December 2024 arXiv papers — page 28

Álvaro Nodar, Irene De León, Danel Arias, Ernesto Mamedaliev

This work explores the potential of the Variational Quantum Eigensolver in solving Dynamic Portfolio Optimization problems surpassing the 100 qubit utility frontier. We systematically analyze how to scale this strategy in complexity and size, from 6 to 112 qubits, by testing different combinations of ansatz and optimizer on a real Quantum Processing Unit. We

Guohao Li, Hongyu Yang, Yifang Men, Di Huang

Generating animatable and editable 3D head avatars is essential for various applications in computer vision and graphics. Traditional 3D-aware generative adversarial networks (GANs), often using implicit fields like Neural Radiance Fields (NeRF), achieve photorealistic and view-consistent 3D head synthesis. However, these methods face limitations in deformat

Nilesh Kumar Sahu, Nandigramam Sai Harshit, Rishabh Uikey, Haroon R. Lone

Social Anxiety Disorder (SAD) significantly impacts individuals' daily lives and relationships. The conventional methods for SAD detection involve physical consultations and self-reported questionnaires, but they have limitations such as time consumption and bias. This paper introduces video analysis as a promising method for early SAD detection. Specificall

Tim Meiler, Yutao Wang, Saurabh Srivastava, Giorgio Adamo

Perovskites have recently brought significant advances to active nanophotonics, offering a unique combination of gain and phase-change properties for tunable light-emitting devices. However, current wavelength-tunable devices often rely on tuning mechanisms or device architectures that lead to slow modulation or bulky setups. In this study, we overcome limit

Xudong Yang, Yifan Wu, Yizhang Zhu, Nan Tang

Chart understanding tasks such as ChartQA and Chart-to-Text involve automatically extracting and interpreting key information from charts, enabling users to query or convert visual data into structured formats. State-of-the-art approaches primarily focus on visual cues from chart images, failing to explicitly incorporate rich textual information (e.g., data

Shaojie Zhou, Jia-Rui Lin, Peng Pan, Yuandong Pan

Deep learning (DL)-based point cloud segmentation is essential for understanding built environment. Despite synthetic point clouds (SPC) having the potential to compensate for data shortage, how synthetic color and mixing proportion impact DL-based segmentation remains a long-standing question. Therefore, this paper addresses this question with extensive exp

Takahiro Matsushita

The Hom complex $\mathrm{Hom}(G, H)$ of graphs is a simplicial complex associated to a pair of graphs $G$ and $H$, and its homotopy type is of interest in the graph coloring problem and the homomorphism reconfiguration problem. In this paper, we show that if $G$ is a connected graph and $H$ is a square-free connected graph, then every connected component of

Darya Parygina, Timofey Mezhuev, Daniil Kuts

Directed fuzzing performs best for targeted program testing via estimating the impact of each input in reaching predefined program points. But due to insufficient analysis of the program structure and lack of flexibility and configurability it can lose efficiency. In this paper, we enhance directed fuzzing with context weights for graph nodes and resolve ind

Siyu Jiao, Haoye Dong, Yuyang Yin, Zequn Jie

Recent works in 3D multimodal learning have made remarkable progress. However, typically 3D multimodal models are only capable of handling point clouds. Compared to the emerging 3D representation technique, 3D Gaussian Splatting (3DGS), the spatially sparse point cloud cannot depict the texture information of 3D objects, resulting in inferior reconstruction

Siyuan Feng, Teruya Yoshinaga, Katsuhiko Hayashi, Koki Washio

Today, manga has gained worldwide popularity. However, the question of how various elements of manga, such as characters, text, and panel layouts, reflect the uniqueness of a particular work, or even define it, remains an unexplored area. In this paper, we aim to quantitatively and qualitatively analyze the visual characteristics of manga works, with a parti

Senbin Zhu, Chenyuan He, Hongde Liu, Pengcheng Dong

In recent years, fine-grained sentiment analysis in finance has gained significant attention, but the scarcity of entity-level datasets remains a key challenge. To address this, we have constructed the largest English and Chinese financial entity-level sentiment analysis datasets to date. Building on this foundation, we propose a novel two-stage sentiment an

Dejie Yang, Zijing Zhao, Yang Liu

Video procedure planning, i.e., planning a sequence of action steps given the video frames of start and goal states, is an essential ability for embodied AI. Recent works utilize Large Language Models (LLMs) to generate enriched action step description texts to guide action step decoding. Although LLMs are introduced, these methods decode the action steps in

