Andrea Montanari

All Publications

• The Landscape of the Spiked Tensor Model COMMUNICATIONS ON PURE AND APPLIED MATHEMATICS Ben Arous, G., Mei, S., Montanari, A., Nica, M. 2019; 72 (11): 2282–2330

  View details for DOI 10.1002/cpa.21861

  View details for Web of Science ID 000486251500002

• Fundamental Limits of Weak Recovery with Applications to Phase Retrieval FOUNDATIONS OF COMPUTATIONAL MATHEMATICS Mondelli, M., Montanari, A. 2019; 19 (3): 703–73

  View details for DOI 10.1007/s10208-018-9395-y

  View details for Web of Science ID 000470144600006

• The spectral norm of random inner-product kernel matrices PROBABILITY THEORY AND RELATED FIELDS Fan, Z., Montanari, A. 2019; 173 (1-2): 27–85

  View details for DOI 10.1007/s00440-018-0830-4

  View details for Web of Science ID 000459396500002

• Optimization of the Sherrington-Kirkpatrick Hamiltonian Montanari, A., IEEE IEEE COMPUTER SOC. 2019: 1417–33

  View details for DOI 10.1109/FOCS.2019.00087

  View details for Web of Science ID 000510015300078

• A Statistical Model for Motifs Detection IEEE TRANSACTIONS ON INFORMATION THEORY Javadi, H., Montanari, A. 2018; 64 (12): 7594–7612

  View details for DOI 10.1109/TIT.2018.2870853

  View details for Web of Science ID 000451257100009

• THE LANDSCAPE OF EMPIRICAL RISK FOR NONCONVEX LOSSES ANNALS OF STATISTICS Mei, S., Bai, Y., Montanari, A. 2018; 46 (6): 2747–74

  View details for DOI 10.1214/17-AOS1637

  View details for Web of Science ID 000443987700008

• Spectral Algorithms for Tensor Completion COMMUNICATIONS ON PURE AND APPLIED MATHEMATICS Montanari, A., Sun, N. 2018; 71 (11): 2381–2425

  View details for DOI 10.1002/cpa.21748

  View details for Web of Science ID 000444411800006

• Generating Random Networks Without Short Cycles OPERATIONS RESEARCH Bayati, M., Montanari, A., Saberi, A. 2018; 66 (5): 1227–46

  View details for DOI 10.1287/opre.2018.1730

  View details for Web of Science ID 000446179500004

• A mean field view of the landscape of two-layer neural networks PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA Mei, S., Montanari, A., Phan-Minh Nguyen 2018; 115 (33): E7665–E7671

  View details for DOI 10.1073/pnas.1806579115

  View details for Web of Science ID 000441638200005

• A mean field view of the landscape of two-layer neural networks. Proceedings of the National Academy of Sciences of the United States of America Mei, S., Montanari, A., Nguyen, P. 2018

  Abstract

  Multilayer neural networks are among the most powerful models in machine learning, yet the fundamental reasons for this success defy mathematical understanding. Learning a neural network requires optimizing a nonconvex high-dimensional objective (risk function), a problem that is usually attacked using stochastic gradient descent (SGD). Does SGD converge to a global optimum of the risk or only to a local optimum? In the former case, does this happen because local minima are absent or because SGD somehow avoids them? In the latter, why do local minima reached by SGD have good generalization properties? In this paper, we consider a simple case, namely two-layer neural networks, and prove that-in a suitable scaling limit-SGD dynamics is captured by a certain nonlinear partial differential equation (PDE) that we call distributional dynamics (DD). We then consider several specific examples and show how DD can be used to prove convergence of SGD to networks with nearly ideal generalization error. This description allows for "averaging out" some of the complexities of the landscape of neural networks and can be used to prove a general convergence result for noisy SGD.

  View details for PubMedID 30054315

• ONLINE RULES FOR CONTROL OF FALSE DISCOVERY RATE AND FALSE DISCOVERY EXCEEDANCE ANNALS OF STATISTICS Javanmard, A., Montanari, A. 2018; 46 (2): 526–54

  View details for DOI 10.1214/17-AOS1559

  View details for Web of Science ID 000431125400004

• Contextual Stochastic Block Models Deshpande, Y., Montanari, A., Mossel, E., Sen, S., Bengio, S., Wallach, H., Larochelle, H., Grauman, K., CesaBianchi, N., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2018

  View details for Web of Science ID 000461852003017

• EXTREMAL CUTS OF SPARSE RANDOM GRAPHS ANNALS OF PROBABILITY Dembo, A., Montanari, A., Sen, S. 2017; 45 (2): 1190-1217

  View details for DOI 10.1214/15-AOP1084

  View details for Web of Science ID 000398966500013

• On the Limitation of Spectral Methods: From the Gaussian Hidden Clique Problem to Rank One Perturbations of Gaussian Tensors IEEE TRANSACTIONS ON INFORMATION THEORY Montanari, A., Reichman, D., Zeitouni, O. 2017; 63 (3): 1572-1579

  View details for DOI 10.1109/TIT.2016.2637959

  View details for Web of Science ID 000395822500015

• Universality of the Elastic Net Error Montanari, A., Phan-Minh Nguyen, IEEE IEEE. 2017: 2338–42

  View details for Web of Science ID 000430345202081

• Inference in Graphical Models via Semidefinite Programming Hierarchies Erdogdu, M. A., Deshpande, Y., Montanari, A., Guyon, Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2017

  View details for Web of Science ID 000452649400040

• How Well Do Local Algorithms Solve Semidefinite Programs? Fan, Z., Montanari, A., Hatami, H., McKenzie, P., King ASSOC COMPUTING MACHINERY. 2017: 604–14

  View details for DOI 10.1145/3055399.3055451

  View details for Web of Science ID 000440317600056

• High dimensional robust M-estimation: asymptotic variance via approximate message passing PROBABILITY THEORY AND RELATED FIELDS Donoho, D., Montanari, A. 2016; 166 (3-4): 935-969

  View details for DOI 10.1007/s00440-015-0675-z

  View details for Web of Science ID 000387502400008

• Phase transitions in semidefinite relaxations PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA Javanmard, A., Montanari, A., Ricci-Tersenghi, F. 2016; 113 (16): E2218-E2223

  Abstract

  Statistical inference problems arising within signal processing, data mining, and machine learning naturally give rise to hard combinatorial optimization problems. These problems become intractable when the dimensionality of the data is large, as is often the case for modern datasets. A popular idea is to construct convex relaxations of these combinatorial problems, which can be solved efficiently for large-scale datasets. Semidefinite programming (SDP) relaxations are among the most powerful methods in this family and are surprisingly well suited for a broad range of problems where data take the form of matrices or graphs. It has been observed several times that when the statistical noise is small enough, SDP relaxations correctly detect the underlying combinatorial structures. In this paper we develop asymptotic predictions for several detection thresholds, as well as for the estimation error above these thresholds. We study some classical SDP relaxations for statistical problems motivated by graph synchronization and community detection in networks. We map these optimization problems to statistical mechanics models with vector spins and use nonrigorous techniques from statistical mechanics to characterize the corresponding phase transitions. Our results clarify the effectiveness of SDP relaxations in solving high-dimensional statistical problems.

  View details for DOI 10.1073/pnas.1523097113

  View details for Web of Science ID 000374393800005

  View details for PubMedID 27001856

  View details for PubMedCentralID PMC4843421

• Non-Negative Principal Component Analysis: Message Passing Algorithms and Sharp Asymptotics IEEE TRANSACTIONS ON INFORMATION THEORY Montanari, A., Richard, E. 2016; 62 (3): 1458-1484

  View details for DOI 10.1109/TIT.2015.2457942

  View details for Web of Science ID 000370954900025

• Sparse PCA via Covariance Thresholding JOURNAL OF MACHINE LEARNING RESEARCH Deshpande, Y., Montanari, A. 2016; 17

  View details for Web of Science ID 000391660400001

• Statistical analysis of a low cost method for multiple disease prediction. Statistical methods in medical research Bayati, M., Bhaskar, S., Montanari, A. 2016: 962280216680242-?

  Abstract

  Early identification of individuals at risk for chronic diseases is of significant clinical value. Early detection provides the opportunity to slow the pace of a condition, and thus help individuals to improve or maintain their quality of life. Additionally, it can lessen the financial burden on health insurers and self-insured employers. As a solution to mitigate the rise in chronic conditions and related costs, an increasing number of employers have recently begun using wellness programs, which typically involve an annual health risk assessment. Unfortunately, these risk assessments have low detection capability, as they should be low-cost and hence rely on collecting relatively few basic biomarkers. Thus one may ask, how can we select a low-cost set of biomarkers that would be the most predictive of multiple chronic diseases? In this paper, we propose a statistical data-driven method to address this challenge by minimizing the number of biomarkers in the screening procedure while maximizing the predictive power over a broad spectrum of diseases. Our solution uses multi-task learning and group dimensionality reduction from machine learning and statistics. We provide empirical validation of the proposed solution using data from two different electronic medical records systems, with comparisons over a statistical benchmark.

  View details for DOI 10.1177/0962280216680242

  View details for PubMedID 27932665

• THE SET OF SOLUTIONS OF RANDOM XORSAT FORMULAE ANNALS OF APPLIED PROBABILITY Ibrahimi, M., Kanoria, Y., Kraning, M., Montanari, A. 2015; 25 (5): 2743-2808

  View details for DOI 10.1214/14-AAP1060

  View details for Web of Science ID 000360868800010

• Finding One Community in a Sparse Graph JOURNAL OF STATISTICAL PHYSICS Montanari, A. 2015; 161 (2): 273-299

  View details for DOI 10.1007/s10955-015-1338-2

  View details for Web of Science ID 000361814400001

• Finding Hidden Cliques of Size root N/e in Nearly Linear Time FOUNDATIONS OF COMPUTATIONAL MATHEMATICS Deshpande, Y., Montanari, A. 2015; 15 (4): 1069-1128

  View details for DOI 10.1007/s10208-014-9215-y

  View details for Web of Science ID 000358262500007

• UNIVERSALITY IN POLYTOPE PHASE TRANSITIONS AND MESSAGE PASSING ALGORITHMS ANNALS OF APPLIED PROBABILITY Bayati, M., Lelarge, M., Montanari, A. 2015; 25 (2): 753-822

  View details for DOI 10.1214/14-AAP1010

  View details for Web of Science ID 000350708000012

• Bargaining dynamics in exchange networks JOURNAL OF ECONOMIC THEORY Bayati, M., Borgs, C., Chayes, J., Kanoria, Y., Montanari, A. 2015; 156: 417-454

  View details for DOI 10.1016/j.jet.2014.02.007

  View details for Web of Science ID 000349728700014

• Computational implications of reducing data to sufficient statistics ELECTRONIC JOURNAL OF STATISTICS Montanari, A. 2015; 9 (2): 2370-2390

  View details for DOI 10.1214/15-EJS1059

  View details for Web of Science ID 000366270900030

• A Low-Cost Method for Multiple Disease Prediction. AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium Bayati, M., Bhaskar, S., Montanari, A. 2015; 2015: 329-338

  Abstract

  Recently, in response to the rising costs of healthcare services, employers that are financially responsible for the healthcare costs of their workforce have been investing in health improvement programs for their employees. A main objective of these so called "wellness programs" is to reduce the incidence of chronic illnesses such as cardiovascular disease, cancer, diabetes, and obesity, with the goal of reducing future medical costs. The majority of these wellness programs include an annual screening to detect individuals with the highest risk of developing chronic disease. Once these individuals are identified, the company can invest in interventions to reduce the risk of those individuals. However, capturing many biomarkers per employee creates a costly screening procedure. We propose a statistical data-driven method to address this challenge by minimizing the number of biomarkers in the screening procedure while maximizing the predictive power over a broad spectrum of diseases. Our solution uses multi-task learning and group dimensionality reduction from machine learning and statistics. We provide empirical validation of the proposed solution using data from two different electronic medical records systems, with comparisons to a statistical benchmark.

