December 2024 arXiv papers — page 105

Shrisha Bharadwaj, Haiwen Feng, Giorgio Becherini, Victoria Fernandez Abrevaya

Manipulating the illumination of a 3D scene within a single image represents a fundamental challenge in computer vision and graphics. This problem has traditionally been addressed using inverse rendering techniques, which involve explicit 3D asset reconstruction and costly ray-tracing simulations. Meanwhile, recent advancements in visual foundation models su

Koushik Paul, Götz Pfeiffer

We provide an algorithmic framework for the computation of explicit representing matrices for all irreducible representations of a generalized symmetric group $\Grin_n$, i.e., a wreath product of cyclic group of order $r$ with the symmetric group $\Symm_n$. The basic building block for this framework is the Specht matrix, a matrix with entries $0$ and $\pm1$

AJ Bu, Doron Zeilberger

We give a very short proof of the fact that the number of $(a,b)$-parking functions of length $n$ equals $a(a+bn)^{n-1}$. This was first proved in 2003 by Kung and Yan, via a very long and torturous route, as a corollary of a more general result. This new version contains a reference to previous work kindly communicated by Richard Stanley

Zhengyu Yin

In this article, we investigate the relationship between the shadowing property of set-valued maps and their associated inverse limit systems. We show that if a set-valued map is expansive and open in the context of set-valued dynamics, then certain induced inverse limit systems have the shadowing property. Additionally, we prove that a continuous set-valued

Victor Sanmartin-Lopez

Let $\Phi$ be a subset of the simple roots of a (possibly non-reduced) abstract root system $\Sigma$, and let $\lambda \in \Sigma$. We define the $\Phi$-string of $\lambda$ as the set of elements in $\Sigma \cup \{0\}$ of the form $\lambda + \sum_{\alpha \in \Phi} n_\alpha \alpha$, where $n_\alpha$ is an integer for each $\alpha \in \Phi$. This notion can be

Youcheng Niu, Jinming Xu, Ying Sun, Li Chai

This paper considers a class of distributed bilevel optimization (DBO) problems with a coupled inner-level subproblem. Existing approaches typically rely on hypergradient estimations involving computationally expensive Hessian evaluation. To address this, we approximate the DBO problem as a minimax problem by properly designing a penalty term that enforces b

Liangda Fang, Xiaoman Wang, Zhang Chen, Kailun Luo

Abstraction is an important and useful concept in the field of artificial intelligence. To the best of our knowledge, there is no syntactic method to compute a sound and complete abstraction from a given low-level basic action theory and a refinement mapping. This paper aims to address this issue.To this end, we first present a variant of situation calculus,

Jin-Yu Liu, Xian-Ling Mao, Tian-Yi Che, Rong-Cheng Tu

Multi-modal hashing methods have gained popularity due to their fast speed and low storage requirements. Among them, the supervised methods demonstrate better performance by utilizing labels as supervisory signals compared with unsupervised methods. Currently, for almost all supervised multi-modal hashing methods, there is a hidden assumption that training s

Cyrus Neary, Nathan Tsao, Ufuk Topcu

We develop compositional learning algorithms for coupled dynamical systems, with a particular focus on electrical networks. While deep learning has proven effective at modeling complex relationships from data, compositional couplings between system components typically introduce algebraic constraints on state variables, posing challenges to many existing dat

Zijie Lou, Gang Cao, Kun Guo, Shaowei Weng

Pixel dependency modeling from tampered images is pivotal for image forgery localization. Current approaches predominantly rely on Convolutional Neural Networks (CNNs) or Transformer-based models, which often either lack sufficient receptive fields or entail significant computational overheads. Recently, State Space Models (SSMs), exemplified by Mamba, have

Shuvankar Gupta, Olajumoke Oluwatobiloba Emmanuel, Yasemin Ozbek, Mingyu Xu

The discovery of two-dimensional van der Waals (vdW) magnetic materials has propelled advancements in technological devices. The Nernst effect, which generates a transverse electric voltage in the presence of a longitudinal thermal gradient, shows great promise for thermoelectric applications. In this work, we report the electronic and thermoelectric transpo

Elliot Eichen, Arvind Aradhya, Oren Collaco

The 7.3 GHz (350 MHz bandwidth) Earth Observation Satellite (EOS) band, while not protected, is used for Passive Sea Surface Temperature (P-SST) measurements that provide important data for weather forecasts, coastal disaster prevention, climate modeling, and oceanographic research. The full 7 GHz band (7.125 to 8.4 GHz), which encompasses these EOS frequenc

