19 papers · ranked by Valyu relevance
Junhao Cai, Taegun An, Chengjun Jin, Sung Il Choi + 2 more
Distributed multi-stage image compression—where visual content traverses multiple processing nodes under varying quality requirements—poses challenges. Progressive methods enable bitstream truncation but underutilize available compute resources; successive compression repeats costly pixel-domain operations and suffers…
Feng Liu, Cheng-yi Yang, Jie Yang, De-li Kong + 3 more
'Jia-yin Qi' 'Zhi-bin Li'] As a distributed storage scheme, the blockchain network lacks storage space has been a long-term concern in this field. At present, there are relatively few research on algorithms and protocols to reduce the storage requirement of blockchain, and the existing research has limitations such as…
Xuan Guang, Ruze Zhang
In this paper, we put forward the model of zero-error distributed function compression system of two binary memoryless sources X and Y as depicted in Fig. 1. In this model, there are two encoders En1 and En2 and one decoder De, connected by two channels (En1 , De ) and (En2 , De ) with the capacity constraints C 1 and…
Steffen Steiner, Abdulrahman Dayo Aminu, Volker Kuehn, Gerhard Bauch + 1 more
'Jan Lewandowsky'] This paper addresses the optimization of distributed compression in a sensor network with partial cooperation among sensors. The widely known Chief Executive Officer (CEO) problem, where each sensor has to compress its measurements locally in order to forward them over capacity limited links to a…
Rui Cao, John H. Bladon, Stephen J. Charczynski, Michael E. Hasselmo + 1 more
The Weber-Fechner law proposes that our perceived sensory input increases with physical input on a logarithmic scale. Hippocampal “time cells” carry a record of recent experience by firing sequentially during a circumscribed period of time after a triggering stimulus. Different cells have “time fields” at different…
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…
Zihao Ren, Lei Wang, Deming Yuan, Hongye Su + 1 more
Optimization Authors: ['Zihao Ren' 'Lei Wang' 'Deming Yuan' 'Hongye Su' 'Guodong Shi'] Several data compressors have been proposed in distributed optimization frameworks of network systems to reduce communication overhead in large-scale applications. In this paper, we demonstrate that effective information compression…
Dmitry Bylinkin, Aleksandr Beznosikov
Optimization Problems under Data Similarity Authors: ['Dmitry Bylinkin' 'Aleksandr Beznosikov'] In recent years, as data and problem sizes have increased, distributed learning has become an essential tool for training highperformance models. However, the communication bottleneck, especially for high-dimensional data…
Derya Malak, Mohammad Reza Deylam Salehi, Berksan Serbetci, Petros Elia + 2 more
'Petros Elia' 'Chintha Tellambura' 'Jun Chen'] The work here studies the communication cost for a multi-server multi-task distributed computation framework, as well as for a broad class of functions and data statistics. Considering the framework where a user seeks the computation of multiple complex (conceivably…
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…
Victoria Erofeeva, Oleg Granichin, Vikentii Pankov, Zeev Volkovich + 1 more
'Ayesha Maqbool'] The paper presents a decentralized, real-time clustering method designed for large-scale, distributed environments such as the Internet of Things (IoT). The approach combines compressed sensing for dimensionality reduction with a consensus protocol for distributed aggregation, enabling each node to…
Dale Zhou, Jason Z. Kim, Adam R. Pines, Valerie J. Sydnor + 6 more
Dimensionality reduction, a form of compression, can simplify representations of information to increase efficiency and reveal general patterns. Yet, this simplification also forfeits information, thereby reducing representational capacity. Hence, the brain may benefit from generating both compressed and uncompressed…
Senik Matinyan, Jan Pieter Abrahams
High-throughput data collection in crystallography poses significant challenges in handling massive amounts of data. Here, we present TERSE, a novel lossless compression algorithm specifically designed for diffraction data. We compare TERSE with the established lossless compression algorithms implemented in gzip, CBF…
Herbert J. Bernstein, Alexei S. Soares, Kimberly Horvat, Jean Jakoncic
New higher-count-rate, integrating, large area X-ray detectors with framing rates as high as 17,400 images per second are beginning to be available. These will soon be used for specialized MX experiments but will require optimal lossy compression algorithms to enable systems to keep up with data throughput. Some…
Anders Andreasen, Maria Bonto, Fernando Montero
– This paper presents a framework for optimisation and techno-economic analysis of various pressurisation pathways for CO2 pipeline transportation. The pressurisation pathways include a conventional compression only case from initial to final pressure, a sub-critical compression part followed by cooling, liquefaction…
Authors not listed
Real-world datasets in chemical engineering and bioengineering processes--such as those from catalytic reactors, multiphase flows, polymerization reactors, bioreactors, and clinical trials--can often be unlabelled or disorganized, rendering the training of existing supervised learning models ineffective at learning the…
Haoning Chen, Minquan Cheng, Zhenhao Huang, Youlong Wu
Computation Cost Authors: ['Haoning Chen' 'Minquan Cheng' 'Zhenhao Huang' 'Youlong Wu'] Abstract—The distributed linearly separable computation problem finds extensive applications across domains such as distributed gradient coding, distributed linear transform, real-time rendering, etc. In this paper, we investigate…
Minquan Cheng, Yongkang Wang, Lingyu Zhang, Youlong Wu
Distributed multi-task learning (DMTL) effectively improves model generalization performance through the collaborative training of multiple related models. However, in large-scale learning scenarios, communication bottlenecks severely limit practical system performance. In this paper, we investigate the communication…
Yingjie Cheng, Gaojun Luo, Xiwang Cao, Martianus Frederic Ezerman + 1 more
'San Ling'] A coded distributed computing (CDC) system aims to reduce the communication load in the MapReduce framework. Such a system has K nodes, N input files, and Q Reduce functions. Each input file is mapped by r nodes and each Reduce function is computed by s nodes. The objective is to achieve the maximum…