November 2024 arXiv papers — page 86

Stavros Venikoudis, Federico De Lillo, Kamiel Janssens, Jishnu Suresh

One potential factor that could impede searches for the stochastic gravitational-wave background (SGWB), arising from the incoherent superposition of a multitude of weak and unresolvable gravitational-wave signals in the Universe, is correlated magnetic noise at Earth-scale distances, such as the Schumann resonances. As the sensitivity of terrestrial detecto

Yuzong Chen, Ahmed F. AbouElhamayed, Xilai Dai, Yang Wang

Large language models (LLMs) have demonstrated remarkable performance across various machine learning tasks. Yet the substantial memory footprint of LLMs significantly hinders their deployment. In this paper, we improve the accessibility of LLMs through BitMoD, an algorithm-hardware co-design solution that enables efficient LLM acceleration at low weight pre

Lavakumar Addepalli, P. K. Pathak

We investigate the phenomenon of correlated emission lasing in a coherently driven single quantum dot coupled to a bimodal photonic crystal cavity, utilizing a master equation to describe the system dynamics. To account for exciton-phonon interactions, we incorporate a non-perturbative approach through a polaron transformed master equation. By analyzing fluc

Yi Huang, Yeonju Go, Jin Huang, Shuhang Li

High-energy large-scale particle colliders generate data at extraordinary rates. Developing real-time high-throughput data compression algorithms to reduce data volume and meet the bandwidth requirement for storage has become increasingly critical. Deep learning is a promising technology that can address this challenging topic. At the newly constructed sPHEN

Gen Ye

The standard model of cosmology, $Λ$CDM, is plagued by various tensions with recent observations. The most pressing one is the Hubble tension, where the observed expansion rate of our Universe is $5σ$ larger than that predicted by $Λ$CDM. Recently, the result from DESI also reveals a new inconsistency between the baryon acoustic oscillation and cosmic microw

Linn E. J. Eriksson, Chao-Chin Yang, Philip J. Armitage

Growing planets interact with their surrounding protoplanetary disk, generating feedback effects that may promote or suppress nearby planet formation. We study how spiral waves launched by planets affect the motion and collisional evolution of particles in the disk. To this end, we perform local 2D hydrodynamical simulations that include a gap-opening planet

DaDong Jiang, Zhihui Ke, Xiaobo Zhou, Zhi Hou

Dynamic scene reconstruction is a long-term challenge in 3D vision. Recent methods extend 3D Gaussian Splatting to dynamic scenes via additional deformation fields and apply explicit constraints like motion flow to guide the deformation. However, they learn motion changes from individual timestamps independently, making it challenging to reconstruct complex

Noga Alon, Nick Gravin, Tristan Pollner, Aviad Rubinstein

We investigate prophet inequalities with competitive ratios approaching $1$, seeking to generalize $k$-uniform matroids. We first show that large girth does not suffice: for all $k$, there exists a matroid of girth $\geq k$ and a prophet inequality instance on that matroid whose optimal competitive ratio is $\frac{1}{2}$. Next, we show $k$-fold matroid union

Mir Faris, Syeda Aynul Karim, Md. Juniadul Islam

In order to address issues with manual vote counting during election procedures, this study intends to examine the viability of using advanced image processing techniques for automated voter counting. The study aims to shed light on how automated systems that utilize cutting-edge technologies like OpenCV, CVZone, and the MOG2 algorithm could greatly increase

Xinchen Luo, Jiangxia Cao, Tianyu Sun, Jinkai Yu

In recent years, with the significant evolution of multi-modal large models, many recommender researchers realized the potential of multi-modal information for user interest modeling. In industry, a wide-used modeling architecture is a cascading paradigm: (1) first pre-training a multi-modal model to provide omnipotent representations for downstream services

Lars Nieradzik, Henrike Stephani, Jördis Sieburg-Rockel, Stephanie Helmling

Wood species identification plays a crucial role in various industries, from ensuring the legality of timber products to advancing ecological conservation efforts. This paper introduces WoodYOLO, a novel object detection algorithm specifically designed for microscopic wood fiber analysis. Our approach adapts the YOLO architecture to address the challenges po

Pierre Delaunay, Xavier Bouthillier, Olivier Breuleux, Satya Ortiz-Gagné

AI workloads, particularly those driven by deep learning, are introducing novel usage patterns to high-performance computing (HPC) systems that are not comprehensively captured by standard HPC benchmarks. As one of the largest academic research centers dedicated to deep learning, Mila identified the need to develop a custom benchmarking suite to address the

