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Emotional Expressions Reconsidered: Challenges to Inferring Emotion From Human Facial Movements (vol 20, pg 1, 2019)

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PSYCHOLOGICAL science IN THE PUBLIC INTEREST 2019年第3期20卷 165-166页

作者： Barrett, L. F. Adolphs, R. Marsella, S. Martinez, A. M. Pollak, S. D. 1 Department of Psychology Northeastern University. 2 Department of Psychiatry Massachusetts General Hospital Boston Massachusetts. 3 Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Boston Massachusetts. 4 Division of Humanities and Social Sciences California Institute of Technology. 5 College of Computer and Information Science Northeastern University. 6 Institute of Neuroscience & Psychology University of Glasgow. 7 Department of Electrical and Computer Engineering and Center for Cognitive and Brain Sciences The Ohio State University. 8 Department of Psychology University of Wisconsin-Madison.

It is commonly assumed that a person's emotional state can be readily inferred from his or her facial movements, typically called or . This assumption influences legal judgments, policy decisions, national security protocols, and educational practices; guides the diagnosis and treatment of psychiatric illness, as well as the development of commercial applications; and pervades everyday social interactions as well as research in other scientific fields such as artificial intelligence, neuroscience, and computer vision. In this article, we survey examples of this widespread assumption, which we refer to as the , and we then examine the scientific evidence that tests this view, focusing on the six most popular emotion categories used by consumers of emotion research: anger, disgust, fear, happiness, sadness, and surprise. The available scientific evidence suggests that people do sometimes smile when happy, frown when sad, scowl when angry, and so on, as proposed by the common view, more than what would be expected by chance. Yet how people communicate anger, disgust, fear, happiness, sadness, and surprise varies substantially across cultures, situations, and even across people within a single situation. Furthermore, similar configurations of facial movements variably express instances of more than one emotion category. In fact, a given configuration of facial movements, such as a scowl, often communicates something other than an emotional state. Scientists agree that facial movements convey a range of information and are important for social communication, emotional or otherwise. But our review suggests an urgent need for research that examines how people move their faces to express emotions and other social information in the variety of contexts that make up everyday life, as well as careful study of the mechanisms by which people perceive instances of emotion in one another. We make specific research recommendations that will yield a more valid picture of how peopl

关键词： emotion perception emotion recognition emotional expression

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Comparative cellular analysis of motor cortex in human, marmoset and mouse (vol 598, pg 111, 2021)

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NATURE 2022年第7904期604卷 E8-E8页

