DOCSLIB.ORG

Multichannel Restoration of Single Channel Images Using a Wavelet

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Multichannel Restoration of Single Channel Images Using a Wavelet Reviewers We thank the following people for their help with reviewing submissions: Scott Aaronson Steve Chien Apostolos Giannopoulos Ittai Abraham Eden Chlamtac Christian Glasser Dimitris Achlioptas Ken Clarkson Michel Goemans Amit Agarwal Henry Cohn Daniel Golovin Gagan Aggarwal Richard Cole Navin Goyal Dorit Aharonov Matthew Cook Vineet Goyal Eric Allender Bill Cunningham Sudipto Guha Andris Ambainis Arthur Czumaj Anupam Gupta Stan Angelov Constantinos Daskalakis Venkatesan Guruswami Dana Angluin Vin de Silva Sean Hallgren Shiri Artstein Nikhil Devanur Danny Harnik Javed Aslam Olivier Devillers Sariel Har-Peled Jim Aspnes Ronald de Wolf Prahladh Harsha Albert Atserias Kedar Dhamdhere Jason Hartline Laszlo Babai Yevgeniy Dodis Tzvika Hartman Dave Bacon Petros Drineas Lane Hemaspaandra Maria-Florina Balcan Bruno Durand Chien-Chung Huang Boaz Barak Zeev Dvir Nicole Immorlica Peter Bartlett Herbert Edelsbrunner Piotr Indyk Paul Beame Yuval Emek Adam Kalai Michael Bender Kousha Etessami Gil Kalai Michael Ben-Or Guy Even Satyen Kale Eli Ben-Sasson Eyal Even-Dar Richard Karp Anne Bergeron Esther Ezra Jonathan Katz Grigoriy Blekherman Gavril Fanica Michael Kearns Gerth Brodal Martin Farach-Colton Julia Kempe Matt Cary Dan Feldmann Krishnaram Kenthapadi Nicolo Cesa-Bianchi Cristina Fernandes Claire Kenyon Amit Chakrabarti Amos Fiat Iordanis Kerenidis Sourav Chakraborty Eldar Fischer Rohit Khandekar Erin Chambers Michael Fischer Tracy Kimbrel Hubert Chan Abie Flaxman Hartmut Klauck Kamalika Chaudhuri Alan Frieze Philip Klein Chandra Chekuri Eli Gafni Robert Kleinberg Jiangzhuo Chen Juan Garay Adam Klivans Ning Chen Jim Geelen Emmanuel Knill xiii Phokion Kolaitis Nick Pippenger Ravi Sundaram Robert Krauthgamer David Pollard Maxim Sviridenko Ravi Kumar Kirk Pruhs Chaitanya Swamy Eyal Kushilevitz Jaikumar Radhakrishnan Mario Szegedy John Lafferty Prasad Raghavendra Kunal Talwar Homin Lee Vijaya Ramachandran Val Tannen James Lee Srinivasa Rao Éva Tardos Leonid Levin R. Ravi Amnon Ta-Shma Moshe Lewenstein S.S. Ravi Sekhar Tatikonda Leonid Libkin Ran Raz Shanghua Teng Katrina Ligett Alexander Razborov Sivan Toledo Guolong Lin Oded Regev Iannis Tourlakis Yehuda Lindell Omer Reingold Luca Trevisan Mohammad Mahdian Peter Richter Levent Tunçel Michael Mahoney Liam Roditty Salil Vadhan Konstantin Makarychev Dan Romik Dieter van Melkebeek Yury Makarychev Alon Rosen Kasturi Varadarajan Yishay Mansour Tim Roughgarden Alexander Vardy Andrew McGregor Atri Rudra Vijay Vazirani Frank McSherry Alexander Russell Santosh Vempala Aranyak Mehta Amin Saberi Elad Verbin Ramgopal Mettu Marie-France Sagot Berthold Vöcking Friedhelm Meyer auf der Heide Amit Sahai Jan Vondrak Vahab Mirrokni Piotr Sankowski Van Vu Michael Mitzenmacher Christian Scheideler Yusu Wang Carlos Mochon Pranab Sen Hoeteck Wee Elchanan Mossel Rocco Servedio Ron Wein S. Muthu Muthukrishnan Paul Seymour Yair Weiss Viswanath Nagarajan Ronen Shaltiel Dror Weitz Assaf Naor Micha Sharir Renato Werneck Moni Naor Bruce Shepherd Kim Whittlesey Kobbi Nissim Micah Sherr Avi Wigderson Yahav Nussbaum Amin Shokrollahi Gordon Wilfong Svetlana Olonetsky Amir Shpilka Ryan Williams Vadim Olshevsky Alistair Sinclair Stephan Winkler Rafail Ostrovsky Mohit Singh Mihalis Yannakakis Martin Pal Alexandrs Slivkins Lisa Zhang Rina Panigrahy Shakhar Smordinski Marius Zimand Rafael Pass Christian Sohler Uri Zwick Erez Petrank Cliff Stein Seth Pettie Dan Suciu xiv.
