December 2024 arXiv papers — page 31

Chenyang Lei, Weiyuan Peng, Guang Zhou, Meiying Zhang

Most autonomous driving (AD) datasets incur substantial costs for collection and labeling, inevitably yielding a plethora of low-quality and redundant data instances, thereby compromising performance and efficiency. Many applications in AD systems necessitate high-quality training datasets using both existing datasets and newly collected data. In this paper,

Erfan Shahriari, Kim Kirstin Peper, Matej Hoffmann, Sami Haddadin

Manipulability analysis is a methodology employed to assess the capacity of an articulated system, at a specific configuration, to produce motion or exert force in diverse directions. The conventional method entails generating a virtual ellipsoid using the system's configuration and model. Yet, this approach poses challenges when applied to systems such as t

Simon Kohaut, Benedict Flade, Daniel Ochs, Devendra Singh Dhami

Advanced Air Mobility (AAM) is a growing field that demands accurate and trustworthy models of legal concepts and restrictions for navigating Unmanned Aircraft Systems (UAS). In addition, any implementation of AAM needs to face the challenges posed by inherently dynamic and uncertain human-inhabited spaces robustly. Nevertheless, the employment of UAS beyond

Lifeng Han, Kilian Evang, Archna Bhatia, Gosse Bouma

Starting in 2003 when the first MWE workshop was held with ACL in Sapporo, Japan, this year, the joint workshop of MWE-UD co-located with the LREC-COLING 2024 conference marked the 20th anniversary of MWE workshop events over the past nearly two decades. Standing at this milestone, we look back to this workshop series and summarise the research topics and me

F. Acerbi, P. Adhikari, P. Agnes, I. Ahmad

DarkSide-20k is a novel liquid argon dark matter detector currently under construction at the Laboratori Nazionali del Gran Sasso (LNGS) of the Istituto Nazionale di Fisica Nucleare (INFN) that will push the sensitivity for Weakly Interacting Massive Particle (WIMP) detection into the neutrino fog. The core of the apparatus is a dual-phase Time Projection Ch

A. V. Nesterov

The asymptotics of a singularly perturbed problem is constructed. describing the transport of a polydisperse impurity in the atmosphere, taking into account the processes of precipitation and wind pick-up, as well as the processes of coagulation - dissociation. The mathematical model of this process represents a differential-operator equation of turbulent di

Tom Baby, Mahendra Kumar Gohil, Bishakh Bhattacharya

In the futuristic agricultural fields compatible with Agriculture 4.0, robots are envisaged to navigate through crops to perform functions like pesticide spraying and fruit harvesting, which are complex tasks due to factors such as non-geometric internal obstacles, space constraints, and outdoor conditions. In this paper, we attempt to employ Deep Reinforcem

Igor de M. Froldi, Carlos Eduardo S. P. Corsino, Hermann Freire

We investigate the Hatsugai-Kohmoto (HK) model on a square lattice, which describes both a Mott insulator at half-filling and a non-Fermi liquid phase on doping. Through the solution of this exactly solvable model with the inclusion of pairing interactions, we demonstrate the emergence of strong pair-density-wave (PDW) fluctuations associated with center-of-

Meltem Aksoy

Large language models (LLMs) have become integral tools in diverse domains, yet their moral reasoning capabilities across cultural and linguistic contexts remain underexplored. This study investigates whether multilingual LLMs, such as GPT-3.5-Turbo, GPT-4o-mini, Llama 3.1, and MistralNeMo, reflect culturally specific moral values or impose dominant moral no

Chenghao Qian, Yuhu Guo, Wenjing Li, Gustav Markkula

3D Gaussian Splatting (3DGS) has gained significant attention for 3D scene reconstruction, but still suffers from complex outdoor environments, especially under adverse weather. This is because 3DGS treats the artifacts caused by adverse weather as part of the scene and will directly reconstruct them, largely reducing the clarity of the reconstructed scene.

Hui Lei, Mei Lu, Yongtang Shi, Jian Sun

Constructing the maximum spanning tree $T$ of an edge-weighted connected graph $G$ is one of the important research topics in computer science and optimization, and the related research results have played an active role in practical applications. In this paper, we are concerned with the ratio of the weighted sum of a spanning tree $T$ of $G$ to the weighted

Gursimran Singh, Xinglu Wang, Yifan Hu, Timothy Yu

Large Multimodal Models (LMMs) extend Large Language Models (LLMs) by handling diverse inputs such as images, audio, and video, but at the cost of adding a multimodal encoding stage that increases both computational and memory overhead. This step negatively affects key Service Level Objectives (SLOs), such as time to first token (TTFT) and time per output to

