November 2024 arXiv papers — page 90

Sreeram Vennam, David Valente, David Herel, Ponnurangam Kumaraguru

Thinking Tokens (TT) have been proposed as an unsupervised method to facilitate reasoning in language models. However, despite their conceptual appeal, our findings show that TTs marginally improves performance and consistently underperforms compared to Chain-of-Thought (CoT) reasoning across multiple benchmarks. We hypothesize that this underperformance ste

Ke Zhang, Zhaoye Zheng, Yurong Guo, Jiacun Wang

Unmanned aerial vehicle (UAV) patrol inspection has emerged as a predominant approach in transmission line monitoring owing to its cost-effectiveness. Detecting defects in transmission lines is a critical task during UAV patrol inspection. However, due to imaging distance and shooting angles, UAV patrol images often suffer from insufficient defect-related vi

Jian Liu, Xu Zhou, Zhuojun Zhou, Le Luo

The quantum hybrid algorithm has become a very promising and speedily method today for solving the larger-scale optimization in the noisy intermediate-scale quantum (NISQ) era. The unit commitment (UC) problem is a fundamental problem in the power system which aims to satisfy a balance load with minimal cost. In this paper, we focus on the implement of the U

Eyal Atias, Michael Assaf

Understanding the dynamics of an epidemic spread is crucial for effective control measures. During the COVID-19 pandemic, quarantines were implemented to minimize infections while mitigating social and economic impacts, raising the question of how to maximize quarantine efficiency. Previous research on periodic quarantines using the susceptible-infected-reco

V. G. Bordo

A source which generates sustained terahertz electromagnetic radiation (oscillator) controllable by an applied voltage is proposed. The structure consists of a nanocomposite slab containing metal nanorods enclosed between two graphene sheets which are electrostatically gated. The principle of its operation is based on a positive feedback which the nanorods p

Runze Ma, Chengxin He, Huiru Zheng, Xinye Wang

Improving the ability to predict protein function can potentially facilitate research in the fields of drug discovery and precision medicine. Technically, the properties of proteins are directly or indirectly reflected in their sequence and structure information, especially as the protein function is largely determined by its spatial properties. Existing app

Shaoying Huang, José Miguel Algarín, Joseba Alonso, Anieyrudh R

Nuclear magnetic resonance instruments are becoming available to the do-it-yourself community. The challenges encountered in the endeavor to build a magnetic resonance imaging instrument from scratch were confronted in a four-day hackathon at Singapore University of Technology and Design in spring 2024. One day was devoted to educational lectures and three d

Shengchao Hu, Yuhang Zhou, Ziqing Fan, Jifeng Hu

Training a generalizable agent to continually learn a sequence of tasks from offline trajectories is a natural requirement for long-lived agents, yet remains a significant challenge for current offline reinforcement learning (RL) algorithms. Specifically, an agent must be able to rapidly adapt to new tasks using newly collected trajectories (plasticity), whi

Boyao Zhou, Shunyuan Zheng, Hanzhang Tu, Ruizhi Shao

Differentiable rendering techniques have recently shown promising results for free-viewpoint video synthesis of characters. However, such methods, either Gaussian Splatting or neural implicit rendering, typically necessitate per-subject optimization which does not meet the requirement of real-time rendering in an interactive application. We propose a general

Harshita Sharma, Valentina Salvatelli, Shaury Srivastav, Kenza Bouzid

There is growing interest in applying AI to radiology report generation, particularly for chest X-rays (CXRs). This paper investigates whether incorporating pixel-level information through segmentation masks can improve fine-grained image interpretation of multimodal large language models (MLLMs) for radiology report generation. We introduce MAIRA-Seg, a seg

Jinhong Wang, Jian Liu, Dongqi Tang, Weiqiang Wang

This paper shows that the autoregressive model is an effective and scalable monocular depth estimator. Our idea is simple: We tackle the monocular depth estimation (MDE) task with an autoregressive prediction paradigm, based on two core designs. First, our depth autoregressive model (DAR) treats the depth map of different resolutions as a set of tokens, and

Zhiming Wang, Mingze Wang, Sheng Xu, Yanjing Li

Remote Sensing Image Change Captioning (RSICC) aims to generate natural language descriptions of surface changes between multi-temporal remote sensing images, detailing the categories, locations, and dynamics of changed objects (e.g., additions or disappearances). Many current methods attempt to leverage the long-sequence understanding and reasoning capabili

