November 2024 arXiv papers — page 24

Mintaek Lim, Seokki Lee, Liyew Woletemaryam Abitew, Sundong Kim

This paper addresses the challenge of enhancing artificial intelligence reasoning capabilities, focusing on logicality within the Abstraction and Reasoning Corpus (ARC). Humans solve such visual reasoning tasks based on their observations and hypotheses, and they can explain their solutions with a proper reason. However, many previous approaches focused only

Jose A. Diaz-Garcia, Julio Amador Diaz Lopez

This comprehensive survey delves into the latest advancements in Relation Extraction (RE), a pivotal task in natural language processing essential for applications across biomedical, financial, and legal sectors. This study highlights the evolution and current state of RE techniques by analyzing 137 papers presented at the Association for Computational Lingu

Dennis J. Klaassen, Lumen A. G. Eek, Alexander N. Rudenko, Esra D. van`t Westende

Realizing a one-dimensional (1D) topological insulator and identifying the lower dimensional limit of two-dimensional (2D) behavior are crucial steps toward developing high-density quantum state networks, advancing topological quantum computing, and exploring dimensionality effects in topological materials. Although 2D topological insulators have been experi

Andreas Horst, Thomas Jahn, Felix Voigtlaender

We study the Besov regularity of wavelet series on $\mathbb{R}^d$ with randomly chosen coefficients. More precisely, each coefficient is a product of a random factor and a parameterized deterministic factor (decaying with the scale $j$ and the norm of the shift $m$). Compared to the literature, we impose relatively mild conditions on the moments of the rando

Jose A. Capitán, Jose Lope-Alba, Juan J. Morales-Ruiz

This paper is devoted to obtain closed form solutions for the semiclassical (or WKB) approximation of the heat kernel propagator of the diffusion equation defined by the constant elasticity variance (CEV) option pricing model. One of the key points is that our calculations are based on the Van Vleck-Morette determinant instead of the Van Vleck determinant us

Yukun Cheng, Wei Chen, Tianwei Hou, Geoffrey Ye Li

Artificial intelligence (AI) provides an alternative way to design channel coding with affordable complexity. However, most existing studies can only learn codes for a given size and rate, typically defined by a fixed network architecture and a set of parameters. The support of multiple code rates is essential for conserving bandwidth under varying channel c

Thai-Binh Nguyen, Alexander Waibel

Speaker-attributed automatic speech recognition (SA-ASR) aims to transcribe speech while assigning transcripts to the corresponding speakers accurately. Existing methods often rely on complex modular systems or require extensive fine-tuning of joint modules, limiting their adaptability and general efficiency. This paper introduces a novel approach, leveragin

Neil C. C. Brown, Pierre Weill-Tessier, Juho Leinonen, Paul Denny

Motivation: Students learning to program often reach states where they are stuck and can make no forward progress. An automatically generated next-step hint can help them make forward progress and support their learning. It is important to know what makes a good hint or a bad hint, and how to generate good hints automatically in novice programming tools, for

Lukas Schichler, Karin Festl, Selim Solmaz, Daniel Watzenig

Path planning for wheeled mobile robots is a critical component in the field of automation and intelligent transportation systems. Car-like vehicles, which have non-holonomic constraints on their movement capability impose additional requirements on the planned paths. Traditional path planning algorithms, such as A* , are widely used due to their simplicity

Thulo Letsele, Mechiel Christiaan Bezuidenhout, the MeerTRAP collaboration

Pulsars are rapidly rotating neutron stars that emit pulses of radiation at regular intervals, typically ranging from milliseconds to seconds. The precise recording and modelling of the arrival times of pulsar emission is known as timing analysis. Rotating radio transients (RRATs) are a subclass of pulsars that emit pulses very sporadically. Because of the s

Ehsan Kabir, Jason D. Bakos, David Andrews, Miaoqing Huang

Transformer neural networks (TNN) excel in natural language processing (NLP), machine translation, and computer vision (CV) without relying on recurrent or convolutional layers. However, they have high computational and memory demands, particularly on resource-constrained devices like FPGAs. Moreover, transformer models vary in processing time across applica

Felix Igelbrink, Marian Renz, Martin Günther, Piper Powell

Semantic mapping is a key component of robots operating in and interacting with objects in structured environments. Traditionally, geometric and knowledge representations within a semantic map have only been loosely integrated. However, recent advances in deep learning now allow full integration of prior knowledge, represented as knowledge graphs or language