Xin Chen, Ben Kang, Wanting Geng, Jiawen Zhu

In this paper, we propose a simple yet unified single object tracking (SOT) framework, dubbed SUTrack. It consolidates five SOT tasks (RGB-based, RGB-Depth, RGB-Thermal, RGB-Event, RGB-Language Tracking) into a unified model trained in a single session. Due to the distinct nature of the data, current methods typically design individual architectures and trai

Hossein Mohammadi, Ali Naseh, Behrad Taghavi

In arXiv:2310.17536, two of the authors studied the function $\mathscr{S}_{\boldsymbol{m}} = S_{\boldsymbol{m}} - \pi \sum_{i=1}^n (m_i - \tfrac{1}{m_i}) \log \mathsf{h}_{i}$ for orbifold Riemann surfaces of signature $(g;m_1,...,m_{n_e};n_p)$ on the generalized Schottky space $\mathfrak{S}_{g,n}(\boldsymbol{m})$. In this paper, we prove the holographic dual

Ali Douaki, Mukhtar Ahmed, Edoardo Longo, Giulia Windisch

In the food industry, innovative packaging solutions are increasingly important for reducing food waste and for contributing to global sustainability efforts. However, current food packaging is generally passive and unable to adapt to changes in the food environment in real-time. To address this, we have developed a battery-less and autonomous smart packagin

Alberto M. Ruiz, José J. Baldoví

Strategies such as chemical substitution, strain engineering and van der Waals stacking offer powerful means to control magnetism in 2D materials, enabling the emergence of novel quantum phenomena. Here, we investigate the magnetic properties of bulk CrSBr$_{1-x}$Cl$_x$ ($x = 0{-}1$) and the strain-dependent switchable magnetic phases in bilayers CrSBr, CrSC

Ruixing Wu, Yiming Yang, Jiakai He, Haifeng Hu

Unsupervised learning visible-infrared person re-identification (USL-VI-ReID) aims to learn modality-invariant features from unlabeled cross-modality datasets and reduce the inter-modality gap. However, the existing methods lack cross-modality clustering or excessively pursue cluster-level association, which makes it difficult to perform reliable modality-in

Azze-Eddine Maredj, Madjid Sadallah

In the rapidly evolving landscape of digital content, the task of summarizing multimedia documents, which encompass textual, visual, and auditory elements, presents intricate challenges. These challenges include extracting pertinent information from diverse formats, maintaining the structural integrity and semantic coherence of the original content, and gene

Sushmita Saha, Deepannita Das, Alestin Mawrie

This study investigates the transport parameters in multi-Weyl semimetals, focusing on their magneto-optical properties and the role of chiral states. The tilting parameter is identified as a key factor in higher-order Weyl nodes, significantly influencing the magneto-optical response. We obtain a generic Landau-level expression for multi-Weyl semimetals, es

A. Vaca, F. Milano

This letter demonstrates how synthetic inertia can be obtained with the control of flexible discrete devices to keep the power balance of power systems, even if the system does not include any synchronous generator or conventional grid-forming converter. The letter also discusses solutions to cycling issues, which can arise due to the interaction of uncoordi

Byeonggwon Lee, Junkyu Park, Khang Truong Giang, Sungho Jo

This study addresses the challenge of online 3D model generation for neural rendering using an RGB image stream. Previous research has tackled this issue by incorporating Neural Radiance Fields (NeRF) or 3D Gaussian Splatting (3DGS) as scene representations within dense SLAM methods. However, most studies focus primarily on estimating coarse 3D scenes rather

Abdelrahman Hussein, Byungki Kim, Kim Verbeken, Tom Depover

Understanding hydrogen-grain boundary (GB) interactions is critical to the analysis of hydrogen embrittlement in metals. This work presents a mesoscale fully kinetic model to investigate the effect of GB misorientation on hydrogen diffusion and trapping using phase-field based representative volume elements (RVEs). The flux equation consists of three terms:

Hai Huang, Shulei Wang, Yan Xia

Recent research in the domain of multimodal unified representations predominantly employs codebook as representation forms, utilizing Vector Quantization(VQ) for quantization, yet there has been insufficient exploration of other quantization representation forms. Our work explores more precise quantization methods and introduces a new framework, Semantic Res

Xiaohan Ye, Xifeng Gao, Kui Wu, Zherong Pan

Robot simulators are indispensable tools across many fields, and recent research has significantly improved their functionality by incorporating additional gradient information. However, existing differentiable robot simulators suffer from non-differentiable singularities, when robots undergo substantial shape changes. To address this, we present the Shape-D

