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Erratum: Towards a muon collider

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The European Physical Journal C 2024年第1期84卷 1-4页

作者： Accettura, Carlotta Adams, Dean Agarwal, Rohit Ahdida, Claudia Aimè, Chiara Amapane, Nicola Amorim, David Andreetto, Paolo Anulli, Fabio Appleby, Robert Apresyan, Artur Apyan, Aram Arsenyev, Sergey Asadi, Pouya Mahmoud, Mohammed Attia Azatov, Aleksandr Back, John Balconi, Lorenzo Bandiera, Laura Barlow, Roger Bartosik, Nazar Barzi, Emanuela Batsch, Fabian Bauce, Matteo Berg, J. Scott Bersani, Andrea Bertarelli, Alessandro Bertolin, Alessandro Black, Kevin Boattini, Fulvio Bogacz, Alex Bonesini, Maurizio Bordini, Bernardo Bottaro, Salvatore Bottura, Luca Braghieri, Alessandro Breschi, Marco Bruhwiler, Natalie Buffat, Xavier Buonincontri, Laura Burrows, Philip N. Burt, Graeme Buttazzo, Dario Caiffi, Barbara Calviani, Marco Calzaferri, Simone Calzolari, Daniele Capdevilla, Rodolfo Carli, Christian Casaburo, Fausto Casarsa, Massimo Castelli, Luca Catanesi, Maria Gabriella Cavallucci, Lorenzo Cavoto, Gianluca Celiberto, Francesco Giovanni Celona, Luigi Cerri, Alessandro Cesarini, Gianmario Cesarotti, Cari Chachamis, Grigorios Chance, Antoine Chen, Siyu Chien, Yang-Ting Chiesa, Mauro Colaleo, Anna Collamati, Francesco Collazuol, Gianmaria Costa, Marco Craig, Nathaniel Curatolo, Camilla Curtin, David Da Molin, Giacomo Dam, Magnus Damerau, Heiko Dasu, Sridhara de Blas, Jorge De Curtis, Stefania De Matteis, Ernesto De Rosa, Stefania Delahaye, Jean-Pierre Denisov, Dmitri Denizli, Haluk Densham, Christopher Dermisek, Radovan Di Luzio, Luca Di Meco, Elisa Di Micco, Biagio Dienes, Keith Diociaiuti, Eleonora Dorigo, Tommaso Dudarev, Alexey Edgecock, Robert Errico, Filippo Fabbrichesi, Marco Farinon, Stefania Ferrari, Anna Somoza, Jose Antonio Ferreira Filthaut, Frank Fiorina, Davide Fol, Elena Forslund, Matthew Franceschini, Roberto Ximenes, Rui Franqueira Gabrielli, Emidio Gallinaro, Michele Garosi, Francesco Giambastiani, Luca Gianelle, Alessio Gilardoni, Simone Giove, Dario Augusto Giraldin, Carlo Glioti, Alfredo Greco, Mario Greljo, Admir Groeber, Ramona Grojean, Christophe Grudiev, Alexej Gu, Jiayin Han, Chengcheng Han, T Organisation Européenne pour la Recherche Nucléaire (CERN) Geneva Switzerland STFC Rutherford Appleton Laboratory (RAL) Oxford UK Computer Science Department University of California (UC) Berkeley USA Dipartimento di Fisica Università di Pavia Pavia Italy INFN Sezione di Pavia Pavia Italy INFN Sezione di Torino Turin Italy Dipartimento di Fisica Università di Torino Turin Italy INFN Sezione di Padova Padua Italy INFN Sezione di Roma Rome Italy School of Physics and Astronomy University of Manchester Manchester UK Fermi National Accelerator Laboratory Batavia USA Department of Physics Brandeis University Waltham USA IRFU CEA University Paris-Saclay Gif-sur-Yvette France Center for Theoretical Physics Massachusetts Institute of Technology Cambridge USA Center for High Energy Physics (CHEP-FU) Fayoum University El-Fayoum Egypt SISSA International School for Advanced Studies Trieste Italy INFN Sezione di Trieste Trieste Italy Department of Physics University of Warwick Coventry UK Dipartimento di Fisica Università di Milano Milan Italy INFN Laboratori Acceleratori e Superconduttività Applicata (LASA) Milan Italy INFN Sezione di Ferrara Ferrara Italy School of Computing and Engineering The University of Huddersfield Huddersfield UK Ohio State University Columbus USA Brookhaven National Laboratory Upton USA INFN Sezione di Genova Genoa Italy University of Wisconsin Wisconsin USA Center for Advanced Studies of Accelerators Jefferson Lab Newport News USA INFN Sezione di Milano Bicocca Milan Italy Dipartimento di Fisica Università di Milano Bicocca Milan Italy Scuola Normale Superiore Pisa Italy INFN Sezione di Pisa Pisa Italy Dipartimento di Ingegneria dell’Energia Elettrica e dell’Informazione Università di Bologna Bologna Italy INFN Sezione di Bologna Bologna Italy Department of Physics University of California (UC) Berkeley USA Dipartimento di Fisica e Astronomia Università di Padova Padoa Italy John Adams Institute University of O

Hapland: A scalable robust Emotion regulation haptic system testbed

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Hapland: A scalable robust Emotion regulation haptic system ...