Songyi Liu, Yongjun Wang, Baoshan Wang, Jian Yan

The quantum mechanics is proved to admit no hidden-variable in 1960s, which means the quantum systems are contextual. Revealing the mathematical structure of quantum mechanics is a significant task. We develop the approach of partial Boolean algebra to characterize the contextuality theory with local consistency and exclusivity, and then prove that the finit

Xiao An, Jiaxing Sun, Zihan Gui, Wei He

The rapid advancement of Large Vision-Language Models (VLMs), both general-domain models and those specifically tailored for remote sensing, has demonstrated exceptional perception and reasoning capabilities in Earth observation tasks. However, a benchmark for systematically evaluating their capabilities in this domain is still lacking. To bridge this gap, w

Sushmita Kumari, Siddharth Gavhale

This study presents a model that examines how families make decisions about having children, managing resources, and planning for their financial security in light of social and economic factors. It explores the balance between the number of children and the quality of life parents wish to provide, including education and future income prospects. By consider

Wenxuan Shi, Yunhang Zhang, Xinyu Xing, Jun Xu

Greybox fuzzing has emerged as a preferred technique for discovering software bugs, striking a balance between efficiency and depth of exploration. While research has focused on improving fuzzing techniques, the importance of high-quality initial seeds remains critical yet often overlooked. Existing methods for seed generation are limited, especially for pro

Jingming Liu, Yumeng Li, Boyuan Xiao, Yichang Jian

Under pure textual modality, Large Language Models (LLMs) have demonstrated remarkable success in complex reasoning tasks by decomposing them into simpler sub-problems. However, Multimodal Large Language Models (MLLMs) still struggle with some seemingly straightforward visual tasks, such as counting and solving jigsaw puzzles. We argue that these tasks chall

Muhammad Al-Zafar Khan, Jamal Al-Karaki, Marwan Omar

In this study, we consider a real-world application of QML techniques to study water quality in the U20A region in Durban, South Africa. Specifically, we applied the quantum support vector classifier (QSVC) and quantum neural network (QNN), and we showed that the QSVC is easier to implement and yields a higher accuracy. The QSVC models were applied for three

Ghazi Shazan Ahmad, Shubham Agarwal, Subrata Mitra, Ryan Rossi

Automated visualization recommendations (vis-rec) help users to derive crucial insights from new datasets. Typically, such automated vis-rec models first calculate a large number of statistics from the datasets and then use machine-learning models to score or classify multiple visualizations choices to recommend the most effective ones, as per the statistics

Dongli Luan, Bo Xue, Huan Liu

We investigate the inverse scattering problem for the massive Thirring model, focusing particularly on cases where the transmission coefficient exhibits $N$ pairs of higher-order poles. Our methodology involves transforming initial data into scattering data via the direct scattering problem. Utilizing two parameter transformations, we examine the asymptotic

Yating Wu, Tao Chu

In silicon electro-optic (EO) Mach-Zehnder interferometer (MZI) switches, crosstalk is typically limited by beam imbalance between the MZI arms, primarily caused by the free carrier absorption loss during routing, thus hindering switch scalability. To address this issue, we propose a low-crosstalk push-pull EO MZI switch by cascading a lightly doped, long ph

Wenyi Yu, Siyin Wang, Xiaoyu Yang, Xianzhao Chen

Full-duplex multimodal large language models (LLMs) provide a unified framework for addressing diverse speech understanding and generation tasks, enabling more natural and seamless human-machine conversations. Unlike traditional modularised conversational AI systems, which separate speech recognition, understanding, and text-to-speech generation into distinc

Shreya Gupta, Boyang Huang, Russell Impagliazzo

The Coin Change problem, also known as the Change-Making problem, is a well-studied combinatorial optimization problem, which involves minimizing the number of coins needed to make a specific change amount using a given set of coin denominations. A natural and intuitive approach to this problem is the greedy algorithm. While the greedy algorithm is not unive

Alexandre Dieguez

For a fixed irrational $\theta > 0$ with a prescribed irrationality measure function, we study the correlation $\int_1^X \Delta(x) \Delta(\theta x) dx$, where $\Delta$ is the Dirichlet error term in the divisor problem. When $\theta$ has a finite irrationality measure, it is known that decorrelation occurs at a rate expressible in terms of this measure. Stro

Uy Dieu Tran, Minh Luu, Phong Ha Nguyen, Khoi Nguyen

Existing Score Distillation Sampling (SDS)-based methods have driven significant progress in text-to-3D generation. However, 3D models produced by SDS-based methods tend to exhibit over-smoothing and low-quality outputs. These issues arise from the mode-seeking behavior of current methods, where the scores used to update the model oscillate between multiple