Tianyi Qu, Jiangchuan Du, Xinran Li

Randomized experiments have been the gold standard for drawing causal inference. The conventional model-based approach has been one of the most popular ways for analyzing treatment effects from randomized experiments, which is often carried through inference for certain model parameters. In this paper, we provide a systematic investigation of model-based ana

German Gritsai, Anastasia Voznyuk, Ildar Khabutdinov, Andrey Grabovoy

The paper describes a system designed by Advacheck team to recognise machine-generated and human-written texts in the monolingual subtask of GenAI Detection Task 1 competition. Our developed system is a multi-task architecture with shared Transformer Encoder between several classification heads. One head is responsible for binary classification between human

Philip D. Mannheim

We present an exact solution to standard model cosmological perturbation theory in a matter-dominated, adiabatic, hydrodynamic era. The solution is in the form of hypergeometric functions. While such functions can oscillate with the sound velocity, they can only do so at high frequency. There is thus a maximum wavelength to these oscillations, with this maxi

Connor W. Herron, Christian Runyon, Isaac Pressgrove, Benjamin C. Beiter

In this work, the joint-control strategy is presented for the humanoid robot, PANDORA, whose structural components are designed to be compliant. As opposed to contemporary approaches which design the elasticity internal to the actuator housing, PANDORA's structural components are designed to be compliant under load or, in other words, structurally elastic. T

Junyong Kim, Hanwen Ren, Ahmed H. Qureshi

Retrieving target objects from unknown, confined spaces remains a challenging task that requires integrated, task-driven active sensing and rearrangement planning. Previous approaches have independently addressed active sensing and rearrangement planning, limiting their practicality in real-world scenarios. This paper presents a new, integrated heuristic-bas

Gabriel Behrendt, Zachary I. Bell, Matthew Hale

Existing works on multi-agent time-varying optimization allow agents to asynchronously communicate and/or compute, but do not allow asynchronous sampling of objectives. Sampling can be difficult to synchronize, and we therefore present a multi-agent optimization framework that allows asynchrony in sampling, communications, and computations for time-varying q

Allison Huang, Yulu Niki Pi, Carlos Mougan

We explore how large language models (LLMs) can be influenced by prompting them to alter their initial decisions and align them with established ethical frameworks. Our study is based on two experiments designed to assess the susceptibility of LLMs to moral persuasion. In the first experiment, we examine the susceptibility to moral ambiguity by evaluating a

Malte Luttermann, Ralf Möller, Marcel Gehrke

Lifted probabilistic inference exploits symmetries in a probabilistic model to allow for tractable probabilistic inference with respect to domain sizes of logical variables. We found that the current state-of-the-art algorithm to construct a lifted representation in form of a parametric factor graph misses symmetries between factors that are exchangeable but

Saugata Basu, Laxmi Parida

We obtain upper bounds, independent of the ambient dimension, for the number of realizable zero-nonzero patterns and (over ordered fields) sign conditions of a finite family of polynomials $\mathcal P$ restricted to an algebraic subset $V$ of affine or projective space. The bounds depend only on $\mathrm{card}(\mathcal P)$ and the degrees of the polynomials

Marianna Pensky

Many statistical applications, such as the Principal Component Analysis, matrix completion, tensor regression and many others, rely on accurate estimation of leading eigenvectors of a matrix. The Davis-Kahan theorem is known to be instrumental for bounding above the distances between matrices $U$ and $\widehat{U}$ of population eigenvectors and their sample

Ziyi Zhang, Li Shen, Sen Zhang, Deheng Ye

Few-step diffusion models enable efficient high-resolution image synthesis but struggle to align with specific downstream objectives due to limitations of existing reinforcement learning (RL) methods in low-step regimes with limited state spaces and suboptimal sample quality. To address this, we propose Stepwise Diffusion Policy Optimization (SDPO), a novel

Jesús López-Fernández, Unai Fernández-Plazaola, José F. París, Luis Díez

In this work we present the results of a measurement campaign carried out in the Mediterranean sea aimed at characterizing the underwater acoustic channel in a wideband at ultrasonic frequencies centered at 80 kHz with a width of 96 kHz, covering two octaves from 32 to 128 kHz. So far, these type of wideband measurements are not found in the literature. Peri

Claas Abert, Florian Bruckner, Andrey Voronov, Martin Lang

We present NeuralMag, a flexible and high-performance open-source Python library for micromagnetic simulations. NeuralMag leverages modern machine learning frameworks, such as PyTorch and JAX, to perform efficient tensor operations on various parallel hardware, including CPUs, GPUs, and TPUs. The library implements a novel nodal finite-difference discretizat

