November 2024 arXiv papers — page 127

Fangzhou Ai, Zhuonan Lin, Jiawei Duan, Vitaliy Lomakin

This work introduces an approach to compute periodic phase diagram of micromagnetic systems by solving a periodic linearized Landau-Lifshitz-Gilbert (LLG) equation using an eigenvalue solver with the Finite Element Method formalism. The linear operator in the eigenvalue problem is defined as a function of the periodic phase shift wave vector. The dispersion

Tharindu B. Hewage, Shashikant Ilager, Maria A. Rodriguez, Rajkumar Buyya

Cloud platforms commonly exploit workload temporal flexibility to reduce their carbon emissions. They suspend/resume workload execution for when and where the energy is greenest. However, increasingly prevalent delay-intolerant real-time workloads challenge this approach. To this end, we present a framework to harvest green renewable energy for real-time wor

Kaiyu Song, Hanjiang Lai

Flow diffusion models (FDMs) have recently shown potential in generation tasks due to the high generation quality. However, the current ordinary differential equation (ODE) solver for FDMs, e.g., the Euler solver, still suffers from slow generation since ODE solvers need many number function evaluations (NFE) to keep high-quality generation. In this paper, w

Kaiying Dou, Wenhui Du, Zhonglin He, Ying Dai

Light-spin coupling is an attractive phenomenon from the standpoints of fundamental physics and device applications, and has spurred rapid development recently. Whereas the current efforts are devoted to trivial magnetism, the interplay between light and nontrivial spin properties of topological magnetism is little known. Here, using first principles, rt-TDD

Amirabbas Afzali, Borna Khodabandeh, Ali Rasekh, Mahyar JafariNodeh

Contrastive learning models have demonstrated impressive abilities to capture semantic similarities by aligning representations in the embedding space. However, their performance can be limited by the quality of the training data and its inherent biases. While Preference Optimization (PO) methods such as Reinforcement Learning from Human Feedback (RLHF) and

Kaiyu Song, Hanjiang Lai, Yan Pan, Kun Yue

Training-free conditional generation based on flow matching aims to leverage pre-trained unconditional flow matching models to perform conditional generation without retraining. Recently, a successful training-free conditional generation approach incorporates conditions via posterior sampling, which relies on the availability of a score function in the uncon

Sajilesh K. P., Roni Anna Gofman, Yuval Nitzav, Avior Almoalem

Exploiting the spin-valley degree of freedom of electrons in materials is a promising avenue for energy-efficient information storage and quantum computing. A key challenge in utilizing spin-valley polarization is the realization of spin-valley locking in bulk systems. Here, we report a comprehensive study of the noncentrosymmetric bulk misfit compound (PbS)

Ludovica Pannitto, Beatrice Bernasconi, Lucia Busso, Flavio Pisciotta

The paper descirbes a first attempt of linking the Italian constructicon to UD resources

J. D. Chan, Y. C. Tay, Brian R. Z. Yen

Distributed ledgers are common in the industry. Some of them can use blockchains as their underlying infrastructure. A blockchain requires participants to agree on its contents. This can be achieved via a consensus protocol, and several BFT (Byzantine Fault Tolerant) protocols have been proposed for this purpose. How do these protocols differ in performance?

Youngseok Yoon, Sangwoo Hong, Hyungjun Joo, Yao Qin

Long-tailed image recognition is a computer vision problem considering a real-world class distribution rather than an artificial uniform. Existing methods typically detour the problem by i) adjusting a loss function, ii) decoupling classifier learning, or iii) proposing a new multi-head architecture called experts. In this paper, we tackle the problem from a

Jingzhe Tang, Tianying Xiao, Xuan Tang, Yongbo Huang

Photomultiplier tubes (PMTs) are extensively employed as photosensors in neutrino and dark matter detection. The precise charge and timing information extracted from the PMT waveform plays a crucial role in energy and vertex reconstruction. In this study, we investigate the deconvolution algorithm utilized for PMT waveform reconstruction, while enhancing the

Linyuan Li, Jianing Qiu, Anujit Saha, Lin Li

As a prominent subfield of Artificial Intelligence Generated Content (AIGC), video generation has achieved notable advancements in recent years. The introduction of Sora-alike models represents a pivotal breakthrough in video generation technologies, significantly enhancing the quality of synthesized videos. Particularly in the realm of biomedicine, video ge