Zhong-Yu Wang, Wei Liang

With the recent measurements of the $D_{s}^{+}\rightarrow \pi^{+}\pi^{+}\pi^{-}$ and $D^{+}\rightarrow \pi^{+}\pi^{+}\pi^{-}$ decays by the LHCb Collaboration, we study these two decay processes by considering the final state interaction formalism. Taking into account the external and internal $W$-emission dominant mechanisms at the quark level, our model ca

Weiguang Zhao, Chenru Jiang, Chengrui Zhang, Jie Sun

Given the diversity of devices and the product upgrades, cross-device research has become an urgent issue that needs to be tackled. To this end, we pioneer in probing the cross-device (cameras & robotics) grasping policy in the 3D open world. Specifically, we construct two real-world grasping setups, employing robotic arms and cameras from completely differe

Jérémie Sublime

In today's world, AI programs powered by Machine Learning are ubiquitous, and have achieved seemingly exceptional performance across a broad range of tasks, from medical diagnosis and credit rating in banking, to theft detection via video analysis, and even predicting political or sexual orientation from facial images. These predominantly deep learning metho

Jingjing Chen, Xiaobo Yin

Bi-directional reflection spectroscopy based on multiple scattering of particulate surfaces is employed in identifying the optical properties of titanium-iron oxide mineral from laboratory reflection measurements.However, the approach suffers from issues including: i) both n and k are to be extracted from a single spectroscopy spectrum, ii) imposing constrai

Dan Li, Philip B. Zhang

Br\"{a}nd\'{e}n and Claesson introduced the concept of mesh patterns in 2011, and since then, these patterns have attracted significant attention in the literature. Subsequently, in 2015, Hilmarsson \emph{et al.} initiated the first systematic study of avoidance of mesh patterns, while Kitaev and Zhang conducted the first systematic study of the distribution

Hyeon-Dong Son, Parada T. P. Hutauruk

In the present study, we explore the properties of generalized parton distributions (GPDs) for the kaon and pion within the framework of the nonlocal chiral quark model (NL$\chi$QM). Valence quark GPDs of the kaon and pion are analyzed with respect to their momentum fraction $x$ and skewness $\xi$ dependencies in the DGLAP and ERBL regions. We observe that t

A. N. Mikhaylov, D. V. Guseinov, A. I. Belov, D. S. Korolev

The stochastic resonance phenomenon has been studied experimentally and theoretically for a state-of-art metal-oxide memristive device based on yttria-stabilized zirconium dioxide and tantalum pentoxide, which exhibits bipolar filamentary resistive switching of anionic type. The effect of white Gaussian noise superimposed on the sub-threshold sinusoidal driv

Yuxuan Wang, Kaige Qu, Wen Wu, Xuemin

In this paper, we propose a novel road side unit (RSU)-assisted cooperative sensing scheme for connected autonomous vehicles (CAVs), with the objective to reduce completion time of sensing tasks. Specifically, LiDAR sensing data of both RSU and CAVs are selectively fused to improve sensing accuracy, and computing resources therein are cooperatively utilized

Christian Rieger, Holger Wendland

In this paper, we show that the approximation of high-dimensional functions, which are effectively low-dimensional, does not suffer from the curse of dimensionality. This is shown first in a general reproducing kernel Hilbert space set-up and then specifically for Sobolev and mixed-regularity Sobolev spaces. Finally, efficient estimates are derived for decid

Salman Ahmadi-Asl, Valentin Leplat, Anh-Huy Phan, Andrzej Cichocki

This paper introduces a novel collaborative neurodynamic model for computing nonnegative Canonical Polyadic Decomposition (CPD). The model relies on a system of recurrent neural networks to solve the underlying nonconvex optimization problem associated with nonnegative CPD. Additionally, a discrete-time version of the continuous neural network is developed.

Duc Anh Vu, Nguyen Tran Cong Duy, Xiaobao Wu, Hoang Minh Nhat

Large Language Models (LLMs) have shown strong in-context learning (ICL) abilities with a few demonstrations. However, one critical challenge is how to select demonstrations to elicit the full potential of LLMs. In this paper, we propose Curriculum Demonstration Selection (CDS), a novel demonstration selection method for ICL. Instead of merely using similari

Cong Wang, Weizhe Yang, Haiping Wang, Renjie Yang

Rapid reconstruction of 2D plasma profiles from line-integral measurements is important in nuclear fusion. This paper introduces a physics-informed model architecture called Onion, that can enhance the performance of models and be adapted to various backbone networks. The model under Onion incorporates physical information by a multiplication process and app