Akanksha, Ritumoni Sarma

In this article, for the finite field $\mathbb{F}_q$, we show that the $\mathbb{F}_q$-algebra $\mathbb{F}_q[x]/\langle f(x) \rangle$ is isomorphic to the product ring $\mathbb{F}_q^{\deg f(x)}$ if and only if $f(x)$ splits over $\mathbb{F}_q$ into distinct factors. We generalize this result to the quotient of the polynomial algebra $\mathbb{F}_q[x_1, x_2,\do

Inpyo Hong, Youngwan Jo, Hyojeong Lee, Sunghyun Ahn

We introduce AKT (Advanced Knowledge Transfer), a novel method to enhance the training ability of low-bit quantized (Q) models in the field of zero-shot quantization (ZSQ). Existing research in ZSQ has focused on generating high-quality data from full-precision (FP) models. However, these approaches struggle with reduced learning ability in low-bit quantizat

Valay Bundele, Karahan Sarıtaş, Bora Kargi, Oğuz Ata Çal

Self-supervised learning (SSL) has emerged as a promising paradigm in medical imaging, addressing the chronic challenge of limited labeled data in healthcare settings. While SSL has shown impressive results, existing studies in the medical domain are often limited in scope, focusing on specific datasets or modalities, or evaluating only isolated aspects of m

Kaixing Yang, Xulong Tang, Haoyu Wu, Qinliang Xue

Dance generation is crucial and challenging, particularly in domains like dance performance and virtual gaming. In the current body of literature, most methodologies focus on Solo Music2Dance. While there are efforts directed towards Group Music2Dance, these often suffer from a lack of coherence, resulting in aesthetically poor dance performances. Thus, we i

Igor Nikonov

A knot invariant is called skein if it is determined by a finite number of skein relations. In the paper we discuss some basic properties of skein invariants and mention some known examples of skein invariants.

Anh-Dung Le

In this paper, we study weak well-posedness of a McKean-Vlasov stochastic differential equations (SDEs) whose drift is density-dependent and whose diffusion is constant. The existence part is due to H\"older stability estimates of the associated Euler-Maruyama scheme. The uniqueness part is due to that of the associated Fokker-Planck equation. We also obtain

Newman Chen, Frédérique Mélanie-Becquet, Jean Barré, Thierry Poibeau

Character recognition is a technique that enables the automated extraction of characters from texts, while coreference resolution establishes connections between various mentions of the same character, collectively facilitating the creation of expansive character networks (Moretti, 2011). Together, these technologies make it possible to navigate and analyze

P. Bantay

We investigate the mutual relations between the centers of different elements in the deconstruction lattice of a 2D conformal model, and show how these can be described using exact sequences of abelian groups. In particular, we exhibit a long exact sequence connecting the centers of higher central quotients.

Xin Chen, Fengyi Zhou, Yan Suo, Cheng Shao

We report the discovery of an ultra-stable antiferromagnetic two-dimensional (2D) CrF3 phase that is energetically more favorable than the traditionally assumed hexagonal structure. Using first-principles calculations and evolutionary structure searches, we identify a new low-energy rectangular configuration of CrF3 with remarkable anisotropic properties. Me

Tadahiro Suhara, Yasutaka Taniguchi, Wataru Horiuchi, Shin Watanabe

The coexistence of various structures, such as diverse shapes and cluster structures, is a fundamental property of atomic nuclei. In neutron-rich nuclei, a core$+n$ structure can compete with nuclear deformation due to the small neutron separation energy. A neutron-rich carbon isotope, $^{17}$C, exemplifies the appearance of the deformation and the core+$n$

Konstantin Jakob, Zhiwei Yun

For a reductive group $G$ over a finite field $k$, and a smooth projective curve $X/k$, we give a motivic counting formula for the number of absolutely indecomposable $G$-bundles on $X$. We prove that the counting can be expressed via the cohomology of the moduli stack of stable parabolic $G$-Higgs bundles on $X$. This result generalizes work of Schiffmann a

SongUng Ri, HyonHui Ju, JinMyong Kim

We first give a note on disjoint hypercyclicity for invertible bilateral pseudo-shifts on $\ell^{p}(\mathbb{Z})$, $1\leq p <\infty$. It is already known that if a tuple of bilateral weighted shifts on $\ell^{p}(\mathbb{Z})$, $1\leq p <\infty$, is disjoint hypercyclic, then non of the weighted shifts is invertible. We show that as for pseudo-shifts which is a

Zhuoyi Pang, Jian-Hui Zhang, Dian-Jun Zhao

We show that the traditional moments approach in lattice QCD, based on operator product expansion (OPE), can be realized in a way that utilizes derivatives in momentum rather than in distance. This also avoids power divergent mixings, and thus allows to extract moments order by order, to all orders in principle. Moreover, by exploiting the symmetry of lattic

