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FULLY TRANSFORMER-BASED BIOMARKER PREDICTION FROM COLORECTAL CANCER HISTOLOGY: A LARGE-SCALE MULTICENTRIC STUDY

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arXiv 2023年

作者： Wagner, Sophia J. Reisenbüchler, Daniel West, Nicholas P. Niehues, Jan Moritz Veldhuizen, Gregory Patrick Quirke, Philip Grabsch, Heike I. van den Brandt, Piet A. Hutchins, Gordon G.A. Richman, Susan D. Yuan, Tanwei Langer, Rupert Jenniskens, Josien Christina Anna Offermans, Kelly Mueller, Wolfram Gray, Richard Gruber, Stephen B. Greenson, Joel K. Rennert, Gad Bonner, Joseph D. Schmolze, Daniel James, Jacqueline A. Loughrey, Maurice B. Salto-Tellez, Manuel Brenner, Hermann Hoffmeister, Michael Truhn, Daniel Schnabel, Julia A. Boxberg, Melanie Peng, Tingying Kather, Jakob Nikolas Helmholtz Munich German Research Center for Environment and Health Munich Germany School of Computation Information and Technology Technical University of Munich Munich Germany Technical University Dresden Dresden Germany Division of Pathology and Data Analytics Leeds Institute of Medical Research at St James’s University of Leeds Leeds United Kingdom Department of Pathology GROW School for Oncology and Developmental Biology Maastricht University Medical Center+ Maastricht Netherlands Department of Epidemiology Maastricht University Medical Center+ Maastricht Netherlands Heidelberg Germany Institute of Pathology und Molecular Pathology Johannes Kepler University Hospital Linz Linz Austria Gemeinschaftspraxis Pathologie Starnberg Germany Nuffield Department of Population Health University of Oxford Oxford United Kingdom Center for Precision Medicine Department of Medical Oncology City of Hope National Medical Center DuarteCA United States Department of Pathology City of Hope Comprehensive Cancer Center DuarteCA United States Department of Community Medicine & Epidemiology Lady Davis Carmel Medical Center Ruth & Bruce Rappaport Faculty of Medicine Technion-Israel Institute of Technology Israel Steve and Cindy Rasmussen Institute for Genomic Medicine Lady Davis Carmel Medical Center Technion Faculty of Medicine Clalit National Cancer Control Center Haifa Israel Precision Medicine Centre of Excellence Health Sciences Building The Patrick G Johnston Centre for Cancer Research Queen’s University Belfast Belfast United Kingdom Regional Molecular Diagnostic Service Belfast Health and Social Care Trust Belfast United Kingdom The Patrick G Johnston Centre for Cancer Research Queen’s University Belfast Belfast United Kingdom Department of Cellular Pathology Belfast Health and Social Care Trust Belfast United Kingdom Centre for Public Health Queen’s University Belfast Belfast United Kingdom Integrated Pathology Unit Institute for Cancer Research Royal Mars

Background: Deep learning (DL) can extract predictive and prognostic biomarkers from routine pathology slides in colorectal cancer. For example, a DL test for the diagnosis of microsatellite instability (MSI) in CRC has been approved in 2022. Current approaches rely on convolutional neural networks (CNNs). Transformer networks are outperforming CNNs and are replacing them in many applications, but have not been used for biomarker prediction in cancer at a large scale. In addition, most DL approaches have been trained on small patient cohorts, which limits their clinical utility. Methods: In this study, we developed a new fully transformer-based pipeline for end-to-end biomarker prediction from pathology slides. We combine a pre-trained transformer encoder and a transformer network for patch aggregation, capable of yielding single and multi-target prediction at patient level. We train our pipeline on over 9,000 patients from 10 colorectal cancer cohorts. Results: A fully transformer-based approach massively improves the performance, generalizability, data efficiency, and interpretability as compared with current state-of-the-art algorithms. After training on a large multicenter cohort, we achieve a sensitivity of 0.97 with a negative predictive value of 0.99 for MSI prediction on surgical resection specimens. We demonstrate for the first time that resection specimen-only training reaches clinical-grade performance on endoscopic biopsy tissue, solving a long-standing diagnostic problem. Interpretation: A fully transformer-based end-to-end pipeline trained on thousands of pathology slides yields clinical-grade performance for biomarker prediction on surgical resections and biopsies. Our new methods are freely available under an open source license. © 2023, CC BY.