作者： Bakken, Trygve E. Jorstad, Nikolas L. Hu, Qiwen Lake, Blue B. Tian, Wei Kalmbach, Brian E. Crow, Megan Hodge, Rebecca D. Krienen, Fenna M. Sorensen, Staci A. Eggermont, Jeroen Yao, Zizhen Aevermann, Brian D. Aldridge, Andrew I. Bartlett, Anna Bertagnolli, Darren Casper, Tamara Castanon, Rosa G. Crichton, Kirsten Daigle, Tanya L. Dalley, Rachel Dee, Nick Dembrow, Nikolai Diep, Dinh Ding, Song-Lin Dong, Weixiu Fang, Rongxin Fischer, Stephan Goldman, Melissa Goldy, Jeff Graybuck, Lucas T. Herb, Brian R. Hou, Xiaomeng Kancherla, Jayaram Kroll, Matthew Lathia, Kanan van Lew, Baldur Li, Yang Eric Liu, Christine S. Liu, Hanqing Lucero, Jacinta D. Mahurkar, Anup McMillen, Delissa Miller, Jeremy A. Moussa, Marmar Nery, Joseph R. Nicovich, Philip R. Niu, Sheng-Yong Orvis, Joshua Osteen, Julia K. Owen, Scott Palmer, Carter R. Pham, Thanh Plongthongkum, Nongluk Poirion, Olivier Reed, Nora M. Rimorin, Christine Rivkin, Angeline Romanow, William J. Sedeno-Cortes, Adriana E. Siletti, Kimberly Somasundaram, Saroja Sulc, Josef Tieu, Michael Torkelson, Amy Tung, Herman Wang, Xinxin Xie, Fangming Yanny, Anna Marie Zhang, Renee Ament, Seth A. Behrens, M. Margarita Bravo, Hector Corrada Chun, Jerold Dobin, Alexander Gillis, Jesse Hertzano, Ronna Hof, Patrick R. Hollt, Thomas Horwitz, Gregory D. Keene, C. Dirk Kharchenko, Peter V. Ko, Andrew L. Lelieveldt, Boudewijn P. Luo, Chongyuan Mukamel, Eran A. Pinto-Duarte, Antonio Preiss, Sebastian Regev, Aviv Ren, Bing Scheuermann, Richard H. Smith, Kimberly Spain, William J. White, Owen R. Koch, Christof Hawrylycz, Michael Tasic, Bosiljka Macosko, Evan Z. McCarroll, Steven A. Ting, Jonathan T. Zeng, Hongkui Zhang, Kun Feng, Guoping Ecker, Joseph R. Linnarsson, Sten Lein, Ed S. Allen Institute for Brain Science Seattle WA USA Department of Physiology and Biophysics University of Washington Seattle WA USA Department of Biomedical Informatics Harvard Medical School Boston MA USA Department of Bioengineering University of California San Diego La Jolla CA USA The Salk Institute for Biological Studies La Jolla CA USA Epilepsy Center of Excellence Department of Veterans Affairs Medical Center Seattle WA USA Stanley Institute for Cognitive Genomics Cold Spring Harbor Laboratory Cold Spring Harbor NY USA Department of Genetics Harvard Medical School Boston MA USA Broad Institute of MIT and Harvard Cambridge MA USA LKEB Department of Radiology Leiden University Medical Center Leiden The Netherlands Pattern Recognition and Bioinformatics group Delft University of Technology Delft The Netherlands J. Craig Venter Institute La Jolla CA USA Department of Pathology University of California San Diego CA USA Division of Vaccine Discovery La Jolla Institute for Immunology La Jolla CA USA Genomic Analysis Laboratory The Salk Institute for Biological Studies La Jolla CA USA Computer Science and Engineering Program University of California San Diego La Jolla CA USA Howard Hughes Medical Institute The Salk Institute for Biological Studies La Jolla CA USA Bioinformatics and Systems Biology Graduate Program University of California San Diego La Jolla CA USA Institute for Genomes Sciences University of Maryland School of Medicine Baltimore MD USA Center for Epigenomics Department of Cellular and Molecular Medicine University of California San Diego La Jolla CA USA Ludwig Institute for Cancer Research La Jolla CA USA Department of Computer Science University of Maryland College Park College Park MD USA Sanford Burnham Prebys Medical Discovery Institute La Jolla CA USA Biomedical Sciences Program School of Medicine University of California San Diego La Jolla CA USA University of Connecticut Storrs CT USA Department

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Reconstructing neuronal anatomy from whole-brain images

arXiv

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arXiv 2019年

作者： Gornet, James Venkataraju, Kannan Umadevi Narasimhan, Arun Turner, Nicholas Lee, Kisuk Seung, H. Sebastian Osten, Pavel Sumbul, Uygar Columbia University Department of Biomedical Engineering New YorkNY United States Cold Spring Harbor Laboratory Department of Neuroscience Cold Spring HarborNY United States Princeton Neuroscience Institute Computer Science Department PrincetonNJ United States Massachusetts Institute of Technology Department of Brain and Cognitive Sciences CambridgeMA United States Allen Institute for Brain Science SeattleWA United States

Reconstructing multiple molecularly defined neurons from individual brains and across multiple brain regions can reveal organizational principles of the nervous system. However, high resolution imaging of the whole brain is a technically challenging and slow process. Recently, oblique light sheet microscopy has emerged as a rapid imaging method that can provide whole brain fluorescence microscopy at a voxel size of 0.4 × 0.4 × 2.5 μm3. On the other hand, complex image artifacts due to whole-brain coverage produce apparent discontinuities in neuronal arbors. Here, we present connectivitypreserving methods and data augmentation strategies for supervised learning of neuroanatomy from light microscopy using neural networks. We quantify the merit of our approach by implementing an end-to-end automated tracing pipeline. Lastly, we demonstrate a scalable, distributed implementation that can reconstruct the large datasets that sub-micron whole-brain images produce. Copyright © 2019, The Authors. All rights reserved.