Qingyu Yin, Chak Tou Leong, Minjun Zhu, Hanqi Yan

The alignment of large language models (LLMs) with human preferences remains a key challenge. While post-training techniques like Reinforcement Learning from Human Feedback (RLHF) and Direct Preference Optimization (DPO) have achieved notable success, they often introduce computational inefficiencies and training instability. In this paper, we propose Featur

Ziwen Gao, Huihang Liu, Xinyu Zhang

The available data in semi-supervised learning usually consists of relatively small sized labeled data and much larger sized unlabeled data. How to effectively exploit unlabeled data is the key issue. In this paper, we write the regression function in the form of a copula and marginal distributions, and the unlabeled data can be exploited to improve the esti

Yaling Ke

In this work, we systematically investigate the impact of ambient noise intensity on the rate modifications of ground-state chemical reactions in an optical cavity under vibrational strong-coupling conditions. To achieve this, we utilize a numerically exact open quantum system approach--the hierarchical equations of motion in twin space, combined with a flex

Avijit Shee, Zhen Huang, Martin Head-Gordon, K. Birgitta Whaley

We have developed a new time propagation method, time-dependent adaptive sampling configuration interaction (TD-ASCI), to describe the dynamics of a strongly correlated system. We employ the short iterative Lanczos (SIL) method as the time-integrator, which provides a unitary, norm-conserving, and stable long-time propagation scheme. We used the TD-ASCI meth

Jia-Wen Li, Gang Su, Bo Gu

To realize ferromagnetic semiconductors with high Curie temperature TC is still a challenge in spintronics. Recent experiments have obtained two-dimensional (2D) room temperature ferromagnetic metals, such as monolayers MnSe2 and Cr3Te6. In this paper, by the density functional theory (DFT) calculations, we proposed a way to obtain 2D high TC ferromagnetic s

Alexander Strang

A stochastic process is at thermodynamic equilibrium if it obeys time-reversal symmetry; forward and reverse time are statistically indistinguishable at steady state. Non-equilibrium processes break time-reversal symmetry by maintaining circulating probability currents. In physical processes, these currents require a continual use and exchange of energy. Acc

I. A. Karkin, A. A. Kirillov, E. P. Savelova

The paper explores the use of various machine learning methods to search for heterogeneous or atypical structures on astronomical maps. The study was conducted on the maps of the cosmic microwave background radiation from the Planck mission obtained at various frequencies. The algorithm used found a number of atypical anomalous structures in the actual maps

Mingyi Wang, Hongqun Zou, Yifan Liu, You Wang

Predicting future motions of road participants is an important task for driving autonomously. Most existing models excel at predicting the marginal trajectory of a single agent, but predicting joint trajectories for multiple agents that are consistent within a scene remains a challenge. Previous research has often focused on marginal predictions, but the imp

Shuai Niu, Jing Ma, Hongzhan Lin, Liang Bai

Interpretation is critical for disease diagnosis, but existing models struggle to balance predictive accuracy with human-understandable rationales. While large language models (LLMs) offer strong reasoning abilities, their clinical use is limited by high computational costs and restricted multimodal reasoning ability. Small language models (SLMs) are efficie

Yan-Jue Lv, Yang Peng, Yong-Kai Liu, Yi Zheng

Thouless pumping with nontrivial topological phases provides a powerful means for the manipulation of matter waves in one-dimensional lattice systems. The band topology is revealed by the quantization of pumped charge. In the context of Thouless pumping, we present a graphical representation for the topological phases characterized by the Chern number of an

Madeline Brown, Christopher Hoffman, Hyojeong Son

The Activated Random Walk (ARW) model is a promising candidate for demonstrating self-organized criticality due to its potential for universality. Recent studies have shown that the ARW model exhibits a well-defined critical density in one dimension, supporting its universality. In this paper, we extend these results by demonstrating that the ARW model on $\

Chia-Hsiang Lin, Tzu-Hsuan Lin, Jocelyn Chanussot

Change detection (CD) is a critical remote sensing technique for identifying changes in the Earth's surface over time. The outstanding substance identifiability of hyperspectral images (HSIs) has significantly enhanced the detection accuracy, making hyperspectral change detection (HCD) an essential technology. The detection accuracy can be further upgraded b

Wei Zhou, Junteng Jia, Leda Sari, Jay Mahadeokar

CTC compressor can be an effective approach to integrate audio encoders to decoder-only models, which has gained growing interest for different speech applications. In this work, we propose a novel CTC compressor based joint speech and text training (CJST) framework for decoder-only ASR. CJST matches speech and text modalities from both directions by explori