关键词： Forecasting

Fault-tolerant quantum computation with static linear optics

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arXiv 2021年

作者： Tzitrin, Ilan Matsuura, Takaya Alexander, Rafael N. Dauphinais, Guillaume Bourassa, J. Eli Sabapathy, Krishna K. Menicucci, Nicolas C. Dhand, Ish Xanadu TorontoONM5G 2C8 Canada Department of Physics University of Toronto TorontoONM5S 1A7 Canada Department of Applied Physics Graduate School of Engineering University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan Centre for Quantum Computation and Communication Technology School of Science RMIT University MelbourneVIC3000 Australia Center for Quantum Information and Control University of New Mexico AlbuquerqueNM87131 United States

The scalability of photonic implementations of fault-tolerant quantum computing based on Gottesman-Kitaev-Preskill (GKP) qubits is injured by the requirements of inline squeezing and reconfigurability of the linear optical network. In this work we propose a topologically error-corrected architecture that does away with these elements at no cost-in fact, at an advantage-to state preparation overheads. Our computer consists of three modules: a 2D array of probabilistic sources of GKP states;a depth-four circuit of static beamsplitters, phase shifters, and short delay lines;and a 2D array of homodyne detectors. The symmetry of our proposed circuit allows us to combine the effects of finite squeezing and uniform photon loss within the noise model, resulting in more comprehensive threshold estimates. These jumps over both architectural and analytical hurdles considerably expedite the construction of a photonic quantum computer. Copyright © 2021, The Authors. All rights reserved.

关键词： Delay circuits

Persistence of excitation for koopman operator represented dynamical systems

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arXiv 2019年

作者： Boddupalli, Nibodh Hasnain, Aqib Yeung, Enoch Department of Mechanical Engineering and the Center for ControlDynamical Systems and Computation University of California Santa Barbara Santa BarbaraCA93106 United States

The increase in complexity of engineered systems and naturally occurring systems, coupled with increasingly data-driven representations of these systems motivates the need for design of experiment frameworks for nonlinear system identification. In the case of a general nonlinear autonomous system (without inputs or control), designing an experiment for identification relies on the selection of persistently exciting flows and their corresponding initial conditions. This work investigates persistence of excitation conditions for discrete-time nonlinear systems that can be represented using Koopman operators. The Koopman operator presents a linear lifted representation of the evolution of a nonlinear system over a space of observable functions. We use this framework to derive spectral conditions for persistence of excitation, relating classical notions of persistence of excitation to the spectral properties of initial conditions, seen through the lens of Koopman observable functions. We illustrate these concepts on a repressilator circuit, a classical biological system from the field of synthetic biology, to motivate the proper selection of initial conditions. We show that when the rank properties of the flow's spectra are degenerate, the trained Koopman operator does not produce robust predictions on test data. Copyright © 2019, The Authors. All rights reserved.

关键词： Design of experiments

One-hundred step measurement-based quantum computation multiplexed in the time domain with 25 MHz clock frequency

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arXiv 2020年

作者： Asavanant, Warit Charoensombutamon, Baramee Yokoyama, Shota Ebihara, Takeru Nakamura, Tomohiro Alexander, Rafael N. Endo, Mamoru Yoshikawa, Jun-ichi Menicucci, Nicolas C. Yonezawa, Hidehiro Furusawa, Akira Department of Applied Physics School of Engineering University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan Centre for Quantum Computation and Communication Technology School of Engineering and Information Technology University of New South Wales CanberraACT2600 Australia Center for Quantum Information and Control Department of Physics and Astronomy University of New Mexico AlbuquerqueNM87131 United States Centre for Quantum Computation and Communication Technology School of Science RMIT University MelbourneVIC3001 Australia

