22 papers · ranked by Valyu relevance
Yuzhu Duan, Ziwen Yang, Xiaoming Duan, Shanying Zhu
This paper designs a communication-efficient distributed optimization algorithm for optimization problems subject to coupled equality constraints. By means of duality theory, the original problem is reformulated to tackle the coupled equality constraints. Furthermore, compressed communication is employed to enhance…
Haonan Wang, Minghui Liwang, Yiguang Hong, Karl H. Johansson + 1 more
In this paper, we propose a unified compression algorithm for distributed nonconvex opitmization with both the locally- and globally-bounded communication compressors, including 1-bit compressors, saturating quantizers, and the globally-bounded compressors with both relative and absolute compression errors, as well as…
S. Das, Subhrakanti Dey
—Compression techniques are essential in distributed optimization and learning algorithms with high-dimensional model parameters, particularly in scenarios with tight communication constraints such as limited bandwidth. This article presents a communication-efficient second-order distributed optimization algorithm…
Wenxiang Lin, Xinglin Pan, Ruibo Fan, Shaohuai Shi + 1 more
Communication has emerged as a critical bottleneck in the distributed training of large language models (LLMs). While numerous approaches have been proposed to reduce communication overhead, the potential of lossless compression has remained largely underexplored since compression and decompression typically consume…
Harsh Vardhan, Da Wang
Distributed high dimensional mean estimation is a common aggregation routine used often in distributed optimization methods. Most of these applications call for a communicationconstrained setting where vectors, whose mean is to be estimated, have to be compressed before sharing. One could independently encode and…
Jiao Xue, Chundong Wang
Introduction Federated learning (FL) is a distributed machine learning paradigm that preserves data privacy and mitigates data silos. Nevertheless, frequent communication between clients and the server often becomes a major bottleneck, restricting training efficiency and scalability. Methods To address this challenge…
Alex Shepherd, Emily Stone, Lucy L. W. Owen
Naturalistic cognition emerges from coordinated interactions among distributed brain systems operating across multiple representational scales. Characterizing this organization remains challenging because cognitively relevant information is embedded within high-dimensional neural activity. Here, we apply Multisubject…
Onur Günlü, Maciej Skorski, H. Vincent Poor, Chi Wan Sung
Semantic communication frameworks aim to convey the underlying significance of data rather than reproducing it exactly, a perspective that enables substantial efficiency gains in settings constrained by latency or bandwidth. Motivated by this shift, we study the rate-distortion-perception (RDP) trade-off for image…
Dohyeon Lee, Juyeon Park, Juheon Lee, Chungha Lee + 2 more
Holotomography (HT) is a label-free, three-dimensional quantitative phase imaging technique that captures refractive index distributions of biological samples at sub-micron resolution. As modern HT systems enable high-throughput and large-scale acquisition, they produce terabyte-scale datasets that require efficient…
Michail Patsakis, Theodore Chronopoulos, Ioannis Mouratidis, Ilias Georgakopoulos-Soares
Genomic data repositories continue to grow as sequencing technologies improve, with the NCBI SRA alone exceeding 47 PB. General-purpose compressors treat bioinformatics files as unstructured byte streams and fail to exploit the structured nature of omics data. We present NYX, a format-aware compression system for…
Tao Guo, Zhangyao Song, Huihui Wu, Yang Li + 1 more
This paper analyzes the semantic rate-distortion problem motivated by task-oriented data compression with side information. The semantic information related to a task is not directly accessible to the encoder but implicitly impacts the observations through a joint probability distribution. The decoder aims to…
Marwa E. Madkour, Salah E. Soliman, Moawad I. Dessouky, Fathi E. Abd El-Samie + 2 more
This research paper presents an efficient data collection scheme for Wireless Sensor Networks (WSNs) that simultaneously compresses and encrypts sensor data to extend network lifespan. To address WSN resource limitations, the scheme combines Compressive Sensing (CS) with Elliptic Curve Cryptography (ECC) and Elliptic…
Meixia Tao, Kai Niu, Youlong Wu
Traditional information theory provides a rigorous foundation for information compression and reliable symbol transmission. However, in emerging applications such as autonomous driving, remote healthcare, and industrial Internet of Things (IoT), the key communication challenge has been shifted from accurate delivery of…
Michail Patsakis, Alexandros Tzanakakis, Ilias Georgakopoulos-Soares
Evo 2 is the largest openly available genomic foundation model, but its forty billion parameter configuration cannot be loaded onto a single 80 GB accelerator, placing genome-scale analysis beyond most laboratories. We present TurboQuant-Bio, an open toolkit that compresses Evo 2’s weights and attention cache to four…
Dale Zhou, Sharon M. Noh, Nora C. Harhen, Nidhi V. Banavar + 3 more
The ability to discriminate similar visual stimuli has been used as an important index of memory function. This ability is widely thought to be supported by expanding the dimensionality of relevant neural codes, such that neural representations for the similar stimuli are maximally distinct, or “separated.” An…
Ray-I Chang, Ting-Wei Hsu, Yu-Han Ke, Wenbing Zhao
Autonomous driving systems (ADSs) increasingly rely on LiDAR sensors for perception. However, the resulting high-volume data places a strain on storage systems and network bandwidth and raises data-privacy concerns. We propose an IoT data engineering framework for processing, transmitting, storing, and retrieving…
K. K. Krishnan Namboodiri, Elizabath Peter, Derya Malak, Petros Elia
—This work establishes the fundamental limits of the classical problem of multi-user distributed computing of linearly separable functions. In particular, we consider a distributed computing setting involving L users, each requesting a linearly separable function over K basis subfunctions from a master node, who is…
Authors not listed
Ni40Co20Fe10Cr10Al18W2 additively manufactured using laser powder bed fusion (LPBF) is among the toughest as-built alloys reported and is a promising candidate for use in extreme environments. However, its behavior under multi-megabar pressure regimes remains unexplored. We used femtosecond in situ X-ray diffraction to…
Alice Tor, Yuxin Wu, Stephen E Clarke, Lisa Yamada + 2 more
The complexity of neural data changes as the brain processes information during events. Universal lossless compression algorithms, which are broadly applicable and grounded in information theory, identify and exploit redundancies in data in order to compress it to essentially-optimal sizes regardless of underlying…
Karla Ivankovic, Anastasios Dimou, Justo Montoya-Gálvez, Riccardo Zucca + 2 more
Understanding how the brain represents information is a central challenge in neuroscience and a practical bottleneck for brain-computer interfaces. Existing analytical tools cannot identify neural representations directly from neural activity data. We introduce MultiPEC, a data-driven method that discovers neural…
Javad Maheri, K. K. Krishnan Namboodiri, Petros Elia
We consider a distributed computing system in which a master node coordinates $N$ workers to evaluate a function over $n$ input files, where this function accepts general decomposition. In particular, we focus on the general case where the requested function admits a $d$-uniform decomposition, meaning that it can be…
Authors not listed
Understanding the complex chemistry of organic materials under dynamic compression is important for many applications, but is challenging due to the large number of reactions occurring at various time scales. Here, we develop a machine learning potential based on Chebyshev polynomials to study the insensitive energetic…