17 papers · ranked by Valyu relevance
Pedro Valdeira, Cláudia Soares, João Xavier
Expectation Maximization (EM) is the standard method to learn Gaussian mixtures. Yet its classic, centralized form is often infeasible, due to privacy concerns and computational and communication bottlenecks. Prior work dealt with data distributed by examples, horizontal partitioning, but we lack a counterpart for data…
Qiongxiu Li, Jaron Skovsted Gundersen, Katrine Tjell, Rafał Wiśniewski + 1 more
'Rafał Wiśniewski' 'Mads Græsbøll Christensen'] Privacy has become a major concern in machine learning. In fact, the federated learning is motivated by the privacy concern as it does not allow to transmit the private data but only intermediate updates. However, federated learning does not always guarantee…
Wendi Yan, Peihang Li, Jiarui Liu, Kaifeng Li + 4 more
Supercoupling in near-zero-index (NZI) media enables geometry-insensitive electromagnetic (EM) transport through narrow channels with near-zero phase delay. However, most studies have focused on single-channel, point-to-point configurations, leaving EM power-flow distribution in complex structures largely unexplored.…
Mohammed Rashid, Jeffrey A. Nanzer
Detector for Distributed Cooperative Spectrum Sensing Authors: ['Mohammed Rashid' 'Jeffrey A. Nanzer'] Distributed cooperative spectrum sensing usually involves a group of unlicensed secondary users (SUs) collaborating to detect the primary user (PU) in the channel, and thereby opportunistically utilize it without…
Ha Hoang, Minh-Huy Nguyen, Vinh Pham-Xuan
In traveling-wave planar-technology antennas, only a certain portion of excitation energy contributes to expected radiation. The remaining significant portion, referred to as residual electromagnetic (EM) energy, has negative effects on overall antenna performance. A systematic design methodology to mitigate such…
Battulga Gankhuu
Conditional matrix variate student t distribution was introduced by Battulga (2024a). In this paper, we propose a new version of the conditional matrix variate student t distribution. The paper provides EM algorithms, which estimate parameters of the conditional matrix variate student t distributions, including general…
Alexandre G. Urzhumtsev
In cryo-electron microscopy, a set of two-dimensional projections collected from different viewing directions may complicate image processing and subsequent model building if the distribution of these views is non-uniform. View distributions are traditionally represented as color-coded two-dimensional diagrams.…
Haotian Li
Machine learning and deep learning are novel and trending approaches to solving real-world scientific problems. Graph machine learning is dedicated to performing learning methods, such as graph neural networks, on non-Euclidean data such as graphs. Molecules, with their natural graph structures, could be analyzed by…
Chandra Thapa, Jun Wen Tang, Alsharif Abuadbba, Yansong Gao + 10 more
'Seyit Camtepe' 'Surya Nepal' 'Mahathir Almashor' 'Yifeng Zheng' 'Giancarlo Fortino' 'Sergio F. Ochoa' 'Weiming Shen' 'Antonio Liotta' 'Yanjun Shi' 'Jonice Oliveira'] The use of artificial intelligence (AI) to detect phishing emails is primarily dependent on large-scale centralized datasets, which has opened it up to a…
Liuyuan He, Ruohua Shi, Wenyao Wang, Yu Cai + 1 more
Accurate analysis of electron microscopy (EM) images is essential for exploring nanoscale biological structures, yet data heterogeneity and fragmented workflows hinder scalable insights. Pretrained on large, diverse datasets, image foundation models provide a robust framework for learning transferable representations…
Shuo Wang, Yongcai Wang, Deying Li, Qianchuan Zhao + 1 more
For a network of robots working in a specific environment, relative localization among robots is the basis for accomplishing various upper-level tasks. To avoid the latency and fragility of long-range or multi-hop communication, distributed relative localization algorithms, in which robots take local measurements and…
David Silva-Sánchez, Erik H. Thiede, Roy R. Lederman, Pilar Cossio
Biomolecules are inherently dynamic, and understanding their conformational ensemble distributions is essential for understanding their dynamics and biological roles. Cryo-electron microscopy (cryo-EM), a technique that images individual biomolecules frozen in a thin layer of amorphous ice, has emerged as a leading…
Erhan Karakoca, Güneş Karabulut Kurt, Ali Görçin
—Due to the unique channel characteristics of Terahertz (THz), comprehensive propagation channel modeling is essential to understand the spectrum and develop reliable communication systems in these bands. In this work, we propose the utilization of the hierarchical Dirichlet process Gamma mixture model (DPGMM) to…
Koffka Khan, Hai Dong
Introduction: Federated Learning (FL) is a distributed machine learning paradigm where a global model is collaboratively trained across multiple decentralized clients without exchanging raw data. This is especially important in sensor networks and edge intelligence, where data privacy, bandwidth constraints, and data…
Swier Garst, Julian Dekker, Marcel Reinders
Federated learning is an upcoming machine learning paradigm which allows data from multiple sources to be used for training of classifiers without the data leaving the source it originally resides. This can be highly valuable for use cases such as medical research, where gathering data at a central location can be…
Authors not listed
Electrochemical impedance spectroscopy (EIS) coupled with distribution of relaxation times (DRT) analysis is a robust framework for characterizing electrochemical systems. However, DRT deconvolution is often plagued by spurious peaks, hindering accurate process identification and quantitative parameter estimation. To…
Authors not listed
Protein conformational landscapes contain the functionally relevant information useful for understanding biological processes. Mapping out conformational landscapes provides valuable insights into protein behaviors and biological phenomena, and has relevance to therapeutic design. While experimental structural biology…