Quantum computers are known to provide algorithmic speed ups over their classical counterparts1. In recent years, approaches based on various physical systems—superconducting qubits2, ion-trap systems3, and photonic systems4,5—have been extensively explored. However, constructing devices at scale required for real-world applications is no trivial task. Among the various approaches, measurement-based quantum computation6,7 \(MBQC\) multiplexed in time domain8 is currently a promising method for addressing the need for scalability. MBQC requires two components: cluster states and programmable measurements. With time-domain multiplexing, the former has been realized on an ultra-large-scale9–12. The latter, however, has remained unrealized, leaving the large-scale cluster states unused. In this work, we make such a measurement system and use it to demonstrate basic quantum operations multiplexed in the time-domain with 25 MHz clock frequency. This programmable measurement system is a key piece for MBQC in time domain. We directly verify transformations of the input states and their nonclassicalities for single-step quantum operations and also observe multi-step quantum operations up to one hundred steps—the largest number ever observed in CV systems. Furthermore, with sufficient squeezing and bosonic qubits, cluster state and programmable measurement system in this work serve as the central components of universal fault-tolerant MBQC13–16. Our work establishes a prototype for scalable quantum computation based on CV optical systems and demonstrates a new and promising road toward full-fledged quantum computation. Copyright © 2020, The Authors. All rights reserved.

关键词： Qubits

Applications of Game Theory in Vehicular Networks: A Survey

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arXiv 2021年

作者： Sun, Zemin Liu, Yanheng Wang, Jian Li, Guofa Anil, Carie Li, Keqiang Guo, Xinyu Sun, Geng Tian, Daxin Cao, Dongpu College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China Institute of Human Factors and Ergonomics College of Mechatronics and Control Engineering Shenzhen University Guangdong Shenzhen518060 China School of Computer Science and Engineering VIT-AP Andhra Pradesh Amaravati522237 India College of Engineering Nanjing Agricultural University Nanjing210031 China State Key Laboratory of Automotive Safety and Energy Department of Automotive Engineering Tsinghua University Beijing100084 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beihang University Beijing100191 China Waterloo Cognitive Autonomous Driving Lab University of Waterloo N2L 3G1 Canada

In the Internet of things (IoT) era, vehicles and other intelligent components in an intelligent transportation system (ITS) are connected, forming vehicular networks (VNs) that provide efficient and safe traffic and ubiquitous access to various applications. However, as the number of nodes in an ITS increases, it is challenging to satisfy a varied and large number of service requests with different quality of service (QoS) and security requirements in highly dynamic VNs. Intelligent nodes in VNs can compete or cooperate for limited network resources to achieve the objective for either an individual or a group. Game theory (GT), a theoretical framework designed for strategic interactions among rational decision makers sharing scarce resources, can be used to model and analyze individual or group behaviors of communicating entities in VNs. This paper primarily surveys the recent developments of GT in solving various challenges of VNs. This survey starts with an introduction to the background of VNs. A review of GT models studied in the VNs is then introduced, including the basic concepts, classifications, and applicable vehicular issues. After discussing the requirements of VNs and the motivation of using GT, a comprehensive literature review on GT applications in dealing with the challenges of current VNs is provided. Furthermore, recent contributions of GT to VNs that are integrated with diverse emerging 5G technologies are surveyed. Finally, the lessons learned are given, and several key research challenges and possible solutions of applying GT in VNs are outlined. Copyright © 2021, The Authors. All rights reserved.

关键词： Quality of service

The 1995-2018 global evolution of the network of amicable and hostile relations among nation-states

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arXiv 2020年

作者： Askarisichani, Omid Singh, Ambuj K. Bullo, Francesco Friedkin, Noah E. Department of Computer Science University of California Santa BarbaraCA93106 United States Center for Control Dynamical Systems and Computation University of California Santa BarbaraCA93106 United States Department of Mechanical Engineering University of California Santa BarbaraCA93106 United States Department of Sociology University of California Santa BarbaraCA93106 United States

We draw on the data collected by the Integrated Crisis Early Warning System on millions of international and regional public news stories, and this system's indicators of the orientation toward a specific nation-state. We construct the networks of international amicable and hostile relations among nation-states that occur in specific time-periods in order to study the global evolution of the network of such international appraisals. Our analysis presents evidence of an evolution of the structure of this network and a model of the probabilistic micro-dynamics of the alterations of international appraisals during the 1995-2018 span of the available data. Our research provides empirical findings on long-standing debates in the interdisciplinary field of work on Structural Balance Theory. Also remarkably, we find that the trajectory of the Frobenius norm of sequential transition probabilities, which govern the evolution of international appraisals among nations, dramatically stabilizes. Copyright © 2020, The Authors. All rights reserved.