M. S. Butuzova, V. A. Guseva, M. A. Gorbachev, A. S. Krivenko

We propose a new method for investigating the evolution of the properties of the blazar brightness variability on timescales from a few hours to a few days. Its essence lies in detecting sequentially located time intervals along the entire light curve, within which it is possible to determine the characteristic time of variability using the structure functio

Mariusz Wozniak, Erik Schnaubelt, Sina Atalay, Bernardo Bordini

High-temperature superconductor (HTS) coated conductors (CC) are often wound into pancake coils with electrical insulation in-between the turns. The copper terminals are used for current injection and conduction cooling. An inherent variation of the critical current along the CC length results from its manufacturing process. This variation causes non-uniform

Xinsong Feng, Ian P. Roberts

This paper leverages stochastic geometry to model, analyze, and optimize multi-band unmanned aerial vehicle (UAV) communication networks operating across low-frequency and millimeter-wave (mmWave) bands. We introduce a novel approach to modeling mmWave antenna gain in such networks, which allows us to better capture and account for interference in our analys

Wanxue Dong, Maria De-Arteaga, Maytal Saar-Tsechansky

Biased human decisions have consequential impacts across various domains, yielding unfair treatment of individuals and resulting in suboptimal outcomes for organizations and society. In recognition of this fact, organizations regularly design and deploy interventions aimed at mitigating these biases. However, measuring human decision biases remains an import

Silvan Käser, Debasish Koner, Markus Meuwly

Atomistic simulations are a powerful tool for studying the dynamics of molecules, proteins, and materials on wide time and length scales. Their reliability and predictiveness, however, depend directly on the accuracy of the underlying potential energy surface (PES). Guided by the principle of parsimony this work introduces KerNN, a combined kernel/neural net

A. D. Mednykh, I. A. Mednykh, G. K. Sokolova

In the present paper, we prove that two discrete rectangular tori are isospectral if and only if they are isomorphic.

Akkarach Sukserm, Jakkapat Seeyangnok, Udomsilp Pinsook

We present a comprehensive investigation of the structural, electronic, magnetic, and vibrational properties of CrSH monolayers in the 1T and 2H phases using density functional theory (DFT)+U calculations with a converged Hubbard U value of 5.54 eV and Born-Oppenheimer molecular dynamics (BO-MD) simulations. The ferromagnetic (FM) 1T-CrSH phase is found to b

Talhah Shamshad Ali Ansari, Rainald Löhner, Roland Wüchner, Harbir Antil

A finite-element method dependant adjoint-based procedure to determine the temperature field of structures based on measured displacements or strains and a set of standard loads is developed and tested. Given a series of force and deformation measurements, the temperature field is obtained by minimizing the adequately weighted differences between the measure

Diandian Gu, Jonathan J. Wylie, Dongdong He, Yvonne M. Stokes

We explore the drawing of an axisymmetric viscoelastic tube subject to inertial and surface tension effects. We adopt the Giesekus constitutive model and derive asymptotic long-wave equations for weakly viscoelastic effects. Intuitively, one might imagine that the elastic stresses should act to prevent hole closure during the drawing process. Surprisingly, o

Hongwei Tan, Rong-Zhen Guo, Jingyi Zhang

In this paper, we investigate the Hawking radiation of the quantum Oppenheimer- Snyde black hole with the tunneling scheme by Parikh and Wilczek. We calculate the emission rate of massless scalar particles. Compared to the traditional results within the framework of General Relativity, our findings include quantum correction terms arising from loop quantum g

Muhammad Ahmad, Francesco Mauro, Manuel Mazzara, Salvatore Distefano

Hyperspectral image (HSI) classification presents inherent challenges due to high spectral dimensionality, significant domain shifts, and limited availability of labeled data. To address these issues, we propose a novel Active Transfer Learning (ATL) framework built upon a Spatial-Spectral Transformer (SST) backbone. The framework integrates multistage trans

Gabriel Torrens, Bartomeu Alorda, Cristian Carmona, Daniel Malagon-Perianez

As minimum area SRAM bit-cells are obtained when using cell ratio and pull-up ratio of 1, we analyze the possibility of decreasing the cell ratio from the conventional values comprised between 1.5-2.5 to 1. The impact of this option on area, power, performance and stability is analyzed showing that the most affected parameter is read stability, although this

Linghu Fan

In this paper, we consider a generalization of the McKay correspondence in positive characteristic regarding the Euler characteristic of crepant resolutions of quotient singularities given by finite subgroups of the special linear group. As the main result, we prove that this generalization holds for groups with a specific semidirect product structure, using

