Gradient free cooperative seeking of a moving source

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Reduced-order models (ROMs) are widely used in scientific computing to tackle high-dimensional systems. However, traditional ROM methods may only partially capture the intrinsic geometric characteristics of the data. These characteristics encompass the und ...

Approximate multipliers (AppMults) are widely employed in deep neural network (DNN) accelerators to reduce the area, delay, and power consumption. However, the inaccuracies of AppMults degrade DNN accuracy, necessitating a retraining process to recover acc ...

Recent years have been marked with the fast-pace diversification and increasing ubiquity of machine learning (ML) applications. Yet, a firm theoretical understanding of the surprising efficiency of neural networks (NNs) to learn from high-dimensional data ...

Motivated by policy gradient methods in the context of reinforcement learning, we derive the first large deviation rate function for the iterates generated by stochastic gradient descent for possibly non-convex objectives satisfying a Polyak-Łojasiewicz co ...

Lipschitz-constrained neural networks have several advantages over unconstrained ones and can be applied to a variety of problems, making them a topic of attention in the deep learning community. Unfortunately, it has been shown both theoretically and empi ...

Stretchable alternating current electroluminescent (ACEL) devices have a bright future in wearable electronics and soft robotics. Still, their market application is hindered by high operating voltages. The voltage can be reduced by increasing the relative ...

Koopmans spectral functionals are a powerful extension of Kohn-Sham density-functional theory (DFT) that enables the prediction of spectral properties with state-of-the-art accuracy. The success of these functionals relies on capturing the effects of elect ...

Machine-learning models based on a point-cloud representation of a physical object are ubiquitous in scientific applications and particularly well-suited to the atomic-scale description of molecules and materials. Among the many different approaches that h ...

Cross-view geo-localization aims at localizing a ground-level query image by matching it to its corresponding geo-referenced aerial view. In real-world scenarios, the task requires accommodating diverse ground images captured by users with varying orientat ...

This paper proposes a tower-foot grounding system model compatible with EMT programs which might be useful for the simulation of lightning transients in overhead lines. The proposed model is based on the solution of the telegrapher's equations and the appl ...

Traffic-responsive signal control is a cost-effective, easy-to-implement, network management strategy, bearing high potential to improve performance in heavily congested networks with dy-namic traffic characteristics. Max Pressure (MP) distributed control ...

Text-to-image models, such as Stable Diffusion, can generate high-quality images from simple textual prompts. With methods such as Textual Inversion, it is possible to expand the vocabulary of these models with additional concepts, by learning the vocabula ...

The tribocorrosion mechanisms of a Ti-Nb-Zr-Ta (TNZT) alloy rubbing against a zirconia ball in a Phosphate Buffered Solution (PBS) were analyzed by considering the mechanical and chemical interactions that lead to the material degradation. To understand th ...

One approach for describing spatiotemporal chaos is to study the unstable invariant sets embedded in the chaotic attractor of the system. While equilibria, periodic orbits, and invariant tori can be computed using existing methods, the numerical identifica ...

In healthcare, data privacy of patients regulations prohibits data from being moved outside the hospital, preventing international medical datasets from being centralized for AI training. Federated learning (FL) is a data privacy-focused method that trains ...

Finding optimal bidding strategies for generation units in electricity markets would result in higher profit. However, it is a challenging problem due to the system uncertainty which is due to the lack of knowledge of the strategies of other generation uni ...

In wearable-based human activity recognition (HAR) research, one of the major challenges is the large intra-class variability problem. The collected activity signal is often, if not always, coupled with noises or bias caused by personal, environmental, or ...

This paper contributes to the intersection of operations research and machine learning in the context of autonomous ridesharing. In this work, autonomous ridesharing operations are reproduced through an event-based simulation approach and are modeled as a ...

Simultaneous prediction of wrist and hand motions is essential for the natural interaction with hand prostheses. In this paper, we propose a novel multi-out Gaussian process (MOGP) model and a multi-task deep learning (MTDL) algorithm to achieve simultaneo ...

The convergence speed of machine learning models trained with Federated Learning is significantly affected by non-independent and identically distributed (non-IID) data partitions, even more so in a fully decentralized setting without a central server. In ...

Gradient free cooperative seeking of a moving source

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OPTIMAL TRANSPORT-INSPIRED DEEP LEARNING FRAMEWORK FOR SLOW-DECAYING KOLMOGOROV \bfitn-WIDTH PROBLEMS: EXPLOITING SINKHORN LOSS AND WASSERSTEIN KERNEL

Gradient Approximation of Approximate Multipliers for High-Accuracy Deep Neural Network Retraining

High-dimensional learning of narrow neural networks

A Large Deviations Perspective on Policy Gradient Algorithms

Improving Lipschitz-Constrained Neural Networks by Learning Activation Functions

High-Permittivity Polysiloxanes for Bright, Stretchable Electroluminescent Devices

Predicting electronic screening for fast Koopmans spectral functional calculations

Wigner kernels: Body-ordered equivariant machine learning without a basis

ConGeo: Robust Cross-View Geo-Localization Across Ground View Variations

Tower-foot grounding model for EMT programs based on transmission line theory and Marti's model

Two-layer adaptive signal control framework for large-scale dynamically-congested networks: Combining efficient Max Pressure with Perimeter Control

VETIM: Expanding the Vocabulary of Text-to-Image Models only with Text

Current and friction transients during tribocorrosion of a biomedical titanium alloy sliding against zirconia

Jacobian-free variational method for computing connecting orbits in nonlinear dynamical systems

Decentralized Federated Learning for Epileptic Seizures Detection in Low-Power Wearable Systems

Multi-agent reinforcement learning with graph convolutional neural networks for optimal bidding strategies of generation units in electricity markets

Learning Disentangled Behaviour Patterns for Wearable-based Human Activity Recognition

A machine learning-driven two-phase metaheuristic for autonomous ridesharing operations

Simultaneous Prediction of Wrist and Hand Motions via Wearable Ultrasound Sensing for Natural Control of Hand Prostheses

D-Cliques: Compensating for Data Heterogeneity with Topology in Decentralized Federated Learning