Mike; Wu, Taoyang

2016-12-01

Phylogenetic networks are rooted, labelled directed acyclic graphswhich are commonly used to represent reticulate evolution. There is a close relationship between phylogenetic networks and multi-labelled trees (MUL-trees). Indeed, any phylogenetic network N can be "unfolded" to obtain a MUL-tree U(N) and, conversely, a MUL-tree T can in certain circumstances be "folded" to obtain aphylogenetic network F(T) that exhibits T. In this paper, we study properties of the operations U and F in more detail. In particular, we introduce the class of stable networks, phylogenetic networks N for which F(U(N)) is isomorphic to N, characterise such networks, and show that they are related to the well-known class of tree-sibling networks. We also explore how the concept of displaying a tree in a network N can be related to displaying the tree in the MUL-tree U(N). To do this, we develop aphylogenetic analogue of graph fibrations. This allows us to view U(N) as the analogue of the universal cover of a digraph, and to establish a close connection between displaying trees in U(N) and reconciling phylogenetic trees with networks.

Agricultural trade networks and patterns of economic development.

PubMed

Shutters, Shade T; Muneepeerakul, Rachata

2012-01-01

International trade networks are manifestations of a complex combination of diverse underlying factors, both natural and social. Here we apply social network analytics to the international trade network of agricultural products to better understand the nature of this network and its relation to patterns of international development. Using a network tool known as triadic analysis we develop triad significance profiles for a series of agricultural commodities traded among countries. Results reveal a novel network "superfamily" combining properties of biological information processing networks and human social networks. To better understand this unique network signature, we examine in more detail the degree and triadic distributions within the trade network by country and commodity. Our results show that countries fall into two very distinct classes based on their triadic frequencies. Roughly 165 countries fall into one class while 18, all highly isolated with respect to international agricultural trade, fall into the other. Only Vietnam stands out as a unique case. Finally, we show that as a country becomes less isolated with respect to number of trading partners, the country's triadic signature follows a predictable trajectory that may correspond to a trajectory of development.

Emergent inequality and self-organized social classes in a network of power and frustration

DOE PAGES

Mahault, Benoit; Saxena, Avadh; Nisoli, Cristiano

2017-02-17

We propose a simple agent-based model on a network to conceptualize the allocation of limited wealth among more abundant expectations at the interplay of power, frustration, and initiative. Concepts imported from the statistical physics of frustrated systems in and out of equilibrium allow us to compare subjective measures of frustration and satisfaction to collective measures of fairness in wealth distribution, such as the Lorenz curve and the Gini index. We find that a completely libertarian, law-of-the-jungle setting, where every agent can acquire wealth from or lose wealth to anybody else invariably leads to a complete polarization of the distribution ofmore » wealth vs. opportunity. This picture is however dramatically ameliorated when hard constraints are imposed over agents in the form of a limiting network of transactions. There, an out of equilibrium dynamics of the networks, based on a competition between power and frustration in the decision-making of agents, leads to network coevolution. The ratio of power and frustration controls different dynamical regimes separated by kinetic transitions and characterized by drastically different values of equality. It also leads, for proper values of social initiative, to the emergence of three self-organized social classes, lower, middle, and upper class. Their dynamics, which appears mostly controlled by the middle class, drives a cyclical regime of dramatic social changes.« less

Emergent inequality and self-organized social classes in a network of power and frustration

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

Mahault, Benoit; Saxena, Avadh; Nisoli, Cristiano

We propose a simple agent-based model on a network to conceptualize the allocation of limited wealth among more abundant expectations at the interplay of power, frustration, and initiative. Concepts imported from the statistical physics of frustrated systems in and out of equilibrium allow us to compare subjective measures of frustration and satisfaction to collective measures of fairness in wealth distribution, such as the Lorenz curve and the Gini index. We find that a completely libertarian, law-of-the-jungle setting, where every agent can acquire wealth from or lose wealth to anybody else invariably leads to a complete polarization of the distribution ofmore » wealth vs. opportunity. This picture is however dramatically ameliorated when hard constraints are imposed over agents in the form of a limiting network of transactions. There, an out of equilibrium dynamics of the networks, based on a competition between power and frustration in the decision-making of agents, leads to network coevolution. The ratio of power and frustration controls different dynamical regimes separated by kinetic transitions and characterized by drastically different values of equality. It also leads, for proper values of social initiative, to the emergence of three self-organized social classes, lower, middle, and upper class. Their dynamics, which appears mostly controlled by the middle class, drives a cyclical regime of dramatic social changes.« less

Emergent inequality and self-organized social classes in a network of power and frustration

PubMed Central

Mahault, Benoit; Saxena, Avadh

2017-01-01

We propose a simple agent-based model on a network to conceptualize the allocation of limited wealth among more abundant expectations at the interplay of power, frustration, and initiative. Concepts imported from the statistical physics of frustrated systems in and out of equilibrium allow us to compare subjective measures of frustration and satisfaction to collective measures of fairness in wealth distribution, such as the Lorenz curve and the Gini index. We find that a completely libertarian, law-of-the-jungle setting, where every agent can acquire wealth from or lose wealth to anybody else invariably leads to a complete polarization of the distribution of wealth vs. opportunity. This picture is however dramatically ameliorated when hard constraints are imposed over agents in the form of a limiting network of transactions. There, an out of equilibrium dynamics of the networks, based on a competition between power and frustration in the decision-making of agents, leads to network coevolution. The ratio of power and frustration controls different dynamical regimes separated by kinetic transitions and characterized by drastically different values of equality. It also leads, for proper values of social initiative, to the emergence of three self-organized social classes, lower, middle, and upper class. Their dynamics, which appears mostly controlled by the middle class, drives a cyclical regime of dramatic social changes. PMID:28212440

Emergent inequality and self-organized social classes in a network of power and frustration.

PubMed

Mahault, Benoit; Saxena, Avadh; Nisoli, Cristiano

2017-01-01

We propose a simple agent-based model on a network to conceptualize the allocation of limited wealth among more abundant expectations at the interplay of power, frustration, and initiative. Concepts imported from the statistical physics of frustrated systems in and out of equilibrium allow us to compare subjective measures of frustration and satisfaction to collective measures of fairness in wealth distribution, such as the Lorenz curve and the Gini index. We find that a completely libertarian, law-of-the-jungle setting, where every agent can acquire wealth from or lose wealth to anybody else invariably leads to a complete polarization of the distribution of wealth vs. opportunity. This picture is however dramatically ameliorated when hard constraints are imposed over agents in the form of a limiting network of transactions. There, an out of equilibrium dynamics of the networks, based on a competition between power and frustration in the decision-making of agents, leads to network coevolution. The ratio of power and frustration controls different dynamical regimes separated by kinetic transitions and characterized by drastically different values of equality. It also leads, for proper values of social initiative, to the emergence of three self-organized social classes, lower, middle, and upper class. Their dynamics, which appears mostly controlled by the middle class, drives a cyclical regime of dramatic social changes.

A class Hierarchical, object-oriented approach to virtual memory management

NASA Technical Reports Server (NTRS)

Russo, Vincent F.; Campbell, Roy H.; Johnston, Gary M.

1989-01-01

The Choices family of operating systems exploits class hierarchies and object-oriented programming to facilitate the construction of customized operating systems for shared memory and networked multiprocessors. The software is being used in the Tapestry laboratory to study the performance of algorithms, mechanisms, and policies for parallel systems. Described here are the architectural design and class hierarchy of the Choices virtual memory management system. The software and hardware mechanisms and policies of a virtual memory system implement a memory hierarchy that exploits the trade-off between response times and storage capacities. In Choices, the notion of a memory hierarchy is captured by abstract classes. Concrete subclasses of those abstractions implement a virtual address space, segmentation, paging, physical memory management, secondary storage, and remote (that is, networked) storage. Captured in the notion of a memory hierarchy are classes that represent memory objects. These classes provide a storage mechanism that contains encapsulated data and have methods to read or write the memory object. Each of these classes provides specializations to represent the memory hierarchy.

Cloud Classification in Polar and Desert Regions and Smoke Classification from Biomass Burning Using a Hierarchical Neural Network

NASA Technical Reports Server (NTRS)

Alexander, June; Corwin, Edward; Lloyd, David; Logar, Antonette; Welch, Ronald

1996-01-01

This research focuses on a new neural network scene classification technique. The task is to identify scene elements in Advanced Very High Resolution Radiometry (AVHRR) data from three scene types: polar, desert and smoke from biomass burning in South America (smoke). The ultimate goal of this research is to design and implement a computer system which will identify the clouds present on a whole-Earth satellite view as a means of tracking global climate changes. Previous research has reported results for rule-based systems (Tovinkere et at 1992, 1993) for standard back propagation (Watters et at. 1993) and for a hierarchical approach (Corwin et al 1994) for polar data. This research uses a hierarchical neural network with don't care conditions and applies this technique to complex scenes. A hierarchical neural network consists of a switching network and a collection of leaf networks. The idea of the hierarchical neural network is that it is a simpler task to classify a certain pattern from a subset of patterns than it is to classify a pattern from the entire set. Therefore, the first task is to cluster the classes into groups. The switching, or decision network, performs an initial classification by selecting a leaf network. The leaf networks contain a reduced set of similar classes, and it is in the various leaf networks that the actual classification takes place. The grouping of classes in the various leaf networks is determined by applying an iterative clustering algorithm. Several clustering algorithms were investigated, but due to the size of the data sets, the exhaustive search algorithms were eliminated. A heuristic approach using a confusion matrix from a lightly trained neural network provided the basis for the clustering algorithm. Once the clusters have been identified, the hierarchical network can be trained. The approach of using don't care nodes results from the difficulty in generating extremely complex surfaces in order to separate one class from all of the others. This approach finds pairwise separating surfaces and forms the more complex separating surface from combinations of simpler surfaces. This technique both reduces training time and improves accuracy over the previously reported results. Accuracies of 97.47%, 95.70%, and 99.05% were achieved for the polar, desert and smoke data sets.

Transitions in Smokers' Social Networks After Quit Attempts: A Latent Transition Analysis.

PubMed

Bray, Bethany C; Smith, Rachel A; Piper, Megan E; Roberts, Linda J; Baker, Timothy B

2016-12-01

Smokers' social networks vary in size, composition, and amount of exposure to smoking. The extent to which smokers' social networks change after a quit attempt is unknown, as is the relation between quitting success and later network changes. Unique types of social networks for 691 smokers enrolled in a smoking-cessation trial were identified based on network size, new network members, members' smoking habits, within network smoking, smoking buddies, and romantic partners' smoking. Latent transition analysis was used to identify the network classes and to predict transitions in class membership across 3 years from biochemically assessed smoking abstinence. Five network classes were identified: Immersed (large network, extensive smoking exposure including smoking buddies), Low Smoking Exposure (large network, minimal smoking exposure), Smoking Partner (small network, smoking exposure primarily from partner), Isolated (small network, minimal smoking exposure), and Distant Smoking Exposure (small network, considerable nonpartner smoking exposure). Abstinence at years 1 and 2 was associated with shifts in participants' social networks to less contact with smokers and larger networks in years 2 and 3. In the years following a smoking-cessation attempt, smokers' social networks changed, and abstinence status predicted these changes. Networks defined by high levels of exposure to smokers were especially associated with continued smoking. Abstinence, however, predicted transitions to larger social networks comprising less smoking exposure. These results support treatments that aim to reduce exposure to smoking cues and smokers, including partners who smoke. Prior research has shown that social network features predict the likelihood of subsequent smoking cessation. The current research illustrates how successful quitting predicts social network change over 3 years following a quit attempt. Specifically, abstinence predicts transitions to networks that are larger and afford less exposure to smokers. This suggests that quitting smoking may expand a person's social milieu rather than narrow it. This effect, plus reduced exposure to smokers, may help sustain abstinence. © The Author 2016. Published by Oxford University Press on behalf of the Society for Research on Nicotine and Tobacco. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

The association between network social capital and self-rated health: pouring old wine in new bottles?

PubMed

Verhaeghe, Pieter-Paul; Pattyn, Elise; Bracke, Piet; Verhaeghe, Mieke; Van De Putte, Bart

2012-03-01

This study examines whether there is an association between network social capital and self-rated health after controlling for social support. Moreover, we distinguish between network social capital that emerges from strong ties and weak ties. We used a cross-sectional representative sample of 815 adults from the Belgian population. Social capital is measured with the position generator and perceived social support with the MOS Social Support-scale. Results suggest that network social capital is associated with self-rated health after adjustment for social support. Because different social classes have access to different sets of resources, resources of friends and family from the intermediate and higher service classes are beneficial for self-rated health, whereas resources of friends and family from the working class appear to be rather detrimental for self-rated health. From a health-promoting perspective, these findings indicate that policy makers should deal with the root causes of socioeconomic disadvantages in society. Copyright © 2011 Elsevier Ltd. All rights reserved.

Robustness analysis of uncertain dynamical neural networks with multiple time delays.

PubMed

Senan, Sibel

2015-10-01

This paper studies the problem of global robust asymptotic stability of the equilibrium point for the class of dynamical neural networks with multiple time delays with respect to the class of slope-bounded activation functions and in the presence of the uncertainties of system parameters of the considered neural network model. By using an appropriate Lyapunov functional and exploiting the properties of the homeomorphism mapping theorem, we derive a new sufficient condition for the existence, uniqueness and global robust asymptotic stability of the equilibrium point for the class of neural networks with multiple time delays. The obtained stability condition basically relies on testing some relationships imposed on the interconnection matrices of the neural system, which can be easily verified by using some certain properties of matrices. An instructive numerical example is also given to illustrate the applicability of our result and show the advantages of this new condition over the previously reported corresponding results. Copyright © 2015 Elsevier Ltd. All rights reserved.

Topological properties of complex networks in protein structures

NASA Astrophysics Data System (ADS)

Kim, Kyungsik; Jung, Jae-Won; Min, Seungsik

2014-03-01

We study topological properties of networks in structural classification of proteins. We model the native-state protein structure as a network made of its constituent amino-acids and their interactions. We treat four structural classes of proteins composed predominantly of α helices and β sheets and consider several proteins from each of these classes whose sizes range from amino acids of the Protein Data Bank. Particularly, we simulate and analyze the network metrics such as the mean degree, the probability distribution of degree, the clustering coefficient, the characteristic path length, the local efficiency, and the cost. This work was supported by the KMAR and DP under Grant WISE project (153-3100-3133-302-350).

Recruitment dynamics in adaptive social networks

NASA Astrophysics Data System (ADS)

Shkarayev, Maxim; Shaw, Leah; Schwartz, Ira

2011-03-01

We model recruitment in social networks in the presence of birth and death processes. The recruitment is characterized by nodes changing their status to that of the recruiting class as a result of contact with recruiting nodes. The recruiting nodes may adapt their connections in order to improve recruitment capabilities, thus changing the network structure. We develop a mean-field theory describing the system dynamics. Using mean-field theory we characterize the dependence of the growth threshold of the recruiting class on the adaptation parameter. Furthermore, we investigate the effect of adaptation on the recruitment dynamics, as well as on network topology. The theoretical predictions are confirmed by the direct simulations of the full system.

Analytical solution for a class of network dynamics with mechanical and financial applications

NASA Astrophysics Data System (ADS)

Krejčí, P.; Lamba, H.; Melnik, S.; Rachinskii, D.

2014-09-01

We show that for a certain class of dynamics at the nodes the response of a network of any topology to arbitrary inputs is defined in a simple way by its response to a monotone input. The nodes may have either a discrete or continuous set of states and there is no limit on the complexity of the network. The results provide both an efficient numerical method and the potential for accurate analytic approximation of the dynamics on such networks. As illustrative applications, we introduce a quasistatic mechanical model with objects interacting via frictional forces and a financial market model with avalanches and critical behavior that are generated by momentum trading strategies.

Security and Dependability Solutions for Networks and Devices

NASA Astrophysics Data System (ADS)

Gücrgens, Sigrid; Fuchs, Andreas

In this chapter we give an overview over the denotation of the SERENITY artefacts S&D Classes, Patterns and Implementations in the context of networks and devices. In order to demonstrate their necessity we sketch an example for confidential and authentic communication and storage that utilizes a trusted platform module, and model the relevant pattern. We then dissociate solutions for network and device related S&D requirements from those targeting the context of organizational or workflow and web services based solutions. Then we give a summary of the broad field of application for network and device solutions. Finally we clarify the meaning and interaction between classes, patterns and implementations by giving some concrete examples.

Predicting the Lifetime of Dynamic Networks Experiencing Persistent Random Attacks.

PubMed

Podobnik, Boris; Lipic, Tomislav; Horvatic, Davor; Majdandzic, Antonio; Bishop, Steven R; Eugene Stanley, H

2015-09-21

Estimating the critical points at which complex systems abruptly flip from one state to another is one of the remaining challenges in network science. Due to lack of knowledge about the underlying stochastic processes controlling critical transitions, it is widely considered difficult to determine the location of critical points for real-world networks, and it is even more difficult to predict the time at which these potentially catastrophic failures occur. We analyse a class of decaying dynamic networks experiencing persistent failures in which the magnitude of the overall failure is quantified by the probability that a potentially permanent internal failure will occur. When the fraction of active neighbours is reduced to a critical threshold, cascading failures can trigger a total network failure. For this class of network we find that the time to network failure, which is equivalent to network lifetime, is inversely dependent upon the magnitude of the failure and logarithmically dependent on the threshold. We analyse how permanent failures affect network robustness using network lifetime as a measure. These findings provide new methodological insight into system dynamics and, in particular, of the dynamic processes of networks. We illustrate the network model by selected examples from biology, and social science.

A Social Network System Based on an Ontology in the Korea Institute of Oriental Medicine

NASA Astrophysics Data System (ADS)

Kim, Sang-Kyun; Han, Jeong-Min; Song, Mi-Young

We in this paper propose a social network based on ontology in Korea Institute of Oriental Medicine (KIOM). By using the social network, researchers can find collaborators and share research results with others so that studies in Korean Medicine fields can be activated. For this purpose, first, personal profiles, scholarships, careers, licenses, academic activities, research results, and personal connections for all of researchers in KIOM are collected. After relationship and hierarchy among ontology classes and attributes of classes are defined through analyzing the collected information, a social network ontology are constructed using FOAF and OWL. This ontology can be easily interconnected with other social network by FOAF and provide the reasoning based on OWL ontology. In future, we construct the search and reasoning system using the ontology. Moreover, if the social network is activated, we will open it to whole Korean Medicine fields.

Network analysis of physics discussion forums and links to course success

NASA Astrophysics Data System (ADS)

Traxler, Adrienne; Gavrin, Andrew; Lindell, Rebecca

2017-01-01

Large introductory science courses tend to isolate students, with negative consequences for long-term retention in college. Many active learning courses build collaboration and community among students as an explicit goal, and social network analysis has been used to track the development and beneficial effects of these collaborations. Here we supplement such work by conducting network analysis of online course discussion forums in two semesters of an introductory physics class. Online forums provide a tool for engaging students with each other outside of class, and offer new opportunities to commuter or non-traditional students with limited on-campus time. We look for correlations between position in the forum network (centrality) and final course grades. Preliminary investigation has shown weak correlations in the very dense full-semester network, so we will consider reduced ''backbone'' networks that highlight the most consistent links between students. Future work and implications for instruction will also be discussed.

Sport, how people choose it: A network analysis approach.

PubMed

Ferreri, Luca; Ivaldi, Marco; Daolio, Fabio; Giacobini, Mario; Rainoldi, Alberto; Tomassini, Marco

2015-01-01

In order to investigate the behaviour of athletes in choosing sports, we analyse data from part of the We-Sport database, a vertical social network that links athletes through sports. In particular, we explore connections between people sharing common sports and the role of age and gender by applying "network science" approaches and methods. The results show a disassortative tendency of athletes in choosing sports, a negative correlation between age and number of chosen sports and a positive correlation between age of connected athletes. Some interesting patterns of connection between age classes are depicted. In addition, we propose a method to classify sports, based on the analyses of the behaviour of people practising them. Thanks to this brand new classifications, we highlight the links of class of sports and their unexpected features. We emphasise some gender dependency affinity in choosing sport classes.

Using PPI network autocorrelation in hierarchical multi-label classification trees for gene function prediction.

PubMed

Stojanova, Daniela; Ceci, Michelangelo; Malerba, Donato; Dzeroski, Saso

2013-09-26

Ontologies and catalogs of gene functions, such as the Gene Ontology (GO) and MIPS-FUN, assume that functional classes are organized hierarchically, that is, general functions include more specific ones. This has recently motivated the development of several machine learning algorithms for gene function prediction that leverages on this hierarchical organization where instances may belong to multiple classes. In addition, it is possible to exploit relationships among examples, since it is plausible that related genes tend to share functional annotations. Although these relationships have been identified and extensively studied in the area of protein-protein interaction (PPI) networks, they have not received much attention in hierarchical and multi-class gene function prediction. Relations between genes introduce autocorrelation in functional annotations and violate the assumption that instances are independently and identically distributed (i.i.d.), which underlines most machine learning algorithms. Although the explicit consideration of these relations brings additional complexity to the learning process, we expect substantial benefits in predictive accuracy of learned classifiers. This article demonstrates the benefits (in terms of predictive accuracy) of considering autocorrelation in multi-class gene function prediction. We develop a tree-based algorithm for considering network autocorrelation in the setting of Hierarchical Multi-label Classification (HMC). We empirically evaluate the proposed algorithm, called NHMC (Network Hierarchical Multi-label Classification), on 12 yeast datasets using each of the MIPS-FUN and GO annotation schemes and exploiting 2 different PPI networks. The results clearly show that taking autocorrelation into account improves the predictive performance of the learned models for predicting gene function. Our newly developed method for HMC takes into account network information in the learning phase: When used for gene function prediction in the context of PPI networks, the explicit consideration of network autocorrelation increases the predictive performance of the learned models. Overall, we found that this holds for different gene features/ descriptions, functional annotation schemes, and PPI networks: Best results are achieved when the PPI network is dense and contains a large proportion of function-relevant interactions.

Social networks and their influence on drinking behaviors: differences related to cognitive impairment in clients receiving alcoholism treatment.

PubMed

Buckman, Jennifer F; Bates, Marsha E; Cisler, Ron A

2007-09-01

Mechanisms of behavioral change that support positive addiction treatment outcomes in individuals with co-occurring alcohol-use disorders and cognitive impairment remain largely unknown. This article combines person- and variable-centered approaches to examine the interrelated influence of cognitive impairment and social support on stability of and changes in drinking behaviors of Project MATCH (Matching Alcoholism Treatments to Client Heterogeneity) outpatients and aftercare clients (N = 1,726) during the first year after their entry into treatment. Latent class analysis identified homogeneous groups of clients based on the nature and extent of social support for abstinence or drinking at treatment entry. Cognitive impairment and drinking outcomes were compared across latent classes, and the interaction between impairment and social support on drinking outcomes was examined using mixture probit regression. Three independent social support classes (frequent positive, limited positive, and negative) were identified. In the outpatient sample, the frequent positive support class had greater cognitive impairment at treatment entry versus other classes, and extent of impairment significantly predicted improved drinking outcomes in this class. In the aftercare sample, the frequent positive and negative support classes had heightened impairment, yet cognitive impairment significantly predicted relatively poorer drinking outcomes in the negative support class only. Cognitive impairment may increase the influence of the social network on the drinking outcomes of persons receiving treatment for alcohol-use disorders, but more research is needed to understand client characteristics that determine whether this influence is more likely to be manifest as increased salience of helping agents or of hindering agents in the social network.

Neural networks in data analysis and modeling for detecting littoral oil-spills by airborne laser fluorosensor remote sensing

NASA Astrophysics Data System (ADS)

Lin, Bin; An, Jubai; Brown, Carl E.; Chen, Weiwei

2003-05-01

In this paper an artificial neural network (ANN) approach, which is based on flexible nonlinear models for a very broad class of transfer functions, is applied for multi-spectral data analysis and modeling of airborne laser fluorosensor in order to differentiate between classes of oil on water surface. We use three types of algorithm: Perceptron Network, Back-Propagation (B-P) Network and Self-Organizing feature Maps (SOM) Network. Using the data in form of 64-channel spectra as inputs, the ANN presents the analysis and estimation results of the oil type on the basis of the type of background materials as outputs. The ANN is trained and tested using sample data set to the network. The results of the above 3 types of network are compared in this paper. It is proved that the training has developed a network that not only fits the training data, but also fits real-world data that the network will process operationally. The ANN model would play a significant role in the ocean oil-spill identification in the future.

Impacts of Climate Change on Operation of the US Rail Network

EPA Science Inventory

The rail network in the US is the largest network within any single country at 140,000 miles of Class 1 tracks. The network is predominantly focused on freight traffic with the exception of key passenger corridors along the eastern seaboard and in the upper Midwest. This extens...

Dynamic Trust Management for Mobile Networks and Its Applications

ERIC Educational Resources Information Center

Bao, Fenye

2013-01-01

Trust management in mobile networks is challenging due to dynamically changing network environments and the lack of a centralized trusted authority. In this dissertation research, we "design" and "validate" a class of dynamic trust management protocols for mobile networks, and demonstrate the utility of dynamic trust management…

77 FR 60680 - Development of the Nationwide Interoperable Public Safety Broadband Network

Federal Register 2010, 2011, 2012, 2013, 2014

2012-10-04

... public comment on the conceptual network architecture presentation made at the FirstNet Board of... business plan considerations. NTIA also seeks comment on the general concept of how to develop applications... network based on a single, nationwide network architecture called for under the Middle Class Tax Relief...

Distributed Spectral Monitoring For Emitter Localization

DTIC Science & Technology

2018-02-12

localization techniques in a DSA sensor network. The results of the research are presented through simulation of localization algorithms, emulation of a...network on a wireless RF environment emulator, and field tests. The results of the various tests in both the lab and field are obtained and analyzed to... are two main classes of localization techniques, and the technique to use will depend on the information available with the emitter. The first class

Cooperation-Controlled Learning for Explicit Class Structure in Self-Organizing Maps

PubMed Central

Kamimura, Ryotaro

2014-01-01

We attempt to demonstrate the effectiveness of multiple points of view toward neural networks. By restricting ourselves to two points of view of a neuron, we propose a new type of information-theoretic method called “cooperation-controlled learning.” In this method, individual and collective neurons are distinguished from one another, and we suppose that the characteristics of individual and collective neurons are different. To implement individual and collective neurons, we prepare two networks, namely, cooperative and uncooperative networks. The roles of these networks and the roles of individual and collective neurons are controlled by the cooperation parameter. As the parameter is increased, the role of cooperative networks becomes more important in learning, and the characteristics of collective neurons become more dominant. On the other hand, when the parameter is small, individual neurons play a more important role. We applied the method to the automobile and housing data from the machine learning database and examined whether explicit class boundaries could be obtained. Experimental results showed that cooperation-controlled learning, in particular taking into account information on input units, could be used to produce clearer class structure than conventional self-organizing maps. PMID:25309950

Classification of conductance traces with recurrent neural networks

NASA Astrophysics Data System (ADS)

Lauritzen, Kasper P.; Magyarkuti, András; Balogh, Zoltán; Halbritter, András; Solomon, Gemma C.

2018-02-01

We present a new automated method for structural classification of the traces obtained in break junction experiments. Using recurrent neural networks trained on the traces of minimal cross-sectional area in molecular dynamics simulations, we successfully separate the traces into two classes: point contact or nanowire. This is done without any assumptions about the expected features of each class. The trained neural network is applied to experimental break junction conductance traces, and it separates the classes as well as the previously used experimental methods. The effect of using partial conductance traces is explored, and we show that the method performs equally well using full or partial traces (as long as the trace just prior to breaking is included). When only the initial part of the trace is included, the results are still better than random chance. Finally, we show that the neural network classification method can be used to classify experimental conductance traces without using simulated results for training, but instead training the network on a few representative experimental traces. This offers a tool to recognize some characteristic motifs of the traces, which can be hard to find by simple data selection algorithms.

Preparation guide for class B software specification documents

NASA Technical Reports Server (NTRS)

Tausworthe, R. C.

1979-01-01

General conceptual requirements and specific application rules and procedures are provided for the production of software specification documents in conformance with deep space network software standards and class B standards. Class B documentation is identified as the appropriate level applicable to implementation, sustaining engineering, and operational uses by qualified personnel. Special characteristics of class B documents are defined.

A Study of Quality of Service Communication for High-Speed Packet-Switching Computer Sub-Networks

NASA Technical Reports Server (NTRS)

Cui, Zhenqian

1999-01-01

With the development of high-speed networking technology, computer networks, including local-area networks (LANs), wide-area networks (WANs) and the Internet, are extending their traditional roles of carrying computer data. They are being used for Internet telephony, multimedia applications such as conferencing and video on demand, distributed simulations, and other real-time applications. LANs are even used for distributed real-time process control and computing as a cost-effective approach. Differing from traditional data transfer, these new classes of high-speed network applications (video, audio, real-time process control, and others) are delay sensitive. The usefulness of data depends not only on the correctness of received data, but also the time that data are received. In other words, these new classes of applications require networks to provide guaranteed services or quality of service (QoS). Quality of service can be defined by a set of parameters and reflects a user's expectation about the underlying network's behavior. Traditionally, distinct services are provided by different kinds of networks. Voice services are provided by telephone networks, video services are provided by cable networks, and data transfer services are provided by computer networks. A single network providing different services is called an integrated-services network.

Agricultural Trade Networks and Patterns of Economic Development

PubMed Central

Shutters, Shade T.; Muneepeerakul, Rachata

2012-01-01

International trade networks are manifestations of a complex combination of diverse underlying factors, both natural and social. Here we apply social network analytics to the international trade network of agricultural products to better understand the nature of this network and its relation to patterns of international development. Using a network tool known as triadic analysis we develop triad significance profiles for a series of agricultural commodities traded among countries. Results reveal a novel network “superfamily” combining properties of biological information processing networks and human social networks. To better understand this unique network signature, we examine in more detail the degree and triadic distributions within the trade network by country and commodity. Our results show that countries fall into two very distinct classes based on their triadic frequencies. Roughly 165 countries fall into one class while 18, all highly isolated with respect to international agricultural trade, fall into the other. Only Vietnam stands out as a unique case. Finally, we show that as a country becomes less isolated with respect to number of trading partners, the country's triadic signature follows a predictable trajectory that may correspond to a trajectory of development. PMID:22768310

Percolation of spatially constraint networks

NASA Astrophysics Data System (ADS)

Li, Daqing; Li, Guanliang; Kosmidis, Kosmas; Stanley, H. E.; Bunde, Armin; Havlin, Shlomo

2011-03-01

We study how spatial constraints are reflected in the percolation properties of networks embedded in one-dimensional chains and two-dimensional lattices. We assume long-range connections between sites on the lattice where two sites at distance r are chosen to be linked with probability p(r)~r-δ. Similar distributions have been found in spatially embedded real networks such as social and airline networks. We find that for networks embedded in two dimensions, with 2<δ<4, the percolation properties show new intermediate behavior different from mean field, with critical exponents that depend on δ. For δ<2, the percolation transition belongs to the universality class of percolation in Erdös-Rényi networks (mean field), while for δ>4 it belongs to the universality class of percolation in regular lattices. For networks embedded in one dimension, we find that, for δ<1, the percolation transition is mean field. For 1<δ<2, the critical exponents depend on δ, while for δ>2 there is no percolation transition as in regular linear chains.

Role models for complex networks

NASA Astrophysics Data System (ADS)

Reichardt, J.; White, D. R.

2007-11-01

We present a framework for automatically decomposing (“block-modeling”) the functional classes of agents within a complex network. These classes are represented by the nodes of an image graph (“block model”) depicting the main patterns of connectivity and thus functional roles in the network. Using a first principles approach, we derive a measure for the fit of a network to any given image graph allowing objective hypothesis testing. From the properties of an optimal fit, we derive how to find the best fitting image graph directly from the network and present a criterion to avoid overfitting. The method can handle both two-mode and one-mode data, directed and undirected as well as weighted networks and allows for different types of links to be dealt with simultaneously. It is non-parametric and computationally efficient. The concepts of structural equivalence and modularity are found as special cases of our approach. We apply our method to the world trade network and analyze the roles individual countries play in the global economy.

Complexity of generic biochemical circuits: topology versus strength of interactions.

PubMed

Tikhonov, Mikhail; Bialek, William

2016-12-06

The historical focus on network topology as a determinant of biological function is still largely maintained today, illustrated by the rise of structure-only approaches to network analysis. However, biochemical circuits and genetic regulatory networks are defined both by their topology and by a multitude of continuously adjustable parameters, such as the strength of interactions between nodes, also recognized as important. Here we present a class of simple perceptron-based Boolean models within which comparing the relative importance of topology versus interaction strengths becomes a quantitatively well-posed problem. We quantify the intuition that for generic networks, optimization of interaction strengths is a crucial ingredient of achieving high complexity, defined here as the number of fixed points the network can accommodate. We propose a new methodology for characterizing the relative role of parameter optimization for topologies of a given class.

A Multi-modal, Discriminative and Spatially Invariant CNN for RGB-D Object Labeling.

PubMed

Asif, Umar; Bennamoun, Mohammed; Sohel, Ferdous

2017-08-30

While deep convolutional neural networks have shown a remarkable success in image classification, the problems of inter-class similarities, intra-class variances, the effective combination of multimodal data, and the spatial variability in images of objects remain to be major challenges. To address these problems, this paper proposes a novel framework to learn a discriminative and spatially invariant classification model for object and indoor scene recognition using multimodal RGB-D imagery. This is achieved through three postulates: 1) spatial invariance - this is achieved by combining a spatial transformer network with a deep convolutional neural network to learn features which are invariant to spatial translations, rotations, and scale changes, 2) high discriminative capability - this is achieved by introducing Fisher encoding within the CNN architecture to learn features which have small inter-class similarities and large intra-class compactness, and 3) multimodal hierarchical fusion - this is achieved through the regularization of semantic segmentation to a multi-modal CNN architecture, where class probabilities are estimated at different hierarchical levels (i.e., imageand pixel-levels), and fused into a Conditional Random Field (CRF)- based inference hypothesis, the optimization of which produces consistent class labels in RGB-D images. Extensive experimental evaluations on RGB-D object and scene datasets, and live video streams (acquired from Kinect) show that our framework produces superior object and scene classification results compared to the state-of-the-art methods.

Airplane detection in remote sensing images using convolutional neural networks

NASA Astrophysics Data System (ADS)

Ouyang, Chao; Chen, Zhong; Zhang, Feng; Zhang, Yifei

2018-03-01

Airplane detection in remote sensing images remains a challenging problem and has also been taking a great interest to researchers. In this paper we propose an effective method to detect airplanes in remote sensing images using convolutional neural networks. Deep learning methods show greater advantages than the traditional methods with the rise of deep neural networks in target detection, and we give an explanation why this happens. To improve the performance on detection of airplane, we combine a region proposal algorithm with convolutional neural networks. And in the training phase, we divide the background into multi classes rather than one class, which can reduce false alarms. Our experimental results show that the proposed method is effective and robust in detecting airplane.

CNN-BLPred: a Convolutional neural network based predictor for β-Lactamases (BL) and their classes.

PubMed

White, Clarence; Ismail, Hamid D; Saigo, Hiroto; Kc, Dukka B

2017-12-28

The β-Lactamase (BL) enzyme family is an important class of enzymes that plays a key role in bacterial resistance to antibiotics. As the newly identified number of BL enzymes is increasing daily, it is imperative to develop a computational tool to classify the newly identified BL enzymes into one of its classes. There are two types of classification of BL enzymes: Molecular Classification and Functional Classification. Existing computational methods only address Molecular Classification and the performance of these existing methods is unsatisfactory. We addressed the unsatisfactory performance of the existing methods by implementing a Deep Learning approach called Convolutional Neural Network (CNN). We developed CNN-BLPred, an approach for the classification of BL proteins. The CNN-BLPred uses Gradient Boosted Feature Selection (GBFS) in order to select the ideal feature set for each BL classification. Based on the rigorous benchmarking of CCN-BLPred using both leave-one-out cross-validation and independent test sets, CCN-BLPred performed better than the other existing algorithms. Compared with other architectures of CNN, Recurrent Neural Network, and Random Forest, the simple CNN architecture with only one convolutional layer performs the best. After feature extraction, we were able to remove ~95% of the 10,912 features using Gradient Boosted Trees. During 10-fold cross validation, we increased the accuracy of the classic BL predictions by 7%. We also increased the accuracy of Class A, Class B, Class C, and Class D performance by an average of 25.64%. The independent test results followed a similar trend. We implemented a deep learning algorithm known as Convolutional Neural Network (CNN) to develop a classifier for BL classification. Combined with feature selection on an exhaustive feature set and using balancing method such as Random Oversampling (ROS), Random Undersampling (RUS) and Synthetic Minority Oversampling Technique (SMOTE), CNN-BLPred performs significantly better than existing algorithms for BL classification.

A QoS scheme for a congestion core network based on dissimilar QoS structures in smart-phone environments.