Liang Wang, Nan Yang, Xingxing Zhang, Xiaolong Huang

We introduce a bootstrapping approach to train long-context language models by exploiting their short-context capabilities only. Our method utilizes a simple agent workflow to synthesize diverse long-context instruction tuning data, thereby eliminating the necessity for manual data collection and annotation. The proposed data synthesis workflow requires only

Shoma Kudo, Satoshi Kagiwada, Hitoshi Iyatomi

Numerous studies have explored image-based automated systems for plant disease diagnosis, demonstrating impressive diagnostic capabilities. However, recent large-scale analyses have revealed a critical limitation: that the diagnostic capability suffers significantly when validated on images captured in environments (domains) differing from those used during

Olga Krivorotko, Tatiana Zvonareva, Andrei Neverov

This paper investigates the identifiability of a spatial mathematical model of the spread of fast-moving epidemics based on the law of acting masses and diffusion processes. The research algorithm is based on global methods of Sobol sensitivity analysis and Bayesian approach, which together allowed to reduce the variation boundaries of unknown parameters for

Qihao Cheng, Da Yan, Tianhao Wu, Zhongyi Huang

Given a graph pair $(G^1, G^2)$, graph edit distance (GED) is defined as the minimum number of edit operations converting $G^1$ to $G^2$. GED is a fundamental operation widely used in many applications, but its exact computation is NP-hard, so the approximation of GED has gained a lot of attention. Data-driven learning-based methods have been found to provid

Xiaowen Ye, Xianghao Yu, Liqun Fu

Intelligent omni-surface (IOS) is a promising technique to enhance the capacity of wireless networks, by reflecting and refracting the incident signal simultaneously. Traditional IOS configuration schemes, relying on all sub-channels' channel state information and user equipments' mobility, are difficult to implement in complex realistic systems. Existing wo

Qin-Wen Luo, Ming-Kun Xie, Ye-Wen Wang, Sheng-Jun Huang

Offline-to-online (O2O) reinforcement learning (RL) provides an effective means of leveraging an offline pre-trained policy as initialization to improve performance rapidly with limited online interactions. Recent studies often design fine-tuning strategies for a specific offline RL method and cannot perform general O2O learning from any offline method. To d

Yuhang Gu, Songbai Chen, Jiliang Jing

The swirling-Kerr black hole is a novel solution of vacuum general relativity and has an extra swirling parameter characterizing the rotation of spacetime background. We have studied the gravitational waves generated by extreme mass ratio inspirals (EMRIs) along eccentric orbits on equatorial plane in this novel swirling spacetime. Our findings indicate that

Xiamiao Zhao, Mei Lu

Let $\mathscr{F}$ be a family of graphs. A graph $G$ is $\mathscr{F}$-free if $G$ does not contain any $F\in \mathcal{F}$ as a subgraph. The general Tur\'an number, denoted by $ex(n, H,\mathscr{F})$, is the maximum number of copies of $H$ in an $n$-vertex $\mathscr{F}$-free graph. Then $ex(n, K_2,\mathscr{F})$, also denote by $ex(n, \mathscr{F})$, is the Tur

Qiong Wu, Panwang Xia, Lei Yu, Yi Liu

Cross-view geo-localization (CVGL) has been widely applied in fields such as robotic navigation and augmented reality. Existing approaches primarily use single images or fixed-view image sequences as queries, which limits perspective diversity. In contrast, when humans determine their location visually, they typically move around to gather multiple perspecti

Fei Zhao, Xueliang Zhang

Acoustic Echo Cancellation (AEC) is an essential speech signal processing technology that removes echoes from microphone inputs to facilitate natural-sounding full-duplex communication. Currently, deep learning-based AEC methods primarily focus on refining model architectures, frequently neglecting the incorporation of knowledge from traditional filter theor

Sheng Liu, Yong-Sheng Zhang

We investigate the quench dynamics of a two-dimensional Rashba spin-orbit coupled Bose-Einstein condensate. Our study focuses on quenching the system from a zero-momentum phase to a plane-wave phase. During this quench, topological defects emerge in the form of vortices. These vortices and anti-vortices exhibit a random spatial distribution with equal number

Maxence Boels, Yang Liu, Prokar Dasgupta, Alejandro Granados

While existing approaches excel at recognising current surgical phases, they provide limited foresight and intraoperative guidance into future procedural steps. Similarly, current anticipation methods are constrained to predicting short-term and single events, neglecting the dense, repetitive, and long sequential nature of surgical workflows. To address thes