Sang Yong Song, Chengyun Hua, Gábor B. Halász, Wonhee Ko

To realize braiding of vortex lines and understand the basic properties of the energy landscape for vortex motion, precise manipulation of superconducting vortices on the nanoscale is required. Here, we reveal that a localized trapping potential powerful enough to pull in the vortex line can be created with nanoscale precision on the surface of an FeSe super

Hugo D. Pasinato

This study constitutes the second phase of a research endeavor aimed at evaluating the feasibility of employing Long Short-Term Memory (LSTM) neural networks as a replacement for Reynolds-Averaged Navier-Stokes (RANS) turbulence models. In the initial phase of this investigation (titled Modeling Turbulent Flows with LSTM Neural Networks, arXiv:2307.13784v1 [

Daniel Bienstock, Tongtong Chen

We study convex optimization problems where disjoint blocks of variables are controlled by binary indicator variables that are also subject to conditions, e.g., cardinality. Several classes of important examples can be formulated in such a way that both the objective and the constraints are separable convex quadratics. We describe a family of polynomial-time

Bernard Helffer, Corentin Léna

In this paper, we study the first eigenvalue of the magnetic Laplacian with Neumann boundary conditions in the unit disk $\mathbb D$ in $\mathbb R^2$. There is a rather complete asymptotic analysis when the constant magnetic field tends to $+\infty$ and some inequalities seem to hold for any value of this magnetic field, leading to rather simple conjectures.

Martina Frau, Poetri Sonya Tarabunga, Mario Collura, Emanuele Tirrito

Understanding how entanglement can be reduced through simple operations is crucial for both classical and quantum algorithms. We investigate the entanglement properties of lattice models hosting conformal field theories cooled via local Clifford operations, a procedure we refer to as stabilizer disentangling. We uncover two distinct regimes: a constant gain

Milad Masroor, Tahir Hassan, Yu Tian, Kevin Wells

Deep learning has achieved remarkable success in image classification and segmentation tasks. However, fairness concerns persist, as models often exhibit biases that disproportionately affect demographic groups defined by sensitive attributes such as race, gender, or age. Existing bias-mitigation techniques, including Subgroup Re-balancing, Adversarial Train

Zoltan Fodor, Antoine Gerardin, Laurent Lellouch, Kalman K. Szabo

We present a lattice QCD calculation of the hadronic light-by-light scattering contribution to the anomalous magnetic moment of the muon using $N_f=2+1+1$ flavors of staggered quarks with masses tuned to their physical values. Our final result, in the continuum limit, reads $a_{\mu}^{\mathrm{hlbl}} = 125.5(11.6)_{\mathrm{stat}}(0.4)_{\mathrm{syst}} \times 10

Yaseen Abd-Elhaleem, Michal Dory, Merav Parter, Oren Weimann

The dual of a planar graph $G$ is a planar graph $G^*$ that has a vertex for each face of $G$ and an edge for each pair of adjacent faces of $G$. The profound relationship between a planar graph and its dual has been the algorithmic basis for solving numerous (centralized) classical problems on planar graphs. In the distributed setting however, the only use

Hongjun Chen, Wencheng Han, Huan Zheng, Jianbing Shen

Recent advancements in sRGB-to-RAW de-rendering have increasingly emphasized metadata-driven approaches to reconstruct RAW data from sRGB images, supplemented by partial RAW information. In image-based de-rendering, metadata is commonly obtained through sampling, whereas in video tasks, it is typically derived from the initial frame. The distinct metadata re

Pierluca Carenza, Roman Pasechnik, Zhi-Wei Wang

Dark Yang-Mills sectors that confine to form stable composite states, known as glueballs, have been traditionally proposed as a potential explanation for cosmological Dark Matter (DM). Earlier studies have established viability of the lightest scalar glueball as a possible DM candidate. In this work, we explore a whole class of effective composite sectors in

Mahsa Nasri, Mehmet Kosa, Leanne Chukoskie, Mohsen Moghaddam

Virtual Reality (VR) has been a beneficial training tool in fields such as advanced manufacturing. However, users may experience a high cognitive load due to various factors, such as the use of VR hardware or tasks within the VR environment. Studies have shown that eye-tracking has the potential to detect cognitive load, but in the context of VR and complex

Marco Flores

By utilizing elementary techniques from toric geometry, we prove sharp cohomological vanishing results for line bundles defined on the blow-up of projective space $\mathbb{P}^n$ at no more than $n+1$ points.

Mingchao Qi, Yuanjin Li, Xing Liu, Zhengxiong Liu

Developing general robotic systems capable of manipulating in unstructured environments is a significant challenge, particularly as the tasks involved are typically long-horizon and rich-contact, requiring efficient skill transfer across different task scenarios. To address these challenges, we propose knowledge graph-based skill library construction method.

