Search · four archives
Search · four archives
24 papers · ranked by Valyu relevance
Ly, Hoang, Soljanin, Emina + 2 more
—Maximum-likelihood (ML) decoding for arbitrary block codes remains fundamentally hard, with worst-case time complexity—measured by the total number of multiplications—being no better than straightforward exhaustive search, which requires q k n operations for an [n, k] q code. This paper introduces a simple…
Marwan Jalaleddine, Jiajie Li, Syed Mohsin Abbas, Warren J. Gross
The high computational cost of approaching the performance of Maximum-likelihood (ML) decoding has limited its practical use for decades. Because the complexity grows exponentially with the message length, researchers have spent years developing algorithms like Ordered Statistics Decoding (OSD), Partial Ordered…
Yajun Fan, Le Zhao, Wencai Yan, Haihua Ma
Reconfigurable intelligent surface (RIS)-aided index modulation (IM) shows great potential for next-generation wireless communications. Nevertheless, obtaining channel state information (CSI) for RIS-based IM incurs high pilot overhead, particularly for multi-domain IM. In this paper, we integrate orthogonal frequency…
Xiaolu Wang, Peter Dayan, Paul M Bays
The activity of neural populations typically encodes more information about sensory or motor variables than can be captured by point estimates of the variables. We present and compare two approaches to quantifying this additional or ancillary information and its relationship to uncertainty: the mutual information…
Luca Schmid, Dominik Sulz, Shrinivas Chimmalgi, Laurent Schmalen
Bayesian inference in high-dimensional discrete-input additive noise models is a fundamental challenge in communication systems, as the support of the required joint a posteriori probability (APP) mass function grows exponentially with the number of unknown variables. In this work, we propose a tensor-train (TT)…
Peng Wang, Eryi Hu
Multi-User MIMO (MU-MIMO) detection plays a pivotal role in modern wireless receivers, yet practical downlink deployments are severely bottlenecked when co-scheduled users employ unknown and highly heterogeneous modulation formats. This paper introduces a joint architecture that seamlessly integrates blind modulation…
Chengwei Zhang, Yifan Du, Siyu Liao
Neural channel decoder, as a data-driven channel decoding strategy, has shown very promising improvement on error-correcting capability over the classical methods. However, the success of those deep learning-based decoder comes at the cost of drastically increased model storage and computational complexity, hindering…
Ying Zhou, Clayton E. Curtis, Daryl Fougnie, Kartik K. Sreenivasan
Models of working memory make fundamentally different commitments to the architecture of individual memories. Information-sparse models conceptualize individual memories single point estimates agnostic to meta-cognitive variables such a uncertainty. In contrast, information-rich models propose memories re encoded s…
Asmaa A. Sharaf, Hussein Seleem, Amany Sarhan, Amira S. Ashour
The necessity for reliable healthcare monitoring following the COVID-19 pandemic has highlighted the limitations of RF-based devices in medical settings. Visible light communication (VLC), which provides inherent security and is resistant to RF interference, is a good alternative. This work proposes a VLC system using…
Akash Doshi, Pinar Sen, Кирилл Иванов, Wei Yang + 6 more
Channel coding from 2G to 5G has assumed the inputs bits at the physical layer to be uniformly distributed. However, hybrid automatic repeat request acknowledgement (HARQ-ACK) bits transmitted in the uplink are inherently non-uniformly distributed. For such sources, significant performance gains could be obtained by…
Mohaimen Mohammed, Mesut Çevik, Stefano Savazzi
This paper presents a Deep Autoencoder-LDPC-OFDM (DAE-LDPC-OFDM) transceiver architecture that integrates a learned belief propagation (BP) decoder to achieve robust, energy-efficient, and adaptive wireless communication. Unlike conventional modular systems that treat encoding, modulation, and decoding as independent…
Chu-Jung Wu, Chien-Ying Lin, Yu-Chih Huang, Jun Chen
In modern communication systems, packets with different blocklengths often coexist, presenting new challenges for interference management and decoding. In scenarios where short-packet transmissions must meet strict latency and reliability requirements, conventional interference cancellation decoding strategies may be…
Wenbo Shi, Wenlong Xie, Jiashen Hu, Lishan Liu + 1 more
Future wireless networks require channel coding schemes that can provide high reliability, low latency, and strong adaptability under finite-blocklength and structurally heterogeneous transmission scenarios. Globally coupled low-density parity-check (GC-LDPC) codes are promising for such systems because their coupled…
Kimonas Provatas, Aris Karatzikos, Charalampos Koilakos, Michail Patsakis + 5 more
Genomic and protein foundation models (GFMs and PFMs) have demonstrated strong performance in learning the language of DNA and proteins, but their use in large-scale sequence generation is limited by the latency of autoregressive decoding. Because every token triggers a forward pass of a large Transformer, whose…
Streit, Julian, Weindel, Franziska + 2 more
—We consider the reconstruction of a codeword from multiple noisy copies that are independently corrupted by insertions, deletions, and substitutions. This problem arises, for example, in DNA data storage. A common code construction uses a concatenated coding scheme that combines an outer linear block code with an…
Authors not listed
Inverse molecular design aims to generate novel chemical structures that satisfy multiple property constraints, yet reinforcement-learning (RL) fine-tuning can be sensitive to how objectives are converted into a scalar reward. Here, we systematically analyze how scalarization choices and stabilization mechanisms shape…
Joshua Calder-Travis, Ruud L. van den Brink, Saanchi Thawani, Lars Schwabe + 1 more
Many human decisions manifest in the outside world as motor actions. Correspondingly, neural signals reflecting decision formation are expressed in neural populations encoding the final action. Some decisions, however, are internal, shaping overt behavior indirectly through the selection of policies or rules that guide…
Antony Mizzi, David M. Walker, Michael Small, José F. F. Mendes
We derive a penalty strength criterion for ridge regression using stochastic complexity, which is a refined variant of the minimum description length principle. Since stochastic complexity does not typically account for the effect of regularization on complexity, despite its ability to simplify models, we are required…
Lorenzo Posani
Neural decoding is a powerful approach for inferring which variables are represented in the activity of a population of neurons, with broad applications ranging from basic neuroscience to clinical settings such as brain-computer interfaces. More recently, decoding has also been used as a cross-validated tool for…
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.…
Authors not listed
Perovskite solar cell performance depends on the joint configuration of materials, interfaces, and layer-specific physical parameters, forming a structured design space that is naturally sequential but rarely modeled as such. This work introduces PervoTransformer, a transformer-based framework that represents complete…
Alexis D MacIntyre, Clément Gaultier, Tobias Goehring
During speech perception, properties of the acoustic stimulus can be reconstructed from the listener’s brain using methods such as electroencephalography (EEG). Most studies employ the amplitude envelope as a target for decoding; however, speech acoustics can be characterised on multiple dimensions, including as…
Authors not listed
The discovery of chemically novel or structurally anomalous metal-organic frameworks (MOFs) is essential for expanding reticular design space and enhancing dataset reliability. We present CHEM-AD (Chemically Unusual Metal–organic Frameworks via Autoencoder-based Detection), a label-free, CPU-efficient pipeline that…
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…