Manuel Bolz, Kevin Brundler, Liam Kane, Panagiotis Patsias

Cryptocurrency markets often face manipulation through prevalent pump-and-dump (P&D) schemes, where self-organized Telegram groups, some exceeding two million members, artificially inflate target cryptocurrency prices. These groups sell premium access to inside information, worsening information asymmetry and financial risks for subscribers and all investors

Zhongwen Wang, Xingfeng Li, Yinghui Sun, Quansen Sun

In recent years, anchor and hash-based multi-view clustering methods have gained attention for their efficiency and simplicity in handling large-scale data. However, existing methods often overlook the interactions among multi-view data and higher-order cooperative relationships during projection, negatively impacting the quality of hash representation in lo

Shun Ishii, Yuki Goto

In this paper, we study certain Kummer characters, which we call the elliptic Soul\'e characters, arising from Galois actions on the pro-$p$ fundamental groups of once-punctured elliptic curves with complex multiplication. In particular, we prove that elliptic Soul\'e characters having values in Tate twists can be written in terms of the Soul\'e characters a

Alexandr Korchemnyi, Alexey K. Kovalev, Aleksandr I. Panov

The idea of disentangled representations is to reduce the data to a set of generative factors that produce it. Typically, such representations are vectors in latent space, where each coordinate corresponds to one of the generative factors. The object can then be modified by changing the value of a particular coordinate, but it is necessary to determine which

Xianjun Gao, Jianchun Liu, Hongli Xu, Shilong Wang

Federated Graph Learning (FGL) has demonstrated the advantage of training a global Graph Neural Network (GNN) model across distributed clients using their local graph data. Unlike Euclidean data (\eg, images), graph data is composed of nodes and edges, where the overall node-edge connections determine the topological structure, and individual nodes along wit

Yuchen Ren, Zhengyu Zhao, Chenhao Lin, Bo Yang

Transferable adversarial examples are known to cause threats in practical, black-box attack scenarios. A notable approach to improving transferability is using integrated gradients (IG), originally developed for model interpretability. In this paper, we find that existing IG-based attacks have limited transferability due to their naive adoption of IG in mode

Matteo Biagiola, Gianluca Ghislotti, Paolo Tonella

Search-based test generators are effective at producing unit tests with high coverage. However, such automatically generated tests have no meaningful test and variable names, making them hard to understand and interpret by developers. On the other hand, large language models (LLMs) can generate highly readable test cases, but they are not able to match the e

Heng-Bo Fan, Ming-Kun Xie, Jia-Hao Xiao, Sheng-Jun Huang

Due to the lack of extensive precisely-annotated multi-label data in real word, semi-supervised multi-label learning (SSMLL) has gradually gained attention. Abundant knowledge embedded in vision-language models (VLMs) pre-trained on large-scale image-text pairs could alleviate the challenge of limited labeled data under SSMLL setting.Despite existing methods

Aryaman Maithani

If $G$ is a linearly reductive group acting rationally on a polynomial ring $S$, then the inclusion $S^{G} \hookrightarrow S$ possesses a unique $G$-equivariant splitting, called the Reynolds operator. We describe algorithms for computing the Reynolds operator for the classical actions as in Weyl's book. The groups are the general linear group, the special l

Huiyu Yang, Yunpeng Wang, Jianchun Wang

Transformer neural operators have recently become an effective approach for surrogate modeling of systems governed by partial differential equations (PDEs). In this paper, we introduce a modified implicit factorized transformer (IFactFormer-m) model which replaces the original chained factorized attention with parallel factorized attention. The IFactFormer-m

Neil Shah, Shirish Karande, Vineet Gandhi

Current Non-Audible Murmur (NAM)-to-speech techniques rely on voice cloning to simulate ground-truth speech from paired whispers. However, the simulated speech often lacks intelligibility and fails to generalize well across different speakers. To address this issue, we focus on learning phoneme-level alignments from paired whispers and text and employ a Text

Ruohong Yang, Peng Hu, Xi Peng, Xiting Liu

Fine-grained clustering is a practical yet challenging task, whose essence lies in capturing the subtle differences between instances of different classes. Such subtle differences can be easily disrupted by data augmentation or be overwhelmed by redundant information in data, leading to significant performance degradation for existing clustering methods. In

Wenjie He, Chunfeng Huang, Rui Guan, Ye Chen

Secure quantum remote sensing (SQRS) uses quantum states to gather information about distant objects or environments while ensuring secure data transmission against eavesdropping. It has potential applications in various fields, including environmental monitoring, military surveillance, and disaster response, where both data accuracy and transmission securit

