Papers
Research papers from arXiv and related sources
Accurate Data-Based State Estimation from Power Loads Inference in Electric Power Grids
Accurate state estimation is a crucial requirement for the reliable operation and control of electric power systems. Here, we construct a data-driven, numerical method to infer missing power load v...
Philippe Jacquod, Laurent Pagnier, Daniel J. Gauthier
Dual-Tree LLM-Enhanced Negative Sampling for Implicit Collaborative Filtering
Negative sampling is a pivotal technique in implicit collaborative filtering (CF) recommendation, enabling efficient and effective training by contrasting observed interactions with sampled unobser...
Jiayi Wu, Zhengyu Wu, Xunkai Li, Rong-Hua Li, Guoren Wang
Reflections on the Future of Statistics Education in a Technological Era
Keeping pace with rapidly evolving technology is a key challenge in teaching statistics. To equip students with essential skills for the modern workplace, educators must integrate relevant technolo...
Craig Alexander, Jennifer Gaskell, Vinny Davies
Online FDR Controlling procedures for statistical SIS Model and its application to COVID19 data
We propose an online false discovery rate (FDR) controlling method based on conditional local FDR (LIS), designed for infectious disease datasets that are discrete and exhibit complex dependencies....
Seohwa Hwang, Junyong Park
Mathematical derivation and verification of the amplitude of LISA's interferometric signals on an ultra-stable interferometer testbed
The Laser Interferometer Space Antenna (LISA) mission aims to detect gravitational waves by interferometrically measuring the change of separation between free-falling test masses (TMs). LISA's int...
Alvise Pizzella, Lennart Wissel, Miguel Dovale-Alvarez, Pablo Martinez Cano, Rodrigo Garcia Alvar...
Weak error approximation for rough and Gaussian mean-reverting stochastic volatility models
For a class of stochastic models with Gaussian and rough mean-reverting volatility that embeds the genuine rough Stein-Stein model, we study the weak approximation rate when using a Euler type sche...
Aurélien Alfonsi, Ahmed Kebaier
Thinking by Subtraction: Confidence-Driven Contrastive Decoding for LLM Reasoning
Recent work on test-time scaling for large language model (LLM) reasoning typically assumes that allocating more inference-time computation uniformly improves correctness. However, prior studies sh...
Lexiang Tang, Weihao Gao, Bingchen Zhao, Lu Ma, Qiao jin, Bang Yang, Yuexian Zou
[Re] Benchmarking LLM Capabilities in Negotiation through Scoreable Games
Large Language Models (LLMs) demonstrate significant potential in multi-agent negotiation tasks, yet evaluation in this domain remains challenging due to a lack of robust and generalizable benchmar...
Jorge Carrasco Pollo, Ioannis Kapetangeorgis, Joshua Rosenthal, John Hua Yao
Properties of Liquid Crystalline Elastomer Foams
We investigate how controlled foaming alters the mechanical dissipation of liquid crystalline elastomers (LCEs). Using thermal expandable microspheres, we generate homogeneous foams with precisely ...
Oliver Dai, Andrew Terentjev, Eugene M. Terentjev
The Dispersed Matter Planet Project Sample -- Detection limits, Occurrence Rates and New Planets
DMPP is a radial-velocity survey that aims to detect planets around stars exhibiting anomalous activity signatures, consistent with the presence of close-in evaporating planets. Here, we report the...
Matthew R. Standing, John R. Barnes, Carole A. Haswell, Adam T. Stevenson, João P. Faria, Erwan Q...
Metrology of Complexity and Implications for the Study of the Emergence of Life
One of the longest standing open problems in science is how life arises from non-living matter. If it is possible to measure this transition in the lab, then it might be possible to understand the ...
Sara Imari Walker
Art Notions in the Age of (Mis)anthropic AI
In this paper, I take the cultural effects of generative artificial intelligence (generative AI) as a context for examining a broader perspective of AI's impact on contemporary art notions. After t...
Dejan Grba
Role and Identity Work of Software Engineering Professionals in the Generative AI Era
The adoption of Generative AI (GenAI) suggests major changes for software engineering, including technical aspects but also human aspects of the professionals involved. One of these aspects is how ...
Jorge Melegati
Computer Vision in Tactical AI Art
AI art comprises a spectrum of creative endeavors that emerge from and respond to the development of artificial intelligence (AI), the expansion of AI-powered economies, and their influence on cult...
Dejan Grba
Noise Mitigation Methods for Digital Visible Light Communication
Visible Light Communication (VLC) using Light Emitting Diodes (LEDs) has gained attention due to its low power consumption, long lifetime, and fast response. However, VLC suffers from optical noise...
Wataru Uemura, Takumi Hamano
Capabilities Ain't All You Need: Measuring Propensities in AI
AI evaluation has primarily focused on measuring capabilities, with formal approaches inspired from Item Response Theory (IRT) being increasingly applied. Yet propensities - the tendencies of model...
Daniel Romero-Alvarado, Fernando Martínez-Plumed, Lorenzo Pacchiardi, Hugo Save, Siddhesh Milind ...
SeedFlood: A Step Toward Scalable Decentralized Training of LLMs
This work presents a new approach to decentralized training-SeedFlood-designed to scale for large models across complex network topologies and achieve global consensus with minimal communication ov...
Jihun Kim, Namhoon Lee
Experimental realization of a photonic weighted graph state for quantum metrology
Quantum metrology seeks to push the boundaries of measurement precision by harnessing quantum phenomena. Conventional methods often rely on maximally entangled resources, with states that are usual...
Unathi Skosana, Byron Alexander, Changhyoup Lee, Mark Tame
Can AI Lower the Barrier to Cybersecurity? A Human-Centered Mixed-Methods Study of Novice CTF Learning
Capture-the-Flag (CTF) competitions serve as gateways into offensive cybersecurity, yet they often present steep barriers for novices due to complex toolchains and opaque workflows. Recently, agent...
Cathrin Schachner, Jasmin Wachter
Click it or Leave it: Detecting and Spoiling Clickbait with Informativeness Measures and Large Language Models
Clickbait headlines degrade the quality of online information and undermine user trust. We present a hybrid approach to clickbait detection that combines transformer-based text embeddings with ling...
Wojciech Michaluk, Tymoteusz Urban, Mateusz Kubita, Soveatin Kuntur, Anna Wroblewska