Designed Semiconductor Network Random Lasers

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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 ...

Accurately estimating model performance poses a significant challenge, particularly in scenarios where the source and target domains follow different data distributions. Most existing performance prediction methods heavily rely on the source data in their ...

We demonstrate a fully hybrid integrated electrooptic comb generator based on a dispersion engineered thin-film Lithium Tantalate microresonator featuring robust turnkey operation and record-broad >200nm span covering the entire telecommunications S, C, an ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

Achieving the regime of single-photon nonlinearities in photonic devices by just exploiting the intrinsic high-order susceptibilities of conventional materials would open the door to practical semiconductor-based quantum photonic technologies. Here we show ...

III-V semiconductors outperform Si in many optoelectronics applications due to their high carrier mobility, efficient light emission and absorption processes, and the possibility to engineer their band gap through alloying. However, complementing Si techno ...

Although often regarded a childhood toy, the design of paper airplanes is subtly complex. The design space and mapping from geometry to distance flown is highly nonlinear and probabilistic where a single airplane design exhibits a multitude of trajectory f ...

This paper presents our approach to predicting future error-related events in a robot-mediated gamified phys- ical training activity for stroke patients. The ability to predict future error under such conditions suggests the existence of distinguishable fe ...

Second harmonic generation microscopy is used to image interfaces or non-centrosymmetric structures and molecules. Two-photon fluorescence microscopy can image molecules within cells without using markers. Thus those technics have lead to advances in our u ...

We introduce a simulation method for dynamic portfolio valuation and risk management building on machine learning with kernels. We learn the dynamic value process of a portfolio from a finite sample of its cumulative cash flow. The learned value process is ...

The increasing use of ground-source heat pumps (GSHPs) for heating and cooling of buildings raises questions regarding the technical potential of GSHPs and their impact on the temperature in the shallow subsurface. In this paper, we develop a method using ...

Machine Learning (ML) is on the rise in medicine, promising improved diagnostic, therapeutic and prognostic clinical tools. While these technological innovations are bound to transform health care, they also bring new ethical concerns to the forefront. One ...

Designed Semiconductor Network Random Lasers

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

Source-Free Domain-Invariant Performance Prediction

Broadband and turnkey Pockels micro-comb with Monolithic Microwave Resonator

Adversarial Training for Jamming-Robust Channel Estimation in OFDM Systems

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

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

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

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

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

Learning of physical systems: from inference to control

Single-photon nonlinearities and blockade from a strongly driven photonic molecule

Nanoscale Mapping of Light Emission in Nanospade-Based InGaAs Quantum Wells Integrated on Si(100): Implications for Dual Light-Emitting Devices

Robotic automation and unsupervised cluster assisted modeling for solving the forward and reverse design problem of paper airplanes

Predictive Analysis of Errors During Robot-Mediated Gamified Training

A tunable femtosecond fiber laser source for multiphoton emission microscopy

Machine learning with kernels for portfolio valuation and risk management

Using Machine Learning to estimate the technical potential of shallow ground-source heat pumps with thermal interference

Towards a pragmatist dealing with algorithmic bias in medical machine learning