Asghar Asgharian-Sardroud, Mohammad Hossein Izanlou, Amin Jabbari, Sepehr Mahmoodian Hamedani

Network function virtualization enables network operators to implement new services through a process called service function chain mapping. The concept of Service Function Chain (SFC) is introduced to provide complex services, which is an ordered set of Network Functions (NF). The network functions of an SFC can be decomposed in several ways into some Virtu

Jonathan Freundlich, Gauri Sharma, Sabine Thater, Mousumi Das

Dark matter is one of the pillars of the current standard model of structure formation: it is assumed to constitute most of the matter in the Universe. However, it can so far only be probed indirectly through its gravitational effects, and its nature remains elusive. In this focus meeting, we discussed different methods used to estimate galaxies' visible and

Qiang Wan, Jun Cui

This paper explores the impact of banking fintech on reducing financial risks in the agricultural supply chain, focusing on the secondary allocation of commercial credit. The study constructs a three-player evolutionary game model involving banks, core enterprises, and SMEs to analyze how fintech innovations, such as big data credit assessment, blockchain, a

G. P. Wu, S. Xue, G. F. Zhang, I. R. Petersen

In this paper, an $\mathscr{H}_2$ norm-based model reduction method for linear quantum systems is presented, which can obtain a physically realizable model with a reduced order for closely approximating the original system. The model reduction problem is described as an optimization problem, whose objective is taken as an $\mathscr{H}_2$ norm of the differen

Bo Chen, Xiaoyu Li, Yingyu Liang, Jiangxuan Long

Characterizing the express power of the Transformer architecture is critical to understanding its capacity limits and scaling law. Recent works provide the circuit complexity bounds to Transformer-like architecture. On the other hand, Rotary Position Embedding ($\mathsf{RoPE}$) has emerged as a crucial technique in modern large language models, offering supe

Bella Specktor-Fadida, Liat Ben-Sira, Dafna Ben-Bashat, Leo Joskowicz

Quality control of structures segmentation in volumetric medical images is important for identifying segmentation errors in clinical practice and for facilitating model development. This paper introduces SegQC, a novel framework for segmentation quality estimation and segmentation error detection. SegQC computes an estimate measure of the quality of a segmen

Li Fan, Jing Yang, Cong Shen

Pre-trained Transformers, through in-context learning (ICL), have demonstrated exceptional capabilities to adapt to new tasks using example prompts \textit{without model update}. Transformer-based wireless receivers, where prompts consist of the pilot data in the form of transmitted and received signal pairs, have shown high estimation accuracy when pilot da

Eliran Sherzer

Tandem queueing networks are widely used to model systems where services are provided in sequential stages. In this study, we assume that each station in the tandem system operates under a general renewal process. Additionally, we assume that the arrival process for the first station is governed by a general renewal process, which implies that arrivals at su

Rose E. Wang, Pawan Wirawarn, Kenny Lam, Omar Khattab

Many open-ended conversations (e.g., tutoring lessons or business meetings) revolve around pre-defined reference materials, like worksheets or meeting bullets. To provide a framework for studying such conversation structure, we introduce Problem-Oriented Segmentation & Retrieval (POSR), the task of jointly breaking down conversations into segments and linkin

Yulong Yang, Haoran Fan, Chenhao Lin, Qian Li

Code Language Models (CLMs) have achieved tremendous progress in source code understanding and generation, leading to a significant increase in research interests focused on applying CLMs to real-world software engineering tasks in recent years. However, in realistic scenarios, CLMs are exposed to potential malicious adversaries, bringing risks to the confid

Mingjun Sheng, Yisheng Song

This paper focuses on the strict copositivity analysis of 4th-order 3-dimensional symmetric tensors. A necessary and sufficient condition is provided for the strict copositivity of a fourth-order symmetric tensor. Subsequently, building upon this conclusion, we discuss the strict copositivity of fourth-order three-dimensional symmetric tensors with its entri

Motoki Omura, Yasuhiro Fujita, Toshiki Kataoka

In the post-training of large language models (LLMs), Reinforcement Learning from Human Feedback (RLHF) is an effective approach to achieve generation aligned with human preferences. Direct Preference Optimization (DPO) allows for policy training with a simple binary cross-entropy loss without a reward model. The objective of DPO is regularized by reverse KL

Rory Jenkins, Xinhua Wang

Subsonic missiles play an important role in modern air-to-air combat scenarios - utilized by the F-35 Lightning II - but require complex Guidance, Navigation and Control systems to manoeuvre with 30G's of acceleration to intercept successfully. Challenges with mathematically modelling and controlling such a dynamic system must be addressed, high frequency no