2017 ACM SIGCHI Conference on Human Factors in Computing Systems, CHI EA 2017

作者： Miri, Pardis Flory, Robert Uusberg, Andero Uusberg, Helen Gross, James J. Isbister, Katherine UC Santa Cruz Department of Computer Science and Engineering United States Intel Labs Hardware Engineer United States Stanford University Department of Psychology United States University of Tartu Institute of Psychology Estonia

ISBN: (纸本)9781450346566

Emotion regulation is crucial for healthy adaptation [29, 16], while emotion dysregulation can lead to the development of mental health disorders such as anxiety or depression [6]. Everyone, one way or another, sometimes fails to effectively regulate their emotions, making this a challenging problem with broad potential impact. In our research, we are exploring three ways in which haptics-enabled wearables can facilitate effective emotion regulation, formulated based on Gross's model of emotion regulation [15]. We hypothesize that an individual-level biofeedback haptic could foster healthier and more effective patterns of emotion regulation. We have designed and implemented HapLand, a scalable, robust biofeedback haptic system testbed to facilitate research-based haptics-enabled wearables design for the purpose of emotion regulation. In this paper, we give an overview of HapLand and our plans for using HapLand for future research. Copyright © 2017 by the Association for Computing Machinery, Inc. (ACM).

关键词： Testbeds

Self-Supervised Visual Planning with Temporal Skip Connections

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arXiv 2017年

作者： Ebert, Frederik Finn, Chelsea Lee, Alex X. Levine, Sergey Department of Electrical Engineering and Computer Sciences UC Berkeley Department of Computer Science Technical University of Munich Germany

In order to autonomously learn wide repertoires of complex skills, robots must be able to learn from their own autonomously collected data, without human supervision. One learning signal that is always available for autonomously collected data is prediction. If a robot can learn to predict the future, it can use this predictive model to take actions to produce desired outcomes, such as moving an object to a particular location. However, in complex open-world scenarios, designing a representation for prediction is difficult. In this work, we instead aim to enable self-supervised robot learning through direct video prediction: instead of attempting to design a good representation, we directly predict what the robot will see next, and then use this model to achieve desired goals. A key challenge in video prediction for robotic manipulation is handling complex spatial arrangements such as occlusions. To that end, we introduce a video prediction model that can keep track of objects through occlusion by incorporating temporal skipconnections. Together with a novel planning criterion and action space formulation, we demonstrate that this model substantially outperforms prior work on video prediction-based control. Our results show manipulation of objects not seen during training, handling multiple objects, and pushing objects around obstructions. These results represent a significant advance in the range and complexity of skills that can be performed entirely with self-supervised robot learning. Copyright © 2017, The Authors. All rights reserved.

关键词： Forecasting

Wastewater sequencing reveals early cryptic SARS-CoV-2 variant transmission

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四川生理科学杂志 2022年第6期44卷 1118-1118页

作者： Smruthi Karthikeyan Department of Pediatrics University of California San Diego La Jolla CA USA Department of Biomedical Informatics University of California San Diego La Jolla California USA Department of Computer Science and Engineering University of California San Diego La Jolla CA USA Department of Bioengineering University of California San Diego La Jolla CA USA Department of Immunology and Microbiology The Scripps Research Institute La Jolla CA USA Expedited COVID Identification Environment (EXCITE) Laboratory Department of Pediatrics University of California San Diego La Jolla CA USA Department of Obstetrics Gynecology and Reproductive Sciences University of California San Diego La Jolla CA USA COVID-19 Detection Investigation Surveillance Clinical and Outbreak Response California Department of Public Health Richmond CA USA Sanford Consortium of Regenerative Medicine University of California San Diego La Jolla CA USA Department of Cellular and Molecular Medicine University of California San Diego La Jolla CA USA Student Health and Well-Being University of California San Diego La Jolla CA USA Center for Computational Biology and Bioinformatics University of California San Diego La Jolla CA USA Institute for Genomic Medicine University of California San Diego La Jolla CA USA Operational Strategic Initiatives University of California San Diego La Jolla CA USA Return to Learn University of California San Diego La Jolla CA USA Student Affairs University of California San Diego La Jolla CA USA Academic Affairs University of California San Diego La Jolla CA USA Department of Pathology University of California San Diego La Jolla CA USA Scripps Health San Diego La Jolla CA USA Rady Children’s Institute for Genomic Medicine San Diego CA USA Sharp Healthcare San Diego CA USA Helix San Mateo CA USA County of San Diego Health and Human Services Agency San Diego CA USA Herbert Wertheim School of Public Health and Human Longevity Science Univers