Load more

Recommended publications
  • Constantinos Daskalakis
    The Work of Constantinos Daskalakis Allyn Jackson How long does it take to solve a problem on a computer? This seemingly innocuous question, unanswerable in general, lies at the heart of computa- tional complexity theory. With deep roots in mathematics and logic, this area of research seeks to understand the boundary between which problems are efficiently solvable by computer and which are not. Computational complexity theory is one of the most vibrant and inven- tive branches of computer science, and Constantinos Daskalakis stands out as one of its brightest young lights. His work has transformed understand- ing about the computational complexity of a host of fundamental problems in game theory, markets, auctions, and other economic structures. A wide- ranging curiosity, technical ingenuity, and deep theoretical insight have en- abled Daskalakis to bring profound new perspectives on these problems. His first outstanding achievement grew out of his doctoral dissertation and lies at the border of game theory and computer science. Game the- ory models interactions of rational agents|these could be individual people, businesses, governments, etc.|in situations of conflict, competition, or coop- eration. A foundational concept in game theory is that of Nash equilibrium. A game reaches a Nash equilibrium if each agent adopts the best strategy possible, taking into account the strategies of the other agents, so that there is no incentive for any agent to change strategy. The concept is named after mathematician John F. Nash, who proved in 1950 that all games have Nash equilibria. This result had a profound effect on economics and earned Nash the Nobel Prize in Economic Sciences in 1994.
    [Show full text]
  • FOCS 2005 Program SUNDAY October 23, 2005
    FOCS 2005 Program SUNDAY October 23, 2005 Talks in Grand Ballroom, 17th floor Session 1: 8:50am – 10:10am Chair: Eva´ Tardos 8:50 Agnostically Learning Halfspaces Adam Kalai, Adam Klivans, Yishay Mansour and Rocco Servedio 9:10 Noise stability of functions with low influences: invari- ance and optimality The 46th Annual IEEE Symposium on Elchanan Mossel, Ryan O’Donnell and Krzysztof Foundations of Computer Science Oleszkiewicz October 22-25, 2005 Omni William Penn Hotel, 9:30 Every decision tree has an influential variable Pittsburgh, PA Ryan O’Donnell, Michael Saks, Oded Schramm and Rocco Servedio Sponsored by the IEEE Computer Society Technical Committee on Mathematical Foundations of Computing 9:50 Lower Bounds for the Noisy Broadcast Problem In cooperation with ACM SIGACT Navin Goyal, Guy Kindler and Michael Saks Break 10:10am – 10:30am FOCS ’05 gratefully acknowledges financial support from Microsoft Research, Yahoo! Research, and the CMU Aladdin center Session 2: 10:30am – 12:10pm Chair: Satish Rao SATURDAY October 22, 2005 10:30 The Unique Games Conjecture, Integrality Gap for Cut Problems and Embeddability of Negative Type Metrics Tutorials held at CMU University Center into `1 [Best paper award] Reception at Omni William Penn Hotel, Monongahela Room, Subhash Khot and Nisheeth Vishnoi 17th floor 10:50 The Closest Substring problem with small distances Tutorial 1: 1:30pm – 3:30pm Daniel Marx (McConomy Auditorium) Chair: Irit Dinur 11:10 Fitting tree metrics: Hierarchical clustering and Phy- logeny Subhash Khot Nir Ailon and Moses Charikar On the Unique Games Conjecture 11:30 Metric Embeddings with Relaxed Guarantees Break 3:30pm – 4:00pm Ittai Abraham, Yair Bartal, T-H.
