Entropy最新文献

{"title":"Centralized Hierarchical Coded Caching Scheme for Two-Layer Network.","authors":"Kun Zhao, Jinyu Wang, Minquan Cheng","doi":"10.3390/e27030316","DOIUrl":"10.3390/e27030316","url":null,"abstract":"<p><p>This paper considers a two-layer hierarchical network, where a server containing <i>N</i> files is connected to K1 mirrors and each mirror is connected to K2 users. Each mirror and each user has a cache memory of size M1 and M2 files, respectively. The server can only broadcast to the mirrors, and each mirror can only broadcast to its connected users. For such a network, we propose a novel coded caching scheme based on two known placement delivery arrays (PDAs). To fully utilize the cache memory of both the mirrors and users, we first treat the mirrors and users as cache nodes of the same type; i.e., the cache memory of each mirror is regarded as an additional part of the connected users' cache, then the server broadcasts messages to all mirrors according to a K1K2-user PDA in the first layer. In the second layer, each mirror first cancels useless file packets (if any) in the received useful messages and forwards them to the connected users, such that each user can decode the requested packets not cached by the mirror, then broadcasts coded subpackets to the connected users according to a K2-user PDA, such that each user can decode the requested packets cached by the mirror. The proposed scheme is extended to a heterogeneous two-layer hierarchical network, where the number of users connected to different mirrors may be different. Numerical comparison showed that the proposed scheme achieved lower coding delays compared to existing hierarchical coded caching schemes at most memory ratio points.</p>","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 3","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11941494/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143729316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clustered Distributed Data Storage Repairing Multiple Failures.","authors":"Shiqiu Liu, Fangwei Ye, Qihui Wu","doi":"10.3390/e27030313","DOIUrl":"10.3390/e27030313","url":null,"abstract":"<p><p>A clustered distributed storage system (DSS), also called a rack-aware storage system, is a distributed storage system in which the nodes are grouped into several clusters. The communication between two clusters may be restricted by their connectivity; that is to say, the communication cost between nodes differs depending on their location. As such, when repairing a failed node, downloading data from nodes that are in the same cluster is much cheaper and more efficient than downloading data from nodes in another cluster. In this article, we consider a scenario in which the failed nodes only download data from nodes in the same cluster, which is an extreme and important case that leverages the fact that the intra-cluster bandwidth is much cheaper than the cross-cluster repair bandwidth. Also, we study the problem of repairing multiple failures in this article, which allows for collaboration within the same cluster, i.e., failed nodes in the same cluster can exchange data with each other. We derive the trade-off between the storage and repair bandwidth for the clustered DSSs and provide explicit code constructions achieving two extreme points in the trade-off, namely the minimum storage clustered collaborative repair (MSCCR) point and the minimum bandwidth clustered collaborative repair (MBCCR) point, respectively.</p>","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 3","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11941202/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143729323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Numerical Investigation of Effect of Nozzle Upper Divergent Angle on Asymmetric Rectangular Section Ejector.","authors":"Manfei Lu, Jingming Dong, Chi Feng, Shuaiyu Song, Miao Zhang, Runfa Wang","doi":"10.3390/e27030312","DOIUrl":"10.3390/e27030312","url":null,"abstract":"<p><p>Ejectors, as widely utilized devices in the field of industrial energy conservation, exhibit a performance that is significantly affected by their structural parameters. However, the study of the influence of nozzle geometry parameters on asymmetric ejector performance is still limited. In this paper, the effect of the nozzle upper divergent angle on the operating characteristics of an asymmetric rectangular section ejector was comprehensively investigated. The results indicated that the entrainment ratio gradually decreased with an increase in the nozzle upper divergent angle, and the maximum decrease could be 20%. At the same time, the relationship between the upper and lower divergent angles was closely linked to the trend of change in the secondary fluid mass flow rate. The analysis of flow characteristics found that the deflection of the central jet was caused by the pressure difference between the walls of the upper and lower divergent sections of the nozzle. Additionally, quantitative analysis of the development of the mixing layer showed that the mass flow rate of the secondary fluid inlet was related to the development of the mixing boundary. Shock wave analysis demonstrated that the deterioration in ejector performance was due to the reduction in the shock wave strength caused by Mach reflection and the increase in the Mach stem height.</p>","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 3","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11941672/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143729248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Coded Distributed Computing Under Combination Networks.","authors":"Yongcheng Yang, Yifei Huang, Xiaohuan Qin, Shenglian Lu","doi":"10.3390/e27030311","DOIUrl":"10.3390/e27030311","url":null,"abstract":"<p><p>Coded distributed computing (CDC) is a powerful approach to reduce the communication overhead in distributed computing frameworks by utilizing coding techniques. In this paper, we focus on the CDC problem in (H,L)-combination networks, where <i>H</i> APs act as intermediate pivots and K=HL workers are connected to different subsets of <i>L</i> APs. Each worker processes a subset of the input file and computes intermediate values (IVs) locally, which are then exchanged via uplink and downlink transmissions through the AP station to ensure that all workers compute their assigned output functions. In this paper, we first novelly characterize the transmission scheme for the shuffle phase from the view point of the coefficient matrix and then obtain the scheme by using the Combined Placement Delivery Array (CPDA). Compared with the baseline scheme, our scheme significantly improves the uplink and downlink communication loads while maintaining the robustness and efficiency of the combined multi-AP network.