October 2024 arXiv papers — page 16

Géza Csima, Jenő Szirmai

In the present paper we deal with non-constant curvature Thurston geometries \cite{M97}, \cite{S}, \cite{Sz22-3},\cite{W06}. We define and determine the generalized trans\-lation-like Apollonius surfaces and thus also bisector surfaces as a special case. Moreover, we give a possible definition of the "surface of a translation-like triangle" in each investiga

Jeongyeon Hwang, Junyoung Park, Hyejin Park, Dongwoo Kim

Retrieval-Augmented Generation (RAG) is an effective approach to enhance the factual accuracy of large language models (LLMs) by retrieving information from external databases, which are typically composed of diverse sources, to supplement the limited internal knowledge of LLMs. However, the standard RAG often risks retrieving incorrect information, as it re

Wei Dong, Yuan Sun, Yiting Yang, Xing Zhang

A common strategy for Parameter-Efficient Fine-Tuning (PEFT) of pre-trained Vision Transformers (ViTs) involves adapting the model to downstream tasks by learning a low-rank adaptation matrix. This matrix is decomposed into a product of down-projection and up-projection matrices, with the bottleneck dimensionality being crucial for reducing the number of lea

Matthew Jenssen, Will Perkins, Aditya Potukuchi, Michael Simkin

We study the following combinatorial counting and sampling problems: can we efficiently sample from the Erd\H{o}s-R\'{e}nyi random graph $G(n,p)$ conditioned on triangle-freeness? Can we efficiently approximate the probability that $G(n,p)$ is triangle-free? These are prototypical instances of forbidden substructure problems ubiquitous in combinatorics. The

Ankita Kumari Jain, Nitish Sharma, Madhav Kanda, Nipun Batra

Respiratory illnesses are a significant global health burden. Respiratory illnesses, primarily Chronic obstructive pulmonary disease (COPD), is the seventh leading cause of poor health worldwide and the third leading cause of death worldwide, causing 3.23 million deaths in 2019, necessitating early identification and diagnosis for effective mitigation. Among

Yizhen Luo, Zikun Nie, Massimo Hong, Suyuan Zhao

Studying protein mutations within amino acid sequences holds tremendous significance in life sciences. Protein language models (PLMs) have demonstrated strong capabilities in broad biological applications. However, due to architectural design and lack of supervision, PLMs model mutations implicitly with evolutionary plausibility, which is not satisfactory to

Ola Rønning, Eric Nalisnick, Christophe Ley, Padhraic Smyth

Stein variational gradient descent (SVGD) [Liu and Wang, 2016] performs approximate Bayesian inference by representing the posterior with a set of particles. However, SVGD suffers from variance collapse, i.e. poor predictions due to underestimating uncertainty [Ba et al., 2021], even for moderately-dimensional models such as small Bayesian neural networks (B

Carlos Martinez-Ranero, Javier Utreras

We prove, assuming resolution of singularities in positive characteristic, an analogue of Siegel's theorem on sum of squares in positive characteristic. The method of proof combines techniques from central simple algebras with model theory and builds on work of Anscombe, Dittmann and Fehm. As an application, we show that, for each finite field $\mathbb{F}$ o

Ankita Nandi, Krishil Gandhi, Mahendra Pratap Singh, Shantanu Chakrabartty

Diverse computing paradigms have emerged to meet the growing needs for intelligent energy-efficient systems. The Margin Propagation (MP) framework, being one such initiative in the analog computing domain, stands out due to its scalability across biasing conditions, temperatures, and diminishing process technology nodes. However, the lack of digital-like aut

Tim Huege, Oliver Krömer

A promising approach to detect high-energy tau neutrinos is through the measurement of impulsive radio emission from horizontal air showers initiated in the Earth's atmosphere. Observations at frequencies between 30 and 80 MHz seem particularly promising -- if high-gain antennas focused at the horizon and blocking out as much as possible of the noisy sky are

Tom A. Lamb, Adam Davies, Alasdair Paren, Philip H. S. Torr

Despite the success of Instruction Tuning (IT) in training large language models (LLMs), such models often leverage spurious or biased features learnt from their training data and can become misaligned, leading to undesired behaviours. While existing techniques can steer model behaviour at inference-time, they are often post-hoc and do not embed steering as

Antoine Tordeux, Tim M. Julitz, Isabelle Müller, Zikai Zhang

In the era of Industry 4.0, system reliability engineering faces both challenges and opportunities. On the one hand, the complexity of cyber-physical systems, the integration of novel numerical technologies, and the handling of large amounts of data pose new difficulties for ensuring system reliability. On the other hand, innovations such as AI-driven progno