  View details for PubMedID 26958164

• On the Concentration of the Number of Solutions of Random Satisfiability Formulas RANDOM STRUCTURES & ALGORITHMS Abbe, E., Montanari, A. 2014; 45 (3): 362-382

  View details for DOI 10.1002/rsa.20501

  View details for Web of Science ID 000341194800002

• Hypothesis Testing in High-Dimensional Regression Under the Gaussian Random Design Model: Asymptotic Theory IEEE TRANSACTIONS ON INFORMATION THEORY Javanmard, A., Montanari, A. 2014; 60 (10): 6522-6554

  View details for DOI 10.1109/TIT.2014.2343629

  View details for Web of Science ID 000342416900042

• Confidence Intervals and Hypothesis Testing for High-Dimensional Regression JOURNAL OF MACHINE LEARNING RESEARCH Javanmard, A., Montanari, A. 2014; 15: 2869-2909

  View details for Web of Science ID 000344638800002

• Accelerated Time-of-Flight Mass Spectrometry IEEE TRANSACTIONS ON SIGNAL PROCESSING Ibrahimi, M., Montanari, A., Moore, G. S. 2014; 62 (15): 3784-3798

  View details for DOI 10.1109/TSP.2014.2329644

  View details for Web of Science ID 000340092600005

• The Replica Symmetric Solution for Potts Models on d-Regular Graphs COMMUNICATIONS IN MATHEMATICAL PHYSICS Dembo, A., Montanari, A., Sly, A., Sun, N. 2014; 327 (2): 551-575

  View details for DOI 10.1007/s00220-014-1956-6

  View details for Web of Science ID 000334052300007

• Information-Theoretically Optimal Compressed Sensing via Spatial Coupling and Approximate Message Passing IEEE TRANSACTIONS ON INFORMATION THEORY Donoho, D. L., Javanmard, A., Montanari, A. 2013; 59 (11): 7434-7464

  View details for DOI 10.1109/TIT.2013.2274513

  View details for Web of Science ID 000325981100031

• Optimal Coding for the Binary Deletion Channel With Small Deletion Probability IEEE TRANSACTIONS ON INFORMATION THEORY Kanoria, Y., Montanari, A. 2013; 59 (10): 6192-6219

  View details for DOI 10.1109/TIT.2013.2262020

  View details for Web of Science ID 000324573500003

• Localization from Incomplete Noisy Distance Measurements FOUNDATIONS OF COMPUTATIONAL MATHEMATICS Javanmard, A., Montanari, A. 2013; 13 (3): 297-345

  View details for DOI 10.1007/s10208-012-9129-5

  View details for Web of Science ID 000318174900001

• Accurate Prediction of Phase Transitions in Compressed Sensing via a Connection to Minimax Denoising IEEE TRANSACTIONS ON INFORMATION THEORY Donoho, D. L., Johnstone, I., Montanari, A. 2013; 59 (6): 3396-3433

  View details for DOI 10.1109/TIT.2013.2239356

  View details for Web of Science ID 000320709800013

• The phase transition of matrix recovery from Gaussian measurements matches the minimax MSE of matrix denoising. Proceedings of the National Academy of Sciences of the United States of America Donoho, D. L., Gavish, M., Montanari, A. 2013; 110 (21): 8405-8410

  Abstract

  Let X(0) be an unknown M by N matrix. In matrix recovery, one takes n < MN linear measurements y(1),…,y(n) of X(0), where y(i) = Tr(A(T)iX(0)) and each A(i) is an M by N matrix. A popular approach for matrix recovery is nuclear norm minimization (NNM): solving the convex optimization problem min ||X||*subject to y(i) =Tr(A(T)(i)X) for all 1 ≤ i ≤ n, where || · ||* denotes the nuclear norm, namely, the sum of singular values. Empirical work reveals a phase transition curve, stated in terms of the undersampling fraction δ(n,M,N) = n/(MN), rank fraction ρ=rank(X0)/min {M,N}, and aspect ratio β=M/N. Specifically when the measurement matrices Ai have independent standard Gaussian random entries, a curve δ*(ρ) = δ*(ρ;β) exists such that, if δ > δ*(ρ), NNM typically succeeds for large M,N, whereas if δ < δ*(ρ), it typically fails. An apparently quite different problem is matrix denoising in Gaussian noise, in which an unknown M by N matrix X(0) is to be estimated based on direct noisy measurements Y =X(0) + Z, where the matrix Z has independent and identically distributed Gaussian entries. A popular matrix denoising scheme solves the unconstrained optimization problem min|| Y-X||(2)(F)/2+λ||X||*. When optimally tuned, this scheme achieves the asymptotic minimax mean-squared error M(ρ;β) = lim(M,N → ∞)inf(λ)sup(rank(X) ≤ ρ · M)MSE(X,X(λ)), where M/N → . We report extensive experiments showing that the phase transition δ*(ρ) in the first problem, matrix recovery from Gaussian measurements, coincides with the minimax risk curve M(ρ)=M(ρ;β) in the second problem, matrix denoising in Gaussian noise: δ*(ρ)=M(ρ), for any rank fraction 0 < ρ < 1 (at each common aspect ratio β). Our experiments considered matrices belonging to two constraint classes: real M by N matrices, of various ranks and aspect ratios, and real symmetric positive-semidefinite N by N matrices, of various ranks.

  View details for DOI 10.1073/pnas.1306110110

  View details for PubMedID 23650360

  View details for PubMedCentralID PMC3666686

• Iterative Coding for Network Coding IEEE TRANSACTIONS ON INFORMATION THEORY Montanari, A., Urbanke, R. L. 2013; 59 (3): 1563-1572

  View details for DOI 10.1109/TIT.2012.2236912

  View details for Web of Science ID 000315120400021

• The mutual information of a class of graphical channels 51st Annual Allerton Conference on Communication, Control, and Computing Abbe, E., Montanari, A. IEEE. 2013: 20–25

  View details for Web of Science ID 000350802400004

• Linear Bandits in High Dimension and Recommendation Systems 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton) Deshpande, Y., Montanari, A. IEEE. 2013: 1750–1754

  View details for Web of Science ID 000320654000242

• Nearly Optimal Sample Size in Hypothesis Testing for High-Dimensional Regression 51st Annual Allerton Conference on Communication, Control, and Computing Javanmard, A., Montanari, A. IEEE. 2013: 1427–1434

  View details for Web of Science ID 000350802400199

• Finding Hidden Cliques of Size in Nearly Linear Time Deshpande, Y., Montanari, A. 2013
• Hypothesis Testing in High-Dimensional Regression under the Gaussian Random Design Model: Asymptotic Theory Javanmard, A., Montanari, A. 2013
• Confidence Intervals and Hypothesis Testing for High-Dimensional Regression Javanmard, A., Montanari, A. 2013
• High Dimensional Robust M-Estimation: Asymptotic Variance via Approximate Message Passing Donoho, D., Montanari, A. 2013
• Factor models on locally tree-like graphs Annals of Probability Dembo, A., Montanari, A., Sun, N. 2013
• Lossy Compression of Discrete Sources via the Viterbi Algorithm IEEE TRANSACTIONS ON INFORMATION THEORY Jalali, S., Montanari, A., Weissman, T. 2012; 58 (4): 2475-2489

  View details for DOI 10.1109/TIT.2011.2178059

  View details for Web of Science ID 000302079800033

• The LASSO Risk for Gaussian Matrices IEEE TRANSACTIONS ON INFORMATION THEORY Bayati, M., Montanari, A. 2012; 58 (4): 1997-2017

  View details for DOI 10.1109/TIT.2011.2174612

  View details for Web of Science ID 000302079800001

• The weak limit of Ising models on locally tree-like graphs PROBABILITY THEORY AND RELATED FIELDS Montanari, A., Mossel, E., Sly, A. 2012; 152 (1-2): 31-51

  View details for DOI 10.1007/s00440-010-0315-6

  View details for Web of Science ID 000299131700002

• Information-Theoretically Optimal Compressed Sensing via Spatial Coupling and Approximate Message Passing IEEE International Symposium on Information Theory Donoho, D. L., Javanmard, A., Montanari, A. IEEE. 2012

  View details for Web of Science ID 000312544301066

• Universality in Polytope Phase Transitions and Message Passing Algorithms Bayati, M., Lelarge, M., Montanari, A. 2012
• The noise sensitivity phase transition in compressed sensing IEEE Transactions on Information Theory Donoho, D., L., Maleki, A., Montanari, A. 2012
• Graphical Models Concepts in Compressed Sensing in Compressed Sensing Montanari, A. Cambridge university PRess. 2012: 1
• ACCELERATED TIME-OF-FLIGHT MASS SPECTROMETRY IEEE Statistical Signal Processing Workshop (SSP) Ibrahimi, M., Montanari, A., Moore, G. S. IEEE. 2012: 432–435

  View details for Web of Science ID 000309943200109

• Subsampling at Information Theoretically Optimal Rates IEEE International Symposium on Information Theory Javanmard, A., Montanari, A. IEEE. 2012

  View details for Web of Science ID 000312544302106

• Universality in Polytope Phase Transitions and Iterative Algorithms IEEE International Symposium on Information Theory Bayati, M., Lelarge, M., Montanari, A. IEEE. 2012

  View details for Web of Science ID 000312544301149

• The Noise-Sensitivity Phase Transition in Compressed Sensing IEEE TRANSACTIONS ON INFORMATION THEORY Donoho, D. L., Maleki, A., Montanari, A. 2011; 57 (10): 6920-6941

  View details for DOI 10.1109/TIT.2011.2165823

  View details for Web of Science ID 000295739000043

• MAJORITY DYNAMICS ON TREES AND THE DYNAMIC CAVITY METHOD ANNALS OF APPLIED PROBABILITY Kanoria, Y., Montanari, A. 2011; 21 (5): 1694-1748

  View details for DOI 10.1214/10-AAP729

  View details for Web of Science ID 000297027800003

• Applications of the Lindeberg Principle in Communications and Statistical Learning IEEE TRANSACTIONS ON INFORMATION THEORY Korada, S. B., Montanari, A. 2011; 57 (4): 2440-2450

  View details for DOI 10.1109/TIT.2011.2112231

  View details for Web of Science ID 000288459100044

• The Dynamics of Message Passing on Dense Graphs, with Applications to Compressed Sensing IEEE International Symposium on Information Theory Bayati, M., Montanari, A. IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. 2011: 764–85

  View details for DOI 10.1109/TIT.2010.2094817

  View details for Web of Science ID 000286514200014

• Subexponential convergence for information aggregation on regular trees 50th IEEE Conference of Decision and Control (CDC)/European Control Conference (ECC) Kanoria, Y., Montanari, A. IEEE. 2011: 5317–5322

  View details for Web of Science ID 000303506205152

• Gossip PCA Korada, S., Montanari, A., Oh, S. 2011
• Accurate Prediction of Phase Transitions in Compressed Sensing via a Connection to Minimax Denoising Donoho, D., Johnstone, I., Montanari, A. 2011
• Information-Theoretically Optimal Compressed Sensing via Spatial Coupling and Approximate Message Passing Donoho, D., Javanmard, A., Montanari, A. 2011
• Distributed Storage for Intermittent Energy Sources: Control Design and Performance Limits Kanoria, Y., Montanari, A., Tse, D., Zhang, B. 2011
• Fast Convergence of Natural Bargaining Dynamics in Exchange Networks 22nd Annual ACM/SIAM Symposium on Discrete Algorithms Kanoria, Y., Bayati, M., Borgs, C., Chayes, J., Montanari, A. SIAM. 2011: 1518–1537

  View details for Web of Science ID 000296182400118

• Localization from Incomplete Noisy Distance Measurements IEEE International Symposium on Information Theory (ISIT) Javanmard, A., Montanari, A. IEEE. 2011: 1584–1588

  View details for Web of Science ID 000297465101155

• Information Theoretic Limits on Learning Stochastic Differential Equations IEEE International Symposium on Information Theory (ISIT) Bento, J., Ibrahimi, M., Montanari, A. IEEE. 2011: 855–859

  View details for Web of Science ID 000297465101007

• Factor models on locally tree-like graphs Dembo, A., Montanari, A., Sun, N. 2011
• Compressed Sensing over l(p)-balls: Minimax Mean Square Error IEEE International Symposium on Information Theory (ISIT) Donoho, D., Johnstone, I., Maleki, A., Montanari, A. IEEE. 2011: 129–133

  View details for Web of Science ID 000297465100027

• RECONSTRUCTION AND CLUSTERING IN RANDOM CONSTRAINT SATISFACTION PROBLEMS SIAM JOURNAL ON DISCRETE MATHEMATICS Montanari, A., Restrepo, R., Tetali, P. 2011; 25 (2): 771-808

  View details for DOI 10.1137/090755862

  View details for Web of Science ID 000292302000024

• The spread of innovations in social networks PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA Montanari, A., Saberi, A. 2010; 107 (47): 20196-20201

  Abstract

  Which network structures favor the rapid spread of new ideas, behaviors, or technologies? This question has been studied extensively using epidemic models. Here we consider a complementary point of view and consider scenarios where the individuals' behavior is the result of a strategic choice among competing alternatives. In particular, we study models that are based on the dynamics of coordination games. Classical results in game theory studying this model provide a simple condition for a new action or innovation to become widespread in the network. The present paper characterizes the rate of convergence as a function of the structure of the interaction network. The resulting predictions differ strongly from the ones provided by epidemic models. In particular, it appears that innovation spreads much more slowly on well-connected network structures dominated by long-range links than in low-dimensional ones dominated, for example, by geographic proximity.