Jiahe Lin, George Michailidis

State-space models (SSMs) offer a powerful framework for dynamical system analysis, wherein the temporal dynamics of the system are assumed to be captured through the evolution of the latent states, which govern the values of the observations. This paper provides a selective review of recent advancements in deep neural network-based approaches for SSMs, and

Yi Feng, Yu Han, Xijing Zhang, Tanghui Li

Inferring the 3D structure of a scene from a single image is an ill-posed and challenging problem in the field of vision-centric autonomous driving. Existing methods usually employ neural radiance fields to produce voxelized 3D occupancy, lacking instance-level semantic reasoning and temporal photometric consistency. In this paper, we propose ViPOcc, which l

Gemma L. Hood

Existence of a large class of exponentially decaying solutions of the nonlinear massive wave equation $\Box_g\psi+\alpha\psi = \mathcal{F}(\psi,\partial\psi)$ on a Kerr--Anti-de Sitter exterior is established via a backwards scattering construction. Exponentially decaying data is prescribed on the future event horizon, and Dirichlet data on the timelike conf

Maryna Borysova, Shikma Bressler, Eilam Gross, Nilotpal Kakati

Precision measurement of hadronic final states presents complex experimental challenges. The study explores the concept of a gaseous Digital Hadronic Calorimeter (DHCAL) and discusses the potential benefits of employing Graph Neural Network (GNN) methods for future collider experiments. In particular, we use GNN to describe calorimeter clusters as point clou

Pedro Miguel Sánchez Sánchez, Enrique Tomás Martínez Beltrán, Miguel Fernández Llamas, Gérôme Bovet

Decentralized Federated Learning (DFL) trains models in a collaborative and privacy-preserving manner while removing model centralization risks and improving communication bottlenecks. However, DFL faces challenges in efficient communication management and model aggregation within decentralized environments, especially with heterogeneous data distributions.

Anand Pillay, Atticus Stonestrom

We give an 'arithmetic regularity lemma' for groups definable in finite fields, analogous to Tao's 'algebraic regularity lemma' for graphs definable in finite fields. More specifically, we show that, for any $M>0$, any finite field $\mathbf{F}$, and any definable group $(G,\cdot)$ in $\mathbf{F}$ and definable subset $D\subseteq G$, each of complexity at mos

Elad Shoham, Hadar Cohen, Khalil Wattad, Havana Rika

Explainable AI (XAI) methods typically focus on identifying essential input features or more abstract concepts for tasks like image or text classification. However, for algorithmic tasks like combinatorial optimization, these concepts may depend not only on the input but also on the current state of the network, like in the graph neural networks (GNN) case.

Isaque P. de Freitas, Nami F. Svaiter, Gustavo O. Heymans

We study a Bose-Einstein condensate under the effects of the non-condensate atomic cloud. We model the resulting linear interaction of the condensate with the atomic gas as a quenched disorder. Using the distributional zeta function method, we obtain a representation for the quenched free energy as a series of integral moments of the partition function. Assu

Derguene Mbaye, Moussa Diallo

In recent years, we are seeing considerable interest in conversational agents with the rise of large language models (LLMs). Although they offer considerable advantages, LLMs also present significant risks, such as hallucination, which hinder their widespread deployment in industry. Moreover, low-resource languages such as African ones are still underreprese

Lucas Hamada

In this paper, we classify the possible torsion subgroup structures of elliptic curves defined over the compositum of all quadratic extensions of the rational number field, whose $j$-invariant is a rational number not equal to 0 or 1728.

Shu-Ao Liao, Jin Zhang, Li-Ping Yang

Using the density matrix renormalization group algorithm, we map the ground-state phase diagram of a two-leg Rydberg ladder array with lattice spacings $a_x=2a_y$. We identify various density wave phases that spontaneously break the translational symmetry or the top-bottom reflection symmetry within the ladder. By increasing the laser detuning from zero, whe

Jing-Cheng Liu, Qiao-Qin Liu, Jun Jason Luo, Jia-jie Wang

We investigate spectral properties of planar Moran measures $\mu_{\{M_n\},\{D_n\}}$ generated by sequences of expanding matrices $\{M_n\}\subset GL(2,\mathbb{Z})$ and digit sets $\{D_n\}\subset\mathbb{Z}^2$, where each digit set has the form $$ D_n = \left\{ \begin{pmatrix} 0 \\ 0 \end{pmatrix}, \begin{pmatrix} \alpha_{n_1} \\ \alpha_{n_2} \end{pmatrix}, \be