Joshua Farrington, Xinhua Wang

Air-to-air missiles are used on many modern military combat aircraft for self-defence. It is imperative for the pilots using the weapons that the missiles hit their target first time. The important goals for a missile control system to achieve are minimising the time constant, overshoot, and settling time of the missile dynamics. The combination of high angl

William Smith, Xinhua Wang

Tilt rotor aircraft combine the benefits of both helicopters and fixed wing aircraft, this makes them popular for a variety of applications, including Search and Rescue and VVIP transport. However, due to the multiple flight modes, significant challenges with regards to the control system design are experienced. The main challenges with VTOL aircraft, comes

Chenbei Lu, Laixi Shi, Zaiwei Chen, Chenye Wu

Reinforcement Learning (RL) algorithms are known to suffer from the curse of dimensionality, which refers to the fact that large-scale problems often lead to exponentially high sample complexity. A common solution is to use deep neural networks for function approximation; however, such approaches typically lack theoretical guarantees. To provably address the

Fen Liu, Shenghai Yuan, Wei Meng, Rong Su

From prehistoric encirclement for hunting to GPS orbiting the earth for positioning, target encirclement has numerous real world applications. However, encircling multiple non-cooperative targets in GPS-denied environments remains challenging. In this work, multiple targets encirclement by using a minimum of two tasking agents, is considered where the relati

Ryoma Sato

Traditionally, recommendation algorithms have been designed for service developers. But recently, a new paradigm called user-side recommender systems has been proposed. User-side recommender systems are built and used by end users, in sharp contrast to traditional provider-side recommender systems. Even if the official recommender system offered by the provi

Longchuan Li, Shuqian He, Qiukai Qi, Ye Cui

Soft robots, while highly adaptable to diverse environments through various actuation methods, still face significant performance boundary due to the inherent properties of materials. These limitations manifest in the challenge of guaranteeing rapid response and large-scale movements simultaneously, ultimately restricting the robots' absolute speed and overa

Stavros Anastassiou

We classify, up to a natural equivalence relation, vector fields of the plane which belong to the kernel of a 1--form. This form can be closed, in which case the vector fields are integrable, or not, in which case the differential of the form defines a, possibly singular, symplectic form. In every case, we provide a fairly complete list of local models for s

Avinash Anand, Akshit Gupta, Nishchay Yadav, Shaurya Bajaj

Bug fixing and code generation have been core research topics in software development for many years. The recent explosive growth in Large Language Models has completely transformed these spaces, putting in reach incredibly powerful tools for both. In this survey, 27 recent papers have been reviewed and split into two groups: one dedicated to Automated Progr

Alhassan S. Yasin, Prabdeep S. Gill

The inherent volatility and dynamic fluctuations within the financial stock market underscore the necessity for investors to employ a comprehensive and reliable approach that integrates risk management strategies, market trends, and the movement trends of individual securities. By evaluating specific data, investors can make more informed decisions. However,

Evangelos Kazakos, Cordelia Schmid, Josef Sivic

We propose a new task, dataset and model for grounded video caption generation. This task unifies captioning and object grounding in video, where the objects in the caption are grounded in the video via temporally consistent bounding boxes. We introduce the following contributions. First, we present a task definition and a manually annotated test dataset for

Muktai Aote, A. V. Deshpande, Kajal Parchake, Anuj Khapekar

A solid electrolyte having the ionic conductivity comparable to that of conventional liquid electrolyte can be used in All Solid State Batteries (ASSB's). The series Li6.75+xLa3-xSrxZr1.75Ta0.25O12 (x = 0 to 0.20) was synthesized to improve the ionic conductivity of garnet Li7La3Zr2O12 (LLZO). The structural, physical and morphological investigations have be

Roozbeh Hazrat, Promit Mukherjee, David Pask, Sujit Kumar Sardar

Given a row-finite higher-rank $k$-graph $\Lambda$, we define a commutative monoid $T_\Lambda$ which is a higher-rank analogue of the talented monoid of a directed graph. The talented monoid $T_\Lambda$ is canonically a $\mathbb{Z}^k$-monoid with respect to the action of state shift. This monoid coincides with the positive cone of the graded Grothendieck gro

K. J. Hepworth, Christopher Church

Data visualizations are inherently rhetorical, and therefore bias-laden visual artifacts that contain both explicit and implicit arguments. The implicit arguments depicted in data visualizations are the net result of many seemingly minor decisions about data and design from inception of a research project through to final publication of the visualization. Da