关键词： Convex optimization

Optimal price of anarchy in cost-sharing games

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arXiv 2019年

作者： Chandan, Rahul Paccagnan, Dario Marden, Jason R. Department of Electrical and Computer Engineering University of California Santa Barbara United States Department of Mechanical Engineering Center of Control Dynamical Systems and Computation UC Santa Barbara United States

The design of distributed algorithms is central to the study of multiagent systems control. In this paper, we consider a class of combinatorial cost-minimization problems and propose a framework for designing distributed algorithms with a priori performance guarantees that are near-optimal. We approach this problem from a game-theoretic perspective, assigning agents cost functions such that the equilibrium efficiency (price of anarchy) is optimized. Once agents’ cost functions have been specified, any algorithm capable of computing a Nash equilibrium of the system inherits a performance guarantee matching the price of anarchy. Towards this goal, we formulate the problem of computing the price of anarchy as a tractable linear program. We then present a framework for designing agents’ local cost functions in order to optimize for the worst-case equilibrium efficiency. Finally, we investigate the implications of our findings when this framework is applied to systems with convex, nondecreasing costs. Copyright © 2019, The Authors. All rights reserved.

关键词： Cost functions

Expertise and confidence explain how social influence evolves along intellective tasks

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arXiv 2020年

作者： Askarisichani, Omid Huang, Elizabeth Y. Sato, Kekoa S. Friedkin, Noah E. Bullo, Francesco Singh, Ambuj K. Department of Computer Science University of California Santa BarbaraCA93106 United States Department of Mechanical Engineering University of California Santa BarbaraCA93106 United States Center for Control Dynamical Systems and Computation University of California Santa BarbaraCA93106 United States Department of Sociology University of California Santa BarbaraCA93106 United States

Discovering the antecedents of individuals’ influence in collaborative environments is an important, practical, and challenging problem. In this paper, we study interpersonal influence in small groups of individuals who collectively execute a sequence of intellective tasks. We observe that along an issue sequence with feedback, individuals with higher expertise and social confidence are accorded higher interpersonal influence. We also observe that low-performing individuals tend to underestimate their high-performing teammate’s expertise. Based on these observations, we introduce three hypotheses and present empirical and theoretical support for their validity. We report empirical evidence on longstanding theories of transactive memory systems, social comparison, and confidence heuristics on the origins of social influence. We propose a cognitive dynamical model inspired by these theories to describe the process by which individuals adjust interpersonal influences over time. We demonstrate the model’s accuracy in predicting individuals’ influence and provide analytical results on its asymptotic behavior for the case with identically performing individuals. Lastly, we propose a novel approach using deep neural networks on a pre-trained text embedding model for predicting the influence of individuals. Using message contents, message times, and individual correctness collected during tasks, we are able to accurately predict individuals’ self-reported influence over time. Extensive experiments verify the accuracy of the proposed models compared to baselines such as structural balance and reflected appraisal model. While the neural networks model is the most accurate, the dynamical model is the most interpretable for influence prediction. Copyright © 2020, The Authors. All rights reserved.

关键词： Forecasting

A data-driven Koopman framework for programming the steady state of biological systems with parametric uncertainty

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arXiv 2019年

作者： Hasnain, Aqib Boddupalli, Nibodh Yeung, Enoch Department of Mechanical Engineering University of California Santa Barbara United States Department of Mechanical Engineering Center for Control Dynamical Systems and Computation Biomolecular Science and Engineering University of California Santa Barbara United States

A central challenge in industrial microbiological applications is engineering of genetic networks to achieve a target yield or steady state concentration. This is a particularly challenging problem when optimizing under novel reaction conditions where canonical models of metabolic pathways are no longer valid. In these scenarios, the biological systems are entirely represented by data for which no direct methods exist to optimize the reaction output. We introduce a datadriven model discovery approach that leverages Koopman operator theory and time-series expression measurements to discover models that predict untested reaction outcomes. These Koopman models allow us to design control strategies to achieve target steady state concentrations for products of interest. We develop a model of the trypotophan pathway and illustrate this steady state programming framework on this pathway. Tryptophan is an essential amino acid and is a valuable product in industrial microbiology. We show how Koopman operator theory can thus be used program the steady state of a genetic network in a data-driven context. Copyright © 2019, The Authors. All rights reserved.

关键词： Biological systems