H. B. Xiao, C. Chen, X. Sui, S. H. Zhang

Rhombohedral multilayer graphene has emerged as an extraordinary platform for investigating exotic quantum states, such as superconductivity and fractional quantum anomalous Hall effects, mainly due to the existence of topological surface flatbands. Despite extensive research efforts, a systematic spectroscopic investigation on the evolution of its electroni

Aaron D. Lanterman

This paper analyzes an unusual active bandpass filter employed in the Buchla Model 295 10 Channel Comb Filter, a synthesizer module developed as part of the Buchla 200 Electric Music Box by Donald Buchla. The filter consists of a peculiar rearrangement of elements in a classic Twin-T configuration; to our knowledge, it has not been previously addressed in th

Jingjing Wang, Xinglin Piao, Zongzhi Gao, Bo Li

Object localization is a hot issue in computer vision area, which aims to identify and determine the precise location of specific objects from image or video. Most existing object localization methods heavily rely on extensive labeled data, which are costly to annotate and constrain their applicability. Therefore, we propose a new Zero-Shot Object Localizati

Jiayi Li, Xile Zhao, Jianli Wang, Chao Wang

Recently, implicit neural representations (INRs) have attracted increasing attention for multi-dimensional data recovery. However, INRs simply map coordinates via a multi-layer perception (MLP) to corresponding values, ignoring the inherent semantic information of the data. To leverage semantic priors from the data, we propose a novel Superpixel-informed INR

Xudong Yan, Songhe Feng, Yang Zhang, Jian Yang

Compositional zero-shot learning (CZSL) aims to recognize novel compositions of attributes and objects learned from seen compositions. Previous works disentangle attributes and objects by extracting shared and exclusive parts between the image pair sharing the same attribute (object), as well as aligning them with pretrained word embeddings to improve unseen

Junxian Li, Simon L. Rydin Myerson, Pankaj Vishe

We present a two-dimensional delta symbol method that facilitates a version of the Kloosterman refinement of the circle method, addressing a question posed by Heath-Brown. As an application, we establish the asymptotic formula for the number of integral points on a non-singular intersection of two integral quadratic forms with at least $10$ variables. Assumi

Nay Myat Min, Long H. Pham, Yige Li, Jun Sun

Large Language Models (LLMs) are vulnerable to backdoor attacks that manipulate outputs via hidden triggers. Existing defense methods--designed for vision/text classification tasks--fail for text generation. We propose Internal Consistency Regularization (CROW), a defense leveraging the observation that backdoored models exhibit unstable layer-wise hidden re

Jing Li, Xueke Chi, Qiufeng Wang, Dahan Wang

Oracle character recognition-an analysis of ancient Chinese inscriptions found on oracle bones-has become a pivotal field intersecting archaeology, paleography, and historical cultural studies. Traditional methods of oracle character recognition have relied heavily on manual interpretation by experts, which is not only labor-intensive but also limits broader

Jingyu Li, Aemon Yat Fei Chiu, Tan Lee

Language mismatch is among the most common and challenging domain mismatches in deploying speaker verification (SV) systems. Adversarial reprogramming has shown promising results in cross-language adaptation for SV. The reprogramming is implemented by padding learnable parameters on the two sides of input speech signals. In this paper, we investigate the rel

QingChang Zhao, Hong Li, Lian Tao, Hua Feng

The geometry of the Comptonization corona in neutron star low-mass X-ray binaries is still unclear. We conducted time-resolved polarimetric analysis of the archival observations of XTE J1701--462 obtained with the \textit{Imaging X-ray Polarimeter Explorer} during its 2022 outburst, and found that the polarization angle (PA) varied significantly with time wh

Bowen Duan, Shiming Chen, Yufei Guo, Guo-Sen Xie

Zero-shot learning (ZSL) aims to leverage additional semantic information to recognize unseen classes. To transfer knowledge from seen to unseen classes, most ZSL methods often learn a shared embedding space by simply aligning visual embeddings with semantic prototypes. However, methods trained under this paradigm often struggle to learn robust embedding spa

Fasong Zheng, Weiliang Chen, Kun Liu, Shaoyang Dai

A new caesium (Cs) fountain clock NIM6 has been developed at the National Institute of Metrology (NIM) in China, for which a comprehensive uncertainty evaluation is presented. A three-dimensional magneto-optical trap (3D MOT) loading optical molasses is employed to obtain more cold atoms rapidly and efficiently with a tunable, uniform density distribution. A

Shaoxiang Qin, Dongxue Zhan, Dingyang Geng, Wenhui Peng

Accurate urban microclimate analysis with wind velocity and temperature is vital for energy-efficient urban planning, supporting carbon reduction, enhancing public health and comfort, and advancing the low-altitude economy. However, traditional computational fluid dynamics (CFD) simulations that couple velocity and temperature are computationally expensive.