关键词： brain mapping

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Cell vibron polariton in the myelin sheath of nerve

arXiv

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arXiv 2019年

作者： Song, Bo Shu, Yousheng School of Optical-Electrical Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research Beijing Normal University 19 Xinjiekou Wai Street Beijing100875 China

Polaritons are arousing tremendous interests in physics and material sciences for their unique and amazing properties, especially including the condensation, lasing without inversion and even room-temperature superfluidity. Herein, we propose a cell vibron polariton (cell-VP): a collectively coherent mode of a photon and all phospholipid molecules in a myelin sheath which is a nervous cell majorly consisting of the phospholipid molecules. Cell-VP can be resonantly self-confined in the myelin sheath under physiological conditions. The observations benefit from the specifically compact, ordered and polar thin-film structure of the sheath, and the relatively strong coupling of the mid-infrared photon with the vibrons of phospholipid tails in the myelin. The underlying physics is revealed to be the collectively coherent superposition of the photon and vibrons, the polariton induced significant enhancement of myelin permittivity, and the resonance of the polariton with the sheath cell. The captured cell-VPs in myelin sheaths may provide a promising way for super-efficient consumption of extra-weak bioenergy and even directly serve for quantum information in the nervous system. These findings further the understanding of neuroscience on the cellular level from the view of quantum mechanics. Copyright © 2019, The Authors. All rights reserved.

关键词： Cell membranes

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Reward Processing in Novelty Seekers: A Transdiagnostic Psychiatric Imaging Biomarker

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BIOLOGICAL PSYCHIATRY 2021年第8期90卷 529-539页

作者： Qi, Shile Schumann, Gunter Bustillo, Juan Turner, Jessica A. Jiang, Rongtao Zhi, Dongmei Fu, Zening Mayer, Andrew R. Vergara, Victor M. Silva, Rogers F. Iraji, Armin Chen, Jiayu Damaraju, Eswar Ma, Xiaohong Yang, Xiao Stevens, Michael Mathalon, Daniel H. Ford, Judith M. Voyvodic, James Mueller, Bryon A. Belger, Aysenil Potkin, Steven G. Preda, Adrian Zhuo, Chuanjun Xu, Yong Chu, Congying Banaschewski, Tobias Barker, Gareth J. Bokde, Arun L. W. Quinlan, Erin Burke Desrivieres, Sylvane Flor, Herta Grigis, Antoine Garavan, Hugh Gowland, Penny Heinz, Andreas Martinot, Jean-Luc Martinot, Marie-Laure Paillere Artiges, Eric Nees, Frauke Orfanos, Dimitri Papadopoulos Paus, Tomas Poustka, Luise Hohmann, Sarah Frohner, Juliane H. Smolka, Michael N. Walter, Henrik Whelan, Robert Calhoun, Vince D. Sui, Jing [a]Tri-institutional Center for Translational Research in Neuroimaging and Data Science Georgia State University Georgia Institute Technology and Emory University Atlanta Georgia [b]Department of Psychiatry University of New Mexico Albuquerque New Mexico [c]Department of Psychology Georgia State University Atlanta Georgia [d]Olin Neuropsychiatry Research Center Hartford Connecticut [e]Department of Psychiatry University of California San Francisco San Francisco California [f]Department of Radiology Duke University Durham North Carolina [g]Department of Psychiatry University of North Carolina at Chapel Hill Chapel Hill North Carolina [h]Department of Psychiatry University of Minnesota Minneapolis Minnesota [i]Department of Psychiatry University of California Irvine Irvine California [j]Departments of Psychiatry and Psychology University of Vermont Burlington Vermont [k]Centre for Population Neuroscience and Stratified Medicine Institute for Science and Technology of Brain-Inspired Intelligence Fudan University Shanghai China [l]University of Chinese Academy of Sciences Beijing China [dd]Department of Computer Science and Engineering Nanjing University of Aeronautics and Astronautics Nanjing China [m]Brainnetome Center and National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China [ee]State Key Laboratory of Cognitive Neuroscience and Learning Beijing Normal University Beijing China [n]Psychiatric Laboratory and Mental Health Center the State Key Laboratory of Biotherapy West China Hospital of Sichuan University Chengdu China [o]Department of Psychiatric-Neuroimaging-Genetics and Morbidity Laboratory Nankai University Affiliated Anding Hospital Tianjin China [p]Department of Humanities and Social Science Shanxi Medical University Taiyuan China [q]Department of Psychiatry and Psychotherapy Charité – Universitätsmedizin Berlin Campus Charité Mitte corporate member of Freie Universität Berlin Humboldt-Univers