Jathin Korrapati, Tanish Baranwal, Rahul Shah

This work explores the theoretical and practical foundations of denoising diffusion probabilistic models (DDPMs) and score-based generative models, which leverage stochastic processes and Brownian motion to model complex data distributions. These models employ forward and reverse diffusion processes defined through stochastic differential equations (SDEs) to

Yunfan Zhang, Changlun Li, Yuyu Luo, Nan Tang

Missing value is a critical issue in data science, significantly impacting the reliability of analyses and predictions. Missing value imputation (MVI) is a longstanding problem because it highly relies on domain knowledge. Large language models (LLMs) have emerged as a promising tool for data cleaning, including MVI for tabular data, offering advanced capabi

Motonari Kambara, Komei Sugiura

This study addresses a task designed to predict the future success or failure of open-vocabulary object manipulation. In this task, the model is required to make predictions based on natural language instructions, egocentric view images before manipulation, and the given end-effector trajectories. Conventional methods typically perform success prediction onl

Yiyuan Ge, Zhihao Chen, Ziyang Wang, Jiaju Kang

The development of deep learning has facilitated the application of person re-identification (ReID) technology in intelligent security. Visible-infrared person re-identification (VI-ReID) aims to match pedestrians across infrared and visible modality images enabling 24-hour surveillance. Current studies relying on unsupervised modality transformations as wel

Sangmin Lee, Seokjun Park, Jeonghun Park, Jinseok Choi

In this paper, we propose a robust and adaptable secure precoding framework designed to encapsulate a intricate scenario where legitimate users have different information security: secure private or normal public information. Leveraging rate-splitting multiple access (RSMA), we formulate the sum secrecy spectral efficiency (SE) maximization problem in downli

Michele Caselle, Elia Cellini, Alessandro Nada

Effective String Theory (EST) is a powerful tool used to study confinement in pure gauge theories by modeling the confining flux tube connecting a static quark-anti-quark pair as a thin vibrating string. Recently, flow-based samplers have been applied as an efficient numerical method to study EST regularized on the lattice, opening the route to study observa

Sanglin Zhao, Jikang Cao

Based on the coupling mechanism between new infrastructure investment level and digital transformation level, a comprehensive index system is constructed. By using entropy weight method, coupling coordination evaluation model, exploratory spatial data analysis (ESDA) and standard deviation ellipse model, the coupling coordination development level of new inf

Xiaoyu Huang, Weidong Chen, Bo Hu, Zhendong Mao

Multivariate time series (MTS) anomaly detection is a critical task that involves identifying abnormal patterns or events in data that consist of multiple interrelated time series. In order to better model the complex interdependence between entities and the various inherent characteristics of each entity, the GNN based methods are widely adopted by existing

Mingqing Chen, Jianguo Huang, Xuehai Huang

This paper is devoted to proposing and analyzing a robust $C^0$ interior penalty method for a gradient-elastic Kirchhoff plate (GEKP) model over a convex polygon. The numerical method is obtained by combining the triangular Hermite element and a $C^0$ interior penalty method, which can avoid the use of higher order shape functions or macroelements. Next, a r

Guoming Li, Jian Yang, Shangsong Liang

Approximation-based spectral graph neural networks, which construct graph filters with function approximation, have shown substantial performance in graph learning tasks. Despite their great success, existing works primarily employ polynomial approximation to construct the filters, whereas another superior option, namely ration approximation, remains underex

Chutian Jiang, Emily Kuang, Mingming Fan

People who are blind or have low vision (BLV) encounter numerous challenges in their daily lives and work. To support them, various haptic assistive tools have been developed. Despite these advancements, the effective utilization of these tools -- including the optimal haptic feedback and on-body stimulation positions for different tasks along with their lim

Hyesong Choi, Daeun Kim, Sungmin Cha, Kwang Moo Yi

In this work, we dive deep into the impact of additive noise in pre-training deep networks. While various methods have attempted to use additive noise inspired by the success of latent denoising diffusion models, when used in combination with masked image modeling, their gains have been marginal when it comes to recognition tasks. We thus investigate why thi

Vitaly V. Chaban

The development of new electrolyte solutions with improved characteristics is a key challenge for creating high-performance batteries, fuel cells, supercapacitors, and other electrochemical devices. The study of the potential energy landscape (PEL) plays an important role in this process, providing information about the interactions between solution componen

Jiawei Yu, Xiang Geng, Yuang Li, Mengxin Ren

Spoken named entity recognition (NER) aims to identify named entities from speech, playing an important role in speech processing. New named entities appear every day, however, annotating their Spoken NER data is costly. In this paper, we demonstrate that existing Spoken NER systems perform poorly when dealing with previously unseen named entities. To tackle

