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arXiv 2017年

作者： Rougier, Nicolas P. Hinsen, Konrad Alexandre, Frédéric Arildsen, Thomas Barba, Lorena Benureau, Fabien C.Y. Titus Brown, C. de Buyl, Pierre Caglayan, Ozan Davison, Andrew P. Delsuc, Marc André Detorakis, Georgios Diem, Alexandra K. Drix, Damien Enel, Pierre Girard, Benoît Guest, Olivia Hall, Matt G. Henriques, Rafael Neto Hinaut, Xavier Jaron, Kamil S. Khamassi, Mehdi Klein, Almar Manninen, Tiina Marchesi, Pietro McGlinn, Dan Metzner, Christoph Petchey, Owen L. Plesser, Hans Ekkehard Poisot, Timothée Ram, Karthik Ram, Yoav Roesch, Etienne Rossant, Cyrille Rostami, Vahid Shifman, Aaron Stimberg, Marcel Stachelek, Joseph Stollmeier, Frank Vaggi, Federico Viejo, Guillaume Vitay, Julien Vostinar, Anya Yurchak, Roman Zito, Tiziano INRIA Bordeaux Sud-Ouest Talence France Institut des Maladies Neurodégénératives Université de Bordeaux CNRS UMR 5293 Bordeaux France LaBRI Université de Bordeaux Bordeaux INP CNRS UMR 5800 Talence France Centre de Biophysique Moléculaire CNRS UPR4301 Orléans France Synchrotron SOLEIL Division Expériences Gif sur Yvette France Department of Electronic Systems Technical Faculty of IT and Design Aalborg University Denmark Department of Mechanical and Aerospace Engineering George Washington University WashingtonDC United States Department of Population Health and Reproduction University of California Davis DavisCA United States Instituut voor Theoretische Fysica KU Leuven Belgium Le Mans France Unité de Neurosciences Information et Complexité CNRS FRE 3693 Gif sur Yvette France Institut de Génétique et de Biologie Moléculaire et Cellulaire INSERM U964 CNRS UMR 7104 Université de Strasbourg Illkirch France Department of Cognitive Sciences University of California Irvine United States Computational Engineering and Design Faculty of Engineering and the Environment University of Southampton United Kingdom Department of Computer Science Humboldt-Universität zu Berlin Friedman Brain Institute Icahn School of Medicine Mount SinaiNY United States Institut des Systèmes Intelligent et de Robotique Sorbonne Universités UPMC Univ Paris 06 CNRS Paris France Experimental Psychology University College London United Kingdom UCL Great Ormond St Institute of Child Health University College London London United Kingdom MRC Cognition and Brain Sciences Unit Cambridge United Kingdom Department of Ecology and Evolution University of Lausanne Lausanne Switzerland Independent scholar Enschede Netherlands BioMediTech Institute and Faculty of Biomedical Sciences and Engineering Tampere University of Technology Tampere Finland Swammerdam Institute for Life Sciences Center for Neuroscience Faculty of Science University of Amsterdam Amsterdam Netherlands Depart

Computer science offers a large set of tools for prototyping, writing, running, testing, validating, sharing and reproducing results, however computational science lags behind. In the best case, authors may provide their source code as a compressed archive and they may feel confident their research is reproducible. But this is not exactly true. Jonathan Buckheit and David Donoho proposed more than two decades ago that an article about computational results is advertising, not scholarship. The actual scholarship is the full software environment, code, and data that produced the result. This implies new workflows, in particular in peer-reviews. Existing journals have been slow to adapt: source codes are rarely requested, hardly ever actually executed to check that they produce the results advertised in the article. Rescience is a peer-reviewed journal that targets computational research and encourages the explicit replication of already published research, promoting new and open-source implementations in order to ensure that the original research can be replicated from its description. To achieve this goal, the whole publishing chain is radically different from other traditional scientific journals. Rescience resides on GitHub where each new implementation of a computational study is made available together with comments, explanations, and software tests. Copyright © 2017, The Authors. All rights reserved.

关键词： Open source software

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Survival microinjection into C. elegans with in vivo observation based on micromanipulation

Survival microinjection into C. elegans with in vivo observa...