</p>","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 3","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11941260/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143729326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Broadcast Channel Cooperative Gain: An Operational Interpretation of Partial Information Decomposition.","authors":"Chao Tian, Shlomo Shamai Shitz","doi":"10.3390/e27030310","DOIUrl":"10.3390/e27030310","url":null,"abstract":"<p><p>Partial information decomposition has recently found applications in biological signal processing and machine learning. Despite its impacts, the decomposition was introduced through an informal and heuristic route, and its exact operational meaning is unclear. In this work, we fill this gap by connecting partial information decomposition to the capacity of the broadcast channel, which has been well studied in the information theory literature. We show that the synergistic information in the decomposition can be rigorously interpreted as the cooperative gain, or a lower bound of this gain, on the corresponding broadcast channel. This interpretation can help practitioners to better explain and expand the applications of the partial information decomposition technique.</p>","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 3","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11941614/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143729201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Democratic Thwarting of Majority Rule in Opinion Dynamics: 1. Unavowed Prejudices Versus Contrarians.","authors":"Serge Galam","doi":"10.3390/e27030306","DOIUrl":"10.3390/e27030306","url":null,"abstract":"<p><p>I study the conditions under which the democratic dynamics of a public debate drives a minority-to-majority transition. A landscape of the opinion dynamics is thus built using the Galam Majority Model (GMM) in a 3-dimensional parameter space for three different sizes, r=2,3,4, of local discussion groups. The related parameters are (p0,k,x), the respective proportions of initial agents supporting opinion A, unavowed tie prejudices breaking in favor of opinion A, and contrarians. Combining <i>k</i> and <i>x</i> yields unexpected and counterintuitive results. In most of the landscape the final outcome is predetermined, with a single-attractor dynamics, independent of the initial support for the competing opinions. Large domains of (k,x) values are found to lead an initial minority to turn into a majority democratically without any external influence. A new alternating regime is also unveiled in narrow ranges of extreme proportions of contrarians. The findings indicate that the expected democratic character of free opinion dynamics is indeed rarely satisfied. The actual values of (k,x) are found to be instrumental to predetermining the final winning opinion independently of p0. Therefore, the conflicting challenge for the predetermined opinion to lose is to modify these values appropriately to become the winner. However, developing a model which could help in manipulating public opinion raises ethical questions. This issue is discussed in the Conclusions.</p>","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 3","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11940988/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143729340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing Visual-Language Prompt Tuning Through Sparse Knowledge-Guided Context Optimization.","authors":"Qiangxing Tian, Min Zhang","doi":"10.3390/e27030301","DOIUrl":"10.3390/e27030301","url":null,"abstract":"<p><p>Prompt tuning visual-language models (VLMs) for specialized tasks often involves leveraging task-specific textual tokens, which can tailor the pre-existing, broad capabilities of a VLM to more narrowly focused applications. This approach, exemplified by CoOp-based methods, integrates mutable textual tokens with categorical tokens to foster nuanced textual comprehension. Nonetheless, such specialized textual insights often fail to generalize beyond the scope of familiar categories, as they tend to overshadow the versatile, general textual knowledge intrinsic to the model's wide-ranging applicability. Addressing this base-novel dilemma, we propose the innovative concept of <b>SparseK</b>nowledge-<b>g</b>uided <b>Co</b>ntext <b>Op</b>timization (Sparse-KgCoOp). This technique aims to fortify the adaptable prompts' capacity to generalize to categories yet unencountered. The cornerstone of Sparse-KgCoOp is based on the premise that reducing the differences between adaptive prompt and their hand-crafted counterparts through sparsification operations can mitigate the erosion of fundamental knowledge. Specifically, Sparse-KgCoOp seeks to narrow the gap between the textual embeddings produced by both the dynamic prompts and the manually devised ones, thus preserving the foundational knowledge while maintaining adaptability. Extensive experiments of several benchmarks demonstrate that the proposed Sparse-KgCoOp is an efficient method for prompt tuning.