Ran Ma, Yixiong Zou, Yuhua Li, Ruixuan Li

Cross-Domain Few-Shot Learning (CDFSL) requires the model to transfer knowledge from the data-abundant source domain to data-scarce target domains for fast adaptation, where the large domain gap makes CDFSL a challenging problem. Masked Autoencoder (MAE) excels in effectively using unlabeled data and learning image's global structures, enhancing model genera

Xiaomin Shi, Zuo Quan Xu

In this paper, we examine a stochastic linear-quadratic control problem characterized by regime switching and Poisson jumps. All the coefficients in the problem are random processes adapted to the filtration generated by Brownian motion and the Poisson random measure for each given regime. The model incorporates two distinct types of controls: the first is a

Cunhang Fan, Enrui Liu, Andong Li, Jianhua Tao

Although the complex spectrum-based speech enhancement(SE) methods have achieved significant performance, coupling amplitude and phase can lead to a compensation effect, where amplitude information is sacrificed to compensate for the phase that is harmful to SE. In addition, to further improve the performance of SE, many modules are stacked onto SE, resultin

Wenzhang Sun, Xiang Li, Donglin Di, Zhuding Liang

Recently, animating portrait images using audio input is a popular task. Creating lifelike talking head videos requires flexible and natural movements, including facial and head dynamics, camera motion, realistic light and shadow effects. Existing methods struggle to offer comprehensive, multifaceted control over these aspects. In this work, we introduce Uni

Yong-Tao Ma, Rui-Hao Bi, Wenjie Dou

Accurate simulation the many-electronic nonadiabatic dynamics process at metal surfaces remains as a significant task. In this work, we present an orbital surface hopping (OSH) algorithm rigorously derived from the orbital quantum classical Liouville equation (o-QCLE) to deal with nonadiabatic dynamics for many-electron systems. This OSH algorithm closely co

Si-Qi Yu, Wei Cheng, Chuang Li, Xiao-Hong Pan

Building on the multiband nature of iron-based superconductors (FeSCs), we have uncovered pronounced anisotropy in Majorana vortex topology arising from the interaction between vortex orientation and multiple electronic topologies. This anisotropy manifests in two distinct vortex configurations: the z-vortex and x-vortex, oriented perpendicular and parallel

Subarsha Banerjee, Soumya Ganguly

Given a graph $G$, the Laplacian matrix of $G$, $L(G)$ is the difference of the adjacency matrix $A(G)$ and $\text{Deg}(G)$, where $\text{Deg}(G)$ is the diagonal matrix of vertex degrees. The distance Laplacian matrix $D^L({G})$ is the difference of the transmission matrix of $G$ and the distance matrix of $G$. In the given paper, we first obtain the Laplac

Shivam Gola

We explore a KSVZ-like extension of the Standard Model with a Dirac fermion and three right-handed neutrinos. PQ symmetry allows the Dirac mass for neutrinos and prevents the Majorana mass. A $\mathcal{Z}_2$ symmetry guarantees the stability of Dirac fermion dark matter. The breakdown of PQ symmetry generates the QCD axion at a high scale. The fermion dark m

Binayak Mukherjee, Natalya S. Fedorova, Jorge Íñiguez-González

Structural softness - often characterized by unstable phonon modes and large electromechanical responses - is a hallmark of ferroelectric perovskites like BaTiO3 or Pb(Ti,Zr)O3. Whether HfO2 ferroelectrics present any such structural softness is still a matter of debate. Here, using first principles calculations, we predict that it is possible to induce stru

Zhaoping Hu, Zongyuan Huang, Jinming Yang, Tao Yang

Human mobility studies how people move to access their needed resources and plays a significant role in urban planning and location-based services. As a paramount task of human mobility modeling, next location prediction is challenging because of the diversity of users' historical trajectories that gives rise to complex mobility patterns and various contexts

Rafael Cabral, Maria De Iorio, Andrew Harris

In this work we investigate the application of advanced object detection techniques to digital numismatics, focussing on the analysis of historical coins. Leveraging models such as Contrastive Language-Image Pre-training (CLIP), we develop a flexible framework for identifying and classifying specific coin features using both image and textual descriptions. B

Zi-Ming Li, Yu-xi Liu

Vector set orthogonal normalization and matrix QR decomposition are fundamental problems in matrix analysis with important applications in many fields. We know that Gram-Schmidt process is a widely used method to solve these two problems. However, the existing methods, including Gram-Schmidt process have problems of high complexity, scaling $O(N^3)$ in the s