BACKGROUND: Dysfunctional reward processing is implicated in multiple mental disorders. Novelty seeking (NS) assesses preference for seeking novel experiences, which is linked to sensitivity to reward environmental cues. METHODS: A subset of 14-year-old adolescents (IMAGEN) with the top 20% ranked high-NS scores was used to identify high-NS-associated multimodal components by supervised fusion. These features were then used to longitudinally predict five different risk scales for the same and unseen subjects (an independent dataset of subjects at 19 years of age that was not used in predictive modeling training at 14 years of age) (within IMAGEN, n z 1100) and even for the corresponding symptom scores of five types of patient cohorts (non-IMAGEN), including drinking (n = 313), smoking (n = 104), attention-deficit/hyperactivity disorder (n = 320), major depressive disorder (n = 81), and schizophrenia (n = 147), as well as to classify different patient groups with diagnostic labels. RESULTS: Multimodal biomarkers, including the prefrontal cortex, striatum, amygdala, and hippocampus, associated with high NS in 14-year-old adolescents were identified. The prediction models built on these features are able to longitudinally predict five different risk scales, including alcohol drinking, smoking, hyperactivity, depression, and psychosis for the same and unseen 19-year-old adolescents and even predict the corresponding symptom scores of five types of patient cohorts. Furthermore, the identified reward-related multimodal features can classify among attention-deficit/hyperactivity disorder, major depressive disorder, and schizophrenia with an accuracy of 87.2%. CONCLUSIONS: Adolescents with higher NS scores can be used to reveal brain alterations in the reward-related system, implicating potential higher risk for subsequent development of multiple disorders. The identified high-NS- associated multimodal reward-related signatures may serve as a transdiagnostic neuroimaging bi

关键词： ADHD Attention-deficit/hyperactivity disorder Major depressive disorders MDD Novelty seeking Reward processing Schizophrenia Substance use

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FLAIR MRI biomarkers of the Normal-Appearing brain Matter (NABM) are related to APOE-4 status and CSF markers Aβ42 and Tau

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Alzheimer's & Dementia 2023年第S3期19卷

作者： Karissa Chan Corinne E. Fischer Houman Khosravani Sandra E. Black Pascal Tyrrell Pejman Jabehdar Maralani Alan R. Moody April Khademi Electrical Computer and Biomedical Engineering Department Ryerson University Toronto ON Canada St. Michael’s Hospital Toronto ON Canada University of Toronto Toronto ON Canada Keenan Research Centre for Biomedical Science St. Michael’s Hospital Toronto ON Canada Faculty of Medicine Department of Psychiatry University of Toronto Toronto ON Canada Institute of Medical Sciences University of Toronto Toronto ON Canada Keenan Research Centre for Biomedical Science the Li Ka Shing Knowledge Institute St. Michael’s Hospital Toronto ON Canada Division of Neurology Department of Medicine University of Toronto Toronto ON Canada Heart and Stroke Foundation Canadian Partnership for Stroke Recovery Toronto ON Canada Sunnybrook Research Institute Toronto ON Canada Hurvitz Brain Sciences Program Sunnybrook Research Institute Toronto ON Canada Toronto Dementia Research Alliance Toronto ON Canada LC Campbell Cognitive Neurology Research Unit Sunnybrook Research Institute Toronto ON Canada Department of Statistical Science University of Toronto Toronto ON Canada Department of Medical Imaging University of Toronto Toronto ON Canada Keenan Research Centre for Biomedical Research the Li Ka Shing Knowledge Institute St. Michael’s Hospital Toronto ON Canada Ryerson University IAMLAB Department of Electrical Computer and Biomedical Engineering Toronto ON Canada