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IEEE/RSJ International Conference on intelligent Robots and systems, IROS 2015

作者： Nakajima, Masahiro Ayamura, Yuki Takeuchi, Masaru Hisamoto, Naoki Pastuhov, Strahil Hasegawa, Yasuhisa Fukuda, Toshio Qiang, Huang Center for Micro-nano Mechatronics Nagoya University Nagoya Aichi Japan Department of Micro-Nano Systems Engineering Nagoya University Nagoya Aichi Japan Division of Biological Science Graduate School of Science Nagoya University Nagoya Aichi Japan Institute for Advanced Research Nagoya University Nagoya Aichi Japan Faculty of Science and Engineering Meijo University Nagoya Aichi Japan Intelligent Robotics Institute School of Mechatronic Engineering Beijing Institute of Technology China

ISBN: (纸本)9781479999941

This study presents the survival microinjection into Caenorhabditis elegans (C. elegans) with in vivo observation based on micromanipulation. The microinjections were achieved with micro-gel beads which are enable to encapsulate chemicals for injection. In this study, a fluorescent material was used to evaluate the injection positional precision inside the C. elegans. The fluorescent microbead was picked up at the tip of a micropipette injection tool and injected by a piezo actuated microinjector. The distance between the injected micro-gel bead and closest nerve axon was measured as 20.3 μm and 16.5 μm by in vivo observation of a confocal microscopy. The two types of pipette tools were used to evaluate the success and survival rates of microinjection, and the smaller pipette (pipette A, 0.8 μm in diameter) showed higher rates as 50 % and 67 % respectively. © 2015 IEEE.

关键词： intelligent robots

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Energy spectrum of cosmic-ray electron and positron from 10 GeV to 3 TeV observed with the calorimetric electron telescope on the international space station

arXiv

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arXiv 2017年

作者： Adriani, O. Akaike, Y. Asano, K. Asaoka, Y. Bagliesi, M.G. Bigongiari, G. Binns, W.R. Bonechi, S. Bongi, M. Brogi, P. Buckley, J.H. Cannady, N. Castellini, G. Checchia, C. Cherry, M.L. Collazuol, G. Di Felice, V. Ebisawa, K. Fuke, H. Guzik, T.G. Hams, T. Hareyama, M. Hasebe, N. Hibino, K. Ichimura, M. Ioka, K. Ishizaki, W. Israel, M.H. Javaid, A. Kasahara, K. Kataoka, J. Kataoka, R. Katayose, Y. Kato, C. Kawanaka, N. Kawakubo, Y. Krawczynski, H.S. Krizmanic, J.F. Kuramata, S. Lomtadze, T. Maestro, P. Marrocchesi, P.S. Messineo, A.M. Mitchell, J.W. Miyake, S. Moiseev, A.A. Mori, K. Mori, M. Mori, N. Motz, H.M. Munakata, K. Murakami, H. Nakahira, S. Nishimura, J. de Nolfo, G.A. Okuno, S. Ormes, J.F. Ozawa, S. Pacini, L. Palma, F. Papini, P. Penacchioni, A.V. Rauch, B.F. Ricciarini, S.B. Sakai, K. Sakamoto, T. Sasaki, M. Shimizu, Y. Shiomi, A. Sparvoli, R. Spillantini, P. Stolzi, F. Takahashi, I. Takayanagi, M. Takita, M. Tamura, T. Tateyama, N. Terasawa, T. Tomida, H. Torii, S. Tsunesada, Y. Uchihori, Y. Ueno, S. Vannuccini, E. Wefel, J.P. Yamaoka, K. Yanagita, S. Yoshida, A. Yoshida, K. Department of Physics University of Florence Via Sansone Sesto Fiorentino1 - 50019 Italy INFN Sezione di Florence Via Sansone Sesto Fiorentino1 - 50019 Italy Department of Physics University of Maryland Baltimore County 1000 Hilltop Circle BaltimoreMD21250 United States Astroparticle Physics Laboratory NASA/GSFC GreenbeltMD20771 United States Institute for Cosmic Ray Research University of Tokyo 5-1-5 Kashiwa-no-Ha Kashiwa Chiba277-8582 Japan Research Institute for Science and Engineering Waseda University 3-4-1 Okubo Shinjuku Tokyo169-8555 Japan JEM Utilization Center Human Spaceflight Technology Directorate Japan Aerospace Exploration Agency 2-1-1 Sengen Tsukuba Ibaraki305-8505 Japan Department of Physical Sciences Earth and Environment University of Siena via Roma 56 Siena53100 Italy INFN Sezione di Pisa Polo Fibonacci Largo B. Pontecorvo Pisa3 - 56127 Italy Department of Physics Washington University One Brookings Drive St. LouisMO63130-4899 United States Department of Physics and Astronomy Louisiana State University 202 Nicholson Hall Baton RougeLA70803 United States Via Madonna del Piano 10 Sesto Fiorentino50019 Italy Department of Physics and Astronomy University of Padova Via Marzolo 8 Padova35131 Italy INFN Sezione di Padova Via Marzolo 8 Padova35131 Italy University of Rome "Tor Vergata" Via della Ricerca Scientifica 1 Rome00133 Italy INFN Sezione di Rome "Tor Vergata" Via della Ricerca Scientifica 1 Rome00133 Italy Institute of Space and Astronautical Science Japan Aerospace Exploration Agency 3-1-1 Yoshinodai Chuo Sagamihara Kanagawa252-5210 Japan CRESST and Astroparticle Physics Laboratory NASA GSFC GreenbeltMD20771 United States St. Marianna University School of Medicine 2-16-1 Sugao Miyamae-ku Kawasaki Kanagawa216-8511 Japan Kanagawa University 3-27-1 Rokkakubashi Kanagawa Yokohama Kanagawa221-8686 Japan Faculty of Science and Technology Graduate School of Science and Technology H