Sebastian Gorgon, Petri Murto, Daniel G. Congrave, Lujo Matasovic

High-spin states in organic molecules offer promising tuneability for quantum technologies. Photogenerated quartet excitons are an extensively studied platform, but their applications are limited by the absence of optical read-out via luminescence. Here we demonstrate a new class of synthetically accessible molecules with quartet-derived luminescence, formed

Qizao Wang, Bin Li, Xiangyang Xue

Large Vision-Language Models (LVLMs) that incorporate visual models and large language models have achieved impressive results across cross-modal understanding and reasoning tasks. In recent years, person re-identification (ReID) has also started to explore cross-modal semantics to improve the accuracy of identity recognition. However, effectively utilizing

Jiacheng Sun, Fajun Li, Xudong Wang, Jing He

Optical metasurface has brought a revolution in label-free molecular sensing, attracting extensive attention. Currently, such sensing approaches are being designed to respond to peak wavelengths with a higher Q factor in the visible and near-infrared regions.Nevertheless, a higher Q factor that enhances light confinement will inevitably deteriorate the wavel

Zerun Wang, Jiafeng Mao, Xueting Wang, Toshihiko Yamasaki

Generative models have become a powerful tool for synthesizing training data in computer vision tasks. Current approaches solely focus on aligning generated images with the target dataset distribution. As a result, they capture only the common features in the real dataset and mostly generate 'easy samples', which are already well learned by models trained on

Euijoon Kwon, Hyun-Myung Chun, Hyunggyu Park, Jae Sung Lee

We derive fluctuation-response inequalities for Markov jump processes that link the fluctuations of general observables to the response to perturbations in the transition rates within a unified framework. These inequalities are derived using the Cram\'er-Rao bound, enabling broader applicability compared to existing fluctuation-response relations formulated

Shih-heng Wang, Jiatong Shi, Chien-yu Huang, Shinji Watanabe

Self-supervised learning (SSL) models have shown exceptional capabilities across various speech-processing tasks. Continuous SSL representations are effective but suffer from high computational and storage demands. On the other hand, discrete SSL representations, although with degraded performance, reduce transmission and storage costs, and improve input seq

Max Schneider, Hendrik Nicolai, Vinzenz Schuh, Matthias Steinhausen

Fuel-lean hydrogen combustion systems hold significant potential for low pollutant emissions, but are also susceptible to intrinsic combustion instabilities. While most research on these instabilities has focused on flames without wall confinement, practical combustors are typically enclosed by walls that strongly influence the combustion dynamics. In part I

N. B. Xiang, X. H. Zhao, L. H. Deng, F. Y. Li

The daily measurements of the disc-integrated solar radio flux, observed by the Radio Solar Telescope Network (RSTN), at 245, 410, 610, 1415, 2695, 4995, and 8800 MHz during the time interval of 1989 January 1 to 2019 December 17, are used to investigate the temporal evolution of radial differential rotation of solar corona using the methods of Ensemble Empi

Jing Qin, Nariya Uchida

Synchronization with noise is important for understanding biophysical processes at nano- and micro-meter scales, such as neuronal firing and flagellar rotations. To understand the energetics of these processes, stochastic thermodynamics approaches are useful. Due to large fluctuations in a small system, ensemble averages of thermodynamic quantities are not s

Mariagrazia Bianchi, Rachel D. Camina, Mark L. Lewis, Emanuele Pacifici

Let $n$ be a non negative integer, and define $D_n$ to be the family of all finite groups having precisely $n$ conjugacy classes of nontrivial subgroups that are not self-normalizing. We are interested in studying the behavior of $D_n$ and its interplay with solvability and nilpotency. We first show that if $G$ belongs to $D_n$ with $n \le 3$, then $G$ is so

Weiqin Zhao, Ziyu Guo, Yinshuang Fan, Yuming Jiang

Due to the large size and lack of fine-grained annotation, Whole Slide Images (WSIs) analysis is commonly approached as a Multiple Instance Learning (MIL) problem. However, previous studies only learn from training data, posing a stark contrast to how human clinicians teach each other and reason about histopathologic entities and factors. Here we present a n

Mathias Staudigl, Simon Weissmann, Tristan van Leeuwen

Inverse problems are key issues in several scientific areas, including signal processing and medical imaging. Data-driven approaches for inverse problems aim for learning model and regularization parameters from observed data samples, and investigate their generalization properties when confronted with unseen data. This approach dictates a statistical approa