Michał Bobula, Tomasz Pawłowski

We study the causal structure for spherically symmetric dust collapse within a model of effective loop quantum gravity in midisuperspace framework. We develop a general strategy (working beyond the dynamical model of our consideration) for constructing double null coordinates, allowing the extraction of conformal diagrams within single coordinate charts. Wit

Panagiotis Tolias, Tobias Dornheim, Jan Vorberger

The density-density correlations of the non-interacting finite temperature electron gas are discussed in detail. Starting from the ideal linear density response function and utilizing general relations from linear response theory, known and novel expressions are derived for the pair correlation function, static structure factor, dynamic structure factor, the

Mohammadreza Bakhshizadeh Mohajer, Luca Barletta, Daniela Tuninetti, Alessandro Tomasoni

Integrated sensing and communication is regarded as a key enabler for next-generation wireless networks. To optimize the transmitted waveform for both sensing and communication, various performance metrics must be considered. This work focuses on sensing, and specifically on the mean square error (MSE) of channel estimation. Given the complexity of deriving

Nicola Menci, Anjan Ananda Sen, Marco Castellano

Recent observations by JWST yield a large abundance of luminous galaxies at $z\gtrsim 10$ compared to that expected in the CDM scenario based on extrapolations of the star formation efficiency measured at lower redshifts. While several astrophysical processes can be responsible for such observations, here we explore to what extent such an effect can be roote

Yujin Wang, Tianyi Xu, Fan Zhang, Tianfan Xue

Image Signal Processors (ISPs) convert raw sensor signals into digital images, which significantly influence the image quality and the performance of downstream computer vision tasks. Designing ISP pipeline and tuning ISP parameters are two key steps for building an imaging and vision system. To find optimal ISP configurations, recent works use deep neural n

Hanyang Chen, Yang Jiang, Shengnan Guo, Xiaowei Mao

The application of reinforcement learning in traffic signal control (TSC) has been extensively researched and yielded notable achievements. However, most existing works for TSC assume that traffic data from all surrounding intersections is fully and continuously available through sensors. In real-world applications, this assumption often fails due to sensor

Jose A. Guridi, Cristobal Cheyre, Qian Yang

Natural language processing (NLP) tools have the potential to boost civic participation and enhance democratic processes because they can significantly increase governments' capacity to gather and analyze citizen opinions. However, their adoption in government remains limited, and harnessing their benefits while preventing unintended consequences remains a c

Ilia Negri, Maura Mezzetti

A discussion on the readiness of Italian universities to address gender-related issues from a regional standpoint is proposed. A statistical analysis is conducted on data of all scholars enrolled in Italian universities from 2000 to 2023 to investigate why the glass ceiling of the full professor position remains so challenging to break in almost all scientif

Antoine Schnepf, Karim Kassab, Jean-Yves Franceschi, Laurent Caraffa

While pre-trained image autoencoders are increasingly utilized in computer vision, the application of inverse graphics in 2D latent spaces has been under-explored. Yet, besides reducing the training and rendering complexity, applying inverse graphics in the latent space enables a valuable interoperability with other latent-based 2D methods. The major challen

Shu Lin, Jiayuan Tian

It is usually believed that physics in off-equilibrium state characterized by hydrodynamic gradient can be equivalently studied using equilibrium state with suitable metric perturbation. We scrutinize this assumption using chiral kinetic theory in curved space, focusing on spin response to hydrodynamic gradient. Two effects of metric perturbation have been i

Siyu Lu, Jixiang Zhang, Min Xi, Nian Li

Plasmonic nanomaterials offer a direct and effective approach to harnessing solar energy. Specifically, plasmonic semiconductors enable a highly efficient light-to-heat conversion process, outperforming noble metals in stability, cost-effectiveness, and accessibility. In this study, a composite 3D printing filament (T-TPU), composed of titanium nitride (TiN)

Nikolay Bazhenov, Vittorio Cipriani, Sanjay Jain, Luca San Mauro

In the last years there has been a growing interest in the study of learning problems associated with algebraic structures. The framework we use models the scenario in which a learner is given larger and larger fragments of a structure from a given target family and is required to output an hypothesis about the structure's isomorphism type. So far researcher

Jonas Becker

In an era where single large language models have dominated the landscape of artificial intelligence for years, multi-agent systems arise as new protagonists in conversational task-solving. While previous studies have showcased their potential in reasoning tasks and creative endeavors, an analysis of their limitations concerning the conversational paradigms

