Related papers: Charge Transport in Interband Cascade Lasers: An A…
During the scheduled high luminosity upgrade of LHC, the world's largest particle physics accelerator at CERN, the position sensitive silicon detectors installed in the vertex and tracking part of the CMS experiment will face more intense…
Task vectors offer a compelling mechanism for accelerating inference in in-context learning (ICL) by distilling task-specific information into a single, reusable representation. Despite their empirical success, the underlying principles…
We study the Traffic Light Control (TLC) problem for a traffic network with multiple intersections in an artery, including the effect of transit delays for vehicles moving from one intersection to the next. The goal is to minimize the…
Continual learning approaches help deep neural network models adapt and learn incrementally by trying to solve catastrophic forgetting. However, whether these existing approaches, applied traditionally to image-based tasks, work with the…
The recent development of Quantum Cascade Lasers (QCLs) represents one of the biggest opportunities for the deployment of a new class of Free Space Optical (FSO) communication systems working in the mid-infrared (Mid-IR) wavelength range.…
Time-resolved spectroscopy-based pump-probe experiments performed on quantum cascade lasers (QCLs) exhibit an initial fast gain recovery followed by a slow tail such that the equilibrium gain is not recovered in a cavity round-trip time.…
Achieving low-threshold infrared stimulated emission in solution-processed quantum dots is critical to enable real-life application including photonic integrated circuits (PICs), LIDAR application and optical telecommunication. However,…
Lidar is a well known optical technology for measuring a target's range and radial velocity. We describe two lidar systems that use entanglement between transmitted signals and retained idlers to obtain significant quantum enhancements in…
We model carrier transport in photonic crystal lasers with lateral current injection through two-dimensional (2D) finite-volume simulations. Though such lasers can achieve ultra-low threshold currents, leakage paths reduce the carrier…
Semiconductor superlattices are interesting for two distinct reasons: the possibility to design their structure (band-width(s),doping, etc.) gives access to a large parameter space where different physical phenomena can be explored.…
Laser-driven (LD) ion acceleration has been explored in a newly constructed short focal length beamline at the BELLA petawatt facility (interaction point 2, iP2). For applications utilizing such LD ion beams, a beam transport system is…
We use first-principle Quantum Monte-Carlo (QMC) simulations and numerical exact diagonalization to analyze the low-frequency charge carrier mobility within a simple tight-binding model of molecular organic semiconductors on a…
Transport of ions in molecular-scale confined spaces is central to all aspects of life and technology: into a crack, it may break steel within days; through a membrane separator, it determines the efficiency of electrochemical energy…
In-context learning (ICL) is one of the most powerful and most unexpected capabilities to emerge in recent transformer-based large language models (LLMs). Yet the mechanisms that underlie it are poorly understood. In this paper, we…
Transformers have demonstrated remarkable in-context learning (ICL) capabilities. The strong ICL performance of transformers is commonly believed to arise from their ability to implicitly execute certain algorithms on the context, thereby…
In-Context Learning (ICL) is an important paradigm for adapting Large Language Models (LLMs) to downstream tasks through a few demonstrations. Despite the great success of ICL, the limitation of the demonstration number may lead to…
Laser-driven ion beams have gained considerable attention for their potential use in multidisciplinary research and technology. Pre-clinical studies into their radiobiological effectiveness have established the prospect of using…
In-context learning (ICL) is a type of prompting where a transformer model operates on a sequence of (input, output) examples and performs inference on-the-fly. In this work, we formalize in-context learning as an algorithm learning problem…
This article comprehensively reviews recent developments and research on deep learning-based (DL-based) techniques for integrated sensing and communication (ISAC) systems. ISAC, which combines sensing and communication functionalities, is…
Accurate modeling of charge transport and both thermal and luminescent radiation is crucial to the understanding and design of radiative thermal energy converters. Charge carrier dynamics in semiconductors are well-described by the…