Background Reduced amyloid-beta-42 (Aβ42) and elevated total tau (t-tau) protein concentrations in the CSF are characteristic of Alzheimer’s disease (AD) pathology [1]. Aβ42 contributes to extracellular plaque formation between neurons [2]. The total level of tau, a microtubule-stabilizer in neuron axons [3], is associated with neuronal death [1]. Additionally, presence of ApoE4 (ε4) alleles is an established identifier for increased risk of AD [4]. Non-invasive imaging-based correlates of the CSF markers are lacking. This study examines correlations of novel FLAIR texture, intensity, and volume biomarkers, along with white matter lesion (WML) burden, with Aβ42, t-tau, and ε4 status. Method FLAIR volumes (n = 4044) from the ADNI database were used for analysis with CSF biomarkers. A subset (n = 921) was used for ε4 analysis based on available data. Normal-appearing brain matter (NABM) was obtained by removing CSF and WML. Macrostructural damage (MAD), microstructural damage (MID), and integrity (MII) texture biomarkers [5] were extracted from each NABM volume (Fig. 1), along with median intensity, NABM/ICV ratio, and normalized WML volume [5]. Relationships between FLAIR biomarkers and AD markers were described using parametric statistical analyses. Statistical significance was set at p<0.05. Result All FLAIR biomarkers were statistically significantly correlated with CSF Aβ42 levels. All features except MID and WML burden were significantly correlated with CSF t-tau levels (Table 1). MAD, MID, and WML burden showed negative correlations with Aβ42, while MAD showed a positive correlation with t-tau. Opposite trends were seen with MII, intensity, and NABM/ICV (Fig. 2). Significant differences between groups without and with one ε4 allele were found in all FLAIR biomarkers except WML burden. Significant differences in MAD, MII and NABM/ICV were found between groups with one and two ε4 alleles (Table 2). Conclusion FLAIR biomarkers were inversely associated with CSF Aβ

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Analyzing ImageNet with spectral relevance analysis: Towards ImageNet un-Hans'ed

arXiv

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arXiv 2019年

作者： Anders, Christopher J. Marinč, Talmaj Neumann, David Samek, Wojciech Müller, Klaus-Robert Lapuschkin, Sebastian Dept. of Electrical Engineering and Computer Science Technische Universität Berlin Germany Dept. of Video Coding and Analytics Fraunhofer Heinrich Hertz Institute Berlin Germany Dept. of Brain and Cognitive Engineering Korea University Seoul Korea Republic of Max Planck Institute for Informatics Saarbrücken Germany

Today's machine learning models for computer vision are typically trained on very large (benchmark) data sets with millions of samples. These may, however, contain biases, artifacts, or errors that have gone unnoticed and are exploited by the model. In the worst case, the trained model may become a 'Clever Hans' predictor that does not learn a valid and generalizable strategy to solve the problem it was trained for, but bases its decisions on spurious correlations in the training data. Recently developed techniques allow to explain individual model decisions and thus to gain deeper insights into the model's prediction strategies. In this paper, we contribute by providing a comprehensive analysis framework based on a scalable statistical analysis of attributions from explanation methods for large data corpora, here ImageNet. Based on a recent technique - Spectral Relevance Analysis (SpRAy) - we propose three technical contributions and resulting findings: (a) novel similarity metrics based on Wasserstein for comparing attributions to allow for the first time scale, translational, and rotational invariant comparisons of attributions, (b) a scalable quantification of artifactual and poisoned classes where the ML models under study exhibit Clever Hans behavior, (c) a cleaning procedure that allows to relief data of artifacts and biases in a systematic manner yielding significantly reduced Clever Hans behavior, i.e. we un-Hans the ImageNet data corpus. Using this novel method set, we provide qualitative and quantitative analyses of the biases and artifacts in ImageNet and demonstrate that the usage of these insights can give rise to improved models and functionally cleaned data corpora. Copyright © 2019, The Authors. All rights reserved.