First results of a cosmic-ray electron + positron spectrum, from 10 GeV to 3 TeV, is presented based upon observations with the CALET instrument on the ISS starting in October, 2015. Nearly a half million electron + positron events are included in the analysis. CALET is an all-calorimetric instrument with total vertical thickness of 30 X0 and a fine imaging capability designed to achieve a large proton rejection and excellent energy resolution well into the TeV energy region. The observed energy spectrum over 30 GeV can be fit with a single power law with a spectral index of - 3.152±0.016 (stat.+ syst.). Possible structure observed above 100 GeV requires further investigation with increased statistics and refined data analysis. Copyright © 2017, The Authors. All rights reserved.

关键词： Cosmology

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Two-dimensional euler PCA for face recognition 21

Two-dimensional euler PCA for face recognition

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21st International Conference on MultiMedia Modeling, MMM 2015

作者： Tan, Huibin Zhang, Xiang Guan, Naiyang Tao, Dacheng Huang, Xuhui Luo, Zhigang Science and Technology on Parallel Distributed Processing Laboratory College of Computer National University of Defense Technology Changsha Hunan410073 China Department of Computer Science and Technology College of Computer National University of Defense Technology Changsha Hunan410073 China Centre for Quantum Computation and Intelligent Systems and the Faculty of Engineering and Information Technology University of Technology Sydney 235 Jones Street UltimoNSW2007 Australia

ISBN: (纸本)9783319144412

Principal component analysis (PCA) projects data on the directions with maximal variances. Since PCA is quite effective in dimension reduction, it has been widely used in computer vision. However, conventional PCA suffers from following deficiencies: 1) it spends much computational costs to handle high-dimensional data, and 2) it cannot reveal the nonlinear relationship among different features of data. To overcome these deficiencies, this paper proposes an efficient two-dimensional Euler PCA (2D-ePCA) algorithm. Particularly, 2D-ePCA learns projection matrix on the 2D pixel matrix of each image without reshaping it into 1D long vector, and uncovers nonlinear relationships among features by mapping data onto complex representation. Since such 2D complex representation induces much smaller kernel matrix and principal subspaces, 2D-ePCA costs much less computational overheads than Euler PCA on large-scale dataset. Experimental results on popular face datasets show that 2D-ePCA outperforms the representative algorithms in terms of accuracy, computational overhead, and robustness. © Springer International Publishing Switzerland 2015.