  View details for DOI 10.1073/pnas.1004098107

  View details for Web of Science ID 000284529000013

  View details for PubMedID 21076030

  View details for PubMedCentralID PMC2996710

• Matrix Completion from Noisy Entries JOURNAL OF MACHINE LEARNING RESEARCH Keshavan, R. H., Montanari, A., Oh, S. 2010; 11: 2057-2078

  View details for Web of Science ID 000282523000005

• Matrix Completion From a Few Entries IEEE TRANSACTIONS ON INFORMATION THEORY Keshavan, R. H., Montanari, A., Oh, S. 2010; 56 (6): 2980-2998

  View details for DOI 10.1109/TIT.2010.2046205

  View details for Web of Science ID 000277880200039

• ISING MODELS ON LOCALLY TREE-LIKE GRAPHS ANNALS OF APPLIED PROBABILITY Dembo, A., Montanari, A. 2010; 20 (2): 565-592

  View details for DOI 10.1214/09-AAP627

  View details for Web of Science ID 000283529500007

• The dynamics of message passing on dense graphs, with applications to compressed sensing 2010 IEEE International Symposium on Information Theory Bayati, M., Montanari, A. IEEE. 2010: 1528–1532

  View details for Web of Science ID 000287512700308

• Learning Networks of Stochastic Differential Equations Bento, J., Ibrahimi, M., Montanari, A. 2010
• Tight Thresholds for Cuckoo Hashing via XORSAT Dietzfelbinger, M., Goerdt, A., Mitzenmacher, M., Montanari, A., Pagh, R., Rink, M. 2010
• Gibbs Measures and Phase Transitions on Sparse Random Graphs Brazilian Journal of Probability and Statistics Dembo, A., Montanari, A. 2008 Brazilian School of Probability. 2010: 1
• Ising models on locally tree-like graphs Annals of Applied Probability Dembo, A., Montanari, A. 2010
• Fast Convergence of Natural Bargaining Dynamics in Exchange Networks Kanoria, Y., Bayati, M., Borgs, C., Chayes, J., Montanari, A. 2010
• The LASSO risk: asymptotic results and real world examples Bayati, M., Bento, J., Montanari, A. 2010
• The Spread of Innovations in Social Networks Montanari, A., Saberi, A. 2010
• Graphical Models Concepts in Compressed Sensing Montanari, A. 2010
• On the deletion channel with small deletion probability 2010 IEEE International Symposium on Information Theory Kanoria, Y., Montanari, A. IEEE. 2010: 1002–1006

  View details for Web of Science ID 000287512700201

• Tight Thresholds for Cuckoo Hashing via XORSAT 37th International Colloquium on Automata, Languages and Programming Dietzfelbinger, M., Goerdt, A., Mitzenmacher, M., Montanari, A., Pagh, R., Rink, M. SPRINGER-VERLAG BERLIN. 2010: 213–225

  View details for Web of Science ID 000286345400019

• An Empirical Scaling Law for Polar Codes 2010 IEEE International Symposium on Information Theory Korada, S. B., Montanari, A., Telatar, E., Urbanke, R. IEEE. 2010: 884–888

  View details for Web of Science ID 000287512700177

• Regularization for Matrix Completion 2010 IEEE International Symposium on Information Theory Keshavan, R. H., Montanari, A. IEEE. 2010: 1503–1507

  View details for Web of Science ID 000287512700303

• Majority dynamics on trees and the dynamic cavity method Annals of Applied Probability Kanoria, Y., Montanari, A. 2010
• Message-passing algorithms for compressed sensing PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA Donoho, D. L., Maleki, A., Montanari, A. 2009; 106 (45): 18914-18919

  Abstract

  Compressed sensing aims to undersample certain high-dimensional signals yet accurately reconstruct them by exploiting signal characteristics. Accurate reconstruction is possible when the object to be recovered is sufficiently sparse in a known basis. Currently, the best known sparsity-undersampling tradeoff is achieved when reconstructing by convex optimization, which is expensive in important large-scale applications. Fast iterative thresholding algorithms have been intensively studied as alternatives to convex optimization for large-scale problems. Unfortunately known fast algorithms offer substantially worse sparsity-undersampling tradeoffs than convex optimization. We introduce a simple costless modification to iterative thresholding making the sparsity-undersampling tradeoff of the new algorithms equivalent to that of the corresponding convex optimization procedures. The new iterative-thresholding algorithms are inspired by belief propagation in graphical models. Our empirical measurements of the sparsity-undersampling tradeoff for the new algorithms agree with theoretical calculations. We show that a state evolution formalism correctly derives the true sparsity-undersampling tradeoff. There is a surprising agreement between earlier calculations based on random convex polytopes and this apparently very different theoretical formalism.

  View details for DOI 10.1073/pnas.0909892106

  View details for Web of Science ID 000271637500010

  View details for PubMedID 19858495

  View details for PubMedCentralID PMC2767368

• The Generalized Area Theorem and Some of its Consequences IEEE TRANSACTIONS ON INFORMATION THEORY Measson, C., Montanari, A., Richardson, T. J., Urbanke, R. 2009; 55 (11): 4793-4821

  View details for DOI 10.1109/TIT.2009.2030457

  View details for Web of Science ID 000271019700001

• Finite-Length Scaling for Iteratively Decoded LDPC Ensembles IEEE TRANSACTIONS ON INFORMATION THEORY Amraoui, A., Montanari, A., Richardson, T., Urbanke, R. 2009; 55 (2): 473-498

  View details for DOI 10.1109/TIT.2008.2009580

  View details for Web of Science ID 000263375500001

• Low-rank Matrix Completion with Noisy Observations: a Quantitative Comparison 47th Annual Allerton Conference on Communication, Control, and Computing Keshavan, R. H., Montanari, A., Oh, S. IEEE. 2009: 1216–1222

  View details for Web of Science ID 000279627100166

• Generating Random Graphs with Large Girth Bayati, M., Montanari, A., Saberi, A. 2009
• Matrix Completion from a Few Entries Keshavan, R., Montanari, A., Oh, S. 2009
• Which graphical models are difficult to learn? Bento, J., Montanari, A. 2009
• An Implementable Scheme for Universal Lossy Compression of Discrete Markov Sources Jalali, S., Montanari, A., Weissman, T. 2009
• Matrix Completion from a Few Entries IEEE International Symposium on Information Theory (ISIT 2009) Keshavan, R. H., Oh, S., Montanari, A. IEEE. 2009: 324–328

  View details for Web of Science ID 000280141400067

• Convergence to Equilibrium in Local Interaction Games 50th Annual IEEE Symposium on Foundations of Computer Science Montanari, A., Saberi, A. IEEE COMPUTER SOC. 2009: 303–312

  View details for DOI 10.1109/FOCS.2009.64

  View details for Web of Science ID 000278330300030

• An Iterative Scheme for Near Optimal and Universal Lossy Compression IEEE Information Theory Workshop on Networking and Information Theory Jalali, S., Montanari, A., Weissman, T. IEEE. 2009: 231–235

  View details for Web of Science ID 000273966100049

• Generating random Tanner-graphs with large girth IEEE Information Theory Workshop (ITW) Bayati, M., Keshavan, R., Montanari, A., Oh, S., Saberi, A. IEEE. 2009: 154–157

  View details for Web of Science ID 000305809200032

• Matrix Completion from Noisy Entries Keshavan, R., Montanari, A., Oh, S. 2009
• Information, Physics, and Computation Mezard, M., Montanari, A. Oxford University Press. 2009
• Generating Random Graphs with Large Girth 20th Annual ACM-SIAM Symposium on Discrete Algorithms Bayati, M., Montanari, A., Saberi, A. SIAM. 2009: 566–575

  View details for Web of Science ID 000281447000063

• Maxwell Construction: The Hidden Bridge Between Iterative and Maximum a Posteriori Decoding IEEE TRANSACTIONS ON INFORMATION THEORY Measson, C., Montanari, A., Urbanke, R. 2008; 54 (12): 5277-5307

  View details for DOI 10.1109/TIT.2008.2006466

  View details for Web of Science ID 000261648100001

• Estimating random variables from random sparse observations EUROPEAN TRANSACTIONS ON TELECOMMUNICATIONS Montanari, A. 2008; 19 (4): 385-403

  View details for DOI 10.1002/ett.1289

  View details for Web of Science ID 000257012200005

• Clusters of solutions and replica symmetry breaking in random k-satisfi ability JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT Montanari, A., Ricci-Tersenghi, F., Semerjian, G. 2008

  View details for DOI 10.1088/1742-5468/2008/04/P04004

  View details for Web of Science ID 000255662000008

• The Slope Scaling Parameter for General Channels, Decoders, and Ensembles IEEE International Symposium on Information Theory Ezri, J., Montanari, A., Oh, S., Urbanke, R. IEEE. 2008: 1443–1447

  View details for Web of Science ID 000260364401009

• Finite Size Scaling for the Core of Large Random Hypergraphs Annals of Applied Probability Dembo, A., Montanari, A. 2008
• Counter Braids: A Novel Counter Architecture for Per-Flow Measurement International Conference on Measurement and Modeling of Computer Systems Lu, Y., Montanari, A., Prabhakar, B., Dharmapurikar, S., Kabbani, A. ASSOC COMPUTING MACHINERY. 2008: 121–32

  View details for Web of Science ID 000266429500011

• Counter Braids: Asymptotic Optimality of the Message Passing Decoding Algorithm 46th Annual Allerton Conference on Communication, Control and Computing Lu, Y., Montanari, A., Prabhakar, B. IEEE. 2008: 209–216

  View details for Web of Science ID 000268229600032

• Computing the threshold shift for general channels IEEE International Symposium on Information Theory Ezri, J., Urbanke, R., Montanari, A., Oh, S. IEEE. 2008: 1448–1452

  View details for Web of Science ID 000260364401010

• An Implementable Scheme for Universal Lossy Compression of Discrete Markov Sources 19th Data Compression Conference Jalali, S., Montanari, A., Weissman, T. IEEE COMPUTER SOC. 2008: 292–301

  View details for DOI 10.1109/DCC.2009.72

  View details for Web of Science ID 000268029900030

• Smooth compression, Gallager bound and Nonlinear sparse-graph codes IEEE International Symposium on Information Theory Montanari, A., Mossel, E. IEEE. 2008: 2474–2478

  View details for Web of Science ID 000260364401219

• Counter Braids IEEE Information Theory Workshop Lu, Y., Montanari, A., Prabhakar, B. IEEE. 2008: 220–221

  View details for Web of Science ID 000259299000048

• Learning Low Rank Matrices from O(n) Entries 46th Annual Allerton Conference on Communication, Control and Computing Keshavan, R. H., Montanari, A., Oh, S. IEEE. 2008: 1365–1372

  View details for Web of Science ID 000268229600194

• How to find good finite-length codes: from art towards science 4th International Symposium on Turbo Codes and Related Topics Amraoui, A., Montanari, A., Urbanke, R. WILEY-BLACKWELL. 2007: 491–508

  View details for DOI 10.1002/ett.1182

  View details for Web of Science ID 000248443400007

• A simple one dimensional glassy Kac model JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT Montanari, A., Sinton, A. 2007

  View details for DOI 10.1088/1742-5468/2007/08/P08004

  View details for Web of Science ID 000249277600007

• Gibbs states and the set of solutions of random constraint satisfaction problems PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA Krzakala, F., Montanari, A., Ricci-Tersenghi, F., Semerjian, G., Zdeborova, L. 2007; 104 (25): 10318-10323

  Abstract

  An instance of a random constraint satisfaction problem defines a random subset (the set of solutions) of a large product space chiN (the set of assignments). We consider two prototypical problem ensembles (random k-satisfiability and q-coloring of random regular graphs) and study the uniform measure with support on S. As the number of constraints per variable increases, this measure first decomposes into an exponential number of pure states ("clusters") and subsequently condensates over the largest such states. Above the condensation point, the mass carried by the n largest states follows a Poisson-Dirichlet process. For typical large instances, the two transitions are sharp. We determine their precise location. Further, we provide a formal definition of each phase transition in terms of different notions of correlation between distinct variables in the problem. The degree of correlation naturally affects the performances of many search/sampling algorithms. Empirical evidence suggests that local Monte Carlo Markov chain strategies are effective up to the clustering phase transition and belief propagation up to the condensation point. Finally, refined message passing techniques (such as survey propagation) may also beat this threshold.