PubMed

Hong, Sung-Ryong; Na, Wonshik; Kang, Jang-Mook

2010-01-01

This study suggests an approach to effective transmission of multimedia content in a rapidly changing Internet environment including smart-phones. Guaranteeing QoS in networks is currently an important research topic. When transmitting Assured Forwarding (AF) packets in a Multi-DiffServ network environment, network A may assign priority in an order AF1, AF2, AF3 and AF4; on the other hand, network B may reverse the order to a priority AF4, AF3, AF2 and AF1. In this case, the AF1 packets that received the best quality of service in network A will receive the lowest in network B, which may result in dropping of packets in network B and vice versa. This study suggests a way to guarantee QoS between hosts by minimizing the loss of AF packet class when one network transmits AF class packets to another network with differing principles. It is expected that QoS guarantees and their experimental value may be utilized as principles which can be applied to various mobile-web environments based on smart-phones.

A QoS Scheme for a Congestion Core Network Based on Dissimilar QoS Structures in Smart-Phone Environments

PubMed Central

Hong, Sung-Ryong; Na, Wonshik; Kang, Jang-Mook

2010-01-01

This study suggests an approach to effective transmission of multimedia content in a rapidly changing Internet environment including smart-phones. Guaranteeing QoS in networks is currently an important research topic. When transmitting Assured Forwarding (AF) packets in a Multi-DiffServ network environment, network A may assign priority in an order AF1, AF2, AF3 and AF4; on the other hand, network B may reverse the order to a priority AF4, AF3, AF2 and AF1. In this case, the AF1 packets that received the best quality of service in network A will receive the lowest in network B, which may result in dropping of packets in network B and vice versa. This study suggests a way to guarantee QoS between hosts by minimizing the loss of AF packet class when one network transmits AF class packets to another network with differing principles. It is expected that QoS guarantees and their experimental value may be utilized as principles which can be applied to various mobile-web environments based on smart-phones. PMID:22163453

Asynchronous networks: modularization of dynamics theorem

NASA Astrophysics Data System (ADS)

Bick, Christian; Field, Michael

2017-02-01

Building on the first part of this paper, we develop the theory of functional asynchronous networks. We show that a large class of functional asynchronous networks can be (uniquely) represented as feedforward networks connecting events or dynamical modules. For these networks we can give a complete description of the network function in terms of the function of the events comprising the network: the modularization of dynamics theorem. We give examples to illustrate the main results.

Quality of service routing in the differentiated services framework

NASA Astrophysics Data System (ADS)

Oliveira, Marilia C.; Melo, Bruno; Quadros, Goncalo; Monteiro, Edmundo

2001-02-01

In this paper we present a quality of service routing strategy for network where traffic differentiation follows the class-based paradigm, as in the Differentiated Services framework. This routing strategy is based on a metric of quality of service. This metric represents the impact that delay and losses verified at each router in the network have in application performance. Based on this metric, it is selected a path for each class according to the class sensitivity to delay and losses. The distribution of the metric is triggered by a relative criterion with two thresholds, and the values advertised are the moving average of the last values measured.

The Effect of Teachers' Social Networks on Teaching Practices and Class Composition

ERIC Educational Resources Information Center

Kim, Chong Min

2011-01-01

Central to this dissertation was an examination of the role teachers' social networks play in schools as living organizations through three studies. The first study investigated the impact of teachers' social networks on teaching practices. Recent evidence suggests that teachers' social networks have a significant effect on teachers' norms,…

Finding a Place To Stand: Negotiating the Spatial Configuration of the Networked Computer Classroom.

ERIC Educational Resources Information Center

Kent-Drury, Roxanne

1998-01-01

Theorizes the spatial dynamics of both traditional and Internet-networked classrooms to reveal that both exhibit indeterminate spatial characteristics, but that network connectivity renders this indeterminacy visible. Argues that networked classrooms need not be disorienting, if students recreate a center by designing a class Web site, creating…

Conservative parallel simulation of priority class queueing networks

NASA Technical Reports Server (NTRS)

Nicol, David

1992-01-01

A conservative synchronization protocol is described for the parallel simulation of queueing networks having C job priority classes, where a job's class is fixed. This problem has long vexed designers of conservative synchronization protocols because of its seemingly poor ability to compute lookahead: the time of the next departure. For, a job in service having low priority can be preempted at any time by an arrival having higher priority and an arbitrarily small service time. The solution is to skew the event generation activity so that the events for higher priority jobs are generated farther ahead in simulated time than lower priority jobs. Thus, when a lower priority job enters service for the first time, all the higher priority jobs that may preempt it are already known and the job's departure time can be exactly predicted. Finally, the protocol was analyzed and it was demonstrated that good performance can be expected on the simulation of large queueing networks.

Conservative parallel simulation of priority class queueing networks

NASA Technical Reports Server (NTRS)

Nicol, David M.

1990-01-01

A conservative synchronization protocol is described for the parallel simulation of queueing networks having C job priority classes, where a job's class is fixed. This problem has long vexed designers of conservative synchronization protocols because of its seemingly poor ability to compute lookahead: the time of the next departure. For, a job in service having low priority can be preempted at any time by an arrival having higher priority and an arbitrarily small service time. The solution is to skew the event generation activity so that the events for higher priority jobs are generated farther ahead in simulated time than lower priority jobs. Thus, when a lower priority job enters service for the first time, all the higher priority jobs that may preempt it are already known and the job's departure time can be exactly predicted. Finally, the protocol was analyzed and it was demonstrated that good performance can be expected on the simulation of large queueing networks.

Dilemma solving by the coevolution of networks and strategy in a 2 x 2 game.

PubMed

Tanimoto, Jun

2007-08-01

A 2 x 2 game model implemented by a coevolution mechanism of both networks and strategy, inspired by the work of Zimmermann and Eguiluz [Phys. Rev. E72, 056118 (2005)] is established. Network adaptation is the manner in which an existing link between two agents is destroyed and how a new one is established to replace it. The strategy is defined as whether an agent offers cooperation (C) or defection (D) . Both the networks and strategy are synchronously renovated in a simulation time step. A series of numerical experiments, considering various 2 x 2 game structures, reveals that the proposed coevolution mechanism can solve dilemmas in several game classes. The effect of solving a dilemma means mutual-cooperation reciprocity (R reciprocity), which is brought about by emerging several cooperative hub agents who have plenty of links. This effect can be primarily observed in game classes of the prisoner's dilemma and stag hunt. The coevolution mechanism, however, seems counterproductive for game classes of leader and hero, where the alternating reciprocity (ST reciprocity) is meaningful.

Identifiability of tree-child phylogenetic networks under a probabilistic recombination-mutation model of evolution.

PubMed

Francis, Andrew; Moulton, Vincent

2018-06-07

Phylogenetic networks are an extension of phylogenetic trees which are used to represent evolutionary histories in which reticulation events (such as recombination and hybridization) have occurred. A central question for such networks is that of identifiability, which essentially asks under what circumstances can we reliably identify the phylogenetic network that gave rise to the observed data? Recently, identifiability results have appeared for networks relative to a model of sequence evolution that generalizes the standard Markov models used for phylogenetic trees. However, these results are quite limited in terms of the complexity of the networks that are considered. In this paper, by introducing an alternative probabilistic model for evolution along a network that is based on some ground-breaking work by Thatte for pedigrees, we are able to obtain an identifiability result for a much larger class of phylogenetic networks (essentially the class of so-called tree-child networks). To prove our main theorem, we derive some new results for identifying tree-child networks combinatorially, and then adapt some techniques developed by Thatte for pedigrees to show that our combinatorial results imply identifiability in the probabilistic setting. We hope that the introduction of our new model for networks could lead to new approaches to reliably construct phylogenetic networks. Copyright © 2018 Elsevier Ltd. All rights reserved.

Connectomic constraints on computation in feedforward networks of spiking neurons.

PubMed

Ramaswamy, Venkatakrishnan; Banerjee, Arunava

2014-10-01

Several efforts are currently underway to decipher the connectome or parts thereof in a variety of organisms. Ascertaining the detailed physiological properties of all the neurons in these connectomes, however, is out of the scope of such projects. It is therefore unclear to what extent knowledge of the connectome alone will advance a mechanistic understanding of computation occurring in these neural circuits, especially when the high-level function of the said circuit is unknown. We consider, here, the question of how the wiring diagram of neurons imposes constraints on what neural circuits can compute, when we cannot assume detailed information on the physiological response properties of the neurons. We call such constraints-that arise by virtue of the connectome-connectomic constraints on computation. For feedforward networks equipped with neurons that obey a deterministic spiking neuron model which satisfies a small number of properties, we ask if just by knowing the architecture of a network, we can rule out computations that it could be doing, no matter what response properties each of its neurons may have. We show results of this form, for certain classes of network architectures. On the other hand, we also prove that with the limited set of properties assumed for our model neurons, there are fundamental limits to the constraints imposed by network structure. Thus, our theory suggests that while connectomic constraints might restrict the computational ability of certain classes of network architectures, we may require more elaborate information on the properties of neurons in the network, before we can discern such results for other classes of networks.

Genetic adaptation of the antibacterial human innate immunity network.

PubMed

Casals, Ferran; Sikora, Martin; Laayouni, Hafid; Montanucci, Ludovica; Muntasell, Aura; Lazarus, Ross; Calafell, Francesc; Awadalla, Philip; Netea, Mihai G; Bertranpetit, Jaume

2011-07-11

Pathogens have represented an important selective force during the adaptation of modern human populations to changing social and other environmental conditions. The evolution of the immune system has therefore been influenced by these pressures. Genomic scans have revealed that immune system is one of the functions enriched with genes under adaptive selection. Here, we describe how the innate immune system has responded to these challenges, through the analysis of resequencing data for 132 innate immunity genes in two human populations. Results are interpreted in the context of the functional and interaction networks defined by these genes. Nucleotide diversity is lower in the adaptors and modulators functional classes, and is negatively correlated with the centrality of the proteins within the interaction network. We also produced a list of candidate genes under positive or balancing selection in each population detected by neutrality tests and showed that some functional classes are preferential targets for selection. We found evidence that the role of each gene in the network conditions the capacity to evolve or their evolvability: genes at the core of the network are more constrained, while adaptation mostly occurred at particular positions at the network edges. Interestingly, the functional classes containing most of the genes with signatures of balancing selection are involved in autoinflammatory and autoimmune diseases, suggesting a counterbalance between the beneficial and deleterious effects of the immune response.

Genetic adaptation of the antibacterial human innate immunity network

PubMed Central

2011-01-01

Background Pathogens have represented an important selective force during the adaptation of modern human populations to changing social and other environmental conditions. The evolution of the immune system has therefore been influenced by these pressures. Genomic scans have revealed that immune system is one of the functions enriched with genes under adaptive selection. Results Here, we describe how the innate immune system has responded to these challenges, through the analysis of resequencing data for 132 innate immunity genes in two human populations. Results are interpreted in the context of the functional and interaction networks defined by these genes. Nucleotide diversity is lower in the adaptors and modulators functional classes, and is negatively correlated with the centrality of the proteins within the interaction network. We also produced a list of candidate genes under positive or balancing selection in each population detected by neutrality tests and showed that some functional classes are preferential targets for selection. Conclusions We found evidence that the role of each gene in the network conditions the capacity to evolve or their evolvability: genes at the core of the network are more constrained, while adaptation mostly occurred at particular positions at the network edges. Interestingly, the functional classes containing most of the genes with signatures of balancing selection are involved in autoinflammatory and autoimmune diseases, suggesting a counterbalance between the beneficial and deleterious effects of the immune response. PMID:21745391

Automatic target recognition using a feature-based optical neural network

NASA Technical Reports Server (NTRS)

Chao, Tien-Hsin

1992-01-01

An optical neural network based upon the Neocognitron paradigm (K. Fukushima et al. 1983) is introduced. A novel aspect of the architectural design is shift-invariant multichannel Fourier optical correlation within each processing layer. Multilayer processing is achieved by iteratively feeding back the output of the feature correlator to the input spatial light modulator and updating the Fourier filters. By training the neural net with characteristic features extracted from the target images, successful pattern recognition with intra-class fault tolerance and inter-class discrimination is achieved. A detailed system description is provided. Experimental demonstration of a two-layer neural network for space objects discrimination is also presented.

Analysis and synthesis of distributed-lumped-active networks by digital computer

NASA Technical Reports Server (NTRS)

1973-01-01

The use of digital computational techniques in the analysis and synthesis of DLA (distributed lumped active) networks is considered. This class of networks consists of three distinct types of elements, namely, distributed elements (modeled by partial differential equations), lumped elements (modeled by algebraic relations and ordinary differential equations), and active elements (modeled by algebraic relations). Such a characterization is applicable to a broad class of circuits, especially including those usually referred to as linear integrated circuits, since the fabrication techniques for such circuits readily produce elements which may be modeled as distributed, as well as the more conventional lumped and active ones.

Dynamics of Opinion Forming in Structurally Balanced Social Networks

PubMed Central

Altafini, Claudio

2012-01-01

A structurally balanced social network is a social community that splits into two antagonistic factions (typical example being a two-party political system). The process of opinion forming on such a community is most often highly predictable, with polarized opinions reflecting the bipartition of the network. The aim of this paper is to suggest a class of dynamical systems, called monotone systems, as natural models for the dynamics of opinion forming on structurally balanced social networks. The high predictability of the outcome of a decision process is explained in terms of the order-preserving character of the solutions of this class of dynamical systems. If we represent a social network as a signed graph in which individuals are the nodes and the signs of the edges represent friendly or hostile relationships, then the property of structural balance corresponds to the social community being splittable into two antagonistic factions, each containing only friends. PMID:22761667

A reduction for spiking integrate-and-fire network dynamics ranging from homogeneity to synchrony.

PubMed

Zhang, J W; Rangan, A V

2015-04-01

In this paper we provide a general methodology for systematically reducing the dynamics of a class of integrate-and-fire networks down to an augmented 4-dimensional system of ordinary-differential-equations. The class of integrate-and-fire networks we focus on are homogeneously-structured, strongly coupled, and fluctuation-driven. Our reduction succeeds where most current firing-rate and population-dynamics models fail because we account for the emergence of 'multiple-firing-events' involving the semi-synchronous firing of many neurons. These multiple-firing-events are largely responsible for the fluctuations generated by the network and, as a result, our reduction faithfully describes many dynamic regimes ranging from homogeneous to synchronous. Our reduction is based on first principles, and provides an analyzable link between the integrate-and-fire network parameters and the relatively low-dimensional dynamics underlying the 4-dimensional augmented ODE.

Majority-Vote Model on Opinion-Dependent Network

NASA Astrophysics Data System (ADS)

Lima, F. W. S.

2013-09-01

We study a nonequilibrium model with up-down symmetry and a noise parameter q known as majority-vote model (MVM) of Oliveira 1992 on opinion-dependent network or Stauffer-Hohnisch-Pittnauer (SHP) networks. By Monte Carlo (MC) simulations and finite-size scaling relations the critical exponents β/ν, γ/ν and 1/ν and points qc and U* are obtained. After extensive simulations, we obtain β/ν = 0.230(3), γ/ν = 0.535(2) and 1/ν = 0.475(8). The calculated values of the critical noise parameter and Binder cumulant are qc = 0.166(3) and U* = 0.288(3). Within the error bars, the exponents obey the relation 2β/ν + γ/ν = 1 and the results presented here demonstrate that the MVM belongs to a different universality class than the equilibrium Ising model on SHP networks, but to the same class as majority-vote models on some other networks.

The topological requirements for robust perfect adaptation in networks of any size.

PubMed

Araujo, Robyn P; Liotta, Lance A

2018-05-01

Robustness, and the ability to function and thrive amid changing and unfavorable environments, is a fundamental requirement for living systems. Until now it has been an open question how large and complex biological networks can exhibit robust behaviors, such as perfect adaptation to a variable stimulus, since complexity is generally associated with fragility. Here we report that all networks that exhibit robust perfect adaptation (RPA) to a persistent change in stimulus are decomposable into well-defined modules, of which there exist two distinct classes. These two modular classes represent a topological basis for all RPA-capable networks, and generate the full set of topological realizations of the internal model principle for RPA in complex, self-organizing, evolvable bionetworks. This unexpected result supports the notion that evolutionary processes are empowered by simple and scalable modular design principles that promote robust performance no matter how large or complex the underlying networks become.

Two classes of bipartite networks: nested biological and social systems.

PubMed

Burgos, Enrique; Ceva, Horacio; Hernández, Laura; Perazzo, R P J; Devoto, Mariano; Medan, Diego

2008-10-01

Bipartite graphs have received some attention in the study of social networks and of biological mutualistic systems. A generalization of a previous model is presented, that evolves the topology of the graph in order to optimally account for a given contact preference rule between the two guilds of the network. As a result, social and biological graphs are classified as belonging to two clearly different classes. Projected graphs, linking the agents of only one guild, are obtained from the original bipartite graph. The corresponding evolution of its statistical properties is also studied. An example of a biological mutualistic network is analyzed in detail, and it is found that the model provides a very good fitting of all the main statistical features. The model also provides a proper qualitative description of the same features observed in social webs, suggesting the possible reasons underlying the difference in the organization of these two kinds of bipartite networks.

Inferring general relations between network characteristics from specific network ensembles.

PubMed

Cardanobile, Stefano; Pernice, Volker; Deger, Moritz; Rotter, Stefan

2012-01-01

Different network models have been suggested for the topology underlying complex interactions in natural systems. These models are aimed at replicating specific statistical features encountered in real-world networks. However, it is rarely considered to which degree the results obtained for one particular network class can be extrapolated to real-world networks. We address this issue by comparing different classical and more recently developed network models with respect to their ability to generate networks with large structural variability. In particular, we consider the statistical constraints which the respective construction scheme imposes on the generated networks. After having identified the most variable networks, we address the issue of which constraints are common to all network classes and are thus suitable candidates for being generic statistical laws of complex networks. In fact, we find that generic, not model-related dependencies between different network characteristics do exist. This makes it possible to infer global features from local ones using regression models trained on networks with high generalization power. Our results confirm and extend previous findings regarding the synchronization properties of neural networks. Our method seems especially relevant for large networks, which are difficult to map completely, like the neural networks in the brain. The structure of such large networks cannot be fully sampled with the present technology. Our approach provides a method to estimate global properties of under-sampled networks in good approximation. Finally, we demonstrate on three different data sets (C. elegans neuronal network, R. prowazekii metabolic network, and a network of synonyms extracted from Roget's Thesaurus) that real-world networks have statistical relations compatible with those obtained using regression models.

Community Wireless Networks

ERIC Educational Resources Information Center

Feld, Harold

2005-01-01

With increasing frequency, communities are seeing the arrival of a new class of noncommercial broadband providers: community wireless networks (CWNs). Utilizing the same wireless technologies that many colleges and universities have used to create wireless networks on campus, CWNs are creating broadband access for free or at costs well below…

Uni10: an open-source library for tensor network algorithms

NASA Astrophysics Data System (ADS)

Kao, Ying-Jer; Hsieh, Yun-Da; Chen, Pochung

2015-09-01

We present an object-oriented open-source library for developing tensor network algorithms written in C++ called Uni10. With Uni10, users can build a symmetric tensor from a collection of bonds, while the bonds are constructed from a list of quantum numbers associated with different quantum states. It is easy to label and permute the indices of the tensors and access a block associated with a particular quantum number. Furthermore a network class is used to describe arbitrary tensor network structure and to perform network contractions efficiently. We give an overview of the basic structure of the library and the hierarchy of the classes. We present examples of the construction of a spin-1 Heisenberg Hamiltonian and the implementation of the tensor renormalization group algorithm to illustrate the basic usage of the library. The library described here is particularly well suited to explore and fast prototype novel tensor network algorithms and to implement highly efficient codes for existing algorithms.

Neural network for nonsmooth pseudoconvex optimization with general convex constraints.

PubMed

Bian, Wei; Ma, Litao; Qin, Sitian; Xue, Xiaoping

2018-05-01

In this paper, a one-layer recurrent neural network is proposed for solving a class of nonsmooth, pseudoconvex optimization problems with general convex constraints. Based on the smoothing method, we construct a new regularization function, which does not depend on any information of the feasible region. Thanks to the special structure of the regularization function, we prove the global existence, uniqueness and "slow solution" character of the state of the proposed neural network. Moreover, the state solution of the proposed network is proved to be convergent to the feasible region in finite time and to the optimal solution set of the related optimization problem subsequently. In particular, the convergence of the state to an exact optimal solution is also considered in this paper. Numerical examples with simulation results are given to show the efficiency and good characteristics of the proposed network. In addition, some preliminary theoretical analysis and application of the proposed network for a wider class of dynamic portfolio optimization are included. Copyright © 2018 Elsevier Ltd. All rights reserved.

Finite-time robust passive control for a class of switched reaction-diffusion stochastic complex dynamical networks with coupling delays and impulsive control

NASA Astrophysics Data System (ADS)

Syed Ali, M.; Yogambigai, J.; Kwon, O. M.

2018-03-01

Finite-time boundedness and finite-time passivity for a class of switched stochastic complex dynamical networks (CDNs) with coupling delays, parameter uncertainties, reaction-diffusion term and impulsive control are studied. Novel finite-time synchronisation criteria are derived based on passivity theory. This paper proposes a CDN consisting of N linearly and diffusively coupled identical reaction- diffusion neural networks. By constructing of a suitable Lyapunov-Krasovskii's functional and utilisation of Jensen's inequality and Wirtinger's inequality, new finite-time passivity criteria for the networks are established in terms of linear matrix inequalities (LMIs), which can be checked numerically using the effective LMI toolbox in MATLAB. Finally, two interesting numerical examples are given to show the effectiveness of the theoretical results.

Class E/F switching power amplifiers

NASA Technical Reports Server (NTRS)

Hajimiri, Seyed-Ali (Inventor); Aoki, Ichiro (Inventor); Rutledge, David B. (Inventor); Kee, Scott David (Inventor)

2004-01-01

The present invention discloses a new family of switching amplifier classes called class E/F amplifiers. These amplifiers are generally characterized by their use of the zero-voltage-switching (ZVS) phase correction technique to eliminate of the loss normally associated with the inherent capacitance of the switching device as utilized in class-E amplifiers, together with a load network for improved voltage and current wave-shaping by presenting class-F.sup.-1 impedances at selected overtones and class-E impedances at the remaining overtones. The present invention discloses a several topologies and specific circuit implementations for achieving such performance.

A Neural Relevance Model for Feature Extraction from Hyperspectral Images, and Its Application in the Wavelet Domain

DTIC Science & Technology

2006-08-01

Nikolas Avouris. Evaluation of classifiers for an uneven class distribution problem. Applied Artificial Intellegence , pages 1-24, 2006. Draft manuscript...data by a hybrid artificial neural network so we may evaluate the classification capabilities of the baseline GRLVQ and our improved GRLVQI. Chapter 4...performance of GRLVQ(I), we compare the results against a baseline classification of the 23-class problem with a hybrid artificial neural network (ANN

Using Facebook to Supplement Participant Pools for Class Research Projects: Should We Like It?

ERIC Educational Resources Information Center

Sciutto, Mark J.

2015-01-01

In-class research projects are a valuable way of providing research experience for undergraduate students in psychology. This article evaluates the use of online social networks to supplement sample recruitment for in-class research projects. Specifically, this article presents a systematic analysis of seven student research projects that…

A new class of finite-time nonlinear consensus protocols for multi-agent systems

NASA Astrophysics Data System (ADS)

Zuo, Zongyu; Tie, Lin

2014-02-01

This paper is devoted to investigating the finite-time consensus problem for a multi-agent system in networks with undirected topology. A new class of global continuous time-invariant consensus protocols is constructed for each single-integrator agent dynamics with the aid of Lyapunov functions. In particular, it is shown that the settling time of the proposed new class of finite-time consensus protocols is upper bounded for arbitrary initial conditions. This makes it possible for network consensus problems that the convergence time is designed and estimated offline for a given undirected information flow and a group volume of agents. Finally, a numerical simulation example is presented as a proof of concept.

Classification of mineral deposits into types using mineralogy with a probabilistic neural network

USGS Publications Warehouse

Singer, Donald A.; Kouda, Ryoichi

1997-01-01

In order to determine whether it is desirable to quantify mineral-deposit models further, a test of the ability of a probabilistic neural network to classify deposits into types based on mineralogy was conducted. Presence or absence of ore and alteration mineralogy in well-typed deposits were used to train the network. To reduce the number of minerals considered, the analyzed data were restricted to minerals present in at least 20% of at least one deposit type. An advantage of this restriction is that single or rare occurrences of minerals did not dominate the results. Probabilistic neural networks can provide mathematically sound confidence measures based on Bayes theorem and are relatively insensitive to outliers. Founded on Parzen density estimation, they require no assumptions about distributions of random variables used for classification, even handling multimodal distributions. They train quickly and work as well as, or better than, multiple-layer feedforward networks. Tests were performed with a probabilistic neural network employing a Gaussian kernel and separate sigma weights for each class and each variable. The training set was reduced to the presence or absence of 58 reported minerals in eight deposit types. The training set included: 49 Cyprus massive sulfide deposits; 200 kuroko massive sulfide deposits; 59 Comstock epithermal vein gold districts; 17 quartzalunite epithermal gold deposits; 25 Creede epithermal gold deposits; 28 sedimentary-exhalative zinc-lead deposits; 28 Sado epithermal vein gold deposits; and 100 porphyry copper deposits. The most common training problem was the error of classifying about 27% of Cyprus-type deposits in the training set as kuroko. In independent tests with deposits not used in the training set, 88% of 224 kuroko massive sulfide deposits were classed correctly, 92% of 25 porphyry copper deposits, 78% of 9 Comstock epithermal gold-silver districts, and 83% of six quartzalunite epithermal gold deposits were classed correctly. Across all deposit types, 88% of deposits in the validation dataset were correctly classed. Misclassifications were most common if a deposit was characterized by only a few minerals, e.g., pyrite, chalcopyrite,and sphalerite. The success rate jumped to 98% correctly classed deposits when just two rock types were added. Such a high success rate of the probabilistic neural network suggests that not only should this preliminary test be expanded to include other deposit types, but that other deposit features should be added.

Specifications of a Simulation Model for a Local Area Network Design in Support of Stock Point Logistics Integrated Communications Environment (SPLICE).

DTIC Science & Technology

1982-10-01

class queueing system with a preemptive -resume priority service discipline, as depicted in Figure 4.2. Concerning a SPLICLAN configuration a node can...processor can be modeled as a single resource, multi-class queueing system with a preemptive -resume priority structure as the one given in Figure 4.2. An...LOCAL AREA NETWORK DESIGN IN SUPPORT OF STOCK POINT LOGISTICS INTEGRATED COMMUNICATIONS ENVIRONMENT (SPLICE) by Ioannis Th. Mastrocostopoulos October

Network-Based Analysis of Software Change Propagation

PubMed Central

Wang, Rongcun; Qu, Binbin

2014-01-01

The object-oriented software systems frequently evolve to meet new change requirements. Understanding the characteristics of changes aids testers and system designers to improve the quality of softwares. Identifying important modules becomes a key issue in the process of evolution. In this context, a novel network-based approach is proposed to comprehensively investigate change distributions and the correlation between centrality measures and the scope of change propagation. First, software dependency networks are constructed at class level. And then, the number of times of cochanges among classes is minded from software repositories. According to the dependency relationships and the number of times of cochanges among classes, the scope of change propagation is calculated. Using Spearman rank correlation analyzes the correlation between centrality measures and the scope of change propagation. Three case studies on java open source software projects Findbugs, Hibernate, and Spring are conducted to research the characteristics of change propagation. Experimental results show that (i) change distribution is very uneven; (ii) PageRank, Degree, and CIRank are significantly correlated to the scope of change propagation. Particularly, CIRank shows higher correlation coefficient, which suggests it can be a more useful indicator for measuring the scope of change propagation of classes in object-oriented software system. PMID:24790557

Network-based analysis of software change propagation.

PubMed

Wang, Rongcun; Huang, Rubing; Qu, Binbin

2014-01-01

The object-oriented software systems frequently evolve to meet new change requirements. Understanding the characteristics of changes aids testers and system designers to improve the quality of softwares. Identifying important modules becomes a key issue in the process of evolution. In this context, a novel network-based approach is proposed to comprehensively investigate change distributions and the correlation between centrality measures and the scope of change propagation. First, software dependency networks are constructed at class level. And then, the number of times of cochanges among classes is minded from software repositories. According to the dependency relationships and the number of times of cochanges among classes, the scope of change propagation is calculated. Using Spearman rank correlation analyzes the correlation between centrality measures and the scope of change propagation. Three case studies on java open source software projects Findbugs, Hibernate, and Spring are conducted to research the characteristics of change propagation. Experimental results show that (i) change distribution is very uneven; (ii) PageRank, Degree, and CIRank are significantly correlated to the scope of change propagation. Particularly, CIRank shows higher correlation coefficient, which suggests it can be a more useful indicator for measuring the scope of change propagation of classes in object-oriented software system.

Network approaches for expert decisions in sports.

PubMed

Glöckner, Andreas; Heinen, Thomas; Johnson, Joseph G; Raab, Markus

2012-04-01

This paper focuses on a model comparison to explain choices based on gaze behavior via simulation procedures. We tested two classes of models, a parallel constraint satisfaction (PCS) artificial neuronal network model and an accumulator model in a handball decision-making task from a lab experiment. Both models predict action in an option-generation task in which options can be chosen from the perspective of a playmaker in handball (i.e., passing to another player or shooting at the goal). Model simulations are based on a dataset of generated options together with gaze behavior measurements from 74 expert handball players for 22 pieces of video footage. We implemented both classes of models as deterministic vs. probabilistic models including and excluding fitted parameters. Results indicated that both classes of models can fit and predict participants' initially generated options based on gaze behavior data, and that overall, the classes of models performed about equally well. Early fixations were thereby particularly predictive for choices. We conclude that the analyses of complex environments via network approaches can be successfully applied to the field of experts' decision making in sports and provide perspectives for further theoretical developments. Copyright © 2011 Elsevier B.V. All rights reserved.

Serotonergic Modulation Differentially Targets Distinct Network Elements within the Antennal Lobe of Drosophila melanogaster

PubMed Central

Sizemore, Tyler R.; Dacks, Andrew M.

2016-01-01

Neuromodulation confers flexibility to anatomically-restricted neural networks so that animals are able to properly respond to complex internal and external demands. However, determining the mechanisms underlying neuromodulation is challenging without knowledge of the functional class and spatial organization of neurons that express individual neuromodulatory receptors. Here, we describe the number and functional identities of neurons in the antennal lobe of Drosophila melanogaster that express each of the receptors for one such neuromodulator, serotonin (5-HT). Although 5-HT enhances odor-evoked responses of antennal lobe projection neurons (PNs) and local interneurons (LNs), the receptor basis for this enhancement is unknown. We used endogenous reporters of transcription and translation for each of the five 5-HT receptors (5-HTRs) to identify neurons, based on cell class and transmitter content, that express each receptor. We find that specific receptor types are expressed by distinct combinations of functional neuronal classes. For instance, the excitatory PNs express the excitatory 5-HTRs, while distinct classes of LNs each express different 5-HTRs. This study therefore provides a detailed atlas of 5-HT receptor expression within a well-characterized neural network, and enables future dissection of the role of serotonergic modulation of olfactory processing. PMID:27845422

Maximization of the Supportable Number of Sensors in QoS-Aware Cluster-Based Underwater Acoustic Sensor Networks

PubMed Central

Nguyen, Thi-Tham; Van Le, Duc; Yoon, Seokhoon

2014-01-01

This paper proposes a practical low-complexity MAC (medium access control) scheme for quality of service (QoS)-aware and cluster-based underwater acoustic sensor networks (UASN), in which the provision of differentiated QoS is required. In such a network, underwater sensors (U-sensor) in a cluster are divided into several classes, each of which has a different QoS requirement. The major problem considered in this paper is the maximization of the number of nodes that a cluster can accommodate while still providing the required QoS for each class in terms of the PDR (packet delivery ratio). In order to address the problem, we first estimate the packet delivery probability (PDP) and use it to formulate an optimization problem to determine the optimal value of the maximum packet retransmissions for each QoS class. The custom greedy and interior-point algorithms are used to find the optimal solutions, which are verified by extensive simulations. The simulation results show that, by solving the proposed optimization problem, the supportable number of underwater sensor nodes can be maximized while satisfying the QoS requirements for each class. PMID:24608009

Maximization of the supportable number of sensors in QoS-aware cluster-based underwater acoustic sensor networks.

PubMed

Nguyen, Thi-Tham; Le, Duc Van; Yoon, Seokhoon

2014-03-07

This paper proposes a practical low-complexity MAC (medium access control) scheme for quality of service (QoS)-aware and cluster-based underwater acoustic sensor networks (UASN), in which the provision of differentiated QoS is required. In such a network, underwater sensors (U-sensor) in a cluster are divided into several classes, each of which has a different QoS requirement. The major problem considered in this paper is the maximization of the number of nodes that a cluster can accommodate while still providing the required QoS for each class in terms of the PDR (packet delivery ratio). In order to address the problem, we first estimate the packet delivery probability (PDP) and use it to formulate an optimization problem to determine the optimal value of the maximum packet retransmissions for each QoS class. The custom greedy and interior-point algorithms are used to find the optimal solutions, which are verified by extensive simulations. The simulation results show that, by solving the proposed optimization problem, the supportable number of underwater sensor nodes can be maximized while satisfying the QoS requirements for each class.

INTERDISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY: Synchronization in Complex Networks with Multiple Connections

NASA Astrophysics Data System (ADS)

Wu, Qing-Chu; Fu, Xin-Chu; Sun, Wei-Gang

2010-01-01

In this paper a class of networks with multiple connections are discussed. The multiple connections include two different types of links between nodes in complex networks. For this new model, we give a simple generating procedure. Furthermore, we investigate dynamical synchronization behavior in a delayed two-layer network, giving corresponding theoretical analysis and numerical examples.

Globally fixed-time synchronization of coupled neutral-type neural network with mixed time-varying delays.

PubMed

Zheng, Mingwen; Li, Lixiang; Peng, Haipeng; Xiao, Jinghua; Yang, Yixian; Zhang, Yanping; Zhao, Hui

2018-01-01

This paper mainly studies the globally fixed-time synchronization of a class of coupled neutral-type neural networks with mixed time-varying delays via discontinuous feedback controllers. Compared with the traditional neutral-type neural network model, the model in this paper is more general. A class of general discontinuous feedback controllers are designed. With the help of the definition of fixed-time synchronization, the upper right-hand derivative and a defined simple Lyapunov function, some easily verifiable and extensible synchronization criteria are derived to guarantee the fixed-time synchronization between the drive and response systems. Finally, two numerical simulations are given to verify the correctness of the results.

Globally fixed-time synchronization of coupled neutral-type neural network with mixed time-varying delays

PubMed Central

2018-01-01

This paper mainly studies the globally fixed-time synchronization of a class of coupled neutral-type neural networks with mixed time-varying delays via discontinuous feedback controllers. Compared with the traditional neutral-type neural network model, the model in this paper is more general. A class of general discontinuous feedback controllers are designed. With the help of the definition of fixed-time synchronization, the upper right-hand derivative and a defined simple Lyapunov function, some easily verifiable and extensible synchronization criteria are derived to guarantee the fixed-time synchronization between the drive and response systems. Finally, two numerical simulations are given to verify the correctness of the results. PMID:29370248

Power Allocation Based on Data Classification in Wireless Sensor Networks

PubMed Central

Wang, Houlian; Zhou, Gongbo

2017-01-01

Limited node energy in wireless sensor networks is a crucial factor which affects the monitoring of equipment operation and working conditions in coal mines. In addition, due to heterogeneous nodes and different data acquisition rates, the number of arriving packets in a queue network can differ, which may lead to some queue lengths reaching the maximum value earlier compared with others. In order to tackle these two problems, an optimal power allocation strategy based on classified data is proposed in this paper. Arriving data is classified into dissimilar classes depending on the number of arriving packets. The problem is formulated as a Lyapunov drift optimization with the objective of minimizing the weight sum of average power consumption and average data class. As a result, a suboptimal distributed algorithm without any knowledge of system statistics is presented. The simulations, conducted in the perfect channel state information (CSI) case and the imperfect CSI case, reveal that the utility can be pushed arbitrarily close to optimal by increasing the parameter V, but with a corresponding growth in the average delay, and that other tunable parameters W and the classification method in the interior of utility function can trade power optimality for increased average data class. The above results show that data in a high class has priorities to be processed than data in a low class, and energy consumption can be minimized in this resource allocation strategy. PMID:28498346

CMPF: class-switching minimized pathfinding in metabolic networks.

PubMed

Lim, Kevin; Wong, Limsoon

2012-01-01

The metabolic network is an aggregation of enzyme catalyzed reactions that converts one compound to another. Paths in a metabolic network are a sequence of enzymes that describe how a chemical compound of interest can be produced in a biological system. As the number of such paths is quite large, many methods have been developed to score paths so that the k-shortest paths represent the set of paths that are biologically meaningful or efficient. However, these approaches do not consider whether the sequence of enzymes can be manufactured in the same pathway/species/localization. As a result, a predicted sequence might consist of groups of enzymes that operate in distinct pathway/species/localization and may not truly reflect the events occurring within cell. We propose a path weighting method CMPF (Class-switching Minimized Pathfinder) to search for routes in a metabolic network which minimizes pathway switching. In biological terms, a pathway is a series of chemical reactions which define a specific function (e.g. glycolysis). We conjecture that routes that cross many pathways are inefficient since different pathways define different metabolic functions. In addition, native routes are also well characterized within pathways, suggesting that reasonable paths should not involve too many pathway switches. Our method can be generalized when reactions participate in a class set (e.g., pathways, species or cellular localization) so that the paths predicted have minimal class crossings. We show that our method generates k-paths that involve the least number of class switching. In addition, we also show that native paths are recoverable and alternative paths deviates less from native paths compared to other methods. This suggests that paths ranked by our method could be a way to predict paths that are likely to occur in biological systems.