Jai G Singla, Bakul Vaghela

Presently, deep learning and convolutional neural networks (CNNs) are widely used in the fields of image processing, image classification, object identification and many more. In this work, we implemented convolutional neural network based modified U-Net model and VGG-UNet model to automatically identify objects from satellite imagery captured using high res

José Edgar Madriz Aguilar, J. O. Valle, M. Montes, C. Romero

In this paper we use the collapse metric obtained by Carneiro and Fabris to calculate the power spectrum associated to gauge invariant fluctuations of the metric during the formation of a primordial scalar black hole at the end of inflation. We assume that local perturbations in the vacuum energy density can collapse by means of the collapse of the local inf

Han Qi, Tao Cai, Xiyue Han

Recently, 3D Gaussian Splatting has dominated novel-view synthesis with its real-time rendering speed and state-of-the-art rendering quality. However, during the rendering process, the use of the Jacobian of the affine approximation of the projection transformation leads to inevitable errors, resulting in blurriness, artifacts and a lack of scene consistency

Jerome Gilles, Tristan Dagobert, Carlo De Franchis

A novel approach is presented in this paper to improve images which are altered by atmospheric turbulence. Two new algorithms are presented based on two combinations of a blind deconvolution block, an elastic registration block and a temporal filter block. The algorithms are tested on real images acquired in the desert in New Mexico by the NATO RTG40 group.

Jerome Gilles, Stephane Landeau, Tristan Dagobert, Philippe Chevalier

This paper deals with the problem of infrared image database generation for ATR assessment purposes. Huge databases are required to have quantitative and objective performance evaluations. We propose a method which superimpose targets and occultants on background under image quality metrics constraints to generate realistic images. We also propose a method t

Zhi-Song Liu, Roland Maier, Andreas Rupp

Finite element methods typically require a high resolution to satisfactorily approximate micro and even macro patterns of an underlying physical model. This issue can be circumvented by appropriate multiscale strategies that are able to obtain reasonable approximations on under-resolved scales. In this paper, we study the implicit neural representation and p

Jerome Gilles, Stephane Landeau, Tristan Dagobert, Philippe Chevalier

In this communication, we propose a method which permits to simulate images of targets in infrared imagery by superimposition of vehicle signatures in background, eventually with occultants. We develop a principle which authorizes us to generate different thermal configurations of target signatures. This method enables us to easily generate huge datasets for

Jianan Ye, Zhaorui Tan, Yijie Hu, Xi Yang

Tabular anomaly detection under the one-class classification setting poses a significant challenge, as it involves accurately conceptualizing "normal" derived exclusively from a single category to discern anomalies from normal data variations. Capturing the intrinsic correlation among attributes within normal samples presents one promising method for learnin

Lihao Zheng, Hongxuan Wang, Xiaocong Li, Jun Ma

Control system optimization has long been a fundamental challenge in robotics. While recent advancements have led to the development of control algorithms that leverage learning-based approaches, such as SafeOpt, to optimize single feedback controllers, scaling these methods to high-dimensional complex systems with multiple controllers remains an open proble

Hue T. B. Do, Meng Zhao, Pengfei Li, Yu Wei Soh

Extreme light confinement down to the atomic scale has been theoretically predicted for ultrathin, Ta-based transition metal dichalcogenides (TMDs). In this work, we experimentally demonstrate in 2H-TaS$_2$ monolayers and bilayers a lateral confinement ratio up to 300 at large wave vectors of $q = 0.15 \, \r{A}^{-1}$, and slow light behaviour with a group ve

Sutthikiat Sungkeetanon, Joseph S. Gaglione, Robert L. Chapman, Tyler M. Kelly

Symplectic integrators offer vastly superior performance over traditional numerical techniques for conservative dynamical systems, but their application to \emph{dissipative} systems is inherently difficult due to dissipative systems' lack of symplectic structure. Leveraging the intrinsic variational structure of higher-order dynamics, this paper presents a

D. K. Durdiev

In this paper, we study the inverse problem of finding a time-dependent multiplier of the right-hand side of a time-fractional one-dimensional diffusion equation with variables coefficients in the case where the usual Cauchy, homogeneous Dirichlet boundary, and an integral overdetermination conditions are given. The overdetermination condition has the form o

Irfan Khan, Snigdharani Panda, Sugam Kumar, Sunita Srivastava

Incorporating nanomaterials into hydrogels allows for the creation of versatile materials with properties that can be precisely tailored by manipulating their nanoscale structures, leading to a wide range of bulk properties. Investigating the structural and property characteristics of composite hydrogels is crucial in tailoring their performance for specific