Neil Shah, Ayan Kashyap, Shirish Karande, Vineet Gandhi

Previous real-time MRI (rtMRI)-based speech synthesis models depend heavily on noisy ground-truth speech. Applying loss directly over ground truth mel-spectrograms entangles speech content with MRI noise, resulting in poor intelligibility. We introduce a novel approach that adapts the multi-modal self-supervised AV-HuBERT model for text prediction from rtMRI

Hao Zhang, Dongjun Yu, Lei Zhang, Guoping Rong

Log statements have become an integral part of modern software systems. Prior research efforts have focused on supporting the decisions of placing log statements, such as where/what to log. With the increasing adoption of Large Language Models (LLMs) for code-related tasks such as code completion or generation, automated approaches for generating log stateme

Bowen Gu, Hao Chen, Ming Lu, Jie Yao

Deep video compression has made significant progress in recent years, achieving rate-distortion performance that surpasses that of traditional video compression methods. However, rate control schemes tailored for deep video compression have not been well studied. In this paper, we propose a neural network-based $\lambda$-domain rate control scheme for deep v

Pochuan Wang, Chen Shen, Masahiro Oda, Chiou-Shann Fuh

In medical imaging, developing generalized segmentation models that can handle multiple organs and lesions is crucial. However, the scarcity of fully annotated datasets and strict privacy regulations present significant barriers to data sharing. Federated Learning (FL) allows decentralized model training, but existing FL methods often struggle with partial l

Shujie Hu, Xurong Xie, Mengzhe Geng, Jiajun Deng

Data-intensive fine-tuning of speech foundation models (SFMs) to scarce and diverse dysarthric and elderly speech leads to data bias and poor generalization to unseen speakers. This paper proposes novel structured speaker-deficiency adaptation approaches for SSL pre-trained SFMs on such data. Speaker and speech deficiency invariant SFMs were constructed in t

Wenhuang Wu, Lulu Guo, Nan Li, Hong Chen

This article proposes a data-driven $H_{\infty}$ control scheme for time-domain constrained systems based on model predictive control formulation. The scheme combines $H_{\infty}$ control and minimax model predictive control, enabling more effective handling of external disturbances and time-domain constraints. First, by leveraging input-output-disturbance d

Joaquín Moraga, José Ignacio Yáñez

A Calabi-Yau pair of index one and complexity zero is toric. Furthermore, a Calabi-Yau pair of index one and complexity one is of cluster type. In this article, we study Calabi-Yau pairs of index one and complexity two. We develop machinery to decide whether a Calabi-Yau of complexity two is of cluster type. This approach reduces the problem to studying del

Pavel Pokhilko, Chia-Nan Yeh, Miguel A. Morales, Dominika Zgid

For molecules and solids, we developed efficient MPI-parallel algorithms for evaluating the second-order exchange term with bare, statically screened, and dynamically screened interactions. We employ the resulting term in a fully self-consistent manner together with scGW, resulting in the following vertex-corrected scGW schemes: scGWSOX, scGWSOSEX, scGW2SOSE

Sundararaju Mugunthan, Venkatasamy Sureshkumar

Recently, in the area of Mobile Edge Computing (MEC) applications, Wu et al. proposed an authentication and key establishment scheme and claimed their protocol is secure. Nevertheless, cryptanalysis shows the scheme fails to provide robustness against key computation attack, mobile user impersonation attack and traceability attack. Vulnerabilities in their s

ChenRui Duan, Zelin Zang, Siyuan Li, Yongjie Xu

Phylogenetic trees elucidate evolutionary relationships among species, but phylogenetic inference remains challenging due to the complexity of combining continuous (branch lengths) and discrete parameters (tree topology). Traditional Markov Chain Monte Carlo methods face slow convergence and computational burdens. Existing Variational Inference methods, whic

Yilei Jiang, Yingshui Tan, Xiangyu Yue

While Multimodal Large Language Models (MLLMs) have made remarkable progress in vision-language reasoning, they are also more susceptible to producing harmful content compared to models that focus solely on text. Existing defensive prompting techniques rely on a static, unified safety guideline that fails to account for the specific risks inherent in differe

Etsuko Itou, Kei Iida, Kotaro Murakami, Daiki Suenaga

We investigate the phase structure and the equation of state (EoS) for dense two-color QCD at low temperatures, $T = 40$ MeV ($32^4$ lattice) and $T = 80$ MeV ($16^4$ lattice). A rich phase structure below the pseudo-critical temperature $T_c$ as a function of quark chemical potential $\mu$ has been revealed. By performing $T = 40$ MeV simulations, essential