Network-centric decision architecture for financial or 1/f data models

NASA Astrophysics Data System (ADS)

Jaenisch, Holger M.; Handley, James W.; Massey, Stoney; Case, Carl T.; Songy, Claude G.

2002-12-01

This paper presents a decision architecture algorithm for training neural equation based networks to make autonomous multi-goal oriented, multi-class decisions. These architectures make decisions based on their individual goals and draw from the same network centric feature set. Traditionally, these architectures are comprised of neural networks that offer marginal performance due to lack of convergence of the training set. We present an approach for autonomously extracting sample points as I/O exemplars for generation of multi-branch, multi-node decision architectures populated by adaptively derived neural equations. To test the robustness of this architecture, open source data sets in the form of financial time series were used, requiring a three-class decision space analogous to the lethal, non-lethal, and clutter discrimination problem. This algorithm and the results of its application are presented here.

Economic viability of access broadband multiservice networks

NASA Astrophysics Data System (ADS)

Castelli, Francesco; Dammicco, Giacinto; Mocci, Ugo

1995-02-01

In this paper the economic viability of alternative architectures for optical access networks providing broad band services to different subscriber classes in a metropolitan environment, is investigated by a specific tool, NEVE (Network Economic Viability Evaluator), developed for broad band multiservice network planning, service evolutionary scenarios assessment, evaluation of tariff strategies and other actions taken at stimulating the demand growth. As the viability target can be achieved in different ways, different studies can be carried out by NEVE. In the paper some of them are discussed, particularly the ones addressed: to evaluate the impact on viability of alternative service scenarios; to determine the critical mass of broad band subscribers and the critical joint service adoption cost; to evaluate cross subsidiary policies among different subscriber classes and services; to perform sensitivity analysis with respect to variations of demand parameters and tariffs.

Evolution of Cooperation in Social Dilemmas on Complex Networks

PubMed Central

Iyer, Swami; Killingback, Timothy

2016-01-01

Cooperation in social dilemmas is essential for the functioning of systems at multiple levels of complexity, from the simplest biological organisms to the most sophisticated human societies. Cooperation, although widespread, is fundamentally challenging to explain evolutionarily, since natural selection typically favors selfish behavior which is not socially optimal. Here we study the evolution of cooperation in three exemplars of key social dilemmas, representing the prisoner’s dilemma, hawk-dove and coordination classes of games, in structured populations defined by complex networks. Using individual-based simulations of the games on model and empirical networks, we give a detailed comparative study of the effects of the structural properties of a network, such as its average degree, variance in degree distribution, clustering coefficient, and assortativity coefficient, on the promotion of cooperative behavior in all three classes of games. PMID:26928428

A high-efficiency low-voltage class-E PA for IoT applications in sub-1 GHz frequency range

NASA Astrophysics Data System (ADS)

Zhou, Chenyi; Lu, Zhenghao; Gu, Jiangmin; Yu, Xiaopeng

2017-10-01

We present and propose a complete and iterative integrated-circuit and electro-magnetic (EM) co-design methodology and procedure for a low-voltage sub-1 GHz class-E PA. The presented class-E PA consists of the on-chip power transistor, the on-chip gate driving circuits, the off-chip tunable LC load network and the off-chip LC ladder low pass filter. The design methodology includes an explicit design equation based circuit components values' analysis and numerical derivation, output power targeted transistor size and low pass filter design, and power efficiency oriented design optimization. The proposed design procedure includes the power efficiency oriented LC network tuning, the detailed circuit/EM co-simulation plan on integrated circuit level, package level and PCB level to ensure an accurate simulation to measurement match and first pass design success. The proposed PA is targeted to achieve more than 15 dBm output power delivery and 40% power efficiency at 433 MHz frequency band with 1.5 V low voltage supply. The LC load network is designed to be off-chip for the purpose of easy tuning and optimization. The same circuit can be extended to all sub-1 GHz applications with the same tuning and optimization on the load network at different frequencies. The amplifier is implemented in 0.13 μm CMOS technology with a core area occupation of 400 μm by 300 μm. Measurement results showed that it provided power delivery of 16.42 dBm at antenna with efficiency of 40.6%. A harmonics suppression of 44 dBc is achieved, making it suitable for massive deployment of IoT devices. Project supported by the National Natural Science Foundation of China (No. 61574125) and the Industry Innovation Project of Suzhou City of China (No. SYG201641).

Analysis of QoS Requirements for e-Health Services and Mapping to Evolved Packet System QoS Classes

PubMed Central

Skorin-Kapov, Lea; Matijasevic, Maja

2010-01-01

E-Health services comprise a broad range of healthcare services delivered by using information and communication technology. In order to support existing as well as emerging e-Health services over converged next generation network (NGN) architectures, there is a need for network QoS control mechanisms that meet the often stringent requirements of such services. In this paper, we evaluate the QoS support for e-Health services in the context of the Evolved Packet System (EPS), specified by the Third Generation Partnership Project (3GPP) as a multi-access all-IP NGN. We classify heterogeneous e-Health services based on context and network QoS requirements and propose a mapping to existing 3GPP QoS Class Identifiers (QCIs) that serve as a basis for the class-based QoS concept of the EPS. The proposed mapping aims to provide network operators with guidelines for meeting heterogeneous e-Health service requirements. As an example, we present the QoS requirements for a prototype e-Health service supporting tele-consultation between a patient and a doctor and illustrate the use of the proposed mapping to QCIs in standardized QoS control procedures. PMID:20976301

Convolutional Neural Network for Histopathological Analysis of Osteosarcoma.

PubMed

Mishra, Rashika; Daescu, Ovidiu; Leavey, Patrick; Rakheja, Dinesh; Sengupta, Anita

2018-03-01

Pathologists often deal with high complexity and sometimes disagreement over osteosarcoma tumor classification due to cellular heterogeneity in the dataset. Segmentation and classification of histology tissue in H&E stained tumor image datasets is a challenging task because of intra-class variations, inter-class similarity, crowded context, and noisy data. In recent years, deep learning approaches have led to encouraging results in breast cancer and prostate cancer analysis. In this article, we propose convolutional neural network (CNN) as a tool to improve efficiency and accuracy of osteosarcoma tumor classification into tumor classes (viable tumor, necrosis) versus nontumor. The proposed CNN architecture contains eight learned layers: three sets of stacked two convolutional layers interspersed with max pooling layers for feature extraction and two fully connected layers with data augmentation strategies to boost performance. The use of a neural network results in higher accuracy of average 92% for the classification. We compare the proposed architecture with three existing and proven CNN architectures for image classification: AlexNet, LeNet, and VGGNet. We also provide a pipeline to calculate percentage necrosis in a given whole slide image. We conclude that the use of neural networks can assure both high accuracy and efficiency in osteosarcoma classification.

47 CFR 36.311 - Network Support/General Support Expenses-Accounts 6110 and 6120 (Class B Telephone Companies...

Code of Federal Regulations, 2014 CFR

2014-10-01

... (Class A Telephone Companies). 36.311 Section 36.311 Telecommunication FEDERAL COMMUNICATIONS COMMISSION..., office equipment, and general purpose computers. (b) The expenses in these account are apportioned among...

47 CFR 36.311 - Network Support/General Support Expenses-Accounts 6110 and 6120 (Class B Telephone Companies...

Code of Federal Regulations, 2013 CFR

2013-10-01

... (Class A Telephone Companies). 36.311 Section 36.311 Telecommunication FEDERAL COMMUNICATIONS COMMISSION..., office equipment, and general purpose computers. (b) The expenses in these account are apportioned among...

47 CFR 36.311 - Network Support/General Support Expenses-Accounts 6110 and 6120 (Class B Telephone Companies...

Code of Federal Regulations, 2012 CFR

2012-10-01

... (Class A Telephone Companies). 36.311 Section 36.311 Telecommunication FEDERAL COMMUNICATIONS COMMISSION..., office equipment, and general purpose computers. (b) The expenses in these account are apportioned among...

The signaling petri net-based simulator: a non-parametric strategy for characterizing the dynamics of cell-specific signaling networks.

PubMed

Ruths, Derek; Muller, Melissa; Tseng, Jen-Te; Nakhleh, Luay; Ram, Prahlad T

2008-02-29

Reconstructing cellular signaling networks and understanding how they work are major endeavors in cell biology. The scale and complexity of these networks, however, render their analysis using experimental biology approaches alone very challenging. As a result, computational methods have been developed and combined with experimental biology approaches, producing powerful tools for the analysis of these networks. These computational methods mostly fall on either end of a spectrum of model parameterization. On one end is a class of structural network analysis methods; these typically use the network connectivity alone to generate hypotheses about global properties. On the other end is a class of dynamic network analysis methods; these use, in addition to the connectivity, kinetic parameters of the biochemical reactions to predict the network's dynamic behavior. These predictions provide detailed insights into the properties that determine aspects of the network's structure and behavior. However, the difficulty of obtaining numerical values of kinetic parameters is widely recognized to limit the applicability of this latter class of methods. Several researchers have observed that the connectivity of a network alone can provide significant insights into its dynamics. Motivated by this fundamental observation, we present the signaling Petri net, a non-parametric model of cellular signaling networks, and the signaling Petri net-based simulator, a Petri net execution strategy for characterizing the dynamics of signal flow through a signaling network using token distribution and sampling. The result is a very fast method, which can analyze large-scale networks, and provide insights into the trends of molecules' activity-levels in response to an external stimulus, based solely on the network's connectivity. We have implemented the signaling Petri net-based simulator in the PathwayOracle toolkit, which is publicly available at http://bioinfo.cs.rice.edu/pathwayoracle. Using this method, we studied a MAPK1,2 and AKT signaling network downstream from EGFR in two breast tumor cell lines. We analyzed, both experimentally and computationally, the activity level of several molecules in response to a targeted manipulation of TSC2 and mTOR-Raptor. The results from our method agreed with experimental results in greater than 90% of the cases considered, and in those where they did not agree, our approach provided valuable insights into discrepancies between known network connectivities and experimental observations.

The Signaling Petri Net-Based Simulator: A Non-Parametric Strategy for Characterizing the Dynamics of Cell-Specific Signaling Networks

PubMed Central

Ruths, Derek; Muller, Melissa; Tseng, Jen-Te; Nakhleh, Luay; Ram, Prahlad T.

2008-01-01

Reconstructing cellular signaling networks and understanding how they work are major endeavors in cell biology. The scale and complexity of these networks, however, render their analysis using experimental biology approaches alone very challenging. As a result, computational methods have been developed and combined with experimental biology approaches, producing powerful tools for the analysis of these networks. These computational methods mostly fall on either end of a spectrum of model parameterization. On one end is a class of structural network analysis methods; these typically use the network connectivity alone to generate hypotheses about global properties. On the other end is a class of dynamic network analysis methods; these use, in addition to the connectivity, kinetic parameters of the biochemical reactions to predict the network's dynamic behavior. These predictions provide detailed insights into the properties that determine aspects of the network's structure and behavior. However, the difficulty of obtaining numerical values of kinetic parameters is widely recognized to limit the applicability of this latter class of methods. Several researchers have observed that the connectivity of a network alone can provide significant insights into its dynamics. Motivated by this fundamental observation, we present the signaling Petri net, a non-parametric model of cellular signaling networks, and the signaling Petri net-based simulator, a Petri net execution strategy for characterizing the dynamics of signal flow through a signaling network using token distribution and sampling. The result is a very fast method, which can analyze large-scale networks, and provide insights into the trends of molecules' activity-levels in response to an external stimulus, based solely on the network's connectivity. We have implemented the signaling Petri net-based simulator in the PathwayOracle toolkit, which is publicly available at http://bioinfo.cs.rice.edu/pathwayoracle. Using this method, we studied a MAPK1,2 and AKT signaling network downstream from EGFR in two breast tumor cell lines. We analyzed, both experimentally and computationally, the activity level of several molecules in response to a targeted manipulation of TSC2 and mTOR-Raptor. The results from our method agreed with experimental results in greater than 90% of the cases considered, and in those where they did not agree, our approach provided valuable insights into discrepancies between known network connectivities and experimental observations. PMID:18463702

Link predication based on matrix factorization by fusion of multi class organizations of the network.

PubMed

Jiao, Pengfei; Cai, Fei; Feng, Yiding; Wang, Wenjun

2017-08-21

Link predication aims at forecasting the latent or unobserved edges in the complex networks and has a wide range of applications in reality. Almost existing methods and models only take advantage of one class organization of the networks, which always lose important information hidden in other organizations of the network. In this paper, we propose a link predication framework which makes the best of the structure of networks in different level of organizations based on nonnegative matrix factorization, which is called NMF 3 here. We first map the observed network into another space by kernel functions, which could get the different order organizations. Then we combine the adjacency matrix of the network with one of other organizations, which makes us obtain the objective function of our framework for link predication based on the nonnegative matrix factorization. Third, we derive an iterative algorithm to optimize the objective function, which converges to a local optimum, and we propose a fast optimization strategy for large networks. Lastly, we test the proposed framework based on two kernel functions on a series of real world networks under different sizes of training set, and the experimental results show the feasibility, effectiveness, and competitiveness of the proposed framework.

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.

Constraint based scheduling for the Goddard Space Flight Center distributed Active Archive Center's data archive and distribution system

NASA Technical Reports Server (NTRS)

Short, Nick, Jr.; Bedet, Jean-Jacques; Bodden, Lee; Boddy, Mark; White, Jim; Beane, John

1994-01-01

The Goddard Space Flight Center (GSFC) Distributed Active Archive Center (DAAC) has been operational since October 1, 1993. Its mission is to support the Earth Observing System (EOS) by providing rapid access to EOS data and analysis products, and to test Earth Observing System Data and Information System (EOSDIS) design concepts. One of the challenges is to ensure quick and easy retrieval of any data archived within the DAAC's Data Archive and Distributed System (DADS). Over the 15-year life of EOS project, an estimated several Petabytes (10(exp 15)) of data will be permanently stored. Accessing that amount of information is a formidable task that will require innovative approaches. As a precursor of the full EOS system, the GSFC DAAC with a few Terabits of storage, has implemented a prototype of a constraint-based task and resource scheduler to improve the performance of the DADS. This Honeywell Task and Resource Scheduler (HTRS), developed by Honeywell Technology Center in cooperation the Information Science and Technology Branch/935, the Code X Operations Technology Program, and the GSFC DAAC, makes better use of limited resources, prevents backlog of data, provides information about resources bottlenecks and performance characteristics. The prototype which is developed concurrently with the GSFC Version 0 (V0) DADS, models DADS activities such as ingestion and distribution with priority, precedence, resource requirements (disk and network bandwidth) and temporal constraints. HTRS supports schedule updates, insertions, and retrieval of task information via an Application Program Interface (API). The prototype has demonstrated with a few examples, the substantial advantages of using HTRS over scheduling algorithms such as a First In First Out (FIFO) queue. The kernel scheduling engine for HTRS, called Kronos, has been successfully applied to several other domains such as space shuttle mission scheduling, demand flow manufacturing, and avionics communications scheduling.

Comparisons of neural networks to standard techniques for image classification and correlation

NASA Technical Reports Server (NTRS)

Paola, Justin D.; Schowengerdt, Robert A.

1994-01-01

Neural network techniques for multispectral image classification and spatial pattern detection are compared to the standard techniques of maximum-likelihood classification and spatial correlation. The neural network produced a more accurate classification than maximum-likelihood of a Landsat scene of Tucson, Arizona. Some of the errors in the maximum-likelihood classification are illustrated using decision region and class probability density plots. As expected, the main drawback to the neural network method is the long time required for the training stage. The network was trained using several different hidden layer sizes to optimize both the classification accuracy and training speed, and it was found that one node per class was optimal. The performance improved when 3x3 local windows of image data were entered into the net. This modification introduces texture into the classification without explicit calculation of a texture measure. Larger windows were successfully used for the detection of spatial features in Landsat and Magellan synthetic aperture radar imagery.

Fuzzy Counter Propagation Neural Network Control for a Class of Nonlinear Dynamical Systems

PubMed Central

Sakhre, Vandana; Jain, Sanjeev; Sapkal, Vilas S.; Agarwal, Dev P.

2015-01-01

Fuzzy Counter Propagation Neural Network (FCPN) controller design is developed, for a class of nonlinear dynamical systems. In this process, the weight connecting between the instar and outstar, that is, input-hidden and hidden-output layer, respectively, is adjusted by using Fuzzy Competitive Learning (FCL). FCL paradigm adopts the principle of learning, which is used to calculate Best Matched Node (BMN) which is proposed. This strategy offers a robust control of nonlinear dynamical systems. FCPN is compared with the existing network like Dynamic Network (DN) and Back Propagation Network (BPN) on the basis of Mean Absolute Error (MAE), Mean Square Error (MSE), Best Fit Rate (BFR), and so forth. It envisages that the proposed FCPN gives better results than DN and BPN. The effectiveness of the proposed FCPN algorithms is demonstrated through simulations of four nonlinear dynamical systems and multiple input and single output (MISO) and a single input and single output (SISO) gas furnace Box-Jenkins time series data. PMID:26366169

Analysis of the error of the developed method of determination the active conductivity reducing the insulation level between one phase of the network and ground, and insulation parameters in a non-symmetric network with isolated neutral with voltage above 1000 V

NASA Astrophysics Data System (ADS)

Utegulov, B. B.

2018-02-01

In the work the study of the developed method was carried out for reliability by analyzing the error in indirect determination of the insulation parameters in an asymmetric network with an isolated neutral voltage above 1000 V. The conducted studies of the random relative mean square errors show that the accuracy of indirect measurements in the developed method can be effectively regulated not only by selecting a capacitive additional conductivity, which are connected between phases of the electrical network and the ground, but also by the selection of measuring instruments according to the accuracy class. When choosing meters with accuracy class of 0.5 with the correct selection of capacitive additional conductivity that are connected between the phases of the electrical network and the ground, the errors in measuring the insulation parameters will not exceed 10%.

Fuzzy Counter Propagation Neural Network Control for a Class of Nonlinear Dynamical Systems.

PubMed

Sakhre, Vandana; Jain, Sanjeev; Sapkal, Vilas S; Agarwal, Dev P

2015-01-01

Fuzzy Counter Propagation Neural Network (FCPN) controller design is developed, for a class of nonlinear dynamical systems. In this process, the weight connecting between the instar and outstar, that is, input-hidden and hidden-output layer, respectively, is adjusted by using Fuzzy Competitive Learning (FCL). FCL paradigm adopts the principle of learning, which is used to calculate Best Matched Node (BMN) which is proposed. This strategy offers a robust control of nonlinear dynamical systems. FCPN is compared with the existing network like Dynamic Network (DN) and Back Propagation Network (BPN) on the basis of Mean Absolute Error (MAE), Mean Square Error (MSE), Best Fit Rate (BFR), and so forth. It envisages that the proposed FCPN gives better results than DN and BPN. The effectiveness of the proposed FCPN algorithms is demonstrated through simulations of four nonlinear dynamical systems and multiple input and single output (MISO) and a single input and single output (SISO) gas furnace Box-Jenkins time series data.

Knowledge Wisdom and Networks: A Project Management Centre of Excellence Example

ERIC Educational Resources Information Center

Walker, Derek H. T.; Christenson, Dale

2005-01-01

Purpose: This conceptual paper aims to explain how "project management centres of excellence (CoEs)", a particular class of knowledge network, can be viewed as providing great potential for assisting project management (PM) teams to make wise decisions. Design/methodology/approach: The paper presents a range of knowledge network types and…

Class Size, School Size and the Size of the School Network

ERIC Educational Resources Information Center

Coupé, Tom; Olefir, Anna; Alonso, Juan Diego

2016-01-01

In many transition countries, including Ukraine, decreases in population and fertility have led to substantial falls in the number of school-aged children. As a consequence, these countries now have school networks that consist of many small schools, leading many countries to consider reorganizing their networks by closing smaller schools and…

Structural Reproduction of Social Networks in Computer-Mediated Communication Forums

ERIC Educational Resources Information Center

Stefanone, M. A.; Gay, G.

2008-01-01

This study explores the relationship between the structure of an existing social network and the structure of an emergent discussion-board network in an undergraduate university class. Thirty-one students were issued with laptop computers that remained in their possession for the duration of the semester. While using these machines, participants'…

Predicting Item Difficulty in a Reading Comprehension Test with an Artificial Neural Network.

ERIC Educational Resources Information Center

Perkins, Kyle; And Others

1995-01-01

This article reports the results of using a three-layer back propagation artificial neural network to predict item difficulty in a reading comprehension test. Three classes of variables were examined: text structure, propositional analysis, and cognitive demand. Results demonstrate that the networks can consistently predict item difficulty. (JL)

Analytic Networks in Music Task Definition.

ERIC Educational Resources Information Center

Piper, Richard M.

For a student to acquire the conceptual systems of a discipline, the designer must reflect that structure or analytic network in his curriculum. The four networks identified for music and used in the development of the Southwest Regional Laboratory (SWRL) Music Program are the variable-value, the whole-part, the process-stage, and the class-member…

A Model for Field Deployment of Wireless Sensor Networks (WSNs) within the Domain of Microclimate Habitat Monitoring

ERIC Educational Resources Information Center

Sanborn, Mark

2011-01-01

Wireless sensor networks (WSNs) represent a class of miniaturized information systems designed to monitor physical environments. These smart monitoring systems form collaborative networks utilizing autonomous sensing, data-collection, and processing to provide real-time analytics of observed environments. As a fundamental research area in…

On Tree-Based Phylogenetic Networks.

PubMed

Zhang, Louxin

2016-07-01

A large class of phylogenetic networks can be obtained from trees by the addition of horizontal edges between the tree edges. These networks are called tree-based networks. We present a simple necessary and sufficient condition for tree-based networks and prove that a universal tree-based network exists for any number of taxa that contains as its base every phylogenetic tree on the same set of taxa. This answers two problems posted by Francis and Steel recently. A byproduct is a computer program for generating random binary phylogenetic networks under the uniform distribution model.

A Bell inequality for a class of multilocal ring networks

NASA Astrophysics Data System (ADS)

Frey, Michael

2017-11-01

Quantum networks with independent sources of entanglement (hidden variables) and nodes that execute joint quantum measurements can create strong quantum correlations spanning the breadth of the network. Understanding of these correlations has to the present been limited to standard Bell experiments with one source of shared randomness, bilocal arrangements having two local sources of shared randomness, and multilocal networks with tree topologies. We introduce here a class of quantum networks with ring topologies comprised of subsystems each with its own internally shared source of randomness. We prove a Bell inequality for these networks, and to demonstrate violations of this inequality, we focus on ring networks with three-qubit subsystems. Three qubits are capable of two non-equivalent types of entanglement, GHZ and W-type. For rings of any number N of three-qubit subsystems, our inequality is violated when the subsystems are each internally GHZ-entangled. This violation is consistently stronger when N is even. This quantitative even-odd difference for GHZ entanglement becomes extreme in the case of W-type entanglement. When the ring size N is even, the presence of W-type entanglement is successfully detected; when N is odd, the inequality consistently fails to detect its presence.

CCSDS Advanced Orbiting Systems Virtual Channel Access Service for QoS MACHETE Model

NASA Technical Reports Server (NTRS)

Jennings, Esther H.; Segui, John S.

2011-01-01

To support various communications requirements imposed by different missions, interplanetary communication protocols need to be designed, validated, and evaluated carefully. Multimission Advanced Communications Hybrid Environment for Test and Evaluation (MACHETE), described in "Simulator of Space Communication Networks" (NPO-41373), NASA Tech Briefs, Vol. 29, No. 8 (August 2005), p. 44, combines various tools for simulation and performance analysis of space networks. The MACHETE environment supports orbital analysis, link budget analysis, communications network simulations, and hardware-in-the-loop testing. By building abstract behavioral models of network protocols, one can validate performance after identifying the appropriate metrics of interest. The innovators have extended the MACHETE model library to include a generic link-layer Virtual Channel (VC) model supporting quality-of-service (QoS) controls based on IP streams. The main purpose of this generic Virtual Channel model addition was to interface fine-grain flow-based QoS (quality of service) between the network and MAC layers of the QualNet simulator, a commercial component of MACHETE. This software model adds the capability of mapping IP streams, based on header fields, to virtual channel numbers, allowing extended QoS handling at link layer. This feature further refines the QoS v existing at the network layer. QoS at the network layer (e.g. diffserv) supports few QoS classes, so data from one class will be aggregated together; differentiating between flows internal to a class/priority is not supported. By adding QoS classification capability between network and MAC layers through VC, one maps multiple VCs onto the same physical link. Users then specify different VC weights, and different queuing and scheduling policies at the link layer. This VC model supports system performance analysis of various virtual channel link-layer QoS queuing schemes independent of the network-layer QoS systems.

Providing QoS guarantee in 3G wireless networks

NASA Astrophysics Data System (ADS)

Chuah, MooiChoo; Huang, Min; Kumar, Suresh

2001-07-01

The third generation networks and services present opportunities to offer multimedia applications and services that meet end-to-end quality of service requirements. In this article, we present UMTS QoS architecture and its requirements. This includes the definition of QoS parameters, traffic classes, the end-to-end data delivery model, and the mapping of end-to-end services to the services provided by the network elements of the UMTS. End-to-end QoS of a user flow is achieved by the combination of the QoS control over UMTS Domain and the IP core Network. In the Third Generation Wireless network, UMTS bearer service manager is responsible to manage radio and transport resources to QoS-enabled applications. The UMTS bearer service consists of the Radio Access Bearer Service between Mobile Terminal and SGSN and Core Network bearer service between SGSN and GGSN. The Radio Access Bearer Service is further realized by the Radio Bearer Service (mostly air interface) and Iu bearer service. For the 3G air interface, one can provide differentiated QoS via intelligent burst allocation scheme, adaptive spreading factor control and weighted fair queueing scheduling algorithms. Next, we discuss the requirements for the transport technologies in the radio access network to provide differentiated QoS to multiple classes of traffic. We discuss both ATM based and IP based transport solutions. Last but not least, we discuss how QoS mechanism is provided in the core network to ensure e2e quality of service requirements. We discuss how mobile terminals that use RSVP as QoS signaling mechanisms can be are supported in the 3G network which may implement only IETF diffserv mechanism. . We discuss how one can map UMTS QoS classes with IETF diffserv code points. We also discuss 2G/3G handover scenarios and how the 2G/3G QoS parameters can be mapped.

Network system effects of mileage fee.

DOT National Transportation Integrated Search

2015-08-01

This project presents a comprehensive investigation about the network effects of MF to facilitate the : developments of proper MF policies. After a practice scan and a review of the recent literature on MF, a multi-class mathematical programming with...

Complex Networks/Foundations of Information Systems

DTIC Science & Technology

2013-03-06

the benefit of feedback or dynamic correlations in coding and protocol. Using Renyi correlation analysis and entropy to model this wider class of...dynamic heterogeneous conditions. Lizhong Zheng, MIT Renyi Channel Correlation Analysis (connected to geometric curvature) Network Channel

A system of IAC neural networks as the basis for self-organization in a sociological dynamical system simulation.

PubMed

Duong, D V; Reilly, K D

1995-10-01

This sociological simulation uses the ideas of semiotics and symbolic interactionism to demonstrate how an appropriately developed associative memory in the minds of individuals on the microlevel can self-organize into macrolevel dissipative structures of societies such as racial cultural/economic classes, status symbols and fads. The associative memory used is based on an extension of the IAC neural network (the Interactive Activation and Competition network). Several IAC networks act together to form a society by virtue of their human-like properties of intuition and creativity. These properties give them the ability to create and understand signs, which lead to the macrolevel structures of society. This system is implemented in hierarchical object oriented container classes which facilitate change in deep structure. Graphs of general trends and an historical account of a simulation run of this dynamical system are presented.

Recruitment dynamics in adaptive social networks

NASA Astrophysics Data System (ADS)

Shkarayev, Maxim S.; Schwartz, Ira B.; Shaw, Leah B.

2013-06-01

We model recruitment in adaptive social networks in the presence of birth and death processes. Recruitment is characterized by nodes changing their status to that of the recruiting class as a result of contact with recruiting nodes. Only a susceptible subset of nodes can be recruited. The recruiting individuals may adapt their connections in order to improve recruitment capabilities, thus changing the network structure adaptively. We derive a mean-field theory to predict the dependence of the growth threshold of the recruiting class on the adaptation parameter. Furthermore, we investigate the effect of adaptation on the recruitment level, as well as on network topology. The theoretical predictions are compared with direct simulations of the full system. We identify two parameter regimes with qualitatively different bifurcation diagrams depending on whether nodes become susceptible frequently (multiple times in their lifetime) or rarely (much less than once per lifetime).

Recruitment dynamics in adaptive social networks.

PubMed

Shkarayev, Maxim S; Schwartz, Ira B; Shaw, Leah B

2013-01-01

We model recruitment in adaptive social networks in the presence of birth and death processes. Recruitment is characterized by nodes changing their status to that of the recruiting class as a result of contact with recruiting nodes. Only a susceptible subset of nodes can be recruited. The recruiting individuals may adapt their connections in order to improve recruitment capabilities, thus changing the network structure adaptively. We derive a mean field theory to predict the dependence of the growth threshold of the recruiting class on the adaptation parameter. Furthermore, we investigate the effect of adaptation on the recruitment level, as well as on network topology. The theoretical predictions are compared with direct simulations of the full system. We identify two parameter regimes with qualitatively different bifurcation diagrams depending on whether nodes become susceptible frequently (multiple times in their lifetime) or rarely (much less than once per lifetime).

End-to-end network models encompassing terrestrial, wireless, and satellite components

NASA Astrophysics Data System (ADS)

Boyarko, Chandler L.; Britton, John S.; Flores, Phil E.; Lambert, Charles B.; Pendzick, John M.; Ryan, Christopher M.; Shankman, Gordon L.; Williams, Ramon P.

2004-08-01

Development of network models that reflect true end-to-end architectures such as the Transformational Communications Architecture need to encompass terrestrial, wireless and satellite component to truly represent all of the complexities in a world wide communications network. Use of best-in-class tools including OPNET, Satellite Tool Kit (STK), Popkin System Architect and their well known XML-friendly definitions, such as OPNET Modeler's Data Type Description (DTD), or socket-based data transfer modules, such as STK/Connect, enable the sharing of data between applications for more rapid development of end-to-end system architectures and a more complete system design. By sharing the results of and integrating best-in-class tools we are able to (1) promote sharing of data, (2) enhance the fidelity of our results and (3) allow network and application performance to be viewed in the context of the entire enterprise and its processes.

Neuromorphic photonic networks using silicon photonic weight banks.

PubMed

Tait, Alexander N; de Lima, Thomas Ferreira; Zhou, Ellen; Wu, Allie X; Nahmias, Mitchell A; Shastri, Bhavin J; Prucnal, Paul R

2017-08-07

Photonic systems for high-performance information processing have attracted renewed interest. Neuromorphic silicon photonics has the potential to integrate processing functions that vastly exceed the capabilities of electronics. We report first observations of a recurrent silicon photonic neural network, in which connections are configured by microring weight banks. A mathematical isomorphism between the silicon photonic circuit and a continuous neural network model is demonstrated through dynamical bifurcation analysis. Exploiting this isomorphism, a simulated 24-node silicon photonic neural network is programmed using "neural compiler" to solve a differential system emulation task. A 294-fold acceleration against a conventional benchmark is predicted. We also propose and derive power consumption analysis for modulator-class neurons that, as opposed to laser-class neurons, are compatible with silicon photonic platforms. At increased scale, Neuromorphic silicon photonics could access new regimes of ultrafast information processing for radio, control, and scientific computing.

Overexpression of the class D MADS-box gene Sl-AGL11 impacts fleshy tissue differentiation and structure in tomato fruits

USDA-ARS?s Scientific Manuscript database

MADS-box transcription factors are key elements of the genetic networks controlling flower and fruit development. Among these, the class D clade are involved in seed, ovule, and funiculus development. The tomato genome comprises two class D genes, Sl-AGL11 and Sl-MBP3, both displaying high expressio...

Direct lifts of coupled cell networks

NASA Astrophysics Data System (ADS)

Dias, A. P. S.; Moreira, C. S.

2018-04-01

In networks of dynamical systems, there are spaces defined in terms of equalities of cell coordinates which are flow-invariant under any dynamical system that has a form consistent with the given underlying network structure—the network synchrony subspaces. Given a network and one of its synchrony subspaces, any system with a form consistent with the network, restricted to the synchrony subspace, defines a new system which is consistent with a smaller network, called the quotient network of the original network by the synchrony subspace. Moreover, any system associated with the quotient can be interpreted as the restriction to the synchrony subspace of a system associated with the original network. We call the larger network a lift of the smaller network, and a lift can be interpreted as a result of the cellular splitting of the smaller network. In this paper, we address the question of the uniqueness in this lifting process in terms of the networks’ topologies. A lift G of a given network Q is said to be direct when there are no intermediate lifts of Q between them. We provide necessary and sufficient conditions for a lift of a general network to be direct. Our results characterize direct lifts using the subnetworks of all splitting cells of Q and of all split cells of G. We show that G is a direct lift of Q if and only if either the split subnetwork is a direct lift or consists of two copies of the splitting subnetwork. These results are then applied to the class of regular uniform networks and to the special classes of ring networks and acyclic networks. We also illustrate that one of the applications of our results is to the lifting bifurcation problem.

Statistical learning from nonrecurrent experience with discrete input variables and recursive-error-minimization equations

NASA Astrophysics Data System (ADS)

Carter, Jeffrey R.; Simon, Wayne E.

1990-08-01

Neural networks are trained using Recursive Error Minimization (REM) equations to perform statistical classification. Using REM equations with continuous input variables reduces the required number of training experiences by factors of one to two orders of magnitude over standard back propagation. Replacing the continuous input variables with discrete binary representations reduces the number of connections by a factor proportional to the number of variables reducing the required number of experiences by another order of magnitude. Undesirable effects of using recurrent experience to train neural networks for statistical classification problems are demonstrated and nonrecurrent experience used to avoid these undesirable effects. 1. THE 1-41 PROBLEM The statistical classification problem which we address is is that of assigning points in ddimensional space to one of two classes. The first class has a covariance matrix of I (the identity matrix) the covariance matrix of the second class is 41. For this reason the problem is known as the 1-41 problem. Both classes have equal probability of occurrence and samples from both classes may appear anywhere throughout the ddimensional space. Most samples near the origin of the coordinate system will be from the first class while most samples away from the origin will be from the second class. Since the two classes completely overlap it is impossible to have a classifier with zero error. The minimum possible error is known as the Bayes error and

From scale-free to Erdos-Rényi networks.

PubMed

Gómez-Gardeñes, Jesús; Moreno, Yamir

2006-05-01

We analyze a model that interpolates between scale-free and Erdos-Rényi networks. The model introduced generates a one-parameter family of networks and allows one to analyze the role of structural heterogeneity. Analytical calculations are compared with extensive numerical simulations in order to describe the transition between these two important classes of networks. Finally, an application of the proposed model to the study of the percolation transition is presented.

Research on Abnormal Detection Based on Improved Combination of K - means and SVDD

NASA Astrophysics Data System (ADS)

Hao, Xiaohong; Zhang, Xiaofeng

2018-01-01

In order to improve the efficiency of network intrusion detection and reduce the false alarm rate, this paper proposes an anomaly detection algorithm based on improved K-means and SVDD. The algorithm first uses the improved K-means algorithm to cluster the training samples of each class, so that each class is independent and compact in class; Then, according to the training samples, the SVDD algorithm is used to construct the minimum superspheres. The subordinate relationship of the samples is determined by calculating the distance of the minimum superspheres constructed by SVDD. If the test sample is less than the center of the hypersphere, the test sample belongs to this class, otherwise it does not belong to this class, after several comparisons, the final test of the effective detection of the test sample.In this paper, we use KDD CUP99 data set to simulate the proposed anomaly detection algorithm. The results show that the algorithm has high detection rate and low false alarm rate, which is an effective network security protection method.