Changwoon Choi, Jeongjun Kim, Geonho Cha, Minkwan Kim

Recent works on dynamic 3D neural field reconstruction assume the input from synchronized multi-view videos whose poses are known. The input constraints are often not satisfied in real-world setups, making the approach impractical. We show that unsynchronized videos from unknown poses can generate dynamic neural fields as long as the videos capture human mot

Vasileios Alevizos, George A Papakostas, Akebu Simasiku, Dimitra Malliarou

While new technologies emerge, human errors always looming. Software supply chain is increasingly complex and intertwined, the security of a service has become paramount to ensuring the integrity of products, safeguarding data privacy, and maintaining operational continuity. In this work, we conducted experiments on the promising open Large Language Models (

Yang Chen, Shuai Fu, Yu Zhang

Soft prompt learning methods are effective for adapting vision-language models (VLMs) to downstream tasks. Nevertheless, empirical evidence reveals a tendency of existing methods that they overfit seen classes and exhibit degraded performance on unseen classes. This limitation is due to the inherent bias in the training data towards the seen classes. To addr

Angel Kodituwakku, Clark Xu, Daniel Rogers, David K. Ahn

Indicators of Compromise (IoCs) play a crucial role in the rapid detection and mitigation of cyber threats. However, the existing body of literature lacks in-depth analytical studies on the temporal aspects of IoC publication, especially when considering up-to-date datasets related to Common Vulnerabilities and Exposures (CVEs). This paper addresses this gap

Tengxue Zhang, Yang Shu, Xinyang Chen, Yifei Long

Pre-trained model assessment for transfer learning aims to identify the optimal candidate for the downstream tasks from a model hub, without the need of time-consuming fine-tuning. Existing advanced works mainly focus on analyzing the intrinsic characteristics of the entire features extracted by each pre-trained model or how well such features fit the target

Quan-Yi Hu

In this work, we study the $B^+\to K^+ E_{\mathrm{miss}}$, $B^0\to K^{*0} E_{\mathrm{miss}}$, and $\Lambda_b^0\to \Lambda^0 E_{\mathrm{miss}}$ decays under three different new physics hypotheses: the heavy new particles, the light neutral vector particles, and the axion-like particles. We find that all three hypotheses can resolve the Belle-II excess, and th

Fanrong Du, Jiuchen Shi, Quan Chen, Li Li

A production microservice application may provide multiple services, queries of a service may have different call graphs, and a microservice may be shared across call graphs. It is challenging to improve the resource efficiency of such complex applications without proper benchmarks, while production traces are too large to be used in experiments. To this end

De-xuan Xu, Zhun Gou, Nan-jing Huang

This paper is concerned with linear quadratic graphon field social control problem where the noises of individual agents are correlated. Compared with the well-studied mean field system, the graphon field system consists of a large number of agents coupled weakly via a weighted undirected graph where each node represents an individual agent. Another notable

Armen Sargsyan, Anahit Gogyan, David Sarkisyan

We have observed laser-induced fluorescence using 456 nm laser radiation, resonant with the 6S1/2-7P3/2 transition in Cs atoms. It includes red emission lines in the range of 580-730 nm and a prominent line at 852 nm corresponding to the 6P3/2-6S1/2 transition. A T-shaped all-sapphire cell with a length of 1 cm, containing Cs atomic vapor and capable of bein

Haotian Qian, YD Chen, Shengtao Lou, Fahad Shahbaz Khan

Dichotomous Image Segmentation (DIS) tasks require highly precise annotations, and traditional dataset creation methods are labor intensive, costly, and require extensive domain expertise. Although using synthetic data for DIS is a promising solution to these challenges, current generative models and techniques struggle with the issues of scene deviations, n

Shitong Fan, Feiyang Xiao, Wenbo Wang, Shuhan Qi

Microphone array techniques are widely used in sound source localization and smart city acoustic-based traffic monitoring, but these applications face significant challenges due to the scarcity of labeled real-world traffic audio data and the complexity and diversity of application scenarios. The DCASE Challenge's Task 10 focuses on using multi-channel audio

Liangliang Guo, Ranran Cai, Zhenhua Zhang, Wenyu Xing

Half-metals are a class of quantum materials with 100% spin-polarization at the Fermi level and have attracted a lot of attention for future spintronic device applications. CrO2 is one of the most promising half-metal candidates, for which the electrical and magnetic properties have been intensively studied in the last several decades. Here, we report the ob

Dima Galat

The recent proliferation of AI-generated content has prompted significant interest in developing reliable detection methods. This study explores techniques for identifying AI-generated text through sentence-level evaluation within hybrid articles. Our findings indicate that ChatGPT-3.5 Turbo exhibits distinct, repetitive probability patterns that enable cons

