December 2024 arXiv papers — page 45

Hyeonjin Kim, Jaejun Yoo

While pruning methods effectively maintain model performance without extra training costs, they often focus solely on preserving crucial connections, overlooking the impact of pruned weights on subsequent fine-tuning or distillation, leading to inefficiencies. Moreover, most compression techniques for generative models have been developed primarily for GANs,

Anh-Tuan Tran, Nguyen Dinh Nama, Nguyen Thi Thanh Nhan, Nguyen Quang Bau

Analytic expressions for the absorption coefficient (AC) of a weak electromagnetic wave (EMW) in 2D Graphene under influence of strong laser radiation are calculated using the quantum kinetic equation (QKE) in the case of electron-optical phonon scattering in both the absence and presence of a magnetic field perpendicular to the graphene sheet. The dependenc

Kosuke Ishizuka

In this paper, we will establish a general method of studying finite-dimensional normed spaces, and apply this method to classifying $3$-dimensional and $4$-dimensional normed spaces over a non-spherically complete field. For this purpose, we will use the spherical completion. From the perspective of the spherical completion, each finite-dimensional normed s

Théo Gherdaoui

We present a necessary condition for the small-time local controllability of multi-input control-affine systems on $R^d$ . This condition is formulated on the vectors of $R^d$ resulting from the evaluation at zero of the Lie brackets of the vector fields: it involves both their direction and their amplitude. The proof is an adaptation to the multi-input case

Xin Song, Zhikai Xue, Guoxiu He, Jiawei Liu

Parameter-efficient fine-tuning (PEFT) methods optimize large language models (LLMs) by modifying or introducing a small number of parameters to enhance alignment with downstream tasks. However, they can result in catastrophic forgetting, where LLMs prioritize new knowledge at the expense of comprehensive world knowledge. A promising approach to mitigate thi

Daoyuan Chen, Yilun Huang, Xuchen Pan, Nana Jiang

Foundation models demand advanced data processing for their vast, multimodal datasets. However, traditional frameworks struggle with the unique complexities of multimodal data. In response, we present Data-Juicer 2.0, a data processing system backed by 100+ data processing operators spanning text, image, video, and audio modalities, supporting more critical

Riku Adachi, Hiroki Kojima, Takashi Ikegami

To further understand the complex behavior of swimming microorganisms, the spontaneous motion of nonliving matter provides essential insights. While substantial research has focused on quantitatively analyzing complex behavioral patterns, characterizing these dynamics aiming for inclusive comparison to the behavior of living systems remains challenging. In t

Zhao Dong, Xuhui Peng

In this paper, we establish ergodic and mixing properties of stochastic 2D Navier-Stokes equations driven by a highly degenerate multiplicative Gaussian noise. The noise could appear in as few as four directions and the intensity of the noise depends on the solution. The case of additive Gaussian noise was treated in Hairer and Mattingly [\emph{Ann. of Math.

Souhir Ben Amor, Smaranda Sgarciu, Taimyra BatzLineiro, Felix Muesgens

Global warming is caused by increasing concentrations of greenhouse gases, particularly carbon dioxide (CO2). A metric used to quantify the change in CO2 emissions is the marginal emission factor, defined as the marginal change in CO2 emissions resulting from a marginal change in electricity demand over a specified period. This paper aims to present two meth

Hao Gui, Lin Hu, Rui Chen, Mingxiao Huang

3D Gaussian Splatting (3DGS) is increasingly attracting attention in both academia and industry owing to its superior visual quality and rendering speed. However, training a 3DGS model remains a time-intensive task, especially in load imbalance scenarios where workload diversity among pixels and Gaussian spheres causes poor renderCUDA kernel performance. We

Youliang Zhang, Ronghui Li, Yachao Zhang, Liang Pan

Extracting physically plausible 3D human motion from videos is a critical task. Although existing simulation-based motion imitation methods can enhance the physical quality of daily motions estimated from monocular video capture, extending this capability to high-difficulty motions remains an open challenge. This can be attributed to some flawed motion clips

Clément Morand, Anne-Laure Ligozat, Aurélie Névéol

The compute requirements associated with training Artificial Intelligence (AI) models have increased exponentially over time. Optimisation strategies aim to reduce the energy consumption and environmental impacts associated with AI, possibly shifting impacts from the use phase to the manufacturing phase in the life-cycle of hardware. This paper investigates