Structural and Functional Characterization of a Caenorhabditis elegans Genetic Interaction Network within Pathways

PubMed Central

Boucher, Benjamin; Lee, Anna Y.; Hallett, Michael; Jenna, Sarah

2016-01-01

A genetic interaction (GI) is defined when the mutation of one gene modifies the phenotypic expression associated with the mutation of a second gene. Genome-wide efforts to map GIs in yeast revealed structural and functional properties of a GI network. This provided insights into the mechanisms underlying the robustness of yeast to genetic and environmental insults, and also into the link existing between genotype and phenotype. While a significant conservation of GIs and GI network structure has been reported between distant yeast species, such a conservation is not clear between unicellular and multicellular organisms. Structural and functional characterization of a GI network in these latter organisms is consequently of high interest. In this study, we present an in-depth characterization of ~1.5K GIs in the nematode Caenorhabditis elegans. We identify and characterize six distinct classes of GIs by examining a wide-range of structural and functional properties of genes and network, including co-expression, phenotypical manifestations, relationship with protein-protein interaction dense subnetworks (PDS) and pathways, molecular and biological functions, gene essentiality and pleiotropy. Our study shows that GI classes link genes within pathways and display distinctive properties, specifically towards PDS. It suggests a model in which pathways are composed of PDS-centric and PDS-independent GIs coordinating molecular machines through two specific classes of GIs involving pleiotropic and non-pleiotropic connectors. Our study provides the first in-depth characterization of a GI network within pathways of a multicellular organism. It also suggests a model to understand better how GIs control system robustness and evolution. PMID:26871911

Dynamic Hop Service Differentiation Model for End-to-End QoS Provisioning in Multi-Hop Wireless Networks

NASA Astrophysics Data System (ADS)

Youn, Joo-Sang; Seok, Seung-Joon; Kang, Chul-Hee

This paper presents a new QoS model for end-to-end service provisioning in multi-hop wireless networks. In legacy IEEE 802.11e based multi-hop wireless networks, the fixed assignment of service classes according to flow's priority at every node causes priority inversion problem when performing end-to-end service differentiation. Thus, this paper proposes a new QoS provisioning model called Dynamic Hop Service Differentiation (DHSD) to alleviate the problem and support effective service differentiation between end-to-end nodes. Many previous works for QoS model through the 802.11e based service differentiation focus on packet scheduling on several service queues with different service rate and service priority. Our model, however, concentrates on a dynamic class selection scheme, called Per Hop Class Assignment (PHCA), in the node's MAC layer, which selects a proper service class for each packet, in accordance with queue states and service requirement, in every node along the end-to-end route of the packet. The proposed QoS solution is evaluated using the OPNET simulator. The simulation results show that the proposed model outperforms both best-effort and 802.11e based strict priority service models in mobile ad hoc environments.

The Effect of Social Interaction on Learning Engagement in a Social Networking Environment

ERIC Educational Resources Information Center

Lu, Jie; Churchill, Daniel

2014-01-01

This study investigated the impact of social interactions among a class of undergraduate students on their learning engagement in a social networking environment. Thirteen undergraduate students enrolled in a course in a university in Hong Kong used an Elgg-based social networking platform throughout a semester to develop their digital portfolios…

An Intelligent Agent Approach for Teaching Neural Networks Using LEGO[R] Handy Board Robots

ERIC Educational Resources Information Center

Imberman, Susan P.

2004-01-01

In this article we describe a project for an undergraduate artificial intelligence class. The project teaches neural networks using LEGO[R] handy board robots. Students construct robots with two motors and two photosensors. Photosensors provide readings that act as inputs for the neural network. Output values power the motors and maintain the…

Game Theoretic Models of Competition and Upgrade Investments in Communication Networks

ERIC Educational Resources Information Center

Wu, Shuang

2010-01-01

In the first part of this dissertation, we study the competition among network service providers in a parallel-link network with the presence of elastic user demand that diminishes both with higher prices and congestion. First we analyze a game where providers strategically price their service for single class of traffic. Later we analyze a game…

Teaching Students How to Integrate and Assess Social Networking Tools in Marketing Communications

ERIC Educational Resources Information Center

Schlee, Regina Pefanis; Harich, Katrin R.

2013-01-01

This research is based on two studies that focus on teaching students how to integrate and assess social networking tools in marketing communications. Study 1 examines how students in marketing classes utilize social networking tools and explores their attitudes regarding the use of such tools for marketing communications. Study 2 focuses on an…

A Comparison of General Diagnostic Models (GDM) and Bayesian Networks Using a Middle School Mathematics Test

ERIC Educational Resources Information Center

Wu, Haiyan

2013-01-01

General diagnostic models (GDMs) and Bayesian networks are mathematical frameworks that cover a wide variety of psychometric models. Both extend latent class models, and while GDMs also extend item response theory (IRT) models, Bayesian networks can be parameterized using discretized IRT. The purpose of this study is to examine similarities and…

Mapping soil landscape as spatial continua: The Neural Network Approach

NASA Astrophysics Data System (ADS)

Zhu, A.-Xing

2000-03-01

A neural network approach was developed to populate a soil similarity model that was designed to represent soil landscape as spatial continua for hydroecological modeling at watersheds of mesoscale size. The approach employs multilayer feed forward neural networks. The input to the network was data on a set of soil formative environmental factors; the output from the network was a set of similarity values to a set of prescribed soil classes. The network was trained using a conjugate gradient algorithm in combination with a simulated annealing technique to learn the relationships between a set of prescribed soils and their environmental factors. Once trained, the network was used to compute for every location in an area the similarity values of the soil to the set of prescribed soil classes. The similarity values were then used to produce detailed soil spatial information. The approach also included a Geographic Information System procedure for selecting representative training and testing samples and a process of determining the network internal structure. The approach was applied to soil mapping in a watershed, the Lubrecht Experimental Forest, in western Montana. The case study showed that the soil spatial information derived using the neural network approach reveals much greater spatial detail and has a higher quality than that derived from the conventional soil map. Implications of this detailed soil spatial information for hydroecological modeling at the watershed scale are also discussed.

"When You See a Normal Person …": Social Class and Friendship Networks among Teenage Students

ERIC Educational Resources Information Center

Papapolydorou, Maria

2014-01-01

This paper draws on social capital theory to discuss the way social class plays out in the friendships of teenage students. Based on data from individual interviews and focus groups with 75 students in four London secondary schools, it is suggested that students tend to form friendships with people who belong to the same social-class background as…

A Connectionist Model of Stimulus Class Formation with a Yes/No Procedure and Compound Stimuli

ERIC Educational Resources Information Center

Tovar, Angel E.; Chavez, Alvaro Torres

2012-01-01

We analyzed stimulus class formation in a human study and in a connectionist model (CM) with a yes/no procedure, using compound stimuli. In the human study, the participants were six female undergraduate students; the CM was a feed-forward back-propagation network. Two 3-member stimulus classes were trained with a similar procedure in both the…

Peers and Academic Achievement: A Longitudinal Study on Selection and Socialization Effects of In-Class Friends

ERIC Educational Resources Information Center

Fortuin, Janna; van Geel, Mitch; Vedder, Paul

2016-01-01

The present study was conducted to analyze whether in-class friends influence each other's grades, and whether adolescents tend to select friends that are similar to them in terms of academic achievement. During 1 academic year, 542 eighth-grade students (M age = 13.3 years) reported on 3 different occasions on their in-class friendship networks.…

Intestinal microbiota and immune related genes in sea cucumber (Apostichopus japonicus) response to dietary β-glucan supplementation

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

Yang, Gang; Xu, Zhenjiang; Tian, Xiangli, E-mail: xianglitian@ouc.edu.cn

β-glucan is a prebiotic well known for its beneficial outcomes on sea cucumber health through modifying the host intestinal microbiota. High-throughput sequencing techniques provide an opportunity for the identification and characterization of microbes. In this study, we investigated the intestinal microbial community composition, interaction among species, and intestinal immune genes in sea cucumber fed with diet supplemented with or without β-glucan supplementation. The results show that the intestinal dominant classes in the control group are Flavobacteriia, Gammaproteobacteria, and Alphaproteobacteria, whereas Alphaproteobacteria, Flavobacteriia, and Verrucomicrobiae are enriched in the β-glucan group. Dietary β-glucan supplementation promoted the proliferation of the family Rhodobacteraceaemore » of the Alphaproteobacteria class and the family Verrucomicrobiaceae of the Verrucomicrobiae class and reduced the relative abundance of the family Flavobacteriaceae of Flavobacteria class. The ecological network analysis suggests that dietary β-glucan supplementation can alter the network interactions among different microbial functional groups by changing the microbial community composition and topological roles of the OTUs in the ecological network. Dietary β-glucan supplementation has a positive impact on immune responses of the intestine of sea cucumber by activating NF-κB signaling pathway, probably through modulating the balance of intestinal microbiota. - Highlights: • Dietary β-glucan supplementation increases the abundance of Rhodobacteraceae and Verrucomicrobiaceae in the intestine. • Dietary β-glucan supplementation changes the topological roles of OTUs in the ecological network. • Dietary β-glucan supplementation has a positive impact on the immune response of intestine of sea cucumber.« less

Development of Genuine Neural Network Prototype Chip

DTIC Science & Technology

1991-01-28

priori distribution is equivalent, and more readily visualized with a rank curve . The sonar signal data consisted of approximately 85% class Target and...15% class Clutter. For this reason, the rank curves for the class Clutter were used for device parameter analysis. R & D STATUS REPORT 1/28/91 N00014...the signal CLASSLD#. Four 10-bit class probabilities are available on the output bus (C0-C9, C16-C25, C32-C41 and C48- C57 ) at each clock cycle. A

Phase-space networks of geometrically frustrated systems.

PubMed

Han, Yilong

2009-11-01

We illustrate a network approach to the phase-space study by using two geometrical frustration models: antiferromagnet on triangular lattice and square ice. Their highly degenerated ground states are mapped as discrete networks such that the quantitative network analysis can be applied to phase-space studies. The resulting phase spaces share some comon features and establish a class of complex networks with unique Gaussian spectral densities. Although phase-space networks are heterogeneously connected, the systems are still ergodic due to the random Poisson processes. This network approach can be generalized to phase spaces of some other complex systems.

Optimizing Nutrient Uptake in Biological Transport Networks

NASA Astrophysics Data System (ADS)

Ronellenfitsch, Henrik; Katifori, Eleni

2013-03-01

Many biological systems employ complex networks of vascular tubes to facilitate transport of solute nutrients, examples include the vascular system of plants (phloem), some fungi, and the slime-mold Physarum. It is believed that such networks are optimized through evolution for carrying out their designated task. We propose a set of hydrodynamic governing equations for solute transport in a complex network, and obtain the optimal network architecture for various classes of optimizing functionals. We finally discuss the topological properties and statistical mechanics of the resulting complex networks, and examine correspondence of the obtained networks to those found in actual biological systems.

Structural Controllability of Temporal Networks with a Single Switching Controller

PubMed Central

Yao, Peng; Hou, Bao-Yu; Pan, Yu-Jian; Li, Xiang

2017-01-01

Temporal network, whose topology evolves with time, is an important class of complex networks. Temporal trees of a temporal network describe the necessary edges sustaining the network as well as their active time points. By a switching controller which properly selects its location with time, temporal trees are used to improve the controllability of the network. Therefore, more nodes are controlled within the limited time. Several switching strategies to efficiently select the location of the controller are designed, which are verified with synthetic and empirical temporal networks to achieve better control performance. PMID:28107538

Observability of Automata Networks: Fixed and Switching Cases.

PubMed

Li, Rui; Hong, Yiguang; Wang, Xingyuan

2018-04-01

Automata networks are a class of fully discrete dynamical systems, which have received considerable interest in various different areas. This brief addresses the observability of automata networks and switched automata networks in a unified framework, and proposes simple necessary and sufficient conditions for observability. The results are achieved by employing methods from symbolic computation, and are suited for implementation using computer algebra systems. Several examples are presented to demonstrate the application of the results.

Separate neural systems support representations for actions and objects during narrative speech in post-stroke aphasia☆

PubMed Central

Gleichgerrcht, Ezequiel; Fridriksson, Julius; Rorden, Chris; Nesland, Travis; Desai, Rutvik; Bonilha, Leonardo

2015-01-01

Background Representations of objects and actions in everyday speech are usually materialized as nouns and verbs, two grammatical classes that constitute the core elements of language. Given their very distinct roles in singling out objects (nouns) or referring to transformative actions (verbs), they likely rely on distinct brain circuits. Method We tested this hypothesis by conducting network-based lesion-symptom mapping in 38 patients with chronic stroke to the left hemisphere. We reconstructed the individual brain connectomes from probabilistic tractography applied to magnetic resonance imaging and obtained measures of production of words referring to objects and actions from narrative discourse elicited by picture naming tasks. Results Words for actions were associated with a frontal network strongly engaging structures involved in motor control and programming. Words for objects, instead, were related to a posterior network spreading across the occipital, posterior inferior temporal, and parietal regions, likely related with visual processing and imagery, object recognition, and spatial attention/scanning. Thus, each of these networks engaged brain areas typically involved in cognitive and sensorimotor experiences equivalent to the function served by each grammatical class (e.g. motor areas for verbs, perception areas for nouns). Conclusions The finding that the two major grammatical classes in human speech rely on two dissociable networks has both important theoretical implications for the neurobiology of language and clinical implications for the assessment and potential rehabilitation and treatment of patients with chronic aphasia due to stroke. PMID:26759789

Detecting and preventing error propagation via competitive learning.

PubMed

Silva, Thiago Christiano; Zhao, Liang

2013-05-01

Semisupervised learning is a machine learning approach which is able to employ both labeled and unlabeled samples in the training process. It is an important mechanism for autonomous systems due to the ability of exploiting the already acquired information and for exploring the new knowledge in the learning space at the same time. In these cases, the reliability of the labels is a crucial factor, because mislabeled samples may propagate wrong labels to a portion of or even the entire data set. This paper has the objective of addressing the error propagation problem originated by these mislabeled samples by presenting a mechanism embedded in a network-based (graph-based) semisupervised learning method. Such a procedure is based on a combined random-preferential walk of particles in a network constructed from the input data set. The particles of the same class cooperate among them, while the particles of different classes compete with each other to propagate class labels to the whole network. Computer simulations conducted on synthetic and real-world data sets reveal the effectiveness of the model. Copyright © 2012 Elsevier Ltd. All rights reserved.

A multiclass vehicular dynamic traffic flow model for main roads and dedicated lanes/roads of multimodal transport network

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

Sossoe, K.S., E-mail: kwami.sossoe@irt-systemx.fr; Lebacque, J-P., E-mail: jean-patrick.lebacque@ifsttar.fr

2015-03-10

We present in this paper a model of vehicular traffic flow for a multimodal transportation road network. We introduce the notion of class of vehicles to refer to vehicles of different transport modes. Our model describes the traffic on highways (which may contain several lanes) and network transit for pubic transportation. The model is drafted with Eulerian and Lagrangian coordinates and uses a Logit model to describe the traffic assignment of our multiclass vehicular flow description on shared roads. The paper also discusses traffic streams on dedicated lanes for specific class of vehicles with event-based traffic laws. An Euler-Lagrangian-remap schememore » is introduced to numerically approximate the model’s flow equations.« less

Food-web structure and network theory: The role of connectance and size

PubMed Central

Dunne, Jennifer A.; Williams, Richard J.; Martinez, Neo D.

2002-01-01

Networks from a wide range of physical, biological, and social systems have been recently described as “small-world” and “scale-free.” However, studies disagree whether ecological networks called food webs possess the characteristic path lengths, clustering coefficients, and degree distributions required for membership in these classes of networks. Our analysis suggests that the disagreements are based on selective use of relatively few food webs, as well as analytical decisions that obscure important variability in the data. We analyze a broad range of 16 high-quality food webs, with 25–172 nodes, from a variety of aquatic and terrestrial ecosystems. Food webs generally have much higher complexity, measured as connectance (the fraction of all possible links that are realized in a network), and much smaller size than other networks studied, which have important implications for network topology. Our results resolve prior conflicts by demonstrating that although some food webs have small-world and scale-free structure, most do not if they exceed a relatively low level of connectance. Although food-web degree distributions do not display a universal functional form, observed distributions are systematically related to network connectance and size. Also, although food webs often lack small-world structure because of low clustering, we identify a continuum of real-world networks including food webs whose ratios of observed to random clustering coefficients increase as a power–law function of network size over 7 orders of magnitude. Although food webs are generally not small-world, scale-free networks, food-web topology is consistent with patterns found within those classes of networks. PMID:12235364

The effect of excluding juveniles on apparent adult olive baboons (Papio anubis) social networks

PubMed Central

Fedurek, Piotr; Lehmann, Julia

2017-01-01

In recent years there has been much interest in investigating the social structure of group living animals using social network analysis. Many studies so far have focused on the social networks of adults, often excluding younger, immature group members. This potentially may lead to a biased view of group social structure as multiple recent studies have shown that younger group members can significantly contribute to group structure. As proof of the concept, we address this issue by investigating social network structure with and without juveniles in wild olive baboons (Papio anubis) at Gashaka Gumti National Park, Nigeria. Two social networks including all independently moving individuals (i.e., excluding dependent juveniles) were created based on aggressive and grooming behaviour. We used knockout simulations based on the random removal of individuals from the network in order to investigate to what extent the exclusion of juveniles affects the resulting network structure and our interpretation of age-sex specific social roles. We found that juvenile social patterns differed from those of adults and that the exclusion of juveniles from the network significantly altered the resulting overall network structure. Moreover, the removal of juveniles from the network affected individuals in specific age-sex classes differently: for example, including juveniles in the grooming network increased network centrality of adult females while decreasing centrality of adult males. These results suggest that excluding juveniles from the analysis may not only result in a distorted picture of the overall social structure but also may mask some of the social roles of individuals belonging to different age-sex classes. PMID:28323851

The effect of excluding juveniles on apparent adult olive baboons (Papio anubis) social networks.

PubMed

Fedurek, Piotr; Lehmann, Julia

2017-01-01

In recent years there has been much interest in investigating the social structure of group living animals using social network analysis. Many studies so far have focused on the social networks of adults, often excluding younger, immature group members. This potentially may lead to a biased view of group social structure as multiple recent studies have shown that younger group members can significantly contribute to group structure. As proof of the concept, we address this issue by investigating social network structure with and without juveniles in wild olive baboons (Papio anubis) at Gashaka Gumti National Park, Nigeria. Two social networks including all independently moving individuals (i.e., excluding dependent juveniles) were created based on aggressive and grooming behaviour. We used knockout simulations based on the random removal of individuals from the network in order to investigate to what extent the exclusion of juveniles affects the resulting network structure and our interpretation of age-sex specific social roles. We found that juvenile social patterns differed from those of adults and that the exclusion of juveniles from the network significantly altered the resulting overall network structure. Moreover, the removal of juveniles from the network affected individuals in specific age-sex classes differently: for example, including juveniles in the grooming network increased network centrality of adult females while decreasing centrality of adult males. These results suggest that excluding juveniles from the analysis may not only result in a distorted picture of the overall social structure but also may mask some of the social roles of individuals belonging to different age-sex classes.

Informal Networks in Youth Transitions in West Germany: Biographical Resource or Reproduction of Social Inequality?

ERIC Educational Resources Information Center

Walther, Andreas; Stauber, Barbara; Pohl, Axel

2005-01-01

This article deals with informal networks and their role in young people's strategies of coping with the uncertainties of transitions to work. The underlying hypothesis is that informal networks have a high potential in this regard that, however, is strongly differentiated according to class and education. Drawing on West German data from the…

Multimedia Network Design Study

DTIC Science & Technology

1989-09-30

manipulation and analysis of the equations involved, thereby providing the application of the great range of powerful mathematical optimization...be treated by this analysis. First, all arrivals to the network have the Poisson distribution, and separate traffic classes may have separate qrrival...different for open and closed networks, so these two situations will be treated separately in the following subsections. 2.3.1 The Computational Process in

Network inoculation: Heteroclinics and phase transitions in an epidemic model

NASA Astrophysics Data System (ADS)

Yang, Hui; Rogers, Tim; Gross, Thilo

2016-08-01

In epidemiological modelling, dynamics on networks, and, in particular, adaptive and heterogeneous networks have recently received much interest. Here, we present a detailed analysis of a previously proposed model that combines heterogeneity in the individuals with adaptive rewiring of the network structure in response to a disease. We show that in this model, qualitative changes in the dynamics occur in two phase transitions. In a macroscopic description, one of these corresponds to a local bifurcation, whereas the other one corresponds to a non-local heteroclinic bifurcation. This model thus provides a rare example of a system where a phase transition is caused by a non-local bifurcation, while both micro- and macro-level dynamics are accessible to mathematical analysis. The bifurcation points mark the onset of a behaviour that we call network inoculation. In the respective parameter region, exposure of the system to a pathogen will lead to an outbreak that collapses but leaves the network in a configuration where the disease cannot reinvade, despite every agent returning to the susceptible class. We argue that this behaviour and the associated phase transitions can be expected to occur in a wide class of models of sufficient complexity.

Finding the probability of infection in an SIR network is NP-Hard

PubMed Central

Shapiro, Michael; Delgado-Eckert, Edgar

2012-01-01

It is the purpose of this article to review results that have long been known to communications network engineers and have direct application to epidemiology on networks. A common approach in epidemiology is to study the transmission of a disease in a population where each individual is initially susceptible (S), may become infective (I) and then removed or recovered (R) and plays no further epidemiological role. Much of the recent work gives explicit consideration to the network of social interactions or disease-transmitting contacts and attendant probability of transmission for each interacting pair. The state of such a network is an assignment of the values {S, I, R} to its members. Given such a network, an initial state and a particular susceptible individual, we would like to compute their probability of becoming infected in the course of an epidemic. It turns out that this and related problems are NP-hard. In particular, it belongs in a class of problems for which no efficient algorithms for their solution are known. Moreover, finding an efficient algorithm for the solution of any problem in this class would entail a major breakthrough in theoretical computer science. PMID:22824138

Dynamic Multiple-Threshold Call Admission Control Based on Optimized Genetic Algorithm in Wireless/Mobile Networks

NASA Astrophysics Data System (ADS)

Wang, Shengling; Cui, Yong; Koodli, Rajeev; Hou, Yibin; Huang, Zhangqin

Due to the dynamics of topology and resources, Call Admission Control (CAC) plays a significant role for increasing resource utilization ratio and guaranteeing users' QoS requirements in wireless/mobile networks. In this paper, a dynamic multi-threshold CAC scheme is proposed to serve multi-class service in a wireless/mobile network. The thresholds are renewed at the beginning of each time interval to react to the changing mobility rate and network load. To find suitable thresholds, a reward-penalty model is designed, which provides different priorities between different service classes and call types through different reward/penalty policies according to network load and average call arrival rate. To speed up the running time of CAC, an Optimized Genetic Algorithm (OGA) is presented, whose components such as encoding, population initialization, fitness function and mutation etc., are all optimized in terms of the traits of the CAC problem. The simulation demonstrates that the proposed CAC scheme outperforms the similar schemes, which means the optimization is realized. Finally, the simulation shows the efficiency of OGA.

A New Local Bipolar Autoassociative Memory Based on External Inputs of Discrete Recurrent Neural Networks With Time Delay.

PubMed

Zhou, Caigen; Zeng, Xiaoqin; Luo, Chaomin; Zhang, Huaguang

In this paper, local bipolar auto-associative memories are presented based on discrete recurrent neural networks with a class of gain type activation function. The weight parameters of neural networks are acquired by a set of inequalities without the learning procedure. The global exponential stability criteria are established to ensure the accuracy of the restored patterns by considering time delays and external inputs. The proposed methodology is capable of effectively overcoming spurious memory patterns and achieving memory capacity. The effectiveness, robustness, and fault-tolerant capability are validated by simulated experiments.In this paper, local bipolar auto-associative memories are presented based on discrete recurrent neural networks with a class of gain type activation function. The weight parameters of neural networks are acquired by a set of inequalities without the learning procedure. The global exponential stability criteria are established to ensure the accuracy of the restored patterns by considering time delays and external inputs. The proposed methodology is capable of effectively overcoming spurious memory patterns and achieving memory capacity. The effectiveness, robustness, and fault-tolerant capability are validated by simulated experiments.

Adaptive architectures for resilient control of networked multiagent systems in the presence of misbehaving agents

NASA Astrophysics Data System (ADS)

Torre, Gerardo De La; Yucelen, Tansel

2018-03-01

Control algorithms of networked multiagent systems are generally computed distributively without having a centralised entity monitoring the activity of agents; and therefore, unforeseen adverse conditions such as uncertainties or attacks to the communication network and/or failure of agent-wise components can easily result in system instability and prohibit the accomplishment of system-level objectives. In this paper, we study resilient coordination of networked multiagent systems in the presence of misbehaving agents, i.e. agents that are subject to exogenous disturbances that represent a class of adverse conditions. In particular, a distributed adaptive control architecture is presented for directed and time-varying graph topologies to retrieve a desired networked multiagent system behaviour. Apart from the existing relevant literature that make specific assumptions on the graph topology and/or the fraction of misbehaving agents, we show that the considered class of adverse conditions can be mitigated by the proposed adaptive control approach that utilises a local state emulator - even if all agents are misbehaving. Illustrative numerical examples are provided to demonstrate the theoretical findings.

Variations in Social Network Type Membership Among Older African Americans, Caribbean Blacks, and Non-Hispanic Whites

PubMed Central

2017-01-01

Abstract Objectives: This study examined race differences in the probability of belonging to a specific social network typology of family, friends, and church members. Method: Samples of African Americans, Caribbean blacks, and non-Hispanic whites aged 55+ were drawn from the National Survey of American Life. Typology indicators related to social integration and negative interactions with family, friendship, and church networks were used. Latent class analysis was used to identify typologies, and latent class multinomial logistic regression was used to assess the influence of race, and interactions between race and age, and race and education on typology membership. Results: Four network typologies were identified: optimal (high social integration, low negative interaction), family-centered (high social integration within primarily the extended family network, low negative interaction), strained (low social integration, high negative interaction), and ambivalent (high social integration and high negative interaction). Findings for race and age and race and education interactions indicated that the effects of education and age on typology membership varied by race. Discussion: Overall, the findings demonstrate how race interacts with age and education to influence the probability of belonging to particular network types. A better understanding of the influence of race, education, and age on social network typologies will inform future research and theoretical developments in this area. PMID:28329871

Intelligent call admission control for multi-class services in mobile cellular networks

NASA Astrophysics Data System (ADS)

Ma, Yufeng; Hu, Xiulin; Zhang, Yunyu

2005-11-01

Scarcity of the spectrum resource and mobility of users make quality of service (QoS) provision a critical issue in mobile cellular networks. This paper presents a fuzzy call admission control scheme to meet the requirement of the QoS. A performance measure is formed as a weighted linear function of new call and handoff call blocking probabilities of each service class. Simulation compares the proposed fuzzy scheme with complete sharing and guard channel policies. Simulation results show that fuzzy scheme has a better robust performance in terms of average blocking criterion.

A novel one-class SVM based negative data sampling method for reconstructing proteome-wide HTLV-human protein interaction networks.

PubMed

Mei, Suyu; Zhu, Hao

2015-01-26

Protein-protein interaction (PPI) prediction is generally treated as a problem of binary classification wherein negative data sampling is still an open problem to be addressed. The commonly used random sampling is prone to yield less representative negative data with considerable false negatives. Meanwhile rational constraints are seldom exerted on model selection to reduce the risk of false positive predictions for most of the existing computational methods. In this work, we propose a novel negative data sampling method based on one-class SVM (support vector machine, SVM) to predict proteome-wide protein interactions between HTLV retrovirus and Homo sapiens, wherein one-class SVM is used to choose reliable and representative negative data, and two-class SVM is used to yield proteome-wide outcomes as predictive feedback for rational model selection. Computational results suggest that one-class SVM is more suited to be used as negative data sampling method than two-class PPI predictor, and the predictive feedback constrained model selection helps to yield a rational predictive model that reduces the risk of false positive predictions. Some predictions have been validated by the recent literature. Lastly, gene ontology based clustering of the predicted PPI networks is conducted to provide valuable cues for the pathogenesis of HTLV retrovirus.

ViNEL: A Virtual Networking Lab for Cyber Defense Education

ERIC Educational Resources Information Center

Reinicke, Bryan; Baker, Elizabeth; Toothman, Callie

2018-01-01

Professors teaching cyber security classes often face challenges when developing workshops for their students: How does one quickly and efficiently configure and deploy an operating system for a temporary learning/testing environment? Faculty teaching these classes spend countless hours installing, configuring and deploying multiple system…

Social Class and School Music

ERIC Educational Resources Information Center

Bates, Vincent C.

2012-01-01

This article takes a practical look at social class in school music by exploring the manifestations and impact of three of its dimensions: financial resources, cultural practices, and social networks. Three suggestions are discussed: provide a free and equal music education for all students, understand and respect each student's cultural…

Object recognition with hierarchical discriminant saliency networks.

PubMed

Han, Sunhyoung; Vasconcelos, Nuno

2014-01-01

The benefits of integrating attention and object recognition are investigated. While attention is frequently modeled as a pre-processor for recognition, we investigate the hypothesis that attention is an intrinsic component of recognition and vice-versa. This hypothesis is tested with a recognition model, the hierarchical discriminant saliency network (HDSN), whose layers are top-down saliency detectors, tuned for a visual class according to the principles of discriminant saliency. As a model of neural computation, the HDSN has two possible implementations. In a biologically plausible implementation, all layers comply with the standard neurophysiological model of visual cortex, with sub-layers of simple and complex units that implement a combination of filtering, divisive normalization, pooling, and non-linearities. In a convolutional neural network implementation, all layers are convolutional and implement a combination of filtering, rectification, and pooling. The rectification is performed with a parametric extension of the now popular rectified linear units (ReLUs), whose parameters can be tuned for the detection of target object classes. This enables a number of functional enhancements over neural network models that lack a connection to saliency, including optimal feature denoising mechanisms for recognition, modulation of saliency responses by the discriminant power of the underlying features, and the ability to detect both feature presence and absence. In either implementation, each layer has a precise statistical interpretation, and all parameters are tuned by statistical learning. Each saliency detection layer learns more discriminant saliency templates than its predecessors and higher layers have larger pooling fields. This enables the HDSN to simultaneously achieve high selectivity to target object classes and invariance. The performance of the network in saliency and object recognition tasks is compared to those of models from the biological and computer vision literatures. This demonstrates benefits for all the functional enhancements of the HDSN, the class tuning inherent to discriminant saliency, and saliency layers based on templates of increasing target selectivity and invariance. Altogether, these experiments suggest that there are non-trivial benefits in integrating attention and recognition.

A unified view on weakly correlated recurrent networks

PubMed Central

Grytskyy, Dmytro; Tetzlaff, Tom; Diesmann, Markus; Helias, Moritz

2013-01-01

The diversity of neuron models used in contemporary theoretical neuroscience to investigate specific properties of covariances in the spiking activity raises the question how these models relate to each other. In particular it is hard to distinguish between generic properties of covariances and peculiarities due to the abstracted model. Here we present a unified view on pairwise covariances in recurrent networks in the irregular regime. We consider the binary neuron model, the leaky integrate-and-fire (LIF) model, and the Hawkes process. We show that linear approximation maps each of these models to either of two classes of linear rate models (LRM), including the Ornstein–Uhlenbeck process (OUP) as a special case. The distinction between both classes is the location of additive noise in the rate dynamics, which is located on the output side for spiking models and on the input side for the binary model. Both classes allow closed form solutions for the covariance. For output noise it separates into an echo term and a term due to correlated input. The unified framework enables us to transfer results between models. For example, we generalize the binary model and the Hawkes process to the situation with synaptic conduction delays and simplify derivations for established results. Our approach is applicable to general network structures and suitable for the calculation of population averages. The derived averages are exact for fixed out-degree network architectures and approximate for fixed in-degree. We demonstrate how taking into account fluctuations in the linearization procedure increases the accuracy of the effective theory and we explain the class dependent differences between covariances in the time and the frequency domain. Finally we show that the oscillatory instability emerging in networks of LIF models with delayed inhibitory feedback is a model-invariant feature: the same structure of poles in the complex frequency plane determines the population power spectra. PMID:24151463

Social networks: Evolving graphs with memory dependent edges

NASA Astrophysics Data System (ADS)

Grindrod, Peter; Parsons, Mark

2011-10-01

The plethora of digital communication technologies, and their mass take up, has resulted in a wealth of interest in social network data collection and analysis in recent years. Within many such networks the interactions are transient: thus those networks evolve over time. In this paper we introduce a class of models for such networks using evolving graphs with memory dependent edges, which may appear and disappear according to their recent history. We consider time discrete and time continuous variants of the model. We consider the long term asymptotic behaviour as a function of parameters controlling the memory dependence. In particular we show that such networks may continue evolving forever, or else may quench and become static (containing immortal and/or extinct edges). This depends on the existence or otherwise of certain infinite products and series involving age dependent model parameters. We show how to differentiate between the alternatives based on a finite set of observations. To test these ideas we show how model parameters may be calibrated based on limited samples of time dependent data, and we apply these concepts to three real networks: summary data on mobile phone use from a developing region; online social-business network data from China; and disaggregated mobile phone communications data from a reality mining experiment in the US. In each case we show that there is evidence for memory dependent dynamics, such as that embodied within the class of models proposed here.

Organization of acute stroke services in Poland - Polish Stroke Unit Network development.

PubMed

Sarzyńska-Długosz, Iwona; Skowrońska, Marta; Członkowska, Anna

2013-01-01

According to the recommendations of stroke organizations, every stroke patient should be treated in a specialized stroke unit (SU). We aimed to evaluate the development of the SU network in Poland during the past decade. In Poland, stroke is treated mainly by neurologists. A questionnaire evaluating structure and staff of neurological departments was sent to all neurological departments in 2003, 2005 and 2007. In 2010, we collected data based on information from the National Health Fund. We divided departments into categories: with a comprehensive SU, with a primary SU unit, and departments without an SU. Primary SUs were further divided into class A SUs (fulfilling criteria of the National Programme of Prevention and Treatment of Stroke Experts - eligible for thrombolysis), class B (conditionally fulfilling criteria), and class C (not fulfilling criteria). Final analyses included 87.4% of departments (194/222) in 2003, 85.5% of departments (188/220) in 2005, and 83.1% of departments (182/219) in 2007. According to the above-mentioned classification there were 20 class A SUs in 2003, 58 in 2005 and 5 comprehensive and 51 class A SUs in 2007. In 2012, based on information from the National Health Fund there were 150 SUs, all fulfilling criteria for thrombolysis, 9 of them comprehensive SUs. The SU network in Poland is developing dynamically but thrombolysis and endovascular procedures are done too rarely. Now it is necessary to improve quality of stroke services and to make organizational changes in the in-hospital stroke pathways as well as to organize continuous education of medical staff.

Neural-Network Simulator

NASA Technical Reports Server (NTRS)

Mitchell, Paul H.

1991-01-01

F77NNS (FORTRAN 77 Neural Network Simulator) computer program simulates popular back-error-propagation neural network. Designed to take advantage of vectorization when used on computers having this capability, also used on any computer equipped with ANSI-77 FORTRAN Compiler. Problems involving matching of patterns or mathematical modeling of systems fit class of problems F77NNS designed to solve. Program has restart capability so neural network solved in stages suitable to user's resources and desires. Enables user to customize patterns of connections between layers of network. Size of neural network F77NNS applied to limited only by amount of random-access memory available to user.

Typing mineral deposits using their grades and tonnages in an artificial neural network

USGS Publications Warehouse

Singer, Donald A.; Kouda, Ryoichi

2003-01-01

A test of the ability of a probabilistic neural network to classify deposits into types on the basis of deposit tonnage and average Cu, Mo, Ag, Au, Zn, and Pb grades is conducted. The purpose is to examine whether this type of system might serve as a basis for integrating geoscience information available in large mineral databases to classify sites by deposit type. Benefits of proper classification of many sites in large regions are relatively rapid identification of terranes permissive for deposit types and recognition of specific sites perhaps worthy of exploring further.Total tonnages and average grades of 1,137 well-explored deposits identified in published grade and tonnage models representing 13 deposit types were used to train and test the network. Tonnages were transformed by logarithms and grades by square roots to reduce effects of skewness. All values were scaled by subtracting the variable's mean and dividing by its standard deviation. Half of the deposits were selected randomly to be used in training the probabilistic neural network and the other half were used for independent testing. Tests were performed with a probabilistic neural network employing a Gaussian kernel and separate sigma weights for each class (type) and each variable (grade or tonnage).Deposit types were selected to challenge the neural network. For many types, tonnages or average grades are significantly different from other types, but individual deposits may plot in the grade and tonnage space of more than one type. Porphyry Cu, porphyry Cu-Au, and porphyry Cu-Mo types have similar tonnages and relatively small differences in grades. Redbed Cu deposits typically have tonnages that could be confused with porphyry Cu deposits, also contain Cu and, in some situations, Ag. Cyprus and kuroko massive sulfide types have about the same tonnages. Cu, Zn, Ag, and Au grades. Polymetallic vein, sedimentary exhalative Zn-Pb, and Zn-Pb skarn types contain many of the same metals. Sediment-hosted Au, Comstock Au-Ag, and low-sulfide Au-quartz vein types are principally Au deposits with differing amounts of Ag.Given the intent to test the neural network under the most difficult conditions, an overall 75% agreement between the experts and the neural network is considered excellent. Among the largestclassification errors are skarn Zn-Pb and Cyprus massive sulfide deposits classed by the neuralnetwork as kuroko massive sulfides—24 and 63% error respectively. Other large errors are the classification of 92% of porphyry Cu-Mo as porphyry Cu deposits. Most of the larger classification errors involve 25 or fewer training deposits, suggesting that some errors might be the result of small sample size. About 91% of the gold deposit types were classed properly and 98% of porphyry Cu deposits were classes as some type of porphyry Cu deposit. An experienced economic geologist would not make many of the classification errors that were made by the neural network because the geologic settings of deposits would be used to reduce errors. In a separate test, the probabilistic neural network correctly classed 93% of 336 deposits in eight deposit types when trained with presence or absence of 58 minerals and six generalized rock types. The overall success rate of the probabilistic neural network when trained on tonnage and average grades would probably be more than 90% with additional information on the presence of a few rock types.

