Wind-Induced Vibration of Stay Cables
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Behavioral Plasticity Through the Modulation of Switch Neurons
Behavioral Plasticity Through the Modulation of Switch Neurons Vassilis Vassiliades, Chris Christodoulou Department of Computer Science, University of Cyprus, 1678 Nicosia, Cyprus Abstract A central question in artificial intelligence is how to design agents ca- pable of switching between different behaviors in response to environmental changes. Taking inspiration from neuroscience, we address this problem by utilizing artificial neural networks (NNs) as agent controllers, and mecha- nisms such as neuromodulation and synaptic gating. The novel aspect of this work is the introduction of a type of artificial neuron we call \switch neuron". A switch neuron regulates the flow of information in NNs by se- lectively gating all but one of its incoming synaptic connections, effectively allowing only one signal to propagate forward. The allowed connection is determined by the switch neuron's level of modulatory activation which is affected by modulatory signals, such as signals that encode some informa- tion about the reward received by the agent. An important aspect of the switch neuron is that it can be used in appropriate \switch modules" in or- der to modulate other switch neurons. As we show, the introduction of the switch modules enables the creation of sequences of gating events. This is achieved through the design of a modulatory pathway capable of exploring in a principled manner all permutations of the connections arriving on the switch neurons. We test the model by presenting appropriate architectures in nonstationary binary association problems and T-maze tasks. The results show that for all tasks, the switch neuron architectures generate optimal adaptive behaviors, providing evidence that the switch neuron model could be a valuable tool in simulations where behavioral plasticity is required. -
Arizona Historic Bridge Inventory | Pages 164-191
NPS Form 10-900-a OMB Approval No. 1024-0018 (8-86) United States Department of the Interior National Park Service National Register of Historic Places Continuation Sheet section number G, H page 156 V E H I C U L A R B R I D G E S I N A R I Z O N A Geographic Data: State of Arizona Summary of Identification and Evaluation Methods The Arizona Historic Bridge Inventory, which forms the basis for this Multiple Property Documentation Form [MPDF], is a sequel to an earlier study completed in 1987. The original study employed 1945 as a cut-off date. This study inventories and evaluates all of the pre-1964 vehicular bridges and grade separations currently maintained in ADOT’s Structure Inventory and Appraisal [SI&A] listing. It includes all structures of all struc- tural types in current use on the state, county and city road systems. Additionally it includes bridges on selected federal lands (e.g., National Forests, Davis-Monthan Air Force Base) that have been included in the SI&A list. Generally not included are railroad bridges other than highway underpasses; structures maintained by federal agencies (e.g., National Park Service) other than those included in the SI&A; structures in private ownership; and structures that have been dismantled or permanently closed to vehicular traffic. There are exceptions to this, however, and several abandoned and/or privately owned structures of particular impor- tance have been included at the discretion of the consultant. The bridges included in this Inventory have not been evaluated as parts of larger road structures or historic highway districts, although they are clearly integral parts of larger highway resources. -
BRIDGE INSPECTION and INTERFEROMETRY by JOSEPH E. KRAJEWSKI a Thesis Submitted to the Faculty of the WORCESTER POLYTECHNIC INST
BRIDGE INSPECTION AND INTERFEROMETRY by JOSEPH E. KRAJEWSKI A Thesis Submitted to the Faculty of the WORCESTER POLYTECHNIC INSTITUTE in partial fulfillment of the requirements for the Degree of Master of Science in Civil Engineering May 2006 APPROVED: Professor Leonard D. Albano, Advisor ________________________________ Professor Cosme Furlong __________________________________________ Professor Malcolm Ray ____________________________________________ Professor Frederick Hart___________________________________________ ABSTRACT With the majority of bridges in the country aging, over capacity and costly to rehabilitate or replace, it is essential that engineers refine their inspection and evaluation techniques. Over the past 130 years the information gathering techniques and methods used by engineers to inspect bridges have changed little. All of the available methods rely on one technique, visual inspection. In addition, over the past 40 years individual bridge inspectors have gone from being information gathers to being solely responsible for the condition rating of bridges they inspect. The reliance on the visual abilities of a single individual to determine the health of a particular bridge has led to inconsistent and sometimes erroneous results. In an effort to provide bridge inspectors and engineers with more reliable inspection and evaluation techniques, this thesis will detail the case for development of a new inspection tool, and the assembly and use of one new tool called Fringe Interferometry. i ACKNOWLEDGMENTS I wish to sincerely thank my advisor, Professor Leonard D. Albano, for his encouragement, suggestions, patience and help in writing this thesis. His willingness to let me go beyond the realm of Civil Engineer and into the far away world of Interferometry (where few if any Civil Engineers have been) is something I will never forget. -