Bappaditya Bhowmik, Deblina Maity

Let $f$ be a conformal (analytic and univalent) map defined on the open unit disk $\D$ of the complex plane $\IC$ that is continuous on the semi-circle $\partial \D^{+}=\{z\in\IC:|z|=1, {\rm{Im}}\,z>0\}$. The existence of a uniform upper bound for the ratio of the length of the image of the horizontal diameter $(-1,1)$ to the length of the image of $\partial

Shohei Koizumi, Yusuke Suzuki

In this paper, we discuss matching extendability of optimal $1$-projective plane graphs (abbreviated as O1PPG), which are drawn on the projective plane $P^2$ so that every edge crosses another edge at most once, and has $n$ vertices and exactly $4n- 4$ edges. We first show that every O1PPG of even order is $1$-extendable. Next, we characterize $2$-extendable

Chuan Zhang, He Yang, Fei Wang, Tuo Zhou

This paper presents a boundary control scheme for prescribed-time (PT) stable of flexible string systems via backstepping method, and the dynamics of such systems modeled by Hamilton's principle is described as second-order hyperbolic partial differential equations (PDEs). Initially, to construct a boundary controller with PT stabilization capacity, a PT sta

Y. Lu, A. Harati, T. Rutowski, R. Oliveira

Depression is a global health concern with a critical need for increased patient screening. Speech technology offers advantages for remote screening but must perform robustly across patients. We have described two deep learning models developed for this purpose. One model is based on acoustics; the other is based on natural language processing. Both models e

Ziyuan Zheng, Qingqing Wu, Wen Chen, Xiangming Wu

Reconfigurable intelligent surfaces enhance wireless systems by reshaping propagation environments. However, dynamic metasurfaces (MSs) with numerous phase-shift elements incur undesired control and hardware costs. In contrast, static MSs (SMSs), configured with static phase shifts pre-designed for specific communication demands, offer a cost-effective alter

Tomek Rutowski, Elizabeth Shriberg, Amir Harati, Yang Lu

Deep learning models are rapidly gaining interest for real-world applications in behavioral health. An important gap in current literature is how well such models generalize over different populations. We study Natural Language Processing (NLP) based models to explore portability over two different corpora highly mismatched in age. The first and larger corpu

Shuyi Wang

Online Learning to Rank (OLTR) optimises ranking models using implicit user feedback, such as clicks. Unlike traditional Learning to Rank (LTR) methods that rely on a static set of training data with relevance judgements to learn a ranking model, OLTR methods update the model continually as new data arrives. Thus, it addresses several drawbacks such as the h

Yanzhe Zhang, Zhonghao Bi, Feiyang Xiao, Xuefeng Yang

This study focuses on the First VoicePrivacy Attacker Challenge within the ICASSP 2025 Signal Processing Grand Challenge, which aims to develop speaker verification systems capable of determining whether two anonymized speech signals are from the same speaker. However, differences between feature distributions of original and anonymized speech complicate thi

Xu Li, Yi Zheng, Haotian Chen, Xiaolei Chen

Large Vision-Language Models (LVLMs) have achieved remarkable success in a wide range of multimodal tasks by integrating pre-trained vision encoders and large language models. However, current LVLMs primarily rely on visual features extracted from the final layers of the vision encoder, overlooking the complementary information available in shallower layers.

Haitao Meng, Chonghao Zhong, Sheng Tang, Lian JunJia

Event cameras are neuromorphically inspired sensors that sparsely and asynchronously report brightness changes. Their unique characteristics of high temporal resolution, high dynamic range, and low power consumption make them well-suited for addressing challenges in monocular depth estimation (e.g., high-speed or low-lighting conditions). However, current ex

Yi-Xiang Hu, Feng Wu, Shaoang Li, Yifang Zhao

Column Generation (CG) is an effective and iterative algorithm to solve large-scale linear programs (LP). During each CG iteration, new columns are added to improve the solution of the LP. Typically, CG greedily selects one column with the most negative reduced cost, which can be improved by adding more columns at once. However, selecting all columns with ne

Jinyu Li, Sheng-Yu Wang, Lu Zhang, Guoyan Ge

High-resolution X-ray spectroscopy is an essential tool in X-ray astronomy, enabling detailed studies of celestial objects and their physical and chemical properties. However, comprehensive mapping of high-resolution X-ray spectra for even simple interstellar and circumstellar molecules is still lacking. In this study, we conducted systematic quantum chemica