Maitri Ganguli

We study the behavior of spread complexity in the context of non-Hermitian many-body localization Transition (MBLT). Our analysis has shown that the singular value spread complexity is capable of distinguishing the ergodic and many-body localization (MBL) phase from the presaturation peak height for the non-hermitian models having time-reversal symmetry (TRS

Mahnaz Rezaei, Jahanfar Abouie, Fariba Nazari

Exchange couplings are fundamental to our understanding of many physical phenomena in condensed matter physics and material science. Model systems provide a controlled environment to investigate such phenomena, effectively. In this study, we employ first-principle calculations based on density functional theory and Green's function (GF) method to explore the

Léo Mathis

We study the expected number of solutions of a system of identically distributed exponential sums with centered Gaussian coefficient and arbitrary variance. We use the Adler and Taylor theory of Gaussian random fields to identify a moment map which allows to express the expected number of solution as an integral over the Newton polytope, in analogy with the

Han Cao, Zhaoyang Zhang, Xiangtian Li, Chufan Wu

In the context of knowledge-driven seq-to-seq generation tasks, such as document-based question answering and document summarization systems, two fundamental knowledge sources play crucial roles: the inherent knowledge embedded within model parameters and the external knowledge obtained through context. Recent studies revealed a significant challenge: when t

Qinglong Cao, Xirui Li, Ding Wang, Chao Ma

Video diffusion models have achieved impressive results in natural scene generation, yet they struggle to generalize to scientific phenomena such as fluid simulations and meteorological processes, where underlying dynamics are governed by scientific laws. These tasks pose unique challenges, including severe domain gaps, limited training data, and the lack of

Huan Lin, Lianghui Ding

Unmanned aerial vehicle (UAV) swarm networks face severe challenges of communication network split (CNS) issues caused by massive damage in hostile environments. In this paper, we propose a new paradigm to restore network connectivity by repositioning remaining UAVs based on damage information within local topologies. Particularly, the locations of destroyed

Zhigang Bao, Daniel Munoz George

From the study of the high order freeness of random matrices, it is known that the order $r$ cumulant of the trace of a polynomial of $N$-dimensional GUE/GOE is of order $N^{2-r}$ if $r$ is fixed. In this work, we extend the study along three directions. First, we also consider generally distributed Wigner matrices with subexponential entries. Second, we inc

Ronghui Han, Duanyu Feng, Hongyu Du, Hao Wang

Accurate time series forecasting, predicting future values based on past data, is crucial for diverse industries. Many current time series methods decompose time series into multiple sub-series, applying different model architectures and training with an end-to-end overall loss for forecasting. However, this raises a question: does this overall loss prioriti

Shweta Didel, Jeewan C Pandey, A. K. Srivastava

In the present paper, we analyze three energetic X-ray flares from the active RS CVn binary HR 1099 using data obtained from XMM-Newton. The flare duration ranges from 2.8 to 4.1 h, with e-folding rise and decay times in the range of 27 to 38 minutes and 1.3 to 2.4 h, respectively, indicating rapid rise and slower decay phases. The flare frequency for HR 109

Abdellah Bulaich Mehamdi, Mathieu Lacroix, Sébastien Martin

Column generation is used alongside Dantzig-Wolfe Decomposition, especially for linear programs having a decomposable pricing step requiring to solve numerous independent pricing subproblems. We propose a filtering method to detect which pricing subproblems may have improving columns, and only those subproblems are solved during pricing. This filtering is do

Emil Geisler

We describe an algorithm to compute the stable multiplicity of a family of irreducible representations in the cohomology of ordered configuration space of the plane. Using this algorithm, we compute the stable multiplicities of all families of irreducibles given by Young diagrams with $23$ boxes or less up to cohomological degree $50$. In particular, this de

Ishfaq Ahmad Wani, Abdul Liman

In this paper, we consider a modified version of Smirnov operator and obtain some Bernstein-type inequalities preserved by this operator. In particular, we prove some compact generalizations of the well-known inequalities of Bernstein, Erd\"{o}s and Lax, Ankeny and Rivlin and others.