Sulim Chun, Ho Jung Lee, In-Kwon Lee

In Redirected Walking (RDW), resets are an overt method that explicitly interrupts users, and they should be avoided to provide a quality user experience. The number of resets depends on the configuration of the physical environment; thus, inappropriate object placement can lead to frequent resets, causing motion sickness and degrading presence. However, est

Xiaopeng Li, Jingtong Gao, Pengyue Jia, Xiangyu Zhao

Multi Scenario Recommendation (MSR) tasks, referring to building a unified model to enhance performance across all recommendation scenarios, have recently gained much attention. However, current research in MSR faces two significant challenges that hinder the field's development: the absence of uniform procedures for multi-scenario dataset processing, thus h

Hangli Ge, Xiaojie Yang, Itsuki Matsunaga, Dizhi Huang

Accurate traffic prediction, especially predicting traffic conditions several days in advance is essential for intelligent transportation systems (ITS). Such predictions enable mid- and long-term traffic optimization, which is crucial for efficient transportation planning. However, the inclusion of diverse external features, alongside the complexities of spa

Wen-Yu Dong, Shaoshi Yang, Wei Lin, Wei Zhao

In harsh environments such as mountainous terrain, dense vegetation areas, or urban landscapes, a single type of unmanned aerial vehicles (UAVs) may encounter challenges like flight restrictions, difficulty in task execution, or increased risk. Therefore, employing multiple types of UAVs, along with satellite assistance, to collaborate becomes essential in s

Cao Thi Vi Ba, Nguyen Quang Bau, Nguyen Dinh Nam, Anh-Tuan Tran

We use the Profile numerical method to calculate the spectral line width, or half width at half maximum (HWHM) of the absorption peaks of multi-photon absorption processes in a two-dimensional graphene system (2DGS) according to important external parameters such as magnetic field and temperature in the presence of strong electromagnetic waves (SEMW). The ap

Srikireddy Dhanunjay Reddy, Pujayita Deb, Tharun Kumar Reddy Bollu

Early and optimal identification of cardiac anomalies, especially Myocardial infarction (MCI) can aid the individual in obtaining prompt medical attention to mitigate the severity. Electrocardiogram (ECG) is a simple non-invasive physiological signal modality, that can be used to examine the electrical activity of heart tissue. Existing methods for MCI detec

J. Gonzalez-Rosa, G. D. Megias, J. A. Caballero, M. B. Barbaro

In this work we compare the SuSAv2 model, based on the superscaling phenomenon and the relativistic mean field theory, with charged-current inclusive neutrino cross sections from the NOvA and MicroBooNE experiments, whose targets are composed primarily by 12 C and 40 Ar, respectively. The neutrino energy in these experiments covers a kinematic range from ten

Lei Li, Yuzhou Peng

We propose in this work a second-order Langevin sampler for the isothermal-isobaric ensemble (the NPT ensemble), preserving a positive volume for the simulation box. We first derive the suitable equations of motion for particles to be coupled with the overdamped Langevin equation of volume by sending the artificial mass of the periodic box to zero in the wor

Liqun Qi, Chunfeng Cui, Ziyan Luo

We define lower triangular tensors, and show that all diagonal entries of such a tensor are eigenvalues of that tensor. We then define lower triangular sub-symmetric tensors, and show that the number of independent entries of a lower triangular sub-symmetric tensor is the same as that of a symmetric tensor of the same order and dimension. We further introduc

Bernd Aichner, Sandra Keppert, Johannes D. Pedarnig, Wolfgang Lang

Few-unit cell thick Bi$_2$Sr$_2$CaCu$_2$O$_{8+x}$ (Bi-2212) layers have recently attracted much interest due to their extreme anisotropy and two-dimensional superconductivity, although they are typically susceptible to ambient conditions. In this study, we report on thin films approximately 13 unit cells thick that are stable in air, exhibit high anisotropy,

Min Lin, Gangwei Xu, Yun Wang, Xianqi Wang

Scene flow methods based on deep learning have achieved impressive performance. However, current top-performing methods still struggle with ill-posed regions, such as extensive flat regions or occlusions, due to insufficient local evidence. In this paper, we propose a novel global-aware scene flow estimation network with global motion propagation, named Flow

Qi Jia, Siyu Ren, Ziheng Qin, Fuzhao Xue

Datasets nowadays are generally constructed from multiple sources and using different synthetic techniques, making data de-noising and de-duplication crucial before being used for post-training. In this work, we propose to perform instruction tuning by iterative data selection (\ApproachName{}). We measure the quality of a sample from complexity and diversit

