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Author Correction: Perceived match between own and observed models' bodies: influence of face, viewpoints, and body size

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Scientific reports 2020年第1期10卷 17120页

作者： Lize De Coster Pablo Sánchez-Herrero Carlos Aliaga Miguel A Otaduy Jorge López-Moreno Ana Tajadura-Jiménez DEI Interactive Systems Group Department of Computer Science and Engineering Universidad Carlos III de Madrid Avenida de la Universidad 30 28911 Leganés Madrid Spain. lcoster@inf.uc3m.es. Seddi Labs Madrid Spain. Modeling and Virtual Reality Group Department of Computer Science Universidad Rey Juan Carlos Madrid Spain. Multimodal Simulation Lab Department of Computer Science and Architecture Computer Systems and Languages Statistics and Operative Investigation Universidad Rey Juan Carlos Madrid Spain. DEI Interactive Systems Group Department of Computer Science and Engineering Universidad Carlos III de Madrid Avenida de la Universidad 30 28911 Leganés Madrid Spain.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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On neural phone recognition of mixed-source ecog signals

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arXiv 2019年

作者： Abdelaziz, Ahmed Hussen Chang, Shuo-Yiin Morgan, Nelson Edwards, Erik Kolossa, Dorothea Ellis, Dan Moses, David A. Chang, Edward F. International Computer Science Institute BerkeleyCA United States Cognitive Signal Processing Group Ruhr-Universität Bochum Germany Electrical Engineering Department Columbia University NY NY United States Department of Neurological Surgery UC San Francisco San FranciscoCA United States Apple Inc. CupertinoCA United States

The emerging field of neural speech recognition (NSR) using electrocorticography has recently attracted remarkable research interest for studying how human brains recognize speech in quiet and noisy surroundings. In this study, we demonstrate the utility of NSR systems to objectively prove the ability of human beings to attend to a single speech source while suppressing the interfering signals in a simulated cocktail party scenario. The experimental results show that the relative degradation of the NSR system performance when tested in a mixed-source scenario is significantly lower than that of automatic speech recognition (ASR). In this paper, we have significantly enhanced the performance of our recently published framework by using manual alignments for initialization instead of the flat start technique. We have also improved the NSR system performance by accounting for the possible transcription mismatch between the acoustic and neural signals. Copyright © 2019, The Authors. All rights reserved.

关键词： Electrophysiology

The Impact of Culture on Learner Behavior in Visual Debuggers

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The Impact of Culture on Learner Behavior in Visual Debugger...

IEEE Symposium on Visual Languages and Human Centric Computing (VL/HCC)

作者： Kyle Thayer Philip J. Guo Katharina Reinecke Paul G. Allen School of Computer Science & Engineering University of Washington Seattle WA USA Dept. of Cognitive Science UC San Diego La Jolla CA USA

People around the world are learning to code using online resources. However, research has found that these learners might not gain equal benefit from such resources, in particular because culture may affect how people learn from and use online resources. We therefore expect to see cultural differences in how people use and benefit from visual debuggers. We investigated the use of one popular online debugger which allows users to execute Python code and navigate bidirectionally through the execution using forward-steps and back-steps. We examined behavioral logs of 78,369 users from 69 countries and conducted an experiment with 522 participants from 82 countries. We found that people from countries that tend to prefer self-directed learning (such as those from countries with a low Power Distance, which tend to be less hierarchical than others) used about twice as many back-steps. We also found that for individuals whose values aligned with instructor-directed learning (those who scored high on a “Conservation” scale), back-steps were associated with less debugging success.

