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Amoruso C, Marinari E, Martin OC, Pagnani A. Scalings of domain wall energies in two dimensional Ising spin glasses. Physical review letters. 2003;91:87201.
Amelino-Camelia G, Arzano M, Procaccini A. A glimpse at the flat-spacetime limit of quantum gravity using the bekenstein argument in reverse. Int.J.Mod.Phys. D13 (2004) 2337-2343. 2005. Abstract
Amelino-Camelia G, Benedetti D, D'Andrea F, Procaccini A. Comparison of relativity theories with observer-independent scales of both velocity and length/mass. Classical and Quantum Gravity. 2003;20:5353-70. Abstract
Amelino-Camelia G, Kowalski-Glikman J, Mandanici G, Procaccini A. Phenomenology of doubly special relativity. Arxiv preprint gr-qc/0312124. 2003. Abstract
Amelino-Camelia G, Kowalski-Glikman J, Mandanici G, Procaccini A. Phenomenology of doubly special relativity. Int.J.Mod.Phys. A20 (2005) 6007-6038. 2003. Abstract
Amelino-Camelia G, Arzano M, Procaccini A. Severe constraints on the loop-quantum-gravity energy-momentum dispersion relation from the black-hole area-entropy law. Physical Review D. 2004;70:107501. Abstract
Altarelli F, Braunstein A, Ramezanpour A, Zecchina R. Stochastic optimization by message passing. JOURNAL OF STATISTICAL MECHANICS: THEORY AND EXPERIMENT. 2011;2011. Abstract
Altarelli F, Braunstein A, Ramezanpour A, Zecchina R. Stochastic Matching Problem. Phys. Rev. Lett.. 2011;106:190601.
Altarelli F, Monasson R, Zamponi F. Can rare SAT formulae be easily recognized? On the efficiency of message-passing algorithms for K-SAT at large clause-to-variable ratios. Journal of Physics A: Mathematical and Theoretical. 2007;40:867-86. Abstract
Altarelli F, Braunstein A, Realpe-Gomez J, Zecchina R. Statistical mechanics of budget-constrained auctions. Journal of Statistical Mechanics: Theory and Experiment. 2009;2009:P07002 (27pp). Abstract
Altarelli F, Monasson R, Zamponi F. Relationship between clustering and algorithmic phase transitions in the random k-XORSAT model and its NP-complete extensions. In: Journal of Physics: Conference Series. Vol 95. Institute of Physics Publishing; 2008. 012013.
Alemi-Neissi A, Baldassi C, Braunstein A, Pagnani A, Zecchina R, Zoccolan D. Information theoretic and machine learning approaches to quantify non-linear visual feature interaction underlying visual object recognition. BMC Neuroscience. 2012;13:1-2.