Jiajun Ma, Chenfei Shi, Yantao Cao, YuWei Zhang

Electron correlation often gives birth to various orders in quantum materials. Recently, a strongly correlated kagome antiferromagnet FeGe is discovered to undergo a charge density wave transition inside the A-type antiferromagnetic state, providing an opportunity to explore the interplay between charge order and magnetism. Here, we reported the observation

Mansur Ziiatdinov, Aliya Khadieva, Kamil Khadiev

Quantum fingerprinting is a technique that maps classical input word to a quantum state. The obtained quantum state is much shorter than the original word, and its processing uses less resources, making it useful in quantum algorithms, communication, and cryptography. One of the examples of quantum fingerprinting is quantum automata algorithm for \(MOD_{p}=\

Ruobin Zhuang, Jianfeng He, Huadan Zheng

Ensuring safety and efficiency in emerging hydrogen-hydrocarbon fuel systems requires accurate measurement of multiple gas components in real time. However, existing detection techniques generally lack the capability to quantitatively measure hydrogen and natural gas constituents simultaneously. Here, we present a novel conductance-photoacoustic spectroscopy

Yu Sang, Hai-Nan Lin

The time series of energy and waiting time of magnetar bursts carry important information about the source activity. In this paper, we investigate the memory and dynamical stability of magnetar bursts from four soft gamma repeater (SGR) sources: SGR 1806$-$20, SGR 1900+14, SGR J1935+2154 and SGR J1550$-$5418. Based on the rescaled range analysis, we quantify

Wenbin Li, Di Yao, Chang Gong, Xiaokai Chu

Trajectory anomaly detection, aiming to estimate the anomaly risk of trajectories given the Source-Destination (SD) pairs, has become a critical problem for many real-world applications. Existing solutions directly train a generative model for observed trajectories and calculate the conditional generative probability $P({T}|{C})$ as the anomaly risk, where $

Md Riyadh, Muqi Li, Felix Haryanto Lie, Jia Long Loh

As data retrieval demands become increasingly complex, traditional search methods often fall short in addressing nuanced and conceptual queries. Vector similarity search has emerged as a promising technique for finding semantically similar information efficiently. However, its effectiveness diminishes when handling intricate queries with contextual nuances.

Karthik Bharath, Huiling Le, Andrew T A Wood, Xi Yan

Empirical Likelihood (EL) is a type of nonparametric likelihood that is useful in many statistical inference problems, including confidence region construction and $k$-sample problems. It enjoys some remarkable theoretical properties, notably Bartlett correctability. One area where EL has potential but is under-developed is in non-Euclidean statistics where

Haiquan Lu, Zhi Yu, Yong Zeng, Shaodan Ma

Passive metal reflectors for communication enhancement have appealing advantages such as ultra low cost, zero energy expenditure, maintenance-free operation, long life span, and full compatibility with legacy wireless systems. To unleash the full potential of passive reflectors for wireless communications, this paper proposes a new passive reflector architec

Rami Wilson

Modern autonomous vehicle simulators feature an ever-growing library of assets, including vehicles, buildings, roads, pedestrians, and more. While this level of customization proves beneficial when creating virtual urban environments, this process becomes cumbersome when intending to train within a digital twin or a duplicate of a real scene. Gaussian splatt

Pham Phuc, Son Vuong, Khang Nguyen, Tuan Dang

Deep learning-based object detection has become ubiquitous in the last decade due to its high accuracy in many real-world applications. With this growing trend, these models are interested in being attacked by adversaries, with most of the results being on classifiers, which do not match the context of practical object detection. In this work, we propose a n

Aleksei Nikolaev

This paper explores the Friedmann field equations within the framework of Lovelock gravity, a natural extension of Einstein's gravity, focusing on both flat and open universes. Utilizing an approach based on independent Riemann tensor components, we derive generalized Friedmann equations for Lovelock gravity and categorize the solutions into Type I and Type

Li Huang, Changming Yue

Analytic continuation is a critical step in quantum many-body computations, connecting imaginary-time or Matsubara Green's functions with real-frequency spectral functions, which can be directly compared to experimental results. However, due to the ill-posed nature of the analytic continuation problems, they have not been completely solved so far. In this pa

Lintao Li, Wei Chen

Low-latency communication has recently attracted considerable attention owing to its potential of enabling delay-sensitive services in next-generation industrial cyber-physical systems. To achieve target average or maximum delay given random arrivals and time-varying channels, buffer-aware scheduling is expected to play a vital role. Evaluating and optimizin

Zhongyuan Shi, Tao Dong, Zhaochu Yang

The local heat transfer coefficient measurement with temperature oscillation induced by periodic thermal perturbation - usually via a Gaussian laser beam, was investigated for the impact of the spikiness (i.e., the standard deviation) elaborated in comparison with the analytical model for dimensional analysis. The statistically more robust technique that rel