Hanyu Guo, Wanchuan Yu, Suzhou Que, Kaiwen Du

In recent years, few-shot action recognition has achieved remarkable performance through spatio-temporal relation modeling. Although a wide range of spatial and temporal alignment modules have been proposed, they primarily address spatial or temporal misalignments at the video level, while the spatio-temporal relationships across different videos at the task

Bowen Zheng, Tohru Ozawa

In this paper, we study a class of variable coefficient Schr\"{o}dinger equations with a linear potential \[i\partial_tu+\nabla\cdot(|x|^b\nabla u)-V(x)u=-|x|^c|u|^pu,\] where $2-n<b\leq0,\ c\geq b-2$ and $0<\textbf{p}_c\leq(2-b)(p+2)$, where $\textbf{p}_c:=np-2c$. In the radial or finite variance case, we firstly prove the global existence and blow-up below

Bowen Zheng, Tohru Ozawa

This paper is dedicated to the blow-up solution for the divergence Schr\"{o}dinger equations with inhomogeneous nonlinearity (dINLS for short) \[i\partial_tu+\nabla\cdot(|x|^b\nabla u)=-|x|^c|u|^pu,\quad\quad u(x,0)=u_0(x),\] where $2-n<b<2$, $c>b-2$, and $np-2c<(2-b)(p+2)$. First, for radial blow-up solutions in $W_b^{1,2}$, we prove an upper bound on the b

Manish Kumar Singh, Chang Quan Lai

Since its introduction more than 60 years ago, the Hashin-Shtrikman upper bound has stood as the theoretical limit for the stiffness of isotropic composites and porous solids, acting as an important reference against which the moduli of heterogeneous structural materials are assessed. Here, we show through first-principles calculations, supported by finite e

L. Lotina, K. Nomura, R. Rodríguez-Guzmán, L. M. Robledo

We present an extensive study of quadrupole-hexadecapole correlation effects in even-even Sm and Gd isotopes with neutron number $N=88-106$. The calculations are performed in the framework of the Gogny energy density functional (EDF) with the D1S parametrization and the $sdg$ interacting boson model (IBM). The quadrupole-hexadecapole constrained self-consist

Jorawar Singh, Vaishali Gulati, Kavita Dorai, Arvind

We design and successfully implement artificial neural networks (ANNs) to detect and classify entanglement for three-qubit systems using limited state features. The overall design principle is a feed forward neural network (FFNN), with the output layer consisting of a single neuron for the detection of genuine multipartite entanglement (GME) and six neurons

Bowen Yuan, Zijian Wang, Mahsa Baktashmotlagh, Yadan Luo

Dataset Distillation (DD) is designed to generate condensed representations of extensive image datasets, enhancing training efficiency. Despite recent advances, there remains considerable potential for improvement, particularly in addressing the notable redundancy within the color space of distilled images. In this paper, we propose AutoPalette, a framework

Vandana Sahdev

The beyond-the-standard-model scenario in this work is motivated from the observations of neutrino masses, anomalous magnetic moments of electron and muon, and dark matter in the Universe. We explain these observations by extending the standard model with two generations of vector-like fermions and an inert scalar doublet, all odd under a $Z_2$ symmetry. The

Zhihong Liu, Long Qian, Zeyang Liu, Lipeng Wan

Decision Transformer (DT) can learn effective policy from offline datasets by converting the offline reinforcement learning (RL) into a supervised sequence modeling task, where the trajectory elements are generated auto-regressively conditioned on the return-to-go (RTG).However, the sequence modeling learning approach tends to learn policies that converge on

DongYu Du, Yue Chan

Sequential recommendation refers to recommending the next item of interest for a specific user based on his/her historical behavior sequence up to a certain time. While previous research has extensively examined Markov chain-based sequential recommendation models, the majority of these studies has focused on the user's historical behavior sequence but has pa

Deepak Ravikumar, Alex Yeo, Yiwen Zhu, Aditya Lakra

The proliferation of big data and analytic workloads has driven the need for cloud compute and cluster-based job processing. With Apache Spark, users can process terabytes of data at ease with hundreds of parallel executors. At Microsoft, we aim at providing a fast and succinct interface for users to run Spark applications, such as through creating simple no

Nicholas Glaze, Tria McNeely, Yiwen Zhu, Matthew Gleeson

Cloud operators have expanded their service offerings, known as Stock Keeping Units (SKUs), to accommodate diverse demands, resulting in increased complexity for customers to select appropriate configurations. In a studied system, only 43% of the resource capacity was correctly chosen. Automated solutions addressing this issue often require enriched data, su

Nathaniel Hanson, Philip Manke, Simon Birkholz, Maximilian Mühlbauer

Machine learning is an important tool for analyzing high-dimension hyperspectral data; however, existing software solutions are either closed-source or inextensible research products. In this paper, we present cuvis.ai, an open-source and low-code software ecosystem for data acquisition, preprocessing, and model training. The package is written in Python and