关键词： Image enhancement

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Charting brain growth in tandem with brain templates at school age

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science Bulletin 2020年第22期65卷 1924-1934,M0004页

作者： Hao-Ming Dong F.Xavier Castellanos Ning Yang Zhe Zhang Quan Zhou Ye He Lei Zhang Ting Xu Avram J.Holmes B.T.Thomas Yeo Feiyan Chen Bin Wang Christian Beckmann Tonya White Olaf Sporns Jiang Qiu Tingyong Feng Antao Chen Xun Liu Xu Chen Xuchu Weng Michael P.Milham Xi-Nian Zuo Research Center for Lifespan Development of Brain and Mind Institute of PsychologyChinese Academy of Sciences(CAS)Beijing 100101China State Key Laboratory of Cognitive Neuroscience and Learning Beijing Normal UniversityBeijing 100875China Department of Child and Adolescent Psychiatry New York University School of MedicineNew YorkNY 10016USA Nathan Kline Institute for Psychiatric Research OrangeburgNew YorkNY 10962USA Department of Psychology University of CASBeijing 100049China Department of Psychological and Brain Sciences Indiana UniversityBloomingtonIN 47405USA Child Mind Institute New YorkNY 10022USA Department of Psychology Yale UniversityNew HavenCT 06511USA Department of Electrical and Computer Engineering Clinical Imaging Research CentreN.1 Institute for Health and Memory Networks ProgramNational University of SingaporeSingapore 117574Singapore Department of Physics Zhejiang UniversityHangzhou 310058China Binzhou Medical University Yantai 264100China Donders Institute Radboud University Medical CentreNijmegen 6525 ENNetherlands Department of Child and Adolescent Psychiatry Erasmus UniversityRotterdam 3000 CBNetherlands Department of Radiology Erasmus University Medical CentreRotterdam 3000 CANetherlands School of Psychology Southwest UniversityChongqing 400715China Institute for Brain Research and Rehabilitation South China Normal UniversityGuangzhou 510631China Institute of Children Health Changzhou Children’s HospitalChangzhou 213003China Key Laboratory of Brain and Education School of Education ScienceNanning Normal UniversityNanning 530001China CAS Key Laboratory of Behavioral Science Institute of PsychologyBeijing 100101China IDG/McGovern Institute for Brain Research Beijing Normal UniversityBeijing 100875China

brain growth charts and age-normed brain templates are essential resources for researchers to eventually contribute to the care of individuals with atypical developmental *** present work generates age-normed brain templates for children and adolescents at one-year intervals and the corresponding growth charts to investigate the influences of age and ethnicity using a common pediatric neuroimaging *** accelerated longitudinal cohorts with the identical experimental design were implemented in the United States and *** magnetic resonance imaging(MRI)of typically developing school-age children(TDC)was obtained up to three times at nominal intervals of1.25 *** protocol generated and compared population-and age-specific brain templates and growth charts,respectively.A total of 674 Chinese pediatric MRI scans were obtained from 457 Chinese TDC and 190 American pediatric MRI scans were obtained from 133 American ***-and age-specific brain templates were used to quantify warp cost,the differences between individual brains and brain *** growth charts for labeled brain network areas were *** analyses of cost functions supported the necessity of age-specific and ethnicitymatched brain templates,which was confirmed by growth chart *** analyses revealed volumetric growth differences between the two ethnicities primarily in lateral frontal and parietal areas,regions which are most variable across individuals in regard to their structure and ***-and ethnicity-specific brain templates facilitate establishing unbiased pediatric brain growth charts,indicating the necessity of the brain charts and brain templates generated in *** templates and growth charts as well as related codes have been made freely available to the public for open neuroscience(https://***/zuoxinian/CCS/tree/master/H3/Growth Charts).