Jeongsu Yu

Text embedding models play a crucial role in natural language processing, particularly in information retrieval, and their importance is further highlighted with the recent utilization of RAG (Retrieval- Augmented Generation). This study presents an efficient fine-tuning methodology encompassing data selection, loss function, and model architecture to enhanc

Qiming Wang, Wenbin Liu

In this work, we present an efficient gradient projection method for solving a class of stochastic optimal control problem with expected integral state constraint. The first order optimality condition system consisting of forward-backward stochastic differential equations and a variational equation is first derived. Then, an efficient gradient projection met

Dhwani Gangal, Sudhava Yadav, K. K. Venkataratnam

This study investigates the nonclassical properties of the inflaton field within the framework of semiclassical gravity by analyzing Cosmological Mandels $Q$ parameter for Squeezed Number States (SNS) and Coherent Squeezed Number States (CSNS). Mandels $Q$ parameter serves as a critical tool for identifying nonclassical states by differentiating between sub-

Lu Chen, Bohan Wang, Chunhua Wang

In this paper, we consider the following Hardy-type mean field equation \[ \left\{ {\begin{array}{*{20}{c}} { - \Delta u-\frac{1}{(1-|x|^2)^2} u = \lambda e^u}, & {\rm in} \ \ B_1,\\ {\ \ \ \ u = 0,} &\ {\rm on}\ \partial B_1, \end{array}} \right. \] \[\] where $\lambda>0$ is small and $B_1$ is the standard unit disc of $\mathbb{R}^2$. Applying the moving pl

Andre Rusli, Makoto Shishido

This study investigates the performance of three popular tokenization tools: MeCab, Sudachi, and SentencePiece, when applied as a preprocessing step for sentiment-based text classification of Japanese texts. Using Term Frequency-Inverse Document Frequency (TF-IDF) vectorization, we evaluate two traditional machine learning classifiers: Multinomial Naive Baye

Ria Rashid, Abhishek Gupta

Analog circuit design can be considered as an optimization problem with the targeted circuit specifications as constraints. When stringent circuit specifications are considered, it is desired to have an optimization methodology that adapts well to heavily constrained search spaces. To this end, we propose a novel Bayesian optimization algorithm with a tiered

Pankaj Saha, Yuko Urakawa

We investigate the effects of local features in the inflationary potential on the preheating dynamics after inflation. We show that a small feature in the potential can enhance the resonance and bring the radiation-like state equation during preheating despite the inflationary potential being a quadratic one. Such localized features may naturally arise due t

Kanghyun Ryu, Jean-Baptiste Bouvier, Shazaib Lalani, Siegfried Eggl

The exponential increase in orbital debris and active satellites will lead to congested orbits, necessitating more frequent collision avoidance maneuvers by satellites. To minimize fuel consumption while ensuring the safety of satellites, enforcing a chance constraint, which poses an upper bound in collision probability with debris, can serve as an intuitive

David Valle, Rubén Capeáns, Alexandre Wagemakers, Miguel A. F. Sanjuán

Chaotic behavior in dynamical systems poses a significant challenge in trajectory control, traditionally relying on computationally intensive physical models. We present a machine learning-based algorithm to compute the minimum control bounds required to confine particles within a region indefinitely, using only samples of orbits that iterate within the regi

Sambit Mishra, Soumya P. Dash, George C. Alexandropoulos

This paper investigates the performance of one- and two-sided amplitude shift keying (ASK) modulations in noncoherent single-input single-output (SISO) wireless communication systems assisted by a reconfigurable intelligent surface (RIS). Novel noncoherent receiver structures are proposed based on the energy of the received symbol and the choice of the modul

Xingcheng Fu, Yisen Gao, Beining Yang, Yuxuan Wu

Dataset condensation has significantly improved model training efficiency, but its application on devices with different computing power brings new requirements for different data sizes. Thus, condensing multiple scale graphs simultaneously is the core of achieving efficient training in different on-device scenarios. Existing efficient works for multi-scale

Jinyuan Chang, Cheng Yong Tang, Yuanzheng Zhu

In this study, we introduce a novel methodological framework called Bayesian Penalized Empirical Likelihood (BPEL), designed to address the computational challenges inherent in empirical likelihood (EL) approaches. Our approach has two primary objectives: (i) to enhance the inherent flexibility of EL in accommodating diverse model conditions, and (ii) to fac