关键词： Python Visualization Navigation Debugging Cultural differences Education Programming

Stochastic gradient MCMC for state space models

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arXiv 2018年

作者： Aicher, Christopher Ma, Yi-An Foti, Nicholas J. Fox, Emily B. Department of Statistics University of Washington WA Singapore Department of Electrical Engineering and Computer Sciences UC BerkeleyCA United States Paul G. Allen School of Computer Science and Engineering University of Washington WA United States

State space models (SSMs) are a flexible approach to modeling complex time series. However, inference in SSMs is often computationally prohibitive for long time series. Stochastic gradient MCMC (SGMCMC) is a popular method for scalable Bayesian inference for large independent data. Unfortunately when applied to dependent data, such as in SSMs, SGMCMC's stochastic gradient estimates are biased as they break crucial temporal dependencies. To alleviate this, we propose stochastic gradient estimators that control this bias by performing additional computation in a 'buffer' to reduce breaking dependencies. Furthermore, we derive error bounds for this bias and show a geometric decay under mild conditions. Using these estimators, we develop novel SGMCMC samplers for discrete, continuous and mixed-type SSMs with analytic message passing. Our experiments on real and synthetic data demonstrate the effectiveness of our SGMCMC algorithms compared to batch MCMC, allowing us to scale inference to long time series with millions of time points. Copyright © 2018, The Authors. All rights reserved.

关键词： State space methods

Area-only method for underwater object tracking using autonomous vehicles

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Area-only method for underwater object tracking using autono...

OCEANS - Europe

作者： Ivan Masmitja Spartacus Gomariz Joaquin Del-Rio Brian Kieft Tom O’Reilly Jacopo Aguzzi Pierre-Jean Bouvet Clara Fannjiang Kakani Katija SARTI Research Group Electronics Department Universitat Politècnica de Catalunya Barcelona Spain Monterey Bay Aquarium Research Institute Moss Landing U.S.A. Marine Science Institute Consejo Superior de Investigaciones Cientfica Barcelona Spain L@bisen SEANERGY Lab. ISEN Brest Yncréa Ouest Brest France Department of Electrical Engineering and Computer Science UC Berkeley Berkeley U.S.A.

The use of autonomous underwater vehicles for ocean research has increased as they have a better cost-to-performance ratio than crewed oceanographic vessels. For example, autonomous surface vehicles (e.g. a Wave Glider) can be used to localise and track targets even in the deep-sea. While other researchers have focused on target tracking using acoustic modems, none explored up to date the area-only target tracking methodology. Here we present such a novel method with commercially available acoustic tags, thereby reducing the costs and complexity over other tracking systems. Moreover, this method can be used to track small targets (e.g., jellyfish) due to the tag's small size. The methodology behind the area-only technique is shown, and results from field tests conducted in Monterey Bay are also presented.

关键词： Target tracking Hidden Markov models Acoustics Probability distribution Animals Receivers Mathematical model

The duality gap for two-Team zero-sum games 8

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The duality gap for two-Team zero-sum games

8th Innovations in Theoretical computer science Conference, ITCS 2017

作者： Schulman, Leonard J Vazirani, Umesh V Caltech Engineering and Applied Science PasadenaMC305-1 United States UC Berkeley Computer Science Berkeley United States

ISBN: (纸本)9783959770293

We consider multiplayer games in which the players fall in two teams of size k, with payoffs equal within, and of opposite sign across, the two teams. In the classical case of k = 1, such zero-sum games possess a unique value, independent of order of play, due to the von Neumann minimax theorem. However, this fails for all k > 1;we can measure this failure by a duality gap, which quantifies the benefit of being the team to commit last to its strategy. In our main result we show that the gap equals 2(1 - 21-k) for m = 2 and 2(1 - m-(1-o1))k) for m > 2, with m being the size of the action space of each player. At a finer level, the cost to a team of individual players acting independently while the opposition employs joint randomness is 1 - 21-k for k = 2, and 1 - m-(1-o(1))k for m > 2. This class of multiplayer games, apart from being a natural bridge between two-player zerosum games and general multiplayer games, is motivated from Biology (the weak selection model of evolution) and Economics (players with shared utility but poor coordination.