Muhammad Fadhil Ginting, Dong-Ki Kim, Sung-Kyun Kim, Bandi Jai Krishna

This paper addresses the problem of task planning for robots that must comply with operational manuals in real-world settings. Task planning under these constraints is essential for enabling autonomous robot operation in domains that require adherence to domain-specific knowledge. Current methods for generating robot goals and plans rely on common sense know

Misao Sasaki, Vicharit Yingcharoenrat, Ying-li Zhang

In the absence of gravity, Coleman's theorem states that the $O(4)$-symmetric instanton solution, which is regular at the origin and exponentially decays at infinity, gives the lowest action. Perturbatively, this implies that any small deformation from $O(4)$-symmetry gives a larger action. In this letter we investigate the possibility of extending this theo

Sara Fraschini, Vladimir Kazeev, Ilaria Perugia

Structured Finite Element Methods (FEMs) based on low-rank approximation in the form of the so-called Quantized Tensor Train (QTT) decomposition (QTT-FEM) have been proposed and extensively studied in the case of elliptic equations. In this work, we design a QTT-FE method for time-domain acoustic wave equations, combining stable low-rank approximation in spa

Yifan Yu, Shengjie Xiu, Daniel P. Palomar

In this paper, we present a novel optimization algorithm designed specifically for estimating state-space models to deal with heavy-tailed measurement noise and constraints. Our algorithm addresses two significant limitations found in existing approaches: susceptibility to measurement noise outliers and difficulties in incorporating constraints into state es

D. N. Yasinskaya, Y. D. Panov

We analyze frustrated states of the one-dimensional dilute Ising chain with charged interacting impurities of two types with mapping of the system to some Markov chain. We perform classification and reveal two types of Markov chains: periodic with period 2 and aperiodic. Frustrated phases with various types of chains have different properties. In phases with

Lechao Cheng, Kaifeng Chen, Jiyang Li, Shengeng Tang

Learning from noisy data has become essential for adapting deep learning models to real-world applications. Traditional methods often involve first evaluating the noise and then applying strategies such as discarding noisy samples, re-weighting, or re-labeling. However, these methods can fall into a vicious cycle when the initial noise evaluation is inaccura

Yun Peng, Akhilesh Deepak Gotmare, Michael Lyu, Caiming Xiong

Large Language Models (LLMs) are widely adopted for assisting in software development tasks, yet their performance evaluations have narrowly focused on the functional correctness of generated code. Human programmers, however, require LLM-generated code to be not only correct but also optimally efficient. We propose PerfCodeGen, a training-free framework that

Ryan Sullivan, Ryan Pégoud, Ameen Ur Rehman, Xinchen Yang

Curriculum learning has been a quiet, yet crucial component of many high-profile successes of reinforcement learning. Despite this, it is still a niche topic that is not directly supported by any of the major reinforcement learning libraries. These methods can improve the capabilities and generalization of RL agents, but often require complex changes to trai

Mohamad Fazelnia, Sara Moshtari, Mehdi Mirakhorli

In the rapidly evolving field of artificial intelligence (AI), the identification, documentation, and mitigation of vulnerabilities are paramount to ensuring robust and secure systems. This paper discusses the minimum elements for AI vulnerability management and the establishment of an Artificial Intelligence Vulnerability Database (AIVD). It presents standa

Aahan Chatterjee

We obtain asymptotic bounds on the number of natural numbers less than $X$ satisfying $\gcd{\left(n,\lfloor P(n) \rfloor\right)}=1$, under some diophantine conditions on the coefficient of $x$ in $P$, and show that the density of such naturals is exactly $\dfrac{1}{\zeta{\left(2\right)}}$.

Haibo Zhang, Toru Nakamura, Takamasa Isohara, Kouichi Sakurai

Recently, an increasing number of laws have governed the useability of users' privacy. For example, Article 17 of the General Data Protection Regulation (GDPR), the right to be forgotten, requires machine learning applications to remove a portion of data from a dataset and retrain it if the user makes such a request. Furthermore, from the security perspectiv

Toru Sasahara

In this paper, we study Hamiltonian stationary Lagrangian surfaces in complex space forms. We first show that when the mean curvature is a non-zero constant, the second fundamental form is parallel. We then consider the case in which the mean curvature is a non-constant harmonic function. Under the additional assumption that the Gaussian curvature is constan