The Question of Algorithmic Personhood and Being
Article The Question of Algorithmic Personhood and Being (Or: On the Tenuous Nature of Human Status and Humanity Tests in Virtual Spaces—Why All Souls Are ‘Necessarily’ Equal When Considered as Energy) Tyler Lance Jaynes Alden March Bioethics Institute, Albany Medical College, Albany, NY 12208, USA; [email protected] Abstract: What separates the unique nature of human consciousness and that of an entity that can only perceive the world via strict logic-based structures? Rather than assume that there is some potential way in which logic-only existence is non-feasible, our species would be better served by assuming that such sentient existence is feasible. Under this assumption, artificial intelligence systems (AIS), which are creations that run solely upon logic to process data, even with self-learning architectures, should therefore not face the opposition they have to gaining some legal duties and protections insofar as they are sophisticated enough to display consciousness akin to humans. Should our species enable AIS to gain a digital body to inhabit (if we have not already done so), it is more pressing than ever that solid arguments be made as to how humanity can accept AIS as being cognizant of the same degree as we ourselves claim to be. By accepting the notion that AIS can and will be able to fool our senses into believing in their claim to possessing a will or ego, we may yet Citation: Jaynes, T.L. The Question have a chance to address them as equals before some unforgivable travesty occurs betwixt ourselves of Algorithmic Personhood and Being and these super-computing beings. -
Identifying and Preserving Historic Bridges
Appendix B—State Departments of Transportation Alabama Department of Transportation Florida Department of Transportation 1409 Coliseum Boulevard 605 Suwannee Street Montgomery, AL 36130 Tallahassee, FL 32399-0450 (334) 242-6311 (850) 488-8541 (334) 262-8041 (fax) (850) 277-3403 (fax) Alaska Department of Transportation & Public Facilities Georgia Department of Transportation 3132 Channel Drive 2 Capital Square Juneau, AK 99801-7898 Atlanta, GA 30334 (907) 465-3900 (404) 656-5206 (907) 586-8365 (fax) (404) 657-8389 (fax) Internet address: [email protected] Arizona Department of Transportation 206 S. 17th Avenue Hawaii Department of Transportation Phoenix, AZ 85007 869 Punchbowl Street (602) 255-7011 Honolulu, HI 96813-5097 (602) 256-7659 (fax) (808) 587-2150 (808) 587-2167 (fax) Arkansas State Highway and Transportation Department State Highway Department Building Idaho Transportation Department P.O. Box 2261, 10324 Interstate 30 3311 W. State Street Little Rock, AR 72203 P.O. Box 7129 (501) 569-2000 Boise, ID 83707 (501) 569-2400 (fax) (208) 334-8000 (208) 334-3858 (fax) California Department of Transportation 1120 N Street Illinois Department of Transportation P.O. Box 942673 2300 S. Dirksen Parkway Sacramento, CA 94273-0001 Springfield, IL 62764 (916) 654-5266 (217) 782-5597 (217) 782-6828 (fax) Colorado Department of Transportation 4201 East Arkansas Avenue Indiana Department of Transportation Denver, CO 80222 Indiana Government Center North (303) 757-9201 100 North Senate Avenue (303) 757-9149 (fax) Indianapolis, IN 46204-2249 (317) 232-5533 Connecticut Department of Transportation (317) 232-0238 (fax) P.O. Box 317546 / 2800 Berlin Turnpike Newington, CT 06131-7546 Iowa Department of Transportation (860) 594-3000 800 Lincoln Way Ames, IA 50010 Delaware Department of Transportation (515) 239-1101 Bay Road, Route 113, P.O. -
A General Principle of Dendritic Constancy – a Neuron's Size And
bioRxiv preprint doi: https://doi.org/10.1101/787911; this version posted October 1, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Dendritic constancy Cuntz et al. A general principle of dendritic constancy – a neuron’s size and shape invariant excitability *Hermann Cuntza,b, Alexander D Birda,b, Marcel Beininga,b,c,d, Marius Schneidera,b, Laura Mediavillaa,b, Felix Z Hoffmanna,b, Thomas Dellerc,1, Peter Jedlickab,c,e,1 a Ernst Strungmann¨ Institute (ESI) for Neuroscience in cooperation with the Max Planck Society, 60528 Frankfurt am Main, Germany b Frankfurt Institute for Advanced Studies, 60438 Frankfurt am Main, Germany c Institute of Clinical Neuroanatomy, Neuroscience Center, Goethe University, 60590 Frankfurt am Main, Germany d Max Planck Insitute for Brain Research, 60438 Frankfurt am Main, Germany e ICAR3R – Interdisciplinary Centre for 3Rs in Animal Research, Justus Liebig University Giessen, 35390 Giessen, Germany 1 Joint senior authors *[email protected] Keywords Electrotonic analysis, Compartmental model, Morphological model, Excitability, Neuronal scaling, Passive normalisation, Cable theory 1/57 bioRxiv preprint doi: https://doi.org/10.1101/787911; this version posted October 1, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. -
