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Truthful mechanisms for agents that value privacy

ACM Transactions on Economics and computation 2016年第3期4卷 1–30页

作者： Chen, Yiling Chong, Stephen Kash, Ian A. Moran, Tal Vadhan, Salil Center for Research on Computation and Society John A. Paulson School of Engineering and Applied Sciences Harvard University 33 Oxford Street CambridgeMA United States Microsoft Research Cambridge 21 Station Road CambridgeCB1 2FB United Kingdom Efi Arazi School of Computer Science IDC Herzliya Israel

Recent work has constructed economic mechanisms that are both truthful and differentially private. In these mechanisms, privacy is treated separately from truthfulness;it is not incorporated in players' utility functions (and doing so has been shown to lead to nontruthfulness in some cases). In this work, we propose a new, general way of modeling privacy in players' utility functions. Specifically, we only assume that if an outcome o has the property that any report of player i would have led to o with approximately the same probability, then o has a small privacy cost to player i. We give three mechanisms that are truthful with respect to our modeling of privacy: for an election between two candidates, for a discrete version of the facility location problem, and for a general social choice problem with discrete utilities (via a VCG-like mechanism). As the number n of players increases, the social welfare achieved by our mechanisms approaches optimal (as a fraction of n). © 2016 ACM.

关键词： Machine design

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Shell feature: a new radiomics descriptor for predicting distant failure after radiotherapy in non-small cell lung cancer and cervix cancer

arXiv

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arXiv 2017年

作者： Hao, Hongxia Zhou, Zhiguo Li, Shulong Maquilan, Genevieve Folkert, Michael R. Iyengar, Puneeth Westover, Kenneth D. Albuquerque, Kevin Liu, Fang Choy, Hak Timmerman, Robert Yang, Lin Wang, Jing School of Computer Science and Technology Xidian University Xi'an710071 China Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education International Research Center for Intelligent Perception and Computation Joint International Research Laboratory of Intelligent Perception and Computation Xidian University Xi'an710071 China Department of Radiation Oncology University of Texas Southwestern Medical Center DallasTX75235 United States School of Biomedical Engineering Southern medical university Guangzhou510515 China Department of Pathology National Cancer Center/Cancer Hospital Chinese Academy of Medical Sciences Beijing100021 China

Purpose To develop and demonstrate a novel tumor shell feature for predicting distant failure in non-small cell lung cancer (NSCLC) and cervical cancer (CC) patients. Patients and Methods The shell predictive model was constructed using pre-treatment positron emission tomography (PET) images from 48 NSCLC patients received stereotactic body radiation therapy (SBRT) and 52 CC patients underwent external beam radiation therapy and concurrent chemotherapy followed with high-dose-rate intracavitary brachytherapy. A shell feature, consisting of outer voxels around the tumor boundary, was extracted from a series of axial PET slices. The hypothesis behind this feature is that non-invasive and invasive tumors may have different morphologic patterns in the tumor periphery, in turn reflecting the differences in radiological presentations in the PET images. The shell’s utility was evaluated by the support vector machine (SVM) classifier in comparison with intensity, geometry, gray level co-occurrence matrix (GLCM)-based texture, neighborhood gray tone difference matrix (NGTDM)-based texture, and a combination of these four features. The results were assessed in terms of accuracy, sensitivity, specificity, and the area under the receiver operating curve (AUC). Results For NSCLC, the AUC achieved by the shell feature was 0.82 while the highest AUC achieved by the other features was 0.76. Similarly, for CC, the AUC achieved by the shell feature was 0.83 while the highest AUC achieved by the other features was 0.76. Also, the difference in performance between shell and the other features was significant (P Copyright © 2017, The Authors. All rights reserved.

关键词： Radiotherapy

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Global contrast based salient region boundary sampling for action recognition 22nd

Global contrast based salient region boundary sampling for a...