关键词： Principal component analysis

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Non-negative low-rank and group-sparse matrix factorization 21

Non-negative low-rank and group-sparse matrix factorization

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21st International Conference on MultiMedia Modeling, MMM 2015

作者： Wu, Shuyi Zhang, Xiang Guan, Naiyang Tao, Dacheng Huang, Xuhui Luo, Zhigang Science and Technology on Parallel Distributed Processing Laboratory College of Computer National University of Defense Technology Changsha Hunan410073 China Department of Computer Science and Technology College of Computer National University of Defense Technology Changsha Hunan410073 China Centre for Quantum Computation & Intelligent Systems and the Faculty of Engineering and Information Technology University of Technology Sydney 235 Jones Street UltimoNSW2007 Australia

ISBN: (纸本)9783319144412

Non-negative matrix factorization (NMF) has been a popular data analysis tool and has been widely applied in computer vision. However, conventional NMF methods cannot adaptively learn grouping structure froma *** paper proposes a non-negative low-rank and group-sparse matrix factorization (NLRGS) method to overcome this deficiency. Particularly, NLRGS captures the relationships among examples by constraining rank of the coefficients meanwhile identifies the grouping structure via group sparsity regularization. By both constraints, NLRGS boosts NMF in both classification and clustering. However, NLRGS is difficult to be optimized because it needs to deal with the low-rank constraint. To relax such hard constraint, we approximate the low-rank constraint with the nuclear norm and then develop an optimization algorithm for NLRGS in the frame of augmented Lagrangian method(ALM). Experimental results of both face recognition and clustering on four popular face datasets demonstrate the effectiveness of NLRGS in quantities. © Springer International Publishing Switzerland 2015.

关键词： Non-negative matrix factorization

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Optimal simulation of Deutsch gates and the Fredkin gate

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Physical Review A 2015年第3期91卷 032302-032302页

作者： Nengkun Yu Mingsheng Ying Institute for Quantum Computing University of Waterloo Waterloo Ontario Canada Department of Mathematics & Statistics University of Guelph Guelph Ontario Canada Center for Quantum Computation and Intelligent Systems (QCIS) Faculty of Engineering and Information Technology University of Technology Sydney NSW 2007 Australia State Key Laboratory of Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology Department of Computer Science and Technology Tsinghua University Beijing 100084 China

In this paper, we study the optimal simulation of the three-qubit unitary using two-qubit gates. First, we completely characterize the two-qubit gate cost of simulating the Deutsch gate (controlled-controlled gate) by generalizing our result on the two-qubit cost of the Toffoli gate. The function of any Deutsch gate is simply a three-qubit controlled-unitary gate and can be intuitively explained as follows: The gate outputs the states of the two control qubits directly, and applies the given one-qubit unitary u on the target qubit only if both the states of the control qubits are |1〉. Previously, it was only known that five two-qubit gates are sufficient for implementing such a gate [Sleator and Weinfurter, Phys. Rev. Lett. 74, 4087 (1995)]. We show that if the determinant of u is 1, four two-qubit gates are optimal. Otherwise, five two-qubit gates are required. For the Fredkin gate (the controlled-swap gate), we prove that five two-qubit gates are necessary and sufficient, which settles the open problem introduced in Smolin and DiVincenzo [Phys. Rev. A 53, 2855 (1996)].

关键词： QUANTUM gates PARTICLE interactions QUBITS QUANTUM information science QUANTUM fluctuations HAMILTONIAN systems QUANTUM states

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A distributed formation-based odor source localization algorithm - design, implementation, and wind tunnel evaluation

A distributed formation-based odor source localization algor...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Jorge M. Soares A. Pedro Aguiar António M. Pascoal Alcherio Martinoli Laboratory of Robotics and Systems in Engineering and Science University of Lisbon Lisboa Portugal Distributed Intelligent Systems and Algorithms Laboratory Ecole Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland Faculty of Engineering University of Porto Research Center for Systems and Technology Porto Portugal

ISBN: (纸本)9781479969241

Robotic odor source localization is a promising tool with numerous applications in safety, search and rescue, and environmental science. In this paper, we present an algorithm for odor source localization using multiple cooperating robots equipped with chemical sensors. Laplacian feedback is employed to maintain the robots in a formation, introducing spatial diversity that is used to better establish the position of the flock relative to the plume and its source. Robots primarily move upwind but use odor information to adjust their position and spacing so that they are centered on the plume and trace its structure. Real-world experiments were performed with an ethanol plume inside a wind tunnel, and used to both validate the algorithm and assess the impact of different formation shapes.

关键词： Robot sensing systems Robot kinematics Algorithm design and analysis Shape Hardware

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Cyber-Security Enhancement of Networked Control systems Using Homomorphic Encryption

Cyber-Security Enhancement of Networked Control Systems Usin...