Felix Gotti, Bangzheng Li

Let $M$ be a cancellative and commutative monoid. A submonoid $N$ of $M$ is called an undermonoid if the Grothendieck groups of $M$ and $N$ coincide. For a given property $\mathfrak{p}$, we are interested in providing an answer to the following main question: does it suffice to check that all undermonoids of $M$ satisfy $\mathfrak{p}$ to conclude that all su

Mariam Hassan, Sebastian Stapf, Ahmad Rahimi, Pedro M B Rezende

We present GEM, a Generalizable Ego-vision Multimodal world model that predicts future frames using a reference frame, sparse features, human poses, and ego-trajectories. Hence, our model has precise control over object dynamics, ego-agent motion and human poses. GEM generates paired RGB and depth outputs for richer spatial understanding. We introduce autore

Seema Satin

Semiclassical stochastic gravity is aimed at studying extended structure formation in the early universe. Rigorous developments in this area include the semiclassical noise and dissipation kernels which are obtained in terms of quantum stress energy tensor composed of scalar fields. The present article forms an important step in an effort to extend the theor

Yuhao Wang, Zhiyuan Zhu, Heyang Liu, Yusheng Liao

Multimodal large language models (MLLMs) excel at multimodal perception and understanding, yet their tendency to generate hallucinated or inaccurate responses undermines their trustworthiness. Existing methods have largely overlooked the importance of refusal responses as a means of enhancing MLLMs reliability. To bridge this gap, we present the Information

Pierre Fraigniaud, Maël Luce, Frédéric Magniez, Ioan Todinca

We show that, for every $k\geq 2$, $C_{2k}$-freeness can be decided in $O(n^{1-1/k})$ rounds in the Broadcast CONGEST model, by a deterministic algorithm. This (deterministic) round-complexity is optimal for $k=2$ up to logarithmic factors thanks to the lower bound for $C_4$-freeness by Drucker et al. [PODC 2014], which holds even for randomized algorithms.

Dan Ristea, Shae McFadden, Ezzeldin Shereen, Madeleine Dwyer

Security vulnerabilities in software can have severe consequences; however, manual vulnerability detection is costly and does not scale, especially as agentic coding frameworks increase the rate of code production. Over the last decade, a large body of research has applied machine learning machine learning to automate vulnerability detection (ML4AVD), yet se

Ling-An Zeng, Guohong Huang, Gaojie Wu, Wei-Shi Zheng

Despite the significant role text-to-motion (T2M) generation plays across various applications, current methods involve a large number of parameters and suffer from slow inference speeds, leading to high usage costs. To address this, we aim to design a lightweight model to reduce usage costs. First, unlike existing works that focus solely on global informati

Igor L. R. Azevedo, Toyotaro Suzumura

Stock market forecasting has been a topic of extensive research, aiming to provide investors with optimal stock recommendations for higher returns. In recent years, this field has gained even more attention due to the widespread adoption of deep learning models. While these models have achieved impressive accuracy in predicting stock behavior, tailoring them

Damian Głodkowski, Piotr Koszmider

We consider the Calkin algebra $\mathcal{Q}(\ell_2)$, i.e., the quotient of the algebra $\mathcal B(\ell_2)$ of all bounded linear operators on the separable Hilbert space $\ell_2$ divided by the ideal $\mathcal K(\ell_2)$ of all compact operators on $\ell_2$. We show that in the Cohen model of set theory ZFC there is no embedding of the product $(c_0(2^\ome

Iqra Altaf

A $\Gamma$-Besicovitch set is a set which contains a rotated copy of $\Gamma$ in every direction. Our main result is the construction of a non-trivial $1$-rectifiable set $\Gamma$ in the plane, for which there exists a 1-dimensional $\Gamma$-Besicovitch set.

Byungjun Kim, Minju Kim, Dayeon Seo, Bugeun Kim

We highlight two significant issues leading to the passivity of current merchant non-player characters (NPCs): pricing and communication. While immersive interactions with active NPCs have been a focus, price negotiations between merchant NPCs and players remain underexplored. First, passive pricing refers to the limited ability of merchants to modify predef

Sukhwan Chung, Daniel Sardak, Jeffrey Cegan, Igor Linkov

Network science is a powerful tool for analyzing transportation networks, offering insights into their structures and enabling the quantification of resilience and robustness. Understanding the underlying structures of transportation networks is crucial for effective infrastructure planning and maintenance. In military contexts, network science is valuable f

