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Statistics and Its Application 1000年第1期8卷 165-190页

作者： Nicolò Cesa-Bianchi Francesco Orabona 1Department of Computer Science and Data Science Research Center Università degli Studi di Milano Milano 20133 Italy email: nicolo.cesa-bianchi@unimi.it 2Department of Electrical and Computer Engineering Boston University Boston Massachusetts 02215 USA email: francesco@***

Online learning is a framework for the design and analysis of algorithms that build predictive models by processing data one at the time. Besides being computationally efficient, online algorithms enjoy theoretical performance guarantees that do not rely on statistical assumptions on the data source. In this review, we describe some of the most important algorithmic ideas behind online learning and explain the main mathematical tools for their analysis. Our reference framework is online convex optimization, a sequential version of convex optimization within which most online algorithms are formulated. More specifically, we provide an in-depth description of online mirror descent and follow the regularized leader, two of the most fundamental algorithms in online learning. As the tuning of parameters is a typically difficult task in sequential data analysis, in the last part of the review we focus on coin-betting, an information-theoretic approach to the design of parameter-free online algorithms with good theoretical guarantees.

关键词： regret minimization convex optimization regression classification

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Measuring the Functioning Human Brain

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Statistics and Its Application 1000年第1期12卷 283-309页

作者： Martin A. Lindquist Bonnie B. Smith Arunkumar Kannan Angela Zhao Brian Caffo 1Department of Biostatistics Johns Hopkins University Baltimore Maryland USA email: mlindqui@jhsph.edu 2Department of Electrical and Computer Engineering Johns Hopkins University Baltimore Maryland USA

The emergence of functional magnetic resonance imaging (fMRI) marked a significant technological breakthrough in the real-time measurement of the functioning human brain in vivo. In part because of their 4D nature (three spatial dimensions and time), fMRI data have inspired a great deal of statistical development in the past couple of decades to address their unique spatiotemporal properties. This article provides an overview of the current landscape in functional brain measurement, with a particular focus on fMRI, highlighting key developments in the past decade. Furthermore, it looks ahead to the future, discussing unresolved research questions in the community and outlining potential research topics for the future.

关键词： fMRI functional magnetic resonance imaging brain connectivity prediction resting-state fMRI statistics

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Noninvasive Brain–Machine Interfaces for Robotic Devices

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Control, Robotics, and Autonomous Systems 1000年第1期4卷 191-214页

作者： Luca Tonin José del R. Millán 1Intelligent Autonomous Systems Laboratory Department of Information Engineering Università degli Studi di Padova 35122 Padova Italy email: luca.tonin@dei.unipd.it 2Department of Electrical and Computer Engineering and Department of Neurology The University of Texas at Austin Austin Texas 78712 USA email: jose.millan@austin.utexas.edu

The last decade has seen a flowering of applications driven by brain–machine interfaces (BMIs), particularly brain-actuated robotic devices designed to restore the independence of people suffering from severe motor disabilities. This review provides an overview of the state of the art of noninvasive BMI-driven devices based on 86 studies published in the last 15 years, with an emphasis on the interactions among the user, the BMI system, and the robot. We found that BMIs are used mostly to drive devices for navigation (e.g., telepresence mobile robots), with BMI paradigms based mainly on exogenous stimulation, and the majority of brain-actuated robots adopt a discrete control strategy. Most critically, in only a few works have disabled people evaluated a brain-actuated robot. The review highlights the most urgent challenges in the field, from the integration between BMI and robotics to the need for a user-centered design to boost the translational impact of BMIs.