Zhenyu Wen, Wanglei Feng, Di Wu, Haozhen Hu

Federated Learning (FL), as a mainstream privacy-preserving machine learning paradigm, offers promising solutions for privacy-critical domains such as healthcare and finance. Although extensive efforts have been dedicated from both academia and industry to improve the vanilla FL, little work focuses on the data pricing mechanism. In contrast to the straightf

Pierre Bouvet, Carlo Bevilacqua, Yogeshwari Ambekar, Giuseppe Antonacci

Brillouin Light Scattering (BLS) spectroscopy is a non-invasive, non-contact, label-free optical technique that can provide information on the mechanical properties of a material on the sub-micron scale. Over the last decade it has seen increased applications in the life sciences, driven by the observed significance of mechanical properties in biological pro

Andrei Egorov, Andrei Vesnin

The Vol-Det Conjecture, formulated by Champanerkar, Kofman and Purcell, states that there exists a specific inequality connecting the hyperbolic volume of an alternating link and its determinant. Among the classes of links for which this conjecture holds are all alternating hyperbolic knots with at most 16 crossings, 2-bridge links, and links that are closur

Giorgio Nicoletti, Daniel M. Busiello

Biological and artificial systems encode information through several complex nonlinear operations, making their exact study a formidable challenge. These internal mechanisms often take place across multiple timescales and process external signals to enable functional output responses. In this work, we focus on two widely implemented paradigms: nonlinear summ

Marcel Scherer

We prove that for every compact, convex subset $K\subset\mathbb{R}^2$ the operator system $A(K)$, consisting of all continuous affine functions on $K$, is hyperrigid in the C*-algebra $C(\mathrm{ex}(K))$. In particular, this result implies that the weak and strong operator topologies coincide on the set $$ \{ T\in\mathcal{B}(H);\ T\ \mathrm{normal}\ \mathrm{

J. A. Muniz, M. Stone, D. T. Stack, M. Jaffe

Arrays of optically trapped neutral atoms are a promising architecture for the realization of quantum computers. In order to run increasingly complex algorithms, it is advantageous to demonstrate high-fidelity and flexible gates between long-lived and highly coherent qubit states. In this work, we demonstrate a universal high-fidelity gate-set with individua

Tao Fan, Yan Kang, Guoqiang Ma, Lixin Fan

By adapting Large Language Models (LLMs) to domain-specific tasks or enriching them with domain-specific knowledge, we can fully harness the capabilities of LLMs. Nonetheless, a gap persists in achieving simultaneous mutual enhancement between the server's LLM and the downstream clients' Small Language Models (SLMs). To address this, we propose FedCoLLM, a n

Ruichuan An, Sihan Yang, Renrui Zhang, Ming Lu

Current vision-language models (VLMs) show exceptional abilities across diverse tasks, such as visual question answering. To enhance user experience, recent studies have investigated VLM personalization to understand user-provided concepts. However, they mainly focus on single concepts, neglecting the existence and interplay of multiple concepts, which limit

Ruikai Chen

This paper explores quadratic forms over finite fields with associated Artin-Schreier curves. Specifically, we investigate quadratic forms of $\mathbb F_{q^n}/\mathbb F_q$ represented by polynomials over $\mathbb F_{q^n}$ with $q$ odd, characterizing them using certain matrices defined by coefficients of the polynomials. In particular, a comprehensive treatm

Willem A. Baan, Tao AN

Astrophysical outflows are ubiquitous across cosmic scales, from stellar to galactic systems. While diverse launching mechanisms have been proposed, we demonstrate that these outflows share a fundamental commonality: their morphology follows the physics of pressure-confined supersonic flows. By extending classical deLaval nozzle theory to account for ambient

Roozbeh Sarenche, Alireza Aghabagherloo, Svetla Nikova, Bart Preneel

The security of Bitcoin protocols is deeply dependent on the incentives provided to miners, which come from a combination of block rewards and transaction fees. As Bitcoin experiences more halving events, the protocol reward converges to zero, making transaction fees the primary source of miner rewards. This shift in Bitcoin's incentivization mechanism, whic

Felipe Pinto, Ricardo C Heitzer, Eitan Dvorquez, Roberto Rodriguez

Quantum spin Hall (QSH) insulators are materials with nontrivial topological properties, characterized by helical edge currents. In 2D strips, the application of a bias voltage along the edge generates a magnetization that can be measured using quantum sensors and magnetometry techniques. In this work, we calculate the magnetic field in the vicinity of the e

