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Deep Functional and Molecular Characterization of a High-Risk Undifferentiated Pleomorphic Sarcoma

Sarcoma 2020年第1期2020卷 6312480页

作者： Berlow, Noah E. Grasso, Catherine S. Quist, Michael J. Cheng, Mingshan Gandour-Edwards, Regina Hernandez, Brian S. Michalek, Joel E. Ryan, Christopher Spellman, Paul Pal, Ranadip Million, Lynn S. Renneker, Mark Keller, Charles Children's Cancer Therapy Development Institute Beaverton 97005 OR United States Electrical and Computer Engineering Texas Tech University Lubbock 79409 TX United States Division of Hematology-Oncology University of California Los Angeles 90095 CA United States Jackson Laboratory Bar Harbor 04609 ME United States Department of Pathology and Laboratory Medicine UC Davis Health System Sacramento 95817 CA United States Department of Epidemiology and Biostatistics University of Texas Health Science Center San Antonio San Antonio 78229 TX United States School of Medicine Oregon Health and Science University Portland 97239 OR United States Department of Radiation Oncology Stanford University Stanford 94305 CA United States Patient-Directed Consultations San Francisco 94116 CA United States Department of Family Medicine University of California San Francisco San Francisco 94143 CA United States

Nonrhabdomyosarcoma soft-tissue sarcomas (STSs) are a class of 50+ cancers arising in muscle and soft tissues of children, adolescents, and adults. Rarity of each subtype often precludes subtype-specific preclinical research, leaving many STS patients with limited treatment options should frontline therapy be insufficient. When clinical options are exhausted, personalized therapy assignment approaches may help direct patient care. Here, we report the results of an adult female STS patient with relapsed undifferentiated pleomorphic sarcoma (UPS) who self-drove exploration of a wide array of personalized Clinical Laboratory Improvement Amendments (CLIAs) level and research-level diagnostics, including state of the art genomic, proteomic, ex vivo live cell chemosensitivity testing, a patient-derived xenograft model, and immunoscoring. Her therapeutic choices were also diverse, including neoadjuvant chemotherapy, radiation therapy, and surgeries. Adjuvant and recurrence strategies included off-label and natural medicines, several immunotherapies, and N-of-1 approaches. Identified treatment options, especially those validated during the in vivo study, were not introduced into the course of clinical treatment but did provide plausible treatment regimens based on FDA-approved clinical agents. © 2020 Noah E. Berlow et al.

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Dialogue with robots to support symbiotic autonomy 7th

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7th International Workshop on Spoken Dialogue systems, IWSDS 2016

作者： Vanzo, Andrea Croce, Danilo Bastianelli, Emanuele Gemignani, Guglielmo Basili, Roberto Nardi, Daniele Department of Computer Control and Management Engineering ‘Antonio Ruberti’ Sapienza University of Rome Rome Italy Department of Enterprise Engineering University of Roma Tor Vergata Rome Italy Department of Civil Engineering and Computer Science Engineering University of Roma Tor Vergata Rome Italy

ISBN: (纸本)9789811025846

Service Robotics is finding solutions to enable effective interaction with users. Among the several issues, the need of adapting robots to the way humans usually communicate is becoming a key and challenging task. In this context the design of robots that understand and reply in Natural Language plays a central role, especially when interactions involve untrained users. In particular, this is even more stressed in the framework of Symbiotic Autonomy, where an interaction is always required for the robot to accomplish a given task. In this article, we propose a framework to model dialogues with robotic platforms, enabling effective and natural dialogic interactions. The framework relies on well-known theories as well as on perceptually informed spoken language understanding processors, giving rise to interactions that are tightly bound to the operating scenario. © Springer science+Business Media Singapore 2017.

关键词： Robots

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Hardness, approximability, and fixed-parameter tractability of the clustered shortest-path tree problem

arXiv

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arXiv 2018年

作者： D’Emidio, Mattia Forlizzi, Luca Frigioni, Daniele Leucci, Stefano Proietti, Guido Gran Sasso Science Institute L’Aquila Italy Department of Information Engineering Computer Science and Mathematics University of L’Aquila L’Aquila Italy Department of Computer Science ETH Zürich Switzerland Istituto di Analisi dei Sistemi e Informatica "Antonio Ruberti" Consiglio Nazionale delle Ricerche Roma Italy

Given an n-vertex non-negatively real-weighted graph G, whose vertices are partitioned into a set of k clusters, a clustered network design problem on G consists of solving a given network design optimization problem on G, subject to some additional constraint on its clusters. In particular, we focus on the classic problem of designing a single-source shortest-path tree, and we analyze its computational hardness when in a feasible solution each cluster is required to form a subtree. We first study the unweighted case, and prove that the problem is NP-hard. However, on the positive side, we show the existence of an approximation algorithm whose quality essentially depends on few parameters, but which remarkably is an O(1)-approximation when the largest out of all the diameters of the clusters is either O(1) or Θ(n). Furthermore, we also show that the problem is fixed-parameter tractable with respect to k or to the number of vertices that belong to clusters of size at least 2. Then, we focus on the weighted case, and show that the problem can be approximated within a tight factor of O(n), and that it is fixed-parameter tractable as well. Finally, we analyze the unweighted single-pair shortest path problem, and we show it is hard to approximate within a (tight) factor of n1−, for any > 0. Copyright © 2018, The Authors. All rights reserved.