Mingxuan Sun, Xing Li, Han Wang

When facing time-variant problems in analog computing, the desirable RNN design requires finite-time convergence and robustness with respect to various types of uncertainties, due to the time-variant nature and difficulties in implementation. It is very worthwhile to explore terminal zeroing neural networks, through examining and applying available attractin

Tunhou Zhang, Dehua Cheng, Yuchen He, Zhengxing Chen

The increasing popularity of deep learning models has created new opportunities for developing AI-based recommender systems. Designing recommender systems using deep neural networks requires careful architecture design, and further optimization demands extensive co-design efforts on jointly optimizing model architecture and hardware. Design automation, such

Hyewon Han, Bogeun Gwak

We investigates the massless scalar perturbations of the Pleba\'nski-Demia\'nski black hole considering the general case that admits all nonzero parameters. This case is the most generic black hole spacetime in general relativity, characterized by mass, spin, acceleration, electric and magnetic charges, NUT parameter, and cosmological constant. Employing con

Muhammad F. A. Chaudhary, Stephanie M. Aguilera, Arie Nakhmani, Joseph M. Reinhardt

Diffeomorphic deformable image registration ensures smooth invertible transformations across inspiratory and expiratory chest CT scans. Yet, in practice, deep learning-based diffeomorphic methods struggle to capture large deformations between inspiratory and expiratory volumes, and therefore lack inverse consistency. Existing methods also fail to account for

B. L. S Prakasa Rao

We study the problem of nonparametric estimation of the linear multiplier function $\theta(t)$ for processes satisfying stochastic differential equations of the type $$dX_t= \theta(t)X_t dt+ \epsilon\; \sigma_1(t,X_t)\sigma_2(t,Y_t)dW_t, X_0=x_0, 0 \leq t \leq T$$ where $\{W_t, t\geq 0\}$ is a standard Brownian motion, $\{Y_t, t\geq 0\}$ is a process adapted

Claudio Corianò, Paul H. Frampton, Dario Melle

We discuss extension of the electroweak gauge group to $SU(3)_L \times SU(3)_R \times U(1)_{XL} \times U(1)_{XR}$. This adds new leptons to the familiar 331-model. The explanation for three light quark-lepton families remains but new particles are introduced which have no TeV scale upper limit on their masses.

Fei He, Lihan Wang

We study the counting function of Steklov eigenvalues on compact manifolds with boundary and obtain its upper bound involving the leading term of Weyl's law. Our estimate can be viewed as a weakened version of P\'{o}lya's Conjecture in the Steklov case on general manifolds. As a byproduct, we also obtain a description about the decay behavior of Steklov eige

Yuejia Zhang, Weiguo Gao, Yingzhou Li

We develop a multi-threaded parallel coordinate descent full configuration interaction algorithm (mCDFCI), for the electronic structure ground-state calculation in the configuration interaction framework. The FCI problem is reformulated as an unconstrained minimization problem, and tackled by a modified block coordinate descent method with a deterministic co

Dan Mangoubi, Daniel Rosenblatt

We ask whether the only multiplicities in the spectrum of the clamped round plate are trivial, i.e., whether all existing multiplicities are due to the isometries of the sphere, or, equivalently, whether any eigenfunction is separated. We prove that any eigenfunction can be expressed as a sum of at most two separated ones, by showing that otherwise the corre

Dongrui Han, Mingyu Cui, Jiawen Kang, Xixin Wu

Grapheme-to-phoneme (G2P) conversion is a crucial step in Text-to-Speech (TTS) systems, responsible for mapping grapheme to corresponding phonetic representations. However, it faces ambiguities problems where the same grapheme can represent multiple phonemes depending on contexts, posing a challenge for G2P conversion. Inspired by the remarkable success of L

Kieran Ricardo, David Lee, Kenneth Duru

Moist thermodynamics is a fundamental driver of atmospheric dynamics across all scales, making accurate modeling of these processes essential for reliable weather forecasts and climate change projections. However, atmospheric models often make a variety of inconsistent approximations in representing moist thermodynamics. These inconsistencies can introduce s

Astha Saini, Petre Stoica, Prabhu Babu, Aakash Arora

This monograph presents a theoretical background and a broad introduction to the Min-Max Framework for Majorization-Minimization (MM4MM), an algorithmic methodology for solving minimization problems by formulating them as min-max problems and then employing majorization-minimization. The monograph lays out the mathematical basis of the approach used to refor

Hongcheng Ding, Xiangyu Shi, Ruiting Deng, Salaar Faroog

This study introduces a novel approach for EUR/USD exchange rate forecasting that integrates deep learning, textual analysis, and particle swarm optimization (PSO). By incorporating online news and analysis texts as qualitative data, the proposed PSO-LSTM model demonstrates superior performance compared to traditional econometric and machine learning models.

