17 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…
Ryan Gabrys, Farzad Farnoud
In this work, we consider the problem of synchronizing two sets of data where the size of the symmetric difference between the sets is small and, in addition, the elements in the symmetric difference are related through the Hamming distance metric. Upper and lower bounds are derived on the minimum amount of information…
Sarah Anhala Cepeda-Humerez, Jakob Ruess, Gašper Tkačik, Alexandre V. Morozov
'Alexandre V. Morozov'] Across diverse biological systems-ranging from neural networks to intracellular signaling and genetic regulatory networks-the information about changes in the environment is frequently encoded in the full temporal dynamics of the network nodes. A pressing data-analysis challenge has thus been to…
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…
Marcelo Firer, Luciano Panek, Jerry Anderson Pinheiro
In this work we explore possibilities for coding and decoding tailor-made for mean squared error evaluation of error in contexts such as image transmission. To do so, we introduce a loss function that expresses the overall performance of a coding and decoding scheme for discrete channels and that exchanges the usual…
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…
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…
Dylan Le, Xue-Xin Wei
Understanding how correlated neural noise affects neural population coding is a basic question in computational and systems neuroscience [1, 2, 3, 4]. Recent theoretical work suggests that shared noise along the stimulus encoding direction is the primary factor that limits information encoding (i.e.…
Guillaume Frèche, Matthieu R. Bloch, Michel Barret
This paper introduces an explicit covert communication code for binary-input asynchronous discrete memoryless channels based on binary polar codes, in which legitimate parties exploit uncertainty created by both the channel noise and the time of transmission to avoid detection by an adversary. The proposed code jointly…
Gabriel Matías Lorenz, Nicola M. Engel, Marco Celotto, Loren Kocillari + 3 more
Information theory has deeply influenced the conceptualization of brain information processing and is a mainstream framework for analyzing how neural networks in the brain process information to generate behavior. Information theory tools have been initially conceived and used to study how information about sensory…
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…
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…
Julie E. Elie, Frédéric E. Theunissen
Although information theoretic approaches have been used extensively in the analysis of the neural code, they have yet to be used to describe how information is accumulated in time while sensory systems are categorizing dynamic sensory stimuli such as speech sounds or visual objects. Here, we present a novel method to…
Nithin Nagaraj, Arun Somani
Error detection is a fundamental need in most computer networks and communication systems in order to combat the effect of noise. Error detection techniques have also been incorporated with lossless data compression algorithms for transmission across communication networks. In this paper, we propose to incorporate a…
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…