A kilobyte rewritable atomic memory

NASA Astrophysics Data System (ADS)

Kalff, F. E.; Rebergen, M. P.; Fahrenfort, E.; Girovsky, J.; Toskovic, R.; Lado, J. L.; Fernández-Rossier, J.; Otte, A. F.

2016-11-01

The advent of devices based on single dopants, such as the single-atom transistor, the single-spin magnetometer and the single-atom memory, has motivated the quest for strategies that permit the control of matter with atomic precision. Manipulation of individual atoms by low-temperature scanning tunnelling microscopy provides ways to store data in atoms, encoded either into their charge state, magnetization state or lattice position. A clear challenge now is the controlled integration of these individual functional atoms into extended, scalable atomic circuits. Here, we present a robust digital atomic-scale memory of up to 1 kilobyte (8,000 bits) using an array of individual surface vacancies in a chlorine-terminated Cu(100) surface. The memory can be read and rewritten automatically by means of atomic-scale markers and offers an areal density of 502 terabits per square inch, outperforming state-of-the-art hard disk drives by three orders of magnitude. Furthermore, the chlorine vacancies are found to be stable at temperatures up to 77 K, offering the potential for expanding large-scale atomic assembly towards ambient conditions.

All-dielectric rod antenna array for terahertz communications

NASA Astrophysics Data System (ADS)

Withayachumnankul, Withawat; Yamada, Ryoumei; Fujita, Masayuki; Nagatsuma, Tadao

2018-05-01

The terahertz band holds a potential for point-to-point short-range wireless communications at sub-terabit speed. To realize this potential, supporting antennas must have a wide bandwidth to sustain high data rate and must have high gain and low dissipation to compensate for the free space path loss that scales quadratically with frequency. Here we propose an all-dielectric rod antenna array with high radiation efficiency, high gain, and wide bandwidth. The proposed array is integral to a low-loss photonic crystal waveguide platform, and intrinsic silicon is the only constituent material for both the antenna and the feed to maintain the simplicity, compactness, and efficiency. Effective medium theory plays a key role in the antenna performance and integrability. An experimental validation with continuous-wave terahertz electronic systems confirms the minimum gain of 20 dBi across 315-390 GHz. A demonstration shows that a pair of such identical rod array antennas can handle bit-error-free transmission at the speed up to 10 Gbit/s. Further development of this antenna will build critical components for future terahertz communication systems.

1 λ × 1.44 Tb/s free-space IM-DD transmission employing OAM multiplexing and PDM.

PubMed

Zhu, Yixiao; Zou, Kaiheng; Zheng, Zhennan; Zhang, Fan

2016-02-22

We report the experimental demonstration of single wavelength terabit free-space intensity modulation direct detection (IM-DD) system employing both orbital angular momentum (OAM) multiplexing and polarization division multiplexing (PDM). In our experiment, 12 OAM modes with two orthogonal polarization states are used to generate 24 channels for transmission. Each channel carries 30 Gbaud Nyquist PAM-4 signal. Therefore an aggregate gross capacity record of 1.44 Tb/s (12 × 2 × 30 × 2 Gb/s) is acheived with a modulation efficiency of 48 bits/symbol. After 0.8m free-space transmission, the bit error rates (BERs) of all the channels are below the 20% hard-decision forward error correction (HD-FEC) threshold of 1.5 × 10(-2). After applying the decision directed recursive least square (DD-RLS) based filter and post filter, the BERs of two polarizations can be reduced from 5.3 × 10(-3) and 7.3 × 10(-3) to 2.2 × 10(-3) and 3.4 × 10(-3), respectively.

Orbital angular momentum in four channel spatial domain multiplexing system for multi-terabit per second communication architectures

NASA Astrophysics Data System (ADS)

Murshid, Syed H.; Muralikrishnan, Hari P.; Kozaitis, Samuel P.

2012-06-01

Bandwidth increase has always been an important area of research in communications. A novel multiplexing technique known as Spatial Domain Multiplexing (SDM) has been developed at the Optronics Laboratory of Florida Institute of Technology to increase the bandwidth to T-bits/s range. In this technique, space inside the fiber is used effectively to transmit up to four channels of same wavelength at the same time. Experimental and theoretical analysis shows that these channels follow independent helical paths inside the fiber without interfering with each other. Multiple pigtail laser sources of exactly the same wavelength are used to launch light into a single carrier fiber in a fashion that resulting channels follow independent helical trajectories. These helically propagating light beams form optical vortices inside the fiber and carry their own Orbital Angular Momentum (OAM). The outputs of these beams appear as concentric donut shaped rings when projected on a screen. This endeavor presents the experimental outputs and simulated results for a four channel spatially multiplexed system effectively increasing the system bandwidth by a factor of four.

Dynamic and scalable audio classification by collective network of binary classifiers framework: an evolutionary approach.

PubMed

Kiranyaz, Serkan; Mäkinen, Toni; Gabbouj, Moncef

2012-10-01

In this paper, we propose a novel framework based on a collective network of evolutionary binary classifiers (CNBC) to address the problems of feature and class scalability. The main goal of the proposed framework is to achieve a high classification performance over dynamic audio and video repositories. The proposed framework adopts a "Divide and Conquer" approach in which an individual network of binary classifiers (NBC) is allocated to discriminate each audio class. An evolutionary search is applied to find the best binary classifier in each NBC with respect to a given criterion. Through the incremental evolution sessions, the CNBC framework can dynamically adapt to each new incoming class or feature set without resorting to a full-scale re-training or re-configuration. Therefore, the CNBC framework is particularly designed for dynamically varying databases where no conventional static classifiers can adapt to such changes. In short, it is entirely a novel topology, an unprecedented approach for dynamic, content/data adaptive and scalable audio classification. A large set of audio features can be effectively used in the framework, where the CNBCs make appropriate selections and combinations so as to achieve the highest discrimination among individual audio classes. Experiments demonstrate a high classification accuracy (above 90%) and efficiency of the proposed framework over large and dynamic audio databases. Copyright © 2012 Elsevier Ltd. All rights reserved.

Velocity and Hierarchical Spread of Epidemic Outbreaks in Scale-Free Networks

NASA Astrophysics Data System (ADS)

Barthélemy, Marc; Barrat, Alain; Pastor-Satorras, Romualdo; Vespignani, Alessandro

2004-04-01

We study the effect of the connectivity pattern of complex networks on the propagation dynamics of epidemics. The growth time scale of outbreaks is inversely proportional to the network degree fluctuations, signaling that epidemics spread almost instantaneously in networks with scale-free degree distributions. This feature is associated with an epidemic propagation that follows a precise hierarchical dynamics. Once the highly connected hubs are reached, the infection pervades the network in a progressive cascade across smaller degree classes. The present results are relevant for the development of adaptive containment strategies.

Attacks on public telephone networks: technologies and challenges

NASA Astrophysics Data System (ADS)

Kosloff, T.; Moore, Tyler; Keller, J.; Manes, Gavin W.; Shenoi, Sujeet

2003-09-01

Signaling System 7 (SS7) is vital to signaling and control in America's public telephone networks. This paper describes a class of attacks on SS7 networks involving the insertion of malicious signaling messages via compromised SS7 network components. Three attacks are discussed in detail: IAM flood attacks, redirection attacks and point code spoofing attacks. Depending on their scale of execution, these attacks can produce effects ranging from network congestion to service disruption. Methods for detecting these denial-of-service attacks and mitigating their effects are also presented.

Data Communications and Networking. Curriculum Improvement Project. Region II.

ERIC Educational Resources Information Center

Easter, Diane

This course curriculum is intended for use by community college instructors and administrators in implementing a data communications networking course. A student course syllabus provides this information: credit hours, catalog description, prerequisites, required text, instructional process, objectives, student evaluation, and class schedule. A…

Intra- Versus Intersex Aggression: Testing Theories of Sex Differences Using Aggression Networks.

PubMed

Wölfer, Ralf; Hewstone, Miles

2015-08-01

Two theories offer competing explanations of sex differences in aggressive behavior: sexual-selection theory and social-role theory. While each theory has specific strengths and limitations depending on the victim's sex, research hardly differentiates between intrasex and intersex aggression. In the present study, 11,307 students (mean age = 14.96 years; 50% girls, 50% boys) from 597 school classes provided social-network data (aggression and friendship networks) as well as physical (body mass index) and psychosocial (gender and masculinity norms) information. Aggression networks were used to disentangle intra- and intersex aggression, whereas their class-aggregated sex differences were analyzed using contextual predictors derived from sexual-selection and social-role theories. As expected, results revealed that sexual-selection theory predicted male-biased sex differences in intrasex aggression, whereas social-role theory predicted male-biased sex differences in intersex aggression. Findings suggest the value of explaining sex differences separately for intra- and intersex aggression with a dual-theory framework covering both evolutionary and normative components. © The Author(s) 2015.

Signaling networks in joint development

PubMed Central

Salva, Joanna E.; Merrill, Amy E.

2016-01-01

Here we review studies identifying regulatory networks responsible for synovial, cartilaginous, and fibrous joint development. Synovial joints, characterized by the fluid-filled synovial space between the bones, are found in high-mobility regions and are the most common type of joint. Cartilaginous joints unite adjacent bones through either a hyaline cartilage or fibrocartilage intermediate. Fibrous joints, which include the cranial sutures, form a direct union between bones through fibrous connective tissue. We describe how the distinct morphologic and histogenic characteristics of these joint classes are established during embryonic development. Collectively, these studies reveal that despite the heterogeneity of joint strength and mobility, joint development throughout the skeleton utilizes common signaling networks via long-range morphogen gradients and direct cell-cell contact. This suggests that different joint types represent specialized variants of homologous developmental modules. Identifying the unifying aspects of the signaling networks between joint classes allows a more complete understanding of the signaling code for joint formation, which is critical to improving strategies for joint regeneration and repair. PMID:27859991

A new class of methods for functional connectivity estimation

NASA Astrophysics Data System (ADS)

Lin, Wutu

Measuring functional connectivity from neural recordings is important in understanding processing in cortical networks. The covariance-based methods are the current golden standard for functional connectivity estimation. However, the link between the pair-wise correlations and the physiological connections inside the neural network is unclear. Therefore, the power of inferring physiological basis from functional connectivity estimation is limited. To build a stronger tie and better understand the relationship between functional connectivity and physiological neural network, we need (1) a realistic model to simulate different types of neural recordings with known ground truth for benchmarking; (2) a new functional connectivity method that produce estimations closely reflecting the physiological basis. In this thesis, (1) I tune a spiking neural network model to match with human sleep EEG data, (2) introduce a new class of methods for estimating connectivity from different kinds of neural signals and provide theory proof for its superiority, (3) apply it to simulated fMRI data as an application.

Convolutional neural network with transfer learning for rice type classification

NASA Astrophysics Data System (ADS)

Patel, Vaibhav Amit; Joshi, Manjunath V.

2018-04-01

Presently, rice type is identified manually by humans, which is time consuming and error prone. Therefore, there is a need to do this by machine which makes it faster with greater accuracy. This paper proposes a deep learning based method for classification of rice types. We propose two methods to classify the rice types. In the first method, we train a deep convolutional neural network (CNN) using the given segmented rice images. In the second method, we train a combination of a pretrained VGG16 network and the proposed method, while using transfer learning in which the weights of a pretrained network are used to achieve better accuracy. Our approach can also be used for classification of rice grain as broken or fine. We train a 5-class model for classifying rice types using 4000 training images and another 2- class model for the classification of broken and normal rice using 1600 training images. We observe that despite having distinct rice images, our architecture, pretrained on ImageNet data boosts classification accuracy significantly.

Updated Guidelines for ANSS Instruments

NASA Astrophysics Data System (ADS)

Evans, J. R.; Hutt, C. R.; Gee, L. S.

2014-12-01

In 2008 the Advanced National Seismic System (ANSS) of the U.S. Geological Survey (USGS) and cooperating universities and institutions issued USGS Open-File Report 2008-1262 (OFR) containing detailed guidelines for the performance of instrumentation to be used by the ANSS. Here we report an update underway to these guidelines to take account of lessons learned, changing technology, and expanding user desires; in a few instances, performance matters that are very hard to test in practice are either modified or removed. Instrument classes are defined in the OFR in terms of amplitude resolution and cost; because relevant technologies have advanced substantially in these six years and a number of groups have begun to explore the use of relatively inexpensive, entirely host installed and operated Class C systems, the guidelines for strong-motion sensors are being expanded to include detailed guidelines for them rather than just anticipating them. As always, Class A systems will form the state-of-the-art backbone of any network, with Class B filling in spatially and in areas otherwise not covered well. Class C systems would be an additional step in making networks denser by providing very inexpensive hardware, installation, and maintenance to fill in additionally between Class A and B sites, for example in a high-seismicity urban area, with Class A sites every 4-6 km, Class B every 2-3 km, and Class C at <1 km spacing. Class C devices would be both installed and maintained by hosts, not institutions, and therefore also would be economical for extending coverage in regions with widely spaced or rare large seismicity, such as the central and eastern U.S.

CNN Newsroom Classroom Guides, March 2001.

ERIC Educational Resources Information Center

Cable News Network, Atlanta, GA.

These classroom guides, designed to accompany the daily CNN (Cable News Network) Newsroom broadcasts for the month of March 2001, provide program rundowns, suggestions for class activities and discussion, student handouts, and a list of related news terms. Top stories include: Seattle earthquake and U.S. economy working class communities fear a…

Middle-Class Mothers on Urban School Selection in Gentrifying Areas

ERIC Educational Resources Information Center

Roberts, Amy; Lakes, Richard D.

2016-01-01

This study examined middle-class mothers' engagement in urban school selection as residents of two gentrifying neighborhoods in Atlanta, Georgia. Gentrifiers levy social capital when activating or exercising agency and create social networks that valorize child-rearing concerns through exchange of information. Thirty mothers with children under…

Occupation, Class, and Social Networks in Urban China

ERIC Educational Resources Information Center

Bian, Yanjie; Breiger, Ronald; Davis, Deborah; Galaskiewicz, Joseph

2005-01-01

China's class structure is changing dramatically in the wake of post-1978 market-oriented economic reforms. The creation of a mixed "market-socialist" economy has eroded the institutional bases of a cadre-dominated social hierarchy and created conditions for a new pattern of social stratification. Although conditions remain dynamic,…

Well-Connected: Exploring Parent Social Networks in a Gentrifying School

ERIC Educational Resources Information Center

Cappelletti, Gina A.

2017-01-01

The enrollment and engagement of middle-class families in historically low-income urban public schools can generate school improvements, including increased resources and expanded extracurricular programming. At the same time, prior research has highlighted the marginalization of low-income parents as one consequence of middle-class parent…

Teaching Service Modelling to a Mixed Class: An Integrated Approach

ERIC Educational Resources Information Center

Deng, Jeremiah D.; Purvis, Martin K.

2015-01-01

Service modelling has become an increasingly important area in today's telecommunications and information systems practice. We have adapted a Network Design course in order to teach service modelling to a mixed class of both the telecommunication engineering and information systems backgrounds. An integrated approach engaging mathematics teaching…

Class, Kinship Density, and Conjugal Role Segregation.

ERIC Educational Resources Information Center

Hill, Malcolm D.

1988-01-01

Studied conjugal role segregation in 150 married women from intact families in working-class community. Found that, although involvement in dense kinship networks was associated with conjugal role segregation, respondents' attitudes toward marital roles and phase of family cycle when young children were present were more powerful predictors of…

A System for Video Surveillance and Monitoring CMU VSAM Final Report

DTIC Science & Technology

1999-11-30

motion-based skeletonization, neural network , spatio-temporal salience Patterns inside image chips, spurious motion rejection, model -based... network of sensors with respect to the model coordinate system, computation of 3D geolocation estimates, and graphical display of object hypotheses...rithms have been developed. The first uses view dependent visual properties to train a neural network classifier to recognize four classes: single

Rethinking Traffic Management: Design of Optimizable Networks

DTIC Science & Technology

2008-06-01

Though this paper used optimization theory to design and analyze DaVinci , op- timization theory is one of many possible tools to enable a grounded...dynamically allocate bandwidth shares. The distributed protocols can be implemented using DaVinci : Dynamically Adaptive VIrtual Networks for a Customized...Internet. In DaVinci , each virtual network runs traffic-management protocols optimized for a traffic class, and link bandwidth is dynamically allocated

The Design of NetSecLab: A Small Competition-Based Network Security Lab

ERIC Educational Resources Information Center

Lee, C. P.; Uluagac, A. S.; Fairbanks, K. D.; Copeland, J. A.

2011-01-01

This paper describes a competition-style of exercise to teach system and network security and to reinforce themes taught in class. The exercise, called NetSecLab, is conducted on a closed network with student-formed teams, each with their own Linux system to defend and from which to launch attacks. Students are expected to learn how to: 1) install…

Cisco Networking Academy Program for high school students: Formative & summative evaluation

NASA Astrophysics Data System (ADS)

Cranford-Wesley, Deanne

This study examined the effectiveness of the Cisco Network Technology Program in enhancing students' technology skills as measured by classroom strategies, student motivation, student attitude, and student learning. Qualitative and quantitative methods were utilized to determine the effectiveness of this program. The study focused on two 11th grade classrooms at Hamtramck High School. Hamtramck, an inner-city community located in Detroit, is racially and ethnically diverse. The majority of students speak English as a second language; more than 20 languages are represented in the school district. More than 70% of the students are considered to be economically at risk. Few students have computers at home, and their access to the few computers at school is limited. Purposive sampling was conducted for this study. The sample consisted of 40 students, all of whom were trained in Cisco Networking Technologies. The researcher examined viable learning strategies in teaching a Cisco Networking class that focused on a web-based approach. Findings revealed that the Cisco Networking Academy Program was an excellent vehicle for teaching networking skills and, therefore, helping to enhance computer skills for the participating students. However, only a limited number of students were able to participate in the program, due to limited computer labs and lack of qualified teaching personnel. In addition, the cumbersome technical language posed an obstacle to students' success in networking. Laboratory assignments were preferred by 90% of the students over lecture and PowerPoint presentations. Practical applications, lab projects, interactive assignments, PowerPoint presentations, lectures, discussions, readings, research, and assessment all helped to increase student learning and proficiency and to enrich the classroom experience. Classroom strategies are crucial to student success in the networking program. Equipment must be updated and utilized to ensure that students are applying practical skills to networking concepts. The results also suggested a high level of motivation and retention in student participants. Students in both classes scored 80% proficiency on the Achievement Motivation Profile Assessment. The identified standard proficiency score was 70%, and both classes exceeded the standard.

Inferring the lithology of borehole rocks by applying neural network classifiers to downhole logs: an example from the Ocean Drilling Program

NASA Astrophysics Data System (ADS)

Benaouda, D.; Wadge, G.; Whitmarsh, R. B.; Rothwell, R. G.; MacLeod, C.

1999-02-01

In boreholes with partial or no core recovery, interpretations of lithology in the remainder of the hole are routinely attempted using data from downhole geophysical sensors. We present a practical neural net-based technique that greatly enhances lithological interpretation in holes with partial core recovery by using downhole data to train classifiers to give a global classification scheme for those parts of the borehole for which no core was retrieved. We describe the system and its underlying methods of data exploration, selection and classification, and present a typical example of the system in use. Although the technique is equally applicable to oil industry boreholes, we apply it here to an Ocean Drilling Program (ODP) borehole (Hole 792E, Izu-Bonin forearc, a mixture of volcaniclastic sandstones, conglomerates and claystones). The quantitative benefits of quality-control measures and different subsampling strategies are shown. Direct comparisons between a number of discriminant analysis methods and the use of neural networks with back-propagation of error are presented. The neural networks perform better than the discriminant analysis techniques both in terms of performance rates with test data sets (2-3 per cent better) and in qualitative correlation with non-depth-matched core. We illustrate with the Hole 792E data how vital it is to have a system that permits the number and membership of training classes to be changed as analysis proceeds. The initial classification for Hole 792E evolved from a five-class to a three-class and then to a four-class scheme with resultant classification performance rates for the back-propagation neural network method of 83, 84 and 93 per cent respectively.

Interplay of Noisy Gene Expression and Dynamics Explains Patterns of Bacterial Operon Organization

NASA Astrophysics Data System (ADS)

Igoshin, Oleg

2011-03-01

Bacterial chromosomes are organized into operons -- sets of genes co-transcribed into polycistronic messenger RNA. Hypotheses explaining the emergence and maintenance of operons include proportional co-regulation, horizontal transfer of intact ``selfish'' operons, emergence via gene duplication, and co-production of physically interacting proteins to speed their association. We hypothesized an alternative: operons can reduce or increase intrinsic gene expression noise in a manner dependent on the post-translational interactions, thereby resulting in selection for or against operons in depending on the network architecture. We devised five classes of two-gene network modules and show that the effects of operons on intrinsic noise depend on class membership. Two classes exhibit decreased noise with co-transcription, two others reveal increased noise, and the remaining one does not show a significant difference. To test our modeling predictions we employed bioinformatic analysis to determine the relationship gene expression noise and operon organization. The results confirm the overrepresentation of noise-minimizing operon architectures and provide evidence against other hypotheses. Our results thereby suggest a central role for gene expression noise in selecting for or maintaining operons in bacterial chromosomes. This demonstrates how post-translational network dynamics may provide selective pressure for organizing bacterial chromosomes, and has practical consequences for designing synthetic gene networks. This work is supported by National Institutes of Health grant 1R01GM096189-01.

Software for hyperspectral, joint photographic experts group (.JPG), portable network graphics (.PNG) and tagged image file format (.TIFF) segmentation

NASA Astrophysics Data System (ADS)

Bruno, L. S.; Rodrigo, B. P.; Lucio, A. de C. Jorge

2016-10-01

This paper presents a system developed by an application of a neural network Multilayer Perceptron for drone acquired agricultural image segmentation. This application allows a supervised user training the classes that will posteriorly be interpreted by neural network. These classes will be generated manually with pre-selected attributes in the application. After the attribute selection a segmentation process is made to allow the relevant information extraction for different types of images, RGB or Hyperspectral. The application allows extracting the geographical coordinates from the image metadata, geo referencing all pixels on the image. In spite of excessive memory consume on hyperspectral images regions of interest, is possible to perform segmentation, using bands chosen by user that can be combined in different ways to obtain different results.

Metadata and network API aspects of a framework for storing and retrieving civil infrastructure monitoring data

NASA Astrophysics Data System (ADS)

Wong, John-Michael; Stojadinovic, Bozidar

2005-05-01

A framework has been defined for storing and retrieving civil infrastructure monitoring data over a network. The framework consists of two primary components: metadata and network communications. The metadata component provides the descriptions and data definitions necessary for cataloging and searching monitoring data. The communications component provides Java classes for remotely accessing the data. Packages of Enterprise JavaBeans and data handling utility classes are written to use the underlying metadata information to build real-time monitoring applications. The utility of the framework was evaluated using wireless accelerometers on a shaking table earthquake simulation test of a reinforced concrete bridge column. The NEESgrid data and metadata repository services were used as a backend storage implementation. A web interface was created to demonstrate the utility of the data model and provides an example health monitoring application.

Multispectral image fusion using neural networks

NASA Technical Reports Server (NTRS)

Kagel, J. H.; Platt, C. A.; Donaven, T. W.; Samstad, E. A.

1990-01-01

A prototype system is being developed to demonstrate the use of neural network hardware to fuse multispectral imagery. This system consists of a neural network IC on a motherboard, a circuit card assembly, and a set of software routines hosted by a PC-class computer. Research in support of this consists of neural network simulations fusing 4 to 7 bands of Landsat imagery and fusing (separately) multiple bands of synthetic imagery. The simulations, results, and a description of the prototype system are presented.

GAPR2: A DTN Routing Protocol for Communications in Challenged, Degraded, and Denied Environments

DTIC Science & Technology

2015-09-01

Transmission Speed Vs. Latency Figure 4.7: Helsinki Simulation Set 2, High Network Load and Small Buffers Analysis of Delivery Ratio in Helsinki Simulation...ipnsig.org/. [17] MANET routing, class notes for CS4554: Network modeling and analysis . 119 [18] S. Basagni et al. Mobile ad hoc networking . John...Wiley & Sons, 2004. [19] E. Royer et al. A review of current routing protocols for ad hoc mobile wireless networks . Personal Communications, IEEE, 6(2

Radio Galaxy Zoo: compact and extended radio source classification with deep learning

NASA Astrophysics Data System (ADS)

Lukic, V.; Brüggen, M.; Banfield, J. K.; Wong, O. I.; Rudnick, L.; Norris, R. P.; Simmons, B.

2018-05-01

Machine learning techniques have been increasingly useful in astronomical applications over the last few years, for example in the morphological classification of galaxies. Convolutional neural networks have proven to be highly effective in classifying objects in image data. In the context of radio-interferometric imaging in astronomy, we looked for ways to identify multiple components of individual sources. To this effect, we design a convolutional neural network to differentiate between different morphology classes using sources from the Radio Galaxy Zoo (RGZ) citizen science project. In this first step, we focus on exploring the factors that affect the performance of such neural networks, such as the amount of training data, number and nature of layers, and the hyperparameters. We begin with a simple experiment in which we only differentiate between two extreme morphologies, using compact and multiple-component extended sources. We found that a three-convolutional layer architecture yielded very good results, achieving a classification accuracy of 97.4 per cent on a test data set. The same architecture was then tested on a four-class problem where we let the network classify sources into compact and three classes of extended sources, achieving a test accuracy of 93.5 per cent. The best-performing convolutional neural network set-up has been verified against RGZ Data Release 1 where a final test accuracy of 94.8 per cent was obtained, using both original and augmented images. The use of sigma clipping does not offer a significant benefit overall, except in cases with a small number of training images.

Brain anatomical networks in world class gymnasts: a DTI tractography study.

PubMed

Wang, Bin; Fan, Yuanyuan; Lu, Min; Li, Shumei; Song, Zheng; Peng, Xiaoling; Zhang, Ruibin; Lin, Qixiang; He, Yong; Wang, Jun; Huang, Ruiwang

2013-01-15

The excellent motor skills of world class gymnasts amaze everyone. People marvel at the way they precisely control their movements and wonder how the brain structure and function of these elite athletes differ from those of non-athletes. In this study, we acquired diffusion images from thirteen world class gymnasts and fourteen matched controls, constructed their anatomical networks, and calculated the topological properties of each network based on graph theory. From a connectivity-based analysis, we found that most of the edges with increased connection density in the champions were linked to brain regions that are located in the sensorimotor, attentional, and default-mode systems. From graph-based metrics, we detected significantly greater global and local efficiency but shorter characteristic path length in the anatomical networks of the champions compared with the controls. Moreover, in the champions we found a significantly higher nodal degree and greater regional efficiency in several brain regions that correspond to motor and attention functions. These included the left precentral gyrus, left postcentral gyrus, right anterior cingulate gyrus and temporal lobes. In addition, we revealed an increase in the mean fractional anisotropy of the corticospinal tract in the champions, possibly in response to long-term gymnastic training. Our study indicates that neuroanatomical adaptations and plastic changes occur in gymnasts' brain anatomical networks either in response to long-term intensive gymnastic training or as an innate predisposition or both. Our findings may help to explain gymnastic skills at the highest levels of performance and aid in understanding the neural mechanisms that distinguish expert gymnasts from novices. Copyright © 2012 Elsevier Inc. All rights reserved.

Social Media and Networking Technologies: An Analysis of Collaborative Work and Team Communication

ERIC Educational Resources Information Center

Okoro, Ephraim A.; Hausman, Angela; Washington, Melvin C.

2012-01-01

Digital communication increases students' learning outcomes in higher education. Web 2.0 technologies encourages students' active engagement, collaboration, and participation in class activities, facilitates group work, and encourages information sharing among students. Familiarity with organizational use and sharing in social networks aids…

Planning Communication Networks to Deliver Educational Services.

ERIC Educational Resources Information Center

Ballard, Richard J.; Eastwood, Lester F., Jr.

As companion to the more general document Telecommunications Media for the Delivery of Educational Programming , this report concentrates on the technical and economic factors affecting the design of only one class of educational networks, dedicated coaxial cable systems. To provide illustrations, possible single and dual dedicated cable networks…

College Students' Nutrition Information Networks.

ERIC Educational Resources Information Center

Hertzler, Ann A.; Frary, Robert B.

1995-01-01

Use of nutrition information networks (consumer market, media, authority, family, and high school classes), food choices, fat practices, and nutrient intake were rated by 179 male and 300 female undergraduates. Family was an important influence; media and consumer market influenced fat practices, especially for women. No source was used very…

Common neighbours and the local-community-paradigm for topological link prediction in bipartite networks

NASA Astrophysics Data System (ADS)

Daminelli, Simone; Thomas, Josephine Maria; Durán, Claudio; Vittorio Cannistraci, Carlo

2015-11-01

Bipartite networks are powerful descriptions of complex systems characterized by two different classes of nodes and connections allowed only across but not within the two classes. Unveiling physical principles, building theories and suggesting physical models to predict bipartite links such as product-consumer connections in recommendation systems or drug-target interactions in molecular networks can provide priceless information to improve e-commerce or to accelerate pharmaceutical research. The prediction of nonobserved connections starting from those already present in the topology of a network is known as the link-prediction problem. It represents an important subject both in many-body interaction theory in physics and in new algorithms for applied tools in computer science. The rationale is that the existing connectivity structure of a network can suggest where new connections can appear with higher likelihood in an evolving network, or where nonobserved connections are missing in a partially known network. Surprisingly, current complex network theory presents a theoretical bottle-neck: a general framework for local-based link prediction directly in the bipartite domain is missing. Here, we overcome this theoretical obstacle and present a formal definition of common neighbour index and local-community-paradigm (LCP) for bipartite networks. As a consequence, we are able to introduce the first node-neighbourhood-based and LCP-based models for topological link prediction that utilize the bipartite domain. We performed link prediction evaluations in several networks of different size and of disparate origin, including technological, social and biological systems. Our models significantly improve topological prediction in many bipartite networks because they exploit local physical driving-forces that participate in the formation and organization of many real-world bipartite networks. Furthermore, we present a local-based formalism that allows to intuitively implement neighbourhood-based link prediction entirely in the bipartite domain.

TTEthernet for Integrated Spacecraft Networks

NASA Technical Reports Server (NTRS)

Loveless, Andrew

2015-01-01

Aerospace projects have traditionally employed federated avionics architectures, in which each computer system is designed to perform one specific function (e.g. navigation). There are obvious downsides to this approach, including excessive weight (from so much computing hardware), and inefficient processor utilization (since modern processors are capable of performing multiple tasks). There has therefore been a push for integrated modular avionics (IMA), in which common computing platforms can be leveraged for different purposes. This consolidation of multiple vehicle functions to shared computing platforms can significantly reduce spacecraft cost, weight, and design complexity. However, the application of IMA principles introduces significant challenges, as the data network must accommodate traffic of mixed criticality and performance levels - potentially all related to the same shared computer hardware. Because individual network technologies are rarely so competent, the development of truly integrated network architectures often proves unreasonable. Several different types of networks are utilized - each suited to support a specific vehicle function. Critical functions are typically driven by precise timing loops, requiring networks with strict guarantees regarding message latency (i.e. determinism) and fault-tolerance. Alternatively, non-critical systems generally employ data networks prioritizing flexibility and high performance over reliable operation. Switched Ethernet has seen widespread success filling this role in terrestrial applications. Its high speed, flexibility, and the availability of inexpensive commercial off-the-shelf (COTS) components make it desirable for inclusion in spacecraft platforms. Basic Ethernet configurations have been incorporated into several preexisting aerospace projects, including both the Space Shuttle and International Space Station (ISS). However, classical switched Ethernet cannot provide the high level of network determinism required by real-time spacecraft applications. Even with modern advancements, the uncoordinated (i.e. event-driven) nature of Ethernet communication unavoidably leads to message contention within network switches. The arbitration process used to resolve such conflicts introduces variation in the time it takes for messages to be forwarded. TTEthernet1 introduces decentralized clock synchronization to switched Ethernet, enabling message transmission according to a time-triggered (TT) paradigm. A network planning tool is used to allocate each device a finite amount of time in which it may transmit a frame. Each time slot is repeated sequentially to form a periodic communication schedule that is then loaded onto each TTEthernet device (e.g. switches and end systems). Each network participant references the synchronized time in order to dispatch messages at predetermined instances. This schedule guarantees that no contention exists between time-triggered Ethernet frames in the network switches, therefore eliminating the need for arbitration (and the timing variation it causes). Besides time-triggered messaging, TTEthernet networks may provide two additional traffic classes to support communication of different criticality levels. In the rate-constrained (RC) traffic class, the frame payload size and rate of transmission along each communication channel are limited to predetermined maximums. The network switches can therefore be configured to accommodate the known worst-case traffic pattern, and buffer overflows can be eliminated. The best-effort (BE) traffic class behaves akin to classical Ethernet. No guarantees are provided regarding transmission latency or successful message delivery. TTEthernet coordinates transmission of all three traffic classes over the same physical connections, therefore accommodating the full spectrum of traffic criticality levels required in IMA architectures. Common computing platforms (e.g. LRUs) can share networking resources in such a way that failures in non-critical systems (using BE or RC communication modes) cannot impact flight-critical functions (using TT communication). Furthermore, TTEthernet hardware (e.g. switches, cabling) can be shared by both TTEthernet and classical Ethernet traffic.

Impact of Multimedia and Network Services on an Introductory Level Course

NASA Technical Reports Server (NTRS)

Russ, John C.

1996-01-01

We will demonstrate and describe the impact of our use of multimedia and network connectivity on a sophomore-level introductory course in materials science. This class services all engineering students, resulting in large (more than 150) class sections with no hands-on laboratory. In 1990 we began to develop computer graphics that might substitute for some laboratory or real-world experiences, and demonstrate relationships hard to show with static textbook images or chalkboard drawings. We created a comprehensive series of modules that cover the entire course content. Called VIMS (Visualizations in Materials Science), these are available in the form of a CD-ROM and also via the internet.

Adaptive neural network backstepping control for a class of uncertain fractional-order chaotic systems with unknown backlash-like hysteresis

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

Wu, Yimin; Lv, Hui, E-mail: lvhui207@gmail.com

In this paper, we consider the control problem of a class of uncertain fractional-order chaotic systems preceded by unknown backlash-like hysteresis nonlinearities based on backstepping control algorithm. We model the hysteresis by using a differential equation. Based on the fractional Lyapunov stability criterion and the backstepping algorithm procedures, an adaptive neural network controller is driven. No knowledge of the upper bound of the disturbance and system uncertainty is required in our controller, and the asymptotical convergence of the tracking error can be guaranteed. Finally, we give two simulation examples to confirm our theoretical results.

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

Lee, Gregory L.; Arnold, Dorian; LeGendre, Matthew

STAT is a light weight debugging tool that gathers and merges stack traces from all of the processes in a parallel application. STAT uses the MRNet tree based overlay network to broadcast commands from the tool front-end to the STAT daemons and for the front-end to gather the traces from the STAT daemons. As the traces propagate through the MRNet network tree, they are merged across all tasks to form a similar function call patterns and to delineate a small set of equivalence classes. A representative task from each of these classes can then be fed into a full featuremore » debugger like TolalView for root cause analysis.« less

Cascades on a class of clustered random networks

NASA Astrophysics Data System (ADS)

Hackett, Adam; Melnik, Sergey; Gleeson, James P.

2011-05-01

We present an analytical approach to determining the expected cascade size in a broad range of dynamical models on the class of random networks with arbitrary degree distribution and nonzero clustering introduced previously in [M. E. J. Newman, Phys. Rev. Lett. PRLTAO0031-900710.1103/PhysRevLett.103.058701103, 058701 (2009)]. A condition for the existence of global cascades is derived as well as a general criterion that determines whether increasing the level of clustering will increase, or decrease, the expected cascade size. Applications, examples of which are provided, include site percolation, bond percolation, and Watts’ threshold model; in all cases analytical results give excellent agreement with numerical simulations.

Socioeconomic correlations and stratification in social-communication networks.

PubMed

Leo, Yannick; Fleury, Eric; Alvarez-Hamelin, J Ignacio; Sarraute, Carlos; Karsai, Márton

2016-12-01

The uneven distribution of wealth and individual economic capacities are among the main forces, which shape modern societies and arguably bias the emerging social structures. However, the study of correlations between the social network and economic status of individuals is difficult due to the lack of large-scale multimodal data disclosing both the social ties and economic indicators of the same population. Here, we close this gap through the analysis of coupled datasets recording the mobile phone communications and bank transaction history of one million anonymized individuals living in a Latin American country. We show that wealth and debt are unevenly distributed among people in agreement with the Pareto principle; the observed social structure is strongly stratified, with people being better connected to others of their own socioeconomic class rather than to others of different classes; the social network appears to have assortative socioeconomic correlations and tightly connected 'rich clubs'; and that individuals from the same class live closer to each other but commute further if they are wealthier. These results are based on a representative, society-large population, and empirically demonstrate some long-lasting hypotheses on socioeconomic correlations, which potentially lay behind social segregation, and induce differences in human mobility. © 2016 The Author(s).