  View details for DOI 10.1073/pnas.0703685104

  View details for Web of Science ID 000247500000006

  View details for PubMedID 17567754

  View details for PubMedCentralID PMC1965511

• Reconstruction for models on random graphs 48th Annual IEEE Symposium on Foundations of Computer Science Gerschenfeld, A., Montanari, A. IEEE COMPUTER SOC. 2007: 194–204

  View details for DOI 10.1109/FOCS.2007.58

  View details for Web of Science ID 000252161900018

• Detailed Network Measurements Using Sparse Graph Counters: The Theory Lu, Y., Montanari, A., Prabhakar, B. 2007
• TP Decoding Lu, Y., Montanari, A., Measson, C. 2007
• Counting Good Truth Assignmants for Random Satisfiability Formulae Montanari, A., Shah, D. 2007
• Solving Constraint Satisfaction Problems through Belief Propagation-guided Decimation Montanari, A., Ricci-Tersenghi, F., Semerjian, G. 2007
• Modern Coding Theory: The Statistical Mechanics and Computer Science Point of View Les Houches Summer School on Mathematical Statistical Physics Montanari, A., Urbanke, R. Elsevier. 2007: 1
• A generalization of the finite-length scaling approach beyond the BEC IEEE International Symposium on Information Theory Ezri, J., Montanari, A., Urbanke, R. IEEE. 2007: 1011–1015

  View details for Web of Science ID 000257010201034

• Asymptotic rate versus design rate IEEE International Symposium on Information Theory Measson, C., Montanari, A., Urbanke, R. IEEE. 2007: 1541–1545

  View details for Web of Science ID 000257010201140

• Rigorous inequalities between length and time scales in glassy systems JOURNAL OF STATISTICAL PHYSICS Montanari, A., Semerjian, G. 2006; 125 (1): 22-54

  View details for DOI 10.1007/s10955-006-9175-y

  View details for Web of Science ID 000241842100002

• On the dynamics of the glass transition on Bethe lattices JOURNAL OF STATISTICAL PHYSICS Montanari, A., Semerjian, G. 2006; 124 (1): 103-189

