检索结果-内蒙古大学图书馆

Generative Adversarial Networks Enable Cross-Modality Super-Resolution in Fluorescence Microscopy

学校读者我要写书评

暂无评论

Microscopy and Microanalysis 2019年第S2期25卷 1228-1229页

作者： Hongda Wang Yair Rivenson Yiyin Jin Zhensong Wei Ronald Gao Harun Günaydın Laurent A. Bentolila Comert Kural Aydogan Ozcan Electrical & Computer Engineering Department University of California Los Angeles Los Angeles USA Bioengineering Department University of California Los Angeles Los Angeles USA California NanoSystems Institute University of California Los Angeles Los Angeles USA Computer Science Department University of California Los Angeles Los Angeles USA Department of Chemistry and Biochemistry University of California Los Angeles Los Angeles USA Department of Physics Ohio State University Columbus USA Biophysics Graduate Program Ohio State University Columbus USA Department of Surgery David Geffen School of Medicine University of California Los Angeles Los Angeles USA.

Ten quick tips for deep learning in biology

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Lee, Benjamin D. Gitter, Anthony Greene, Casey S. Raschka, Sebastian Maguire, Finlay Titus, Alexander J. Kessler, Michael D. Lee, Alexandra J. Chevrette, Marc G. Stewart, Paul Allen Britto-Borges, Thiago Cofer, Evan M. Yu, Kun-Hsing Carmona, Juan Jose Fertig, Elana J. Kalinin, Alexandr A. Signal, Beth Lengerich, Benjamin J. Triche, Timothy J. Boca, Simina M. In-Q-Tel Labs School of Engineering and Applied Sciences Harvard University Department of Genetics Harvard Medical School United States Department of Biostatistics and Medical Informatics University of Wisconsin-Madison MadisonWI United States Morgridge Institute for Research MadisonWI United States Department of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Biochemistry and Molecular Genetics University of Colorado School of Medicine AuroraCO United States Center for Health AI University of Colorado School of Medicine AuroraCO United States Department of Statistics University of Wisconsin Madison United States Faculty of Computer Science Dalhousie University Canada University of New Hampshire Bioeconomy.XYZ United States Department of Oncology Johns Hopkins University United States Institute for Genome Sciences University of Maryland School of Medicine United States Genomics and Computational Biology Graduate Program University of Pennsylvania United States Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania United States Wisconsin Institute for Discovery Department of Plant Pathology University of Wisconsin-Madison United States Department of Biostatistics and Bioinformatics Moffitt Cancer Center TampaFL United States Section of Bioinformatics and Systems Cardiology Klaus Tschira Institute for Integrative Computational Cardiology University Hospital Heidelberg Germany University Hospital Heidelberg Germany Lewis-Sigler Institute for Integrative Genomics Princeton University PrincetonNJ United States Graduate Program in Quantitative and Computational Biology Princeton University PrincetonNJ United States Department of Biomedical Informatics Harvard Medical School United States Department of Pathology Brigham and Women's Hospital United States Philips Healthcare CambridgeMA United States Philips Research

Machine learning is a modern approach to problem-solving and task automation. In particular, machine learning is concerned with the development and applications of algorithms that can recognize patterns in data and use them for predictive modeling. Artificial neural networks are a particular class of machine learning algorithms and models that evolved into what is now described as deep learning. Given the computational advances made in the last decade, deep learning can now be applied to massive data sets and in innumerable contexts. Therefore, deep learning has become its own subfield of machine learning. In the context of biological research, it has been increasingly used to derive novel insights from high-dimensional biological data. To make the biological applications of deep learning more accessible to scientists who have some experience with machine learning, we solicited input from a community of researchers with varied biological and deep learning interests. These individuals collaboratively contributed to this manuscript's writing using the GitHub version control platform and the Manubot manuscript generation toolset. The goal was to articulate a practical, accessible, and concise set of guidelines and suggestions to follow when using deep learning. In the course of our discussions, several themes became clear: the importance of understanding and applying machine learning fundamentals as a baseline for utilizing deep learning, the necessity for extensive model comparisons with careful evaluation, and the need for critical thought in interpreting results generated by deep learning, among others. © 2021, CC BY.

