14 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…
Hamid Karimi-Rouzbahani, Alexandra Woolgar
Neural codes are reflected in complex neural activation patterns. Conventional electroencephalography (EEG) decoding analyses summarize activations by averaging/down-sampling signals within the analysis window. This diminishes informative fine-grained patterns. While previous studies have proposed distinct statistical…
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…
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…
Saurabh Bhardwaj, Yizhi Wang, Guoqiang Yu, Yue Wang
Deep learning models have been widely used in many supervised learning applications. However, these models suffer from overfitting due to various types of uncertainty with deteriorating performance when facing data biases, class imbalance, or noise propagation. The Information-Set Deep learning (ISDL) architectures…
Maria Kromm, Mariana P Branco, Mathijs Raemaekers, Nick F Ramsey
Implantable brain-computer interfaces (iBCIs) aim to restore communication in individuals with severe motor impairments. For good iBCI performance, it is important to target an optimal location. In this study, we used high-resolution 7-Tesla functional magnetic resonance imaging (fMRI) to map the spatial distribution…
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…
Bixue Song, Yongxin Feng, Yang Wang, T. Aaron Gulliver
Polar codes are closer to the Shannon limit with lower complexity in coding and decoding. As traditional decoding techniques suffer from high latency and low throughput, with the development of deep learning technology, some deep learning-based decoding methods have been proposed to solve these problems. Usually, the…
Ian Daly
Neural decoding models can be used to decode neural representations of visual, acoustic, or semantic information. Recent studies have demonstrated neural decoders that are able to decode accoustic information from a variety of neural signal types including electrocortiography (ECoG) and the electroencephalogram (EEG).…
Alejandro Santos‐Mayo, Faith Gilbert, Laura Ahumada, Caitlin Traiser + 4 more
'Hannah Engle' 'Christian Panitz' 'Mingzhou Ding' 'Andreas Keil'] Title: ABSTRACT Neuroimaging research has increasingly used decoding techniques, in which multivariate statistical methods identify patterns in neural data that allow the classification of experimental conditions or participant groups. Typically, the…
Edgar Beck, Carsten Bockelmann, Armin Dekorsy, Changchuan Yin
Motivated by the recent success of Machine Learning (ML) tools in wireless communications, the idea of semantic communication by Weaver from 1949 has gained attention. It breaks with Shannon’s classic design paradigm by aiming to transmit the meaning of a message, i.e., semantics, rather than its exact version and…
Dor Tsur, Haim Permuter, Kichun Lee
Despite the widespread use of information measures in analyzing probabilistic systems, effective visualization tools for understanding complex dependencies in sequential data are scarce. In this work, we introduce the information matrix (InfoMat), a novel and intuitive matrix representation of information transfer in…
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…