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22nd International Conference on MultiMedia Modeling, MMM 2016

作者： Xu, Zengmin Hu, Ruimin Chen, Jun Chen, Huafeng Li, Hongyang National Engineering Research Center for Multimedia Software School of Computer Wuhan University Wuhan China Collaborative Innovation Center of Geospatial Technology Wuhan China School of Mathematics and Computing Science Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9783319276700

Although the excellent representation ability of improved Dense Trajectory (iDT) based features for action video had been proved on several action datasets, the performance of action recognition still suffers from large camera motion of videos. In this paper, we improve the iDT method by advancing a novel salient region boundary based dense sampling strategy, which reduces the number of trajectories while preserves the discriminative power. We first implement the iDT sampling based on motion boundary image, then introduce a global contrast based salient object segmentation method in interest points sampling step of action recognition. To overcome the flaws of global color contrast-based salient region sampling, we apply morphological gradient to generate a more robust mask for sampling dense points, as motion boundaries are much clearer. To evaluate the proposed method, we conduct extensive experiments on two benchmarks including HMDB51 and UCF 50. The results show that our sampling strategy can improve the performance of action recognition with minor computational cost of mask production. In particular, on the HMDB51 dataset, the improvement over the original iDT result is 3%. Meanwhile, any other dense features of action recognition can achieve more competitive performance by utilizing our sampling strategy and Fisher vector encoding method simply. © Springer International Publishing Switzerland 2016.

关键词： Trajectories

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Markov Brains: A technical introduction

arXiv

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arXiv 2017年

作者： Hintze, Arend Edlund, Jeffrey A. Olson, Randal S. Knoester, David B. Schossau, Jory Albantakis, Larissa Tehrani-Saleh, Ali Kvam, Peter Sheneman, Leigh Goldsby, Heather Bohm, Clifford Adami, Christoph Department of Computer Science & Engineering Michigan State University Department of Integrative Biology Michigan State University BEACON Center for the Study of Evolution in Action Michigan State University Computation & Neural Systems California Institute of Technology Institute for Biomedical Informatics University of Pennsylvania Department of Psychiatry University of Wisconsin Department of Psychological & Brain Sciences Indiana University Department of Microbiology & Molecular Genetics Michigan State University Department of Physics & Astronomy Michigan State University

Markov Brains are a class of evolvable artificial neural networks (ANN). They differ from conventional ANNs in many aspects, but the key difference is that instead of a layered architecture, with each node performing the same function, Markov Brains are networks built from individual computational components. These computational components interact with each other, receive inputs from sensors, and control motor outputs. The function of the computational components, their connections to each other, as well as connections to sensors and motors are all subject to evolutionary optimization. Here we describe in detail how a Markov Brain works, what techniques can be used to study them, and how they can be evolved. Copyright © 2017, The Authors. All rights reserved.

关键词： Optimization

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Privacy odometers and filters: Pay-as-you-go composition 30

Privacy odometers and filters: Pay-as-you-go composition

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30th Annual Conference on neural Information Processing Systems, NIPS 2016

作者： Rogers, Ryan Roth, Aaron Ullman, Jonathan Vadhan, Salil Department of Applied Mathematics and Computational Science University of Pennsylvania United States Department of Computer and Information Sciences University of Pennsylvania United States College of Computer and Information Science Northeastern University United States Center for Research on Computation and Society John A. Paulson School of Engineering and Applied Sciences Harvard University United States

In this paper we initiate the study of adaptive composition in differential privacy when the length of the composition, and the privacy parameters themselves can be chosen adaptively, as a function of the outcome of previously run analyses. This case is much more delicate than the setting covered by existing composition theorems, in which the algorithms themselves can be chosen adaptively, but the privacy parameters must be fixed up front. Indeed, it isn't even clear how to define differential privacy in the adaptive parameter setting. We proceed by defining two objects which cover the two main use cases of composition theorems. A privacy filter is a stopping time rule that allows an analyst to halt a computation before his pre-specified privacy budget is exceeded. A privacy odometer allows the analyst to track realized privacy loss as he goes, without needing to pre-specify a privacy budget. We show that unlike the case in which privacy parameters are fixed, in the adaptive parameter setting, these two use cases are distinct. We show that there exist privacy filters with bounds comparable (up to constants) with existing privacy composition theorems. We also give a privacy odometer that nearly matches non-adaptive private composition theorems, but is sometimes worse by a small asymptotic factor. Moreover, we show that this is inherent, and that any valid privacy odometer in the adaptive parameter setting must lose this factor, which shows a formal separation between the filter and odometer use-cases. © 2016 NIPS Foundation - All Rights Reserved.