关键词： Approximation algorithms

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Sysevr: A framework for using deep learning to detect software vulnerabilities

arXiv

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arXiv 2018年

作者： Li, Zhen Zou, Deqing Xu, Shouhuai Jin, Hai Zhu, Yawei Chen, Zhaoxuan Services Computing Technology and System Lab Cluster and Grid Computing Lab Big Data Security Engineering Research Center School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Department of Computer Science University of Texas at San Antonio San AntonioTX78249 United States

The detection of software vulnerabilities (or vulnerabilities for short) is an important problem that has yet to be tackled, as manifested by many vulnerabilities reported on a daily basis. This calls for machine learning methods to automate vulnerability detection. Deep learning is attractive for this purpose because it does not require human experts to manually define features. Despite the tremendous success of deep learning in other domains, its applicability to vulnerability detection is not systematically understood. In order to fill this void, we propose the first systematic framework for using deep learning to detect vulnerabilities. The framework, dubbed Syntax-based, Semantics-based, and Vector Representations (SySeVR), focuses on obtaining program representations that can accommodate syntax and semantic information pertinent to vulnerabilities. Our experiments with 4 software products demonstrate the usefulness of the framework: we detect 15 vulnerabilities that are not reported in the National Vulnerability Database. Among these 15 vulnerabilities, 7 are unknown and have been reported to the vendors, and the other 8 have been "silently" patched by the vendors when releasing newer versions of the products. Copyright © 2018, The Authors. All rights reserved.

关键词： Syntactics

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Training deep networks without learning rates through coin betting

arXiv

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arXiv 2017年

作者： Orabona, Francesco Tommasi, Tatiana Department of Computer Science Stony Brook University NY United States Department of Computer Control and Management Engineering Antonio Ruberti Sapienza University of Rome Italy

Deep learning methods achieve state-of-the-art performance in many application scenarios. Yet, these methods require a significant amount of hyperparameters tuning in order to achieve the best results. In particular, tuning the learning rates in the stochastic optimization process is still one of the main bottlenecks. In this paper, we propose a new stochastic gradient descent procedure for deep networks that does not require any learning rate setting. Contrary to previous methods, we do not adapt the learning rates nor we make use of the assumed curvature of the objective function. Instead, we reduce the optimization process to a game of betting on a coin and propose a learning-rate-free optimal algorithm for this scenario. Theoretical convergence is proven for convex and quasi-convex functions and empirical evidence shows the advantage of our algorithm over popular stochastic gradient algorithms. Copyright © 2017, The Authors. All rights reserved.

关键词： Learning algorithms

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Asynchronous parallel algorithms for nonconvex big-data optimization part II: Complexity and numerical results

arXiv

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arXiv 2017年

作者： Cannelli, Loris Facchinei, Francisco Kungurtsev, Vyacheslav Scutari, Gesualdo School of Industrial Engineering Purdue University United States Department of Computer Control and Management Engineering Antonio Ruberti University of Rome La Sapienza Roma Italy Dept. of Computer Science Faculty of Electrical Engineering Czech Technical University in Prague Czech Republic

We present complexity and numerical results for a new asynchronous parallel algorithmic method for the minimization of the sum of a smooth nonconvex function and a convex nonsmooth regularizer, subject to both convex and nonconvex constraints. The proposed method hinges on successive convex approximation techniques and a novel probabilistic model that captures key elements of modern computational architectures and asynchronous implementations in a more faithful way than state-of-the-art models. In the companion paper [3] we provided a detailed description on the probabilistic model and gave convergence results for a diminishing stepsize version of our method. Here, we provide theoretical complexity results for a fixed stepsize version of the method and report extensive numerical comparisons on both convex and nonconvex problems demonstrating the efficiency of our approach. Copyright © 2017, The Authors. All rights reserved.

关键词： Constrained optimization

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Recent Advance in Content-based Image Retrieval: A literature survey

arXiv

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arXiv 2017年

作者： Zhou, Wengang Li, Houqiang Fellow, Tian I.E.E.E. CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei230027 China Department of Computer Science University of Texas San Antonio San AntonioTX78249 United States

—The explosive increase and ubiquitous accessibility of visual data on the Web have led to the prosperity of research activity in image search or retrieval. With the ignorance of visual content as a ranking clue, methods with text search techniques for visual retrieval may suffer inconsistency between the text words and visual content. Content-based image retrieval (CBIR), which makes use of the representation of visual content to identify relevant images, has attracted sustained attention in recent two decades. Such a problem is challenging due to the intention gap and the semantic gap problems. Numerous techniques have been developed for content-based image retrieval in the last decade. The purpose of this paper is to categorize and evaluate those algorithms proposed during the period of 2003 to 2016. We conclude with several promising directions for future research. Copyright © 2017, The Authors. All rights reserved.