As SARS-CoV-2 continues to spread and evolve,detecting emerging variants early is critical for public health *** lineage prevalence by clinical testing is infeasible at scale,especially in areas with limited resources,participation,or testing/sequencing capacity,which can also introduce ***-CoV-2 RNA concentration in wastewater successfully tracks regional infection dynamics and provides less biased abundance estimates than clinical testing4,*** virus genomic sequences in wastewater would improve community prevalence estimates and detect emerging variants.

关键词： wastewater Wastewater testing

Superconducting phases in lithium decorated graphene LiC6

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arXiv 2018年

作者： Gholami, Rouhollah Moradian, Rostam Moradian, Sina Pickett, Warren E. Physics Department Faculty of Science Razi University Kermanshah Iran Nano science and nano technology research center Razi University Kermanshah Iran Department of Electrical and Computer Engineering University of Central Florida OrlandoFL United States Department of Physics UC Davis One Shield Avenue DavisCA95616 United States

A study of possible superconducting phases of graphene has been constructed in detail. A realistic tight binding model, fit to ab initio calculations, accounts for the Li-decoration of graphene with broken lattice symmetry, and includes s and d symmetry Bloch character that influences the gap symmetries that can arise. The resulting seven hybridized Li-C orbitals that support nine possible bond pairing amplitudes. The gap equation is solved for all possible gap symmetries. One band is weakly dispersive near the Fermi energy along ⌈ → M where its Bloch wave function has linear combination of dx2-y2and dxy character, and is responsible for dx2-y2and dxypairing with lowest pairing energy in our model. These symmetries almost preserve properties from a two band model of pristine graphene. Another part of this band, along K → ⌈, is nearly degenerate with upper s band that favors extended s wave pairing which is not found in two band model. Upon electron doping to a critical chemical potential μ1= 0.22eV the pairing potential decreases, then increases until a second critical value μ2=1.3 eV at which a phase transition to a distorted s-wave occurs. The distortion of d- or s-wave phases are a consequence of decoration which is not appear in two band pristine model. In the pristine graphene these phases convert to usual d-wave or extended s-wave pairing. Copyright © 2018, The Authors. All rights reserved.

关键词： Graphene

Stochastic neural networks for hierarchical reinforcement learning

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arXiv 2017年

作者： Florensa, Carlos Duan, Yan Abbeel, Pieter UC Berkeley Department of Electrical Engineering and Computer Science United States International Computer Science Institute United States OpenAI United States

Deep reinforcement learning has achieved many impressive results in recent years. However, tasks with sparse rewards or long horizons continue to pose significant challenges. To tackle these important problems, we propose a general framework that first learns useful skills in a pre-training environment, and then leverages the acquired skills for learning faster in downstream tasks. Our approach brings together some of the strengths of intrinsic motivation and hierarchical methods: the learning of useful skill is guided by a single proxy reward, the design of which requires very minimal domain knowledge about the downstream tasks. Then a high-level policy is trained on top of these skills, providing a significant improvement of the exploration and allowing to tackle sparse rewards in the downstream tasks. To efficiently pre-train a large span of skills, we use Stochastic Neural Networks combined with an information-theoretic regularizer. Our experiments1 show2 that this combination is effective in learning a wide span of interpretable skills in a sample-efficient way, and can significantly boost the learning performance uniformly across a wide range of downstream tasks. Copyright © 2017, The Authors. All rights reserved.

关键词： Deep learning

A machine learning approach to routing

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arXiv 2017年

作者： Valadarsky, Asaf Schapira, Michael Shahaf, Dafna Tamar, Aviv School of Computer Science and Engineering Hebrew University of Jerusalem Israel Dept. of Electrical Engineering and Computer Sciences UC Berkeley United States

Recently, much attention has been devoted to the question of whether/when traditional network protocol design, which relies on the application of algorithmic insights by human experts, can be replaced by a data-driven (i.e., machine learning) approach. We explore this question in the context of the arguably most fundamental networking task: routing. Can ideas and techniques from machine learning (ML) be leveraged to automatically generate "good" routing configurations? We focus on the classical setting of intradomain traffic engineering. We observe that this context poses significant challenges for data-driven protocol design. Our preliminary results regarding the power of data-driven routing suggest that applying ML (specifically, deep reinforcement learning) to this context yields high performance and is a promising direction for further research. We outline a research agenda for ML-guided routing. Copyright © 2017, The Authors. All rights reserved.