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Face Recognition by Humans and Machines: Three Fundamental Advances from Deep Learning

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Vision Science 1000年第1期7卷 543-570页

作者： Alice J. O'Toole Carlos D. Castillo 2Department of Electrical and Computer Engineering Johns Hopkins University Baltimore Maryland 21218 USA email: carlosdc@jhu.edu

Deep learning models currently achieve human levels of performance on real-world face recognition tasks. We review scientific progress in understanding human face processing using computational approaches based on deep learning. This review is organized around three fundamental advances. First, deep networks trained for face identification generate a representation that retains structured information about the face (e.g., identity, demographics, appearance, social traits, expression) and the input image (e.g., viewpoint, illumination). This forces us to rethink the universe of possible solutions to the problem of inverse optics in vision. Second, deep learning models indicate that high-level visual representations of faces cannot be understood in terms of interpretable features. This has implications for understanding neural tuning and population coding in the high-level visual cortex. Third, learning in deep networks is a multistep process that forces theoretical consideration of diverse categories of learning that can overlap, accumulate over time, and interact. Diverse learning types are needed to model the development of human face processing skills, cross-race effects, and familiarity with individual faces.

关键词： face recognition deep convolutional networks human learning machine learning facial features cross-race effects face space

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Extrachromosomal DNA in Cancer

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Genomics and Human Genetics 1000年第1期23卷 29-52页

作者： Vineet Bafna Paul S. Mischel 1Department of Computer Science and Engineering and Halıcıoğlu Data Science Institute University of California San Diego La Jolla California USA email: vbafna@cs.ucsd.edu 2Department of Pathology and ChEM-H Stanford University School of Medicine Stanford California USA email: pmischel@stanford.edu

In cancer, complex genome rearrangements and other structural alterations, including the amplification of oncogenes on circular extrachromosomal DNA (ecDNA) elements, drive the formation and progression of tumors. ecDNA is a particularly challenging structural alteration. By untethering oncogenes from chromosomal constraints, it elevates oncogene copy number, drives intratumoral genetic heterogeneity, promotes rapid tumor evolution, and results in treatment resistance. The profound changes in DNA shape and nuclear architecture generated by ecDNA alter the transcriptional landscape of tumors by catalyzing new types of regulatory interactions that do not occur on chromosomes. The current suite of tools for interrogating cancer genomes is well suited for deciphering sequence but has limited ability to resolve the complex changes in DNA structure and dynamics that ecDNA generates. Here, we review the challenges of resolving ecDNA form and function and discuss the emerging tool kit for deciphering ecDNA architecture and spatial organization, including what has been learned to date about how this dramatic change in shape alters tumor development, progression, and drug resistance.

关键词： ecDNA cancer homogeneously staining regions HSRs ecDNA evolution ecDNA hubs enhancer hijacking

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Genomic Data Compression

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Biomedical Data Science 1000年第1期2卷 19-37页

作者： Mikel Hernaez Dmitri Pavlichin Tsachy Weissman Idoia Ochoa 1Carl R. Woese Institute for Genomic Biology University of Illinois at Urbana–Champaign Urbana Illinois 61801 USA email: mhernaez@illinois.edu 2Department of Electrical Engineering Stanford University Stanford California 94305 USA 3Department of Electrical and Computer Engineering University of Illinois at Urbana–Champaign Urbana Illinois 61801 USA

Recently, there has been growing interest in genome sequencing, driven by advances in sequencing technology, in terms of both efficiency and affordability. These developments have allowed many to envision whole-genome sequencing as an invaluable tool for both personalized medical care and public health. As a result, increasingly large and ubiquitous genomic data sets are being generated. This poses a significant challenge for the storage and transmission of these data. Already, it is more expensive to store genomic data for a decade than it is to obtain the data in the first place. This situation calls for efficient representations of genomic information. In this review, we emphasize the need for designing specialized compressors tailored to genomic data and describe the main solutions already proposed. We also give general guidelines for storing these data and conclude with our thoughts on the future of genomic formats and compressors.

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A Perspective on Incentive Design: Challenges and Opportunities

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Control, Robotics, and Autonomous Systems 1000年第1期2卷 305-338页

作者： Lillian J. Ratliff Roy Dong Shreyas Sekar Tanner Fiez 1Department of Electrical Engineering University of Washington Seattle Washington 98195 USA email: ratliffl@uw.edu 2Department of Electrical and Computer Engineering University of Illinois Urbana-Champaign Urbana Illinois 61801 USA

The increasingly tight coupling between humans and system operations in domains ranging from intelligent infrastructure to e-commerce has led to a challenging new class of problems founded on a well-established area of research: incentive design. There is a clear need for a new tool kit for designing mechanisms that help coordinate self-interested parties while avoiding unexpected outcomes in the face of information asymmetries, exogenous uncertainties from dynamic environments, and resource constraints. This article provides a perspective on the current state of the art in incentive design from three core communities—economics, control theory, and machine learning—and highlights interesting avenues for future research at the interface of these domains.

