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We consider the problem of distributing a file in a network of storage nodes whose storage budget is limited but at least equals to the size file. We first generate $T$ encoded symbols (from the file) which are then distributed among the…

Information Theory · Computer Science 2010-05-31 Mohsen Sardari , Ricardo Restrepo , Faramarz Fekri , Emina Soljanin

Many Random Number Generators (RNG) are available nowadays; they are divided in two categories, hardware RNG, that provide "true" random numbers, and algorithmic RNG, that generate pseudo random numbers (PRNG). Both types usually generate…

Information Theory · Computer Science 2018-09-28 Andrea C. G. Mennucci

Reversible state transformations under entanglement non-increasing operations give rise to entanglement measures. It is well known that asymptotic local operations and classical communication (LOCC) are required to get a simple operational…

Quantum Physics · Physics 2013-05-29 Ashish V. Thapliyal , John A. Smolin

One of the main problems for the future of practical quantum computing is to stabilize the computation against unwanted interactions with the environment and imperfections in the applied operations. Existing proposals for quantum memories…

Quantum Physics · Physics 2007-05-23 Emanuel Knill , Raymond Laflamme

In this paper, we show that coding can be used in storage area networks (SANs) to improve various quality of service metrics under normal SAN operating conditions, without requiring additional storage space. For our analysis, we develop a…

Information Theory · Computer Science 2013-05-30 Ulric J. Ferner , Tong Wang , Muriel Médard , Emina Soljanin

When a quantum system is distributed to spatially separated parties, it is natural to consider how the system evolves when the parties perform local quantum operations with classical communication (LOCC). However, the structure of LOCC…

Quantum Physics · Physics 2020-05-07 Eric Chitambar , Julio I. de Vicente , Mark W. Girard , Gilad Gour

We address the fundamental task of converting $n$ uses of an unknown unitary transformation into a quantum state (i.e., storage) and later retrieval of the transformation. Specifically, we consider the case where the unknown unitary is…

This paper considers a source, which employs random linear coding (RLC) to encode a message, a legitimate destination, which can recover the message if it gathers a sufficient number of coded packets, and an eavesdropper. The probability of…

Information Theory · Computer Science 2022-03-24 Ioannis Chatzigeorgiou

We start with the task of discriminating finitely many multipartite quantum states using LOCC protocols, with the goal to optimize the probability of correctly identifying the state. We provide two different methods to show that finitely…

Quantum Physics · Physics 2021-04-13 Debbie Leung , Andreas Winter , Nengkun Yu

Learning and compression are driven by the common aim of identifying and exploiting statistical regularities in data, which opens the door for fertile collaboration between these areas. A promising group of compression techniques for…

Machine Learning · Computer Science 2021-02-02 Fernando E. Rosas , Pedro A. M. Mediano , Michael Gastpar

In this paper, we propose a network coding (NC) based approach to ultra-reliable low-latency communication (URLLC) over erasure channels. In transmitting multiple data packets, we demonstrate that the use of random NC can improve the…

Information Theory · Computer Science 2021-11-23 Jinho Choi

We investigate the distribution of the volume and coordination number associated to each particle in a jammed packing of monodisperse hard sphere using the mesoscopic ensemble developed in Nature 453, 606 (2008). Theory predicts an…

Soft Condensed Matter · Physics 2015-05-13 Ping Wang , Chaoming Song , Yuliang Jin , Kun Wang , Hernan A. Makse

We establish a tight characterization of the worst-case rates for the excess risk of agnostic learning with sample compression schemes and for uniform convergence for agnostic sample compression schemes. In particular, we find that the…

Machine Learning · Computer Science 2018-05-22 Steve Hanneke , Aryeh Kontorovich

Distributional linear quadratic regulator (LQR) is a new framework that integrates the distributional reinforcement learning and classical LQR, which offers a new way to study the random return instead of the expected cost. Unlike iterative…

Optimization and Control · Mathematics 2025-10-28 Ruyi Teng , Dan Wang , Wei Chen , Yulong Gao

Locally recoverable (LRC) codes have recently been a focus point of research in coding theory due to their theoretical appeal and applications in distributed storage systems. In an LRC code, any erased symbol of a codeword can be recovered…

Information Theory · Computer Science 2018-05-16 Abhishek Agarwal , Alexander Barg , Sihuang Hu , Arya Mazumdar , Itzhak Tamo

We introduce a novel framework for implementing error-correction in constrained systems. The main idea of our scheme, called Quantized-Constraint Concatenation (QCC), is to employ a process of embedding the codewords of an error-correcting…

Information Theory · Computer Science 2023-02-07 Dor Elimelech , Tom Meyerovitch , Moshe Schwartz

We construct a randomized vector quantizer which has a smaller maximum error compared to all known lattice quantizers with the same entropy for dimensions 5, 6, ..., 48, and also has a smaller mean squared error compared to known lattice…

Information Theory · Computer Science 2025-10-29 Chih Wei Ling , Cheuk Ting Li

We present a numerical study of the reversals and reorientations of the large scale circulation (LSC) of convective fluid in a cylindrical container of aspect ratio one. We take Prandtl number to be 0.7 and Rayleigh numbers in the range…

Fluid Dynamics · Physics 2011-02-14 Pankaj Kumar Mishra , Arnab De , Mahendra K. Verma , V. Eswaran

We investigate the large-scale circulation (LSC) of turbulent Rayleigh-B\'enard convection in a large box of aspect ratio $\Gamma =32$ for Rayleigh numbers up to $Ra=10^9$ and at a fixed Prandtl number $Pr=1$. A conditional averaging…

The outstanding performance of large foundational models across diverse tasks, from computer vision to speech and natural language processing, has significantly increased their demand. However, storing and transmitting these models poses…