Biraj Silwal

Contextual Embeddings have yielded state-of-the-art results in various natural language processing tasks. However, these embeddings are constrained by models requiring large amounts of data and huge computing power. This is an issue for low-resource languages like Nepali as the amount of data available over the internet is not always sufficient for the model

Shun Takahashi, Yuzo Kinuta, Seiya Ito, Hiroki Ohnishi

We experimentally demonstrated a circularly polarized cavity mode in a GaAs-based chiral photonic crystal (PhC) containing a planar defect. Low-temperature photoluminescence measurements of InAs quantum dots (QDs) embedded in the planar defect revealed a polarization bandgap for left-handed circularly polarized light in the near-infrared spectrum. Within thi

Krishna Patra, Chidella Srinivasa Rao

In this article, we investigate monotonicity of limit wave speed of periodic traveling wave solutions for a perturbed generalized KdV equation via Abelian integral. We have answered an open problem outlined by Yan et al. (2014) and the conjecture proposed by Ouyang et al. (2022). Geometric singular perturbation theory allows for the reduction of a three-dime

Shuhei Watanabe

Expected Improvement (EI) is arguably the most widely used acquisition function in Bayesian optimization. However, it is often challenging to enhance the performance with EI due to its sensitivity to numerical precision. Previously, Hutter et al. (2009) tackled this problem by using Gaussian process trained on the log-transformed objective function and it wa

Davide Davoli, Martin Avanzini, Tamara Rezk

The efficacy of address space layout randomization has been formally demonstrated in a shared-memory model by Abadi et al., contingent on specific assumptions about victim programs. However, modern operating systems, implementing layout randomization in the kernel, diverge from these assumptions and operate on a separate memory model with communication throu

Jiabao Du, Shusen Zhao, Xiaoyu Lu, Lu Jiang

Ultrafast laser direct writing inside materials has garnered significant attention for its applications in techniques like two-photon polymerization, stealth dicing and vertical slicing. 4H-Silicon Carbide (4H-SiC) vertical slicing has wide potentials from research to industry due to low kerf loss and high slicing speed. In this paper, to improve the vertica

Mingxing Rao, Bohan Jiang, Daniel Moyer

In the present work we present Training Noise Token (TNT) Pruning for vision transformers. Our method relaxes the discrete token dropping condition to continuous additive noise, providing smooth optimization in training, while retaining discrete dropping computational gains in deployment settings. We provide theoretical connections to Rate-Distortion literat

Xinlin Cao, Arpan Mukherjee, Mourad Sini

The excitation of plasmonic nanoparticles by incident electromagnetic waves at frequencies near their subwavelength resonances induces localized heat generation in the surrounding medium. We develop a mathematical framework to rigorously quantify this heat generation in systems of arbitrarily distributed nanoparticles. 1. For an arbitrary discrete distributi

Hao Wang, Erjia Xiao, Wenbo Mu, Songhuan He

Due to the high sensitivity of qubits to environmental noise, which leads to decoherence and information loss, active quantum error correction(QEC) is essential. Surface codes represent one of the most promising fault-tolerant QEC schemes, but they require decoders that are accurate, fast, and scalable to large-scale quantum platforms. In all types of decode

Kabir Bakhshaei, Sajad Salavatidezfouli, Giovanni Stabile, Gianluigi Rozza

Patient-specific modeling of cardiovascular flows with high-fidelity is challenging due to its dependence on accurately estimated velocity boundary profiles, which are essential for precise simulations and directly influence wall shear stress calculations - key in predicting cardiovascular diseases like atherosclerosis. This data, often derived from in vivo

Bo Lin, Shangwen Wang, Liqian Chen, Xiaoguang Mao

As software vulnerabilities increase in both volume and complexity, vendors often struggle to repair them promptly. Automated vulnerability repair has emerged as a promising solution to reduce the burden of manual debugging and fixing activities. However, existing techniques exclusively focus on repairing the vulnerabilities at the source code level, which h

Brandon Willnecker, Mervlyn Moodley

The implementation of adaptive genetic algorithms (AGA) for optimization problems has proven to be superior than many other methods due to its nature of producing more robust and high quality solutions. Considering the complexity involved in many-body simulations, a novel AGA is proposed for applications to such systems and is specifically used to determine

Yunwoo Lee, Jungwon Park, H. Jin Kim

This letter presents a distributed trajectory planning method for multi-agent aerial tracking. The proposed method uses a Dynamic Buffered Voronoi Cell (DBVC) and a Dynamic Inter-Visibility Cell (DIVC) to formulate the distributed trajectory generation. Specifically, the DBVC and the DIVC are time-variant spaces that prevent mutual collisions and occlusions