Thien-Minh Nguyen, Ziyu Cao, Kailai Li, William Talbot

In this paper, we propose a third-order, i.e., white-noise-on-jerk, Gaussian Process (GP) Trajectory Representation (TR) framework for continuous-time (CT) motion estimation (ME) tasks. Our framework features a unified trajectory representation that encapsulates the kinematic models of both $SO(3)\times\mathbb{R}^3$ and $SE(3)$ pose representations. This enc

Colin Jahel, Pierre Perruchaud

The classical de Finetti Theorem classifies the $\mathrm{Sym}(\mathbb N)$-invariant probability measures on $[0,1]^{\mathbb N}$. More precisely it states that those invariant measures are combinations of measures of the form $\nu^{\otimes\mathbb N}$ where $\nu$ is a measure on $[0,1]$. Recently, Jahel--Tsankov generalized this theorem showing that under cond

Chiara Bigi, Matteo Dürrnagel, Lennart Klebl, Armando Consiglio

Among many-body instabilities in correlated quantum systems, electronic nematicity, defined by the spontaneous breaking of rotational symmetry, has emerged as a critical phenomenon, particularly within high-temperature superconductors. Recently, this behavior has been identified in CsTi$_3$Bi$_5$, a member of the AV$_3$Sb$_5$ (A = K, Rb, Cs) kagome family, r

Thi Lien Nguyen, Bao Quoc Tang

Large time dynamics of reaction-diffusion systems modeling some irreversible reaction networks are investigated. Depending on initial masses, these networks possibly possess boundary equilibria, where some of the chemical concentrations are completely used up. In the absence of these equilibria, we show an explicit convergence to equilibrium by a modified en

Yihao Wu, Di Zhao, Jingfeng Zhang, Yun Sing Koh

Reliable re-identification of individuals within large wildlife populations is crucial for biological studies, ecological research, and wildlife conservation. Classic computer vision techniques offer a promising direction for Animal Re-identification (Animal ReID), but their backbones' close-set nature limits their applicability and generalizability. Despite

Stefan Zeppetzauer, Leonardo Assis Morais, Xin He, Gerard Milburn

A driven linear oscillator and a feedback mechanism are two necessary elements of any classical periodic clock. Here, we introduce a novel, fully quantum clock using a driven oscillator in the quantum regime and coherent quantum feedback. We show that if we treat the model semiclassically, this system supports limit cycles, or self-sustained oscillations, as

Bora Caglayan, Mingxue Wang, John D. Kelleher, Shen Fei

NL2SQL (Natural Language to Structured Query Language) transformation has seen wide adoption in Business Intelligence (BI) applications in recent years. However, existing NL2SQL benchmarks are not suitable for production BI scenarios, as they are not designed for common business intelligence questions. To address this gap, we have developed a new benchmark f

Swagata Bhunia, Soumyadip Chatterjee, Ritam Sarkar, Dhiman Nag

The demand for GaN Nanowires (NWs)-based optoelectronic devices has rapidly increased over the past few years due to its superior crystalline quality compare to their planar counterparts. However, NWs-based devices face significant challenges because of number of surface states, basal plane stacking faults and coalescence related defect states. While the ori

Yazhong Jiang, Lisong Shi, Chih-Yung Wen

The upwind conservation element and solution element (CESE) scheme is an alternative discontinuity-capturing numerical approach to solving hyperbolic conservation laws. To evaluate the numerical properties of this spatiotemporal coupled scheme, a formal analysis is conducted on the upwind CESE discretization applied to the linear advection problem. The modif

Mingxian Li, Hao Sun, Yingtie Lei, Xiaofeng Zhang

Document images are often degraded by various stains, significantly impacting their readability and hindering downstream applications such as document digitization and analysis. The absence of a comprehensive stained document dataset has limited the effectiveness of existing document enhancement methods in removing stains while preserving fine-grained detail

J. Kluson

We study Born-Infeld inspired gravity in covariant canonical formalism. We determine corresponding Hamiltonian and equations of motion.

Diego Córdoba, Luis Martínez-Zoroa

We establish the existence of smooth, finite-energy solutions to the 2D incompressible porous media equation (IPM), with a compactly supported uniformly smooth source, which develop singularities in finite time.