关键词： Content based retrieval

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Design and operation of strategic inventory control system for drug delivery in healthcare industry

引用

IFAC-PapersOnLine 2016年第12期49卷 904-909页

作者： Stecca, Giuseppe Baffo, Ilaria Kaihara, Toshiya Institute of Systems Analysis and Computer Science “Antonio Ruberti” National Research Council CNR - IASI Via dei Taurini 19 Rome00185 Italy DEIM-Industrial Engineering School University of Tuscia Viterbo01100 Italy Graduate School of System Informatics University of Kobe kobe Japan

Several factors such as the raise in healthcare expenditures impose growing pressures to improve efficiency in healthcare industry Industrial engineering and operations research can be effectively used at this aim. This paper examines the optimization of a hospital's inventory costs where the central pharmacy of the hospital assumes a role of central decision maker for drugs’ purchasing and their distribution to internal wards. Inventory control strategies integrating combinatorial based mechanisms and classical inventory control has been developed in order to optimize inventory costs of drugs logistics. The developed work includes into the decisional model also a partial decisional independence of wards and the special requirements of drugs delivery. Results and tests of the model's implementation on real data are reported to evaluate the goodness of offered tool and to discuss of potential real application. © 2016

关键词： Controlled drug delivery Decision making Hospitals Industrial research Inventory control Operations research Production control Supply chains Targeted drug delivery Control strategies Decision makers Design and operations Healthcare industry Inventory costs Production planning and scheduling Real applications

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Ghost penalties in nonconvex constrained optimization: Diminishing stepsizes and iteration complexity

arXiv

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arXiv 2017年

作者： Facchinei, Francisco Kungurtsev, Vyacheslav Lampariello, Lorenzo Scutari, Gesualdo Sapienza University of Rome Department of Computer Management Engineering Antonio Ruberti Via Ariosto 25 Rome00185 Italy Czech Technical University in Prague Department of Computer Science Faculty of Electrical Engineering Roma Tre University Department of Business Studies Via D'Amico 77 Rome00145 Italy Purdue University School of Industrial Engineering West-LafayetteIN United States

We consider, for the first time, general diminishing stepsize methods for nonconvex, constrained optimiza-tion problems. We show that by using directions obtained in an SQP-like fashion convergence to generalized stationary points can be proved. In order to do so, we make use of classical penalty functions in an uncon-ventional way. In particular, penalty functions only enter in the theoretical analysis of convergence while the algorithm itself is penalty-free. We then consider the iteration complexity of this method and some variants where the stepsize is either kept constant or decreased according to very simple rules. We establish convergence to -approximate stationary points in at most O(σ -2), O(σ -3), or O(σ-4) iterations according to the assumptions made on the problem. These complexity results complement nicely the very few existing results in the field. Copyright © 2017, The Authors. All rights reserved.

关键词： Constrained optimization

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Improved computation of individual ZPD in a distance learning system

Improved computation of individual ZPD in a distance learnin...

引用

International Conference on Information Technology Based Higher Education and Training, ITHET

作者： Sara De Cristofano Maria De Marsico Andrea Sterbini Marco Temperini Department of Computer Science Sapienza University of Rome Rome Italy Department of Computer Control and Management Engineering Antonio Ruberti Sapienza University of Rome Rome Italy

ISBN: (纸本)9781509007790

This paper builds upon theoretical studies in the field of social constructivism. Lev Vygotsky is considered one of the greatest representatives of this research line, with his theory of the Zone of Proximal Development (ZPD). Our work aims at integrating this concept in the practice of a computer-assisted learning system. For each learner, the system stores a model summarizing the current Student Knowledge (SK). Each educational activity is specified through the deployed content, the skills required to tackle it, and those acquired, and is further annotated by the effort estimated for the task. The latter may change from one student to another, given the already achieved competence. A suitable weighting of the robustness (certainty) of student's skills, stored in SK, and their combination are used to verify the inclusion of a learning activity in the student's ZPD. With respect to our previous work, the algorithm for the calculation of the ZPD of the individual student has been optimized, by enhancing the certainty weighting policy, and a graphical display of the ZPD has been added. Thanks to the latter, the student can get a clear vision of the learning paths that he/she can presently tackle. This both facilitates the educational process, and helps developing the metacognitive ability self-assessment.

关键词： Problem-solving computer aided instruction Collaboration Context Electronic learning Learning systems

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