Miao Fan, Jizhou Huang, An Zhuo, Ying Li

The value assessment of private properties is an attractive but challenging task which is widely concerned by a majority of people around the world. A prolonged topic among us is ``\textit{how much is my house worth?}''. To answer this question, most experienced agencies would like to price a property given the factors of its attributes as well as the demogr

Jieshan Chen, Zhen Wang, Jiamou Sun, Zhenchang Xing

Mobile apps are essential in daily life but frequently employ deceptive patterns, such as visual emphasis or linguistic nudging, to manipulate user behavior. Existing research largely relies on manual detection, which is time-consuming and cannot keep pace with rapidly evolving apps. Although recent work has explored automated approaches, these methods are l

Sreedevi V, Kavita Lalwani

A major challenge for extended human spaceflights in deep space is the dangerous exposure to space radiation. In previous studies aluminium has been used as a multilayer shielding material in GCR space radiation environment with high dose equivalent. To further reduce the dose equivalent, shielding effectiveness of various metal hydrides in GCR free space en

Renshuai Tao, Haoyu Wang, Yuzhe Guo, Hairong Chen

To detect prohibited items in challenging categories, human inspectors typically rely on images from two distinct views (vertical and side). Can AI detect prohibited items from dual-view X-ray images in the same way humans do? Existing X-ray datasets often suffer from limitations, such as single-view imaging or insufficient sample diversity. To address these

Seunghun Lee

We give two criteria for a divisor on complex smooth projective variety to be ample using the multiplier ideal sheaf and the model category.

Wenyan Yu, Weihua Yang, Xing-hua Yang

The charged current weak interaction can distinguish quark flavors, it provides a valid method to determine (transverse momentum dependent) parton distribution functions in high energy reactions by utilizing tagged hadrons. In this paper, we calculate the charged current semi-inclusive deeply inelastic neutrino and anti-neutrino scattering of $N=Z$ nuclei. S

Xiao-Hua Fan, Zu-Xing Yang, Peng-Hui Chen, Zhi-Pan Li

We simulate ultra-central collisions of prolate uranium-uranium nuclei at intermediate energies using the isospin-dependent Boltzmann-Uehling-Uhlenbeck model to investigate the impact of momentum anisotropy on spatial geometric effects. By defining the quadrupole deformation parameter in momentum space $\beta_\text{p}$, we establish an ellipsoidal Fermi surf

Arjun Agarwal, Sayan Bandyapadhyay

In this work, we develop Extraction Theorems for classes of geometric objects with small extraction numbers. These classes include intervals, axis-parallel segments, axis-parallel rays, and octants. We investigate these classes of objects and prove small bounds on the extraction numbers. The tightness of these bounds is demonstrated by examples with matching

Haoyu Wang, Renshuai Tao, Wei Wang, Yunchao Wei

Detecting prohibited items in X-ray security imagery is a challenging yet crucial task. With the rapid advancement of deep learning, object detection algorithms have been widely applied in this area. However, the distribution of object classes in real-world prohibited item detection scenarios often exhibits a distinct long-tailed distribution. Due to the uni

Akshat Sharma, Hangliang Ding, Jianping Li, Neel Dani

How to efficiently serve LLMs in practice has become exceptionally challenging due to their prohibitive memory and computation requirements. In this study, we investigate optimizing the KV cache, whose memory footprint poses a critical bottleneck in LLM inference, especially when dealing with long context tasks. To tackle the challenge, we introduce MiniKV,

Asmita Mukherjee, Sudeep Saha, Ravi Singh

We compute the contribution of the gluonic component of the energy-momentum tensor (EMT) to the angular momentum density in various decompositions. We use the light-front Hamiltonian technique, and a two-component formalism in light-front gauge, where the constrained degrees of freedom are eliminated. Instead of a nucleon, we consider a simple composite spin

Chon In Leong, I-Ling Chung, Kin-Fong Chao, Jun-You Wang

The goal of music style transfer is to convert a music performance by one instrument into another while keeping the musical contents unchanged. In this paper, we investigate another style transfer scenario called ``failed-music style transfer''. Unlike the usual music style transfer where the content remains the same and only the instrumental characteristics

Aoxiang Wang, Ya-Nan Zhu, Jufri Setianegara, Yuting Lin

Background: Intensity-modulated proton therapy (IMPT) using pencil beam technique scans tumor in a layer by layer, then spot by spot manner. It can provide highly conformal dose to tumor targets and spare nearby organs-at-risk (OAR). Fast delivery of IMPT can improve patient comfort and reduce motion-induced uncertainties. Since energy layer switching time d