    [Show full text]
  • More Revenue from Two Samples Via Factor Revealing Sdps
    EC’20 Session 2e: Revenue Maximization More Revenue from Two Samples via Factor Revealing SDPs CONSTANTINOS DASKALAKIS, Massachusetts Institute of Technology MANOLIS ZAMPETAKIS, Massachusetts Institute of Technology We consider the classical problem of selling a single item to a single bidder whose value for the item is drawn from a regular distribution F, in a “data-poor” regime where F is not known to the seller, and very few samples from F are available. Prior work [9] has shown that one sample from F can be used to attain a 1/2-factor approximation to the optimal revenue, but it has been challenging to improve this guarantee when more samples from F are provided, even when two samples from F are provided. In this case, the best approximation known to date is 0:509, achieved by the Empirical Revenue Maximizing (ERM) mechanism [2]. We improve this guarantee to 0:558, and provide a lower bound of 0:65. Our results are based on a general framework, based on factor-revealing Semidefnite Programming relaxations aiming to capture as tight as possible a superset of product measures of regular distributions, the challenge being that neither regularity constraints nor product measures are convex constraints. The framework is general and can be applied in more abstract settings to evaluate the performance of a policy chosen using independent samples from a distribution and applied on a fresh sample from that same distribution. CCS Concepts: • Theory of computation → Algorithmic game theory and mechanism design; Algo- rithmic mechanism design. Additional Key Words and Phrases: revenue maximization, two samples, mechanism design, semidefnite relaxation ACM Reference Format: Constantinos Daskalakis and Manolis Zampetakis.
    [Show full text]
  • Privacy Loss in Apple's Implementation of Differential
    Privacy Loss in Apple’s Implementation of Differential Privacy on MacOS 10.12 Jun Tang Aleksandra Korolova Xiaolong Bai University of Southern California University of Southern California Tsinghua University [email protected] [email protected] [email protected] Xueqiang Wang Xiaofeng Wang Indiana University Indiana University [email protected] [email protected] ABSTRACT 1 INTRODUCTION In June 2016, Apple made a bold announcement that it will deploy Differential privacy [7] has been widely recognized as the lead- local differential privacy for some of their user data collection in ing statistical data privacy definition by the academic commu- order to ensure privacy of user data, even from Apple [21, 23]. nity [6, 11]. Thus, as one of the first large-scale commercial de- The details of Apple’s approach remained sparse. Although several ployments of differential privacy (preceded only by Google’s RAP- patents [17–19] have since appeared hinting at the algorithms that POR [10]), Apple’s deployment is of significant interest to privacy may be used to achieve differential privacy, they did not include theoreticians and practitioners alike. Furthermore, since Apple may a precise explanation of the approach taken to privacy parameter be perceived as competing on privacy with other consumer com- choice. Such choice and the overall approach to privacy budget use panies, understanding the actual privacy protections afforded by and management are key questions for understanding the privacy the deployment of differential privacy in its desktop and mobile protections provided by any deployment of differential privacy. OSes may be of interest to consumers and consumer advocate In this work, through a combination of experiments, static and groups [16].
    [Show full text]
  • Testing Ising Models
    Testing Ising Models The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. Citation Daskalakis, Constantinos et al. “Testing Ising Models.” IEEE Transactions on Information Theory, 65, 11 (November 2019): 6829 - 6852 © 2019 The Author(s) As Published 10.1109/TIT.2019.2932255 Publisher Institute of Electrical and Electronics Engineers (IEEE) Version Author's final manuscript Citable link https://hdl.handle.net/1721.1/129990 Terms of Use Creative Commons Attribution-Noncommercial-Share Alike Detailed Terms http://creativecommons.org/licenses/by-nc-sa/4.0/ Testing Ising Models∗ § Constantinos Daskalakis† Nishanth Dikkala‡ Gautam Kamath July 12, 2019 Abstract Given samples from an unknown multivariate distribution p, is it possible to distinguish whether p is the product of its marginals versus p being far from every product distribution? Similarly, is it possible to distinguish whether p equals a given distribution q versus p and q being far from each other? These problems of testing independence and goodness-of-fit have received enormous attention in statistics, information theory, and theoretical computer science, with sample-optimal algorithms known in several interesting regimes of parameters. Unfortu- nately, it has also been understood that these problems become intractable in large dimensions, necessitating exponential sample complexity. Motivated by the exponential lower bounds for general distributions as well as the ubiquity of Markov Random Fields (MRFs) in the modeling of high-dimensional distributions, we initiate the study of distribution testing on structured multivariate distributions, and in particular the prototypical example of MRFs: the Ising Model. We demonstrate that, in this structured setting, we can avoid the curse of dimensionality, obtaining sample and time efficient testers for independence and goodness-of-fit.