关键词： Budget control

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Author Correction: Multi-label Deep Learning for Gene Function Annotation in Cancer Pathways

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Scientific reports 2018年第1期8卷 8995页

作者： Renchu Guan Xu Wang Mary Qu Yang Yu Zhang Fengfeng Zhou Chen Yang Yanchun Liang Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Computer Science and Technology Jilin University Changchun 130012 China. MidSouth Bioinformatics Center and Joint Bioinformatics Ph.D. Program of University of Arkansas at Little Rock and Univ. of Arkansas Medical Sciences Little Rock AR 72204 USA. Institute of Information Engineering Chinese Academy of Sciences School of Cyber Security University of Chinese Academy of Sciences Beijing 100093 China. College of Earth Sciences Jilin University Changchun 130061 China. yangc616@***. Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Computer Science and Technology Jilin University Changchun 130012 China. ycliang@***. Zhuhai Laboratory of Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Zhuhai College of Jilin University Zhuhai 519041 China. ycliang@***.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has not been fixed in the paper.

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Aperiodic Multilayer Graphene Based Tunable and Switchable Thermal Emitter at Mid-Infrared Frequencies

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ECS Meeting Abstracts 2017年第29期MA2017-01卷

作者： Safura Sharifi Yaser Mohammadi Banadaki Chenglong You Simon Lorenzo Georgios Veronis Jonathan P. Dowling Center for Computation and Technology School of Electrical Engineering & Computer Science LSU Southern University and A&M College - Baton Rouge Hearne Institute for Theoretical Physics LSU Hearne Institute for Theoretical Physics Department of Physics and Astronomy LSU School of Electrical Engineering & Computer Science LSU

Graphene has attracted an explosion of interest for photonic applications as it provides a degree of freedom to manipulate electromagnetic waves. In this work, we propose a new one-dimensional system composed of multiple graphene and hexagonal boron nitride (hBN) with varying aperiodic thicknesses in between the graphene layers. A hybrid optimization method, consisting of a micro-genetic global optimization algorithm coupled to a local optimization algorithm, indicates that absorption efficiency close to 100% can be achieved at very narrow frequency range under normal incidence. This efficiency can be significantly decreased by tuning the chemical potential and the corresponding carrier density in graphene. It thus enables a promising way to design electrically controllable thermal emitter. Fig. 1(a) shows the proposed device structure composed of 23 graphene layers and 22 alternating layers of hexagonal boron nitride (hBN) insulator, which sandwiches between two thick silicon carbide (SiC) layers. The hBN has two-dimensional atomic structure similar to graphene and has been proposed as its promising complementary insulator layer. The ambient temperature of 873 K is assumed, in which the semi-infinite tungsten substrate creates the thermal emission of black body radiation with maximum emission at infrared range. Since the tungsten substrate is taken to be semi-infinite, the transmittance is identically zero (Absorption = 1-Reflection) and the calculated absorption can be equated to emittance because of Kirchhoff's second law and conservation of energy. The Kubo formula is used to extract the optical conductivity and consequently the refractive index of graphene while the experimental data is used for the wavelength-dependent indices of refraction of other materials. Fig. 1(b) shows the normalized power radiated per unit area and unit wavelength by the structure. In order to find the optimized structure with narrowband thermal emittance, the chemical potential initia

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Optimized mid-infrared thermal emitters for applications in aircraft countermeasures

Optimized mid-infrared thermal emitters for applications in ...

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Frontiers in Optics, FiO 2016

作者： Lorenzo, Simón G. You, Chenglong Granier, Christopher H. Veronis, Georgios Dowling, Jonathan P. Hearne Institute for Theoretical Physics and Department of Physics and Astronomy Louisiana State University Baton RougeLA70803 United States School of Electrical Engineering and Computer Science Louisiana State University Baton RougeLA70803 United States Center for Computation and Technology Louisiana State University Baton RougeLA70803 United States

An optimized aperiodic photonic crystal structure is capable of near-blackbody and broad angle thermal emission at aircraft engine temperatures over the three to five mm atmospheric transmission band. © OSA 2016.