关键词： Learning algorithms

FINDING CONTINUITY AND DISCONTINUITY IN FISH SCHOOLS VIA INTEGRATED INFORMATION THEORY

学校读者我要写书评

暂无评论

arXiv 2018年

作者： Niizato, Takayuki Sakamoto, Kotaro Mototake, Yoh-ichi Murakami, Hisashi Tomaru, Takenori Hoshika, Tomotaro Fukushima, Toshiki Faculty of Engineering Information and Systems University of Tsukuba Japan Human Biology Program University of Tsukuba Japan Graduate School of Frontier Sciences University of Tokyo Japan Research Center for Advanced Science and Technology University of Tokyo Japan Department of Computer Science and Engineering Toyohashi University of Technology Japan

Collective behaviour is known to be the result of diverse dynamics and is sometimes likened to a living system. Although many studies have revealed the dynamics of various collective behaviours, their main focus was on the information process inside the collective, not on the whole system itself. For example, the qualitative difference between two elements and three elements as a system has rarely been investigated. Tononi et al. have proposed Integrated Information Theory (IIT) to measure the degree of consciousness Φ. IIT postulates that the amount of information loss caused by certain partitions is equivalent to the degree of information integration in the system. This measure is not only useful for estimating the degree of consciousness but can also be applied to more general network systems. Here we applied IIT (in particular, IIT 3.0 using PyPhi) to analyse real fish schools (Plecoglossus altivelis). Our hypothesis in this study is a very simple one: a living system evolves to raise its Φ value. If we accept this hypothesis, IIT reveals the existence of continuous and discontinuous properties as group size varies. For example, leadership in the fish school emerged for a school size of four or above;but not below three. Furthermore, this transition was not observed by measuring mutual information or in a simple Boids model. This result suggests that integrated information Φ can reveal some inherent properties which cannot be observed using other measures. We also discuss how the fish recognition of the figure-ground relation, that is, what determines the relevant ON and OFF states, may reveal various optimal paths for obtaining the functional evolution of collective behaviour. Copyright © 2018, The Authors. All rights reserved.

关键词： Information theory

Noninvasive Glucose Sensor Based on Parity-Time Symmetry

学校读者我要写书评

暂无评论

Physical Review Applied 2019年第4期11卷 044049-044049页

作者： Yun Jing Zhang Hoyeong Kwon Mohammad-Ali Miri Efthymios Kallos Helena Cano-Garcia Mei Song Tong Andrea Alu Department of Electrical and Computer Engineering The University of Texas at Austin Austin 78712 USA Department of Electronic Science and Technology Tongji University Shanghai 201804 China Department of Physics Queens College of the City University of New York Queens New York 11367 USA Physics Program Graduate Center City University of New York New York 10016 USA Medical Wireless Sensing Ltd London E1 2AX UK King’s College London London WC2R 2LS UK Photonics Initiative Advanced Science Research Center City University of New York New York 10031 USA Department of Electrical Engineering City College of The City University of New York New York 10031 USA

We discuss a noninvasive technique to detect glucose changes with enhanced sensitivity based on parity-time (PT) symmetry. We detect glucose level changes within the skin by measuring the frequency shift in the electromagnetic resonance induced within a PT-symmetric system that sandwiches the tissue sample under analysis. Even though the sample itself is lossy, and therefore, resonances would be damped, the introduction of balanced gain and loss enables an efficient sensing mechanism that bypasses the conventional limitations of passive sensing schemes. Our results indicate that the resonance shift can be made fairly linear with respect to the glucose concentration variations, and the expected accuracy is large. We also investigate a realistic system to implement the noninvasive PT-symmetric glucose sensor using loop antennas and negative impedance converters, exploring its sensitivity with respect to design errors and disorder.

关键词： Medical physics Optical parametric oscillators & amplifiers Optoelectronics Metamaterials Solid-state detectors PT-symmetry Microwave techniques Resonance techniques

Cryptocurrencies for Smart Territories: an exploratory study

学校读者我要写书评

暂无评论

Cryptocurrencies for Smart Territories: an exploratory study

International Joint Conference on Neural Networks (IJCNN)

作者： Alexandre C. Barbosa Thays A. Oliveira Vitor N. Coelho Graduate Program in Sustainable Territorial Development Universitá Degli Studi di Padova Italy and Katholieke Universiteit Leuven Belgium Universitè Paris 1 Pantheon-Sorbonne France Graduate Program in Engineering and Information and Communication Technologies Universitat Pompeu Fabra Barcelona Spain Institute of Computer Science Universidade Federal Fluminense Niterói RJ Brazil