Lei Yang, Shaoyang Xu, Jianxiang Peng, Shaolin Zhu

Large language models (LLMs) based on the Transformer architecture usually have their context length limited due to the high training cost. Recent advancements extend the context window by adjusting the scaling factors of RoPE and fine-tuning. However, suboptimal initialization of these factors results in increased fine-tuning costs and reduced performance a

Yilei Jiang, Weihong Li, Yiyuan Zhang, Minghong Cai

While Diffusion Models (DM) exhibit remarkable performance across various image generative tasks, they nonetheless reflect the inherent bias presented in the training set. As DMs are now widely used in real-world applications, these biases could perpetuate a distorted worldview and hinder opportunities for minority groups. Existing methods on debiasing DMs u

Fanbo Sun, Youjun Deng

This paper is concerned with the open problem proposed in Ammari et. al. Commun. Math.Phys, 2013. We first investigate the existence and uniqueness of Generalized Polarization Tensors (GPTs) vanishing structures locally in both two and three dimension by fixed point theorem. Employing the Brouwer Degree Theory and the local uniqueness, we prove that for any

Gustaf Ahdritz, Aravind Gollakota, Parikshit Gopalan, Charlotte Peale

We give a principled method for decomposing the predictive uncertainty of a model into aleatoric and epistemic components with explicit semantics relating them to the real-world data distribution. While many works in the literature have proposed such decompositions, they lack the type of formal guarantees we provide. Our method is based on the new notion of

Kai Luo, Tingguang Wang, Xinguo Ren

Direct minimization method on the complex Stiefel manifold in Kohn-Sham density functional theory is formulated to treat both finite and extended systems in a unified manner. This formulation is well-suited for scenarios where straightforward iterative diagonalization becomes challenging, especially when the Aufbau principle is not applicable. We present the

Hila Levi, Guy Heller, Dan Levi

As working with large datasets becomes standard, the task of accurately retrieving images containing objects of interest by an open set textual query gains practical importance. The current leading approach utilizes a pre-trained CLIP model without any adaptation to the target domain, balancing accuracy and efficiency through additional post-processing. In t

Fatemah Alharthi, Dhruvil Solanki, Ishmael Apachigawo, Jianfeng Xiao

Parkinsons disease (PD) is considered one of the most frequent neurological diseases in the world. There is a need to study the early and efficient biomarkers of Parkinsons, such as changes in structural disorders like DNA and chromatin, especially at the subcellular level in the human brain. We used two techniques, Partial wave spectroscopy (PWS) and Invers

Zijun Gao

Accurate heterogeneous treatment effect (HTE) estimation is essential for personalized recommendations, making it important to evaluate and compare HTE estimators. Traditional assessment methods are inapplicable due to missing counterfactuals. Current HTE evaluation methods rely on additional estimation or matching on test data, often ignoring the uncertaint

Zhun Gou, Nan-Jing Huang, Xian-Jun Long, Jian-Hao Kang

This paper investigates the non-zero-sum linear-quadratic stochastic Stackelberg differential games with affine constraints, which depend on both the follower's response and the leader's strategy. With the help of the stochastic Riccati equations and the Lagrangian duality theory, the feedback expressions of optimal strategies of the follower and the leader

Stanislav Mosny, Boris Muha, Sebastian Schwarzacher, Justin T. Webster

Time-periodic weak solutions for a coupled hyperbolic-parabolic system are obtained. A linear heat and wave equation are considered on two respective $d$-dimensional spatial domains that share a common $(d-1)$-dimensional interface $\Gamma$. The system is only partially damped, leading to an indeterminate case for existing theory (Galdi et al., 2014). We con

Xinkai Du, Quanjie Han, Chao Lv, Yan Liu

Open-domain Question Answering (QA) has garnered substantial interest by combining the advantages of faithfully retrieved passages and relevant passages generated through Large Language Models (LLMs). However, there is a lack of definitive labels available to pair these sources of knowledge. In order to address this issue, we propose an unsupervised and simp

Lirika Solaa, Youdinghuan Chen, Samantha K. Murphy, V. S. Subrahmanian

Climate change is becoming a widely recognized risk factor of farmer-herder conflict in Africa. Using an 8 year dataset (Jan 2015 to Sep 2022) of detailed weather and terrain data across four African nations, we apply statistical and machine learning methods to analyze pastoral conflict. We test hypotheses linking these variables with pastoral conflict withi