关键词： Growth charts Neuroimaging Ethnicity brain templates Development

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Semantic categories of artifacts and animals reflect efficient coding

arXiv

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arXiv 2019年

作者： Zaslavsky, Noga Regier, Terry Tishby, Naftali Kemp, Charles Edmond and Lily Safra Center for Brain Sciences Hebrew University Jerusalem9190401 Israel Department of Linguistics University of California BerkeleyCA94720 United States Cognitive Science Program University of California BerkeleyCA94720 United States Benin School of Computer Science and Engineering Hebrew University Jerusalem9190401 Israel School of Psychological Sciences University of Melbourne ParkvilleVIC3010 Australia

It has been argued that semantic categories across languages reflect pressure for efficient communication. Recently, this idea has been cast in terms of a general information-theoretic principle of efficiency, the Information Bottleneck (IB) principle, and it has been shown that this principle accounts for the emergence and evolution of named color categories across languages, including soft structure and patterns of inconsistent naming. However, it is not yet clear to what extent this account generalizes to semantic domains other than color. Here we show that it generalizes to two qualitatively different semantic domains: names for containers, and for animals. First, we show that container naming in Dutch and French is nearoptimal in the IB sense, and that IB broadly accounts for soft categories and inconsistent naming patterns in both languages. Second, we show that a hierarchy of animal categories derived from IB captures cross-linguistic tendencies in the growth of animal taxonomies. Taken together, these findings suggest that fundamental information-theoretic principles of efficient coding may shape semantic categories across languages and across domains. Copyright © 2019, The Authors. All rights reserved.

关键词： Animals

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Self-Awareness for Autonomous Systems

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PROCEEDINGS OF THE IEEE 2020年第7期108卷 971-975页

作者： Dutt, Nikil Regazzoni, Carlo S. Rinner, Bernhard Yao, Xin Nikil Dutt (Fellow IEEE) received the Ph.D. degree from the University of Illinois at Urbana–Champaign Champaign IL USA in 1989.""He is currently a Distinguished Professor of computer science (CS) cognitive sciences and electrical engineering and computer sciences (EECS) with the University of California at Irvine Irvine CA USA. He is a coauthor of seven books. His research interests include embedded systems electronic design automation (EDA) computer architecture distributed systems healthcare Internet of Things (IoT) and brain-inspired architectures and computing.""Dr. Dutt is a Fellow of ACM. He was a recipient of the IFIP Silver Core Award. He has received numerous best paper awards. He serves as the Steering Committee Chair of the IEEE/ACM Embedded Systems Week (ESWEEK). He is also on the steering organizing and program committees of several premier EDA and embedded system design conferences and workshops. He has served on the Editorial Boards for the IEEE Transactions on Very Large Scale Integration (VLSI) Systems and the ACM Transactions on Embedded Computing Systems and also previously served as the Editor-in-Chief (EiC) for the ACM Transactions on Design Automation of Electronic Systems. He served on the Advisory Boards of the IEEE Embedded Systems Letters the ACM Special Interest Group on Embedded Systems the ACM Special Interest Group on Design Automationt and the ACM Transactions on Embedded Computing Systems. Carlo S. Regazzoni (Senior Member IEEE) received the M.S. and Ph.D. degrees in electronic and telecommunications engineering from the University of Genoa Genoa Italy in 1987 and 1992 respectively.""He is currently a Full Professor of cognitive telecommunications systems with the Department of Electrical Electronics and Telecommunication Engineering and Naval Architecture (DITEN) University of Genoa and a Co-Ordinator of the Joint Doctorate on Interactive and Cognitive Environments (JDICE) international Ph.D. course started initially as EU Erasmus Mundus Project and

Autonomous systems are able to make decisions and potentially take actions without direct human intervention, which requires some knowledge about the system and its environment as well as goal-oriented reasoning. In computer systems, one can derive such behavior from the concept of a rational agent with autonomy (“control over its own actions”), reactivity (“react to events from the environment”), proactivity (“act on its own initiative”), and sociality (“interact with other agents”) as fundamental properties \n[1]\n. Autonomous systems will undoubtedly pervade into our everyday lives, and we will find them in a variety of domains and applications including robotics, transportation, health care, communications, and entertainment to name a few. \nThe articles in this month’s special issue cover concepts and fundamentals, architectures and techniques, and applications and case studies in the exciting area of self-awareness in autonomous systems.

关键词： Special issues and sections Autonomous systems Self-aware Robot sensing systems Computational modeling

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