  View details for DOI 10.1007/s10955-006-9103-1

  View details for Web of Science ID 000240126900006

• Precision electroweak measurements on the Z resonance PHYSICS REPORTS-REVIEW SECTION OF PHYSICS LETTERS Schael, S., Barate, R., Bruneliere, R., Buskulic, D., De Bonis, I., Decamp, D., Ghez, P., Goy, C., Jezequel, S., Lees, J. P., Lucotte, A., Martin, F., Merle, E., Minard, M. N., Nief, J. Y., Odier, P., Pietrzyk, B., Trocme, B., Bravo, S., Casado, M. P., Chmeissani, M., Comas, P., Crespo, J. M., Fernandez, E., Fernandez-Bosman, M., Garrido, L., Grauges, E., Juste, A., Martinez, M., Merino, G., Miquel, R., Mir, L. M., Orteu, S., Pacheco, A., Park, I. C., Perlas, J., Riu, I., Ruiz, H., Sanchez, F., Colaleo, A., Creanza, D., De Filippis, N., de Palma, M., Iaselli, G., Maggi, G., Maggi, M., Nuzzo, S., Ranieri, A., Raso, G., Ruggieri, F., Selvaggi, G., Silvestris, L., Tempesta, P., Tricomi, A., Zito, G., Huang, X., Lin, J., Ouyang, Q., Wang, T., Xie, Y., Xu, R., Xue, S., Zhang, J., Zhang, L., Zhao, W., Abbaneo, D., Bazarko, A., Becker, U., Boix, G., Bird, F., Blucher, E., Bonvicini, B., Bright-Thomas, P., Barklow, T., Buchmuller, O., Cattaneo, M., Cerutti, F., Ciulli, V., Clerbaux, B., Drevermann, H., Forty, R. W., Frank, M., Greening, T. C., Hagelberg, R., Halley, A. W., Gianotti, F., Girone, M., Hansen, J. B., Harvey, J., Jacobsen, R., Hutchcroft, D. E., Janot, R., JOST, B., Knobloch, J., Kado, M., Lehraus, I., Lazeyras, P., Maley, R., Mato, P., May, J., Moutussi, A., Pepe-Altarelli, M., Ranjard, F., Rolandi, L., Schlatter, D., Schmitt, B., Schneider, O., Tejessy, W., Teubert, F., Tomalin, I. R., Tournefier, E., Veenhof, R., Valassi, A., Wiedenmann, W., Wright, A. E., Ajaltouni, Z., Badaud, F., Chazelle, G., Deschamps, O., Dessagne, S., Falvard, A., Ferdi, C., Fayolle, D., Gay, P., Guicheney, C., Henrard, P., Jousset, J., Michel, B., Monteil, S., Montret, J. C., Pallin, D., Pascolo, J. M., Perret, P., Podlyski, F., Bertelsen, H., Fernley, T., Hansen, J. D., Hansen, J. R., Hansen, P. H., Kraan, A. C., Lindahl, A., Mollerud, R., Nilsson, B. S., Rensch, B., Waananen, A., Daskalakis, G., Kyriakis, A., Markou, C., Simopoulou, E., Siotis, I., Vayaki, A., Zachariadou, K., Blondel, A., Bonneaud, G., Brient, J. C., Machefert, E., Rouge, A., Rumpf, M., Swynghedauw, M., Tanaka, R., Verderi, M., Videau, H., Ciulli, V., Focardi, E., Parrini, G., Zachariadou, K., Corden, M., Georgiopoulos, C., Antonelli, A., Antonelli, M., Bencivenni, G., Bologna, G., Bossi, F., Campana, P., Capon, G., Cerutti, F., Chiarella, V., Felici, G., Laurelli, P., Mannocchi, G., Murtas, G. P., Passalacqua, L., Picchi, P., Colrain, P., Ten Have, I., Hughes, I. S., Kennedy, J., Knowles, I. G., Lynch, J. G., Morton, W. T., Negus, P., O'Shea, V., Raine, C., Reeves, P., Scarr, J. M., Smith, K., Thompson, A. S., Turnbull, R. M., Wasserbaech, S., Buchmuller, O., Cavanaugh, R., Dhamotharan, S., Geweniger, C., Hanke, P., Hansper, G., Hepp, V., Kluge, E. E., Putzer, A., Sommer, J., Stenzel, H., Tittel, K., Werner, W., Wunsch, M., Beuselinck, R., Binnie, D. M., Cameron, W., Davies, G., Dornan, P. J., Goodsir, S., Marinelli, N., Martin, E. B., NASH, J., Nowell, J., Rutherford, S. A., Sedgbeer, J. K., THOMPSON, J. C., White, R., Williams, M. D., Ghete, V. M., Girtler, P., Kneringer, E., Kuhn, D., Rudolph, G., Bouhova-Thacker, E., Bowdery, C. K., Buck, P. G., Clarke, D. P., Ellis, G., Finch, A. J., Foster, F., Hughes, G., Jones, R. W., Keemer, N. R., Pearson, M. R., Robertson, N. A., Sloan, T., Smizanska, M., Snow, S. W., Williams, M. I., van der Aa, O., Delaere, C., Leibenguth, G., Lemaitre, V., Bauerdick, L. A., Blumenschein, U., van Gemmeren, P., Giehl, I., Holldorfer, F., Jakobs, K., Kasemann, M., Kayser, F., Kleinknecht, K., Muller, A. S., Quast, G., Renk, B., Rohne, E., Sander, H. G., Schmeling, S., Wachsmuth, H., Wanke, R., Zeitnitz, C., Ziegler, T., Aubert, J. J., Benchouk, C., Bonissent, A., Carr, J., Coyle, P., Curtil, C., Ealet, A., Etienne, F., Fouchez, D., Motsch, F., Payre, P., Rousseau, D., Tilquin, A., Talby, M., Thulasides, M., Aleppo, M., Antonelli, M., Ragusa, F., Buscher, V., David, A., Dietl, H., Ganis, G., Huttmann, K., Lutjens, G., Mannert, C., Manner, W., Moser, H. G., Settles, R., Seywerd, H., Stenzel, H., Villegas, M., Wiedenmann, W., Wolf, G., Azzurri, P., Boucrot, J., Callot, O., Chen, S., Cordier, A., Davier, M., Duflot, L., Grivaz, J. F., Heusse, P., Jacholkowska, A., Le Diberder, F., Lefrancois, J., Mutz, A. M., Schune, M. H., Serin, L., Veillet, J. J., Videau, I., Zerwas, D., Azzurri, P., Bagliesi, G., Bettarini, S., Boccali, T., Bozzi, C., Calderini, G., Dell'Orso, R., Fantechi, R., Ferrante, I., Fidecaro, F., Foa, L., Giammanco, A., Giassi, A., Gregorio, A., Ligabue, F., Lusiani, A., Marrocchesi, P. S., Messineo, A., Palla, F., Rizzo, G., Sanguinetti, G., Sciaba, A., Sguazzoni, G., Spagnolo, P., Steinberger, J., Tenchini, R., Venturi, A., Vannini, C., Venturi, A., Verdini, P. G., Awunor, O., Blair, G. A., Cowan, G., Garcia-Bellido, A., Green, M. G., Medcalf, T., Misiejuk, A., Strong, J. A., Teixeira-Dias, P., Botterill, D. R., Clifft, R. W., Edgecock, T. R., Edwards, M., Haywood, S. J., Norton, P. R., Tomalin, I. R., Ward, J. J., Bloch-Devaux, B., Boumediene, D., Colas, P., Emery, S., Fabbro, B., Kozanecki, W., Lancon, E., Lemaire, M. C., Locci, E., Perez, P., Rander, J., Renardy, J. F., Roussarie, A., Schuller, J. P., Schwindling, J., Tuchming, B., Vallage, B., Black, S. N., Dann, J. H., Kim, H. Y., Konstantinidis, N., Litke, A. M., McNeil, M. A., Taylor, G., Booth, C. N., Cartwright, S., Combley, F., Hodgson, P. N., LEHTO, M., Thompson, L. F., Affholderbach, K., Barberio, E., Bohrer, A., Brandt, S., Burkhardt, H., Feigl, E., Grupen, C., Hess, J., Lutters, G., Meinhard, H., Minguet-Rodriguez, J., Mirabito, L., Misiejuk, A., Neugebauer, E., Ngac, A., Prange, G., Rivera, F., Saraiva, P., Schafer, U., Sieler, U., Smolik, L., Stephan, F., Trier, H., Apollonio, M., Borean, C., Bosisio, L., Della Marina, R., Giannini, G., Gobbo, B., Musolino, G., Pitis, L., He, H., Kim, H., Putz, J., Rothberg, J., Armstrong, S. R., Bellantoni, L., Berkelman, K., Cinabro, D., Conway, J. S., Cranmer, K., Elmer, P., Feng, Z., Ferguson, D. P., Gao, Y., Gonzalez, S., Grahl, J., Harton, J. L., Hayes, O. J., Hu, H., Jin, S., Johnson, R. P., Kile, J., McNamara, P. A., Nielsen, J., Orejudos, W., Pan, Y., Saadi, Y., Scott, I. J., Sharma, V., Walsh, A. M., Walsh, J., Wear, J., von Wimmersperg-Toeller, J. H., Wiedenmann, W., Wu, J., Wu, S. L., Wu, X., Yamartino, J. M., Zobernig, G., Dissertori, G., Abdallah, J., Abreu, P., Adam, W., Adye, T., Adzic, P., Ajinenko, I., Albrecht, T., Alderweireld, T., Alekseev, G. D., Alemany-Fernandez, R., Allmendinger, T., Allport, P. P., Almehed, S., Amaldi, U., Amapane, N., Amato, S., Anashkin, E., Anassontzis, E. G., Andersson, P., Andreazza, A., Andringa, S., Anjos, N., Antilous, P., Apel, W. D., Arnoud, Y., Ask, S., Asman, B., Augustin, J. E., Augustinus, A., Baillon, P., Ballestrero, A., Bambade, P., Barao, F., Barbiellini, G., Barbier, R., Bardin, D., Barker, G., Baroncelli, A., Battaglia, M., Baubillier, M., Becks, K. H., Begalli, M., Behrmann, A., Beilliere, P., Belokopytov, Y., Belous, K., Ben-Haim, E., Benekos, N., Benvenuti, A., Berat, C., Berggren, M., Berntzon, L., Bertini, D., Bertrand, D., Besancon, M., Besson, N., Bianchi, F., Bigi, M., Bilenky, M. S., Bizouard, M. A., Bloch, D., Blom, M., Bluj, M., Bonesini, M., Bonivento, W., Boonekamp, M., Booth, P. S., Borgland, A. W., Borisov, G., Bosio, C., Botner, O., Boudinov, E., Bouquet, B., Bourdarios, C., Bowcock, T. J., Boyko, I., Bozovic, I., Bozzo, M., Bracko, M., Branchini, P., Brenke, T., Brenner, R., Brodet, E., Bruckman, P., Brunet, J. M., Bugge, L., Buran, T., Burgsmueller, T., Buschbeck, B., Buschmann, P., Cabrera, S., Caccia, M., Calvi, M., Rozas, A. J., Camporesi, T., Canale, V., Canepa, M., Carena, F., Carroll, L., Caso, C., Gimenez, M. V., Castro, N., Cattai, A., Cavallo, F., Cerruti, C., Chabaud, V., Chapkin, M., Charpentier, P., Chaussard, L., Checchia, P., Chelkov, G. A., Chen, M., Chierici, R., Chliapnikov, R., Chochula, P., Chorowicz, V., Chudoba, J., Chung, S. U., Cieslik, K., Collins, P., Colomer, M., Contri, R., Cortina, E., Cosme, G., Cossuti, F., Costa, M. J., Cowell, J. H., Crawley, H. B., Crennell, D., Crepe, S., Crosetti, G., Cuevas, J., Czellar, S., D'Hondt, J., Dalmagne, B., Dalmau, J., Damgaard, G., Davenport, M., da Silva, T., Da Silva, W., Deghorain, A., Della Ricca, G., Delpierre, P., Demaria, N., De Angelis, A., De Boer, W., de Brabandere, S., De Clercq, C., De Lotto, B., De Maria, N., De Min, A., de Paula, L., Dijkstra, H., Di Ciaccio, L., Di Diodato, A., Di Simone, A., Djannati, A., Dolbeau, J., Doroba, K., Dracos, M., Drees, J., Drees, K. A., Dris, M., Duperrin, A., Durand, J. D., Ehret, R., Eigen, G., Ekelof, T., Ekspong, G., Ellert, M., Elsing, M., Engel, J. P., Erzen, B., Santo, M. C., Falk, E., Fanourakis, G., Fassouliotis, D., Fayot, J., Feindt, M., Fenyuk, A., Fernandez, J., Ferrari, P., Ferrer, A., Ferrer-Ribas, E., Ferro, F., Fichet, S., Firestone, A., Fischer, P. A., Flagmeyer, U., Foeth, H., Fokitis, E., Fontanelli, F., Franek, B., Frodesen, A. G., Fruhwirth, R., Fulda-Quenzer, F., Fuster, J., Galloni, A., Gamba, D., Gamblin, S., Gandelman, M., Garcia, C., Garcia, J., GASPAR, C., Gaspar, M., Gasparini, U., Gavillet, P., Gazis, E., Gele, D., Gerber, J. P., Gerdyukov, L., Ghodbane, N., Gil, I., Glege, F., Gokieli, R., Golob, B., Gomez-Ceballos, G., Goncalves, P., Caballero, I. G., Gopal, G., Gorn, L., Gorski, M., Gouz, Y., Gracco, V., Grahl, J., Graziani, E., Green, C., Grefrath, A., Grimm, H. J., Gris, P., Grosdidier, G., Grzelak, K., Gunther, M., Guy, J., Haag, C., Hahn, F., Hahn, S., Hallgren, A., Hamacher, K., Hamilton, K., Hansen, J., Harris, F. J., Haug, S., Hauler, F., Hedberg, V., Heising, S., Hennecke, M., Henriques, R., Hernandez, J. J., Herquet, P., Herr, H., Hessing, T. L., Heuser, J. M., Higon, E., Hoffman, J., Holmgren, S. O., Holt, P. J., Holthuizen, D., Hoorelbeke, S., Houlden, M. A., Hrubec, J., Huber, M., Huet, K., Hughes, G. J., Hultqvist, K., Jackson, J. N., Jacobsson, R., Jalocha, P., Janik, R., Jarlskog, C., Jarlskog, G., Jarry, P., Jean-Marie, B., Jeans, D., Johansson, E. K., Johansson, P. D., Jonsson, P., Joram, C., Juillot, P., Jungermann, L., Kapusta, F., Karafasoulis, K., Katsanevas, S., Katsoufis, E., Keranen, R., Kernel, G., Kersevan, B. P., Kerzel, U., Khomenko, B. A., Khovanski, N. N., Kiiskinen, A., King, B. T., Kinvig, A., Kjaer, N. J., Klapp, O., Klein, H., Kluit, P., Knoblauch, D., Kokkinias, P., Konopliannikov, A., Koratzinos, M., Kostioukhine, V., Kourkoumelis, C., Kouznetsov, O., Krammer, M., Kreuter, C., Kriznic, E., Krstic, J., Krumstein, Z., Kubinec, P., Kucewicz, W., Kucharczyk, M., Kurowska, J., Kurvinen, K., Lamsa, J., Lanceri, L., Lane, D. W., Langefeld, P., Lapin, V., Laugier, J. P., Lauhakangas, R., Leder, G., Ledroit, F., Lefebure, V., Leinonen, L., Leisos, A., Leitner, R., Lemonne, J., Lenzen, G., Lepeltier, V., Lesiak, T., Lethuillier, M., Libby, J., Liebig, W., Liko, D., Lipniacka, A., Lippi, I., Loerstad, B., Lokajicek, M., Loken, J. G., Lopes, J. H., Lopez, J. M., Lopez-Femandez, R., Loukas, D., Lutz, P., Lyons, L., MacNaughton, J., Mahon, J. R., Maio, A., Malek, A., Malmgren, T. G., Maltezos, S., Malychev, V., Mandl, F., Marco, J., Marco, R., Marechal, B., Margoni, M., Marin, J. C., Mariotti, C., Markou, A., Martinez-Rivero, C., Martinez-Vidal, F., Garcia, S. M., Masik, J., Mastroyiannopoulos, N., Matorras, F., Matteuzzi, C., Matthiae, G., Mazik, J., Mazzucato, F., Mazzucato, M., McCubbin, M., McKay, R., McNulty, R., Meroni, C., Meyer, W. T., Miagkov, A., Migliore, E., Mirabito, L., Mitaroff, W., Mjoernmark, U., Moa, T., Moch, M., Moeller, R., Moenig, K., Monge, R., Montenegro, J., Moraes, D., Moreau, X., Moreno, S., Morettini, P., Morton, G., Mueller, U., Muenich, K., Mulders, M., Mulet-Marquis, C., Mundim, L., Muresan, R., Murray, W., Muryn, B., Myatt, G., Myklebust, T., Naraghi, F., Nassiakou, M., Navarria, F., Navas, S., Nawrocki, K., Negri, P., Neufeld, N., Neumann, W., Neumeister, N., Nicolaidou, R., Nielsen, B. S., Nieuwenhuizen, M., Niezurawski, P., Nikolaenko, V., Nikolenko, M., Nomokonov, V., Normand, A., Nygren, A., Oblakowska-Mucha, A., Obraztsov, V., Olshevski, A., Onofre, A., Orava, R., Orazi, G., Osterberg, K., Ouraou, A., Oyanguren, A., Paganini, P., Paganoni, M., Paiano, S., Pain, R., Paiva, R., Palacios, J. P., Palka, H., Papadopoulou, T. D., Papageorgiou, K., Pape, L., Parkes, C., Parodi, F., Parzefall, U., Passeri, A., Passon, O., Pavel, T., Pegoraro, M., Peralta, L., Perepelitsa, V., Pernicka, M., Perrotta, A., Petridou, C., Petrolini, A., Philips, H. T., Piana, G., Piedra, J., Pieri, L., Pierre, F., Pimenta, M., Piotto, E., Podobnik, T., Poireau, V., Pol, M. E., Polok, G., Polycarpo, E., Poropat, P., Pozdniakov, V., Privitera, R., Pukhaeva, N., Pullia, A., Radojicic, D., Ragazzi, S., Rahmani, H., Rakoczy, D., Rames, J., Ramler, L., Ratoff, P. N., Read, A., Rebecchi, P., Redaelli, N. G., Regler, M., Rehn, J., Reid, D., Reinhardt, R., Renton, P., Resvanis, L. K., Richard, F., Ridky, J., Rinaudo, G., Ripp-Baudot, I., Rivero, M., Rodriguez, D., Rohne, O., Romero, A., Ronchese, P., Rosenberg, E. I., Rosinsky, P., Roudeau, R., Rovelli, T., Royon, C., Ruhlmann-Kleider, V., Ruiz, A., Ryabtchikov, D., Saarikko, H., Sacquin, Y., Sadovsky, A., Sajot, G., Salmi, L., Salt, J., Sampsonidis, D., Sannino, M., Savoy-Navarro, A., Scheidle, T., Schneider, H., Schwemling, P., Schwering, B., Schwickerath, U., Schyns, M. A., Scuri, F., Seager, P., Sedykh, Y., Segar, A., Seibert, N., Sekulin, R., Shellard, R. C., Sheridan, A., Siebel, M., Silvestre, R., Simard, L., Simonetto, F., Sisakian, A., Skaali, T. B., Smadja, G., Smirnov, N., Smirnova, O., Smith, G. R., Sokolov, A., Sopczak, A., Sosnowski, R., Spassov, T., Spiriti, E., Sponholz, P., Squarcia, S., Stampfer, D., Stanescu, C., Stanic, S., Stanitzki, M., Stapnes, S., Stevenson, K., Stocchi, A., Strauss, J., Strub, R., Stugu, B., Szczekowski, M., Szeptycka, M., Szumlak, T., Tabarelli, T., Taffard, A. C., Tegenfeldt, F., Terranova, F., Thomas, J., Tilquin, A., Timmermans, J., Tinti, N., Tkatchev, L., Tobin, M., Todorov, T., Todorovova, S., Toet, D. Z., Tomaradze, A., Tome, B., Tonazzo, A., Tortora, L., Tortosa, P., Transtromer, G., Travnicek, P., Treille, D., Tristram, G., Trochimczuk, M., Trombini, A., Troncon, C., Tsirou, A., Turluer, M. L., Tyapkin, I. A., Tyapkin, P., Tzamarias, S., Ullaland, O., Uvarov, V., Valenti, G., Vallazza, E., Vander Velde, C., Van Apeldoorn, G. W., van Dam, P., Van den Boeck, W., Van Doninck, W. K., Van Eldik, J., Van Lysebetten, A., van Remortel, N., Van Vulpen, I., Vassilopoulos, N., Vegni, G., Velos, F., Ventura, L., Venus, W., Verbeure, F., Verdier, P., Verlato, M., Vertogradov, L. S., Verzi, V., Vilanova, D., Vitale, L., Vlasov, E., Vodopyanov, A. S., Vollmer, C., Voulgaris, G., Vrba, V., Wahlen, H., Walck, C., Washbrook, A. J., Weiser, C., Wetherell, A. M., Wicke, D., Wickens, J., Wilkinson, G., Winter, M., Witek, M., Wlodek, T., Wolf, G., Yi, J., Yushchenko, O., Zaitsev, A., Zalewska, A., Zalewski, P., Zavrtanik, D., Zevgolatakos, E., Zhuravlov, V., Zimin, N. I., Zintchenko, A., Zoller, P., Zucchelli, G. C., Zumerle, G., Zupan, M., Acciarri, M., Achard, P., Adriani, O., Aguilar-Benitez, M., Alcaraz, J., Alemanni, G., Allaby, J., Aloisio, A., Alviggi, M. G., Ambrosi, G., Anderhub, H., Andreev, V. P., Angelescu, T., Anselmo, F., Arefiev, A., Azemoon, T., Aziz, T., Bagnaia, P., Bajo, A., Baksay, G., Baksay, L., Balandras, A., Baldew, S. V., Ball, R. C., Banerjee, S., Banerjee, S., Barczyk, A., Barillere, R., Barone, L., Bartalini, P., Basile, M., Batalova, N., Battiston, R., Bay, A., Becattini, F., Becker, U., Behner, F., Bellucci, L., Berbeco, R., Berdugo, J., Berges, P., Bertucci, B., Betev, B. L., Bhattacharya, S., Biasini, M., Biglietti, M., Biland, A., Blaising, J. J., Blyth, S. C., Bobbink, B. J., Bohm, A., Boldizsar, L., Borgia, B., Bottai, S., Bourilkov, D., Bourquin, M., Braccini, S., Branson, J. G., Brigljevic, V., Brochu, F., Brock, I. C., Buffini, A., Buijs, A., Burger, J. D., Burger, W. J., Button, A., Cai, X. D., Campanelli, M., Capell, M., Romeo, G. C., Carlino, G., Cartacci, A., Casaus, J., Castellini, G., Cavallari, E., Cavallo, N., Cecchi, C., Cerrada, M., Cesaroni, F., Chamizo, M., Chang, Y. H., Chaturvedi, U. K., Chemarin, M., Chen, A., Chen, G., Chen, G. M., Chen, H. F., Chen, H. S., Chiefari, G., Cifarelli, L., Cindolo, F., Civinini, C., Clare, I., Clare, R., Coignet, G., Colijn, A. P., Colino, N., Costantini, S., Cotorobai, F., Cozzoni, B., de la Cruz, B., Csilling, A., Cucciarelli, S., Dai, T. S., van Dalen, J. A., D'Alessandro, R., de Asmundis, R., Debreczeni, J., Deglon, P., Degre, P., Dehmelt, K., Deiters, K., della Volpe, D., Delmeire, E., Denes, P., DeNotaristefani, F., De Salvo, A., Diemoz, M., Dierckxsens, M., van Dierendonck, D., Di Lodovico, F., Dionisi, C., Dittmar, M., Dominguez, A., DORIA, A., Dova, M. T., Duchesneau, D., Dufournaud, D., Duda, M., Duinker, P., Duran, I., Dutta, S., Echenard, B., Eline, A., El Hage, A., El Mamouni, H., Engler, A., Eppling, F. J., Erne, F. C., Extermann, P., Fabre, M., Faccini, R., Falagan, M. A., Falciano, S., Favara, A., Fay, J., Fedin, O., Felcini, M., Ferguson, T., Ferroni, F., Fesefeldt, H., Fiandrini, E., Field, J. H., Filthaut, F., Fisher, P. H., Fisher, W., Fisk, I., Forconi, G., Fredj, L., Freudenreich, K., Furetta, C., Galaktionov, Y., Ganguli, S. N., Garcia-Abia, P., Gataullin, M., Gau, S. S., Gentile, S., Gheordanescu, N., Giagu, S., Gong, Z. F., Grenier, G., Grimm, O., Gruenewald, M. W., Guida, M., van Gulik, R., van Gupta, V. K., Gurtu, A., Gutay, L. J., Haas, D., Hasan, A., Hatzifotiadou, D., Hebbeker, T., Herve, A., Hidas, P., Hirschfelder, J., Hofer, H., Hohlmann, M., Holzner, G., Hoorani, H., Hou, S. R., Lashvili, I., Innocente, V., Jin, B. N., Jindal, P., Jones, L. W., deJong, P., Josa-Mutuberria, I., Khan, R. A., Kaur, M., Kienzle-Focacci, M. N., Kim, D., Kim, J. K., Kirkby, J., Kiss, D., Kittel, W., Klimentov, A., Konig, A. C., Koffeman, E., Kopal, M., Kopp, A., Koutsenko, V., Kraber, M., Kraemer, R. W., Krenz, W., Kruger, A., Kuijten, H., Kunin, A., de Guevara, P. L., Laktineh, I., Landi, G., Lassila-Perini, K., Lebeau, M., Lebedev, A., Lebrun, P., Lecomte, P., Lecoq, P., Le Coultre, P., Lee, H. J., Le Goff, J. M., Leiste, R., Leonardi, E., Levtchenko, M., Levtchenko, P., Li, C., Likhoded, S., Lin, C. H., Lin, W. T., Linde, F. L., Lista, L., Liu, Z. A., Lohmann, W., Longo, E., Lu, Y. S., Lu, W., Lubelsmeyer, K., Luci, C., Luckey, D., Luminari, L., Lugnier, L., Lustermann, W., Ma, W. G., Maity, M., Malgeri, L., Malinin, A., Mana, C., Mangeol, D., Mans, J., Marchesini, P., Marian, G., Martin, J. P., Marzano, F., Massaro, G. G., Mazumdar, K., McNeil, R. R., Mele, S., Merola, L., Merk, M., Meschini, M., Metzger, W. J., von der Mey, M., Mihul, A., Milcent, H., Mirabelli, G., Mnich, J., Mohanty, G. B., Molnar, P., Monteleoni, B., Moulik, T., Muanza, G. S., Muheim, F., Muijs, A. J., Musicar, B., Musy, M., Nagy, S., Natale, S., Napolitano, M., Nessi-Tedaldi, F., Newman, H., Niessen, T., Nisati, A., Novak, I., Nowak, H., Ofierzynski, R., Organtini, G., Oulianov, A., Pal, I., Palomares, C., Pandoulas, D., Paoletti, S., Paoloni, A., Paolucci, P., Paramatti, R., Park, H. K., Park, I. H., Pascale, G., Passaleva, G., Patricelli, S., Paul, T., Pauluzzi, M., Paus, C., Pauss, F., Peach, D., Pedace, M., Pensotti, S., Perret-Gallix, D., Petersen, B., Piccolo, D., Pierella, F., Pieri, M., Pioppi, M., Piroue, P. A., Pistolesi, E., Plyaskin, V., Pohl, M., Pojidaev, V., Postema, H., Pothier, J., Produit, N., Prokofiev, D. O., Prokofiev, D., Quartieri, J., Rahal-Callot, G., Rahaman, M. A., Raics, P., Raja, N., Ramelli, R., Rancoita, P. G., Ranieri, R., Raspereza, A., Razis, P., Ren, D., Rescigno, M., Reucroft, S., van Rhee, T., Riemann, S., Riles, K., Robohm, A., Rodin, J., Roe, B. P., Romero, L., Rosca, A., Rosemann, C., Rosenbleck, C., Rosier-Lees, S., Roth, S., Rubio, J. A., RUGGIERO, G., Ruschmeier, D., Rykaczewski, H., Sakharov, A., Saremi, S., Sarkar, S., Salicio, J., Sanchez, E., Sanders, M. P., Sarakinos, M. E., Schafer, C., Schegelsky, V., Schmidt-Kaerst, S., Schmitz, D., Schopper, H., Schotanus, D. J., Schwering, G., Sciacca, C., Sciarrino, D., Seganti, A., Servoli, L., Shevchenko, S., Shivarov, N., Shoutko, V., Shumilov, E., Shvorob, A., Siedenburg, T., Son, D., Smith, B., Souga, C., Spillantini, P., Steuer, M., Stickland, D. P., Stone, A., Stone, H., Stoyanov, B., Straessner, A., Sudhakar, K., Sultanov, G., Sun, L. Z., Sushkov, S., Suter, H., Swain, J. D., Szillasi, Z., Sztaricskai, T., Tang, X. W., Tarjan, P., Tauscher, L., Taylor, L., Tellili, B., Teyssier, D., Timmermans, C., Ting, S. C., Ting, S. M., Tonwar, S. C., TOTH, J., Tully, C., Tung, K. L., Uchida, Y., Ulbricht, J., Uwer, U., Valente, E., Van de Walle, R. T., Vasquez, R., Veszpremi, V., Vesztergombi, G., Vetlitsky, I., Vicinanza, D., Viertel, G., Villa, S., Vivargent, M., Vlachos, S., Vodopianov, I., Vogel, H., Vogt, H., Vorobiev, I., Vorobyov, A. A., Vorvolakos, A., Wadhwa, M., Wallraff, W., Wang, Q., Wang, X. L., Wang, Z. M., Weber, A., Weber, M., Wienemann, P., Wilkens, H., Wu, S. X., Wynhoff, S., Xia, L., Xu, Z. Z., Yamamoto, J., Yang, B. Z., Yang, C. G., Yang, H. J., Yang, M., Ye, J. B., Yeh, S. C., You, J. M., Zalite, A., Zalite, Y., Zhang, Z. P., Zhao, J., Zhu, G. Y., Zhu, R. Y., Zhuang, H. L., Zichichi, A., Zilizi, G., Zimmermann, B., Zoller, M., Abbiendi, G., Acton, P. D., Ainsley, C., Akesson, P. F., Alexander, G., Allison, J., Allport, P. P., Altekamp, N., Amaral, P., Ametewee, K., Anagnostou, G., Anderson, K. J., Anderson, S., Arcelli, S., Armitage, J. C., Asai, S., Ashby, S. F., Ashton, P., Astbury, A., Axen, D., Azuelos, G., Bahan, G. A., Bailey, I., Baines, J. T., Ball, A. H., Banks, J., Barberio, E., Barillari, T., Barker, G. J., Barlow, R. J., Barnett, S., Bartoldus, R., Batley, R. J., Beaudoin, G., Bechtle, P., Bechtluft, J., Beck, A., Becker, J., Beeston, C., Behnke, T., Bell, A. N., Bell, K. W., Bell, P. J., Bella, G., Bellerive, A., Benelli, G., Bentvelsen, S., Berlich, P., Bethke, S., Biebel, O., Binder, U., Blobel, V., Bloodworth, I. J., Bloomer, J. E., Bock, P., Boden, B., Bohme, J., Boeriu, O., Bonacorsi, D., Bosch, H. M., Bougerolle, S., Bouterneur, M., Bouwens, B. T., Brabson, B. B., Braibant, S., Breuker, H., Bright-Thomas, P., Brigliadori, L., Brown, R. M., Brun, R., Burgin, R., Buesser, K., Buijs, A., Burckhart, H. J., Burgard, C., Cammin, J., Campana, S., Capiluppi, P., Carnegie, R. K., Caron, B., Carter, A. A., Carter, J. R., Chang, C. Y., Charlesworth, C., Charlton, D. G., Chrin, J. T., Chrisman, D., Chu, S. L., Ciocca, C., Clarke, P. E., Clay, E., Clayton, J. C., Cohen, I., Collins, W. J., Conboy, J. E., Cooke, O. C., Cooper, M., Couch, M., Couchman, J., Coupland, M., Silva, E. D., Coxe, R. L., Csilling, A., Cuffiani, M., Dado, S., Dallapiccola, C., Dallavalle, G. M., Dallison, S., Darling, C., de