Miao Fan, Jizhou Huang, Haifeng Wang

With the increased popularity of mobile devices, Web mapping services have become an indispensable tool in our daily lives. To provide user-satisfied services, such as location searches, the point of interest (POI) database is the fundamental infrastructure, as it archives multimodal information on billions of geographic locations closely related to people's

Yanyan Li, Yixin Fang, Federico Tombari, Gim Hee Lee

Sparse Multi-view Images can be Learned to predict explicit radiance fields via Generalizable Gaussian Splatting approaches, which can achieve wider application prospects in real-life when ground-truth camera parameters are not required as inputs. In this paper, a novel generalizable Gaussian Splatting method, SmileSplat, is proposed to reconstruct pixel-ali

Miguel Zabaleta, Joel Lehman

Do horror writers have worse childhoods than other writers? Though biographical details are known about many writers, quantitatively exploring such a qualitative hypothesis requires significant human effort, e.g. to sift through many biographies and interviews of writers and to iteratively search for quantitative features that reflect what is qualitatively o

Temitope Adeyeha, Chetraj Pandey, Berkay Aydin

Accurate and reliable predictions of solar flares are essential due to their potentially significant impact on Earth and space-based infrastructure. Although deep learning models have shown notable predictive capabilities in this domain, current evaluations often focus on accuracy while neglecting interpretability and reliability--factors that are especially

Deepak Gupta, Dina Demner-Fushman, William Hersh, Steven Bedrick

With the advancement of large language models (LLMs), the biomedical domain has seen significant progress and improvement in multiple tasks such as biomedical question answering, lay language summarization of the biomedical literature, clinical note summarization, etc. However, hallucinations or confabulations remain one of the key challenges when using LLMs

Gwanghyun Kim, Suh Yoon Jeon, Seunggyu Lee, Se Young Chun

We present PersonaCraft, a framework for controllable and occlusion-robust full-body personalized image synthesis of multiple individuals in complex scenes. Current methods struggle with occlusion-heavy scenarios and complete body personalization, as 2D pose conditioning lacks 3D geometry, often leading to ambiguous occlusions and anatomical distortions, and

Haonan Han, Xiangzuo Wu, Huan Liao, Zunnan Xu

Recently, text-to-motion models have opened new possibilities for creating realistic human motion with greater efficiency and flexibility. However, aligning motion generation with event-level textual descriptions presents unique challenges due to the complex relationship between textual prompts and desired motion outcomes. To address this, we introduce AToM,

Corey Bregman, Anatoly Libgober, Kejia Zhu

Motivated by the question of whether braid groups are CAT(0), we investigate the CAT(0) behavior of fundamental groups of plane curve complements and certain universal families. If $C$ is the branch locus of a generic projection of a smooth, complete intersection surface to $\PP^2$, we show that $\pi_1(\PP^2\setminus C)$ is CAT(0). In the other direction, we

Jiaxiong Qiu, Liu Liu, Xinjie Wang, Tianwei Lin

Recently, 3D Gaussian Splatting (3DGS) has achieved impressive performance on indoor surface reconstruction and 3D open-vocabulary segmentation. This paper presents GLS, a unified framework of 3D surface reconstruction and open-vocabulary segmentation based on 3DGS. GLS extends two fields by improving their sharpness and smoothness. For indoor surface recons

Faraz Tahmasebi, Yian Wang, Benji Y. H. Huang, Hyoukjun Kwon

Recent research has shown that large language models (LLMs) can utilize low-precision floating point (FP) quantization to deliver high efficiency while maintaining original model accuracy. In particular, recent works have shown the effectiveness of non-power-of-two precisions, such as FP6 and FP5, and diverse sensitivity to low-precision arithmetic of LLM la

Zhang Cheng, Yanxia Wang

Deep learning-based appearance gaze estimation methods are gaining popularity due to their high accuracy and fewer constraints from the environment. However, existing high-precision models often rely on deeper networks, leading to problems such as large parameters, long training time, and slow convergence. In terms of this issue, this paper proposes a novel

Yalcin Tur, Vedat Cicek, Tufan Cinar, Elif Keles

Pulmonary Embolism (PE) is a serious cardiovascular condition that remains a leading cause of mortality and critical illness, underscoring the need for enhanced diagnostic strategies. Conventional clinical methods have limited success in predicting 30-day in-hospital mortality of PE patients. In this study, we present a new algorithm, called PEP-Net, for 30-