Tiejin Chen, Kaishen Wang, Hua Wei

Jailbreaking methods, which induce Multi-modal Large Language Models (MLLMs) to output harmful responses, raise significant safety concerns. Among these methods, gradient-based approaches, which use gradients to generate malicious prompts, have been widely studied due to their high success rates in white-box settings, where full access to the model is availa

Tomoharu Furudoi, Takumi Takahashi, Shinsuke Ibi, Hideki Ochiai

This paper considers a discrete-valued signal estimation scheme based on a low-complexity Bayesian optimal message passing algorithm (MPA) for solving massive linear inverse problems under highly correlated measurements. Gaussian belief propagation (GaBP) can be derived by applying the central limit theorem (CLT)-based Gaussian approximation to the sum-produ

Yue Zeng, Yao-jia Zhang, Nan-jing Huang

The main goal of this paper is to investigate the multi-parameter stability result for a stochastic fractional differential variational inequality with L\'{e}vy jump (SFDVI with L\'{e}vy jump) under some mild conditions. We verify that Mosco convergence of the perturbed set implies point convergence of the projection onto the Hilbert space consisting of spec

Li Yu

Due to the scarcity of labeled samples in Image Quality Assessment (IQA) datasets, numerous recent studies have proposed multi-task based strategies, which explore feature information from other tasks or domains to boost the IQA task. Nevertheless, multi-task strategies based No-Reference Image Quality Assessment (NR-IQA) methods encounter several challenges

Umangi Jain, Ashkan Mirzaei, Igor Gilitschenski

We introduce GaussianCut, a new method for interactive multiview segmentation of scenes represented as 3D Gaussians. Our approach allows for selecting the objects to be segmented by interacting with a single view. It accepts intuitive user input, such as point clicks, coarse scribbles, or text. Using 3D Gaussian Splatting (3DGS) as the underlying scene repre

Tianxing Mei, Yingying Fan, Jinchi Lv

We offer theoretical and empirical insights into the impact of exogenous randomness on the effectiveness of random forests with tree-building rules independent of training data. We formally introduce the concept of exogenous randomness and identify two types of commonly existing randomness: Type I from feature subsampling, and Type II from tie-breaking in tr

Yuval Efron, Shyamal Patel, Cliff Stein

We give an algorithm for the fully-dynamic carpooling problem with recourse: Edges arrive and depart online from a graph $G$ with $n$ nodes according to an adaptive adversary. Our goal is to maintain an orientation $H$ of $G$ that keeps the discrepancy, defined as $\max_{v \in V} |\text{deg}_H^+(v) - \text{deg}_H^-(v)|$, small at all times. We present a simp

Renata Kallosh, Andrei Linde

We investigate the global structure of the recently discovered family of $SL(2,\mathbb{Z})$-invariant potentials describing inflationary $\alpha$-attractors. These potentials have an inflationary plateau consisting of the fundamental domain and its images fully covering the upper part of the Poincar\'e half-plane. Meanwhile, the lower part of the half-plane

Xueyu Du, Lilian Zhang, Chengjun Ji, Xinchan Luo

Due to the advantages of high computational efficiency and small memory requirements, filter-based visual inertial odometry (VIO) has a good application prospect in miniaturized and payload-constrained embedded systems. However, the filter-based method has the problem of insufficient accuracy. To this end, we propose the State transformation and Pose-only VI

Akash Vijayakumar, Atmanand M A, Abhilash Somayajula

This paper presents an approach for autonomous docking of a fully actuated autonomous surface vessel using expert demonstration data. We frame the docking problem as an imitation learning task and employ inverse reinforcement learning (IRL) to learn a reward function from expert trajectories. A two-stage neural network architecture is implemented to incorpor

Rajko Nenadov, Lander Verlinde

For a field $\mathbb{F}$ and integers $d, k$ and $\ell$, a set $A \subseteq \mathbb{F}^d$ is called $(k,\ell)$-nearly orthogonal if all vectors in $A$ are non-self-orthogonal and every $k+1$ vectors in $A$ contain $\ell + 1$ pairwise orthogonal vectors. Recently, Haviv, Mattheus, Milojevi\'{c} and Wigderson have improved the lower bound on nearly orthogonal