Vladmir Sicca, Tianxiang Xia, Mathïs Fédérico, Philip John Gorinski

We introduce a new symbolic solver for geometry, called Newclid, which is based on AlphaGeometry. Newclid contains a symbolic solver called DDARN (derived from DDAR-Newclid), which is a significant refactoring and upgrade of AlphaGeometry's DDAR symbolic solver by being more user-friendly - both for the end user as well as for a programmer wishing to extend

Inam Mirza, Juraj Sládek, Yoann Levy, Alexander V. Bulgakov

Using different laser irradiation patterns to modify of silicon surface, it has been demonstrated that, at rather small overlapping between irradiation spots, highly regular laser-induced periodic surface structures (LIPSS) can be produced already starting from the second laser pulse, provided that polarization direction coincides with the scanning direction

Songyu Sun, Xiao Dong, Yanliang Sha, Quan Chen

High-speed serial links are fundamental to energy-efficient and high-performance computing systems such as artificial intelligence, 5G mobile and automotive, enabling low-latency and high-bandwidth communication. Transmitters (TXs) within these links are key to signal quality, while their modeling presents challenges due to nonlinear behavior and dynamic int

Zixuan He, Charalambos D. Charalambous, Photios A. Stavrou

This paper deals with the computation of a non-asymptotic lower bound by means of the nonanticipative rate-distortion function (NRDF) on the discrete-time zero-delay variable-rate lossy compression problem for discrete Markov sources with per-stage, single-letter distortion. First, we derive a new information structure of the NRDF for Markov sources and sing

Chenyang Jiang, Donggyu Kim, Alejandra Quintos, Yazhen Wang

Reinforcement Learning (RL) has proven effective in solving complex decision-making tasks across various domains, but challenges remain in continuous-time settings, particularly when state dynamics are governed by stochastic differential equations (SDEs) with jump components. In this paper, we address this challenge by introducing the Mean-Square Bipower Var

Jacques Fries, Javier Diaz, Marie Jardat, Ignacio Pagonabarraga

The formation of condensates is now considered as a major organization principle of eukaryotic cells. Several studies have recently shown that the properties of these condensates are affected by enzymatic reactions. We propose here a simple generic model to study the interplay between two enzyme populations and a two-state protein. In one state, the protein

Yasuo Oda, Merritt P. Losert, Jason P. Kestner

We present a scalable protocol for suppressing errors during electron spin shuttling in silicon quantum dots. The approach maps the valley Hamiltonian to a Landau-Zener problem to model the nonadiabatic dynamics in regions of small valley splitting. An optimization refines the shuttling velocity profile over a single small segment of the shuttling path. The

Jinhao Jiang, Zhipeng Chen, Yingqian Min, Jie Chen

Recently, test-time scaling has garnered significant attention from the research community, largely due to the substantial advancements of the o1 model released by OpenAI. By allocating more computational resources during the inference phase, large language models~(LLMs) can extensively explore the solution space by generating more thought tokens or diverse

Francesco Pappone, Federico Califano, Marco Tafani

Accurately determining the geographic origin of mineral samples is pivotal for applications in geology, mineralogy, and material science. Leveraging the comprehensive Raman spectral data from the RRUFF database, this study introduces a novel machine learning framework aimed at geolocating mineral specimens at the country level. We employ a one-dimensional Co

Jinwoo Lee, Kyogu Lee

Music captioning has emerged as a promising task, fueled by the advent of advanced language generation models. However, the evaluation of music captioning relies heavily on traditional metrics such as BLEU, METEOR, and ROUGE which were developed for other domains, without proper justification for their use in this new field. We present cases where traditiona

Chan Ho Park, Ka Leong Cheng, Zhicheng Wang, Qifeng Chen

Generalizable Neural Radiance Field (GNeRF) across scenes has been proven to be an effective way to avoid per-scene optimization by representing a scene with deep image features of source images. However, despite its potential for real-world applications, there has been limited research on the robustness of GNeRFs to different types of degradation present in

Werner Krauth

We discuss Hamiltonian Monte Carlo (HMC) and event-chain Monte Carlo (ECMC) for the one-dimensional chain of particles with harmonic interactions and benchmark them against local reversible Metropolis algorithms. While HMC achieves considerable speedup with respect to local reversible Monte Carlo algorithms, its autocorrelation functions of global observable

Ike Obi, Rohan Pant, Srishti Shekhar Agrawal, Maham Ghazanfar

LLMs are increasingly fine-tuned using RLHF datasets to align them with human preferences and values. However, very limited research has investigated which specific human values are operationalized through these datasets. In this paper, we introduce Value Imprint, a framework for auditing and classifying the human values embedded within RLHF datasets. To inv