Paweł Mąka, Yusuf Can Semerci, Jan Scholtes, Gerasimos Spanakis

In this paper, we investigate the role of attention heads in Context-aware Machine Translation models for pronoun disambiguation in the English-to-German and English-to-French language directions. We analyze their influence by both observing and modifying the attention scores corresponding to the plausible relations that could impact a pronoun prediction. Ou

Christophe Gissinger

This review article summarizes two decades of laboratory research aimed at understanding the dynamics of accretion disks, with particular emphasis on magnetohydrodynamic experiments involving liquid metals and plasmas. First, the Taylor-Couette experiments demonstrated the generation of magnetorotational instability (MRI) in liquid metals, and highlighted ho

Haili Ye, Stephan Goerttler, Fei He

Electroencephalogram (EEG) is a valuable technique to record brain electrical activity through electrodes placed on the scalp. Analyzing EEG signals contributes to the understanding of neurological conditions and developing brain-computer interface. Graph Signal Processing (GSP) has emerged as a promising method for EEG spatial-temporal analysis, by further

Haisheng Lu, Yujie Fu, Fan Zhang, Le Zhang

Medical image segmentation is a critical component of clinical practice, and the state-of-the-art MedSAM model has significantly advanced this field. Nevertheless, critiques highlight that MedSAM demands substantial computational resources during inference. To address this issue, the CVPR 2024 MedSAM on Laptop Challenge was established to find an optimal bal

Han Zhu, Gaofeng Cheng, Qingwei Zhao, Pengyuan Zhang

The performance of automatic speech recognition models often degenerates on domains not covered by the training data. Domain adaptation can address this issue, assuming the availability of the target domain data in the target language. However, such assumption does not stand in many real-world applications. To make domain adaptation more applicable, we addre

Danny Segev

In this paper, we present long-awaited algorithmic advances toward the efficient construction of near-optimal replenishment policies for a true inventory management classic, the economic warehouse lot scheduling problem. While this paradigm has accumulated a massive body of surrounding literature since its inception in the late '50s, we are still very much i

Bohan Li, Xin Jin, Jianan Wang, Yukai Shi

Recent diffusion models have demonstrated remarkable performance in both 3D scene generation and perception tasks. Nevertheless, existing methods typically separate these two processes, acting as a data augmenter to generate synthetic data for downstream perception tasks. In this work, we propose OccScene, a novel mutual learning paradigm that integrates fin

Thomas Willwacher

We extend the rational homotopy theory of operads developed by B. Fresse to several types of modules over operads.

Siddhesh Sharad Kashid, Sachin Verma, Abhishek Maurya, Manjushree Maity

Hybrid magnonics, exploring the coupling between magnons and quantum systems, is an exciting field for developing next-generation information technologies. Achieving a strong and tunable magnon-magnon coupling (MMC) in confined nanomagnets is crucial for the on-chip integration of these hybrid systems and advancing the field. In this work, we numerically inv

Ziang Zhou, Zhihao Ding, Jieming Shi, Qing Li

Graph Neural Networks (GNNs) are pivotal in graph-based learning, particularly excelling in node classification. However, their scalability is hindered by the need for multi-hop data during inference, limiting their application in latency-sensitive scenarios. Recent efforts to distill GNNs into multi-layer perceptrons (MLPs) for faster inference often underu

Abed Saif Ahmed Alghawli, Tamara Radivilova

Automated, secure software development is an important task of digitalization, which is solved with the DevSecOps approach. An important part of the DevSecOps approach is continuous risk assessment, which is necessary to identify and evaluate risk factors. Combining the development cycle with continuous risk assessment creates synergies in software developme

Phillip Heiler, Asbjørn Kaufmann, Bezirgen Veliyev

This paper provides a solution to the evaluation of treatment effects in selective samples when neither instruments nor parametric assumptions are available. We provide sharp bounds for average treatment effects under a conditional monotonicity assumption for all principal strata, i.e. units characterizing the complete intensive and extensive margins. Most i

Shi-Jiang Chen, Jun-Xian Wang, Jia-Lai Kang, Wen-Yong Kang

A core sample of 59 unobscured type 1 AGNs with simultaneous XMM-Newton X-ray and UV observations is compiled from archive to probe the nature of soft X-ray excess (SE). In the first paper of this series, our focus centers on scrutinizing the spectral profile of the soft excess. Of the sources, $\approx$ 71% (42/59) exhibit powerlaw-like (po-like) soft exces

Hanxiao Lu, Hongyu Cai, Yiming Liang, Antonio Bianchi

Language model approaches have recently been integrated into binary analysis tasks, such as function similarity detection and function signature recovery. These models typically employ a two-stage training process: pre-training via Masked Language Modeling (MLM) on machine code and fine-tuning for specific tasks. While MLM helps to understand binary code str