Aven-Le Zhou, Wei Wu, Yu-Ao Wang, Kang Zhang

With the advancement of neural generative capabilities, the art community has increasingly embraced GenAI (Generative Artificial Intelligence), particularly large text-to-image models, for producing aesthetically compelling results. However, the process often lacks determinism and requires a tedious trial-and-error process as users often struggle to devise e

Y. S. Teo, S. U. Shringarpure, S. Cho, H. Jeong

Quantum-information processing and computation with bosonic qubits are corruptible by noise channels. Using interferometers and photon-subtraction gadgets (PSGs) accompanied by linear amplification and attenuation, we establish linear-optical methods to mitigate and suppress bosonic noise channels. We first show that by employing amplifying and attenuating P

Rawad Melhem, Riad Hamadeh, Assef Jafar

The CEEMDAN algorithm is one of the modern methods used in the analysis of non-stationary signals. This research presents a study of the effectiveness of this method in audio source separation to know the limits of its work. It concluded two conditions related to frequencies and amplitudes of mixed signals to be separated by CEEMDAN. The performance of the a

Hodaka Kikuchi, Makoto Ozeki, Nobuyuki Kurita, Shinichiro Asai

We performed inelastic neutron scattering experiments on single-crystal samples of the honeycomb lattice magnet, ilmenite NiTiO3. Below the Neel temperature of 22 K, spin wave excitations with a band energy of 3.7 meV were observed. The neutron energy spectra were well-reproduced by modeling the system as a ferromagnetic honeycomb lattice with antiferromagne

P Sangeerth, Abolfazl Lavaei, Pushpak Jagtap

In this paper, we introduce the notion of simulation-gap functions to formally quantify the potential gap between an approximate nominal mathematical model and the high-fidelity simulator representation of a real system. Given a nominal mathematical model alongside a quantified simulation gap, the system can be conceptualized as one characterized by bounded

L. K. Duchaniya, B. Mishra

This study explored the behavior of the $f(T, \mathcal{T})$ cosmological model with the use of various data set combinations. We also compared the results for this model between the Pantheon+ (without SH0ES) and the Pantheon+\&SH0ES (with SH0ES) data sets. Additionally, we incorporated data from BAO along with $H_0$ priors. We observed that integrating SH0ES

Albert Munyeshyaka, Praveen Kumar Dhankar, Joseph Ntahompagaze

In the present work, we investigate cosmological perturbations of viscous modified chaplygin gas model. Using 1 + 3 covariant formalism, we define covariant and gauge invariant gradient variables, which after the application of scalar decomposition and harmonic decomposition techniques together with redshift transformation method, provide the energy overdens

Sai Samrat Kankanala, Akshara Soman, Sriram Ganapathy

Speech comprehension is an involuntary task for the healthy human brain, yet the understanding of the mechanisms underlying this brain functionality remains obscure. In this paper, we aim to quantify the role of acoustic and semantic information streams in complex listening conditions. We propose a paradigm to understand the encoding of the speech cues in el

Yuancheng Zhan, Hui Zhang, Rebecca Erbanni, Andreas Burger

Quantum evolution is crucial for the understanding of complex quantum systems. However, current implementations of time evolution on quantum photonic platforms are limited by both low photon generation efficiency and high propagation loss, making photon detection difficult. Furthermore, the single-layer complexity of most implementations cannot support multi

Ray Wai Man Kong, Theodore Ho Tin Kong, Miao Yi, Zerui Zhang

Automated machinery design for garment manufacturing is essential for improving productivity, consistency, and quality. This paper focuses on the development of new pulling gear for automated pant bottom hem sewing machines. Traditionally, these machines require manual intervention to guide the bottom hem sewing process, which often leads to inconsistent sti

Ranmin Wang, Limin Zhuang, Hongkun Chen, Boyan Xu

The advancement of medical image segmentation techniques has been propelled by the adoption of deep learning techniques, particularly UNet-based approaches, which exploit semantic information to improve the accuracy of segmentations. However, the order of organs in scanned images has been disregarded by current medical image segmentation approaches based on

Guochen Yan, Xunkai Li, Luyuan Xie, Qingni Shen

Federated Graph Learning (FGL) has emerged as a promising paradigm for breaking data silos among distributed private graphs. In practical scenarios involving heterogeneous distributed graph data, personalized Federated Graph Learning (pFGL) aims to enhance model utility by training personalized models tailored to client needs. However, existing pFGL methods

Cheng-Yen Yang, Hsiang-Wei Huang, Wenhao Chai, Zhongyu Jiang

The Segment Anything Model 2 (SAM 2) has demonstrated strong performance in object segmentation tasks but faces challenges in visual object tracking, particularly when managing crowded scenes with fast-moving or self-occluding objects. Furthermore, the fixed-window memory approach in the original model does not consider the quality of memories selected to co