The smart city debate will still be the scope of business, policy-making and territorial planning in the following years. Based on that, the paper aims to propose an interdisciplinary conceptualization of smart territories. The goal is to promote the contrary movement of megacities creation and also consolidate the geographical aspect of smartness. The concept was formulated based on the elements of sustainable territorial development, strategic spatial planning and smart governance. In addition, taking hand of a parallel literature review, the study explores the potential of creating complementary cryptocurrencies as a strategy of territorial planning. This term was built based on already existing complementary currency systems in distributed ledger technologies scope. The idea provided presents a great potential for poverty alleviation by bringing together financial and digital inclusion.

关键词： Planning Smart cities Collaboration Decision making Government Cryptography Cultural differences

Pandemics are catalysts of scientific novelty: Evidence from COVID-19

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Liu, Meijun Bu, Yi Chen, Chongyan Xu, Jian Li, Daifeng Leng, Yan Freeman, Richard Barry Meyer, Eric Yoon, Wonjin Sung, Mujeen Jeong, Minbyul Lee, Jinhyuk Kang, Jaewoo Min, Chao Song, Min Zhai, Yujia Ding, Ying Institute for Global Public Policy Fudan University Shanghai China Department of Information Management Peking University Beijing China School of Information University of Texas at Austin AustinTX United States School of Information Management Sun Yat-Sen University Guangzhou China McCombs School of Business University of Texas at Austin AustinTX United States Department of Economics Harvard University CambridgeMA United States CambridgeMA United States Department of Computer Science and Engineering Korea University Seoul Korea Republic of Interdisciplinary Graduate Program in Bioinformatics Korea University Seoul Korea Republic of School of Information Management Nanjing University Nanjing China Department of Library and Information Science Yonsei University Seoul Korea Republic of Department of Information Resource Management School of Management Tianjin Normal University Tianjin China School of Information Management Wuhan University Wuhan China Dell Medical School University of Texas at Austin AustinTX United States

Scientific novelty drives the efforts to invent new vaccines and solutions during the pandemic. First-time collaboration and international collaboration are two pivotal channels to expand teams' search activities for a broader scope of resources required to address the global challenge, which might facilitate the generation of novel ideas. Our analysis of 98,981 coronavirus papers suggests that scientific novelty measured by the BioBERT model that is pre-trained on 29 million PubMed articles, and first-time collaboration increased after the outbreak of COVID-19, and international collaboration witnessed a sudden decrease. During COVID-19, papers with more first-time collaboration were found to be more novel and international collaboration did not hamper novelty as it had done in the normal periods. The findings suggest the necessity of reaching out for distant resources and the importance of maintaining a collaborative scientific community beyond nationalism during a pandemic. Copyright © 2020, The Authors. All rights reserved.

关键词： Coronavirus

Inverse-designed photonic circuits for fully passive, bias-free Kerr-based nonreciprocal transmission and routing

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Yang, Ki Youl Skarda, Jinhie Cotrufo, Michele Dutt, Avik Ahn, Geun Ho Vercruysse, Dries Fan, Shanhui Alù, Andrea Vucković, Jelena E. L. Ginzton Laboratory Stanford University StanfordCA94305 United States Department of Electrical and Computer Engineering University of Texas at Austin AustinTX78712 United States Photonics Initiative Advanced Science Research Center City University of New York New YorkNY10031 United States Physics Program Graduate Center City University of New York New YorkNY10016 United States Department of Electrical Engineering City University of New York New YorkNY10031 United States

Nonreciprocal devices such as isolators and circulators are key enabling technologies for communication systems, both at microwave and optical frequencies. While nonreciprocal devices based on magnetic effects are available for free-space and fibre-optic communication systems, their on-chip integration has been challenging, primarily due to the concomitant high insertion loss, weak magneto-optical effects, and material incompatibility. We show that Kerr nonlinear resonators can be used to achieve all-passive, low-loss, bias-free, broadband nonreciprocal transmission and routing for applications in photonic systems such as chip-scale LIDAR. A multi-port nonlinear Fano resonator is used as an on-chip, all-optical router for frequency comb based distance measurement. Since time-reversal symmetry imposes stringent limitations on the operating power range and transmission of a single nonlinear resonator, we implement a cascaded Fano-Lorentzian resonator system that overcomes these limitations and significantly improves the insertion loss, bandwidth and non-reciprocal power range of current state-of-the-art devices. This work provides a platform-independent design for nonreciprocal transmission and routing that are ideally suited for photonic integration. Copyright © 2019, The Authors. All rights reserved.