Luqi Wang, Wenbao Jiang

As a cornerstone of blockchain technology in the 3.0 era, smart contracts play a pivotal role in the evolution of blockchain systems. In order to address the limitations of existing smart contract vulnerability detection models with regard to their generalisation capability, an AF-STip smart contract vulnerability detection framework incorporating efficient

Bat-Sheva Einbinder, Liran Ringel, Yaniv Romano

The risk-controlling prediction sets (RCPS) framework is a general tool for transforming the output of any machine learning model to design a predictive rule with rigorous error rate control. The key idea behind this framework is to use labeled hold-out calibration data to tune a hyper-parameter that affects the error rate of the resulting prediction rule. H

Lizhong Wang, Xingzhong Xu, Jiping Zhang

In this paper, we focus on the subgroups control $p$-fusion, and we improve the Theorem B of [4] for odd prime. For odd prime, we prove that elementary abelian subgroups of rank at least 2 can control $p$-fusion(see our Theorem B).

Chetan Verma, Archit Agarwal

Pre-trained models excel on NLI benchmarks like SNLI and MultiNLI, but their true language understanding remains uncertain. Models trained only on hypotheses and labels achieve high accuracy, indicating reliance on dataset biases and spurious correlations. To explore this issue, we applied the Universal Adversarial Attack to examine the model's vulnerabiliti

Andrea Cristina McGlinchey, Peter J Barclay

Following the universal availability of generative AI systems with the release of ChatGPT, automatic detection of deceptive text created by Large Language Models has focused on domains such as academic plagiarism and "fake news". However, generative AI also poses a threat to the livelihood of creative writers, and perhaps to literary culture in general, thro

Tzalik Maimon, Shirley Alus, Gil Kedar

Co-Channel Interference (CCI) is a fundamental problem in wireless communication networks. It is a well-studied problem in the field. As channels use the same frequency, interference in the radio waves occurs which, in turn, reduces the capacity of the interfered channels. There is a need to use the least number of frequencies as communication networks advan

Songgaojun Deng, Maarten de Rijke

Time series forecasting is vital in many real-world applications, yet developing models that generalize well on unseen relevant domains -- such as forecasting web traffic data on new platforms/websites or estimating e-commerce demand in new regions -- remains underexplored. Existing forecasting models often struggle with domain shifts in time series data, as

Yujie Zhang, Bingyang Cui, Qi Yang, Zhu Li

Text-to-3D generation has achieved remarkable progress in recent years, yet evaluating these methods remains challenging for two reasons: i) Existing benchmarks lack fine-grained evaluation on different prompt categories and evaluation dimensions. ii) Previous evaluation metrics only focus on a single aspect (e.g., text-3D alignment) and fail to perform mult

Nesreen Mufid

Nowadays, electric robots play big role in many fields as they can replace humans and/or decrease the amount of load on humans. There are several types of robots that are present in the daily life, some of them are fully controlled by humans while others are programmed to be self-controlled. In addition there are self-control robots with partial human contro

Jin Li, Zitong Yu, Ziqiang He, Z. Jane Wang

Imperceptible adversarial attacks have recently attracted increasing research interests. Existing methods typically incorporate external modules or loss terms other than a simple $l_p$-norm into the attack process to achieve imperceptibility, while we argue that such additional designs may not be necessary. In this paper, we rethink the essence of impercepti

Jiahao Yuan, Zixiang Di, Shangzixin Zhao, Zhiqing Cui

Large language models (LLMs) face challenges in aligning with diverse cultural values despite their remarkable performance in generation, which stems from inherent monocultural biases and difficulties in capturing nuanced cultural semantics. Existing methods struggle to adapt to unknown culture after fine-tuning. Inspired by cultural geography across five co

S. V. Bolokhov, Milena Skvortsova

A correspondence between two distinct spectral problems, quasinormal modes and grey-body factors, has recently been established for a wide class of black holes. Here, we demonstrate that a similar correspondence exists for a broad class of traversable wormholes and verify it using several well-known examples.