Dimitrios Mataragkas, Alexandros Vasilopoulos, Nikolaos G. Fytas, Dong-Hee Kim

We report on numerical simulations of the two-dimensional spin-$1$ Blume-Capel ferromagnet embedded in a triangular lattice. Utilizing a range of Monte Carlo and finite-size scaling techniques, we explore several critical aspects along the crystal field--temperature ($\Delta, T$) transition line. Wang-Landau simulations measuring the joint density of states

Liangqi Lei, Keke Gai, Jing Yu, Liehuang Zhu

The personalization techniques of diffusion models succeed in generating images with specific concepts. This ability also poses great threats to copyright protection and network security since malicious users can generate unauthorized content and disinformation relevant to a target concept. Model watermarking is an effective solution to trace the malicious g

Yuhong Chen, Xiaobing Dai, Martin Buss, Fangzhou Liu

In this work, we develop an analytical framework that integrates opinion dynamics with a recommendation system. By incorporating elements such as collaborative filtering, we provide a precise characterization of how recommendation systems shape interpersonal interactions and influence opinion formation. Moreover, the property of the coevolution of both opini

Jakob Schoenleber

The threshold region for double-deeply virtual Compton scattering (DDVCS) is discussed. I derive a resummation formula for the (partonic) threshold logarithms in the flavor non-singlet case. The resummations can be done by using (re)factorization theorems for the coefficient functions near the partonic thresholds. As a byproduct, we obtain the leading term i

Duncan V. Mifsud, Sándor Góbi, Péter Herczku, Béla Sulik

The radiation chemistry and physics of solid N2O have been increasingly studied due to its potential presence on the surfaces of cold, outer Solar System bodies. However, to date, no study has investigated systematically the influence of temperature on this chemistry and physics. In this present study, crystalline N2O ices were irradiated using 2 keV electro

Alessio Notari, Michele Redi, Andrea Tesi

We critically review the evidence for time-varying dark energy from recent Baryon Acoustic Oscillations (BAO) and Supernova (SN) observations. First, we show that such evidence is present at the 3$\sigma$ level, even without the new BAO data from the dark energy Spectroscopic Instrument (DESI), by instead using BAO data from the dark energy Survey (DES), com

Hao Yin, Mark Hutter, Christian Wagner, F. Stefan Tautz

To take full advantage of twisted bilayers of graphene or other two-dimensional materials, it is essential to precisely control the twist angle between the stacked layers, as this parameter determines the properties of the heterostructure. In this context, a growth routine using borazine as a surfactant molecule on SiC(0001) surfaces has been reported, leadi

Xianlong Wang, Hewen Pan, Hangtao Zhang, Minghui Li

Robotic manipulation policies are increasingly empowered by \textit{large language models} (LLMs) and \textit{vision-language models} (VLMs), leveraging their understanding and perception capabilities. Recently, inference-time attacks against robotic manipulation have been extensively studied, yet backdoor attacks targeting model supply chain security in rob

Zeinab Nezami, Maryam Hafeez, Karim Djemame, Syed Ali Raza Zaidi

6G's AI native vision of embedding advance intelligence in the network while bringing it closer to the user requires a systematic evaluation of Generative AI (GenAI) models on edge devices. Rapidly emerging solutions based on Open RAN (ORAN) and Network-in-a-Box strongly advocate the use of low-cost, off-the-shelf components for simpler and efficient deploym

Junjie Yang, Matthieu Labeau, Florence d'Alché-Buc

Structured prediction involves learning to predict complex structures rather than simple scalar values. The main challenge arises from the non-Euclidean nature of the output space, which generally requires relaxing the problem formulation. Surrogate methods build on kernel-induced losses or more generally, loss functions admitting an Implicit Loss Embedding,

Jiawei Li, Xinyue Liang, Junlong Zhang, Yizhe Yang

Process supervision enhances the performance of large language models in reasoning tasks by providing feedback at each step of chain-of-thought reasoning. However, due to the lack of effective process supervision methods, even advanced large language models are prone to logical errors and redundant reasoning. We claim that the effectiveness of process superv

Ke-Jian He, Guo-Ping Li, Chen-Yu Yang, Xiao-Xiong Zeng

By employing ray-tracing techniques, we investigate the shadow images of rotating Bardeen black holes surrounded by perfect fluid dark matter. In this work, two models are considered for the background light source, namely the celestial light source model and the thin accretion disk model. Regarding the celestial light source, the investigation focuses on th