Gang Dang, Dainhui Wang

Recurrent stochastic configuration networks (RSCNs) have shown promise in modelling nonlinear dynamic systems with order uncertainty due to their advantages of easy implementation, less human intervention, and strong approximation capability. This paper develops the original RSCNs with block increments, termed block RSCNs (BRSCNs), to further enhance the lea

Heon-Gyu Kwak, Gi-Hwan Shin, Yeon-Woo Choi, Dong-Hoon Lee

In this paper, we propose a conceptual framework for personalized brain-computer interface (BCI) applications, which can offer an enhanced user experience by customizing services to individual preferences and needs, based on endogenous electroencephalography (EEG) paradigms including motor imagery (MI), speech imagery (SI), and visual imagery. The framework

Shubhadeep Chakraborty, Bo Wang, Ram Tiwari, Samiran Ghosh

Cluster or group randomized trials (CRTs) are increasingly used for both behavioral and system-level interventions, where entire clusters are randomly assigned to a study condition or intervention. Apart from the assigned cluster-level analysis, investigating whether an intervention has a differential effect for specific subgroups remains an important issue,

Kazuo Aoyama, Kazumi Saito

This paper presents an accelerated spherical K-means clustering algorithm for large-scale and high-dimensional sparse document data sets. We design an algorithm working in an architecture-friendly manner (AFM), which is a procedure of suppressing performance-degradation factors such as the numbers of instructions, branch mispredictions, and cache misses in C

Cheng Liu, Cheng Zhang, Shi-Pu Gu, Xing-Fu Wang

Quantum secure direct communication (QSDC) enables the message sender to directly send secure messages to the receiver through the quantum channel without keys. Device-independent (DI) and measurement-device-independent (MDI) QSDC protocols can enhance QSDC's practical security in theory. DI QSDC requires extremely high global detection efficiency and has qu

Chensheng Peng, Chengwei Zhang, Yixiao Wang, Chenfeng Xu

We present DeSiRe-GS, a self-supervised gaussian splatting representation, enabling effective static-dynamic decomposition and high-fidelity surface reconstruction in complex driving scenarios. Our approach employs a two-stage optimization pipeline of dynamic street Gaussians. In the first stage, we extract 2D motion masks based on the observation that 3D Ga

Hideo Bannai, Tomohiro I, Yuto Nakashima

The Burrows-Wheeler transform (BWT) is a reversible transform that converts a string $w$ into another string $\mathsf{BWT}(w)$. The size of the run-length encoded BWT (RLBWT) can be interpreted as a measure of repetitiveness in the class of representations called dictionary compression which are essentially representations based on copy and paste operations.

Kenji Fukushima, Syo Kamata

This is a pedagogical review of the possible connection between the stochastic quantization in physics and the diffusion models in machine learning. For machine-learning applications, the denoising diffusion model has been established as a successful technique, which is formulated in terms of the stochastic differential equation (SDE). In this review, we foc

Anthony Nguyen

Graphical User Interface (GUI) grounding plays a crucial role in enhancing the capabilities of Vision-Language Model (VLM) agents. While general VLMs, such as GPT-4V, demonstrate strong performance across various tasks, their proficiency in GUI grounding remains suboptimal. Recent studies have focused on fine-tuning these models specifically for zero-shot GU

Kyle O'Brien, David Majercak, Xavier Fernandes, Richard Edgar

Responsible deployment of language models requires mechanisms for refusing unsafe prompts while preserving model performance. While most approaches modify model weights through additional training, we explore an alternative: steering model activations at inference time via amplifying sparse autoencoder (SAE) features that mediate refusal. This work uncovers

Peng Shu, Junhao Chen, Zhengliang Liu, Hui Wang

Large Language Models (LLMs) have demonstrated remarkable success across a wide range of tasks and domains. However, their performance in low-resource language translation, particularly when translating into these languages, remains underexplored. This gap poses significant challenges, as linguistic barriers hinder the cultural preservation and development o

Yaquan Fang, Christina Gao, Ying-Ying Li, Jing Shu

Numerous challenges persist in High Energy Physics (HEP), the addressing of which requires advancements in detection technology, computational methods, data analysis frameworks, and phenomenological designs. We provide a concise yet comprehensive overview of recent progress across these areas, in line with advances in quantum technology. We will discuss the