关键词： Insertion losses

Identification of gene expression signatures for psoriasis classification using machine learning techniques

学校读者我要写书评

暂无评论

Medicine in Omics 2021年 1卷

作者： Nguyen Quoc Khanh Le Duyen Thi Do Trinh-Trung-Duong Nguyen Ngan Thi Kim Nguyen Truong Nguyen Khanh Hung Nguyen Thi Thu Trang Professional Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical University Taipei City 106 Taiwan Research Center for Artificial Intelligence in Medicine Taipei Medical University Taipei City 106 Taiwan Graduate Institute of Biomedical Informatics Taipei Medical University Taipei City 106 Taiwan Department of Computer Science and Engineering Yuan Ze University Chung-Li 32003 Taiwan School of Nutrition and Health Sciences Taipei Medical University Taipei City 110 Taiwan International Master/Ph.D. Program in Medicine College of Medicine Taipei Medical University Taipei City 110 Taiwan Orthopedic and Trauma Department Cho Ray Hospital Ho Chi Minh City Viet Nam International PhD Program for Cell Therapy and Regeneration Medicine College of Medicine Taipei Medical University Taipei City 110 Taiwan

Psoriasis classification requires the accurate identification of the lesional types for the early and effective diagnosis and it is worth interesting that the normal and psoriasis cell tissues exhibit different gene expression. Therefore, gene expression data is an effective source for psoriasis classification and there is a challenge regarding the selection of suitable gene signatures for its purpose. In this present study, the gene expression-based microarray data were used and 35 expression features linked with psoriasis were utilized to feed into our machine learning model. Overall, the performance of our model based on 35 mentioned-above features surpassed that of other state-of-the-art classifiers with an average accuracy of 98.3%, recall of 98.6%, and precision of 98% in 5-fold cross-validation tests. We also validate our model on two different sets of psoriasis and the performance results are significant. These results have suggested that our 35 expression signatures have been identified as key features for classifying samples between lesion and non-lesion. More specifically, the expression levels of few genes i.e., FABP5 , TGM1 , or BCAR3 are discovered as newly potential biomarkers for psoriasis classification and treatment with high confidence. This study, therefore, could shed light on developing the prediction models for psoriasis classification and treatment using gene expression profiles.

关键词： Psoriasis classification Random forest Gene expression Feature selection Machine learning FABP5 Dermatology

Geometric Methods for Robust Data Analysis in High Dimension

学校读者我要写书评

暂无评论

arXiv 2017年

作者： Anderson, Joseph Timothy Graduate Program in Department of Computer Science and Engineering The Ohio State University

Data-driven applications are growing. Machine learning and data analysis now finds both scientific and industrial application in biology, chemistry, geology, medicine, and physics. These applications rely on large quantities of data gathered from automated sensors and user input. Furthermore, the dimensionality of many datasets is extreme: more details are being gathered about single user interactions or sensor readings. All of these applications encounter problems with a common theme: use observed data to make inferences about the world. Our work obtains the first provably efficient algorithms for Independent Component Analysis (ICA) in the presence of heavy-tailed data. The main tool in this result is the centroid body (a well-known topic in convex geometry), along with optimization and random walks for sampling from a convex body. This is the first algorithmic use of the centroid body and it is of independent theoretical interest, since it effectively replaces the estimation of covariance from samples, and is more generally accessible. We demonstrate that ICA is itself a powerful geometric primitive. That is, having access to an efficient algorithm for ICA enables us to efficiently solve other important problems in machine learning. The first such reduction is a solution to the open problem of efficiently learning the intersection of n + 1 halfspaces in Rn, posed in [43]. This reduction relies on a non-linear transformation of samples from such an intersection of halfspaces (i.e. a simplex) to samples which are approximately from a linearly transformed product distribution. Through this transformation of samples, which can be done efficiently, one can then use an ICA algorithm to recover the vertices of the intersection of halfspaces. Finally, we again use ICA as an algorithmic primitive to construct an efficient solution to the widely-studied problem of learning the parameters of a Gaussian mixture model. Our algorithm again transforms samples from a Gaussian mi

关键词： Independent component analysis