More Opportunities than Wealth: Inequality and Emergent Social Classes in a Network of Power and Frustration

NASA Astrophysics Data System (ADS)

Nisoli, Cristiano; Mahault, Benoit; Saxena, Avadh

We introduce a minimal agent-based model to qualitatively conceptualize the allocation of limited wealth among more abundant opportunities. There the interplay of power, satisfaction and frustration determines the distribution, concentration, and inequality of wealth. Our framework allows us to compare subjective measures of frustration and satisfaction to collective measures of fairness in wealth distribution, such as the Lorenz curve and the Gini index. We find that a completely libertarian, law-of-the-jungle setting, where every agent can acquire wealth from, or lose wealth to, anybody else invariably leads to large inequality. The picture is however dramatically modified when hard constraints are imposed over agents, and they are limited to share wealth with neighbors on a network. We address dynamical societies via an out of equilibrium coevolution of the network, driven by a competition between power and frustration. The ratio between power and frustration controls different dynamical regimes separated by kinetic transitions and characterized by drastically different values of the indices of equality. In particular, it leads to the emergence of three self-organized social classes, lower, middle, and upper class, whose interactions drive a cyclical regime.

Socioeconomic correlations and stratification in social-communication networks

PubMed Central

Leo, Yannick; Fleury, Eric; Sarraute, Carlos

2016-01-01

The uneven distribution of wealth and individual economic capacities are among the main forces, which shape modern societies and arguably bias the emerging social structures. However, the study of correlations between the social network and economic status of individuals is difficult due to the lack of large-scale multimodal data disclosing both the social ties and economic indicators of the same population. Here, we close this gap through the analysis of coupled datasets recording the mobile phone communications and bank transaction history of one million anonymized individuals living in a Latin American country. We show that wealth and debt are unevenly distributed among people in agreement with the Pareto principle; the observed social structure is strongly stratified, with people being better connected to others of their own socioeconomic class rather than to others of different classes; the social network appears to have assortative socioeconomic correlations and tightly connected ‘rich clubs’; and that individuals from the same class live closer to each other but commute further if they are wealthier. These results are based on a representative, society-large population, and empirically demonstrate some long-lasting hypotheses on socioeconomic correlations, which potentially lay behind social segregation, and induce differences in human mobility. PMID:27974571

Duplicate retention in signalling proteins and constraints from network dynamics.

PubMed

Soyer, O S; Creevey, C J

2010-11-01

Duplications are a major driving force behind evolution. Most duplicates are believed to fix through genetic drift, but it is not clear whether this process affects all duplications equally or whether there are certain gene families that are expected to show neutral expansions under certain circumstances. Here, we analyse the neutrality of duplications in different functional classes of signalling proteins based on their effects on response dynamics. We find that duplications involving intermediary proteins in a signalling network are neutral more often than those involving receptors. Although the fraction of neutral duplications in all functional classes increase with decreasing population size and selective pressure on dynamics, this effect is most pronounced for receptors, indicating a possible expansion of receptors in species with small population size. In line with such an expectation, we found a statistically significant increase in the number of receptors as a fraction of genome size in eukaryotes compared with prokaryotes. Although not confirmative, these results indicate that neutral processes can be a significant factor in shaping signalling networks and affect proteins from different functional classes differently. © 2010 The Authors. Journal Compilation © 2010 European Society For Evolutionary Biology.

A queueing network model to analyze the impact of parallelization of care on patient cycle time.

PubMed

Jiang, Lixiang; Giachetti, Ronald E

2008-09-01

The total time a patient spends in an outpatient facility, called the patient cycle time, is a major contributor to overall patient satisfaction. A frequently recommended strategy to reduce the total time is to perform some activities in parallel thereby shortening patient cycle time. To analyze patient cycle time this paper extends and improves upon existing multi-class open queueing network model (MOQN) so that the patient flow in an urgent care center can be modeled. Results of the model are analyzed using data from an urgent care center contemplating greater parallelization of patient care activities. The results indicate that parallelization can reduce the cycle time for those patient classes which require more than one diagnostic and/ or treatment intervention. However, for many patient classes there would be little if any improvement, indicating the importance of tools to analyze business process reengineering rules. The paper makes contributions by implementing an approximation for fork/join queues in the network and by improving the approximation for multiple server queues in both low traffic and high traffic conditions. We demonstrate the accuracy of the MOQN results through comparisons to simulation results.

Results from Three Years of Ka-Band Propagation Characterization at Svalbard, Norway

NASA Technical Reports Server (NTRS)

Nessel, James; Zemba, Michael; Morse, Jacquelynne

2015-01-01

Over the next several years, NASA plans to launch several earth science missions which are expected to achieve data throughputs of 5-40 terabits per day transmitted from low earth orbiting spacecraft to ground stations. The current S-band and X-band frequency allocations in use by NASA, however, are incapable of supporting the data rates required to meet this demand. As such, NASA is in the planning stages to upgrade its existing Near Earth Network (NEN) polar ground stations to support Ka-band (25.5-27 GHz) operations. Consequently, it installed and operated a Ka-band radiometer at the Svalbard site. Svalbard was chosen as the appropriate site for two primary reasons: (1) Svalbard will be the first site to be upgraded to Ka-band operations within the NEN Polar Network enhancement plan, and (2) there exists a complete lack of Ka-band propagation data at this site (as opposed to the Fairbanks, AK NEN site, which has 5 years of characterization collected during the Advanced Communications Technology becomes imperative that characterization of propagation effects at these NEN sites is conducted to determine expected system Satellite (ACTS) campaign). processing and provide the Herein, we discuss the data three-year measurement results performance, particularly at low elevation angles ((is) less than 10 deg) from the ongoing Ka-band propagation characterization where spacecraft signal acquisition typically occurs. Since May 2011, NASA Glenn Research Center has installed and operated a Ka-band radiometer at the NEN site located in Svalbard, Norway. The Ka-band radiometer monitors the water vapor line, as well as 4 frequencies around 26.5 GHz at a fixed 10 deg elevation angle. Three-year data collection results indicate good campaign at Svalbard, Norway. Comparison of these results with the ITU models and existing ERA profile data indicates very good agreement when the 2010 rain maps and cloud statistics are used. Finally, the Svalbard data is used to derive the expected atmospheric margin requirements for this site agreement with models and comparable performance to necessary to maintain total system availability levels for the previously characterized northern latitude sites in the United States, i.e., Fairbanks, Alaska. The Svalbard data is used to upcoming Joint Polar Satellite System (JPSS) launch in the derive availability results for an upcoming earth-observation 2017/2022 timeframes. mission, JPSS-1, and indicate a requirement of 4 dB of atmospheric attenuation margin necessary to close the link with 99% overall system availability for the expected LEO orbital cycle, as observed from the Svalbard location.

Results from Three Years of Ka-band Propagation Characterization at Svalbard, Norway

NASA Technical Reports Server (NTRS)

Nessel, James A.; Zemba, Michael; Morse, Jacquelynne

2015-01-01

Over the next several years, NASA plans to launch several earth science missions which are expected to achieve data throughputs of 5-40 terabits per day transmitted from low earth orbiting spacecraft to ground stations. The current S-band and X-band frequency allocations in use by NASA, however, are incapable of supporting the data rates required to meet this demand. As such, NASA is in the planning stages to upgrade its existing Near Earth Network (NEN) polar ground stations to support Ka-band (25.5-27 GHz) operations. Consequently, it installed and operated a Ka-band radiometer at the Svalbard site. Svalbard was chosen as the appropriate site for two primary reasons: (1) Svalbard will be the first site to be upgraded to Ka-band operations within the NEN Polar Network enhancement plan, and (2) there exists a complete lack of Ka-band propagation data at this site (as opposed to the Fairbanks, AK NEN site, which has 5 years of characterization collected during the Advanced Communications Technology becomes imperative that characterization of propagation effects at these NEN sites is conducted to determine expected system Satellite (ACTS) campaign). processing and provide the Herein, we discuss the data three-year measurement results performance, particularly at low elevation angles ((is) less than 10 deg) from the ongoing Ka-band propagation characterization where spacecraft signal acquisition typically occurs. Since May 2011, NASA Glenn Research Center has installed and operated a Ka-band radiometer at the NEN site located in Svalbard, Norway. The Ka-band radiometer monitors the water vapor line, as well as 4 frequencies around 26.5 GHz at a fixed 10 deg elevation angle. Three-year data collection results indicate good campaign at Svalbard, Norway. Comparison of these results with the ITU models and existing ERA profile data indicates very good agreement when the 2010 rain maps and cloud statistics are used. Finally, the Svalbard data is used to derive the expected atmospheric margin requirements for this site agreement with models and comparable performance to necessary to maintain total system availability levels for the previously characterized northern latitude sites in the United States, i.e., Fairbanks, Alaska. The Svalbard data is used to upcoming Joint Polar Satellite System (JPSS) launch in the derive availability results for an upcoming earth-observation 2017/2022 timeframes. mission, JPSS-1, and indicate a requirement of 4 dB of atmospheric attenuation margin necessary to close the link with 99% overall system availability for the expected LEO orbital cycle, as observed from the Svalbard location.

Young People Take Their Rightful Places as Full and Contributing Members of a World Class Workforce: Philadelphia Youth Network Annual Report 2006

ERIC Educational Resources Information Center

Philadelphia Youth Network, 2006

2006-01-01

The title of this year's annual report has particular meaning for all of the staff at the Philadelphia Youth Network. The phrase derives from Philadelphia Youth Network's (PYN's) new vision statement, developed as part of its recent strategic planning process, which reads: All of our city's young people take their rightful places as full and…

Introduction to Concepts in Artificial Neural Networks

NASA Technical Reports Server (NTRS)

Niebur, Dagmar

1995-01-01

This introduction to artificial neural networks summarizes some basic concepts of computational neuroscience and the resulting models of artificial neurons. The terminology of biological and artificial neurons, biological and machine learning and neural processing is introduced. The concepts of supervised and unsupervised learning are explained with examples from the power system area. Finally, a taxonomy of different types of neurons and different classes of artificial neural networks is presented.

A Participatory Evaluation of the Use of Social Networking Tools in a High School Math Class

ERIC Educational Resources Information Center

Wormald, Randy J.

2012-01-01

As we move into the 21st century, the needs of our students are more variable than ever. There has been a proliferation of social networking usage in society yet there has been little use of those emerging tools in schools as a means to enhance student learning. It is a common practice in school districts to block social networking sites and…

Time-Frequency Cross Mutual Information Analysis of the Brain Functional Networks Underlying Multiclass Motor Imagery.

PubMed

Gong, Anmin; Liu, Jianping; Chen, Si; Fu, Yunfa

2018-01-01

To study the physiologic mechanism of the brain during different motor imagery (MI) tasks, the authors employed a method of brain-network modeling based on time-frequency cross mutual information obtained from 4-class (left hand, right hand, feet, and tongue) MI tasks recorded as brain-computer interface (BCI) electroencephalography data. The authors explored the brain network revealed by these MI tasks using statistical analysis and the analysis of topologic characteristics, and observed significant differences in the reaction level, reaction time, and activated target during 4-class MI tasks. There was a great difference in the reaction level between the execution and resting states during different tasks: the reaction level of the left-hand MI task was the greatest, followed by that of the right-hand, feet, and tongue MI tasks. The reaction time required to perform the tasks also differed: during the left-hand and right-hand MI tasks, the brain networks of subjects reacted promptly and strongly, but there was a delay during the feet and tongue MI task. Statistical analysis and the analysis of network topology revealed the target regions of the brain network during different MI processes. In conclusion, our findings suggest a new way to explain the neural mechanism behind MI.

A latent class analysis of friendship network types and their predictors in the second half of life.

PubMed

Miche, Martina; Huxhold, Oliver; Stevens, Nan L

2013-07-01

Friendships contribute uniquely to well-being in (late) adulthood. However, studies on friendship often ignore interindividual differences in friendship patterns. The aim of this study was to investigate such differences including their predictors. The study builds on Matthews's qualitative model of friendship styles. Matthews distinguished 3 approaches to friendship differing by number of friends, duration of friendships, and emotional closeness. We used latent class analysis to identify friendship network types in a sample of middle-aged and older adults aged 40-85 years (N = 1,876). Data came from the German Aging Survey (DEAS). Our analysis revealed 4 distinct friendship network types that were in high congruence with Matthews's typology. We identified these as a discerning style, which focuses on few close relationships, an independent style, which refrains from close engagements, and 2 acquisitive styles that both acquire new friends across their whole life course but differ regarding the emotional closeness of their friendships. Socioeconomic status, gender, health, and network-disturbing and network-sustaining variables predicted affiliations with network types. We argue that future studies should consider a holistic view of friendships in order to better understand the association between friendships and well-being in the second half of life.

Simulating Issue Networks in Small Classes using the World Wide Web.

ERIC Educational Resources Information Center

Josefson, Jim; Casey, Kelly

2000-01-01

Provides background information on simulations and active learning. Discusses the use of simulations in political science courses. Describes a simulation exercise where students performed specific institutional role playing, simulating the workings of a single congressional issue network, based on the reauthorization of the Endangered Species Act.…

Introducing Artificial Neural Networks through a Spreadsheet Model

ERIC Educational Resources Information Center

Rienzo, Thomas F.; Athappilly, Kuriakose K.

2012-01-01

Business students taking data mining classes are often introduced to artificial neural networks (ANN) through point and click navigation exercises in application software. Even if correct outcomes are obtained, students frequently do not obtain a thorough understanding of ANN processes. This spreadsheet model was created to illuminate the roles of…

Guided Practice: Use of Low-Cost Networking.

ERIC Educational Resources Information Center

Gersten, Russell; And Others

This study investigated the effectiveness of the use of computer networking in providing guided practice in teaching reading comprehension to middle school students (grades 6-8) in remedial reading class. (Guided practice is defined as the phase of instruction immediately following the presentation of a new skill, concept, or strategy, in which…

Incorporating Covariates into Stochastic Blockmodels

ERIC Educational Resources Information Center

Sweet, Tracy M.

2015-01-01

Social networks in education commonly involve some form of grouping, such as friendship cliques or teacher departments, and blockmodels are a type of statistical social network model that accommodate these grouping or blocks by assuming different within-group tie probabilities than between-group tie probabilities. We describe a class of models,…

Locating Hate Speech in the Networked Writing Classroom.

ERIC Educational Resources Information Center

Catalano, Tim

Many instructors are planning to teach their writing classes in the networked computer classroom. Through the use of electronic mail, course listservs, and chat programs, the instructor is offered the opportunity to facilitate a more egalitarian classroom discourse that creates a strong sense of community, not only between students, but also…

English Writing via a Social Networking Platform

ERIC Educational Resources Information Center

Yu, Wei-Chieh Wayne

2018-01-01

This study examined students' perceptions of completing an English writing class via a social networking platform. Participants were 162 aboriginal students between 18 and 23 years of age at a nursing college in southern Taiwan. Different ethnicities were defined and represented by different memberships of indigenous groups or tribes, also known…

78 FR 70848 - Special Conditions: Boeing Model 777-200, -300, and -300ER Series Airplanes; Aircraft Electronic...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-11-27

... the EFB architecture and existing airplane network systems. The applicable airworthiness regulations..., software-configurable avionics, and fiber-optic avionics networks. The proposed Class 3 EFB architecture is... existing regulations and guidance material did not anticipate this type of system architecture or...

78 FR 70849 - Special Conditions: Boeing Model 777-200, -300, and -300ER Series Airplanes; Aircraft Electronic...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-11-27

... the EFB architecture and existing airplane network systems. The applicable airworthiness regulations..., software-configurable avionics, and fiber-optic avionics networks. The proposed Class 3 EFB architecture is... existing regulations and guidance material did not anticipate this type of system architecture or...

Laboratory Experiments for Network Security Instruction

ERIC Educational Resources Information Center

Brustoloni, Jose Carlos

2006-01-01

We describe a sequence of five experiments on network security that cast students successively in the roles of computer user, programmer, and system administrator. Unlike experiments described in several previous papers, these experiments avoid placing students in the role of attacker. Each experiment starts with an in-class demonstration of an…

Integration of Continuous-Time Dynamics in a Spiking Neural Network Simulator.

PubMed

Hahne, Jan; Dahmen, David; Schuecker, Jannis; Frommer, Andreas; Bolten, Matthias; Helias, Moritz; Diesmann, Markus

2017-01-01

Contemporary modeling approaches to the dynamics of neural networks include two important classes of models: biologically grounded spiking neuron models and functionally inspired rate-based units. We present a unified simulation framework that supports the combination of the two for multi-scale modeling, enables the quantitative validation of mean-field approaches by spiking network simulations, and provides an increase in reliability by usage of the same simulation code and the same network model specifications for both model classes. While most spiking simulations rely on the communication of discrete events, rate models require time-continuous interactions between neurons. Exploiting the conceptual similarity to the inclusion of gap junctions in spiking network simulations, we arrive at a reference implementation of instantaneous and delayed interactions between rate-based models in a spiking network simulator. The separation of rate dynamics from the general connection and communication infrastructure ensures flexibility of the framework. In addition to the standard implementation we present an iterative approach based on waveform-relaxation techniques to reduce communication and increase performance for large-scale simulations of rate-based models with instantaneous interactions. Finally we demonstrate the broad applicability of the framework by considering various examples from the literature, ranging from random networks to neural-field models. The study provides the prerequisite for interactions between rate-based and spiking models in a joint simulation.

A single network adaptive critic (SNAC) architecture for optimal control synthesis for a class of nonlinear systems.

PubMed

Padhi, Radhakant; Unnikrishnan, Nishant; Wang, Xiaohua; Balakrishnan, S N

2006-12-01

Even though dynamic programming offers an optimal control solution in a state feedback form, the method is overwhelmed by computational and storage requirements. Approximate dynamic programming implemented with an Adaptive Critic (AC) neural network structure has evolved as a powerful alternative technique that obviates the need for excessive computations and storage requirements in solving optimal control problems. In this paper, an improvement to the AC architecture, called the "Single Network Adaptive Critic (SNAC)" is presented. This approach is applicable to a wide class of nonlinear systems where the optimal control (stationary) equation can be explicitly expressed in terms of the state and costate variables. The selection of this terminology is guided by the fact that it eliminates the use of one neural network (namely the action network) that is part of a typical dual network AC setup. As a consequence, the SNAC architecture offers three potential advantages: a simpler architecture, lesser computational load and elimination of the approximation error associated with the eliminated network. In order to demonstrate these benefits and the control synthesis technique using SNAC, two problems have been solved with the AC and SNAC approaches and their computational performances are compared. One of these problems is a real-life Micro-Electro-Mechanical-system (MEMS) problem, which demonstrates that the SNAC technique is applicable to complex engineering systems.

Integration of Continuous-Time Dynamics in a Spiking Neural Network Simulator

PubMed Central

Hahne, Jan; Dahmen, David; Schuecker, Jannis; Frommer, Andreas; Bolten, Matthias; Helias, Moritz; Diesmann, Markus

2017-01-01

Contemporary modeling approaches to the dynamics of neural networks include two important classes of models: biologically grounded spiking neuron models and functionally inspired rate-based units. We present a unified simulation framework that supports the combination of the two for multi-scale modeling, enables the quantitative validation of mean-field approaches by spiking network simulations, and provides an increase in reliability by usage of the same simulation code and the same network model specifications for both model classes. While most spiking simulations rely on the communication of discrete events, rate models require time-continuous interactions between neurons. Exploiting the conceptual similarity to the inclusion of gap junctions in spiking network simulations, we arrive at a reference implementation of instantaneous and delayed interactions between rate-based models in a spiking network simulator. The separation of rate dynamics from the general connection and communication infrastructure ensures flexibility of the framework. In addition to the standard implementation we present an iterative approach based on waveform-relaxation techniques to reduce communication and increase performance for large-scale simulations of rate-based models with instantaneous interactions. Finally we demonstrate the broad applicability of the framework by considering various examples from the literature, ranging from random networks to neural-field models. The study provides the prerequisite for interactions between rate-based and spiking models in a joint simulation. PMID:28596730

Information Processing Concepts: A Cure for "Technofright." Information Processing in the Electronic Office. Part 1: Concepts.

ERIC Educational Resources Information Center

Popyk, Marilyn K.

1986-01-01

Discusses the new automated office and its six major technologies (data processing, word processing, graphics, image, voice, and networking), the information processing cycle (input, processing, output, distribution/communication, and storage and retrieval), ergonomics, and ways to expand office education classes (versus class instruction). (CT)

Choosing Colleges. How Social Class and Schools Structure Opportunity.

ERIC Educational Resources Information Center

McDonough, Patricia M.

This study examines the ways in which social class and high school guidance operations combine to shape a high school student's perceptions of her opportunities for a college education. It is also an analysis of the intersection of family, friends, and school network effects and how they create an individual's biography. Students connect with…

Heterogeneous Associations of Second-Graders' Learning in Robotics Class

ERIC Educational Resources Information Center

Cho, Eunji; Lee, Kyunghwa; Cherniak, Shara; Jung, Sung Eun

2017-01-01

Drawing on Latour's (Reassembling the social: an introduction to actor--network-theory, Oxford University Press, New York, 2005), this manuscript discusses a study of a robotics class in a public, Title I elementary school. Compared with theoretical frameworks (e.g., constructivism and constructionism) dominant in the field of early childhood…

49 CFR 1180.1 - General policy statement for merger or control of at least two Class I railroads.

Code of Federal Regulations, 2012 CFR

2012-10-01

... Transportation (Continued) SURFACE TRANSPORTATION BOARD, DEPARTMENT OF TRANSPORTATION RULES OF PRACTICE RAILROAD... the railroad industry (including Class II and III carriers) is a network of competing and... weight in our analysis. Applicants shall make a good faith effort to calculate the net public benefits...

49 CFR 1180.1 - General policy statement for merger or control of at least two Class I railroads.

Code of Federal Regulations, 2013 CFR

2013-10-01

... Transportation (Continued) SURFACE TRANSPORTATION BOARD, DEPARTMENT OF TRANSPORTATION RULES OF PRACTICE RAILROAD... the railroad industry (including Class II and III carriers) is a network of competing and... weight in our analysis. Applicants shall make a good faith effort to calculate the net public benefits...

49 CFR 1180.1 - General policy statement for merger or control of at least two Class I railroads.

Code of Federal Regulations, 2011 CFR

2011-10-01

... Transportation (Continued) SURFACE TRANSPORTATION BOARD, DEPARTMENT OF TRANSPORTATION RULES OF PRACTICE RAILROAD... the railroad industry (including Class II and III carriers) is a network of competing and... weight in our analysis. Applicants shall make a good faith effort to calculate the net public benefits...

49 CFR 1180.1 - General policy statement for merger or control of at least two Class I railroads.

Code of Federal Regulations, 2014 CFR

2014-10-01

... Transportation (Continued) SURFACE TRANSPORTATION BOARD, DEPARTMENT OF TRANSPORTATION RULES OF PRACTICE RAILROAD... the railroad industry (including Class II and III carriers) is a network of competing and... weight in our analysis. Applicants shall make a good faith effort to calculate the net public benefits...

Utilization of Social Media in Marketing Classes

ERIC Educational Resources Information Center

Allen, Charlotte

2013-01-01

The goal of this paper is to highlight how instructors may integrate the different social media into various marketing classes. The paper will address the major social networks, and then follow with discussions of microblogging, media sites, and social gaming. Given that there is a great deal of research highlighting the effectiveness of utilizing…

Virtual Office Hours as Cyberinfrastructure: The Case Study of Instant Messaging

ERIC Educational Resources Information Center

Balayeva, Jeren; Quan-Haase, Anabel

2009-01-01

Although out-of-class communication enhances students' learning experience, students' use of office hours has been limited. As the learning infrastructures of the social sciences and humanities have undergone a range of changes since the diffusion of digital networks, new opportunities emerge to increase out-of-class communication. Hence, it is…

Challenges in Integrating a Complex Systems Computer Simulation in Class: An Educational Design Research

ERIC Educational Resources Information Center

Loke, Swee-Kin; Al-Sallami, Hesham S.; Wright, Daniel F. B.; McDonald, Jenny; Jadhav, Sheetal; Duffull, Stephen B.

2012-01-01

Complex systems are typically difficult for students to understand and computer simulations offer a promising way forward. However, integrating such simulations into conventional classes presents numerous challenges. Framed within an educational design research, we studied the use of an in-house built simulation of the coagulation network in four…

Adding Interactivity to a Non-Interative Class

ERIC Educational Resources Information Center

Rogers, Gary; Krichen, Jack

2004-01-01

The IT 3050 course at Capella University is an introduction to fundamental computer networking. This course is one of the required courses in the Bachelor of Science in Information Technology program. In order to provide a more enriched learning environment for learners, Capella has significantly modified this class (and others) by infusing it…

Ace: Action-Communication-Expression. IMPACT II: Houston's Teacher-to-Teacher Network.

ERIC Educational Resources Information Center

McIntyre, Margie

The Action-Communication-Expression program, an extension of a speech communication class in a Houston (Texas) high school, involves visual and concrete communication, such as photography, script writing, and filmmaking. Students in two speech classes work in small groups of four or five, independently of the teacher, after receiving initial…

Parental Involvement and University Graduate Employment in China

ERIC Educational Resources Information Center

Liu, Dian

2016-01-01

In the expanded higher education in China, middle-class students are found to have better access to job information than their underprivileged counterparts; they also gain better jobs in the labour market. Researchers have turned to social capital theory to explain this phenomenon, claiming that middle-class students with wider social network and…

Neuroanatomically Separable Effects of Imageability and Grammatical Class during Single-Word Comprehension

ERIC Educational Resources Information Center

Bedny, Marina; Thompson-Schill, Sharon L.

2006-01-01

The present study characterizes the neural correlates of noun and verb imageability and addresses the question of whether components of the neural network supporting word recognition can be separately modified by variations in grammatical class and imageability. We examined the effect of imageability on BOLD signal during single-word comprehension…

Toward the virtual classroom

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

Pihlman, M.; Dirks, D.H.

1990-01-03

The Lawrence Livermore National Laboratory (LLNL) encourages its employees to remotely attend classes given by Stanford University, University of California at Davis, and the National Technological University (NTU). To improve the quality of education for LLNL employees, we are cooperating with Stanford University in upgrading the Stanford Instructional Television Network (SITN). A dedicated high-speed communication link (Tl) between Stanford and LLNL will be used for enhanced services such as videoconferencing, real time classnotes distribution, and electronic distribution of homework assignments. The new network will also allow students to take classes from their offices with the ability to ask the professormore » questions via an automatically dialed telephone call. As part of this upgrade, we have also proposed a new videoconferencing based classroom environment where students taking remote classes would feel as though they are attending the live class. All paperwork would be available in near real time and students may converse normally with, and see, other remote students as though they were all in the same physical location. We call this the Virtual Classroom.'' 1 ref., 6 figs.« less

African American Extended Family and Church-Based Social Network Typologies.

PubMed

Nguyen, Ann W; Chatters, Linda M; Taylor, Robert Joseph

2016-12-01

We examined social network typologies among African American adults and their sociodemographic correlates. Network types were derived from indicators of the family and church networks. Latent class analysis was based on a nationally representative sample of African Americans from the National Survey of American Life. Results indicated four distinct network types: ambivalent, optimal, family centered, and strained. These four types were distinguished by (a) degree of social integration, (b) network composition, and (c) level of negative interactions. In a departure from previous work, a network type composed solely of nonkin was not identified, which may reflect racial differences in social network typologies. Further, the analysis indicated that network types varied by sociodemographic characteristics. Social network typologies have several promising practice implications, as they can inform the development of prevention and intervention programs.

Compressive Feedback Control Design for Spatially Distributed Systems

DTIC Science & Technology

2017-01-03

NecSys 2015 & 2016 Abstract The goal of this research is the development of new fundamental insights and methodologies to exploit structural properties of...Measures One of the simplest class of dynamical networks that our proposed methodology can be explained in a simple setting is the class of first–order...developed a novel methodology to obtain tight lower and upper bounds for the class of systemic measures. In the following, some of the key ideas behind our

Kinetic signature of fractal-like filament networks formed by orientational linear epitaxy.

PubMed

Hwang, Wonmuk; Eryilmaz, Esma

2014-07-11

We study a broad class of epitaxial assembly of filament networks on lattice surfaces. Over time, a scale-free behavior emerges with a 2.5-3 power-law exponent in filament length distribution. Partitioning between the power-law and exponential behaviors in a network can be used to find the stage and kinetic parameters of the assembly process. To analyze real-world networks, we develop a computer program that measures the network architecture in experimental images. Application to triaxial networks of collagen fibrils shows quantitative agreement with our model. Our unifying approach can be used for characterizing and controlling the network formation that is observed across biological and nonbiological systems.

Neural-Network Quantum States, String-Bond States, and Chiral Topological States

NASA Astrophysics Data System (ADS)

Glasser, Ivan; Pancotti, Nicola; August, Moritz; Rodriguez, Ivan D.; Cirac, J. Ignacio

2018-01-01

Neural-network quantum states have recently been introduced as an Ansatz for describing the wave function of quantum many-body systems. We show that there are strong connections between neural-network quantum states in the form of restricted Boltzmann machines and some classes of tensor-network states in arbitrary dimensions. In particular, we demonstrate that short-range restricted Boltzmann machines are entangled plaquette states, while fully connected restricted Boltzmann machines are string-bond states with a nonlocal geometry and low bond dimension. These results shed light on the underlying architecture of restricted Boltzmann machines and their efficiency at representing many-body quantum states. String-bond states also provide a generic way of enhancing the power of neural-network quantum states and a natural generalization to systems with larger local Hilbert space. We compare the advantages and drawbacks of these different classes of states and present a method to combine them together. This allows us to benefit from both the entanglement structure of tensor networks and the efficiency of neural-network quantum states into a single Ansatz capable of targeting the wave function of strongly correlated systems. While it remains a challenge to describe states with chiral topological order using traditional tensor networks, we show that, because of their nonlocal geometry, neural-network quantum states and their string-bond-state extension can describe a lattice fractional quantum Hall state exactly. In addition, we provide numerical evidence that neural-network quantum states can approximate a chiral spin liquid with better accuracy than entangled plaquette states and local string-bond states. Our results demonstrate the efficiency of neural networks to describe complex quantum wave functions and pave the way towards the use of string-bond states as a tool in more traditional machine-learning applications.

Incorporation of spatial interactions in location networks to identify critical geo-referenced routes for assessing disease control measures on a large-scale campus.

PubMed

Wen, Tzai-Hung; Chin, Wei Chien Benny

2015-04-14

Respiratory diseases mainly spread through interpersonal contact. Class suspension is the most direct strategy to prevent the spread of disease through elementary or secondary schools by blocking the contact network. However, as university students usually attend courses in different buildings, the daily contact patterns on a university campus are complicated, and once disease clusters have occurred, suspending classes is far from an efficient strategy to control disease spread. The purpose of this study is to propose a methodological framework for generating campus location networks from a routine administration database, analyzing the community structure of the network, and identifying the critical links and nodes for blocking respiratory disease transmission. The data comes from the student enrollment records of a major comprehensive university in Taiwan. We combined the social network analysis and spatial interaction model to establish a geo-referenced community structure among the classroom buildings. We also identified the critical links among the communities that were acting as contact bridges and explored the changes in the location network after the sequential removal of the high-risk buildings. Instead of conducting a questionnaire survey, the study established a standard procedure for constructing a location network on a large-scale campus from a routine curriculum database. We also present how a location network structure at a campus could function to target the high-risk buildings as the bridges connecting communities for blocking disease transmission.

Evolutionary fuzzy ARTMAP neural networks for classification of semiconductor defects.

PubMed

Tan, Shing Chiang; Watada, Junzo; Ibrahim, Zuwairie; Khalid, Marzuki

2015-05-01

Wafer defect detection using an intelligent system is an approach of quality improvement in semiconductor manufacturing that aims to enhance its process stability, increase production capacity, and improve yields. Occasionally, only few records that indicate defective units are available and they are classified as a minority group in a large database. Such a situation leads to an imbalanced data set problem, wherein it engenders a great challenge to deal with by applying machine-learning techniques for obtaining effective solution. In addition, the database may comprise overlapping samples of different classes. This paper introduces two models of evolutionary fuzzy ARTMAP (FAM) neural networks to deal with the imbalanced data set problems in a semiconductor manufacturing operations. In particular, both the FAM models and hybrid genetic algorithms are integrated in the proposed evolutionary artificial neural networks (EANNs) to classify an imbalanced data set. In addition, one of the proposed EANNs incorporates a facility to learn overlapping samples of different classes from the imbalanced data environment. The classification results of the proposed evolutionary FAM neural networks are presented, compared, and analyzed using several classification metrics. The outcomes positively indicate the effectiveness of the proposed networks in handling classification problems with imbalanced data sets.

Individual social capital, neighbourhood deprivation, and self-rated health in England.

PubMed

Verhaeghe, Pieter-Paul; Tampubolon, Gindo

2012-07-01

Individual social capital is increasingly considered to be an important determinant of an individual's health. This study examines the extent to which individual social capital is associated with self-rated health and the extent to which individual social capital mediates t.he relationship between neighbourhood deprivation and self-rated health in an English sample. Individual social capital was conceptualized and operationalized in both the social cohesion- and network resource tradition, using measures of generalized trust, social participation and social network resources. Network resources were measured with the position generator. Multilevel analyses were applied to wave 2 and 3 of the Taking Part Surveys of England, which consist of face-to-face interviews among the adult population in England (N(i) = 25,366 respondents, N(j) = 12,388 neighbourhoods). The results indicate that generalized trust, participation with friends and relatives and having network members from the salariat class are positively associated with self-rated health. Having network members from the working class is, however, negatively related to self-rated health. Moreover, these social capital elements are partly mediating the negative relationship between neighbourhood deprivation and self-rated health. Copyright © 2012 Elsevier Ltd. All rights reserved.

Birth of an abstraction: a dynamical systems account of the discovery of an elsewhere principle in a category learning task.

PubMed

Tabor, Whitney; Cho, Pyeong W; Dankowicz, Harry

2013-01-01

Human participants and recurrent ("connectionist") neural networks were both trained on a categorization system abstractly similar to natural language systems involving irregular ("strong") classes and a default class. Both the humans and the networks exhibited staged learning and a generalization pattern reminiscent of the Elsewhere Condition (Kiparsky, 1973). Previous connectionist accounts of related phenomena have often been vague about the nature of the networks' encoding systems. We analyzed our network using dynamical systems theory, revealing topological and geometric properties that can be directly compared with the mechanisms of non-connectionist, rule-based accounts. The results reveal that the networks "contain" structures related to mechanisms posited by rule-based models, partly vindicating the insights of these models. On the other hand, they support the one mechanism (OM), as opposed to the more than one mechanism (MOM), view of symbolic abstraction by showing how the appearance of MOM behavior can arise emergently from one underlying set of principles. The key new contribution of this study is to show that dynamical systems theory can allow us to explicitly characterize the relationship between the two perspectives in implemented models. © 2013 Cognitive Science Society, Inc.

Support or competition? How online social networks increase physical activity: A randomized controlled trial.

PubMed

Zhang, Jingwen; Brackbill, Devon; Yang, Sijia; Becker, Joshua; Herbert, Natalie; Centola, Damon

2016-12-01

To identify what features of online social networks can increase physical activity, we conducted a 4-arm randomized controlled trial in 2014 in Philadelphia, PA. Students (n = 790, mean age = 25.2) at an university were randomly assigned to one of four conditions composed of either supportive or competitive relationships and either with individual or team incentives for attending exercise classes. The social comparison condition placed participants into 6-person competitive networks with individual incentives. The social support condition placed participants into 6-person teams with team incentives. The combined condition with both supportive and competitive relationships placed participants into 6-person teams, where participants could compare their team's performance to 5 other teams' performances. The control condition only allowed participants to attend classes with individual incentives. Rewards were based on the total number of classes attended by an individual, or the average number of classes attended by the members of a team. The outcome was the number of classes that participants attended. Data were analyzed using multilevel models in 2014. The mean attendance numbers per week were 35.7, 38.5, 20.3, and 16.8 in the social comparison, the combined, the control, and the social support conditions. Attendance numbers were 90% higher in the social comparison and the combined conditions (mean = 1.9, SE = 0.2) in contrast to the two conditions without comparison (mean = 1.0, SE = 0.2) (p = 0.003). Social comparison was more effective for increasing physical activity than social support and its effects did not depend on individual or team incentives.

Inferring network structure from cascades.

PubMed

Ghonge, Sushrut; Vural, Dervis Can

2017-07-01

Many physical, biological, and social phenomena can be described by cascades taking place on a network. Often, the activity can be empirically observed, but not the underlying network of interactions. In this paper we offer three topological methods to infer the structure of any directed network given a set of cascade arrival times. Our formulas hold for a very general class of models where the activation probability of a node is a generic function of its degree and the number of its active neighbors. We report high success rates for synthetic and real networks, for several different cascade models.

A Prior for Neural Networks utilizing Enclosing Spheres for Normalization

NASA Astrophysics Data System (ADS)

v. Toussaint, U.; Gori, S.; Dose, V.

