A Practical Influence Approximation for Privacy-Preserving Data Filtering in Federated Learning

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Distributed constraint optimization (DCOP) is a framework in which multiple agents with private constraints (or preferences) cooperate to achieve a common goal optimally. DCOPs are applicable in several multi-agent coordination/allocation problems, such as ...

Mechanical resonators are widely used in sensors, transducers and optomechanical systems, where mechanical dissipation sets the ultimate limit to performance. Over the past 15 years, the quality factors in strained mechanical resonators have increased by f ...

A range of behavioral and contextual factors, including eating and drinking behavior, mood, social context, and other daily activities, can significantly impact an individual's quality of life and overall well-being. Therefore, inferring everyday life aspe ...

A key challenge across many disciplines is to extract meaningful information from data which is often obscured by noise. These datasets are typically represented as large matrices. Given the current trend of ever-increasing data volumes, with datasets grow ...

The COVID-19 pandemic has led to a significant increase in working from home worldwide, making the workfrom-home (WFH) setting a crucial context for studying the influence of indoor environmental quality (IEQ) on workers' well-being and productivity. A nar ...

The 70 kDa heat shock protein (Hsp70) chaperones control protein homeostasis in all ATP-containing cellular compartments. J-domain proteins (JDPs) coevolved with Hsp70s to trigger ATP hydrolysis and catalytically upload various substrate polypeptides in ne ...

At room temperature, mechanical motion driven by the quantum backaction of light has been observed only in pioneering experiments in which an optical restoring force controls the oscillator stiffness1,2. For solid-state mechanical resonators in which oscil ...

This paper proposes a data-driven control design method for nonlinear systems that builds upon the Koopman operator framework. In particular, the Koopman operator is used to lift the nonlinear dynamics to a higher-dimensional space where the so-called obse ...

The performance of machine learning algorithms is conditioned by the availability of training datasets, which is especially true for the field of nondestructive evaluation. Here we propose one reconfigurable specimen instead of numerous reference specimens ...

Electric vehicles (EVs) are becoming more popular due to environmental consciousness. The limited availability of charging stations (CSs), compared to the number of EVs on the road, has led to increased range anxiety and a higher frequency of CS queries du ...

Implanted medical devices (IMDs) have been widely developed to support the monitoring and recording of biological data inside the body or brain. Wirelessly powered IMDs, a subset of implantable electronics, have been proposed to eliminate the limitations r ...

A Practical Influence Approximation for Privacy-Preserving Data Filtering in Federated Learning

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Differentially private multi-agent constraint optimization

Ultrahigh-quality-factor micro- and nanomechanical resonators using dissipation dilution

Generalization and Personalization of Machine Learning for Multimodal Mobile Sensing in Everyday Life

Spectral Estimators for High-Dimensional Matrix Inference

A state-of-the-art, systematic review of indoor environmental quality studies in work-from-home settings

Autorepression of yeast Hsp70 cochaperones by intramolecular interactions involving their J-domains

Room-temperature quantum optomechanics using an ultralow noise cavity

Data-Driven Control Synthesis using Koopman Operator: A Robust Approach

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

A Privacy-Preserving Querying Mechanism with High Utility for Electric Vehicles

Integrated Wireless Power, Data Communication, and Thermal Sensing System for Autonomous Multisite Brain Implants