    [Show full text]
  • The Complexity of Nash Equilibria by Constantinos Daskalakis Diploma
    The Complexity of Nash Equilibria by Constantinos Daskalakis Diploma (National Technical University of Athens) 2004 A dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Computer Science in the Graduate Division of the University of California, Berkeley Committee in charge: Professor Christos H. Papadimitriou, Chair Professor Alistair J. Sinclair Professor Satish Rao Professor Ilan Adler Fall 2008 The dissertation of Constantinos Daskalakis is approved: Chair Date Date Date Date University of California, Berkeley Fall 2008 The Complexity of Nash Equilibria Copyright 2008 by Constantinos Daskalakis Abstract The Complexity of Nash Equilibria by Constantinos Daskalakis Doctor of Philosophy in Computer Science University of California, Berkeley Professor Christos H. Papadimitriou, Chair The Internet owes much of its complexity to the large number of entities that run it and use it. These entities have different and potentially conflicting interests, so their interactions are strategic in nature. Therefore, to understand these interactions, concepts from Economics and, most importantly, Game Theory are necessary. An important such concept is the notion of Nash equilibrium, which provides us with a rigorous way of predicting the behavior of strategic agents in situations of conflict. But the credibility of the Nash equilibrium as a framework for behavior-prediction depends on whether such equilibria are efficiently computable. After all, why should we expect a group of rational agents to behave in a fashion that requires exponential time to be computed? Motivated by this question, we study the computational complexity of the Nash equilibrium. We show that computing a Nash equilibrium is an intractable problem.
    [Show full text]
  • On the Cryptographic Hardness of Finding a Nash Equilibrium
    On the Cryptographic Hardness of Finding a Nash Equilibrium Nir Bitansky∗ Omer Panethy Alon Rosenz August 14, 2015 Abstract We prove that finding a Nash equilibrium of a game is hard, assuming the existence of indistin- guishability obfuscation and one-way functions with sub-exponential hardness. We do so by showing how these cryptographic primitives give rise to a hard computational problem that lies in the complexity class PPAD, for which finding Nash equilibrium is complete. Previous proposals for basing PPAD-hardness on program obfuscation considered a strong “virtual black-box” notion that is subject to severe limitations and is unlikely to be realizable for the programs in question. In contrast, for indistinguishability obfuscation no such limitations are known, and recently, several candidate constructions of indistinguishability obfuscation were suggested based on different hardness assumptions on multilinear maps. Our result provides further evidence of the intractability of finding a Nash equilibrium, one that is extrinsic to the evidence presented so far. ∗MIT. Email: [email protected]. yBoston University. Email: [email protected]. Supported by the Simons award for graduate students in theoretical computer science and an NSF Algorithmic foundations grant 1218461. zEfi Arazi School of Computer Science, IDC Herzliya, Israel. Email: [email protected]. Supported by ISF grant no. 1255/12 and by the ERC under the EU’s Seventh Framework Programme (FP/2007-2013) ERC Grant Agreement n. 307952. 1 Introduction The notion of Nash equilibrium is fundamental to game theory. While a mixed Nash equilibrium is guar- anteed to exist in any game [Nas51], there is no known polynomial-time algorithm for finding one.