Xiangming Wang, Haijin Zeng, Jiaoyang Chen, Sheng Liu

In real-world scenarios, complex data such as multispectral images and multi-frame videos inherently exhibit robust low-rank property. This property is vital for multi-dimensional inverse problems, such as tensor completion, spectral imaging reconstruction, and multispectral image denoising. Existing tensor singular value decomposition (t-SVD) definitions re

Xutao Liao, Shaohui Li, Yuhui Xu, Zhi Li

Recent low-rank training methods, such as GaLore, have significantly reduced the memory required to optimize large language models (LLMs). However, these methods often suffer from time-consuming low-rank projection estimations. In particular, the singular value decomposition (SVD) in GaLore can consume more than 80\% of the total training time. To address th

Lien P. Le, Xuan-Hien Nguyen Thi, Thu Nguyen, Michael A. Riegler

Healthcare time series data is vital for monitoring patient activity but often contains noise and missing values due to various reasons such as sensor errors or data interruptions. Imputation, i.e., filling in the missing values, is a common way to deal with this issue. In this study, we compare imputation methods, including Multiple Imputation with Random F

Martin Bohnert

A nondegenerate toric hypersurface of negative Kodaira dimension can be characterized by the empty Fine interior of its Newton polytope according to recent work by Victor Batyrev, where the Fine interior is the rational subpolytope consisting of all points which have an integral distance of at least 1 to all integral supporting hyperplanes of the Newton poly

Hangya Wang, Wangtao Lu

This paper is concerned with three-dimensional acoustic wave scattering in two-layer media, where the two homogeneous layers are separated by a locally perturbed plane featuring an axially symmetric perturbation. A fast novel boundary integral equation (BIE) method is proposed to solve the scattering problem within a cylindrical perfectly matched layer (PML)

Juliana Gerard, Sahajpreet Singh, Morgan Macleod, Michael McKay

This study investigates students' perceptions of Generative Artificial Intelligence (GenAI), with a focus on Higher Education institutions in Northern Ireland and India. We collect quantitative Likert ratings and qualitative comments from 1211 students on their awareness and perceptions of AI and investigate variations in attitudes toward AI across instituti

Jing Zhang, Shuzhen Sun, Peng Zhang, Guangxing Cao

Transformer-based large language models exhibit groundbreaking capabilities, but their storage and computational costs are prohibitively high, limiting their application in resource-constrained scenarios. An effective approach is to eliminate redundant model parameters and computational costs while incorporating efficient expert-derived knowledge structures

Chuang Yu, Yunpeng Liu, Jinmiao Zhao, Xiangyu Yue

Recently, cross-spectral image patch matching based on feature relation learning has attracted extensive attention. However, performance bottleneck problems have gradually emerged in existing methods. To address this challenge, we make the first attempt to explore a stable and efficient bridge between descriptor learning and metric learning, and construct a

Haisong Xia, Wanyue Xu, Zuobai Zhang, Zhongzhi Zhang

For random walks on graph $\mathcal{G}$ with $n$ vertices and $m$ edges, the mean hitting time $H_j$ from a vertex chosen from the stationary distribution to vertex $j$ measures the importance for $j$, while the Kemeny constant $\mathcal{K}$ is the mean hitting time from one vertex to another selected randomly according to the stationary distribution. In thi

Keyi Wang, Jaisal Patel, Charlie Shen, Daniel Kim

Financial large language models (FinLLMs) have been applied to various tasks in business, finance, accounting, and auditing. Complex financial regulations and standards are critical to financial services, which LLMs must comply with. However, FinLLMs' performance in understanding and interpreting financial regulations has rarely been studied. Therefore, we o

Pengqian Lu, Jie Lu, Anjin Liu, Guangquan Zhang

Concept drift, characterized by unpredictable changes in data distribution over time, poses significant challenges to machine learning models in streaming data scenarios. Although error rate-based concept drift detectors are widely used, they often fail to identify drift in the early stages when the data distribution changes but error rates remain constant.

W. Schweiger, W. H. Klink

This paper deals with the partial solution of the energy-eigenvalue problem for one-dimensional Schr\"odinger operators of the form $H_N=X_0^2+V_N$, where $V_N=X_N^2+\alpha X_{N-1}$ is a polynomial potential of degree $(2N-2)$ and $X_i$ are the generators of an irreducible representation of a particular nilpotent group $\mathcal{G}_N$. Algebraization of the

Chenying Lin

Given an essentially atoral Laurent polynomial $P$, we show an equidistribution theorem for the function $\operatorname{log}|P|$ on specific subsets of Galois orbits of torsion points of the $d$-dimensional algebraic torus $\mathbb{G}^d_m(\overline{\mathbb{Q}})$. The specific subsets under consideration are the preimages of $d$-dimensional polytopes within t