Yachao Yuan, Yu Huang, Yingwen Wu, Jin Wang

Semi-supervised Learning plays a crucial role in network anomaly detection applications, however, learning anomaly patterns with limited labeled samples is not easy. Additionally, the lack of interpretability creates key barriers to the adoption of semi-supervised frameworks in practice. Most existing interpretation methods are developed for supervised/unsup

Lorenzo Iorio

Recently, a perturbative calculation to the first post-Newtonian order has shown that the analytically worked out Lense-Thirring precession of the orbital angular momentum of a test particle following a circular path around a massive spinning primary is able to explain the measured features of the jet precession of the supermassive black hole at the centre o

Matthew Pierson, Zia Mehrabi

Surprisingly a number of Earth's waterways remain unmapped, with a significant number in low and middle income countries. Here we build a computer vision model (WaterNet) to learn the location of waterways in the United States, based on high resolution satellite imagery and digital elevation models, and then deploy this in novel environments in the African c

Jaheer Khan, Swarup E, Rakshit Ramesh

This evaluate the performance of geospatial image processing using two distinct data storage formats: Zarr and TIFF. Geospatial images, converted to numerous applications like environmental monitoring, urban planning, and disaster management. Traditional Tagged Image File Format is mostly used because it is simple and compatible but may lack by performance l

Bengy Tsz Tsun Wong, Zehai Pang, Yi Yang

We theoretically propose a scheme to synthesize photonic non-Abelian electric field and spin-orbit coupling (SOC) in the synthetic frequency dimension based on a polarization-multiplexed time-modulated ring resonator. Inside the ring resonator, the cascade of polarization-dependent phase modulation, polarization rotation, and phase retardation enables a phot

Dharmendra Kumar Singh, Chinmay Sharma

Probability distribution theory helps in studying the impact of various dimensions in life while the Mittag-Leffler function and bicomplex are used in electromagnetism, quantum mechanics, and signal theory. Considering the importance of both, the purpose of this paper is to introduce bicomplex Mittag-Leffler distribution theory with the help of the bicomplex

Subhasis Ghora, Tarakanta Nayak, Soumen Pal, Pooja Phogat

It is proved that the Chebyshev&#39;s method applied to an entire function $f$ is a rational map if and only if $f(z) = p(z) e^{q(z)}$, for some polynomials $p$ and $q$. These are referred to as rational Chebyshev maps, and their fixed points are discussed in this article. It is seen that $\infty$ is a parabolic fixed point with multiplicity one bigger than

Yungi Kim, Hyunsoo Ha, Seonghoon Yang, Sukyung Lee

Creating high-quality, large-scale datasets for large language models (LLMs) often relies on resource-intensive, GPU-accelerated models for quality filtering, making the process time-consuming and costly. This dependence on GPUs limits accessibility for organizations lacking significant computational infrastructure. To address this issue, we introduce the Li

Yang Chen, Jingcai Guo, Song Guo, Dacheng Tao

Zero-shot skeleton action recognition is a non-trivial task that requires robust unseen generalization with prior knowledge from only seen classes and shared semantics. Existing methods typically build the skeleton-semantics interactions by uncontrollable mappings and conspicuous representations, thereby can hardly capture the intricate and fine-grained rela

Scarlett Raine, Frederic Maire, Niko Suenderhauf, Tobias Fischer

Underwater surveys provide long-term data for informing management strategies, monitoring coral reef health, and estimating blue carbon stocks. Advances in broad-scale survey methods, such as robotic underwater vehicles, have increased the range of marine surveys but generate large volumes of imagery requiring analysis. Computer vision methods such as semant

Jiongcheng Li

Optimization problems, arise in many practical applications, from the view points of both theory and numerical methods. Especially, significant improvement in deep learning training came from the Quasi-Newton methods. Quasi-Newton search directions provide an attractive alternative to Newton's method in that they do not require computation of the Hessian and

Ranjan Sapkota, Achyut Paudel, Manoj Karkee

Currently, deep learning-based instance segmentation for various applications (e.g., Agriculture) is predominantly performed using a labor-intensive process involving extensive field data collection using sophisticated sensors, followed by careful manual annotation of images, presenting significant logistical and financial challenges to researchers and organ

Ting-Wai Chiu

We investigate the spatial $ z $-correlators of meson operators in $ N_f=2+1+1+1 $ lattice QCD with optimal domain-wall quarks across eight temperatures ranging from 325 to 3250 MeV. The meson operators include a complete set of Dirac bilinears for ten flavor combinations. Our findings reveal a hierarchical restoration of chiral symmetry in QCD with $ (u, d,