Zihang Qiu, Chaojie Li, Zhongyang Wang, Renyou Xie

Accurate prediction helps to achieve supply-demand balance in energy systems, supporting decision-making and scheduling. Traditional models, lacking AI-assisted automation, rely on experts, incur high costs, and struggle with sparse data prediction. To address these challenges, we propose the Energy Forecasting Large Language Model (EF-LLM), which integrates

Michiel Van Kenhove, Maximilian Seidler, Friedrich Vandenberghe, Warre Dujardin

The rapid expansion of Internet of Things (IoT), edge, and embedded devices in the past decade has introduced numerous challenges in terms of security and configuration management. Simultaneously, advances in cloud-native development practices have greatly enhanced the development experience and facilitated quicker updates, thereby enhancing application secu

Romina Wild, Felix Wodaczek, Vittorio Del Tatto, Bingqing Cheng

Feature selection is essential in the analysis of molecular systems and many other fields, but several uncertainties remain: What is the optimal number of features for a simplified, interpretable model that retains essential information? How should features with different units be aligned, and how should their relative importance be weighted? Here, we introd

Semin Kim, Jaehoon Yoo, Jinwoo Kim, Yeonwoo Cha

In this work, we investigate a method for simulation-free training of Neural Ordinary Differential Equations (NODEs) for learning deterministic mappings between paired data. Despite the analogy of NODEs as continuous-depth residual networks, their application in typical supervised learning tasks has not been popular, mainly due to the large number of functio

Yihua Shao, Yan Gu, Siyu Chen, Haiyang Liu

Large language models (LLMs) show impressive performance in solving complex language tasks. However, its large number of parameters presents significant challenges for the deployment. So, compressing LLMs to low bits can enable to deploy on resource-constrained devices. To address this problem, we propose gradient-aware weight quantization (GWQ), the first q

Lucas S. Flores, Amanda de Azevedo-Lopes, Chadi M. Saad-Roy, Arne Traulsen

Social dilemmas where the good of a group is at odds with individual interests are usually considered as static -- the dilemma does not change over time. In the COVID-19 pandemic, social dilemmas occurred in the mitigation of epidemic spread: Should I reduce my contacts or wear a mask to protect others? In the context of respiratory diseases, which are predo

Yanchu Guan, Dong Wang, Yan Wang, Haiqing Wang

Autonomous mobile app interaction has become increasingly important with growing complexity of mobile applications. Developing intelligent agents that can effectively navigate and interact with mobile apps remains a significant challenge. In this paper, we propose an Explainable Behavior Cloning LLM Agent (EBC-LLMAgent), a novel approach that combines large

Ibrahim Gokcan, Ali Hikmet Deger

The research aims to construct a new type of matrix called the Fibonacci-Hessenberg-Lorentz matrix by multiplying Fibonacci-Hessenberg matrices with Lorentz matrix multiplication. The study will start by examining the properties of Hessenberg and tridiagonal matrices and then focus on developing the Fibonacci-Hessenberg matrix using Fibonacci sequences. By m

Ya-Zheng Tao, Rui-Hong Gao, Hong-Bo Jin, Zhen-Xiang Hao

Space-based gravitational wave observatories, such as LISA, Taiji, and TianQin, employ long-baseline laser interferometry, necessitating displacement measurement sensitivity at 1 pm/$\sqrt{Hz}$ level. A significant challenge in achieving this precision is the coupling noise arising from far-field wavefront errors (WFE) and laser pointing jitter. This paper p

Maryam Sadat Mirkamali, David G. Cory

Bipartite entangled states between a qubit and macroscopically distinct states of a mesoscopic system, known as micro-macro entangled states, are emerging resources for quantum information processing. One main challenge in generating such states in the lab is their fragility to environmental noise. We analyze this fragility in detail for single particle nois

Yonghui Xia, Jianglong Xiao, Jianshe Yu

This paper delves into a systematically reduced plant system proposed by Ja\"ibi et al. [Phys. D, 2020] in arid area. They used the method of geometric singular perturbation to study the existence of abundant orbits. Instead, we deliberate the stability and distributed patterns of this system. For a non-diffusive scenario for the model, we scrutinize the loc

Steve Yuwono, Ahmar Kamal Hussain, Dorothea Schwung, Andreas Schwung

In this study, we introduce Modular State-based Stackelberg Games (Mod-SbSG), a novel game structure developed for distributed self-learning in modular manufacturing systems. Mod-SbSG enhances cooperative decision-making among self-learning agents within production systems by integrating State-based Potential Games (SbPG) with Stackelberg games. This hierarc

Zhan Zhuang, Xiequn Wang, Yulong Zhang, Wei Li

Low-Rank Adaptation (LoRA) is a parameter-efficient technique for rapidly fine-tuning foundation models. In standard LoRA training dynamics, models tend to quickly converge to a local optimum near the initialization. However, this local optimum may not be ideal for out-of-distribution data or tasks such as merging and pruning. In this work, we propose a nove

