Blogue
Hydrogen-poor Superluminous Supernovae with Bumpy Light Curves Powered by Precessing Magnetars
Recent observations and statistical studies have revealed that a significant fraction of hydrogen-poor superluminous supernovae (SLSNe-I) exhibit light curves that deviate from the smooth evolution predicted by the magnetar-powered model, instead showing one or more bumps after the primary peak. Cependant, the...
Monte Carlo simulation of GRB data to test Lorentz-invariance violation
Lorentz-invariance violation (LV) at energy scales approaching the Planck regime serves as a critical probe for understanding quantum gravity phenomenology. Astrophysical observations of gamma-ray bursts (GRBs) present a promising avenue for testing LV-induced spectral lag phenomena; however, interpretations are complicated by degeneracies...
Transfer Learning for High-dimensional Reduced Rank Time Series Models
The objective of transfer learning is to enhance estimation and inference in a target data by leveraging knowledge gained from additional sources. Recent studies have explored transfer learning for independent observations in complex, high-dimensional models assuming sparsity, yet research on time series...
You Sense Only Once Beneath: Ultra-Light Real-Time Underwater Object Detection
Despite the remarkable achievements in object detection, the model’s accuracy and efficiency still require further improvement under challenging underwater conditions, such as low image quality and limited computational resources. To address this, we propose an Ultra-Light Real-Time Underwater Object Detection framework, You...
A Time Series Analysis of Malware Uploads to Programming Language Ecosystems
Software ecosystems built around programming languages have greatly facilitated software development. At the same time, their security has increasingly been acknowledged as a problem. To this end, the paper examines the previously overlooked longitudinal aspects of software ecosystem security, focusing on malware...
Advancing Embodied Agent Security: From Safety Benchmarks to Input Moderation
Embodied agents exhibit immense potential across a multitude of domains, making the assurance of their behavioral safety a fundamental prerequisite for their widespread deployment. Cependant, existing research predominantly concentrates on the security of general large language models, lacking specialized methodologies for establishing...
Spin and energy diffusion vs. subdiffusion in disordered spin chains
While the high-temperature spin diffusion in spin chains with random local fields has been the subject of numerous studies concerning the phenomenon of many-body localization (MBL), the energy diffusion in the same models has been much less explored. We show that energy...
Riesz transform, function spaces and their applications on infinite dimensional compact groups
On a compact connected group $G$, consider the infinitesimal generator $-L$ of a central symmetric Gaussian convolution semigroup $(\mu_t)_{t>0}$. We establish several regularity results of the solution to the Poisson equation $LU=F$, both in strong and weak senses. To this end, we...
SeaLLM: Service-Aware and Latency-Optimized Resource Sharing for Large Language Model Inference
Large language models (LLMs) with different architectures and sizes have been developed. Serving each LLM with dedicated GPUs leads to resource waste and service inefficiency due to the varying demand of LLM requests. A common practice is to share multiple LLMs. Cependant,...
From predictions to confidence intervals: an empirical study of conformal prediction methods for in-context learning
Transformers have become a standard architecture in machine learning, demonstrating strong in-context learning (ICL) abilities that allow them to learn from the prompt at inference time. Cependant, uncertainty quantification for ICL remains an open challenge, particularly in noisy regression tasks. This paper...