Zhu-Ling Deng, Xiang-Dong Li

While there are over a dozen known neutron star (NS) symbiotic X-ray binaries (SyXBs) in the Galaxy, none SyXBs containing a black hole (BH) have been detected. We address this problem by incorporating binary population synthesis and the accretion properties of BHs fed by the wind from red giant companions. We investigate the impact of different supernova me

Pingjie Wang, Liudan Zhao, Zihan Zhao, Miao He

Accurate and efficient auscultation-based diagnostics are vital for early disease detection, especially in resource-limited settings where specialized clinical expertise is scarce. Traditional auscultation, which heavily depends on clinician experience, suffers from significant inter-observer variability, while existing AI models often falter due to the limi

YeongHyeon Park, Myung Jin Kim, Hyeong Seok Kim

A pre-trained visual-language model, contrastive language-image pre-training (CLIP), successfully accomplishes various downstream tasks with text prompts, such as finding images or localizing regions within the image. Despite CLIP's strong multi-modal data capabilities, it remains limited in specialized environments, such as medical applications. For this pu

Vedran Jelic, Stefanie Adams, Daniel Maldonado-Lopez, Ismail A. Buliyaminu

Light-induced phase transitions offer a method to dynamically modulate topological states in bulk complex materials. Yet, next-generation devices demand nanoscale architectures with contact resistances near the quantum limit and precise control over local electronic properties. The layered material WTe$_2$ has gained attention as a likely Weyl semimetal, wit

Md Arid Hasan, Krishno Dey

The recent advancement of edge computing enables researchers to optimize various deep learning architectures to employ them in edge devices. In this study, we aim to optimize Xception architecture which is one of the most popular deep learning algorithms for computer vision applications. The Xception architecture is highly effective for object detection task

Qi-Yuan Nie, Wen-Han Dai, Hao Ma, Qian Yue

We search for nuclear recoil signals of dark matter models with a light mediator using data taken from a p-type point-contact germanium detector of the CDEX-10 experiment at the China Jinping Underground Laboratory. The 90% confidence level upper limits on the DM-nucleon interaction cross section from 205.4 kg-day exposure data are derived, excluding new par

Yuhei Iwata, Masanori Akimoto, Tomoki Matsuoka, Keiichi Maeda

We report on radio follow-up observations of the nearby Type II supernova, SN 2023ixf, spanning from 1.7 to 269.9 days after the explosion, conducted using three very long baseline interferometers (VLBIs), which are the Japanese VLBI Network (JVN), the VLBI Exploration of Radio Astrometry (VERA), and the Korean VLBI Network (KVN). In three observation epochs

Qiankun Gao, Jiarui Meng, Chengxiang Wen, Jie Chen

The online reconstruction of dynamic scenes from multi-view streaming videos faces significant challenges in training, rendering and storage efficiency. Harnessing superior learning speed and real-time rendering capabilities, 3D Gaussian Splatting (3DGS) has recently demonstrated considerable potential in this field. However, 3DGS can be inefficient in terms

Neelkamal Somisetty, Swaroop Darbha

This paper addresses the lateral control of Autonomous and Connected Vehicles (ACVs) in a platoon executing an Emergency Lane Change (ELC) maneuver. These maneuvers are typically triggered by emergency signals from the front or rear of the platoon in response to the need to avoid obstacles or allow other vehicles to pass. The study assumes that ACVs maintain

F. Qi, L. Ni, C. Xu

We introduce a film score generation framework to harmonize visual pixels and music melodies utilizing a latent diffusion model. Our framework processes film clips as input and generates music that aligns with a general theme while offering the capability to tailor outputs to a specific composition style. Our model directly produces music from video, utilizi

Bingqing Song, Boran Han, Shuai Zhang, Jie Ding

While the Transformer architecture has achieved remarkable success across various domains, a thorough theoretical foundation explaining its optimization dynamics is yet to be fully developed. In this study, we aim to bridge this understanding gap by answering the following two core questions: (1) Which types of Transformer architectures allow Gradient Descen

Pouyan Sajadi, Mahya Qorbani, Sobhan Moosavi, Erfan Hassannayebi

Traffic accidents pose a significant threat to public safety, resulting in numerous fatalities, injuries, and a substantial economic burden each year. The development of predictive models capable of real-time forecasting of post-accident impact using readily available data can play a crucial role in preventing adverse outcomes and enhancing overall safety. H