Shenghong He, Chao Yu

Real-time bidding (RTB) plays a pivotal role in online advertising ecosystems. Advertisers employ strategic bidding to optimize their advertising impact while adhering to various financial constraints, such as the return-on-investment (ROI) and cost-per-click (CPC). Primarily focusing on bidding with fixed budget constraints, traditional approaches cannot ef

Wenkui Du, Yuchao Yi, Ziyi Zhao

In this paper, we prove a Wulff inequality for $n$-dimensional minimal submanifolds with boundary in $\mathbb{R}^{n+m}$, where we associate a nonnegative anisotropic weight $\Phi: S^{n+m-1}\to \mathbb{R}^{+}$ to the boundary of minimal submanifolds. The Wulff inequality constant depends only on $m$ and $n$, and is independent of the weights. The inequality i

Yinghui Li, Qianyu Zhou, Jingyu Gong, Ye Zhu

Point Transformers (PoinTr) have shown great potential in point cloud completion recently. Nevertheless, effective domain adaptation that improves transferability toward target domains remains unexplored. In this paper, we delve into this topic and empirically discover that direct feature alignment on point Transformer's CNN backbone only brings limited impr

Xi Chen, Cory Pecinovsky, Eva Korblova, Matthew A. Glaser

Polarization flutter, produced by an applied AC electric field drives an equilibrium ferroelectric nematic ($\mathrm{N_F}$) liquid crystal (LC) through a transition into a dissipative active ferroelectric nematic state exhibiting strong elasto-hydrodynamic intermolecular interaction. In such a fluttering ferroelectric, the typical equilibrium $\mathrm{N_F}$

Wenxiao Zhan, Siqi Yang, Minghui Liu, Francesco Hautmann

We report a study of the determination of the intrinsic transverse momentum of partons, the intrinsic $k_T$, from the dilepton transverse momentum $p_T$ in Drell-Yan (DY) production at hadron colliders. The result shows that a good sensitivity to the intrinsic $k_T$ distribution is achieved by measuring relative ratios between the cross sections of suitably

Ligang Jin, Yingli Kang, Xuding Zhu

The concept of DP-coloring of graphs was introduced by Dvo\v{r}\'{a}k and Postle, and was used to prove that planar graphs without cycles of length from $4$ to $8$ are $3$-choosable. In the same paper, they proposed a more natural and stronger claim that such graphs are DP-$3$-colorable. This paper confirms that claim by proving a stronger result that planar

Guanxing Fu, Xiaomin Shi, Zuo Quan Xu

We study a stochastic control problem with regime switching arising in an optimal liquidation problem with dark pools and multiple regimes. The new feature of this model is that it introduces a system of BSDEs with jumps and with singular terminal values, which appears in literature for the first time. The existence result for this system is obtained. As a r

Slobodan Mitrović, Anish Mukherjee, Piotr Sankowski, Wen-Horng Sheu

We design a deterministic algorithm for the $(1+\epsilon)$-approximate maximum matching problem. Our primary result demonstrates that this problem can be solved in $O(\epsilon^{-6})$ semi-streaming passes, improving upon the $O(\epsilon^{-19})$ pass-complexity algorithm by [Fischer, Mitrovi\'c, and Uitto, STOC'22]. This contributes substantially toward resol

Chun Wang Chau, Tian Xiang, Shuai A. Chen, K. T. Law

Our previous understanding of transport in disordered system depends on the assumption that there is a well-defined Fermi velocity. The Fermi velocity determines important length scales in the system such as the diffusion length and localization length. However, nearly flat band materials with vanishing Fermi velocity, it is uncertain how to understand the d

Huiyuan Tian, Bonan Xu, Shijian Li, Gang Pan

Knowledge Distillation (KD) has achieved widespread success in compressing large Vision Transformers (ViTs), but a unified theoretical framework for both ViTs and KD is still lacking. In this paper, we propose SpectralKD, a novel unified analytical framework that offers deeper insights into ViTs and optimizes KD via spectral analysis. Our model-wise analysis

Pham Ky Anh, Trinh Ngoc Hai, Nguyen Van Manh

In this paper, based on the Tikhonov regularization technique, we study a monotone general variational inequality (GVI) by considering an associated strongly monotone GVI, depending on a regularization parameter $\alpha,$ such that the latter admits a unique solution $x_\alpha$ which tends to some solution of the initial GVI, as $\alpha \to 0.$ However, inst

Jana Dunfield

To design type systems that use subtyping, we have to make tradeoffs. Deep subtyping is more expressive than shallow subtyping, because deep subtyping compares the entire structure of types. However, shallow subtyping is easier to reason about. By eta-expanding source programs, we can get the effect of deep subtyping with less of its complexity. An early pap