Simulation and Analysis of Neuro-Memristive Hybrid Circuits
Simulation and analysis of neuro-memristive hybrid circuits João Alexandre da Silva Pereira Reis Mestrado em Física Departamento de Física e Astronomia 2016 Orientador Paulo de Castro Aguiar, Investigador Auxiliar do Instituto de Investigação e Inovação em Saúde da Universidade do Porto Coorientador João Oliveira Ventura, Investigador Auxiliar do Departamento de Física e Astronomia da Universidade do Porto Todas as correções determinadas pelo júri, e só essas, foram efetuadas. O Presidente do Júri, Porto, ______/______/_________ U P M’ P Simulation and analysis of neuro-memristive hybrid circuits Advisor: Author: Dr. Paulo A João Alexandre R Co-Advisor: Dr. João V A dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science A at A Department of Physics and Astronomy Faculty of Science of University of Porto II FCUP II Simulation and analysis of neuro-memristive hybrid circuits FCUP III Simulation and analysis of neuro-memristive hybrid circuits III Acknowledgments First and foremost, I need to thank my dissertation advisors Dr. Paulo Aguiar and Dr. João Ven- tura for their constant counsel, however basic my doubts were or which wall I ran into. Regardless of my stubbornness to stick to my way to research and write, they were always there for me. Of great importance, because of our shared goals, Catarina Dias and Mónica Cerquido helped me have a fixed and practical outlook to my research. During the this dissertation, I attended MemoCIS, a training school of memristors, which helped me have a more concrete perspective on state of the art research on technical details, modeling considerations and concrete proposed and realized applications. -
5. Neuromorphic Chips
Neuromorphic chips for the Artificial Brain Jaeseung Jeong, Ph.D Program of Brain and Cognitive Engineering, KAIST Silicon-based artificial intelligence is not efficient Prediction, expectation, and error Artificial Information processor vs. Biological information processor Core 2 Duo Brain • 65 watts • 10 watts • 291 million transistors • 100 billion neurons • >200nW/transistor • ~100pW/neuron Comparison of scales Molecules Channels Synapses Neurons CNS 0.1nm 10nm 1mm 0.1mm 1cm 1m Silicon Transistors Logic Multipliers PIII Parallel Gates Processors Motivation and Objective of neuromorphic engineering Problem • As compared to biological systems, today’s intelligent machines are less efficient by a factor of a million to a billion in complex environments. • For intelligent machines to be useful, they must compete with biological systems. Human Cortex Computer Simulation for Cerebral Cortex 20 Watts 1010 Watts 10 Objective I.4 Liter 4x 10 Liters • Develop electronic, neuromorphic machine technology that scales to biological level for efficient artificial intelligence. 9 Why Neuromorphic Engineering? Interest in exploring Interest in building neuroscience neurally inspired systems Key Advantages • The system is dynamic: adaptation • What if our primitive gates were a neuron computation? a synapse computation? a piece of dendritic cable? • Efficient implementations compute in their memory elements – more efficient than directly reading all the coefficients. Biology and Silicon Devices Similar physics of biological channels and p-n junctions -
Physiological Role of AMPAR Nanoscale Organization at Basal State and During Synaptic Plasticities Benjamin Compans
Physiological role of AMPAR nanoscale organization at basal state and during synaptic plasticities Benjamin Compans To cite this version: Benjamin Compans. Physiological role of AMPAR nanoscale organization at basal state and during synaptic plasticities. Human health and pathology. Université de Bordeaux, 2017. English. NNT : 2017BORD0700. tel-01753429 HAL Id: tel-01753429 https://tel.archives-ouvertes.fr/tel-01753429 Submitted on 29 Mar 2018 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. THÈSE PRÉSENTÉE POUR OBTENIR LE GRADE DE DOCTEUR DE L’UNIVERSITÉ DE BORDEAUX ÉCOLE DOCTORALE DES SCIENCES DE LA VIE ET DE LA SANTE SPÉCIALITÉ NEUROSCIENCES Par Benjamin COMPANS Rôle physiologique de l’organisation des récepteurs AMPA à l’échelle nanométrique à l’état basal et lors des plasticités synaptiques Sous la direction de : Eric Hosy Soutenue le 19 Octobre 2017 Membres du jury Stéphane Oliet Directeur de Recherche CNRS Président Jean-Louis Bessereau PU/PH Université de Lyon Rapporteur Sabine Levi Directeur de Recherche CNRS Rapporteur Ryohei Yasuda Directeur de Recherche Max Planck Florida Institute Examinateur Yukiko Goda Directeur de Recherche Riken Brain Science Institute Examinateur Daniel Choquet Directeur de Recherche CNRS Invité 1 Interdisciplinary Institute for NeuroSciences (IINS) CNRS UMR 5297 Université de Bordeaux Centre Broca Nouvelle-Aquitaine 146 Rue Léo Saignat 33076 Bordeaux (France) 2 Résumé Le cerveau est formé d’un réseau complexe de neurones responsables de nos fonctions cognitives et de nos comportements. -
Identification and Ageing of Yellow-Breasted Bunting and Separation from Chestnut Bunting