Hong-Tao Zheng, Xiang-Fa Zhou, Guang-Can Guo, Zheng-Wei Zhou

We propose a scheme to detect the Unruh effect in a circularly rotated Unruh-DeWitt detector enclosed within a cylindrical cavity. This technique relies on the enhanced atomic spontaneous emission rate related to the counter-rotating coupling between the detector and massless scalar fields. Our analysis demonstrates that the integration of a cylindrical cavi

Amy Iris Parker

Full-packet encryption is a technique used by modern evasive Virtual Private Networks (VPNs) to avoid protocol-based flagging from censorship models by disguising their traffic as random noise on the network. Traditional methods for censoring full-packet-encryption based VPN protocols requires assuming a substantial amount of collateral damage, as other non-

Gui Zhang, Yichao Yao, Ziyan Zeng, Minyu Feng

Reputation and punishment are significant guidelines for regulating individual behavior in human society, and those with a good reputation are more likely to be imitated by others. In addition, society imposes varying degrees of punishment for behaviors that harm the interests of groups with different reputations. However, conventional pairwise interaction r

Muhammad Ahmad, Manuel Mazzara, Salvatore Distefano, Adil Mehmood Khan

Hyperspectral image classification (HSIC) has gained significant attention because of its potential in analyzing high-dimensional data with rich spectral and spatial information. In this work, we propose the Differential Spatial-Spectral Transformer (DiffFormer), a novel framework designed to address the inherent challenges of HSIC, such as spectral redundan

Yindan Luo, Jiaxin Cai

Deep learning is an advanced technology that relies on large-scale data and complex models for feature extraction and pattern recognition. It has been widely applied across various fields, including computer vision, natural language processing, and speech recognition. In recent years, deep learning has demonstrated significant potential in the realm of prote

Thomas Rückstieß, Alana Huang, Robin Vujanic

Despite the popularity and widespread use of semi-structured data formats such as JSON, end-to-end supervised learning applied directly to such data remains underexplored. We present ORIGAMI (Object RepresentatIon via Generative Autoregressive ModellIng), a transformer-based architecture that directly processes nested key/value pairs while preserving their h

Yin Qixuan

Sentiment analysis is a crucial task in natural language processing (NLP) with applications in public opinion monitoring, market research, and beyond. This paper introduces a three-class sentiment classification method for Weibo comments using Long Short-Term Memory (LSTM) networks to discern positive, neutral, and negative sentiments. LSTM, as a deep learni

Xinyuan Wu, Lili Wang, Ruoyu Chen, Bowen Liu

Fundus fluorescein angiography (FFA) is critical for diagnosing retinal vascular diseases, but beginners often struggle with image interpretation. This study develops FFA Sora, a text-to-video model that converts FFA reports into dynamic videos via a Wavelet-Flow Variational Autoencoder (WF-VAE) and a diffusion transformer (DiT). Trained on an anonymized dat

Balder ten Cate, Raoul Koudijs, Ana Ozaki

Labeled examples (i.e., positive and negative examples) are an attractive medium for communicating complex concepts. They are useful for deriving concept expressions (such as in concept learning, interactive concept specification, and concept refinement) as well as for illustrating concept expressions to a user or domain expert. We investigate the power of l

Akane Tsuboya, Yu Kono, Tatsuji Takahashi

The objective of a reinforcement learning agent is to discover better actions through exploration. However, typical exploration techniques aim to maximize rewards, often incurring high costs in both exploration and learning processes. We propose a novel deep reinforcement learning method, which prioritizes achieving an aspiration level over maximizing expect

Fuhua Jia, Xiaoying Yang, Mengshen Yang, Yang Li

Performing simultaneous localization and mapping (SLAM) in low-visibility conditions, such as environments filled with smoke, dust and transparent objets, has long been a challenging task. Sensors like cameras and Light Detection and Ranging (LiDAR) are significantly limited under these conditions, whereas ultrasonic sensors offer a more robust alternative.