Alicia Vidler, Toby Walsh

The over-the-counter (OTC) government bond markets are characterised by their bilateral trading structures, which pose unique challenges to understanding and ensuring market stability and liquidity. In this paper, we develop a bespoke ABM that simulates market-maker interactions within a stylised government bond market. The model focuses on the dynamics of l

Joar Skalse, Alessandro Abate

The aim of inverse reinforcement learning (IRL) is to infer an agent's preferences from observing their behaviour. Usually, preferences are modelled as a reward function, $R$, and behaviour is modelled as a policy, $\pi$. One of the central difficulties in IRL is that multiple preferences may lead to the same observed behaviour. That is, $R$ is typically und

Chuang Yu, Jinmiao Zhao, Yunpeng Liu, Sicheng Zhao

Recently, single-frame infrared small target (SIRST) detection with single point supervision has drawn wide-spread attention. However, the latest label evolution with single point supervision (LESPS) framework suffers from instability, excessive label evolution, and difficulty in exerting embedded network performance. Inspired by organisms gradually adapting

Mahdi Abolghasemi, Daniele Girolimetto, Tommaso Di Fonzo

Wind power forecasting is essential for managing daily operations at wind farms and enabling market operators to manage power uncertainty effectively in demand planning. This paper explores advanced cross-temporal forecasting models and their potential to enhance forecasting accuracy. First, we propose a novel approach that leverages validation errors, rathe

Jiancheng Huang, Yi Huang, Jianzhuang Liu, Donghao Zhou

Text-conditional image editing is a practical AIGC task that has recently emerged with great commercial and academic value. For real image editing, most diffusion model-based methods use DDIM Inversion as the first stage before editing. However, DDIM Inversion often results in reconstruction failure, leading to unsatisfactory performance for downstream editi

Weilin Li, Karl Otness, Kui Ren, Donsub Rim

The approximate discrete Radon transform (ADRT) is a hierarchical multiscale approximation of the Radon transform. In this paper, we factor the ADRT into a product of linear transforms that resemble convolutions and derive an explicit spectral decomposition of each factor. We further show that this implies -- for data lying in the range of the ADRT -- that t

Changhao Liu, Weidong Mei, Peilan Wang, Yinuo Meng

Intelligent reflecting surface (IRS) is composed of numerous passive reflecting elements and can be mounted on unmanned aerial vehicles (UAVs) to achieve six-dimensional (6D) movement by adjusting the UAV's three-dimensional (3D) location and 3D orientation simultaneously. Hence, in this paper, we investigate a new UAV-enabled passive 6D movable antenna (6DM

Mohammadreza Salehi, Nikolaos Apostolikas, Efstratios Gavves, Cees G. M. Snoek

In the realm of novelty detection, accurately identifying outliers in data without specific class information poses a significant challenge. While current methods excel in single-object scenarios, they struggle with multi-object situations due to their focus on individual objects. Our paper suggests a novel approach: redefining `normal' at the object level i

Jan Naudts, Jun Zhang

We revisit the work of Rieffel and van Daele on pairs of subspaces of a real Hilbert space, while relaxing as much as possible the assumption that all the relevant subspaces are in general positions with respect to each other. We work out, in detail, how two real projection operators lead to the construction of a complex Hilbert space where the theory of the

Kâzım Büyükboduk, Manisha Ganguly

This article presents an approach to the algebraic functional equation for Selmer complexes, which in turn have applications in the Iwasawa theoretic study of Rankin-Selberg products of the Hida and Coleman families. Our treatment establishes the functional equation for algebraic $p$-adic $L$-functions (which are given in terms of characteristic ideals of Se

Ruiwei Xiao, Ying-Jui Tseng, Hanqi Li, Hsuan Nieu

Interest in K-12 AI Literacy education has surged in the past year, yet large-scale learning data remains scarce despite considerable efforts in developing learning materials and running summer programs. To make larger scale dataset available and enable more replicable findings, we developed an intelligent online learning platform featuring AI Literacy modul

Ali Kohan, Mohamad Roshanzamir, Roohallah Alizadehsani

Genetic Network Programming (GNP) is an evolutionary algorithm that extends Genetic Programming (GP). It is typically used in agent control problems. In contrast to GP, which employs a tree structure, GNP utilizes a directed graph structure. During the evolutionary process, the connections between nodes change to discover the optimal strategy. Due to the lar

Minlie Huang, Yingkang Wang, Shiyao Cui, Pei Ke

We have witnessed superhuman intelligence thanks to the fast development of large language models and multimodal language models. As the application of such superhuman models becomes more and more popular, a critical question arises here: how can we ensure superhuman models are still safe, reliable and aligned well to human values? In this position paper, we