Wenqian Du, Ran Long, João Moura, Jiayi Wang

Dual-arm mobile manipulators can transport and manipulate large-size objects with simple end-effectors. To interact with dynamic environments with strict safety and compliance requirements, achieving whole-body motion planning online while meeting various hard constraints for such highly redundant mobile manipulators poses a significant challenge. We tackle

Geng Li, Haozhi Cao, Mingyang Liu, Chenxi Jiang

Non-rigid point cloud registration is a critical challenge in 3D scene understanding, particularly in surgical navigation. Although existing methods achieve excellent performance when trained on large-scale, high-quality datasets, these datasets are prohibitively expensive to collect and annotate, e.g., organ data in authentic medical scenarios. With insuffi

Safwan Labbi, Daniil Tiapkin, Lorenzo Mancini, Paul Mangold

In this paper, we present the Federated Upper Confidence Bound Value Iteration algorithm ($\texttt{Fed-UCBVI}$), a novel extension of the $\texttt{UCBVI}$ algorithm (Azar et al., 2017) tailored for the federated learning framework. We prove that the regret of $\texttt{Fed-UCBVI}$ scales as $\tilde{\mathcal{O}}(\sqrt{H^3 |\mathcal{S}| |\mathcal{A}| T / M})$,

Cristina Carata, Ana-Luisa Chelaru

In recent years, cryptocurrencies have enjoyed increased popularity in all domains. Thus, in this context, it is important to understand how these digital assets can be transmitted, both legally and efficiently, in the event of the death of their owner. The present paper analyses the mechanisms of cryptocurrencies, analysing from a technical point of view as

Zébulon Goriely, Richard Diehl Martinez, Andrew Caines, Lisa Beinborn

Language models are typically trained on large corpora of text in their default orthographic form. However, this is not the only option; representing data as streams of phonemes can offer unique advantages, from deeper insights into phonological language acquisition to improved performance on sound-based tasks. The challenge lies in evaluating the impact of

Nuno J. Alves

We consider the space of functions almost in $L_p$ and endow it with the topology of asymptotic $L_p$-convergence. This yields a completely metrizable topological vector space which, on finite measure spaces, coincides with the space of measurable functions equipped with the topology of (local) convergence in measure. We investigate analogs of classical resu

Atakan Coban, David Dzsotjan, Stefan Küchemann, Jürgen Durst

Quantum cryptography is a central topic in the quantum technology field that is particularly important for secure communication. The training of qualified experts in this field is necessary for continuous development. However, the abstract and complex nature of quantum physics makes the topic difficult to understand. Augmented reality (AR) allows otherwise i

Dasong Li, John Man Shun Ma

In this paper we prove two backward uniqueness theorems for extrinsic geometric flow of possibly non-compact hypersurfaces in general ambient complete Riemannian manifolds. These are applicable to a wide range of extrinsic geometric flow, including the mean curvature flow, inverse mean curvature flow, Gauss curvature flow and so on.

Łukasz Bondaruk, Jakub Kubiak, Mateusz Czyżnikiewicz

This paper presents a system developed for submission to Poleval 2024, Task 3: Polish Automatic Speech Recognition Challenge. We describe Voicebox-based speech synthesis pipeline and utilize it to augment Conformer and Whisper speech recognition models with synthetic data. We show that addition of synthetic speech to training improves achieved results signif

Elzbieta Gradauskaite, Anouk S. Goossens, Xiaoyan Li, Lucía Iglesias

Layered perovskite-based compounds offer a range of unconventional properties enabled by their naturally anisotropic structure. While most renowned for the superconductivity observed in the Ruddlesden-Popper phases, many of these layered compounds are also ferroelectric and exhibit a sizeable in-plane polarization. Among these, the Carpy-Galy phases (A${_n}$

Shengkai Zhang, Nianhong Jiao, Tian Li, Chaojie Yang

We propose an effective method for inserting adapters into text-to-image foundation models, which enables the execution of complex downstream tasks while preserving the generalization ability of the base model. The core idea of this method is to optimize the attention mechanism related to 2D feature maps, which enhances the performance of the adapter. This a

Aabhaas Vineet Mallik, Loris Maria Cangemi, Amikam Levy, Emanuele G. Dalla Torre

Gate-based quantum computers are an innovative tool for experimentally studying the core principles of quantum mechanics. This work presents the first observation of quantum anomalous heat flow between two qubits and investigates the role of mid-circuit measurements in this context. Using mid-circuit measurements, we designed quantum circuits that violate th

Amit Bracha, Thomas Dagès, Ron Kimmel

When matching parts of a surface to its whole, a fundamental question arises: Which points should be included in the matching process? The issue is intensified when using isometry to measure similarity, as it requires the validation of whether distances measured between pairs of surface points should influence the matching process. The approach we propose tr

