20 papers · ranked by Valyu relevance
Alexander Churbanov, Stephen Winters-Hilt
Background Ionic current blockade signal processing, for use in nanopore detection, offers a promising new way to analyze single molecule properties with potential implications for DNA sequencing. The α-Hemolysin transmembrane channel interacts with a translocating molecule in a nontrivial way, frequently evidenced by…
Sanvesh Srivastava, Glen DePalma, Chuanhai Liu
The family of Expectation-Maximization (EM) algorithms provides a general approach to fitting flexible models for large and complex data. The expectation (E) step of EM-type algorithms is time consuming in massive data applications because it requires multiple passes through the full data. We address this problem by…
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…
Yang Weng, Wendong Xiao, Lihua Xie
Distributed estimation of Gaussian mixtures has many applications in wireless sensor network (WSN), and its energy-efficient solution is still challenging. This paper presents a novel diffusion-based EM algorithm for this problem. A diffusion strategy is introduced for acquiring the global statistics in EM algorithm in…
Xin Wei, Chunguang Li, Liang Zhou, Li Zhao + 1 more
Distributed density estimation in sensor networks has received much attention due to its broad applicability. When encountering high-dimensional observations, a mixture of factor analyzers (MFA) is taken to replace mixture of Gaussians for describing the distributions of observations. In this paper, we study…
Natalia A. Schmid, Marwan Alkhweldi, Matthew C. Valenti
—The problem of distributed estimation of a parametric physical field is stated as a maximum likelihood estimation problem. Sensor observations are distorted by additive white Gaussian noise. Prior to data transmission, each sensor quantizes its observation to M levels. The quantized data are then communicated over…
Mengshuo Jia, Shaowei Huang, Zhiwen Wang, Chen Shen
—Building the conditional probability distribution of wind power forecast errors benefits both wind farms (WFs) and independent system operators (ISOs). Establishing the joint probability distribution of wind power and the corresponding forecast data of spatially correlated WFs is the foundation for deriving the…
Chanseok Park, Min Wang, Refah Mohammed Alotaibi, Hoda Rezk
A load-sharing system is defined as a parallel system whose load will be redistributed to its surviving components as each of the components fails in the system. Our focus is on making statistical inference of the parameters associated with the lifetime distribution of each component in the system. In this paper, we…
Thomas Macrina, Kisuk Lee, Ran Lu, Nicholas L. Turner + 42 more
3D electron microscopy (EM) has been successful at mapping invertebrate nervous systems, but the approach has been limited to small chunks of mammalian brains. To scale up to larger volumes, we have built a computational pipeline for processing petascale image datasets acquired by serial section EM, a popular form of…
Sami Ullah, Ghulam Abbas, Muhammad Waqas, Ziaul Haq Abbas + 3 more
'Shanshan Tu' 'Ibrahim A. Hameed' 'Omprakash Kaiwartya'] In Vehicular Adhoc Networks (VANETs), disseminating Emergency Messages (EMs) to a maximum number of vehicles with low latency and low packet loss is critical for road safety. However, avoiding the broadcast storm and dealing with large-scale dissemination of EMs…
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…
Lukas Hecker, Rebekka Rupprecht, Ludger Tebartz van Elst, Juergen Kornmeier
EEG and MEG are well-established non-invasive methods in neuroscientific research and clinical diagnostics. Both methods provide a high temporal but low spatial resolution of brain activity. In order to gain insight about the spatial dynamics of the EEG one has to solve the inverse problem, which means that more than…
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…
Yanqing Xu, Bo Wang, Enbin Song, Qingjiang Shi + 1 more
The traditional centralized baseband processing architecture is faced with the bottlenecks of high computation complexity and excessive fronthaul communication, especially when the number of antennas at the base station (BS) is large. To cope with these two challenges, the decentralized baseband processing (DPB)…
Parth Paritosh, Nikolay Atanasov, Sonia Martı́nez
—Developing efficient solutions for inference problems in intelligent sensor networks is crucial for the next generation of location, tracking, and mapping services. This paper develops a scalable distributed probabilistic inference algorithm that applies to continuous variables, intractable posteriors and large-scale…
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…
Joseph E. Arnold, Graeme M. Day
The synthesis and experimental testing of energetic materials can be hazardous, but their many industrial and military applications necessitate their constant research and development. We evaluate computational methods for predicting the crystal structures of energetic molecular organic crystals from their molecular…
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…