Yichen Shen, Ping-Yen Hsieh, Sashank Kaushik Sridhar, Samantha Feldman

Squeezed light, with its quantum noise reduction capabilities, has emerged as a powerful resource in quantum information processing and precision metrology. To reach noise reduction levels such that a quantum advantage is achieved, off-chip squeezers are typically used. The development of on-chip squeezed light sources, particularly in nanophotonic platforms

Haoyi Jia, Abhilasha Dave, Julia Gonski, Ryan Herbst

To fully exploit the physics potential of current and future high energy particle colliders, machine learning (ML) can be implemented in detector electronics for intelligent data processing and acquisition. The implementation of ML in real-time at colliders requires very low latencies that are unachievable with a software-based approach, requiring optimizati

Hui Zhang, Fu Liu

Among adversarial attacks against sequential recommender systems, model extraction attacks represent a method to attack sequential recommendation models without prior knowledge. Existing research has primarily concentrated on the adversary's execution of black-box attacks through data-free model extraction. However, a significant gap remains in the literatur

Jacopo Borga, Sky Cao, Jasper Shogren-Knaak

We give a sum over weighted planar surfaces formula for Wilson loop expectations in the large-$N$ limit of strongly coupled lattice Yang-Mills theory, in any dimension. The weights of each surface are simple and expressed in terms of products of signed Catalan numbers. In establishing our results, the main novelty is to convert a recursive relation for Wilso

Tomohiro Ohigashi, Kazushi Maruo, Takashi Sozu, Masahiko Gosho

When incorporating historical control data into the analysis of current randomized controlled trial data, it is critical to account for differences between the datasets. When the cause of the difference is an unmeasured factor and adjustment for observed covariates only is insufficient, it is desirable to use a dynamic borrowing method that reduces the impac

Bangshuai Zhu, Dexi Shao, Cuiying Pei, Qi Wang

The abundant chemical compositions in ternary hydrides bring much more possibility to explore high temperature superconductors under lower pressure. Here we constructed 115 ternary hydrides on the basis of the elements substitution using 16 metal elements within 5 reported prototype structures. We conducted a three-step approach to screen and study these can

Michal Hubert, Tomáš Maleček, Kyo-Hoon Ahn, Martin Míšek

Following the recent observation of anomalous Hall effect in antiferromagnetic hexagonal MnTe thin films, related phenomena at finite frequencies have come into focus. Magnetic circular dichroism (MCD) is the key material property here. In the x-ray range, the XMCD has already been demonstrated and used to visualise domains via photoemission electron microsc

Antonio Norelli

Rooted in the explosion of deep learning over the past decade, this thesis spans from AlphaGo to ChatGPT to empirically examine the fundamental concepts needed to realize the vision of an artificial scientist: a machine with the capacity to autonomously generate original research and contribute to the expansion of human knowledge. The investigation begins wi

Riccardo Panico, Giorgio Ciliberto, Giovanni Italo Martone, Thibault Congy

We present a combined experimental and theoretical investigation of the formation and decay kinetics of vortices in two dimensional, compressible quantum turbulence. We follow the temporal evolution of a quantum fluid of exciton polaritons, hybrid light matter quasiparticles, and measure both phase and modulus of the order parameter in the turbulent regime.

Carolina Arata, François Arleo, Benjamin Audurier, Alberto Baldisseri

This document summarizes the prospective physics plans of the French QCD and Heavy-Ion community, including the experimental programs at the LHC Run 5 and beyond and future colliders at CERN, within the context of the French contribution to the update of the European Strategy in Particle Physics (ESPPU 2025), as discussed in the workshop on European Strategy

Ding Zhang, Jane Downer, Can Chen, Ren Wang

Graph classification is essential for understanding complex biological systems, where molecular structures and interactions are naturally represented as graphs. Traditional graph neural networks (GNNs) perform well on static tasks but struggle in dynamic settings due to catastrophic forgetting. We present Perturbed and Sparsified Continual Graph Learning (PS

Ross Gardiner, Sareh Rowands, Benno I. Simmons

Camera traps, combined with AI, have emerged as a way to achieve automated, scalable biodiversity monitoring. However, the passive infrared (PIR) sensors that trigger camera traps are poorly suited for detecting small, fast-moving ectotherms such as insects. Insects comprise over half of all animal species and are key components of ecosystems and agriculture

Lijie Hu, Chenyang Ren, Huanyi Xie, Khouloud Saadi

Contrastive learning, commonly applied in large-scale multimodal models, often relies on data from diverse and often unreliable sources, which can include misaligned or mislabeled text-image pairs. This frequently leads to robustness issues and hallucinations, ultimately causing performance degradation. Data valuation is an efficient way to detect and trace

