Source-Free Domain-Invariant Performance Prediction

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Reflective writing is known as a useful method in learning sciences to improve the metacognitive skills of students. However, students struggle to structure their reflections properly, limiting the possible learning gains. Previous works in educational tec ...

Data analyses in particle physics rely on an accurate simulation of particle collisions and a detailed simulation of detector effects to extract physics knowledge from the recorded data. Event generators together with a geant-based simulation of the detect ...

The rapid advent of high-throughput omics technologies has created an exponential growth in biological data, often outpacing our ability to derive molecular insights. Large-language models have shown a way out of this data deluge in natural language proces ...

Operators from various industries have been pushing the adoption of wireless sensing nodes for industrial monitoring, and such efforts have produced sizeable condition monitoring datasets that can be used to build diagnosis algorithms capable of warning ma ...

Monitoring forests, in particular their response to climate and land use change, requires studying long time scales. While efficient deep learning methods have been developed to process short time series of satellite imagery, leveraging long time series of ...

Vision Transformers (ViTs) demonstrate remarkable performance in image classification through visual-token interaction learning, particularly when equipped with local information via region attention or convolutions. Although such architectures improve the ...

Orthogonal frequency-division multiplexing (OFDM) is widely used to mitigate inter-symbol interference (ISI) from multipath fading. However, the open nature of wireless OFDM systems makes them vulnerable to jamming attacks. In this context, pilot jamming i ...

Distributed learning has become a promising computational parallelism paradigm that enables a wide scope of intelligent applications from the Internet of Things (IoT) to autonomous driving and the healthcare industry. This paper studies distributed learnin ...

This paper presents a comparative analysis of two methodologies for estimating unknown parameters in a Vehicle Dynamic Model (VDM)-based sensor fusion framework for small drones. Focusing on a delta-wing drone, we conduct open-air wind tunnel experiments t ...

Conventional lasers typically support a well‐defined comb of modes. Coupling many resonators together to form larger complex cavities enables the design of the spatial and spectral distribution of modes, for sensitive and controllable on‐chip light sources ...

As a follow-up to the CEA-EPFL PETALE experimental program on stainless steel nuclear data, the EPFL initiated an open-loop pile-oscillation experimental program in the CROCUS reactor: BLOOM. A reproduction of the critical experiments of PETALE, the progra ...

Physics-informed machine learning (PIML) is a set of methods and tools that systematically integrate machine learning (ML) algorithms with physical constraints and abstract mathematical models developed in scientific and engineering domains. As opposed to ...

Most recent test-time adaptation methods focus on only classification tasks, use specialized network architectures, destroy model calibration or rely on lightweight information from the source domain. To tackle these issues, this paper proposes a novel Tes ...

We consider the problem of learning a target function corresponding to a deep, extensive-width, non-linear neural network with random Gaussian weights. We consider the asymptotic limit where the number of samples, the input dimension and the network width ...

Purpose: To perform one-shot retinal artery and vein segmentation with cross-modality artery-vein (AV) soft -label pretraining.Design: Cross-sectional study.Subjects: The study included 6479 color fundus photography (CFP) and arterial-venous fundus fluores ...

The limiting distributions of statistics used to test hypotheses about parameters on the boundary of their domains may provide very poor approximations to the finite-sample behaviour of these statistics, even for very large samples. We review theoretical w ...

To characterize a physical system to behave as desired, either its underlying governing rules must be known a priori or the system itself be accurately measured. The complexity of full measurements of the system scales with its size. When exposed to real-w ...

Source-Free Domain-Invariant Performance Prediction

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Metacognition meets AI: Empowering reflective writing with large language models

Reweighting simulated events using machine-learning techniques in the CMS experiment

Towards multimodal foundation models in molecular cell biology

Federated learning with uncertainty-based client clustering for fleet-wide fault diagnosis

Multi-temporal forest monitoring in the Swiss Alps with knowledge-guided deep learning

AdaNCA: Neural Cellular Automata as Adaptors for More Robust Vision Transformer

Adversarial Training for Jamming-Robust Channel Estimation in OFDM Systems

One-Bit Byzantine-Tolerant Distributed Learning via Over-The-Air Computation

Identifying Aerodynamics of Delta-Wing Drones for Model-Based Navigation: A Comparative Study

Designed Semiconductor Network Random Lasers

Design of an Open-Loop Pile-Oscillation Program in the CROCUS Reactor

Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems

TeSLA: Test-Time Self-Learning With Automatic Adversarial Augmentation

Bayes-optimal Learning of Deep Random Networks of Extensive-width

Forest Drought Impact Prediction based on Spatio-temporal Satellite Imagery and Weather Forecasts -- A Spatio-Temporal Approach using Convolutional LSTM Models

One-shot Retinal Artery and Vein Segmentation via Cross-modality Pretraining

Improved inference for a boundary parameter

Learning of physical systems: from inference to control