Mujadded Al Rabbani Alif

Accurate vehicle detection is essential for the development of intelligent transportation systems, autonomous driving, and traffic monitoring. This paper presents a detailed analysis of YOLO11, the latest advancement in the YOLO series of deep learning models, focusing exclusively on vehicle detection tasks. Building upon the success of its predecessors, YOL

Haiyue Yuan, Ali Raza, Nikolay Matyunin, Jibesh Patra

The development of technologies has prompted a paradigm shift in the automotive industry, with an increasing focus on connected services and autonomous driving capabilities. This transformation allows vehicles to collect and share vast amounts of vehicle-specific and personal data. While these technological advancements offer enhanced user experiences, they

Antoine Tordeux, Cécile Appert-Rolland, Alexandre Nicolas, Armin Seyfried

In our urbanised societies, the management and regulation of traffic and pedestrian flows is of considerable interest for public safety, economic development, and the conservation of the environment. However, modelling and controlling the collective dynamics of vehicles and pedestrians raises several challenges. Not only are the individual entities self-prop

Minas Gadalla, Sotiris Nikoletseas, José Roberto de A. Amazonas

This research explores the interdisciplinary interaction between psychoanalysis and computer science, suggesting a mutually beneficial exchange. Indeed, psychoanalytic concepts can enrich technological applications involving unconscious, elusive aspects of the human factor, such as social media and other interactive digital platforms. Conversely, computer sc

Pietro Noah Crestaz, Gokhan Alcan, Ville Kyrki

Hybrid dynamical systems pose significant challenges for effective planning and control, especially when additional constraints such as obstacle avoidance, state boundaries, and actuation limits are present. In this letter, we extend the recently proposed Hybrid iLQR method [1] to handle state and input constraints within an indirect optimization framework,

Romeo Orsolino, Mykhaylo Marfeychuk, Mariana de Paula Assis Fonseca, Mario Baggetta

Robotic manipulation of fresh fruits and vegetables, including the grasping of multiple loose items, has a strong industrial need but it still is a challenging task for robotic manipulation. This paper outlines the distinctive manipulation strategies used by humans to pick loose fruits and vegetables with the aim to better adopt them for robotic manipulation

Koen Decancq, Vanesa Jorda

We employ a flexible parametric model to estimate global income, health, and education distributions from 1980 to 2015. Using these marginal distributions within a copula-based framework, we construct a global joint distribution of well-being. This approach allows us to specifically analyze the impact of dependency structures on global well-being inequality.

Jae Hyeon Cho, Minkyung Park, Byung-Jun Lee

Direct Preference Optimization (DPO) trains a language model using human preference data, bypassing the explicit reward modeling phase of Reinforcement Learning from Human Feedback (RLHF). By iterating over sentence pairs in a preference dataset, DPO enhances generation quality by increasing the likelihood of producing preferred sentences over less favored o

Dani Rose J Marattukalam, Ashutosh Dwibedi, Sourodeep De, Sabyasachi Ghosh

A high baryon density and strong magnetic fields are expected in peripheral collisions in heavy ion collision experiments, such as the upcoming CBM experiment at FAIR in Germany and NICA in Russia. Such densities are also likely in the core of massive neutron stars, possibly with mixed quark-hadron phases. We employed the chiral effective model to obtain the

Giovanni Caldarelli, Alberto Guandalini, Francesco Macheda, Francesco Mauri

We consider the dynamical electronic response function in theoretical frameworks that include nonlocal exchange interactions, such as the Bethe-Salpeter equation with the frequency independent approximation of the screened interaction, Hartree-Fock, and range-separated Hybrid DFT approaches. Within these pictures, we demonstrate that any time-dependent elect

Youcheng Huang, Fengbin Zhu, Jingkun Tang, Pan Zhou

Visual Language Models (VLMs) are vulnerable to adversarial attacks, especially those from adversarial images, which is however under-explored in literature. To facilitate research on this critical safety problem, we first construct a new laRge-scale Adervsarial images dataset with Diverse hArmful Responses (RADAR), given that existing datasets are either sm

Ziqiao Wang, Yongyi Mao

In this work, we introduce novel information-theoretic generalization bounds using the conditional $f$-information framework, an extension of the traditional conditional mutual information (MI) framework. We provide a generic approach to derive generalization bounds via $f$-information in the supersample setting, applicable to both bounded and unbounded loss