Jong, S., De Roeck, A., De Wolf, E. A., Debu, P., Deng, H., Deninno, M. M., Dervan, P., Desch, K., Dieckmann, A., Dienes, B., Dittmar, M., Dixit, M. S., Donkers, M., Doucet, M., Dubbert, J., Duboscq, J. E., Duchovni, E., Duckeck, G., Duerdoth, I. P., Dumas, D. J., Eckerlin, G., Edwards, J. E., Elcombe, P. A., Estabrooks, P. G., Etzion, E., Evans, H. G., Evans, M., Fabbri, F., Fanti, M., Fath, P., Feld, L., Ferrari, P., Fiedler, F., Fierro, M., Fincke-Keeler, M., Fischer, H. M., Fleck, I., Folman, R., Fong, D. G., Ford, M., Foucher, M., Frey, A., Furtjes, A., Fukui, H., Fukunaga, C., Futyan, D. I., Gagnon, P., Gaidot, A., Ganel, O., Gary, J. W., Gascon, J., Gascon-Shotkin, S. M., Gaycken, G., Geddes, N. I., Geich-Gimbel, C., Gensler, S. W., Gentit, F. X., Geralis, T., Giacomelli, G., Giacomelli, P., Giacomelli, R., Gibson, V., Gibson, W. R., Gillies, J. D., Gingrich, D. M., Giunta, M., Glenzinski, D., Goldberg, J., Goodrick, M. J., Gorn, W., Graham, K., Grandi, C., Grant, F. C., Gross, E., Grunhaus, J., Gruwe, M., Gunther, P. O., Gupta, A., Hagemann, J., Hajdu, C., Hamann, M., Hanson, G. G., Hansroul, M., Hapke, M., Harder, K., Harel, A., Hargrovet, C. K., Harin-Dirac, M., Harrison, P. F., Hart, P. A., Hartmann, C., Hattersley, P. M., Hauschild, M., Hawkes, C. M., Hawkings, R., Heflin, E., Hemingway, R. J., Hensel, C., Herten, G., Heuer, R. D., Hildreth, M. D., Hill, J. C., Hillier, S. J., Hilse, T., Hinshaw, D. A., Ho, C., Hoare, J., Hobbs, J. D., Hobson, P. R., Hochman, D., Hocker, A., Hoffman, K., Holl, B., Homer, R. J., Honma, A. K., Horvath, D., Hossain, K. R., Hou, S. R., Howard, R., Howarth, C. P., Huntemeyer, P., Hughes-Jones, R. E., Humbert, R., Hutchcroft, D. E., Igo-Kemenes, P., Ihssen, H., Imrie, D. C., Ingram, M., Ishii, K., Jacob, F. R., Janissen, A. C., Jawahery, A., Jeffreys, P. W., Jeremie, H., Jimack, M., Jobes, M., Joly, T., Jones, C. R., Jones, G., Jones, M., Jones, R. W., Jost, U., Jovanovic, P., Jui, C., Jobes, M., Joly, A., Jones, C. R., Jones, G., Jones, M., Jones, R. W., Jost, U., Jovanovic, P., Jui, C., Junk, T. R., Kanaya, N., KANZAKI, J., Karapetian, G., Karlen, D., Kartvelishvili, V., Kawagoe, K., Kawamoto, T., Keeler, R. K., Kellogg, R. G., Kennedy, B. W., Kim, D. H., King, B. J., Kirk, J., Klein, K., Kleinwort, C., Klem, D. E., Klier, A., Kluth, S., Kobayashi, T., Kobel, M., Kopke, L., Koetke, D. S., Kokott, T. P., Komamiya, S., Kormos, L., Kowalewski, R., Kramer, T., Kral, J. F., Kress, T., Kreutzmann, H., Krieger, P., von Krogh, J., Kroll, J., Krop, D., Kruger, K., Kuhl, T., Kupper, M., Kuwano, M., Kyberd, P., Lafferty, G. D., Lafoux, H., Lahmann, R., Lai, W. P., Lamarche, F., Landsman, H., Lanske, D., Larson, W. J., Lauber, J., Lautenschlager, S. R., Lawson, I., Layter, J. G., Lazic, D., Le Du, P., Leblanc, P., Lee, A. M., Lefebvre, E., Lehto, M. H., Leins, A., Lellouch, D., Lennert, P., Leroy, C., Lessard, L., Letts, J., Levegrun, S., Levinson, L., Lewis, C., Liebisch, R., Lillich, J., Littlewood, C., Lloyd, A. W., Lloyd, S. L., Loebinger, F. K., Long, G. D., Lorah, J. M., Lorazo, B., Losty, M. J., Lou, X. C., Lu, J., Ludwig, A., Ludwig, J., Luig, A., Macchiolo, A., Macpherson, A., Mader, W., Mattig, P., Malik, A., Mannelli, M., Marcellini, S., Marchant, T. E., Maringer, G., Markus, C., Martin, A., Martin, J. P., Martinez, G., Masetti, G., Mashimo, T., Matthews, W., Maur, U., McDonald, W. J., McGowan, R. F., McKenna, J., Mckigney, E. A., McMahon, T. J., McNab, A. I., McNutt, J., McPherson, A. C., McPherson, R. A., Meijers, F., Mendez-Lorenzo, P., Menges, W., Menke, S., Menszner, D., Merritt, F. S., Mes, H., Meyer, J., Meyer, N., Michelini, A., Middleton, R. P., Mihara, S., Mikenberg, G., Mildenberger, J., Miller, D. J., Milstene, C., Mir, R., Moed, S., Mohr, W., Moisan, C., Montanari, A., Mori, T., Morii, M., Moss, M. W., Mouthuy, T., Muller, U., Murphy, P. G., Mutter, A., Nagai, K., NAKAMURA, I., Nanjo, H., Neal, H. A., Nellen, B., Nguyen, H. H., Nijjhar, B., Nisius, R., Nozaki, M., Oakham, F. G., Odorici, F., Ogg, M., Ogren, H. O., Oh, A., Oh, H., Okpara, A., Oldershaw, N. J., OMORI, T., O'Neale, S. W., O'Neill, B. P., Oram, C. J., Oreglia, M. J., Orito, S., Pahl, C., Palinkas, J., Palmonari, F., Pansart, J. P., Panzer-Steindel, B., Paschievici, P., Pasztor, G., Pater, J. R., Patrick, G. N., Pawley, S. J., Paz-Jaoshvili, N., Pearce, M. J., Petzold, S., Pfeifenschneider, P., Pfister, P., Pilcher, J. E., Pinfold, J., Pitman, D., Plane, D. E., Poffenberger, P., Poli, B., Polok, J., Pooth, O., Posthaus, A., Pouladdej, A., del Poz, L. A., Prebys, E., Pritchard, T. W., Przybycien, M., Przysiezniak, H., Quadt, A., Quast, G., Rabbertz, K., Raith, B., Redmond, M. W., Rees, D. L., Rembser, C., Renkel, P., Richards, G. E., Rick, H., Rigby, D., Riles, K., Robins, S. A., Robinson, D., Rodning, N., Rollnik, A., Roney, J. M., Rooke, A., Ros, E., Rosati, S., Roscoe, K., Rossberg, S., Rossi, A. M., Rosvick, M., Routenburg, P., Rozen, Y., Runge, K., Runolfsson, O., Ruppel, U., Rust, D. R., Rylko, R., Sachs, K., Saeki, T., Sahr, O., Sanghera, S., Sarkisyan, E. K., Sasaki, M., Sbarra, C., Schaile, A. D., Schaile, O., Schappert, W., Scharf, F., Scharff-Hansen, P., Schenk, P., Schieck, J., Schmitt, B., von der Schmitt, H., Schmitt, S., Schorner-Sadenius, T., Schreiber, S., Schroder, M., Schutz, P., Schultz-Coulon, H. C., Schulz, M., Schumacher, M., Schwarz, J., Schwick, C., Schwiening, J., Scott, W. G., Settles, M., Seuster, R., Shears, T. G., Shen, B. C., Shepherd-Themistocleous, C. H., Sherwood, P., Shypit, R., Simon, A., Singh, P., Siroli, G. P., Sittler, A., Skillman, A., Skuja, A., Smith, A. M., Smith, T. J., Snow, G. A., Sobie, R., Soldner-Rembold, S., Spagnolo, S., Spano, F., Springer, R. W., Sproston, M., Stahl, A., Starks, M., Steiert, M., Stephens, K., Steuerer, J., Stier, H. E., Stockhausen, B., Stoll, K., Strohmer, R., Strom, D., Strumia, F., Stumpf, L., Surrow, B., Szymanski, P., Tafirout, R., Takeda, H., Takeshita, T., Talbot, S. D., Tanaka, S., Taras, P., Tarem, S., Tasevsky, M., Taylor, R. J., Tecchio, M., Teixeira-Dias, P., Tesch, N., Teuscher, R., Thackray, N. J., Thiergen, M., Thomas, J., Thomson, M. A., von Torne, E., Torrence, E., Towers, S., Toya, D., Trocsanyi, Z., Tran, P., Transtromer, G., Trefzger, T., Tresilian, N. J., Trigger, I., Tscheulin, M., Tsukamoto, T., Tsur, E., Turcot, A. S., Tumer-Watson, M. F., Tysarczyk-Niemeyer, G., Ueda, I., Ujvari, B., Utzat, P., Vachon, B., Van den Plas, D., Van Kooten, R., VanDalen, G. J., Vannerem, P., Vasseur, G., Vertesi, R., Verzocchi, M., Vikas, P., Vincter, M., Virtue, C. J., Vokurka, E. H., Vollmer, C. F., Voss, H., Vossebeld, J., Wackerle, F., Wagner, A., Wagner, D. L., Wahl, C., Walker, J. P., Waller, D., Ward, C. P., Ward, D. R., Ward, J. J., Watkins, P. M., Watson, A. T., Watson, N. K., Weber, M., Weber, P., Weisz, S., Wels, P. S., Wengler, T., Wermes, N., Wetterling, D., Weymann, M., Whalley, M. A., White, J. S., Wilkens, B., Wilson, J. A., Wilson, G. W., Wingerter, I., Winterer, V. H., Wlodek, T., Wolf, G., Wood, N. C., Wotton, S., Wyatt, T. R., Yaari, R., Yamashita, S., Yang, Y., Yeaman, A., Yekutieli, G., Yurko, M., Zacek, V., Zacharov, I., Zer-Zion, D., Zeuner, W., Zivkovic, L., Zorn, G. T., Abe, K., Abe, K., Abe, T., Abt, I., Acton, P. D., Adam, I., Agnew, G., Akagi, T., Akimoto, H., Allen, N. J., Ash, W. W., Aston, D., Bacchetta, N., Baird, K. G., Baltay, C., Band, H. R., Barakat, M. B., Baranko, G. J., Bardon, O., Barklow, T. L., Bashindzhagian, G. L., Battiston, R., Bauer, J. M., Bazarko, A. O., Bean, A., Bellodi, G., Ben-David, R., Benvenuti, A. C., Berger, R., Biasini, M., Bienz, T., Bilei, G. M., Bisello, D., Blaylock, G., Bogart, J., Bolen, B., Bolton, T., Bower, G. R., Brau, J. E., Breidenbach, M., Bugg, W. M., Burke, D., Burnett, T. H., Burrows, P. N., Busza, W., Calcaterra, A., Caldwell, D. O., Camanzi, B., Carpinelli, M., Carr, J., Cassell, R., Castaldi, R., Castro, A., Cavalli-Sforza, M., Chadwick, G. B., Chou, A., Church, E., Claus, R., Cohn, H. O., Coller, J. A., Convery, M. R., Cook, V., Cotton, R., Cowan, R. F., Coyle, P. A., Coyne, D. G., Crawford, G., D'Oliveira, A., Damerell, C. J., Daoudi, M., Dasu, S., De Groot, N., de Sangro, R., De Simone, P., De Simone, S., Dell'Orso, R., Dervan, P. J., Dima, M., Dong, D. N., Doser, M., Du, P. Y., Dubois, R., Duboscq, J. E., Eigen, G., Eisenstein, B. I., Elia, R., Erdos, E., Erofeeva, I., Eschenburg, V., Etzion, E., Fahey, S., Falciai, D., Fan, C., Fernandez, J. P., Fero, M. J., Flood, K., Frey, R., Friedman, J. I., Furuno, K., Garwin, E. L., Gillman, T., Gladding, G., Gonzalez, S., Hallewell, G. D., Hart, E. L., Harton, J. L., Hasan, A., Hasegawa, Y., Hasuko, K., Hedges, S., Hertzbach, S. S., Hildreth, M. D., Hitlin, D. G., Honma, A., Huber, J. S., Huffer, M. E., Hughes, E. W., Huynh, X., Hwang, H., Iwasaki, M., Iwasaki, Y., Izen, J. M., Jackson, D. J., Jacques, P., Jaros, J. A., Jiang, Z. Y., Johnson, A. S., Johnson, J. R., Johnson, R. A., Junk, T., Kajikawa, R., Kalelkar, M., Kamyshkov, Y. A., Kang, H. J., Karliner, I., Kawahara, H., Kelsey, M. H., Kendall, H. W., Kim, Y. D., King, M., King, R., Kofler, R., Krishna, N. M., Kwon, Y., Labs, J. F., Kroeger, R. S., Langston, M., Lath, A., Lauber, J. A., Leith, D. W., Lia, V., Lin, C., Liu, M. X., Loreti, M., Lu, A., Lynch, H. L., Ma, J., Mancinelli, G., Manly, S., Mantovani, G., Markiewicz, T. W., Maruyama, T., Masuda, H., Mazzucato, H., McGowan, J. F., McKemey, A. K., Meadows, B. T., Messner, R., Mockett, P. M., Moffeit, K. C., Moore, T. B., Morii, M., Mours, B., Muller, D., Mueller, G., Murzin, V., Nagamine, T., Narita, S., Nauenberg, U., Neal, H., Nesom, G., Nussbaum, M., Ohnishi, Y., Oishi, N., Onoprienko, D., Osborne, L. S., Panvini, R. S., Park, C. H., Park, H., Pavel, T. J., Peruzzi, I., Pescara, L., Piccolo, M., Piemontese, L., Pieroni, E., Pitts, K. T., Plano, R. J., Prepost, R., Prescott, C. Y., Punkar, G., Quigley, J., Ratcliff, B. N., Reeves, K., Reeves, T. W., Reidy, J., Reinertsen, P. L., Rensing, P. E., Rochester, L. S., Rothberg, J. E., Rowson, P. C., Russell, J. J., Saxton, O. H., Schalk, T., Schindler, R. H., Schneekloth, U., Schumm, B. A., Schwiening, J., Seiden, A., Sen, S., Serbo, V. V., Servoli, L., Shaevitz, M. H., Shank, J. T., Shapiro, G., Sherden, D. J., Shmakov, K. D., Simopoulos, C., Sinev, N. B., Smith, S. R., Smy, M. B., Snyder, J. A., Sokoloff, M. D., Staengle, H., Stahl, A., Starner, P., Steiner, H., Steiner, R., Strauss, M. G., Su, D., Suekane, F., Sugiyama, A., Suzuki, A., Suzuki, S., Swartz, M., Szumilo, A., Takahashi, T., Taylor, F. E., Thaler, J. J., Thom, J., Torrence, E., Trandafir, A. I., Turk, J. D., Usher, T., Va'vra, J., Vannini, C., Vella, E., Venuti, J. P., Verdier, R., Verdini, P. G., Wagner, D. L., Wagner, S. R., Waite, A. P., Walston, S., Wang, J., Watts, S. J., Weidemann, A. W., Weiss, E. R., Whitaker, J. S., White, S. L., Wickens, F. J., Williams, D. A., Williams, D. C., Williams, S. H., Willocq, S., Wilson, R. J., Wisniewski, W. J., Wittlin, J. L., Woods, M., Word, G. B., Wright, T. R., Wyss, J., Yamamoto, R. K., Yamartino, J. M., Yang, X. Q., Yashima, J., Yellin, S. J., Young, C. C., Yuta, H., Zapalac, G., Zdarko, R. W., Zeitlin, C., Zhou, J. 2006; 427 (5-6): 257-454