关键词： Biology

Publisher Correction: The effect of large-scale anti-contagion policies on the COVID-19 pandemic

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Nature 2020年第7824期585卷 E7页

作者： Solomon Hsiang Daniel Allen Sébastien Annan-Phan Kendon Bell Ian Bolliger Trinetta Chong Hannah Druckenmiller Luna Yue Huang Andrew Hultgren Emma Krasovich Peiley Lau Jaecheol Lee Esther Rolf Jeanette Tseng Tiffany Wu Global Policy Laboratory Goldman School of Public Policy UC Berkeley Berkeley CA USA. shsiang@berkeley.edu. National Bureau of Economic Research Cambridge MA USA. shsiang@berkeley.edu. Centre for Economic Policy Research London UK. shsiang@berkeley.edu. Global Policy Laboratory Goldman School of Public Policy UC Berkeley Berkeley CA USA. Agricultural & Resource Economics UC Berkeley Berkeley CA USA. Manaaki Whenua - Landcare Research Auckland New Zealand. Energy & Resources Group UC Berkeley Berkeley CA USA. Electrical Engineering & Computer Science Department UC Berkeley Berkeley CA USA.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Utilizing stability criteria in choosing feature selection methods yields reproducible results in microbiome data

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arXiv 2020年

作者： Jiang, Lingjing Haiminen, Niina Carrieri, Anna-Paola Huang, Shi Vázquez-Baeza, Yoshiki Parida, Laxmi Kim, Ho-Cheol Swafford, Austin D. Knight, Rob Natarajan, Loki Division of Biostatistics University of California San Diego San diegoCA92093 United States IBM T. J. Watson Research Center Yorktown HeightsNY10598 United States IBM Research UK Hartree Center Warrington United Kingdom Center for Microbiome Innovation Jacobs School of Engineering UC San Diego San diegoCA92093 United States Department of Pediatrics University of California San Diego San diegoCA92093 United States Scalable Knowledge Intelligence IBM Research-Almaden San JoseCA95120 United States Department of Computer Science and Engineering University of California San Diego San diegoCA92093 United States Department of Bioengineering University of California San Diego San diegoCA92093 United States

Feature selection is indispensable in microbiome data analysis, but it can be particularly challenging as microbiome data sets are high-dimensional, underdetermined, sparse and compositional. Great efforts have recently been made on developing new methods for feature selection that handle the above data characteristics, but almost all methods were evaluated based on performance of model predictions. However, little attention has been paid to address a fundamental question: how appropriate are those evaluation criteria? Most feature selection methods often control the model fit, but the ability to identify meaningful subsets of features cannot be evaluated simply based on the prediction accuracy. If tiny changes to the training data would lead to large changes in the chosen feature subset, then many of the biological features that an algorithm has found are likely to be a data artifact rather than real biological signal. This crucial need of identifying relevant and reproducible features motivated the reproducibility evaluation criterion such as Stability, which quantifies how robust a method is to perturbations in the data. In our paper, we compare the performance of popular model prediction metric MSE and proposed reproducibility criterion Stability in evaluating four widely used feature selection methods in both simulations and experimental microbiome applications. We conclude that Stability is a preferred feature selection criterion over MSE because it better quantifies the reproducibility of the feature selection method. © 2020, CC BY.

关键词： Forecasting

The limits and potentials of deep learning for robotics

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arXiv 2018年

作者： Sünderhauf, Niko Brock, Oliver Scheirer, Walter Hadsell, Raia Fox, Dieter Leitner, Jürgen Upcroft, Ben Abbeel, Pieter Burgard, Wolfram Milford, Michael Corke, Peter Brisbane Australia Robotics and Biology Laboratory Technische Universität Berlin Germany Department of Computer Science and Engineering University of Notre Dame IN United States DeepMind London United Kingdom Paul G. Allen School of Computer Science & Engineering University of Washington WA United States Oxbotica Ltd. Oxford United Kingdom UC Berkeley Department of Electrical Engineering and Computer Sciences CA United States Department of Computer Science University of Freiburg Germany

The application of deep learning in robotics leads to very specific problems and research questions that are typically not addressed by the computer vision and machine learning communities. In this paper we discuss a number of robotics-specific learning, reasoning, and embodiment challenges for deep learning. We explain the need for better evaluation metrics, highlight the importance and unique challenges for deep robotic learning in simulation, and explore the spectrum between purely data-driven and model-driven approaches. We hope this paper provides a motivating overview of important research directions to overcome the current limitations, and help fulfill the promising potentials of deep learning in robotics. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep learning