Fanpu Cao, Shu Yang, Zhengjian Chen, Ye Liu

Transformer-based models have achieved remarkable success in multivariate time series forecasting (MTSF) by capturing long-range dependencies. However, their widespread adoption is hindered by the quadratic computational complexity of self-attention, which limits scalability on high-dimensional sequences. To address this challenge, we propose the Inverted Se

Enbo Huang, Yuan Zhang, Faliang Huang, Guangyu Zhang

Person image synthesis with controllable body poses and appearances is an essential task owing to the practical needs in the context of virtual try-on, image editing and video production. However, existing methods face significant challenges with details missing, limbs distortion and the garment style deviation. To address these issues, we propose a Disentan

Ken Shiozaki, Jing-Yuan Chen

Topological invariants in band theory are often formulated assuming that Bloch wave functions are smoothly defined over the Brillouin zone (BZ). However, first-principles band calculations typically provide Bloch states only at discrete points in the BZ, rendering standard continuum-based approaches inapplicable. In this work, we focus on the second Stiefel-

Tatiana Lastovina, Oleg Usoltsev, Furkan Isik, Andriy Budnyk

We report on a systematic study of the Cd, Zn, Se, and S elemental distributions across the interfaces in CdSe/Cd1-xZnxS core-shell and CdSe/CdS/Cd1-xZnxS core-crown-shell quantum wells with the CdSe core thickness ranging from 3.5 to 5.5 ML. By processing the XAS data, we observe that the Cd-Se bonds dominate at the CdSe/Cd1-xZnxS core-shell interface of st

Beatrice Acciaio, Songyan Hou, Gudmund Pammer

The adapted Wasserstein distance is a metric for quantifying distributional uncertainty and assessing the sensitivity of stochastic optimization problems on time series data. A computationally efficient alternative to it, is provided by the entropically regularized adapted Wasserstein distance. Suffering from similar shortcomings as classical optimal transpo

Naihuan Jing, Yu Wu

In this paper, we use vertex operator techniques to compute character values on unipotent classes of $\GL_n(\mathbb F_q)$. By realizing the Grothendieck ring $R_G=\bigoplus_{n\geq0}^\infty R(\GL_n(\mathbb F_q))$ as Fock spaces, we formulate the Murnanghan-Nakayama rule of $\GL_n(\mathbb F_q)$ between Schur functions colored by an orbit $\phi$ of linear chara

Alehegn Minale Chanie, Dawit Kflie, Getamesay Haile

Vehicular Ad-hoc Network (VANET) is a subset of Mobile Ad-hoc Network (MANET) enabling communication between vehicles for safety, traffic updates, entertainment, and data sharing. Due to the high mobility in VANETs, routing messages to their final destination is challenging. Various protocols, such as broadcasting, multicasting, and geo-casting, are used to

Sen Peng, Jijia Yang, Mingyue Wang, Jianfei He

Diffusion-based text-to-image models have shown immense potential for various image-related tasks. However, despite their prominence and popularity, customizing these models using unauthorized data also brings serious privacy and intellectual property issues. Existing methods introduce protective perturbations based on adversarial attacks, which are applied

Pranshu Malviya, Goncalo Mordido, Aristide Baratin, Reza Babanezhad Harikandeh

Efficiently exploring complex loss landscapes is key to the performance of deep neural networks. While momentum-based optimizers are widely used in state-of-the-art setups, classical momentum can still struggle with large, misaligned gradients, leading to oscillations. To address this, we propose Torque-Aware Momentum (TAM), which introduces a damping factor

Jingyi Wang, Haowei Wang, Cosmin G. Petra, Nai-Yuan Chiang

Bayesian optimization (BO) with Gaussian process (GP) surrogate models is a powerful black-box optimization method. Acquisition functions are a critical part of a BO algorithm as they determine how the new samples are selected. Some of the most widely used acquisition functions include upper confidence bound (UCB) and Thompson sampling (TS). The convergence

Yi-Jie Wang, Xuan Luo, Hua-Xing Chen, Er-Liang Cui

We study the $P$-wave bottom baryons of the $SU(3)$ flavor antitriplet and systematically calculate their strong decay properties, including their $D$-wave decays into ground-state bottom baryons with light pseudoscalar mesons and $S$-wave decays into ground-state bottom baryons with light vector mesons. Together with Refs.~\cite{Tan:2023opd,Yang:2019cvw,Yan

Mequanent Argaw Muluneh, Yan-Tsung Peng, Li Su

Despite its musicological, cultural, and religious significance, the Ethiopian Orthodox Tewahedo Church (EOTC) chant is relatively underrepresented in music research. Historical records, including manuscripts, research papers, and oral traditions, confirm Saint Yared's establishment of three canonical EOTC chanting modes during the 6th century. This paper at