  View details for DOI 10.1016/j.physrep.2005.12.006

  View details for Web of Science ID 000236815000001

• The Asymptotic Error Floor of LDPC Ensembles Under BP Decoding Montanari, A. 2006
• Finite-Length Scaling for Gallager A Ezri, J., Montanari, A., Urbanke, R. 2006
• Two Lectures on Iterative Coding and Statistical Mechanics Les Houches Summer School on Mathematical Statistical Physics Montanari, A. Elsevier. 2006: 1
• Analysis of Belief propagation for Non-Linear Problems: The Example of CDMA (or: How to Prove Tanaka's Formula) Montanari, A., Tse, D. 2006
• Approximate analysis of search algorithms with “physical” methods Phase Transitions and Algorithmic Complexity Cocco, S., Monasson, R., Montanari, A., Semerjian, G. Oxford. 2006: 1
• How to Find Good Finite-Length Codes: From Art Towards Science European Transactions on Telecommunications Amraoui, A., Montanari, A., Urbanke, R. 2006
• From large scale rearrangements to mode coupling phenomenology in model glasses PHYSICAL REVIEW LETTERS Montanari, A., Semerjian, G. 2005; 94 (24)

  View details for DOI 10.1103/PhysRevLett.94.247201

  View details for Web of Science ID 000230092000053

• Why We Can Not Surpass Capacity: The Matching Condition Measson, C., Montanari, A., Urbanke, R. 2005
• From Large Scale Rearrangements to Mode Coupling Phenomenology Physical Review Letters Montanari, A., Semerjian, G. 2005
• How to Compute Loop Corrections to Bethe Approximation Journal of Statistical Mechanics Montanari, A., Rizzo, T. 2005
• Tight bounds for LDPC and LDGM codes under MAP decoding IEEE Transactions on Information Theory Montanari, A. 2005
• Belief Propagation Based Multi–User Detection Montanari, A., Prabhakar, B., Tse, D. 2005
• Further results on finite-length scaling for iteratively decoded LDPC ensembles IEEE International Symposium on Information Theory Amraoui, A., Urbanke, R., Montanari, A., Richardson, T. IEEE. 2004: 103–103

  View details for Web of Science ID 000223202600103

• Finite-Length Scaling and Finite-Length Shift for Low-Density Parity-Check Codes Amraoui, A., Montanari, A., Richardson, T., Urbanke, R. 2004
• Instability of one-step replica-symmetry-broken phase in satisfiability problems Journal of Physics A Montanari, A., Parisi, G., Ricci-Tersenghi, F. 2004
• The Phase Diagram of Random Heteropolymers Physical Review Letters Montanari, A., Mueller, M., Mezard, M. 2004
• Glassy phases in Random Heteropolymers with correlated sequences Journal of Chemical Physics Mueller, M., Montanari, A., Mezard, M. 2004
• On the cooling-schedule dependence of the dynamics of mean-field glasses Physical Review B Montanari, A., Ricci-Tersenghi, F. 2004
• Life Above Threshold: From List Decoding to Area Theorem and MSE Measson, C., Montanari, A., Richardson, T., Urbanke, R. 2004
• Maxwell's construction: The hidden bridge between maximum-likelihood and iterative decoding IEEE International Symposium on Information Theory Measson, C., Montanari, A., Urbanke, M. IEEE. 2004: 225–225

  View details for Web of Science ID 000223202600225

• Weight distributions of LDPC code ensembles: Combinatorics meets statistical physics IEEE International Symposium on Information Theory Di, C. Y., Montanari, A., Urbanke, R. IEEE. 2004: 102–102

  View details for Web of Science ID 000223202600102

• Tight bounds for LDPC codes under MAP decoding Montanari, A. 2004
• On the stochastic dynamics of disordered spin models Journal of Statistical Physics Semerjian, G., Cugliandolo, L., F., Montanari, A. 2004
• On the nature of the low-temperature phase in discontinuous mean-field spin glasses EUROPEAN PHYSICAL JOURNAL B Montanari, A., Ricci-Tersenghi, F. 2003; 33 (3): 339-346

  View details for DOI 10.1140/epjb/e2003-00174-7

  View details for Web of Science ID 000183776500012

• Finite-length scaling for iteratively decoded LDPC ensembles Amraoui, A., Montanari, A., Richardson, T., Urbanke, R. 2003
• A microscopic description of the aging dynamics: fluctuation-dissipation relations, effective temperature and heterogeneities Physical Review Letters Montanari, A., Ricci-Tersenghi, F. 2003
• Alternative translation techniques for propositional and first-order modal logics JOURNAL OF AUTOMATED REASONING Montanari, A., POLICRITI, A., Slanina, M. 2002; 28 (4): 397-415

  View details for Web of Science ID 000176183400003

• The dynamic phase transition for decoding algorithms Physical Review E Franz, S., Leone, M., Montanari, A., Ricci-Tersenghi, F. 2002; 66
• Optimizing searches via rare events Physical Review Letters Montanari, A., Zecchina, R. 2002
• Finite-size scaling and metastable states of good codes Montanari, A. 2001
• Statistical mechanics and turbo codes Montanari, A., Sourlas, N. 2001
• Asymptotically free models and discrete non-Abelian groups Physics Letters B Caracciolo, S., Montanari, A., Pelissetto, A. 2001
• Hairpin formation and elongation of biomolecules Physical Review Letters Montanari, A., Mezard, M. 2001
• Spin models on Platonic solids and asymptotic freedom Caracciolo, S., Montanari, A., Pelissetto, A. 2001
• Discrete non-Abelian groups and asymptotically free models Caracciolo, S., Montanari, A., Pelissetto, A. 2001
• The glassy phase of Gallager codes The European Physical Journal B Montanari, A. 2001
• A guided tour through some extensions of the event calculus COMPUTATIONAL INTELLIGENCE Cervesato, I., Franceschet, M., Montanari, A. 2000; 16 (2): 307-347

  View details for Web of Science ID 000086463100007

• Turbo codes: the phase transition The European Physical Journal B Montanari, A. 2000
• The statistical mechanics of turbo codes The European Physical Journal B Montanari, A., Sourlas, N. 2000
• Operator product expansion on the lattice: a numerical test in the two-dimensional non-linear sigma-model Journal of High Energy Physics Caracciolo, S., Montanari, A., Pelissetto, A. 2000
• A general modal framework for the Event Calculus and its skeptical and credulous variants JOURNAL OF LOGIC PROGRAMMING Cervesato, I., Montanari, A. 1999; 38 (2): 111-164

  View details for Web of Science ID 000077705000001

• Testing the efficiency of different improvement programs Nuclear Physics B Caracciolo, S., Montanari, A., Pelissetto, A. 1999
• Composite operators from operator product expansion: what can go wrong? Caracciolo, S., Montanari, A., Pelissetto, A. 1999
• Event Calculus with explicit quantifiers 5th International Workshop on Temporal Representation and Reasoning Cervesato, I., Franceschet, M., Montanari, A. I E E E, COMPUTER SOC PRESS. 1998: 81–88

  View details for Web of Science ID 000074225400013

• Operator product expansion and non-perturbative renormalization Caracciolo, S., Montanari, A., Pelissetto, A. 1998
• Improved actions for the two-dimensional sigma-model Caracciolo, S., Montanari, A., Pelissetto, A. 1997
• COEVAL AR-40/AR-39 AGES OF 65.0 MILLION YEARS AGO FROM CHICXULUB CRATER MELT ROCK AND CRETACEOUS-TERTIARY BOUNDARY TEKTITES SCIENCE Swisher, C. C., GrajalesNishimura, J. M., Montanari, A., MARGOLIS, S. V., Claeys, P., Alvarez, W., Renne, P., CedilloPardo, E., Maurrasse, F. J., Curtis, G. H., Smit, J., McWilliams, M. O. 1992; 257 (5072): 954-958

  Abstract

  (40)Ar/(39)Ar dating of drill core samples of a glassy melt rock recovered from beneath a massive impact breccia contained within the 180-kilometer subsurface Chicxulub crater in Yucatán, Mexico, has yielded well-behaved incremental heating spectra with a mean plateau age of 64.98 +/- 0.05 million years ago (Ma). The glassy melt rock of andesitic composition was obtained from core 9 (1390 to 1393 meters) in the Chicxulub 1 well. The age of the melt rock is virtually indistinguishable from (40)Ar/(39)Ar ages obtained on tektite glass from Beloc, Haiti, and Arroyo el Mimbral, northeastern Mexico, of 65.01 +/- 0.08 Ma (mean plateau age for Beloc) and 65.07 +/- 0.10 Ma (mean total fusion age for both sites). The (40)Ar/(39)Ar ages, in conjunction with geochemical and petrological similarities, strengthen the recent suggestion that the Chicxulub structure is the source for the Haitian and Mexican tektites and is a viable candidate for the Cretaceous-Tertiary boundary impact site.

  View details for Web of Science ID A1992JH82700032

  View details for PubMedID 17789640