    [Show full text]
  • Mathematisches Forschungsinstitut Oberwolfach Complexity Theory
    Mathematisches Forschungsinstitut Oberwolfach Report No. 54/2015 DOI: 10.4171/OWR/2015/54 Complexity Theory Organised by Peter B¨urgisser, Berlin Oded Goldreich, Rehovot Madhu Sudan, Cambridge MA Salil Vadhan, Cambridge MA 15 November – 21 November 2015 Abstract. Computational Complexity Theory is the mathematical study of the intrinsic power and limitations of computational resources like time, space, or randomness. The current workshop focused on recent developments in various sub-areas including arithmetic complexity, Boolean complexity, communication complexity, cryptography, probabilistic proof systems, pseu- dorandomness and randomness extraction. Many of the developments are related to diverse mathematical fields such as algebraic geometry, combinato- rial number theory, probability theory, representation theory, and the theory of error-correcting codes. Mathematics Subject Classification (2010): 68-06, 68Q01, 68Q10, 68Q15, 68Q17, 68Q25, 94B05, 94B35. Introduction by the Organisers The workshop Complexity Theory was organized by Peter B¨urgisser (TU Berlin), Oded Goldreich (Weizmann Institute), Madhu Sudan (Harvard), and Salil Vadhan (Harvard). The workshop was held on November 15th–21st 2015, and attended by approximately 50 participants spanning a wide range of interests within the field of Computational Complexity. The plenary program, attended by all participants, featured fifteen long lectures and five short (8-minute) reports by students and postdocs. In addition, intensive interaction took place in smaller groups. The Oberwolfach Meeting on Complexity Theory is marked by a long tradition and a continuous transformation. Originally starting with a focus on algebraic and Boolean complexity, the meeting has continuously evolved to cover a wide variety 3050 Oberwolfach Report 54/2015 of areas, most of which were not even in existence at the time of the first meeting (in 1972).
    [Show full text]
  • The Gödel Prize 2020 - Call for Nominatonn
    The Gödel Prize 2020 - Call for Nominatonn Deadline: February 15, 2020 The Gödel Prize for outntanding papern in the area of theoretial iomputer niienie in nponnored jointly by the European Annoiiaton for Theoretial Computer Siienie (EATCS) and the Annoiiaton for Computng Maihinery, Speiial Innterent Group on Algorithmn and Computaton Theory (AC M SInGACT) The award in prenented annually, with the prenentaton taaing plaie alternately at the Innternatonal Colloquium on Automata, Languagen, and Programming (InCALP) and the AC M Symponium on Theory of Computng (STOC) The 28th Gödel Prize will be awarded at the 47th Innternatonal Colloquium on Automata, Languagen, and Programming to be held during 8-12 July, 2020 in Beijing The Prize in named in honour of Kurt Gödel in reiogniton of hin major iontributonn to mathematial logii and of hin interent, diniovered in a leter he wrote to John von Neumann nhortly before von Neumann’n death, in what han beiome the famoun “P vernun NP” quenton The Prize iniluden an award of USD 5,000 Award Committee: The 2020 Award Commitee ionnintn of Samnon Abramnay (Univernity of Oxford), Anuj Dawar (Chair, Univernity of Cambridge), Joan Feigenbaum (Yale Univernity), Robert Krauthgamer (Weizmann Innnttute), Daniel Spielman (Yale Univernity) and David Zuiaerman (Univernity of Texan, Auntn) Eligibility: The 2020 Prize rulen are given below and they nupernede any diferent interpretaton of the generii rule to be found on webniten of both SInGACT and EATCS Any renearih paper or nerien of papern by a ningle author or by
    [Show full text]
  • Lecture 23 — Differentially Private Machine Learning the Netflix Prize
    CS 480/680: Introduction to Machine Learning Winter 2021 Lecture 23 | Differentially Private Machine Learning Prof. Gautam Kamath Machine learning algorithms are frequently trained on sensitive data sets, containing private infor- mation belonging to individuals. We will see that without proper care, machine learning systems might leak this sensitive information. We will then discuss differential privacy, a strong notion of data privacy which is intended to prevent disclosures of this nature. We conclude by discussing its application to a differentially private version of stochastic gradient descent. The Netflix Prize One case study of data anonymization gone wrong is the Netflix Prize competition. Netflix is a very data-driven and statistically-minded company: many of their hit TV shows are conceived based on user data, and their fabled recommendation algorithm is tuned to optimize user engagement. Between 2006 and 2009, they hosted a contest, challenging researchers to improve their recommen- dation engine. The grand prize was a highly-publicized US$1,000,000, claimed by a team named BellKor's Pragmatic Chaos, based on matrix factorization techniques. In order to help teams design their strategies, Netflix provided a training dataset of user data. Each datapoint consisted of an (anonymized) user ID, movie ID, rating, and date. Netflix assured users that the data was appropriately de-anonymized to protect individual privacy. Indeed, the Video Privacy Protection Act of 1988 requires them to do this. One's media consumption history is generally considered to be sensitive or private information, as one might consume media associated with certain minority groups (including of a political or sexual nature).