Chao Fan, Fangsheng Wu, Ali Mostafavi

Online social networks are increasingly being utilized for collective sense making and information processing in disasters. However, the underlying mechanisms that shape the dynamics of collective intelligence in online social networks during disasters is not fully understood. To bridge this gap, we examine the mechanisms of collective information processing

Jinyuan Chang, Jing He, Chen Lin, Qiwei Yao

High-dimensional time series analysis has become increasingly important in fields such as finance, economics, and biology. The two primary tasks for high-dimensional time series analysis are modeling and statistical inference, which aim to capture the underlying dynamic structure and investigate valuable information in the data. This paper presents the HDTSA

Deepak K. Roy, Mukul Kabir

The quest for room-temperature nanoscale magnets remains a central challenge, driven by their promising applications in quantum technologies. Layered $4d$ and $5d$ transition metal oxides with high magnetic ordering temperatures offer significant potential in this context. We explore ultrathin \ce{SrRu2O6} nanosheets using first-principles calculations, comp

Beibei Yu, Tao Shen, Hongbin Na, Ling Chen

Remote-sensing mineral exploration is critical for identifying economically viable mineral deposits, yet it poses significant challenges for multimodal large language models (MLLMs). These include limitations in domain-specific geological knowledge and difficulties in reasoning across multiple remote-sensing images, further exacerbating long-context issues.

Xin Gao, Yang Lin, Ruiqing Li, Yasha Wang

Data mining and knowledge discovery are essential aspects of extracting valuable insights from vast datasets. Neural topic models (NTMs) have emerged as a valuable unsupervised tool in this field. However, the predominant objective in NTMs, which aims to discover topics maximizing data likelihood, often lacks alignment with the central goals of data mining a

Yueyang Li, Zijian Kang, Shengyu Gong, Wenhao Dong

Decoding neural visual representations from electroencephalogram (EEG)-based brain activity is crucial for advancing brain-machine interfaces (BMI) and has transformative potential for neural sensory rehabilitation. While multimodal contrastive representation learning (MCRL) has shown promise in neural decoding, existing methods often overlook semantic consi

Zihao Li, Dongqi Fu, Mengting Ai, Jingrui He

Knowledge graphs (KGs), which store an extensive number of relational facts, serve various applications. Recently, personalized knowledge graphs (PKGs) have emerged as a solution to optimize storage costs by customizing their content to align with users' specific interests within particular domains. In the real world, on one hand, user queries and their unde

Chayut Wongkamthong

In social science research, understanding latent structures in populations through survey data with categorical responses is a common and important task. Traditional methods like Factor Analysis and Latent Class Analysis have limitations, particularly in handling categorical data and accommodating mixed memberships in latent structures, respectively. Moreove

Lanhang Ye, Toshiyuki Yamamoto

Modular autonomous vehicles (MAVs) represent a transformative paradigm in the rapidly advancing field of autonomous vehicle technology. The integration of modularity offers numerous advantages, poised to reshape urban mobility systems and foster innovation in this emerging domain. Although publications on MAVs have only gained traction in the past five years

Daniel Tan, Neftali Watkinson Medina

Unlike repetitions in Western Chess where all repetitions are draws, repetitions in Chinese Chess could result in a win, draw, or loss depending on the kind of repetition being made by both players. One of the biggest hurdles facing Chinese Chess application development is a proper system for judging games correctly. This paper introduces a complete algorith

Jaeheun Jung, Jaehyuk Lee, Changhae Jung, Hanyoung Kim

Shock waves caused by earthquakes can be devastating. Generating realistic earthquake-caused ground motion waveforms help reducing losses in lives and properties, yet generative models for the task tend to generate subpar waveforms. We present High-fidelity Earthquake Groundmotion Generation System (HEGGS) and demonstrate its superior performance using earth

Hugo Prod'homme, Philipp del Hougne

The design of large complex wave systems (filters, networks, vacuum-electronic devices, metamaterials, smart radio environments, etc.) requires repeated evaluations of the scattering parameters resulting from complex connections between constituent subsystems. Instead of starting each new evaluation from scratch, we propose a computationally efficient method

Yujie Lin, Jingyao Liu, Yan Gao, Ante Wang

Metaphor detection, a critical task in natural language processing, involves identifying whether a particular word in a sentence is used metaphorically. Traditional approaches often rely on supervised learning models that implicitly encode semantic relationships based on metaphor theories. However, these methods often suffer from a lack of transparency in th

Jianjian Yin, Yi Chen, Zhichao Zheng, Junsheng Zhou

Semi-supervised semantic segmentation has attracted considerable attention for its ability to mitigate the reliance on extensive labeled data. However, existing consistency regularization methods only utilize high certain pixels with prediction confidence surpassing a fixed threshold for training, failing to fully leverage the potential supervisory informati

Harsh Joshi

This research paper presents the development of a lightweight and efficient computer vision pipeline aimed at assisting farmers in detecting orange diseases using minimal resources. The proposed system integrates advanced object detection, classification, and segmentation models, optimized for deployment on edge devices, ensuring functionality in resource-li