关键词： Deep learning

Author Correction: Pathway and network analysis of more than 2500 whole cancer genomes

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Nature communications 2022年第1期13卷 7566页

作者： Matthew A Reyna David Haan Marta Paczkowska Lieven P C Verbeke Miguel Vazquez Abdullah Kahraman Sergio Pulido-Tamayo Jonathan Barenboim Lina Wadi Priyanka Dhingra Raunak Shrestha Gad Getz Michael S Lawrence Jakob Skou Pedersen Mark A Rubin David A Wheeler Søren Brunak Jose M G Izarzugaza Ekta Khurana Kathleen Marchal Christian von Mering S Cenk Sahinalp Alfonso Valencia Jüri Reimand Joshua M Stuart Benjamin J Raphael Department of Computer Science Princeton University Princeton NJ 08540 USA. Department of Biomedical Informatics Emory University Atlanta GA 30322 USA. Department of Biomolecular Engineering and UC Santa Cruz Genomics Institute University of California Santa Cruz Santa Cruz CA 95060 USA. Computational Biology Program Ontario Institute for Cancer Research Toronto Ontario Canada. Department of Information Technology IDLab Ghent University IMEC Ghent the Netherlands. Department of Plant Biotechnology and Bioinformatics Ghent University Ghent the Netherlands. Barcelona Supercomputing Center (BSC) Barcelona 08034 Spain. Department of Clinical and Molecular Medicine Faculty of Medicine and Health Sciences Norwegian University of Science and Technology Trondheim Norway. Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics University of Zurich CH-8057 Zurich Switzerland. Department of Pathology and Molecular Pathology University Hospital Zurich CH-8091 Zurich Switzerland. Department of Physiology and Biophysics Weill Cornell Medicine New York NY 10065 USA. Vancouver Prostate Centre 2660 Oak Street Vancouver BC V6H 3Z6 Canada. The Broad Institute of MIT and Harvard Cambridge MA 02124 USA. Massachusetts General Hospital Center for Cancer Research Charlestown MA 02129 USA. Harvard Medical School 250 Longwood Avenue Boston MA 02115 USA. Massachusetts General Hospital Department of Pathology Boston MA 02114 USA. Department of Molecular Medicine (MOMA) Aarhus University Hospital Aarhus Denmark. Bioinformatics Research Centre (BiRC) Aarhus University Aarhus Denmark. Human Genome Sequencing Center Baylor College of Medicine Houston TX 77030 USA. DTU Bioinformatics Department of Bio and Health Informatics Technical University of Denmark Kemitorvet 2800 Kongens Lyngby Denmark. Novo Nordisk Foundation Center for Protein Research Faculty of Health and Medical Sciences University of Copenhagen 2200 Copenhagen Denmark. De

Towards robust neural networks via random self-ensemble

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arXiv 2017年

作者： Liu, Xuanqing Cheng, Minhao Zhang, Huan Hsieh, Cho-Jui Electrical and Computer Science UC Davis DavisCA95616 United States Electrical and Computer Engineering UC Davis DavisCA95616 United States Department of Statistics UC Davis DavisCA95616 United States

Recent studies have revealed the vulnerability of deep neural networks: A small adversarial perturbation that is imperceptible to human can easily make a well-trained deep neural network misclassify. This makes it unsafe to apply neural networks in security-critical applications. In this paper, we propose a new defense algorithm called Random Self-Ensemble (RSE) by combining two important concepts: randomness and ensemble. To protect a targeted model, RSE adds random noise layers to the neural network to prevent the strong gradient-based attacks, and ensembles the prediction over random noises to stabilize the performance. We show that our algorithm is equivalent to ensemble an infinite number of noisy models f∊ without any additional memory overhead, and the proposed training procedure based on noisy stochastic gradient descent can ensure the ensemble model has a good predictive capability. Our algorithm significantly outperforms previous defense techniques on real data sets. For instance, on CIFAR-10 with VGG network (which has 92% accuracy without any attack), under the strong C&W attack within a certain distortion tolerance, the accuracy of unprotected model drops to less than 10%, the best previous defense technique has 48% accuracy, while our method still has 86% prediction accuracy under the same level of attack. Finally, our method is simple and easy to integrate into any neural network. Copyright © 2017, The Authors. All rights reserved.

关键词： Deep neural networks