Yan Ma, De-Shun Zhang, Cheng-Qun Pang, Zhi-Feng Sun

In this work, we studied the $Z_{c}(3900)$ state within the framework of effective field theory. We firstly show the construction of the Lagrangian describing meson-meson-meson and meson-diquark-diquark interactions. By using the Feynman rule, we calculate the effective potentials corresponding to the coupled channels of $D\bar{D}^{*}/D^{*}\bar{D}$ and $S_{c

Akshay Singh, Andrea Sanna, Sudip Bhattacharyya, Sudiip Chakraborty

IGR J17591--2342, a transient accretion-powered millisecond X-ray pulsar, was discovered during its 2018 outburst. Here, we present a timing and spectral analysis of the source using {\it AstroSat} data of the same outburst. From the timing analysis, we obtain updated values of binary orbital parameters, which reveal an average pulsar spin frequency of 527.4

Tiankai Yang, Yi Nian, Shawn Li, Ruiyao Xu

Anomaly detection (AD) is an important machine learning task with many real-world uses, including fraud detection, medical diagnosis, and industrial monitoring. Within natural language processing (NLP), AD helps detect issues like spam, misinformation, and unusual user activity. Although large language models (LLMs) have had a strong impact on tasks such as

Zouhaïr Mouayn

We rederive the expression of the integrated density of states for \ the sub-Laplacian on Heisenberg groups $\mathbb{H}_{n}$ by using its resolvent kernel.

Ryo Kitabayashi, Hiroyuki Sato, Akihiro Hirakawa

Basket trials in oncology enroll multiple patients with cancer harboring identical gene alterations and evaluate their response to targeted therapies across cancer types. Several existing methods have extended a Bayesian hierarchical model borrowing information on the response rates in different cancer types to account for the heterogeneity of drug effects.

Da Li, Junping Yin, Jin Xu, Xinxin Li

Extracting interpretable equations from observational datasets to describe complex natural phenomena is one of the core goals of artificial intelligence. This field is known as symbolic regression (SR). In recent years, Transformer-based paradigms have become a new trend in SR, addressing the well-known problem of inefficient search. However, the modal heter

Juntao Dai, Yaodong Yang, Qian Zheng, Gang Pan

A key aspect of Safe Reinforcement Learning (Safe RL) involves estimating the constraint condition for the next policy, which is crucial for guiding the optimization of safe policy updates. However, the existing Advantage-based Estimation (ABE) method relies on the infinite-horizon discounted advantage function. This dependence leads to catastrophic errors i

Hezha O. Rasul, Dlzar D. Ghafour, Bakhtyar K. Aziz, Bryar A. Hassan

The drug development process is a critical challenge in the pharmaceutical industry due to its time-consuming nature and the need to discover new drug potentials to address various ailments. The initial step in drug development, drug target identification, often consumes considerable time. While valid, traditional methods such as in vivo and in vitro approac

Yi Zhang, Melody Huang, Kosuke Imai

To test scientific theories and develop individualized treatment rules, researchers often wish to learn heterogeneous treatment effects that can be consistently found across diverse populations and contexts. We consider the problem of generalizing heterogeneous treatment effects (HTE) based on data from multiple sites. A key challenge is that a target popula

Vishal Bhardwaj, Lekshmi Rajagopal, Lorenzo Arici, Matan Bocarsly

The coexistence of superconductivity and magnetism within a single material system represents a long-standing goal in condensed matter physics. Van der Waals-based moir\'e superlattices provide an exceptional platform for exploring competing and coexisting broken symmetry states. Alternating twisted trilayer graphene (TTG) exhibits robust superconductivity a

Iraj Safaei, Soma Heydari, Milad Solbi, Kayoomars Karami

Here, a single field inflationary model driven by a mutated hilltop potential, a subclass of the hilltop models of inflation, is investigated. To constrain the parameter space, we employ the latest $r-n_{\rm s}$ constraints from Planck 2018, BICEP/Keck 2018, and the Atacama Cosmology Telescope (ACT) data, alongside reheating parameters $N_{\rm{re}}$, $T_{\rm

Alessia Nota, Dominik Nowak, Chiara Saffirio

We consider the magnetic Lorentz gas proposed by Bobylev et al. [4], which describes a point particle moving in a random distribution of hard-disk obstacles in $\mathbb{R}^2$ under the influence of a constant magnetic field perpendicular to the plane. We show that, in the coupled low-density and diffusion limit, when the intensity of the magnetic field is sm