Suchir Salhan, Richard Diehl Martinez, Zébulon Goriely, Paula Buttery

Curriculum Learning has been a popular strategy to improve the cognitive plausibility of Small-Scale Language Models (SSLMs) in the BabyLM Challenge. However, it has not led to considerable improvements over non-curriculum models. We assess whether theoretical linguistic acquisition theories can be used to specify more fine-grained curriculum learning strate

M. J. Mardanov, T. K. Melikov, G. V. Hajiyeva

This article explores minimum of an extremal in the variational problem with delay under the degeneracy of the Weierstrass condition. Here for study the minimality of extremal, variations of the Weierstrass type are used in two forms: in the form of variations on the right with respect to the given point, and in the form of variations on the left with respec

Itay Yona, Ilia Shumailov, Jamie Hayes, Nicholas Carlini

Mixture-of-Experts (MoE) models improve the efficiency and scalability of dense language models by routing each token to a small number of experts in each layer. In this paper, we show how an adversary that can arrange for their queries to appear in the same batch of examples as a victim's queries can exploit Expert-Choice-Routing to fully disclose a victim'

Haowen Xiao, Guanghui Liu, Xinyi Gao, Yang Li

Self-supervised learning (SSL) has shown remarkable data representation capabilities across a wide range of datasets. However, when applied to real-world datasets with long-tailed distributions, performance on multiple downstream tasks degrades significantly. Recently, the community has begun to focus more on self-supervised long-tailed learning. Some works

Riccardo Aliberti, Paolo Beltrame, Ettore Budassi, Carlo M. Carloni Calame

We present the results of Phase I of an ongoing review of Monte Carlo tools relevant for low-energy hadronic cross sections. This includes a detailed comparison of Monte Carlo codes for electron-positron scattering into a muon pair, pion pair, and electron pair, for scan and radiative-return experiments. After discussing the various approaches that are used

Imad Ali Shah, Fahad Mumtaz Malik, Muhammad Waqas Ashraf

Computer vision researchers have extensively worked on fundamental infrared visual recognition for the past few decades. Among various approaches, deep learning has emerged as the most promising candidate. However, Infrared Small Object Segmentation (ISOS) remains a major focus due to several challenges including: 1) the lack of effective utilization of loca

Luisa Beghin, Lorenzo Cristofaro, Federico Polito

The definition of generalized random processes in Gel'fand sense allows to extend well-known stochastic models, such as the fractional Brownian motion, and study the related fractional pde's, as well as stochastic differential equations in distributional sense. By analogy with the construction (in the infinite-dimensional white-noise space) of the latter, we

Pradeep R. Nair

Recent reports indicate that perovskite based light emitting diodes (LEDs) have achieved an external quantum efficiency (EQE) of 32% - rather an internal quantum efficiency close to 100%. Much of this improved performance is attributed to the role of excitons. While the experimental trends are encouraging, the recombination parameters estimated through exten

Osamu Sato

The FASER experiment studies the neutral decay products from LHC collision of 13.6 TeV centre of mass energy at 480m distant away. There could be Beyond Standard Model (BSM) particles such like dark photons or axion like particles etc.., and also high energy neutrinos. The neutrino target is an Emulsion Cloud Chamber with tungsten plates who can measure all

Dominik Kirstein, Christian Kremer

We generalise the classical Bass-Heller-Swan decomposition for the K-theory of (twisted) Laurent algebras to a splitting for general localising invariants of certain categories of twisted automorphisms. As an application, we obtain splitting formulas for Waldhausen's A-theory of mapping tori and for the K-theory of certain tensor algebras. We identify the Ni

Fernando Lucatelli Nunes, Rui Prezado

Effective descent morphisms, originally defined in Grothendieck descent theory, form a class of special morphisms within a category. Essentially, an effective descent morphism enables bundles over its codomain to be fully described as bundles over its domain endowed with additional algebraic structure, called descent data. Like the study of epimorphisms, stu

Paolo Marcellini, Antonella Nastasi, Cintia Pacchiano Camacho

We propose some general growth conditions on the function $% f=f\left( x,\xi \right) $, including the so-called natural growth, or polynomial, or $p,q-$growth conditions, or even exponential growth, in order to obtain that any local minimizer of the energy integral $\;\int_{\Omega }f\left( x,Du\right) dx\,$ is locally Lipschitz continuous in $\Omega $. In fa

Leonardo Ranaldi, Marco Valentino, Andrè Freitas

Retrieval-augmented generation (RAG) has emerged as a critical mechanism in contemporary NLP to support Large Language Models(LLMs) in systematically accessing richer factual context. However, the integration of RAG mechanisms brings its inherent challenges, as LLMs need to deal with potentially noisy contexts. Recent studies have shown that LLMs still strug

