18 papers · ranked by Valyu relevance
Niklas Gassner, Julia Lieb, Abhinaba Mazumder, Michael Schaller
In this paper, we present a framework for generic decoding of convolutional codes, which allows us to do cryptanalysis of code-based systems that use convolutional codes as public keys. We then apply this framework to information set decoding, study success probabilities and give tools to choose variables. Finally, we…
Martin Bossert, Rebekka Schulz, Sebastian Bitzer
—The binary primitive BCH codes are cyclic and are constructed by choosing a subset of the cyclotomic cosets. Which subset is chosen determines the dimension, the minimum distance and the weight distribution of the BCH code. We construct possible BCH codes and determine their coderate, true minimum distance and the…
Jia Lu, Ryan Tsoi, Nan Luo, Yuanchi Ha + 8 more
Dynamical systems often generate distinct outputs according to different initial conditions, and one can infer the corresponding input configuration given an output. This property captures the essence of information encoding and decoding. Here, we demonstrate the use of self-organized patterns, combined with machine…
Thach V. Bui
Neural coding is an important tool to discover the inner workings of mind. In this work, we propose and consider a simple but novel self-decoding model for neural coding based on the principle that the neuron body represents ongoing stimulus while dendrites are used to store that stimulus as a memory. In particular…
Kairui Tian, Rongke Liu, Zheng Lu, Xiaowei Wu + 3 more
'Jinhong Yuan' 'Min Qiu'] The newly developed automorphism ensemble decoder (AED) leverages the rich automorphisms of Reed-Muller (RM) codes to achieve near maximum likelihood (ML) performance at short code lengths. However, the performance gain of AED comes at the cost of high complexity, as the ensemble size required…
Jia Lu, Ryan Tsoi, Nan Luo, Yuanchi Ha + 8 more
'Minjun Kwak' 'Yasa Baig' 'Nicole Moiseyev' 'Shari Tian' 'Alison Zhang' 'Neil Zhenqiang Gong' 'Lingchong You'] Title: Summary Dynamical systems often generate distinct outputs according to different initial conditions, and one can infer the corresponding input configuration given an output. This property captures the…
Franziska Gerken, Alana Darcher, Pedro J Gonçalves, Rachel Rapp + 8 more
The human medial temporal lobe (MTL), a region implicated in memory and high-level cognition, contains neurons that respond selectively to stimuli belonging to specific categories, such as individual people, landmarks, or objects. However, these neurons have been largely studied via static, isolated presentations of…
Cheuk Ting Li
We propose using confusion hypergraphs (hyperconfusions) as a model of information. In contrast to the conventional approach using random variables, we can now perform conjunction, disjunction and implication of information, forming a Heyting algebra. Using the connection between Heyting algebra and intuitionistic…
Timo Hillmann, Lucas Berent, Armanda O. Quintavalle, Jens Eisert + 2 more
'Robert Wille' 'Joschka Roffe'] Quantum low-density parity-check codes are a promising candidate for fault-tolerant quantum computing with considerably reduced overhead compared to the surface code. However, the lack of a practical decoding algorithm remains a barrier to their implementation. In this work, we introduce…
Jingcheng Zhang, Lei Chen, Jinlin Sun, Shumin Li + 5 more
DNA has emerged as a compelling archival storage medium, offering unprecedented information density and millennia-scale durability. Despite its promise, DNA-based data storage faces critical challenges due to error-prone processes during DNA synthesis, storage, and sequencing. In this study, we introduce Gungnir, a…
Jan Lewandowsky, Gerhard Bauch, Maximilian Stark, Jerry D. Gibson
Digital communication receivers extract information about the transmitted data from the received signal in subsequent processing steps, such as synchronization, demodulation and channel decoding. Technically, the receiver-side signal processing for conducting these tasks is complex and hence causes bottleneck…
Roberto Maffulli, Miguel A. Casal, Marco Celotto, Stefano Zucca + 3 more
Information theory provides a popular and principled framework for the analysis of neural data. It allows to uncover in an assumption-free way how neurons encode and transmit information, capturing both linear and non-linear coding mechanisms and including the information carried by interactions of any order. To…
Authors not listed
Quantum state tomography has been widely used to reconstruct the quantum state of a system from a set of informationally-complete measurements. Obtaining enough information about, e.g., the wavefunction of a molecule allows its complete characterization. On the other hand, deep learning models for molecular property…
Tagir Akhmetshin, Arkadii Lin, Timur Madzhidov, Alexandre Varnek
Autoencoders represent a promising technique for the inverse quantitative structure-activity relationship (QSAR) task. However, undesirable bias, such as atom ordering, affects the neighbourhood behaviour of autoencoders’ latent space and, consequently, usage of the latent vectors as variables in machine-learning…
Aida Koch, Alix Petit
In this article, we will analyze in detail the coding limit of an individual sequence by introducing the latest developments brought by the Set Shaping Theory. This new theory made us realize that there is a huge difference between source entropy and zero order empirical entropy. Understanding the differences between…
Vahe Galstyan, Age Tjalma, Pieter Rein ten Wolde
Efficient signal representation is essential for the functioning of living and artificial systems operating under resource constraints. A widely recognized framework for deriving such representations is the information bottleneck method, which yields the optimal strategy for encoding a random variable, such as the…
Michail Gkagkos, Charalambos D. Charalambous, Eduard Jorswieck
The main focus of this paper is the derivation of the structural properties of the test channels of Wyner’s operational information rate distortion function (RDF), $R¯(Δ_{X})$, for arbitrary abstract sources and, subsequently, the derivation of additional properties for a tuple of multivariate correlated, jointly…
Xinhao Fan, Shreesh P Mysore
A cornerstone of our understanding of both biological and artificial neural networks is that they store information in the strengths of connections among the constituent neurons. However, in contrast to the well-established theory for quantifying information encoded by the firing patterns of neural networks, little is…