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We discuss the performance of Low-Density-Parity-Check (LDPC) codes decoded by means of Linear Programming (LP) at moderate and large Signal-to-Noise-Ratios (SNR). Utilizing a combination of the previously introduced pseudo-codeword-search…

Information Theory · Computer Science 2007-07-13 Michael Chertkov , Mikhail Stepanov

In this paper, a new chaotic pseudo-random number generator (PRNG) is proposed. It combines the well-known ISAAC and XORshift generators with chaotic iterations. This PRNG possesses important properties of topological chaos and can…

Cryptography and Security · Computer Science 2010-12-22 Christophe Guyeux , Qianxue Wang , Jacques M. Bahi

We propose a new DRAM-based true random number generator (TRNG) that leverages DRAM cells as an entropy source. The key idea is to intentionally violate the DRAM access timing parameters and use the resulting errors as the source of…

Hardware Architecture · Computer Science 2018-12-27 Jeremie S. Kim , Minesh Patel , Hasan Hassan , Lois Orosa , Onur Mutlu

We consider the fundamental problem of constructing fast and small circuits for binary addition. We propose a new algorithm with running time $\mathcal O(n \log_2 n)$ for constructing linear-size $n$-bit adder circuits with a significantly…

Data Structures and Algorithms · Computer Science 2024-05-24 Ulrich Brenner , Anna Silvanus

We study the Fourier spectrum of functions $f\colon \{0,1\}^{mk} \to \{-1,0,1\}$ which can be written as a product of $k$ Boolean functions $f_i$ on disjoint $m$-bit inputs. We prove that for every positive integer $d$, \[ \sum_{S \subseteq…

Computational Complexity · Computer Science 2019-02-08 Chin Ho Lee

Generating random and pseudorandom numbers with a deterministic system is a long-standing challenge in theoretical research and engineering applications. Several pseudorandom number generators based on the inversive congruential method have…

Discrete Mathematics · Computer Science 2024-01-17 Xiaoxiong Lu , Chengqing Li , Bo Zhou

The Minimum Circuit Size Problem for Partial Functions ($MCSP^*$) is hard assuming the Exponential Time Hypothesis (ETH) (Ilango, 2020). This breakthrough hardness result leveraged a characterization of the optimal $\{\land, \lor, \neg\}$…

Computational Complexity · Computer Science 2025-11-24 Marco Carmosino , Ngu Dang , Tim Jackman

With the widespread use of communication technologies, cryptosystems are therefore critical to guarantee security over open networks as the Internet. Pseudo-random number generators (PRNGs) are fundamental in cryptosystems and information…

Cryptography and Security · Computer Science 2011-12-06 Jacques M. Bahi , Jean-François Couchot , Christophe Guyeux , Qianxue Wang

Quantum error correction is the building block for constructing fault-tolerant quantum processors that can operate reliably even if its constituting elements are corrupted by decoherence. In this context, real-time decoding is a necessity…

Quantum Physics · Physics 2024-02-15 Antonio deMarti iOlius , Josu Etxezarreta Martinez

We present a tree-based construction of LDPC codes that have minimum pseudocodeword weight equal to or almost equal to the minimum distance, and perform well with iterative decoding. The construction involves enumerating a $d$-regular tree…

Information Theory · Computer Science 2007-07-13 Christine Kelley , Deepak Sridhara , Joachim Rosenthal

Random number generators (RNG) are essential elements in many cryptographic systems. True random number generators (TRNG) rely upon sources of randomness from natural processes such as those arising from quantum mechanics phenomena. We…

Calculating the diameter of an undirected graph requires quadratic running time under the Strong Exponential Time Hypothesis and this barrier works even against any approximation better than 3/2. For planar graphs with positive edge…

Data Structures and Algorithms · Computer Science 2025-07-08 Michał Włodarczyk

In this paper we give improved constructions of several central objects in the literature of randomness extraction and tamper-resilient cryptography. Our main results are: (1) An explicit seeded non-malleable extractor with error $\epsilon$…

Computational Complexity · Computer Science 2016-08-02 Xin Li

We present an efficient parallel derandomization method for randomized algorithms that rely on concentrations such as the Chernoff bound. This settles a classic problem in parallel derandomization, which dates back to the 1980s. Consider…

Data Structures and Algorithms · Computer Science 2023-11-27 Mohsen Ghaffari , Christoph Grunau

$\mathbf F_2$-linear pseudorandom number generators are very popular due to their high speed, to the ease with which generators with a sizable state space can be created, and to their provable theoretical properties. However, they suffer…

Data Structures and Algorithms · Computer Science 2022-03-29 David Blackman , Sebastiano Vigna

Deep neural networks have achieved great success both in computer vision and natural language processing tasks. However, mostly state-of-art methods highly rely on external training or computing to improve the performance. To alleviate the…

Machine Learning · Computer Science 2020-09-25 Ming Yan , Xueli Xiao , Joey Tianyi Zhou , Yi Pan

One-way state generators (OWSG) are natural quantum analogs to classical one-way functions. We consider statistically-verifiable OWSGs (sv-OWSG), which are potentially weaker objects than OWSGs. We show that O(n/log(n))-copy sv-OWSGs (n…

Quantum Physics · Physics 2025-01-13 Rishabh Batra , Rahul Jain

Randomness extractors, which extract high quality (almost-uniform) random bits from biased random sources, are important objects both in theory and in practice. While there have been significant progress in obtaining near optimal…

Computational Complexity · Computer Science 2018-06-12 Kuan Cheng , Xin Li

Controlling small size trapping sets and short cycles can result in LDPC codes with large minimum distance $d_{\min}$. We prove that short cycles with a chord are the root of several trapping sets and eliminating these cycles increases…

Information Theory · Computer Science 2020-08-05 Farzane Amirzade , Mohammad-Reza~Sadeghi , Daniel Panario

Deep generative models (DGMs) for graphs achieve impressively high expressive power thanks to very efficient and scalable neural networks. However, these networks contain non-linearities that prevent analytical computation of many standard…

Machine Learning · Computer Science 2025-08-12 Martin Rektoris , Milan Papež , Václav Šmídl , Tomáš Pevný