Serena Dipierro, Enrico Valdinoci, Mary Vaughan

We study the higher-order asymptotic development of a nonlocal phase transition energy in bounded domains and with prescribed external boundary conditions. The energy under consideration has fractional order $2s \in (0,1)$ and a first-order asymptotic development in the $\Gamma$-sense as described by the fractional perimeter functional. We prove that there i

Han Cheng Lie, Alexander Munteanu

We develop and analyze data subsampling techniques for Poisson regression, the standard model for count data $y\in\mathbb{N}$. In particular, we consider the Poisson generalized linear model with ID- and square root-link functions. We consider the method of coresets, which are small weighted subsets that approximate the loss function of Poisson regression up

Alexander Heinlein, Sebastian Kinnewig, Thomas Wick

In this work, restricted additive Schwarz (RAS) and optimized restricted additive Schwarz (ORAS) preconditioners from the Trilinos package FROSch (Fast and Robust Overlapping Schwarz) are employed to solve model problems implemented using deal.II (differential equations analysis library). Therefore, a Tpetra-based interface for coupling deal.II and FROSch is

J. Quetzalcoatl Toledo-Marin, Sebastian Gonzalez, Hao Jia, Ian Lu

Particle collisions at accelerators such as the Large Hadron Collider, recorded and analyzed by experiments such as ATLAS and CMS, enable exquisite measurements of the Standard Model and searches for new phenomena. Simulations of collision events at these detectors have played a pivotal role in shaping the design of future experiments and analyzing ongoing o

Sean Enis Cody, Sebastian Scher, Iain McDonald, Albert Zijlstra

Identifying stars belonging to different classes is vital in order to build up statistical samples of different phases and pathways of stellar evolution. In the era of surveys covering billions of stars, an automated method of identifying these classes becomes necessary. Many classes of stars are identified based on their emitted spectra. In this paper, we u

Prashanth S, Binoy Krishna De, Shubham Kumar Parate, Kartick Biswas

Phase change materials (PCMs) that exhibit volatile resistive switching are promising for emulating neuronal oscillators. Charge transfer insulators, such as ReNiO3 (where Re represents rare earth metals like Pr, Nd, Sm, Eu...), form a family of PCMs with tunable metal-insulator transition (MIT) temperatures across a broad range. Notably, MIT can be adjusted

Jianxiong Li, Boyang Li, Zhuoqiang Guo, Mingzhen Li

Physical phenomena such as chemical reactions, bond breaking, and phase transition require molecular dynamics (MD) simulation with ab initio accuracy ranging from milliseconds to microseconds. However, previous state-of-the-art neural network based MD packages such as DeePMD-kit can only reach 4.7 nanoseconds per day on the Fugaku supercomputer. In this pape

Jan Ernsting, Phillip Nikolas Beeken, Lynn Ogoniak, Jacqueline Kockwelp

Testis size is known to be one of the main predictors of male fertility, usually assessed in clinical workup via palpation or imaging. Despite its potential, population-level evaluation of testicular volume using imaging remains underexplored. Previous studies, limited by small and biased datasets, have demonstrated the feasibility of machine learning for te

Feihong Shen, Chao Li, Yifeng Geng, Yongjian Deng

Image retargeting is the task of adjusting the aspect ratio of images to suit different display devices or presentation environments. However, existing retargeting methods often struggle to balance the preservation of key semantics and image quality, resulting in either deformation or loss of important objects, or the introduction of local artifacts such as

Victor de Mora Losada, Riccardo Della Monica, Ivan de Martino, Mariafelicia De Laurentis

The Parameterized Post-Newtonian (PPN) formalism offers an agnostic framework for evaluating theories of gravity that extend beyond General Relativity. Departures from General Relativity are represented by a set of dimensionless parameters that, at the first order in the expansion, reduce to $\beta$ and $\gamma$, which describe deviations in spatial curvatur

P. Agnes, Q. Berger, M. Bomben, M. Campestrini

The Xenon-Argon Technology (X-ArT) collaboration presents a study on the dynamics of pure and xenon-doped liquid argon (LAr) scintillation. Using two types of silicon photomultipliers sensitive to different wavelength ranges, we provide evidence in favor of a contribution from long-lived (>10 $\mu$s) extreme ultraviolet (EUV) lines emitted from argon atomic

Karan Bania, Tanmay Verlekar

Video-based gait analysis can be defined as the task of diagnosing pathologies, such as ataxia, using videos of patients walking in front of a camera. This paper presents a graph convolution network called AtGCN for detecting ataxic gait and identifying its severity using 2D videos. The problem is especially challenging as the deviation of an ataxic gait fro