Identification and ageing of Yellow-breasted Bunting and separation from Chestnut Bunting Jari Peltomäki & Jukka Jantunen ellow-breasted Emberiza aureola and Chest- Poland (5), Spain (1) and Sweden (24+). Chestnut Y nut Buntings E rutila are two species of Bunting is a much rarer vagrant in Europe and its which the breeding and wintering areas are pre- occurrence is clouded by the possibility of dominantly situated in the Eastern Palearctic. The escaped birds. The adult male being a colourful breeding areas of Yellow-breasted Bunting, how- bird, Chestnut Bunting is a popular cagebird and, ever, extend well into the Western Palearctic, therefore, most records from Europe are general- covering large parts of Russia and reaching into ly thought to concern escapes. There are, how- Finland, whereas Chestnut Bunting’s breeding ever, a few records of immature birds ‘at the right areas extend just west of Lake Baikal in Russia. time and the right place’. These autumn occur- Yellow-breasted and Chestnut Buntings are long- rences of first-winter birds, suggestive of genuine distance migrants and winter in south-eastern vagrancy, have been in the Netherlands (5 No- Asia and both species occur as vagrants in (west- vember 1937), Norway (13-15 October 1974), ern) Europe. Yellow-breasted Bunting is a regular Malta (November 1983) and former Yugoslavia vagrant in Europe outside its breeding area with (10 October 1987). Although Vinicombe & Cot- annual records in Britain (mainly on the northern tridge (1996) list Chestnut Bunting as an Eastern isles of -
Draft Nstac Report to the President On
THE PRESIDENT’S NATIONAL SECURITY TELECOMMUNICATIONS ADVISORY COMMITTEE DRAFT NSTAC REPORT TO THE PRESIDENT DRAFTon Communications Resiliency TBD Table of Contents Executive Summary .......................................................................................................ES-1 Introduction ........................................................................................................................1 Scoping and Charge.............................................................................................................2 Subcommittee Process ........................................................................................................3 Summary of Report Structure ...............................................................................................3 The Future State of ICT .......................................................................................................4 ICT Vision ...........................................................................................................................4 Wireline Segment ............................................................................................................5 Satellite Segment............................................................................................................6 Wireless 5G/6G ..............................................................................................................7 Public Safety Communications ..........................................................................................8 -
What Are Computational Neuroscience and Neuroinformatics? Computational Neuroscience
Department of Mathematical Sciences B12412: Computational Neuroscience and Neuroinformatics What are Computational Neuroscience and Neuroinformatics? Computational Neuroscience Computational Neuroscience1 is an interdisciplinary science that links the diverse fields of neu- roscience, computer science, physics and applied mathematics together. It serves as the primary theoretical method for investigating the function and mechanism of the nervous system. Com- putational neuroscience traces its historical roots to the the work of people such as Andrew Huxley, Alan Hodgkin, and David Marr. Hodgkin and Huxley's developed the voltage clamp and created the first mathematical model of the action potential. David Marr's work focused on the interactions between neurons, suggesting computational approaches to the study of how functional groups of neurons within the hippocampus and neocortex interact, store, process, and transmit information. Computational modeling of biophysically realistic neurons and dendrites began with the work of Wilfrid Rall, with the first multicompartmental model using cable theory. Computational neuroscience is distinct from psychological connectionism and theories of learning from disciplines such as machine learning,neural networks and statistical learning theory in that it emphasizes descriptions of functional and biologically realistic neurons and their physiology and dynamics. These models capture the essential features of the biological system at multiple spatial-temporal scales, from membrane currents, protein and chemical coupling to network os- cillations and learning and memory. These computational models are used to test hypotheses that can be directly verified by current or future biological experiments. Currently, the field is undergoing a rapid expansion. There are many software packages, such as NEURON, that allow rapid and systematic in silico modeling of realistic neurons.