Yan Liu, Zhihua Guo, Zhihao Ma, Shao-Ming Fei

The Kirkwood-Dirac distribution, serving as an informationally complete representation of a quantum state, has recently garnered increasing attention. We investigate the Kirkwood-Dirac classicality with respect to mutually unbiased bases. For prime dimensional Hilbert spaces, {we demonstrate that a quantum state exhibits Kirkwood-Dirac classicality for two d

Asmaa Hakami, Raneem Alqarni, Asmaa Muqaibil, Nahed Alowidi

Websites have become increasingly important in people's lives, fulfilling a wide range of needs across various domains such as shopping, education, news, and booking. Among the most heavily used website categories are online shopping platforms, whose usage has particularly increased during the COVID-19 pandemic, as they eliminate time and geographical barrie

Arash Pourdamghani, Chen Avin, Robert Sama, Maryam Shiran

Distributed systems often serve dynamic workloads and resource demands evolve over time. Such a temporal behavior stands in contrast to the static and demand-oblivious nature of most data structures used by these systems. In this paper, we are particularly interested in consistent hashing, a fundamental building block in many large distributed systems. Our w

Hoomaan Maskan, Paniz Halvachi, Suvrit Sra, Alp Yurtsever

We introduce an extension of the Difference of Convex Algorithm (DCA) in the form of a randomized block coordinate approach for problems with separable structure. For $n$ coordinate-blocks and $k$ iterations, our main result proves a non-asymptotic convergence rate of $O(n/k)$ in expectation, with respect to a stationarity measure based on a Forward-Backward

Kieran Calvert, Karmen Grizelj, Andrey Krutov, Pavle Pandžić

We show how to use Clifford algebra techniques to describe the de Rham cohomology ring of equal rank compact symmetric spaces $G/K$. In particular, for $G/K=U(n)/U(k)\times U(n-k)$, we obtain a new way of multiplying Schur polynomials, i.e., computing the Littlewood-Richardson coefficients. The corresponding multiplication on the Clifford algebra side is, in

Noam Zilberstein, Alexandra Silva, Joseph Tassarotti

Although randomization has long been used in distributed computing, formal methods for reasoning about probabilistic concurrent programs have lagged behind. No existing program logics can express specifications about the full distributions of outcomes resulting from programs that are both probabilistic and concurrent. To address this, we introduce Probabilis

Miriam Patricolo, Marcel Gievers, Kilian Fraboulet, Aiman Al-Eryani

We extend the recently introduced single-boson exchange formulation to the computation of the self-energy from the Schwinger--Dyson equation (SDE). In particular, we derive its expression both in diagrammatic and in physical channels. The simple form of the single-boson exchange SDE, involving only the bosonic propagator and the fermion-boson vertex, but not

Antonio Pereira, Alba Villarino, Aser Cortines, Samuel Mugel

The application of Tensor Networks (TN) in quantum computing has shown promise, particularly for data loading. However, the assumption that data is readily available often renders the integration of TN techniques into Quantum Monte Carlo (QMC) inefficient, as complete probability distributions would have to be calculated classically. In this paper the tensor

Hui Chen, Yunhua Zhao, Kostadin Damevski

The changesets (or patches) that fix open source software vulnerabilities form critical datasets for various machine learning security-enhancing applications, such as automated vulnerability patching and silent fix detection. These patch datasets are derived from extensive collections of historical vulnerability fixes, maintained in databases like the Common

Nazanin Sedaghati, Masoud Kargar, Sina Abbaskhani

Human activity recognition, facilitated by smart devices, has recently garnered significant attention. Deep learning algorithms have become pivotal in daily activities, sports, and healthcare. Nevertheless, addressing the challenge of extracting features from sensor data processing necessitates the utilization of diverse algorithms in isolation, subsequently

Lasha Berezhiani, Gia Dvali, Otari Sakhelashvili

We present a formulation of coherent states as of consistent quantum description of classical configurations in the BRST-invariant quantization of electrodynamics. The quantization with proper gauge-fixing is performed on the vacuum of the theory, whereas other backgrounds are obtained as BRST-invariant coherent states. One of the key insights is the possibi

Julio Melio, Solenn Riedel, Ali Azadbakht, Silvana A. Caipa Cure

Catalytic microswimmers typically swim close to walls due to hydrodynamic and/or phoretic effects. The walls in turn are known to affect their propulsion, making it difficult to single out the contributions that stem from particle-based catalytic propulsion only, thereby preventing an understanding of the propulsion mechanism. Here, we use acoustic tweezers