Théo Matricon, Nathanaël Fijalkow, Guillaume Lagarde

Many approaches to program synthesis perform a combinatorial search within a large space of programs to find one that satisfies a given specification. To tame the search space blowup, previous works introduced probabilistic and neural approaches to guide this combinatorial search by inducing heuristic cost functions. Best-first search algorithms ensure to se

Jukka Ruohonen

In security engineering, including software security engineering, there is a well-known design paradigm telling to prefer safe and secure defaults. The paper presents a systematization of knowledge (SoK) of this paradigm by the means of a systematic mapping study and a scoping review of relevant literature. According to the mapping and review, the paradigm h

Meixia Lin, Yangjing Zhang

Common clustering methods, such as $k$-means and convex clustering, group similar vector-valued observations into clusters. However, with the increasing prevalence of matrix-valued observations, which often exhibit low rank characteristics, there is a growing need for specialized clustering techniques for these data types. In this paper, we propose a low ran

Ufuk Beyaztas, Han Lin Shang, Gizel Bakicierler Sezer, Abhijit Mandal

We introduce a spatial function-on-function regression model to capture spatial dependencies in functional data by integrating spatial autoregressive techniques with functional principal component analysis. The proposed model addresses a critical gap in functional regression by enabling the analysis of functional responses influenced by spatially correlated

Shahin Hasanbeigi

The objective of this study is to present a novel, efficient, and fast direct method for solving linear systems of equations whose coefficient matrix is a tridiagonal Quasi-Toeplitz matrix. Such matrices are frequently encountered in the discretization of second-order differential equation problems with Neumann boundary conditions, the discretization of Quas

Helia Mohamadi, Mohammad Ali Keyvanrad, Mohammad Reza Mohammadi

Domain adaptation, a pivotal branch of transfer learning, aims to enhance the performance of machine learning models when deployed in target domains with distinct data distributions. This is particularly critical for object detection tasks, where domain shifts (caused by factors such as lighting conditions, viewing angles, and environmental variations) can l

Chayan Karmakar

In this paper we compute the character values of highest weight representations for classical groups of types A_n, B_n, C_n, D_n and the Exceptional group G_2 at all conjugacy classes of order 2. We prove that these character values, if nonzero, can be expressed either as a product involving the dimensions of two highest weight representations from classical

Artyom Stitsyuk, Jaesik Choi

In recent years, the application of transformer-based models in time-series forecasting has received significant attention. While often demonstrating promising results, the transformer architecture encounters challenges in fully exploiting the temporal relations within time series data due to its attention mechanism. In this work, we design eXponential Patch

Hechong Chen, Zihan Liu, Shengdi Lian, Qingying Quan

We report experimental observation and theoretical explanation of novel propagation regimes for optical beams in an artificial nonlinear material with outstanding photorefractive properties. Nondiffractive beams, which keep their shapes invariant in the free space, feature self-splitting from the middle in two separating secondary beams, due to the light-mat

Nicolas Devatine, Louis Abraham

Assessing the extent of human edits on texts generated by Large Language Models (LLMs) is crucial to understanding the human-AI interactions and improving the quality of automated text generation systems. Existing edit distance metrics, such as Levenshtein, BLEU, ROUGE, and TER, often fail to accurately measure the effort required for post-editing, especiall

Eric Marberg, Brendan Pawlowski

We study an inductive method of computing initial ideals and Gr\"obner bases for families of ideals in a polynomial ring. This method starts from a given set of pairs $(I,J)$ where $I$ is any ideal and $J$ is a monomial ideal contained in the initial ideal of $I$. These containments become a system of equalities if one can establish a particular transition r

Yuying Chen, Zhengxi Zhang, Chi-Kin Lai, Yun Liang

We investigate the scattering halos resulting from collisions between discrete momentum components in the time-of-flight expansion of interaction-tunable $^6\rm Li_2$ molecular Bose-Einstein condensates. A key highlight of this study is the observation of the influence of interactions on the collisional scattering process. We measure the production of scatte

Nick Oh

This position paper defends post-hoc explainability methods as legitimate tools for scientific knowledge production in machine learning. Addressing criticism of these methods' reliability and epistemic status, we develop a philosophical framework grounded in mediated understanding and bounded factivity. We argue that scientific insights can emerge through st

Young-Ju Lee, Jongho Park

We present new convergence analyses for parallel subspace correction methods for unconstrained semicoercive and nearly semicoercive convex optimization problems, generalizing the theory of singular and nearly singular linear problems to a class of nonlinear problems. Our results demonstrate that the elegant theoretical framework developed for singular and ne