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Mapping the Multiscale Proteomic Organization of Cellular and Disease Phenotypes

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Biomedical Data Science 1000年第1期7卷 369-389页

作者： Anthony Cesnik Leah V. Schaffer Ishan Gaur Mayank Jain Trey Ideker Emma Lundberg 1Department of Bioengineering Stanford University Stanford California USA email: emmalu@stanford.edu 2Department of Medicine University of California San Diego La Jolla California USA email: tideker@health.ucsd.edu 3Departments of Computer Science and Engineering and Bioengineering University of California San Diego La Jolla California USA 4Department of Pathology Stanford University Palo Alto California USA 5Science for Life Laboratory School of Engineering Sciences in Chemistry Biotechnology and Health KTH Royal Institute of Technology Stockholm Sweden 6Chan Zuckerberg Biohub San Francisco California USA

While the primary sequences of human proteins have been cataloged for over a decade, determining how these are organized into a dynamic collection of multiprotein assemblies, with structures and functions spanning biological scales, is an ongoing venture. Systematic and data-driven analyses of these higher-order structures are emerging, facilitating the discovery and understanding of cellular phenotypes. At present, knowledge of protein localization and function has been primarily derived from manual annotation and curation in resources such as the Gene Ontology, which are biased toward richly annotated genes in the literature. Here, we envision a future powered by data-driven mapping of protein assemblies. These maps can capture and decode cellular functions through the integration of protein expression, localization, and interaction data across length scales and timescales. In this review, we focus on progress toward constructing integrated cell maps that accelerate the life sciences and translational research.

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Control of Low-Inertia Power Systems

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Control, Robotics, and Autonomous Systems 1000年第1期6卷 415-445页

作者： Florian Dörfler Dominic Groß 1Automatic Control Laboratory ETH Zurich Zurich Switzerland email: dorfler@ethz.ch 2Department of Electrical and Computer Engineering University of Wisconsin–Madison Madison Wisconsin USA email: dominic.gross@wisc.edu

Electric power systems are undergoing an unprecedented transition from fossil fuel–based power plants to low-inertia systems that rely predominantly on power electronics and renewable energy resources. This article reviews the resulting control challenges and modeling fallacies, at both the device and system level, and focuses on novel aspects or classical concepts that need to be revised in light of the transition to low-inertia systems. To this end, we survey the literature on modeling of low-inertia systems, review research on the control of grid-connected power converters, and discuss the frequency dynamics of low-inertia systems. Moreover, we discuss system-level services from a control perspective. Overall, we conclude that the system-theoretic mindset is essential to bridge different research communities and understand the complex interactions of power electronics, electric machines, and their controls in large-scale low-inertia power systems.

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Mixed Semiconductor Alloys for Optical Devices

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Chemical and Biomolecular engineering 1000年第1期4卷 187-209页

作者： Thomas F. Kuech Luke J. Mawst April S. Brown 1Department of Chemical and Biological Engineering 2Department of Electrical and Computer Engineering University of Wisconsin–Madison Madison Wisconsin 53706 email: kuech@engr.wisc.edu mawst@engr.wisc.edu abrown@ee.duke.edu 3Department of Electrical and Computer Engineering Duke University Durham North Carolina 27708

There is an increasing technological need for a wider array of semiconducting materials that will allow greater control over the physical and electronic structure within multilayer heterostructures. This need has led to an expansion in the range of semiconducting alloys explored and used in new applications. These alloy semiconductors are often complicated by a limited range of miscibility. The current research has focused on the properties, stability, and detailed chemistry required to realize these materials. The use of synthetic conditions that permit the growth of these alloys to be dominated by kinetic rather than mass-transport considerations has allowed many of these nominally unstable materials to be grown and used in device structures. These materials have found important applications within optical communications as emitters and detectors and in solid-state lighting.

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