</p>","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 3","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11941247/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143729356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Minimizing System Entropy: A Dual-Phase Optimization Approach for EV Charging Scheduling.","authors":"Wenpeng Yuan, Lin Guan","doi":"10.3390/e27030303","DOIUrl":"10.3390/e27030303","url":null,"abstract":"<p><p>To address the electric vehicle (EV) charging scheduling problem in rural distribution networks, this study proposes a novel two-phase optimization strategy that combines particle swarm optimization (PSO) and Q-learning for global optimization and real-time adaptation. In the first stage, PSO is used to generate an initial charging plan that minimizes voltage deviations and line overloads while maximizing user satisfaction. In the second phase, a Q-learning approach dynamically adjusts the plan based on real-time grid conditions and feedback. The strategy reduces the system's entropy by minimizing the uncertainty and disorder in power distribution caused by variable EV charging loads. Experimental results on a 33-bus distribution system under baseline and high-load scenarios demonstrate significant improvements over conventional dispatch methods, with voltage deviation reduced from 5.8% to 1.9%, maximum load factor reduced from 95% to 82%, and average customer satisfaction increased from 75% to 88%. While the computation time increases compared to standalone PSO (66 min vs. 34 min), the enhanced grid stability and customer satisfaction justify the trade-off. By effectively minimizing system entropy and balancing grid reliability with user convenience, the proposed two-phase strategy offers a practical and robust solution for integrating EVs into rural power systems.</p>","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 3","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11940855/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143729479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multilabel Classification for Entry-Dependent Expert Selection in Distributed Gaussian Processes.","authors":"Hamed Jalali, Gjergji Kasneci","doi":"10.3390/e27030307","DOIUrl":"10.3390/e27030307","url":null,"abstract":"<p><p>By distributing the training process, local approximation reduces the cost of the standard Gaussian process. An ensemble method aggregates predictions from local Gaussian experts, each trained on different data partitions, under the assumption of perfect diversity among them. While this assumption ensures tractable aggregation, it is frequently violated in practice. Although ensemble methods provide consistent results by modeling dependencies among experts, they incur a high computational cost, scaling cubically with the number of experts. Implementing an expert-selection strategy reduces the number of experts involved in the final aggregation step, thereby improving efficiency. However, selection approaches that assign a fixed set of experts to each data point cannot account for the unique properties of individual data points. This paper introduces a flexible expert-selection approach tailored to the characteristics of individual data points. To achieve this, we frame the selection task as a multi-label classification problem in which experts define the labels, and each data point is associated with specific experts. We discuss in detail the prediction quality, efficiency, and asymptotic properties of the proposed solution. We demonstrate the efficiency of the proposed method through extensive numerical experiments on synthetic and real-world datasets. This strategy is easily extendable to distributed learning scenarios and multi-agent models, regardless of Gaussian assumptions regarding the experts.</p>","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 3","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11941380/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143729483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Variability Identification and Uncertainty Evolution Characteristic Analysis of Hydrological Variables in Anhui Province, China.","authors":"Xia Bai, Jinhuang Yu, Yule Li, Juliang Jin, Chengguo Wu, Rongxing Zhou","doi":"10.3390/e27030305","DOIUrl":"10.3390/e27030305","url":null,"abstract":"<p><p>Variability identification and uncertainty characteristic analysis, under the impacts of climate change and human activities, is beneficial for accurately predicting the future evolution trend of hydrological variables. In this study, based on the evolution trend and characteristic analyses of historical precipitation and temperature sequences from monthly, annual, and interannual scales through the Linear Tendency Rate (LTR) index, as well as its variability point identification using the M-K trend test method, we further utilized three cloud characteristic parameters comprising the average <i>Ex</i>, entropy <i>En</i>, and hyper-entropy <i>He</i> of the Cloud Model (CM) method to quantitatively reveal the uncertainty features corresponding to the diverse cloud distribution of precipitation and temperature sample scatters. And then, through an application analysis of the proposed research framework in Anhui Province, China, the following can be summarized from the application results: (1) The annual precipitation of Anhui Province presented a remarkable decreasing trend from south to north and an annual increasing trend from 1960 to 2020, especially in the southern area, with the LTR index equaling 55.87 mm/10a, and the annual average temperature of the entire provincial area also presented an obvious increasing trend from 1960 to 2020, with LTR equaling about 0.226 °C/10a. (2) The uncertainty characteristic of the precipitation series was evidently intensified after the variability points in 2013 and 2014 in the southern and provincial areas, respectively, according to the derived values of entropy <i>En</i> and hyper-entropy <i>He</i>, which are basically to the contrary for the historical annual average temperature series in southern Anhui Province. (3) The obtained result was basically consistent with the practical statistics of historical hydrological and disaster data, indicating that the proposed research methodologies can be further applied in related variability diagnosis analyses of non-stationary hydrological variables.</p>","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 3","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11941455/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143729458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}