Yuying Wang, Yichen Li, Haozhao Wang, Lei Zhao

Cross-Project Defect Prediction (CPDP) poses a non-trivial challenge to construct a reliable defect predictor by leveraging data from other projects, particularly when data owners are concerned about data privacy. In recent years, Federated Learning (FL) has become an emerging paradigm to guarantee privacy information by collaborative training a global model

Yang Cao, Jiayan Huo, Yingyu Liang, Zhenmei Shi

The Rotary Position Embedding (RoPE) mechanism has become a powerful enhancement to the Transformer architecture, which enables models to capture token relationships when encoding positional information. However, the RoPE mechanisms make the computations of attention mechanisms more complicated, which makes efficient algorithms challenging. Earlier research

Linhao Zhang, Daoguang Zan, Quanshun Yang, Zhirong Huang

Large Language Models (LLMs) have advanced rapidly in recent years, with their applications in software engineering expanding to more complex repository-level tasks. GitHub issue resolving is a key challenge among these tasks. While recent approaches have made progress on this task, they focus on textual data within issues, neglecting visual data. However, t

WonTae Hwang, Kyunghwan Song

The greatest integer that does not belong to a numerical semigroup $S$ is called the Frobenius number of $S$, and finding the Frobenius number is called the Frobenius problem. In this paper, we solve the Frobenius problem for the numerical semigroups generated by binomial coefficients. As applications, we provide some nontrivial identities among binomial coe

Pengbin Feng, Yankaiqi Li, Yijiashun Qi, Xiaojun Guo

This study proposes a multi-task learning framework based on ResNeXt, aiming to solve the problem of feature extraction and task collaborative optimization in financial data mining. Financial data usually has the complex characteristics of high dimensionality, nonlinearity, and time series, and is accompanied by potential correlations between multiple tasks,

Evan C. Johnson

Ecologists have long argued about the strength of density dependence and population regulation, respectively defined as the short-term and long-term rates of return to equilibrium. Here, I give three arguments for the intractability of population regulation. First, the ecological literature flip-flops on the strength of evidence for population regulation; by

Minh-Duc Nguyen, Phuong Mai Dinh, Quang-Huy Nguyen, Long P. Hoang

Expensive multi-objective optimization problems (EMOPs) are common in real-world scenarios where evaluating objective functions is costly and involves extensive computations or physical experiments. Current Pareto set learning methods for such problems often rely on surrogate models like Gaussian processes to approximate the objective functions. These surrog

Kumar Balasubramanian, Sanjeev Kumar Pandey, Renu Joshi, Varsha Vasudevan

Let $F$ be a non-Archimedean local field of characteristic zero and $G=\GL(2,F)$. Let $n\geq 2$ be a positive integer and $\widetilde{G}=\widetilde{\GL}(2,F)$ be the $n$-fold metaplectic cover of $G$. Let $\pi$ be an irreducible smooth representation of $G$ and $\pi^{\vee}$ be the contragredient of $\pi$. Let $\tau$ be an involutive anti-automorphism of $G$

Ashutosh Nayak, Prajwal NJ, Sameeksha Keshav, Kavitha S. N.

Recommender systems create enormous value for businesses and their consumers. They increase revenue for businesses while improving the consumer experience by recommending relevant products amidst huge product base. Product bundling is an exciting development in the field of product recommendations. It aims at generating new bundles and recommending exciting

Nicholas J. Pritchard

Quantum computing promises solutions to classically difficult and new-found problems through controlling the subtleties of quantum computing. The Quantum Approximate Optimisation Algorithm (QAOA) is a recently proposed quantum algorithm designed to tackle difficult combinatorial optimisation problems utilising both quantum and classical computation. The hybr

Tesshu Hanaka, Yasuaki Kobayashi, Kazuhiro Kurita, Yasuko Matsui

A tree is said to be even if for every pair of distinct leaves, the length of the unique path between them is even. In this paper we discuss the problem of determining whether an input graph has a spanning even tree. Hofmann and Walsh [Australas. J Comb. 35, 2006] proved that this problem can be solved in polynomial time on bipartite graphs. In contrast to t

Gongyu Chen, Haomin Zhang, Chaofan Ding, Zihao Chen

One fascinating aspect of pre-trained Audio-Language Models (ALMs) learning is their impressive zero-shot generalization capability and test-time adaptation (TTA) methods aiming to improve domain performance without annotations. However, previous test time adaptation (TTA) methods for ALMs in zero-shot classification tend to be stuck in incorrect model predi