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IEEE Annual Conference on Decision and Control

作者： Kiminao Kogiso Takahiro Fujita Faculty of Mechanical Engineering and Intelligent Systems University of Electro-Communications Chofu Tokyo 182-8585 Japan Graduate School of Information Science Nara Institute of Science and Technology Ikoma Nara 630-0192 Japan

ISBN: (纸本)9781479978878

This paper proposes a new concept of controller encryption for enhancement of the cyber-security of networked control systems and presents how to encrypt a linear controller using our modified homomorphic encryption schemes based on public-key RSA and ElGamal encryption systems. A remarkable advantage of the controller encryption is to be able to conceal several informations processed inside the controller device, such as controller parameters, references (recipes), measurements, control commands, and parameters of plant models in the internal model principal, maintaining an original function of the controller. Therefore, even if malicious users hacked the controller device by unauthorized accesses, it would take much time and cost to decipher and steal the control system's information. Finally, numerical examples confirm that only the scrambled parameters and signals can be seen in the controller device of the security-enhanced networked control system.

关键词： Networked control systems control systems Network security Controllers Homomorphic encryption Security Enhancement linear controllers Decryption cryptography commands unauthorized access

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Multi-DL-ReSuMe: Multiple neurons Delay Learning Remote Supervised Method

Multi-DL-ReSuMe: Multiple neurons Delay Learning Remote Supe...

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International Joint Conference on Neural Networks

作者： Aboozar Taherkhani Ammar Belatreche Yuhua Li Liam P. Maguire Intelligent Systems Research Centre University of Ulster Londonderry U.K School of Computing and Intelligent Systems University of Ulster Londonderry U.K School of Computing Science and Engineering University of Salford Manchester U.K Faculty of Computing and Engineering University of Ulster Londonderry U.K

ISBN: (纸本)9781479919611

Spikes are an important part of information transmission between neurons in the biological brain. Biological evidence shows that information is carried in the timing of individual action potentials, rather than only the firing rate. Spiking neural networks are devised to capture more biological characteristics of the brain to construct more powerful intelligent systems. In this paper, we extend our newly proposed supervised learning algorithm called DL-ReSuMe (Delay Learning Remote Supervised Method) to train multiple neurons to classify spatiotemporal spiking patterns. In this method, a number of neurons instead of a single neuron is trained to perform the classification task. The simulation results show that a population of neurons has significantly higher processing ability compared to a single neuron. It is also shown that the performance of Multi-DL-ReSuMe (Multiple DL-ReSuMe) is increased when the number of desired spikes is increased in the desired spike trains to an appropriate number.

关键词： classification delay learning spiking neural network supervised learning spatiotemporal patterns Semi-supervised Learning Neurons Action Potentials spike data transmission spiking Remote TRAIN spikes

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Multivariate spearman's ρ for aggregating ranks using copulas

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2016年第1期17卷

作者： Kevin Murphy Bernhard Schölkopf Justin Bedö Cheng Soon Ong Google MPI for Intelligent Systems The Walter and Eliza Hall Institute Victoria Australia and The Department of Computing and Information Systems The University of Melbourne VIC Australia Data61 CSIRO Canberra ACT Australia and Research School of Computer Science The Australian National University Australia and The Department of Electrical and Electronic Engineering The University of Melbourne VIC Australia

We study the problem of rank aggregation: given a set of ranked lists, we want to form a consensus ranking. Furthermore, we consider the case of extreme lists: i.e., only the rank of the best or worst elements are known. We impute missing ranks and generalise Spearman's ρ to extreme ranks. Our main contribution is the derivation of a non-parametric estimator for rank aggregation based on multivariate extensions of Spearman's ρ, which measures correlation between a set of ranked lists. Multivariate Spearman's ρ is defined using copulas, and we show that the geometric mean of normalised ranks maximises multivariate correlation. Motivated by this, we propose a weighted geometric mean approach for learning to rank which has a closed form least squares solution. When only the best (top-k) or worst (bottom-k) elements of a ranked list are known, we impute the missing ranks by the average value, allowing us to apply Spearman's ρ. We discuss an optimistic and pessimistic imputation of missing values, which respectively maximise and minimise correlation, and show its effect on aggregating university rankings. Finally, we demonstrate good performance on the rank aggregation benchmarks MQ2007 and MQ2008.

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