ISBN: (纸本)9781943580194

关键词： Aircraft engines

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Author Correction: Altered structural brain asymmetry in autism spectrum disorder in a study of 54 datasets

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Nature communications 2021年第1期12卷 7260页

作者： Merel C Postema Daan van Rooij Evdokia Anagnostou Celso Arango Guillaume Auzias Marlene Behrmann Geraldo Busatto Filho Sara Calderoni Rosa Calvo Eileen Daly Christine Deruelle Adriana Di Martino Ilan Dinstein Fabio Luis S Duran Sarah Durston Christine Ecker Stefan Ehrlich Damien Fair Jennifer Fedor Xin Feng Jackie Fitzgerald Dorothea L Floris Christine M Freitag Louise Gallagher David C Glahn Ilaria Gori Shlomi Haar Liesbeth Hoekstra Neda Jahanshad Maria Jalbrzikowski Joost Janssen Joseph A King Xiang Zhen Kong Luisa Lazaro Jason P Lerch Beatriz Luna Mauricio M Martinho Jane McGrath Sarah E Medland Filippo Muratori Clodagh M Murphy Declan G M Murphy Kirsten O'Hearn Bob Oranje Mara Parellada Olga Puig Alessandra Retico Pedro Rosa Katya Rubia Devon Shook Margot J Taylor Michela Tosetti Gregory L Wallace Fengfeng Zhou Paul M Thompson Simon E Fisher Jan K Buitelaar Clyde Francks Department of Language & Genetics Max Planck Institute for Psycholinguistics Nijmegen The Netherlands. Department of Cognitive Neuroscience Donders Institute for Brain Cognition and Behaviour Donders Centre for Cognitive Neuroimaging Radboud University Medical Centre Nijmegen The Netherlands. Bloorview Research Institute Holland Bloorview Kids Rehabilitation Hospital and Department of Pediatrics University of Toronto Toronto ON USA. Child and Adolescent Psychiatry Department Gregorio Marañón General University Hospital School of Medicine Universidad Complutense IiSGM CIBERSAM Madrid Spain. Institut de Neurosciences de la Timone UMR 7289 Aix Marseille Université CNRS Marseille France. Department of Psychology Carnegie Mellon University Pittsburgh PA USA. Laboratory of Psychiatric Neuroimaging (LIM-21) Departamento e Instituto de Psiquiatria Hospital das Clinicas HCFMUSP Faculdade de Medicina Universidade de Sao Paulo Sao Paulo SP Brazil. IRCCS Stella Maris Foundation viale del Tirreno 331 56128 Pisa Italy. Department of Clinical and Experimental Medicine University of Pisa Pisa Italy. Department of Child and Adolescent Psychiatry and Psychology Hospital Clinic Psychiatry Unit Department of Medicine 2017SGR881 University of Barcelona IDIBAPS CIBERSAM Barcelona Spain. Department of Forensic and Neurodevelopmental Sciences Institute of Psychiatry Psychology & Neuroscience King's College London London UK. Institute for Pediatric Neuroscience NYU Child Study Center New York NY USA. Department of Psychology Ben-Gurion University of the Negev Beer Sheva Israel. Brain Center Rudolf Magnus Department of Psychiatry University Medical Center Utrecht Utrecht The Netherlands. Department of Child and Adolescent Psychiatry Psychosomatics and Psychotherapy University Hospital Goethe University Frankfurt am Main Frankfurt Germany. Imaging Genetics Center Mark and Mary Stevens Neuroimaging and Informatics Institute Keck School of Medicine of USC University o

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Privacy Odometers and Filters: Pay-as-you-Go Composition 16

Privacy Odometers and Filters: Pay-as-you-Go Composition

引用

Annual Conference on neural Information Processing Systems

作者： Ryan Rogers Aaron Roth Jonathan Ullman Salil Vadhan Department of Applied Mathematics and Computational Science University of Pennsylvania Department of Computer and Information Sciences University of Pennsylvania College of Computer and Information Science Northeastern University Center for Research on Computation & Society and John A. Paulson School of Engineering & Applied Sciences Harvard University

ISBN: (纸本)9781510838819

关键词： Privacy parameter setting Remuneration Wages Filters psychoanalyst Stopping time Theorem

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