Di Yu, Xin Du, Linshan Jiang, Huijing Zhang

The energy efficiency of deep spiking neural networks (SNNs) aligns with the constraints of resource-limited edge devices, positioning SNNs as a promising foundation for intelligent applications leveraging the extensive data collected by these devices. To address data privacy concerns when deploying SNNs on edge devices, federated learning (FL) facilitates c

Vinay Prithyani, Mohsin Mohammed, Richa Gadgil, Ricardo Buitrago

We build upon time-series classification by leveraging the capabilities of Vision Language Models (VLMs). We find that VLMs produce competitive results after two or less epochs of fine-tuning. We develop a novel approach that incorporates graphical data representations as images in conjunction with numerical data. This approach is rooted in the hypothesis th

Bo Jiang, Wanrong Zhang, Donghang Lu, Jian Du

Local Differential Privacy (LDP) protocols enable the collection of randomized client messages for data analysis, without the necessity of a trusted data curator. Such protocols have been successfully deployed in real-world scenarios by major tech companies like Google, Apple, and Microsoft. In this paper, we propose a Generalized Count Mean Sketch (GCMS) pr

Fengyi Wu, Simin Liu, Haoan Wang, Bingjie Tao

Optimization-based approaches dominate infrared small target detection as they leverage infrared imagery's intrinsic low-rankness and sparsity. While effective for single-frame images, they struggle with dynamic changes in multi-frame scenarios as traditional spatial-temporal representations often fail to adapt. To address these challenges, we introduce a Ne

Xiaoye Wang

The rapid development of cloud-native architecture has promoted the widespread application of container technology, but the optimization problems in container scheduling and resource management still face many challenges. This paper proposes a container scheduling method based on multi-objective optimization, which aims to balance key performance indicators

Yi'an Zhou, Xiaoli Yan, Zhike Xue, Liheng Yang

Aims. This study aims to investigate the deviation of the intensity ratio of the \ion{Si}{IV} 1394 \AA\ and 1403 \AA\ emission lines from the expected value of 2 in the optically thin regime, as observed in many recent studies. Methods. We analyzed the integrated intensity ratio ($R$) and the wavelength-dependent ratio ($r(\Delta\lambda)$) in a small bifurca

Faisal Alkaabneh, Sam Jotham Sutharson

As an important means of providing medical services in developing countries and remote areas, Mobile Health Clinics (MHCs) focus on distributing medical supplies and providing basic health needs to underserved communities. In this paper, we propose a new model for the mobile health clinics with resupply from a truck and heterogeneous demand. In addition to t

Mahan Tafreshipour, Aaron Imani, Eric Huang, Eduardo Almeida

The adoption of Large Language Models (LLMs) is reshaping software development as developers integrate these LLMs into their applications. In such applications, prompts serve as the primary means of interacting with LLMs. Despite the widespread use of LLM-integrated applications, there is limited understanding of how developers manage and evolve prompts. Thi

Kaifang Long, Guoyang Xie, Lianbo Ma, Jiaqi Liu

Existing efforts to boost multimodal fusion of 3D anomaly detection (3D-AD) primarily concentrate on devising more effective multimodal fusion strategies. However, little attention was devoted to analyzing the role of multimodal fusion architecture (topology) design in contributing to 3D-AD. In this paper, we aim to bridge this gap and present a systematic s

Abel C. H. Chen

With the advancement of quantum computing technologies, recent years have seen increasing efforts to identify cryptographic methods resistant to quantum attacks and to establish post-quantum cryptography (PQC) approaches. Among these, hash-based digital signature algorithms (DSAs) are a notable category of PQC. Hash functions are not only utilized in digital

Shota Nishijima, Asuka Takai

Methods for converting the tacit knowledge of experts into explicit knowledge have drawn increasing attention. Gaze data has emerged as a valuable approach in this effort. However, the effective transfer of tacit knowledge remains a challenge. No studies have directly compared the effects of gaze-based and annotation-based guidance or adequately examined the

Yueqian Wang, Xiaojun Meng, Yuxuan Wang, Jianxin Liang

Multi-modal multi-party conversation (MMC) is a less studied yet important topic of research due to that it well fits real-world scenarios and thus potentially has more widely-used applications. Compared with the traditional multi-modal conversations, MMC requires stronger character-centered understanding abilities as there are many interlocutors appearing i