    [Show full text]
  • SIGACT FY'06 Annual Report July 2005
    SIGACT FY’06 Annual Report July 2005 - June 2006 Submitted by: Richard E. Ladner, SIGACT Chair 1. Awards Gödel Prize: Manindra Agrawal, Neeraj Kayal, and Nitin Saxena for their paper "PRIMES is in P" in the Annals of Mathematics 160, 1-13, 2004. The Gödel Prize is awarded jointly by SIGACT and EATCS. Donald E. Knuth Prize: Mihalis Yannakakis for his seminal and prolific research in computational complexity theory, database theory, computer-aided verification and testing, and algorithmic graph theory. The Knuth Prize is given jointly by SIGACT and IEEE Computer Society TCMFC. Paris Kanellakis Theory and Practice Award: Gerard J. Holzmann, Robert P. Kurshan, Moshe Y. Vardi, and Pierre Wolper recognizing their development of automata-theoretic techniques for reactive-systems verification, and the practical realization of powerful formal-verification tools based on these techniques. This award is an ACM award sponsored in part by SIGACT. Edsger W. Dijkstra Prize in Distributed Computing: Marshal Pease, Robert Shostak, and Leslie Lamport for their paper “Reaching agreement in the presence of faults” in Journal of the ACM 27 (2): 228-234, 1980. The Dijkstra Prize is given jointly by SIGACT and SIGOPS. ACM-SIGACT Distinguished Service Award: Tom Leighton for his exemplary work as Chair of SIGACT, Editorships, Program Chairships and the establishment of permanent endowments for SIGACT awards. Best Paper Award at STOC 2006: Irit Dinur for “The PCP Theorem via Gap Amplification”. SIGACT Danny Lewin Best Student Paper Award at STOC 2006: Anup Rao and Jakob Nordstrom for “Extractors for a Constant Number of Polynomial Min-Entropy Independent Sources” (Anup Rao) and “Narrow Proofs May Be Spacious: Separating Space and Width in Resolution” (Jakob Nordstrom).
    [Show full text]
  • The Gödel Prize 2019 - Call for Nominations Deadline: February 15, 2019
    The Gödel Prize 2019 - Call for Nominations Deadline: February 15, 2019 The Gödel Prize for outstanding papers in the area of theoretical computer science is sponsored jointly by the European Association for Theoretical Computer Science (EATCS) and the Association for Computing Machinery, Special Interest Group on Algorithms and Computation Theory (ACM SIGACT). The award is presented annually, with the presentation taking place alternately at the International Colloquium on Automata, Languages, and Programming (ICALP) and the ACM Symposium on Theory of Computing (STOC). The 27th Gödel Prize will be awarded at 51st Annual ACM Symposium on the Theory of Computing to be held during June 23-26, 2019 in Phoenix, AZ. The Prize is named in honor of Kurt Gödel in recognition of his major contributions to mathematical logic and of his interest, discovered in a letter he wrote to John von Neumann shortly before von Neumann’s death, in what has become the famous “P versus NP” question. The Prize includes an award of USD 5,000. Award Committee: The 2019 Award Committee consists of Anuj Dawar (Cambridge University), Robert Krauthgamer (Weizmann Institute), Joan Feigenbaum (Yale University), Giuseppe Persiano (Università di Salerno), Omer Reingold (Chair, Stanford University) and Daniel Spielman (Yale University). Eligibility: The 2019 Prize rules are given below and they supersede any different interpretation of the generic rule to be found on websites of both SIGACT and EATCS. Any research paper or series of papers by a single author or by a team of authors is deemed eligible if: - The main results were not published (in either preliminary or final form) in a journal or conference proceedings before January 1st, 2006.
    [Show full text]