2004-11-01

Neural Networks are famous for their advantageous flexibility for problems when there is insufficient knowledge to set up a proper model. On the other hand this flexibility can cause over-fitting and can hamper the generalization properties of neural networks. Many approaches to regularize NN have been suggested but most of them based on ad-hoc arguments. Employing the principle of transformation invariance we derive a general prior in accordance with the Bayesian probability theory for a class of feedforward networks. Optimal networks are determined by Bayesian model comparison verifying the applicability of this approach.

Inferring network structure from cascades

NASA Astrophysics Data System (ADS)

Ghonge, Sushrut; Vural, Dervis Can

2017-07-01

Many physical, biological, and social phenomena can be described by cascades taking place on a network. Often, the activity can be empirically observed, but not the underlying network of interactions. In this paper we offer three topological methods to infer the structure of any directed network given a set of cascade arrival times. Our formulas hold for a very general class of models where the activation probability of a node is a generic function of its degree and the number of its active neighbors. We report high success rates for synthetic and real networks, for several different cascade models.

Neural networks for learning and prediction with applications to remote sensing and speech perception

NASA Astrophysics Data System (ADS)

Gjaja, Marin N.

1997-11-01

Neural networks for supervised and unsupervised learning are developed and applied to problems in remote sensing, continuous map learning, and speech perception. Adaptive Resonance Theory (ART) models are real-time neural networks for category learning, pattern recognition, and prediction. Unsupervised fuzzy ART networks synthesize fuzzy logic and neural networks, and supervised ARTMAP networks incorporate ART modules for prediction and classification. New ART and ARTMAP methods resulting from analyses of data structure, parameter specification, and category selection are developed. Architectural modifications providing flexibility for a variety of applications are also introduced and explored. A new methodology for automatic mapping from Landsat Thematic Mapper (TM) and terrain data, based on fuzzy ARTMAP, is developed. System capabilities are tested on a challenging remote sensing problem, prediction of vegetation classes in the Cleveland National Forest from spectral and terrain features. After training at the pixel level, performance is tested at the stand level, using sites not seen during training. Results are compared to those of maximum likelihood classifiers, back propagation neural networks, and K-nearest neighbor algorithms. Best performance is obtained using a hybrid system based on a convex combination of fuzzy ARTMAP and maximum likelihood predictions. This work forms the foundation for additional studies exploring fuzzy ARTMAP's capability to estimate class mixture composition for non-homogeneous sites. Exploratory simulations apply ARTMAP to the problem of learning continuous multidimensional mappings. A novel system architecture retains basic ARTMAP properties of incremental and fast learning in an on-line setting while adding components to solve this class of problems. The perceptual magnet effect is a language-specific phenomenon arising early in infant speech development that is characterized by a warping of speech sound perception. An unsupervised neural network model is proposed that embodies two principal hypotheses supported by experimental data--that sensory experience guides language-specific development of an auditory neural map and that a population vector can predict psychological phenomena based on map cell activities. Model simulations show how a nonuniform distribution of map cell firing preferences can develop from language-specific input and give rise to the magnet effect.

Multi-class geospatial object detection based on a position-sensitive balancing framework for high spatial resolution remote sensing imagery

NASA Astrophysics Data System (ADS)

Zhong, Yanfei; Han, Xiaobing; Zhang, Liangpei

2018-04-01

Multi-class geospatial object detection from high spatial resolution (HSR) remote sensing imagery is attracting increasing attention in a wide range of object-related civil and engineering applications. However, the distribution of objects in HSR remote sensing imagery is location-variable and complicated, and how to accurately detect the objects in HSR remote sensing imagery is a critical problem. Due to the powerful feature extraction and representation capability of deep learning, the deep learning based region proposal generation and object detection integrated framework has greatly promoted the performance of multi-class geospatial object detection for HSR remote sensing imagery. However, due to the translation caused by the convolution operation in the convolutional neural network (CNN), although the performance of the classification stage is seldom influenced, the localization accuracies of the predicted bounding boxes in the detection stage are easily influenced. The dilemma between translation-invariance in the classification stage and translation-variance in the object detection stage has not been addressed for HSR remote sensing imagery, and causes position accuracy problems for multi-class geospatial object detection with region proposal generation and object detection. In order to further improve the performance of the region proposal generation and object detection integrated framework for HSR remote sensing imagery object detection, a position-sensitive balancing (PSB) framework is proposed in this paper for multi-class geospatial object detection from HSR remote sensing imagery. The proposed PSB framework takes full advantage of the fully convolutional network (FCN), on the basis of a residual network, and adopts the PSB framework to solve the dilemma between translation-invariance in the classification stage and translation-variance in the object detection stage. In addition, a pre-training mechanism is utilized to accelerate the training procedure and increase the robustness of the proposed algorithm. The proposed algorithm is validated with a publicly available 10-class object detection dataset.

Improved mine blast algorithm for optimal cost design of water distribution systems

NASA Astrophysics Data System (ADS)

Sadollah, Ali; Guen Yoo, Do; Kim, Joong Hoon

2015-12-01

The design of water distribution systems is a large class of combinatorial, nonlinear optimization problems with complex constraints such as conservation of mass and energy equations. Since feasible solutions are often extremely complex, traditional optimization techniques are insufficient. Recently, metaheuristic algorithms have been applied to this class of problems because they are highly efficient. In this article, a recently developed optimizer called the mine blast algorithm (MBA) is considered. The MBA is improved and coupled with the hydraulic simulator EPANET to find the optimal cost design for water distribution systems. The performance of the improved mine blast algorithm (IMBA) is demonstrated using the well-known Hanoi, New York tunnels and Balerma benchmark networks. Optimization results obtained using IMBA are compared to those using MBA and other optimizers in terms of their minimum construction costs and convergence rates. For the complex Balerma network, IMBA offers the cheapest network design compared to other optimization algorithms.

PAM4 based symmetrical 112-Gbps long-reach TWDM-PON

NASA Astrophysics Data System (ADS)

Wu, Liyu; Gao, Fan; Zhang, Minming; Fu, Songnian; Deng, Lei; Choi, Michael; Chang, Donald; Lei, Gordon K. P.; Liu, Deming

2018-02-01

We experimentally demonstrate cost effective symmetrical 112-Gbps long-reach passive optical network (LR-PON) over 70-km standard signal mode fiber (SSMF), based on pulse amplitude modulation (PAM)-4. Four 10G-class directly modulated lasers (DMLs) at C-band are used for achieving 4 × 28-Gbps downstream transmission, while two 18G-class DMLs at O-band are used to realize 2 × 56-Gbps upstream transmission, without any optical amplification in optical distributed network (ODN). Both dispersion compensation fiber (DCF) for downstream signal and praseodymium-doped fiber amplifier (PDFA) for upstream signal are equipped at optical line terminal (OLT). Meanwhile, sparse Volterra filter (SVF) equalizer is proposed to mitigate the transmission impairments with substantial reduction of computation complexity. Finally, we can successfully provide a loss budget of 33 dB per downstream wavelength channel, indicating of 64 optical network units (ONUs) with more than 1.25 Gbps per ONU.

Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals.

PubMed

Acharya, U Rajendra; Oh, Shu Lih; Hagiwara, Yuki; Tan, Jen Hong; Adeli, Hojjat

2017-09-27

An encephalogram (EEG) is a commonly used ancillary test to aide in the diagnosis of epilepsy. The EEG signal contains information about the electrical activity of the brain. Traditionally, neurologists employ direct visual inspection to identify epileptiform abnormalities. This technique can be time-consuming, limited by technical artifact, provides variable results secondary to reader expertise level, and is limited in identifying abnormalities. Therefore, it is essential to develop a computer-aided diagnosis (CAD) system to automatically distinguish the class of these EEG signals using machine learning techniques. This is the first study to employ the convolutional neural network (CNN) for analysis of EEG signals. In this work, a 13-layer deep convolutional neural network (CNN) algorithm is implemented to detect normal, preictal, and seizure classes. The proposed technique achieved an accuracy, specificity, and sensitivity of 88.67%, 90.00% and 95.00%, respectively. Copyright © 2017 Elsevier Ltd. All rights reserved.

Reconfigurable origami-inspired acoustic waveguides

PubMed Central

Babaee, Sahab; Overvelde, Johannes T. B.; Chen, Elizabeth R.; Tournat, Vincent; Bertoldi, Katia

2016-01-01

We combine numerical simulations and experiments to design a new class of reconfigurable waveguides based on three-dimensional origami-inspired metamaterials. Our strategy builds on the fact that the rigid plates and hinges forming these structures define networks of tubes that can be easily reconfigured. As such, they provide an ideal platform to actively control and redirect the propagation of sound. We design reconfigurable systems that, depending on the externally applied deformation, can act as networks of waveguides oriented along one, two, or three preferential directions. Moreover, we demonstrate that the capability of the structure to guide and radiate acoustic energy along predefined directions can be easily switched on and off, as the networks of tubes are reversibly formed and disrupted. The proposed designs expand the ability of existing acoustic metamaterials and exploit complex waveguiding to enhance control over propagation and radiation of acoustic energy, opening avenues for the design of a new class of tunable acoustic functional systems. PMID:28138527

On the continuous differentiability of inter-spike intervals of synaptically connected cortical spiking neurons in a neuronal network.

PubMed

Kumar, Gautam; Kothare, Mayuresh V

2013-12-01

We derive conditions for continuous differentiability of inter-spike intervals (ISIs) of spiking neurons with respect to parameters (decision variables) of an external stimulating input current that drives a recurrent network of synaptically connected neurons. The dynamical behavior of individual neurons is represented by a class of discontinuous single-neuron models. We report here that ISIs of neurons in the network are continuously differentiable with respect to decision variables if (1) a continuously differentiable trajectory of the membrane potential exists between consecutive action potentials with respect to time and decision variables and (2) the partial derivative of the membrane potential of spiking neurons with respect to time is not equal to the partial derivative of their firing threshold with respect to time at the time of action potentials. Our theoretical results are supported by showing fulfillment of these conditions for a class of known bidimensional spiking neuron models.

Non-Markovian Infection Spread Dramatically Alters the Susceptible-Infected-Susceptible Epidemic Threshold in Networks

NASA Astrophysics Data System (ADS)

Van Mieghem, P.; van de Bovenkamp, R.

2013-03-01

Most studies on susceptible-infected-susceptible epidemics in networks implicitly assume Markovian behavior: the time to infect a direct neighbor is exponentially distributed. Much effort so far has been devoted to characterize and precisely compute the epidemic threshold in susceptible-infected-susceptible Markovian epidemics on networks. Here, we report the rather dramatic effect of a nonexponential infection time (while still assuming an exponential curing time) on the epidemic threshold by considering Weibullean infection times with the same mean, but different power exponent α. For three basic classes of graphs, the Erdős-Rényi random graph, scale-free graphs and lattices, the average steady-state fraction of infected nodes is simulated from which the epidemic threshold is deduced. For all graph classes, the epidemic threshold significantly increases with the power exponents α. Hence, real epidemics that violate the exponential or Markovian assumption can behave seriously differently than anticipated based on Markov theory.

Epidemics on interconnected networks

NASA Astrophysics Data System (ADS)

Dickison, Mark; Havlin, S.; Stanley, H. E.

2012-06-01

Populations are seldom completely isolated from their environment. Individuals in a particular geographic or social region may be considered a distinct network due to strong local ties but will also interact with individuals in other networks. We study the susceptible-infected-recovered process on interconnected network systems and find two distinct regimes. In strongly coupled network systems, epidemics occur simultaneously across the entire system at a critical infection strength βc, below which the disease does not spread. In contrast, in weakly coupled network systems, a mixed phase exists below βc of the coupled network system, where an epidemic occurs in one network but does not spread to the coupled network. We derive an expression for the network and disease parameters that allow this mixed phase and verify it numerically. Public health implications of communities comprising these two classes of network systems are also mentioned.

A machine learning pipeline for automated registration and classification of 3D lidar data

NASA Astrophysics Data System (ADS)

Rajagopal, Abhejit; Chellappan, Karthik; Chandrasekaran, Shivkumar; Brown, Andrew P.

2017-05-01

Despite the large availability of geospatial data, registration and exploitation of these datasets remains a persis- tent challenge in geoinformatics. Popular signal processing and machine learning algorithms, such as non-linear SVMs and neural networks, rely on well-formatted input models as well as reliable output labels, which are not always immediately available. In this paper we outline a pipeline for gathering, registering, and classifying initially unlabeled wide-area geospatial data. As an illustrative example, we demonstrate the training and test- ing of a convolutional neural network to recognize 3D models in the OGRIP 2007 LiDAR dataset using fuzzy labels derived from OpenStreetMap as well as other datasets available on OpenTopography.org. When auxiliary label information is required, various text and natural language processing filters are used to extract and cluster keywords useful for identifying potential target classes. A subset of these keywords are subsequently used to form multi-class labels, with no assumption of independence. Finally, we employ class-dependent geometry extraction routines to identify candidates from both training and testing datasets. Our regression networks are able to identify the presence of 6 structural classes, including roads, walls, and buildings, in volumes as big as 8000 m3 in as little as 1.2 seconds on a commodity 4-core Intel CPU. The presented framework is neither dataset nor sensor-modality limited due to the registration process, and is capable of multi-sensor data-fusion.

Topology of Innovation Spaces in the Knowledge Networks Emerging through Questions-And-Answers

PubMed Central

Andjelković, Miroslav; Tadić, Bosiljka; Mitrović Dankulov, Marija; Rajković, Milan; Melnik, Roderick

2016-01-01

The communication processes of knowledge creation represent a particular class of human dynamics where the expertise of individuals plays a substantial role, thus offering a unique possibility to study the structure of knowledge networks from online data. Here, we use the empirical evidence from questions-and-answers in mathematics to analyse the emergence of the network of knowledge contents (or tags) as the individual experts use them in the process. After removing extra edges from the network-associated graph, we apply the methods of algebraic topology of graphs to examine the structure of higher-order combinatorial spaces in networks for four consecutive time intervals. We find that the ranking distributions of the suitably scaled topological dimensions of nodes fall into a unique curve for all time intervals and filtering levels, suggesting a robust architecture of knowledge networks. Moreover, these networks preserve the logical structure of knowledge within emergent communities of nodes, labeled according to a standard mathematical classification scheme. Further, we investigate the appearance of new contents over time and their innovative combinations, which expand the knowledge network. In each network, we identify an innovation channel as a subgraph of triangles and larger simplices to which new tags attach. Our results show that the increasing topological complexity of the innovation channels contributes to network’s architecture over different time periods, and is consistent with temporal correlations of the occurrence of new tags. The methodology applies to a wide class of data with the suitable temporal resolution and clearly identified knowledge-content units. PMID:27171149

Relationships among classes of self-oscillating transistor parallel inverters. [dc to square wave converter circuits for power conditioning

NASA Technical Reports Server (NTRS)

Wilson, T. G.; Lee, F. C. Y.; Burns, W. W., III; Owen, H. A., Jr.

1974-01-01

A procedure is developed for classifying dc-to-square-wave two-transistor parallel inverters used in power conditioning applications. The inverters are reduced to equivalent RLC networks and are then grouped with other inverters with the same basic equivalent circuit. Distinction between inverter classes is based on the topology characteristics of the equivalent circuits. Information about one class can then be extended to another class using the basic oscillation theory and the concept of duality. Oscillograms from test circuits confirm the validity of the procedure adopted.

ORA User’s Guide 2012

DTIC Science & Technology

2012-06-11

places, resources, knowledge sets or other common Node Classes*. 285 This example will use the Stargate dataset (SG-1). This dataset is included...create a new Meta-Network. Below is the NodeSet for Stargate with the original 16 node NodeSet. 376 From the main menu select, Actions > Add...measures by simply gauging their size visually and intuitively. First, visualize one of your networks. Below is the Stargate agent x event network to

New results for global exponential synchronization in neural networks via functional differential inclusions.

PubMed

Wang, Dongshu; Huang, Lihong; Tang, Longkun

2015-08-01

This paper is concerned with the synchronization dynamical behaviors for a class of delayed neural networks with discontinuous neuron activations. Continuous and discontinuous state feedback controller are designed such that the neural networks model can realize exponential complete synchronization in view of functional differential inclusions theory, Lyapunov functional method and inequality technique. The new proposed results here are very easy to verify and also applicable to neural networks with continuous activations. Finally, some numerical examples show the applicability and effectiveness of our main results.

Modular analysis of biological networks.

PubMed

Kaltenbach, Hans-Michael; Stelling, Jörg

2012-01-01

The analysis of complex biological networks has traditionally relied on decomposition into smaller, semi-autonomous units such as individual signaling pathways. With the increased scope of systems biology (models), rational approaches to modularization have become an important topic. With increasing acceptance of de facto modularity in biology, widely different definitions of what constitutes a module have sparked controversies. Here, we therefore review prominent classes of modular approaches based on formal network representations. Despite some promising research directions, several important theoretical challenges remain open on the way to formal, function-centered modular decompositions for dynamic biological networks.

Frequency Domain Analysis of Narx Neural Networks

NASA Astrophysics Data System (ADS)

Chance, J. E.; Worden, K.; Tomlinson, G. R.

1998-06-01

A method is proposed for interpreting the behaviour of NARX neural networks. The correspondence between time-delay neural networks and Volterra series is extended to the NARX class of networks. The Volterra kernels, or rather, their Fourier transforms, are obtained via harmonic probing. In the same way that the Volterra kernels generalize the impulse response to non-linear systems, the Volterra kernel transforms can be viewed as higher-order analogues of the Frequency Response Functions commonly used in Engineering dynamics; they can be interpreted in much the same way.

Finite-time synchronization of uncertain coupled switched neural networks under asynchronous switching.

PubMed

Wu, Yuanyuan; Cao, Jinde; Li, Qingbo; Alsaedi, Ahmed; Alsaadi, Fuad E

2017-01-01

This paper deals with the finite-time synchronization problem for a class of uncertain coupled switched neural networks under asynchronous switching. By constructing appropriate Lyapunov-like functionals and using the average dwell time technique, some sufficient criteria are derived to guarantee the finite-time synchronization of considered uncertain coupled switched neural networks. Meanwhile, the asynchronous switching feedback controller is designed to finite-time synchronize the concerned networks. Finally, two numerical examples are introduced to show the validity of the main results. Copyright © 2016 Elsevier Ltd. All rights reserved.

An Optimizing Space Data-Communications Scheduling Method and Algorithm with Interference Mitigation, Generalized for a Broad Class of Optimization Problems

NASA Technical Reports Server (NTRS)

Rash, James

2014-01-01

NASA's space data-communications infrastructure-the Space Network and the Ground Network-provide scheduled (as well as some limited types of unscheduled) data-communications services to user spacecraft. The Space Network operates several orbiting geostationary platforms (the Tracking and Data Relay Satellite System (TDRSS)), each with its own servicedelivery antennas onboard. The Ground Network operates service-delivery antennas at ground stations located around the world. Together, these networks enable data transfer between user spacecraft and their mission control centers on Earth. Scheduling data-communications events for spacecraft that use the NASA communications infrastructure-the relay satellites and the ground stations-can be accomplished today with software having an operational heritage dating from the 1980s or earlier. An implementation of the scheduling methods and algorithms disclosed and formally specified herein will produce globally optimized schedules with not only optimized service delivery by the space data-communications infrastructure but also optimized satisfaction of all user requirements and prescribed constraints, including radio frequency interference (RFI) constraints. Evolutionary algorithms, a class of probabilistic strategies for searching large solution spaces, is the essential technology invoked and exploited in this disclosure. Also disclosed are secondary methods and algorithms for optimizing the execution efficiency of the schedule-generation algorithms themselves. The scheduling methods and algorithms as presented are adaptable to accommodate the complexity of scheduling the civilian and/or military data-communications infrastructure within the expected range of future users and space- or ground-based service-delivery assets. Finally, the problem itself, and the methods and algorithms, are generalized and specified formally. The generalized methods and algorithms are applicable to a very broad class of combinatorial-optimization problems that encompasses, among many others, the problem of generating optimal space-data communications schedules.

Models and simulation of 3D neuronal dendritic trees using Bayesian networks.

PubMed

López-Cruz, Pedro L; Bielza, Concha; Larrañaga, Pedro; Benavides-Piccione, Ruth; DeFelipe, Javier

2011-12-01

Neuron morphology is crucial for neuronal connectivity and brain information processing. Computational models are important tools for studying dendritic morphology and its role in brain function. We applied a class of probabilistic graphical models called Bayesian networks to generate virtual dendrites from layer III pyramidal neurons from three different regions of the neocortex of the mouse. A set of 41 morphological variables were measured from the 3D reconstructions of real dendrites and their probability distributions used in a machine learning algorithm to induce the model from the data. A simulation algorithm is also proposed to obtain new dendrites by sampling values from Bayesian networks. The main advantage of this approach is that it takes into account and automatically locates the relationships between variables in the data instead of using predefined dependencies. Therefore, the methodology can be applied to any neuronal class while at the same time exploiting class-specific properties. Also, a Bayesian network was defined for each part of the dendrite, allowing the relationships to change in the different sections and to model heterogeneous developmental factors or spatial influences. Several univariate statistical tests and a novel multivariate test based on Kullback-Leibler divergence estimation confirmed that virtual dendrites were similar to real ones. The analyses of the models showed relationships that conform to current neuroanatomical knowledge and support model correctness. At the same time, studying the relationships in the models can help to identify new interactions between variables related to dendritic morphology.

Learning through Social Networking Sites--The Critical Role of the Teacher

ERIC Educational Resources Information Center

Callaghan, Noelene; Bower, Matt

2012-01-01

This comparative case study examined factors affecting behaviour and learning in social networking sites (SNS). The behaviour and learning of two classes completing identical SNS based modules of work was observed and compared. All student contributions to the SNS were analysed, with the cognitive process dimension of the Revised Bloom's Taxonomy…

Incorporating Social Networking Sites into Traditional Pedagogy: A Case of Facebook

ERIC Educational Resources Information Center

Naghdipour, Bakhtiar; Eldridge, Nilgün Hancioglu

2016-01-01

The use of online social networking sites for educational purposes or expanding curricular opportunities has recently sparked debates in scholarly forums. This potential, however, has yet to attract sufficient attention in second language classes, and particularly in English as a Foreign Language (EFL) contexts. The current study explores the…

Critical Thinking about Literature through Computer Networking.

ERIC Educational Resources Information Center

Long, Thomas L.; Pedersen, Christine

A computer-oriented, classroom-based research project was conducted at Thomas Nelson Community College in Hampton, Virginia, to explore the ways in which students in a composition and literature class might use a local area network (LAN) as a catalyst to critical thinking, to construct a decentralized classroom, and to use various forms of…

Distributed intelligent scheduling of FMS

NASA Astrophysics Data System (ADS)

Wu, Zuobao; Cheng, Yaodong; Pan, Xiaohong

1995-08-01

In this paper, a distributed scheduling approach of a flexible manufacturing system (FMS) is presented. A new class of Petri nets called networked time Petri nets (NTPN) for system modeling of networking environment is proposed. The distributed intelligent scheduling is implemented by three schedulers which combine NTPN models with expert system techniques. The simulation results are shown.

Correlation between Academic and Skills-Based Tests in Computer Networks

ERIC Educational Resources Information Center

Buchanan, William

2006-01-01

Computing-related programmes and modules have many problems, especially related to large class sizes, large-scale plagiarism, module franchising, and an increased requirement from students for increased amounts of hands-on, practical work. This paper presents a practical computer networks module which uses a mixture of online examinations and a…

Classifying galaxy spectra at 0.5 < z < 1 with self-organizing maps

NASA Astrophysics Data System (ADS)

Rahmani, S.; Teimoorinia, H.; Barmby, P.

2018-05-01

The spectrum of a galaxy contains information about its physical properties. Classifying spectra using templates helps elucidate the nature of a galaxy's energy sources. In this paper, we investigate the use of self-organizing maps in classifying galaxy spectra against templates. We trained semi-supervised self-organizing map networks using a set of templates covering the wavelength range from far ultraviolet to near infrared. The trained networks were used to classify the spectra of a sample of 142 galaxies with 0.5 < z < 1 and the results compared to classifications performed using K-means clustering, a supervised neural network, and chi-squared minimization. Spectra corresponding to quiescent galaxies were more likely to be classified similarly by all methods while starburst spectra showed more variability. Compared to classification using chi-squared minimization or the supervised neural network, the galaxies classed together by the self-organizing map had more similar spectra. The class ordering provided by the one-dimensional self-organizing maps corresponds to an ordering in physical properties, a potentially important feature for the exploration of large datasets.

Adaptive oscillator networks with conserved overall coupling: Sequential firing and near-synchronized states

NASA Astrophysics Data System (ADS)

Picallo, Clara B.; Riecke, Hermann

2011-03-01

Motivated by recent observations in neuronal systems we investigate all-to-all networks of nonidentical oscillators with adaptive coupling. The adaptation models spike-timing-dependent plasticity in which the sum of the weights of all incoming links is conserved. We find multiple phase-locked states that fall into two classes: near-synchronized states and splay states. Among the near-synchronized states are states that oscillate with a frequency that depends only very weakly on the coupling strength and is essentially given by the frequency of one of the oscillators, which is, however, neither the fastest nor the slowest oscillator. In sufficiently large networks the adaptive coupling is found to develop effective network topologies dominated by one or two loops. This results in a multitude of stable splay states, which differ in their firing sequences. With increasing coupling strength their frequency increases linearly and the oscillators become less synchronized. The essential features of the two classes of states are captured analytically in perturbation analyses of the extended Kuramoto model used in the simulations.

Networked differential GPS system

NASA Technical Reports Server (NTRS)

Sheynblat, Leonid (Inventor); Kalafus, Rudolph M. (Inventor); Loomis, Peter V. W. (Inventor); Mueller, K. Tysen (Inventor)

1994-01-01

An embodiment of the present invention relates to a worldwide network of differential GPS reference stations (NDGPS) that continually track the entire GPS satellite constellation and provide interpolations of reference station corrections tailored for particular user locations between the reference stations Each reference station takes real-time ionospheric measurements with codeless cross-correlating dual-frequency carrier GPS receivers and computes real-time orbit ephemerides independently. An absolute pseudorange correction (PRC) is defined for each satellite as a function of a particular user's location. A map of the function is constructed, with iso-PRC contours. The network measures the PRCs at a few points, so-called reference stations and constructs an iso-PRC map for each satellite. Corrections are interpolated for each user's site on a subscription basis. The data bandwidths are kept to a minimum by transmitting information that cannot be obtained directly by the user and by updating information by classes and according to how quickly each class of data goes stale given the realities of the GPS system. Sub-decimeter-level kinematic accuracy over a given area is accomplished by establishing a mini-fiducial network.

Enhancing Employability Skills by Bringing Literature Back into the Foreign Language Class: The "Litinclass" Website

ERIC Educational Resources Information Center

Almeida, Ana Bela; Puig, Idoya

2017-01-01

The international research network, "Literature in the Foreign Language Class" ("Litinclass"), was created with a view of exploring and sharing ideas on the numerous skills and benefits that can be derived from language learning through literature. This paper focuses on how literature can have an important role in the…

A Case Study of Using Facebook in an EFL English Writing Class: The Perspective of a Writing Teacher

ERIC Educational Resources Information Center

Yu, Li-Tang

2014-01-01

The purpose of this study was to address a writing teacher's perspective about integrating Facebook, a social networking site, into a university-level English writing course in Taiwan. Data, including interviews with the teacher and class postings on Facebook, were analyzed inductively, qualitatively, and interpretively, resulting in three…

Using Facebook Groups to Encourage Science Discussions in a Large-Enrollment Biology Class

ERIC Educational Resources Information Center

Pai, Aditi; McGinnis, Gene; Bryant, Dana; Cole, Megan; Kovacs, Jennifer; Stovall, Kyndra; Lee, Mark

2017-01-01

This case study reports the instructional development, impact, and lessons learned regarding the use of Facebook as an educational tool within a large enrollment Biology class at Spelman College (Atlanta, GA). We describe the use of this social networking site to (a) engage students in active scientific discussions, (b) build community within the…

Designing an Engaged Swarm: Toward a "Techne" for Multi-Class, Interdisciplinary Collaborations with Nonprofit Partners

ERIC Educational Resources Information Center

McCarthy, Seán

2016-01-01

This essay proposes a model of university-community partnership called "an engaged swarm" that mobilizes networks of students from across classes and disciplines to work with off-campus partners such as nonprofits. Based on theories that translate the distributed, adaptive, and flexible activity of actors in biological systems to…

Comparing Educational Trajectories of Two Chinese Students and One Latina Student, a Social Capital Approach

ERIC Educational Resources Information Center

Prado, Jose M.

2009-01-01

This qualitative study compares and analyzes the social network experiences of two working-class Chinese students from immigrant families (Sally, Alex) to those of one working-class Latina student from an immigrant family (Elizabeth). Theory holds that these students would have difficulty obtaining educational resources and support (i.e., social…

Measuring Social Capital among First-Generation and Non-First-Generation, Working-Class, White Males

ERIC Educational Resources Information Center

Moschetti, Roxanne; Hudley, Cynthia

2008-01-01

Social capital is a useful theory for understanding the experiences of working class, first-generation college students. Social capital is the value of a relationship that provides support and assistance in a given social situation. According to social capital theory, networks of relationships can aid students in managing an otherwise unfamiliar…

Patterns of harm reduction service utilization and HIV incidence among people who inject drugs in Ukraine: A two-part latent profile analysis.

PubMed

Ompad, Danielle C; Wang, Jiayu; Dumchev, Konstantin; Barska, Julia; Samko, Maria; Zeziulin, Oleksandr; Saliuk, Tetiana; Varetska, Olga; DeHovitz, Jack

2017-05-01

Program utilization patterns are described within a large network of harm reduction service providers in Ukraine. The relationship between utilization patterns and HIV incidence is determined among people who inject drugs (PWID) controlling for oblast-level HIV incidence and treatment/syringe coverage. Data were extracted from the network's monitoring and evaluation database (January 2011-September 2014, n=327,758 clients). Latent profile analysis was used to determine harm reduction utilization patterns using the number of HIV tests received annually and the number of condoms, syringes, and services (i.e., information and counseling sessions) received monthly over a year. Cox proportional hazards regression determined the relations between HIV seroconversion and utilization class membership. In the final 4-class model, class 1 (34.0% of clients) received 0.1 HIV tests, 1.3 syringes, 0.6 condom and minimal counseling and information sessions per month; class 2 (33.6%) received 8.6 syringes, 3.2 condoms, and 0.5 HIV tests and counseling and information sessions; class 3 (19.1%) received 1 HIV test, 11.9 syringes, 4.3 condoms, and 0.7 information and counseling sessions; class 4 (13.3%) received 1 HIV test, 26.1 syringes, 10.3 condoms, and 1.8 information and 1.9 counseling sessions. Class 4 clients had significantly decreased risk for HIV seroconversion as compared to those in class 1 after controlling for oblast-level characteristics. Injection drug use continues to be a major mode of HIV transmission in Ukraine, making evaluation of harm reduction efforts in reducing HIV incidence among PWID critical. These analyses suggest that receiving more syringes and condoms decreased risk of HIV. Scaling up HIV testing and harm reduction services is warranted. Copyright © 2016. Published by Elsevier B.V.

Deep Flare Net (DeFN) Model for Solar Flare Prediction

NASA Astrophysics Data System (ADS)

Nishizuka, N.; Sugiura, K.; Kubo, Y.; Den, M.; Ishii, M.

2018-05-01

We developed a solar flare prediction model using a deep neural network (DNN) named Deep Flare Net (DeFN). This model can calculate the probability of flares occurring in the following 24 hr in each active region, which is used to determine the most likely maximum classes of flares via a binary classification (e.g., ≥M class versus

Ontology-based literature mining and class effect analysis of adverse drug reactions associated with neuropathy-inducing drugs.

PubMed

Hur, Junguk; Özgür, Arzucan; He, Yongqun

2018-06-07

Adverse drug reactions (ADRs), also called as drug adverse events (AEs), are reported in the FDA drug labels; however, it is a big challenge to properly retrieve and analyze the ADRs and their potential relationships from textual data. Previously, we identified and ontologically modeled over 240 drugs that can induce peripheral neuropathy through mining public drug-related databases and drug labels. However, the ADR mechanisms of these drugs are still unclear. In this study, we aimed to develop an ontology-based literature mining system to identify ADRs from drug labels and to elucidate potential mechanisms of the neuropathy-inducing drugs (NIDs). We developed and applied an ontology-based SciMiner literature mining strategy to mine ADRs from the drug labels provided in the Text Analysis Conference (TAC) 2017, which included drug labels for 53 neuropathy-inducing drugs (NIDs). We identified an average of 243 ADRs per NID and constructed an ADR-ADR network, which consists of 29 ADR nodes and 149 edges, including only those ADR-ADR pairs found in at least 50% of NIDs. Comparison to the ADR-ADR network of non-NIDs revealed that the ADRs such as pruritus, pyrexia, thrombocytopenia, nervousness, asthenia, acute lymphocytic leukaemia were highly enriched in the NID network. Our ChEBI-based ontology analysis identified three benzimidazole NIDs (i.e., lansoprazole, omeprazole, and pantoprazole), which were associated with 43 ADRs. Based on ontology-based drug class effect definition, the benzimidazole drug group has a drug class effect on all of these 43 ADRs. Many of these 43 ADRs also exist in the enriched NID ADR network. Our Ontology of Adverse Events (OAE) classification further found that these 43 benzimidazole-related ADRs were distributed in many systems, primarily in behavioral and neurological, digestive, skin, and immune systems. Our study demonstrates that ontology-based literature mining and network analysis can efficiently identify and study specific group of drugs and their associated ADRs. Furthermore, our analysis of drug class effects identified 3 benzimidazole drugs sharing 43 ADRs, leading to new hypothesis generation and possible mechanism understanding of drug-induced peripheral neuropathy.

Use of a personal computer for dynamical engineering illustrations in a classroom and over an instructional TV network

NASA Technical Reports Server (NTRS)

Watson, V. R.

1983-01-01

A personal computer has been used to illustrate physical phenomena and problem solution techniques in engineering classes. According to student evaluations, instruction of concepts was greatly improved through the use of these illustrations. This paper describes the class of phenomena that can be effectively illustrated, the techniques used to create these illustrations, and the techniques used to display the illustrations in regular classrooms and over an instructional TV network. The features of a personal computer required to apply these techniques are listed. The capabilities of some present personal computers are discussed and a forecast of the capabilities of future personal computers is presented.

Priority and Negotiation Based Dynamic Spectrum Allocation Scheme for Multiple Radio Access Network Operators

NASA Astrophysics Data System (ADS)

Kim, Hoon; Hyon, Taein; Lee, Yeonwoo

Most of previous works have presented the dynamic spectrum allocation (DSA) gain achieved by utilizing the time or regional variations in traffic demand between multi-network operators (NOs). In this paper, we introduce the functionalities required for the entities related with the spectrum sharing and allocation and propose a spectrum allocation algorithm while considering the long-term priority between NOs, the priority between multiple class services, and the urgent bandwidth request. To take into account the priorities among the NOs and the priorities of multiple class services, a spectrum sharing metric (SSM) is proposed, while a negotiation procedure is proposed to treat the urgent bandwidth request.

Small worlds in space: Synchronization, spatial and relational modularity

NASA Astrophysics Data System (ADS)

Brede, M.

2010-06-01

In this letter we investigate networks that have been optimized to realize a trade-off between enhanced synchronization and cost of wire to connect the nodes in space. Analyzing the evolved arrangement of nodes in space and their corresponding network topology, a class of small-world networks characterized by spatial and network modularity is found. More precisely, for low cost of wire optimal configurations are characterized by a division of nodes into two spatial groups with maximum distance from each other, whereas network modularity is low. For high cost of wire, the nodes organize into several distinct groups in space that correspond to network modules connected on a ring. In between, spatially and relationally modular small-world networks are found.

Expert networks in CLIPS

NASA Technical Reports Server (NTRS)

Hruska, S. I.; Dalke, A.; Ferguson, J. J.; Lacher, R. C.

1991-01-01

Rule-based expert systems may be structurally and functionally mapped onto a special class of neural networks called expert networks. This mapping lends itself to adaptation of connectionist learning strategies for the expert networks. A parsing algorithm to translate C Language Integrated Production System (CLIPS) rules into a network of interconnected assertion and operation nodes has been developed. The translation of CLIPS rules to an expert network and back again is illustrated. Measures of uncertainty similar to those rules in MYCIN-like systems are introduced into the CLIPS system and techniques for combining and hiring nodes in the network based on rule-firing with these certainty factors in the expert system are presented. Several learning algorithms are under study which automate the process of attaching certainty factors to rules.

The use of artificial neural networks in experimental data acquisition and aerodynamic design

NASA Technical Reports Server (NTRS)

Meade, Andrew J., Jr.

1991-01-01

It is proposed that an artificial neural network be used to construct an intelligent data acquisition system. The artificial neural networks (ANN) model has a potential for replacing traditional procedures as well as for use in computational fluid dynamics validation. Potential advantages of the ANN model are listed. As a proof of concept, the author modeled a NACA 0012 airfoil at specific conditions, using the neural network simulator NETS, developed by James Baffes of the NASA Johnson Space Center. The neural network predictions were compared to the actual data. It is concluded that artificial neural networks can provide an elegant and valuable class of mathematical tools for data analysis.