Yuki Sato, Soma Nagahama, Ilya Belopolski, Ryutaro Yoshimi

Unconventional superconductivity is often accompanied by non-Fermi liquid (NFL) behavior, which emerges near a quantum critical point (QCP) - a point where an electronic ordered phase is terminated at absolute zero under non-thermal parameters. While nematic orders, characterized by broken rotational symmetry, are sometimes found in unconventional supercondu

R. Sharma, Y. B. Guo

Understanding thermal stress evolution in metal additive manufacturing (AM) is crucial for producing high-quality components. Recent advancements in machine learning (ML) have shown great potential for modeling complex multiphysics problems in metal AM. While physics-based simulations face the challenge of high computational costs, conventional data-driven M

Buzhen Huang, Jingyi Ju, Yuan Shu, Yangang Wang

Dynamic multi-person mesh recovery has broad applications in sports broadcasting, virtual reality, and video games. However, current multi-view frameworks rely on a time-consuming camera calibration procedure. In this work, we focus on multi-person motion capture with uncalibrated cameras, which mainly faces two challenges: one is that inter-person interacti

Mequanent Argaw Muluneh, Yan-Tsung Peng, Worku Abebe Degife, Nigussie Abate Tadesse

Computational music research plays a critical role in advancing music production, distribution, and understanding across various musical styles worldwide. Despite the immense cultural and religious significance, the Ethiopian Orthodox Tewahedo Church (EOTC) chants are relatively underrepresented in computational music research. This paper contributes to this

Zixiao Gu, Mengtian Li, Ruhua Chen, Zhongxia Ji

As demand from the film and gaming industries for 3D scenes with target styles grows, the importance of advanced 3D stylization techniques increases. However, recent methods often struggle to maintain local consistency in color and texture throughout stylized scenes, which is essential for maintaining aesthetic coherence. To solve this problem, this paper in

Lillian St. Kleess

We present a rigorously formulated, novel operator-based framework that merges ideas from condensed matter physics, continuum mechanics, and quantum-inspired theory to analyze collective virion behavior in complex environments. By modeling metabolically inert virions, whose main interactions are Coulombic and Lennard-Jones, as nodes in a viral lattice linked

Chao Min, Liwei Wang

We consider the symmetric gap probability distributions of certain Freud unitary ensembles. This problem is related to the Hankel determinants generated by the Freud weights supported on the complement of a symmetric interval. By using Chen and Ismail's ladder operator approach, we obtain the difference equations satisfied by the recurrence coefficients for

Shingo Ayabe, Takuto Otomo, Hiroshi Kera, Kazuhiko Kawamoto

Offline reinforcement learning, which learns solely from datasets without environmental interaction, has gained attention. This approach, similar to traditional online deep reinforcement learning, is particularly promising for robot control applications. Nevertheless, its robustness against real-world challenges, such as joint actuator faults in robots, rema

Yuheng Yang

Human skeleton-based action recognition has long been an indispensable aspect of artificial intelligence. Current state-of-the-art methods tend to consider only the dependencies between connected skeletal joints, limiting their ability to capture non-linear dependencies between physically distant joints. Moreover, most existing approaches distinguish action

Nirmalya Thakur

Long COVID continues to challenge public health by affecting a significant segment of individuals who have recovered from acute SARS-CoV-2 infection yet endure prolonged and often debilitating symptoms. Social media has emerged as a vital resource for those seeking real-time information, peer support, and validating their health concerns related to Long COVI

Tan Nguyen, Coy D. Heldermon, Corey Toler-Franklin

We present a novel method that extends the self-attention mechanism of a vision transformer (ViT) for more accurate object detection across diverse datasets. ViTs show strong capability for image understanding tasks such as object detection, segmentation, and classification. This is due in part to their ability to leverage global information from interaction

Tenta Tani, Soma Miki, Hiroki Mori, Minori Goto

We theoretically investigate the flow of information in an interacting two-skyrmion system confined in a box at finite temperature. By numerical simulations based on the Thiele-Langevin equation, we demonstrate that the skyrmion motion cannot be fully described by the master equation, highlighting the nontrivial dynamics. Particularly, due to the chiral moti

Apoorv Thapliyal, Vinay Lanka, Swathi Baskaran

ObitoNet employs a Cross Attention mechanism to integrate multimodal inputs, where Vision Transformers (ViT) extract semantic features from images and a point cloud tokenizer processes geometric information using Farthest Point Sampling (FPS) and K Nearest Neighbors (KNN) for spatial structure capture. The learned multimodal features are fed into a transform