Implementation of a tactical voice/data network over FDDI. [Fiber Distributed Data Interface

NASA Technical Reports Server (NTRS)

Bergman, L. A.; Halloran, F.; Martinez, J.

1988-01-01

An asynchronous high-speed fiber-optic local-area network is described that simultaneously supports packet data traffic with synchronous TI voice traffic over a standard asynchronous FDDI (fiber distributed data interface) token-ring channel. A voice interface module was developed that parses, buffers, and resynchronizes the voice data to the packet network. The technique is general, however, and can be applied to any deterministic class of networks, including multitier backbones. In addition, the higher layer packet data protocols may operate independently of those for the voice, thereby permitting great flexibility in reconfiguring the network. Voice call setup and switching functions are performed external to the network with PABX equipment.

An evolutionary algorithm that constructs recurrent neural networks.

PubMed

Angeline, P J; Saunders, G M; Pollack, J B

1994-01-01

Standard methods for simultaneously inducing the structure and weights of recurrent neural networks limit every task to an assumed class of architectures. Such a simplification is necessary since the interactions between network structure and function are not well understood. Evolutionary computations, which include genetic algorithms and evolutionary programming, are population-based search methods that have shown promise in many similarly complex tasks. This paper argues that genetic algorithms are inappropriate for network acquisition and describes an evolutionary program, called GNARL, that simultaneously acquires both the structure and weights for recurrent networks. GNARL's empirical acquisition method allows for the emergence of complex behaviors and topologies that are potentially excluded by the artificial architectural constraints imposed in standard network induction methods.

Stabilization of model-based networked control systems

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

Miranda, Francisco; Instituto Politécnico de Viana do Castelo, Viana do Castelo; Abreu, Carlos

2016-06-08

A class of networked control systems called Model-Based Networked Control Systems (MB-NCSs) is considered. Stabilization of MB-NCSs is studied using feedback controls and simulation of stabilization for different feedbacks is made with the purpose to reduce the network trafic. The feedback control input is applied in a compensated model of the plant that approximates the plant dynamics and stabilizes the plant even under slow network conditions. Conditions for global exponential stabilizability and for the choosing of a feedback control input for a given constant time between the information moments of the network are derived. An optimal control problem to obtainmore » an optimal feedback control is also presented.« less

Collective network for computer structures

DOEpatents

Blumrich, Matthias A [Ridgefield, CT; Coteus, Paul W [Yorktown Heights, NY; Chen, Dong [Croton On Hudson, NY; Gara, Alan [Mount Kisco, NY; Giampapa, Mark E [Irvington, NY; Heidelberger, Philip [Cortlandt Manor, NY; Hoenicke, Dirk [Ossining, NY; Takken, Todd E [Brewster, NY; Steinmacher-Burow, Burkhard D [Wernau, DE; Vranas, Pavlos M [Bedford Hills, NY

2011-08-16

A system and method for enabling high-speed, low-latency global collective communications among interconnected processing nodes. The global collective network optimally enables collective reduction operations to be performed during parallel algorithm operations executing in a computer structure having a plurality of the interconnected processing nodes. Router devices ate included that interconnect the nodes of the network via links to facilitate performance of low-latency global processing operations at nodes of the virtual network and class structures. The global collective network may be configured to provide global barrier and interrupt functionality in asynchronous or synchronized manner. When implemented in a massively-parallel supercomputing structure, the global collective network is physically and logically partitionable according to needs of a processing algorithm.

Analysis and design of continuous class-E power amplifier at sub-nominal condition

NASA Astrophysics Data System (ADS)

Chen, Peng; Yang, Kai; Zhang, Tianliang

2017-12-01

The continuous class-E power amplifier at sub-nominal condition is proposed in this paper. The class-E power amplifier at continuous mode means it can be high efficient on a series matching networks while at sub-nominal condition means it only requires the zero-voltage-switching condition. Comparing with the classical class-E power amplifier, the proposed design method releases two additional design freedoms, which increase the class-E power amplifier's design flexibility. Also, the proposed continuous class-E power amplifier at sub-nominal condition can perform high efficiency over a broad bandwidth. The performance study of the continuous class-E power amplifier at sub-nominal condition is derived and the design procedure is summarised. The normalised switch voltage and current waveforms are investigated. Furthermore, the influences of different sub-nominal conditions on the power losses of the switch-on resistor and the output power capability are also discussed. A broadband continuous class-E power amplifier based on a Gallium Nitride (GaN) transistor is designed and testified to verify the proposed design methodology. The measurement results show, it can deliver 10-15 W output power with 64-73% power-added efficiency over 1.4-2.8 GHz.

Region stability analysis and tracking control of memristive recurrent neural network.

PubMed

Bao, Gang; Zeng, Zhigang; Shen, Yanjun

2018-02-01

Memristor is firstly postulated by Leon Chua and realized by Hewlett-Packard (HP) laboratory. Research results show that memristor can be used to simulate the synapses of neurons. This paper presents a class of recurrent neural network with HP memristors. Firstly, it shows that memristive recurrent neural network has more compound dynamics than the traditional recurrent neural network by simulations. Then it derives that n dimensional memristive recurrent neural network is composed of [Formula: see text] sub neural networks which do not have a common equilibrium point. By designing the tracking controller, it can make memristive neural network being convergent to the desired sub neural network. At last, two numerical examples are given to verify the validity of our result. Copyright © 2017 Elsevier Ltd. All rights reserved.

Structural factoring approach for analyzing stochastic networks

NASA Technical Reports Server (NTRS)

Hayhurst, Kelly J.; Shier, Douglas R.

1991-01-01

The problem of finding the distribution of the shortest path length through a stochastic network is investigated. A general algorithm for determining the exact distribution of the shortest path length is developed based on the concept of conditional factoring, in which a directed, stochastic network is decomposed into an equivalent set of smaller, generally less complex subnetworks. Several network constructs are identified and exploited to reduce significantly the computational effort required to solve a network problem relative to complete enumeration. This algorithm can be applied to two important classes of stochastic path problems: determining the critical path distribution for acyclic networks and the exact two-terminal reliability for probabilistic networks. Computational experience with the algorithm was encouraging and allowed the exact solution of networks that have been previously analyzed only by approximation techniques.

The multiscale backbone of the human phenotype network based on biological pathways.

PubMed

Darabos, Christian; White, Marquitta J; Graham, Britney E; Leung, Derek N; Williams, Scott M; Moore, Jason H

2014-01-25

Networks are commonly used to represent and analyze large and complex systems of interacting elements. In systems biology, human disease networks show interactions between disorders sharing common genetic background. We built pathway-based human phenotype network (PHPN) of over 800 physical attributes, diseases, and behavioral traits; based on about 2,300 genes and 1,200 biological pathways. Using GWAS phenotype-to-genes associations, and pathway data from Reactome, we connect human traits based on the common patterns of human biological pathways, detecting more pleiotropic effects, and expanding previous studies from a gene-centric approach to that of shared cell-processes. The resulting network has a heavily right-skewed degree distribution, placing it in the scale-free region of the network topologies spectrum. We extract the multi-scale information backbone of the PHPN based on the local densities of the network and discarding weak connection. Using a standard community detection algorithm, we construct phenotype modules of similar traits without applying expert biological knowledge. These modules can be assimilated to the disease classes. However, we are able to classify phenotypes according to shared biology, and not arbitrary disease classes. We present examples of expected clinical connections identified by PHPN as proof of principle. We unveil a previously uncharacterized connection between phenotype modules and discuss potential mechanistic connections that are obvious only in retrospect. The PHPN shows tremendous potential to become a useful tool both in the unveiling of the diseases' common biology, and in the elaboration of diagnosis and treatments.

Variations in Social Network Type Membership Among Older African Americans, Caribbean Blacks, and Non-Hispanic Whites.

PubMed

Nguyen, Ann W

2017-07-01

This study examined race differences in the probability of belonging to a specific social network typology of family, friends, and church members. Samples of African Americans, Caribbean blacks, and non-Hispanic whites aged 55+ were drawn from the National Survey of American Life. Typology indicators related to social integration and negative interactions with family, friendship, and church networks were used. Latent class analysis was used to identify typologies, and latent class multinomial logistic regression was used to assess the influence of race, and interactions between race and age, and race and education on typology membership. Four network typologies were identified: optimal (high social integration, low negative interaction), family-centered (high social integration within primarily the extended family network, low negative interaction), strained (low social integration, high negative interaction), and ambivalent (high social integration and high negative interaction). Findings for race and age and race and education interactions indicated that the effects of education and age on typology membership varied by race. Overall, the findings demonstrate how race interacts with age and education to influence the probability of belonging to particular network types. A better understanding of the influence of race, education, and age on social network typologies will inform future research and theoretical developments in this area. © The Author 2017. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Convergence dynamics and pseudo almost periodicity of a class of nonautonomous RFDEs with applications

NASA Astrophysics Data System (ADS)

Fan, Meng; Ye, Dan

2005-09-01

This paper studies the dynamics of a system of retarded functional differential equations (i.e., RF=Es), which generalize the Hopfield neural network models, the bidirectional associative memory neural networks, the hybrid network models of the cellular neural network type, and some population growth model. Sufficient criteria are established for the globally exponential stability and the existence and uniqueness of pseudo almost periodic solution. The approaches are based on constructing suitable Lyapunov functionals and the well-known Banach contraction mapping principle. The paper ends with some applications of the main results to some neural network models and population growth models and numerical simulations.

Stellar Oscillations Network Group

NASA Astrophysics Data System (ADS)

Grundahl, F.; Kjeldsen, H.; Christensen-Dalsgaard, J.; Arentoft, T.; Frandsen, S.

2007-06-01

Stellar Oscillations Network Group (SONG) is an initiative aimed at designing and building a network of 1m-class telescopes dedicated to asteroseismology and planet hunting. SONG will have 8 identical telescope nodes each equipped with a high-resolution spectrograph and an iodine cell for obtaining precision radial velocities and a CCD camera for guiding and imaging purposes. The main asteroseismology targets for the network are the brightest (V < 6) stars. In order to improve performance and reduce maintenance costs the instrumentation will only have very few modes of operation. In this contribution we describe the motivations for establishing a network, the basic outline of SONG and the expected performance.

VSAT communications networks - An overview

NASA Astrophysics Data System (ADS)

Chakraborty, D.

1988-05-01

The very-small-aperture-terminal (VSAT) fixed satellite communication network is a star network in which many dispersed micro terminals attempt to send data in a packet form through a random access/time-division multiple-access (RA/TDMA) satellite channel with transmission delay. The basic concept of the VSAT and its service potential are discussed. Two classes of traffic are addressed, namely, business-oriented low-rate-data traffic and bulk data traffic of corporate networks. Satellite access, throughput, and delay are considered. The size of the network population that can be served in an RA/TDMA environment is calculated. User protocols are examined. A typical VSAT business scenario is described.

Asymptotic theory of time varying networks with burstiness and heterogeneous activation patterns

NASA Astrophysics Data System (ADS)

Burioni, Raffaella; Ubaldi, Enrico; Vezzani, Alessandro

2017-05-01

The recent availability of large-scale, time-resolved and high quality digital datasets has allowed for a deeper understanding of the structure and properties of many real-world networks. The empirical evidence of a temporal dimension prompted the switch of paradigm from a static representation of networks to a time varying one. In this work we briefly review the framework of time-varying-networks in real world social systems, especially focusing on the activity-driven paradigm. We develop a framework that allows for the encoding of three generative mechanisms that seem to play a central role in the social networks’ evolution: the individual’s propensity to engage in social interactions, its strategy in allocate these interactions among its alters and the burstiness of interactions amongst social actors. The functional forms and probability distributions encoding these mechanisms are typically data driven. A natural question arises if different classes of strategies and burstiness distributions, with different local scale behavior and analogous asymptotics can lead to the same long time and large scale structure of the evolving networks. We consider the problem in its full generality, by investigating and solving the system dynamics in the asymptotic limit, for general classes of ties allocation mechanisms and waiting time probability distributions. We show that the asymptotic network evolution is driven by a few characteristics of these functional forms, that can be extracted from direct measurements on large datasets.

An event- and network-level analysis of college students' maximum drinking day.

PubMed

Meisel, Matthew K; DiBello, Angelo M; Balestrieri, Sara G; Ott, Miles Q; DiGuiseppi, Graham T; Clark, Melissa A; Barnett, Nancy P

2018-04-01

Heavy episodic drinking is common among college students and remains a serious public health issue. Previous event-level research among college students has examined behaviors and individual-level characteristics that drive consumption and related consequences but often ignores the social network of people with whom these heavy drinking episodes occur. The main aim of the current study was to investigate the network of social connections between drinkers on their heaviest drinking occasions. Sociocentric network methods were used to collect information from individuals in the first-year class (N=1342) at one university. Past-month drinkers (N=972) reported on the characteristics of their heaviest drinking occasion in the past month and indicated who else among their network connections was present during this occasion. Average max drinking day indegree, or the total number of times a participant was nominated as being present on another students' heaviest drinking occasion, was 2.50 (SD=2.05). Network autocorrelation models indicated that max drinking day indegree (e.g., popularity on heaviest drinking occassions) and peers' number of drinks on their own maximum drinking occasions were significantly associated with participant maximum number of drinks, after controlling for demographic variables, pregaming, and global network indegree (e.g., popularity in the entire first-year class). Being present at other peers' heaviest drinking occasions is associated with greater drinking quantities on one's own heaviest drinking occasion. These findings suggest the potential for interventions that target peer influences within close social networks of drinkers. Copyright © 2017 Elsevier Ltd. All rights reserved.

Functional architecture of Escherichia coli: new insights provided by a natural decomposition approach.

PubMed

Freyre-González, Julio A; Alonso-Pavón, José A; Treviño-Quintanilla, Luis G; Collado-Vides, Julio

2008-10-27

Previous studies have used different methods in an effort to extract the modular organization of transcriptional regulatory networks. However, these approaches are not natural, as they try to cluster strongly connected genes into a module or locate known pleiotropic transcription factors in lower hierarchical layers. Here, we unravel the transcriptional regulatory network of Escherichia coli by separating it into its key elements, thus revealing its natural organization. We also present a mathematical criterion, based on the topological features of the transcriptional regulatory network, to classify the network elements into one of two possible classes: hierarchical or modular genes. We found that modular genes are clustered into physiologically correlated groups validated by a statistical analysis of the enrichment of the functional classes. Hierarchical genes encode transcription factors responsible for coordinating module responses based on general interest signals. Hierarchical elements correlate highly with the previously studied global regulators, suggesting that this could be the first mathematical method to identify global regulators. We identified a new element in transcriptional regulatory networks never described before: intermodular genes. These are structural genes that integrate, at the promoter level, signals coming from different modules, and therefore from different physiological responses. Using the concept of pleiotropy, we have reconstructed the hierarchy of the network and discuss the role of feedforward motifs in shaping the hierarchical backbone of the transcriptional regulatory network. This study sheds new light on the design principles underpinning the organization of transcriptional regulatory networks, showing a novel nonpyramidal architecture composed of independent modules globally governed by hierarchical transcription factors, whose responses are integrated by intermodular genes.

Video-based face recognition via convolutional neural networks

NASA Astrophysics Data System (ADS)

Bao, Tianlong; Ding, Chunhui; Karmoshi, Saleem; Zhu, Ming

2017-06-01

Face recognition has been widely studied recently while video-based face recognition still remains a challenging task because of the low quality and large intra-class variation of video captured face images. In this paper, we focus on two scenarios of video-based face recognition: 1)Still-to-Video(S2V) face recognition, i.e., querying a still face image against a gallery of video sequences; 2)Video-to-Still(V2S) face recognition, in contrast to S2V scenario. A novel method was proposed in this paper to transfer still and video face images to an Euclidean space by a carefully designed convolutional neural network, then Euclidean metrics are used to measure the distance between still and video images. Identities of still and video images that group as pairs are used as supervision. In the training stage, a joint loss function that measures the Euclidean distance between the predicted features of training pairs and expanding vectors of still images is optimized to minimize the intra-class variation while the inter-class variation is guaranteed due to the large margin of still images. Transferred features are finally learned via the designed convolutional neural network. Experiments are performed on COX face dataset. Experimental results show that our method achieves reliable performance compared with other state-of-the-art methods.

Comparison of four approaches to a rock facies classification problem

USGS Publications Warehouse

Dubois, M.K.; Bohling, Geoffrey C.; Chakrabarti, S.

2007-01-01

In this study, seven classifiers based on four different approaches were tested in a rock facies classification problem: classical parametric methods using Bayes' rule, and non-parametric methods using fuzzy logic, k-nearest neighbor, and feed forward-back propagating artificial neural network. Determining the most effective classifier for geologic facies prediction in wells without cores in the Panoma gas field, in Southwest Kansas, was the objective. Study data include 3600 samples with known rock facies class (from core) with each sample having either four or five measured properties (wire-line log curves), and two derived geologic properties (geologic constraining variables). The sample set was divided into two subsets, one for training and one for testing the ability of the trained classifier to correctly assign classes. Artificial neural networks clearly outperformed all other classifiers and are effective tools for this particular classification problem. Classical parametric models were inadequate due to the nature of the predictor variables (high dimensional and not linearly correlated), and feature space of the classes (overlapping). The other non-parametric methods tested, k-nearest neighbor and fuzzy logic, would need considerable improvement to match the neural network effectiveness, but further work, possibly combining certain aspects of the three non-parametric methods, may be justified. ?? 2006 Elsevier Ltd. All rights reserved.

New neural-networks-based 3D object recognition system

NASA Astrophysics Data System (ADS)

Abolmaesumi, Purang; Jahed, M.

1997-09-01

Three-dimensional object recognition has always been one of the challenging fields in computer vision. In recent years, Ulman and Basri (1991) have proposed that this task can be done by using a database of 2-D views of the objects. The main problem in their proposed system is that the correspondent points should be known to interpolate the views. On the other hand, their system should have a supervisor to decide which class does the represented view belong to. In this paper, we propose a new momentum-Fourier descriptor that is invariant to scale, translation, and rotation. This descriptor provides the input feature vectors to our proposed system. By using the Dystal network, we show that the objects can be classified with over 95% precision. We have used this system to classify the objects like cube, cone, sphere, torus, and cylinder. Because of the nature of the Dystal network, this system reaches to its stable point by a single representation of the view to the system. This system can also classify the similar views to a single class (e.g., for the cube, the system generated 9 different classes for 50 different input views), which can be used to select an optimum database of training views. The system is also very flexible to the noise and deformed views.

Optimal Control-Based Adaptive NN Design for a Class of Nonlinear Discrete-Time Block-Triangular Systems.

PubMed

Liu, Yan-Jun; Tong, Shaocheng

2016-11-01

In this paper, we propose an optimal control scheme-based adaptive neural network design for a class of unknown nonlinear discrete-time systems. The controlled systems are in a block-triangular multi-input-multi-output pure-feedback structure, i.e., there are both state and input couplings and nonaffine functions to be included in every equation of each subsystem. The design objective is to provide a control scheme, which not only guarantees the stability of the systems, but also achieves optimal control performance. The main contribution of this paper is that it is for the first time to achieve the optimal performance for such a class of systems. Owing to the interactions among subsystems, making an optimal control signal is a difficult task. The design ideas are that: 1) the systems are transformed into an output predictor form; 2) for the output predictor, the ideal control signal and the strategic utility function can be approximated by using an action network and a critic network, respectively; and 3) an optimal control signal is constructed with the weight update rules to be designed based on a gradient descent method. The stability of the systems can be proved based on the difference Lyapunov method. Finally, a numerical simulation is given to illustrate the performance of the proposed scheme.

Field-theoretic approach to fluctuation effects in neural networks

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

Buice, Michael A.; Cowan, Jack D.; Mathematics Department, University of Chicago, Chicago, Illinois 60637

A well-defined stochastic theory for neural activity, which permits the calculation of arbitrary statistical moments and equations governing them, is a potentially valuable tool for theoretical neuroscience. We produce such a theory by analyzing the dynamics of neural activity using field theoretic methods for nonequilibrium statistical processes. Assuming that neural network activity is Markovian, we construct the effective spike model, which describes both neural fluctuations and response. This analysis leads to a systematic expansion of corrections to mean field theory, which for the effective spike model is a simple version of the Wilson-Cowan equation. We argue that neural activity governedmore » by this model exhibits a dynamical phase transition which is in the universality class of directed percolation. More general models (which may incorporate refractoriness) can exhibit other universality classes, such as dynamic isotropic percolation. Because of the extremely high connectivity in typical networks, it is expected that higher-order terms in the systematic expansion are small for experimentally accessible measurements, and thus, consistent with measurements in neocortical slice preparations, we expect mean field exponents for the transition. We provide a quantitative criterion for the relative magnitude of each term in the systematic expansion, analogous to the Ginsburg criterion. Experimental identification of dynamic universality classes in vivo is an outstanding and important question for neuroscience.« less

Using ALFA for high throughput, distributed data transmission in the ALICE O2 system

NASA Astrophysics Data System (ADS)

Wegrzynek, A.; ALICE Collaboration

2017-10-01

ALICE (A Large Ion Collider Experiment) is a heavy-ion detector designed to study the physics of strongly interacting matter (the Quark-Gluon Plasma at the CERN LHC (Large Hadron Collider). ALICE has been successfully collecting physics data in Run 2 since spring 2015. In parallel, preparations for a major upgrade of the computing system, called O2 (Online-Offline), scheduled for the Long Shutdown 2 in 2019-2020, are being made. One of the major requirements of the system is the capacity to transport data between so-called FLPs (First Level Processors), equipped with readout cards, and the EPNs (Event Processing Node), performing data aggregation, frame building and partial reconstruction. It is foreseen to have 268 FLPs dispatching data to 1500 EPNs with an average output of 20 Gb/s each. In overall, the O2 processing system will operate at terabits per second of throughput while handling millions of concurrent connections. The ALFA framework will standardize and handle software related tasks such as readout, data transport, frame building, calibration, online reconstruction and more in the upgraded computing system. ALFA supports two data transport libraries: ZeroMQ and nanomsg. This paper discusses the efficiency of ALFA in terms of high throughput data transport. The tests were performed with multiple FLPs pushing data to multiple EPNs. The transfer was done using push-pull communication patterns and two socket configurations: bind, connect. The set of benchmarks was prepared to get the most performant results on each hardware setup. The paper presents the measurement process and final results - data throughput combined with computing resources usage as a function of block size. The high number of nodes and connections in the final set up may cause race conditions that can lead to uneven load balancing and poor scalability. The performed tests allow us to validate whether the traffic is distributed evenly over all receivers. It also measures the behaviour of the network in saturation and evaluates scalability from a 1-to-1 to a N-to-M solution.

Ranking of critical species to preserve the functionality of mutualistic networks using the k-core decomposition

PubMed Central

García-Algarra, Javier; Pastor, Juan Manuel; Iriondo, José María

2017-01-01

Background Network analysis has become a relevant approach to analyze cascading species extinctions resulting from perturbations on mutualistic interactions as a result of environmental change. In this context, it is essential to be able to point out key species, whose stability would prevent cascading extinctions, and the consequent loss of ecosystem function. In this study, we aim to explain how the k-core decomposition sheds light on the understanding the robustness of bipartite mutualistic networks. Methods We defined three k-magnitudes based on the k-core decomposition: k-radius, k-degree, and k-risk. The first one, k-radius, quantifies the distance from a node to the innermost shell of the partner guild, while k-degree provides a measure of centrality in the k-shell based decomposition. k-risk is a way to measure the vulnerability of a network to the loss of a particular species. Using these magnitudes we analyzed 89 mutualistic networks involving plant pollinators or seed dispersers. Two static extinction procedures were implemented in which k-degree and k-risk were compared against other commonly used ranking indexes, as for example MusRank, explained in detail in Material and Methods. Results When extinctions take place in both guilds, k-risk is the best ranking index if the goal is to identify the key species to preserve the giant component. When species are removed only in the primary class and cascading extinctions are measured in the secondary class, the most effective ranking index to identify the key species to preserve the giant component is k-degree. However, MusRank index was more effective when the goal is to identify the key species to preserve the greatest species richness in the second class. Discussion The k-core decomposition offers a new topological view of the structure of mutualistic networks. The new k-radius, k-degree and k-risk magnitudes take advantage of its properties and provide new insight into the structure of mutualistic networks. The k-risk and k-degree ranking indexes are especially effective approaches to identify key species to preserve when conservation practitioners focus on the preservation of ecosystem functionality over species richness. PMID:28533969

Ranking of critical species to preserve the functionality of mutualistic networks using the k-core decomposition.

PubMed

García-Algarra, Javier; Pastor, Juan Manuel; Iriondo, José María; Galeano, Javier

2017-01-01

Network analysis has become a relevant approach to analyze cascading species extinctions resulting from perturbations on mutualistic interactions as a result of environmental change. In this context, it is essential to be able to point out key species, whose stability would prevent cascading extinctions, and the consequent loss of ecosystem function. In this study, we aim to explain how the k -core decomposition sheds light on the understanding the robustness of bipartite mutualistic networks. We defined three k -magnitudes based on the k -core decomposition: k -radius, k -degree, and k -risk. The first one, k -radius, quantifies the distance from a node to the innermost shell of the partner guild, while k -degree provides a measure of centrality in the k -shell based decomposition. k -risk is a way to measure the vulnerability of a network to the loss of a particular species. Using these magnitudes we analyzed 89 mutualistic networks involving plant pollinators or seed dispersers. Two static extinction procedures were implemented in which k -degree and k -risk were compared against other commonly used ranking indexes, as for example MusRank, explained in detail in Material and Methods. When extinctions take place in both guilds, k -risk is the best ranking index if the goal is to identify the key species to preserve the giant component. When species are removed only in the primary class and cascading extinctions are measured in the secondary class, the most effective ranking index to identify the key species to preserve the giant component is k -degree. However, MusRank index was more effective when the goal is to identify the key species to preserve the greatest species richness in the second class. The k -core decomposition offers a new topological view of the structure of mutualistic networks. The new k -radius, k -degree and k -risk magnitudes take advantage of its properties and provide new insight into the structure of mutualistic networks. The k -risk and k -degree ranking indexes are especially effective approaches to identify key species to preserve when conservation practitioners focus on the preservation of ecosystem functionality over species richness.

Rational pain management in complex regional pain syndrome 1 (CRPS 1)--a network meta-analysis.

PubMed

Wertli, Maria M; Kessels, Alphons G H; Perez, Roberto S G M; Bachmann, Lucas M; Brunner, Florian

2014-09-01

Guidelines for complex regional pain syndrome (CRPS) 1 advocate several substance classes to reduce pain and support physical rehabilitation, but guidance about which agent should be prioritized when designing a therapeutic regimen is not provided. Using a network meta-analytic approach, we examined the efficacy of all agent classes investigated in randomized clinical trials of CRPS 1 and provide a rank order of various substances stratified by length of illness duration. In this study a network meta-analysis was conducted. The participants of this study were patients with CRPS 1. Searches in electronic, previous systematic reviews, conference abstracts, book chapters, and the reference lists of relevant articles were performed. Eligible studies were randomized controlled trials comparing at least one analgesic agent with placebo or with another analgesic and reporting efficacy in reducing pain. Summary efficacy stratified by symptom duration and length of follow-up was computed across all substance classes. Two authors independently extracted data. In total, 16 studies were included in the analysis. Bisphosphonates appear to be the treatment of choice in early stages of CRPS 1. The effects of calcitonin surpass that of bisphosphonates and other substances as a short-term medication in more chronic stages of the illness. While most medications showed some efficacy on short-term follow-up, only bisphosphonates, NMDA analogs, and vasodilators showed better long-term pain reduction than placebo. For some drug classes, only a few studies were available and many studies included a small group of patients. Insufficient data were available to analyze efficacy on disability. This network meta-analysis indicates that a rational pharmacological treatment strategy of pain management should consider bisphosphonates in early CRPS 1 and a short-term course of calcitonin in later stages. While most medications showed some efficacy on short-term follow-up, only bisphosphonates, NMDA analogs and vasodilators showed better long-term pain reduction than placebo. Wiley Periodicals, Inc.

Social Networking and Pedagogical Variations: An Integrated Approach for Effective Interpersonal and Group Communications Skills Development

ERIC Educational Resources Information Center

Okoro, Ephraim

2012-01-01

Electronic communication and social networking are effective and useful tools in the process of teaching and learning and have increasingly improved the quality of students' learning outcomes in higher education in recent years. The system encourages and supports students' active engagement, collaboration, and participation in class activities and…

An Online Social Networking Approach to Reinforce Learning of Rocks and Minerals

ERIC Educational Resources Information Center

Kennelly, Patrick

2009-01-01

Numerous and varied methods are used in introductory Earth science and geology classes to help students learn about rocks and minerals, such as classroom lectures, laboratory specimen identification, and field trips. This paper reports on a method using online social networking. The choice of this forum was based on two criteria. First, many…

Social Networking in an Intensive English Program Classroom: A Language Socialization Perspective

ERIC Educational Resources Information Center

Reinhardt, Jonathon; Zander, Victoria

2011-01-01

This ongoing project seeks to investigate the impact, inside and outside of class, of instruction focused on developing learner awareness of social-networking site (SNS) use in an American Intensive English Program (IEP). With language socialization as an interpretative framework (Duff, in press; Ochs, 1988; Watson-Gegeo, 2004), the project uses a…

Social Networking: Developing Intercultural Competence and Fostering Autonomous Learning

ERIC Educational Resources Information Center

Vurdien, Ruby

2014-01-01

With the emergence of Web 2.0, the incorporation of internet-based social networking tools is becoming increasingly popular in the foreign language classes of today. This form of social interaction provides students with the opportunity to express and share their views with their peers, and to create profiles as well as online communities of…

Blindness and Computer Networking at iTEC [Information Technology Education Center].

ERIC Educational Resources Information Center

Goins, Shannon

A new program to train blind and visually impaired individuals to design and run a computer network has been developed. The program offers the Microsoft Certified Systems Engineer (MCSE) training. The program, which began in February 2001, recently graduated its first class of students, who are currently completing 1-month internships to complete…

Are Student Evaluations of Teaching Effectiveness Valid for Measuring Student Learning Outcomes in Business Related Classes? A Neural Network and Bayesian Analyses

ERIC Educational Resources Information Center

Galbraith, Craig S.; Merrill, Gregory B.; Kline, Doug M.

2012-01-01

In this study we investigate the underlying relational structure between student evaluations of teaching effectiveness (SETEs) and achievement of student learning outcomes in 116 business related courses. Utilizing traditional statistical techniques, a neural network analysis and a Bayesian data reduction and classification algorithm, we find…

Statistics of premixed flame cells

NASA Technical Reports Server (NTRS)

Noever, David A.

1991-01-01

The statistics of random cellular patterns in premixed flames are analyzed. Agreement is found with a variety of topological relations previously found for other networks, namely, Lewis's law and Aboav's law. Despite the diverse underlying physics, flame cells are shown to share a broad class of geometric properties with other random networks-metal grains, soap foams, bioconvection, and Langmuir monolayers.

Can a Social Networking Site Support Afterschool Group Learning of Mandarin?

ERIC Educational Resources Information Center

Yang, Yang; Crook, Charles; O'Malley, Claire

2014-01-01

Schools are often encouraged to facilitate extra-curricular learning within their own premises. This study addresses the potential of social networking sites (SNS) for supporting such out-of-class study. Given concerns that learning on these sites may happen at a surface level, we adopted self-determination theory for designing a social networking…

Teradata University Network: A No Cost Web-Portal for Teaching Database, Data Warehousing, and Data-Related Subjects

ERIC Educational Resources Information Center

Jukic, Nenad; Gray, Paul

2008-01-01

This paper describes the value that information systems faculty and students in classes dealing with database management, data warehousing, decision support systems, and related topics, could derive from the use of the Teradata University Network (TUN), a free comprehensive web-portal. A detailed overview of TUN functionalities and content is…

78 FR 63196 - Sunshine Act Meeting; Open Commission Meeting; Monday, October, 28, 2013

Federal Register 2010, 2011, 2012, 2013, 2014

2013-10-23

...: Implementing HOMELAND SECURITY. Public Safety Broadband Provisions of the Middle Class Tax Relief and Job... Safety Network in the 700 MHz Band (PS Docket No. 06-229); Service Rules for the 698-746, 747-762 and 777... adopting technical rules for the 700 MHz broadband spectrum licensed to the First Responder Network...

77 FR 62461 - Implementing Public Safety Broadband Provisions of the Middle Class Tax Relief and Job Creation...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-10-15

.... SUMMARY: In this document, the Public Safety and Homeland Security Bureau (Bureau) of the Commission... Spectrum Act) governing deployment of a nationwide public safety broadband network in the 700 MHz band...), and the D Block to the First Responder Network Authority (FirstNet). By eliminating any confusion or...

Using SPEEDES to simulate the blue gene interconnect network

NASA Technical Reports Server (NTRS)

Springer, P.; Upchurch, E.

2003-01-01

JPL and the Center for Advanced Computer Architecture (CACR) is conducting application and simulation analyses of BG/L in order to establish a range of effectiveness for the Blue Gene/L MPP architecture in performing important classes of computations and to determine the design sensitivity of the global interconnect network in support of real world ASCI application execution.

Social Networking Tools in a University Setting: A Student's Perspective

ERIC Educational Resources Information Center

Haytko, Diana L.; Parker, R. Stephen

2012-01-01

As Professors, we are challenged to reach ever-changing cohorts of college students as they flow through our classes and our lives. Technological advancements happen daily and we need to decide which, if any, to incorporate into our classrooms. Our students constantly check Facebook, Twitter, MySpace and other online social networks. Should we be…

Using an Electronic Network To Create a Read Context for High School Writing.

ERIC Educational Resources Information Center

Schwartz, Jeffrey

In an effort to broaden the context for classroom writing by providing an audience other than the teacher and classmates, a study used microcomputers, a modem and an electronic mail service to set up communications with classes in other communities. Two classes (27 students) at Sewickley Academy in Pennsylvania communicated for a semester with two…

Summer Site Visits 95. Workplace Education Class Establishes Innovative Networking between Casco Bay Partners.

ERIC Educational Resources Information Center

University of Southern Maine, Gorham.

Employees of Hannaford Brothers Company, Nichols Portland, and Wood Structures in the Casco Bay area of Maine participated in a series of tours at each worksite for three consecutive Tuesdays in August 1995. Each group of employees, some of whom were limited English speakers engaged in workplace literacy classes, planned a tour of their facility,…

Model selection for anomaly detection

NASA Astrophysics Data System (ADS)

Burnaev, E.; Erofeev, P.; Smolyakov, D.

2015-12-01

Anomaly detection based on one-class classification algorithms is broadly used in many applied domains like image processing (e.g. detection of whether a patient is "cancerous" or "healthy" from mammography image), network intrusion detection, etc. Performance of an anomaly detection algorithm crucially depends on a kernel, used to measure similarity in a feature space. The standard approaches (e.g. cross-validation) for kernel selection, used in two-class classification problems, can not be used directly due to the specific nature of a data (absence of a second, abnormal, class data). In this paper we generalize several kernel selection methods from binary-class case to the case of one-class classification and perform extensive comparison of these approaches using both synthetic and real-world data.

An Empirical Typology of Social Networks and Its Association With Physical and Mental Health: A Study With Older Korean Immigrants

PubMed Central

Jang, Yuri; Lee, Beom S.; Ko, Jung Eun; Haley, William E.; Chiriboga, David A.

2015-01-01

Objectives. In the context of social convoy theory, the purposes of the study were (a) to identify an empirical typology of the social networks evident in older Korean immigrants and (b) to examine its association with self-rated health and depressive symptoms. Method. The sample consisted of 1,092 community-dwelling older Korean immigrants in Florida and New York. Latent class analyses were conducted to identify the optimal social network typology based on 8 indicators of interpersonal relationships and activities. Bivariate and multivariate analyses were conducted to examine how the identified social network typology was associated with self-rating of health and depressive symptoms. Results. Results from the latent class analysis identified 6 clusters as being most optimal, and they were named diverse, unmarried/diverse, married/coresidence, family focused, unmarried/restricted, and restricted. Memberships in the clusters of diverse and married/coresidence were significantly associated with more favorable ratings of health and lower levels of depressive symptoms. Discussion. Notably, no distinct network solely composed of friends was identified in the present sample of older immigrants; this may reflect the disruptions in social convoys caused by immigration. The findings of this study promote our understanding of the unique patterns of social connectedness in older immigrants. PMID:23887929

A key heterogeneous structure of fractal networks based on inverse renormalization scheme

NASA Astrophysics Data System (ADS)

Bai, Yanan; Huang, Ning; Sun, Lina

2018-06-01

Self-similarity property of complex networks was found by the application of renormalization group theory. Based on this theory, network topologies can be classified into universality classes in the space of configurations. In return, through inverse renormalization scheme, a given primitive structure can grow into a pure fractal network, then adding different types of shortcuts, it exhibits different characteristics of complex networks. However, the effect of primitive structure on networks structural property has received less attention. In this paper, we introduce a degree variance index to measure the dispersion of nodes degree in the primitive structure, and investigate the effect of the primitive structure on network structural property quantified by network efficiency. Numerical simulations and theoretical analysis show a primitive structure is a key heterogeneous structure of generated networks based on inverse renormalization scheme, whether or not adding shortcuts, and the network efficiency is positively correlated with degree variance of the primitive structure.