December 2024 arXiv papers — page 102

Qinyi Lu, Jiale Cheng, Wei Kang, Nan Liu

The growing privacy concerns in distributed learning have led to the widespread adoption of secure aggregation techniques in distributed machine learning systems, such as federated learning. Motivated by a coded gradient aggregation problem in a user-helper-master hierarchical network setting with straggling communication links, we formulate a new secure hie

Dongyang Jin, Chao Fan, Weihua Chen, Shiqi Yu

The gait, as a kind of soft biometric characteristic, can reflect the distinct walking patterns of individuals at a distance, exhibiting a promising technique for unrestrained human identification. With largely excluding gait-unrelated cues hidden in RGB videos, the silhouette and skeleton, though visually compact, have acted as two of the most prevailing ga

Zekai Li, Jintu Zheng, Ji Liu, Han Liu

Recently, large language models (LLMs) have demonstrated superior performance across various tasks by adhering to scaling laws, which significantly increase model size. However, the huge computation overhead during inference hinders the deployment in industrial applications. Many works leverage traditional compression approaches to boost model inference, but

Claudia Contardi, Emanuele Dolera, Stefano Favaro

The Ewens-Pitman model is a distribution for random partitions of the set $\{1,\ldots,n\}$, with $n\in\mathbb{N}$, indexed by parameters $\alpha \in [0,1)$ and $\theta>-\alpha$, such that $\alpha=0$ is the Ewens model in population genetics. The large $n$ asymptotic behaviour of the number $K_{n}$ of blocks in the Ewens-Pitman random partition has been exten

Alexandros A. Voudouris

We consider a distributed voting problem with a set of agents that are partitioned into disjoint groups and a set of obnoxious alternatives. Agents and alternatives are represented by points in a metric space. The goal is to compute the alternative that maximizes the total distance from all agents using a two-step mechanism which, given some information abou

Bowen Xie, Sheng Zhou, Zhisheng Niu, Hao Wu

Future Vehicle-to-Everything (V2X) scenarios require high-speed, low-latency, and ultra-reliable communication services, particularly for applications such as autonomous driving and in-vehicle infotainment. Dense heterogeneous cellular networks, which incorporate both macro and micro base stations, can effectively address these demands. However, they introdu

Andrea Francesco Battaglia, Säm Krucker

The Reuven Ramaty High Energy Solar Spectrocopy Imager (RHESSI) $\gamma$-ray observations of the extraordinary GOES X25 flare SOL2003-10-28T11:10 are revisited to investigate previously reported conclusions that flare-accelerated electrons and protons precipitate along spatially separated flare loops. In contrast to previous works which reconstructed 2.223 M

Zijian Gu, Jianwei Ma, Yan Huang, Honghao Wei

Millimeter-wave radar plays a vital role in 3D object detection for autonomous driving due to its all-weather and all-lighting-condition capabilities for perception. However, radar point clouds suffer from pronounced sparsity and unavoidable angle estimation errors. To address these limitations, incorporating a camera may partially help mitigate the shortcom

Lingkai Meng, Long Yuan, Xuemin Lin, Chengjie Li

Bipartite graphs are commonly used to model relationships between two distinct entities in real-world applications, such as user-product interactions, user-movie ratings and collaborations between authors and publications. A butterfly (a 2x2 bi-clique) is a critical substructure in bipartite graphs, playing a significant role in tasks like community detectio

Jiajun Gong, Wei Cai, Siyuan Liang, Zhong Guan

Website Fingerprinting (WF) aims to deanonymize users on the Tor network by analyzing encrypted network traffic. Recent deep-learning-based attacks show high accuracy on undefended traces. However, they struggle against modern defenses that use tactics like injecting dummy packets and delaying real packets, which significantly degrade classification performa

Shunta Takahashi

Anyon condensation in wormhole geometries is investigated in the Virasoro TQFT (VTQFT) formulation, a proposed reformulation of 3d AdS quantum gravity. We first review some elementary techniques of VTQFT and summarize a gauging scheme for non-invertible symmetries referred to as anyon condensation. We then exhibit that anyon condensation is applicable to VTQ

Minxin Zhang, Fuqun Han, Yat Tin Chow, Stanley Osher

This work concerns the zeroth-order global minimization of continuous nonconvex functions with a unique global minimizer and possibly multiple local minimizers. We formulate a theoretical framework for inexact proximal point (IPP) methods for global optimization, establishing convergence guarantees under mild assumptions when either deterministic or stochast

Wonje Choi, Woo Kyung Kim, SeungHyun Kim, Honguk Woo

For embodied reinforcement learning (RL) agents interacting with the environment, it is desirable to have rapid policy adaptation to unseen visual observations, but achieving zero-shot adaptation capability is considered as a challenging problem in the RL context. To address the problem, we present a novel contrastive prompt ensemble (ConPE) framework which

Moming Duan, Rui Zhao, Linshan Jiang, Nigel Shadbolt

As model parameter sizes scale into the billions and training consumes zettaFLOPs of computation, the reuse of Machine Learning (ML) assets and collaborative development have become increasingly prevalent in the ML community. These ML assets, including models, datasets, and software, may originate from various sources and be published under different license

Yuxuan Xia, Ángel F. García-Fernández, Johan Karlsson, Kuo-Chu Chang

This paper presents a probabilistic generalization of the Generalized Optimal Sub-Pattern Assignment (GOSPA) metric, termed P-GOSPA. The GOSPA metric has been widely used to evaluate the distance between finite sets, particularly in multi-object estimation applications. The P-GOSPA extends GOSPA into the space of multi-Bernoulli densities, incorporating inhe

Zhong Xu, Jun Huang, Hongyan Wu, Rui Chen

High silicon steel with 6.5% silicon content is the best because of its excellent magnetic properties, such as high saturation magnetization, high resistivity, low iron loss and near zero magnetostriction. High silicon steel can greatly save energy, and reduce the weight and size of electrical appliances. This has a very important application prospect for en

Young-Jae Park, Doyi Kim, Minseok Seo, Hae-Gon Jeon

Accurate precipitation forecasting is crucial for early warnings of disasters, such as floods and landslides. Traditional forecasts rely on ground-based radar systems, which are space-constrained and have high maintenance costs. Consequently, most developing countries depend on a global numerical model with low resolution, instead of operating their own rada

Jianhua Zhang, Li Yu, Shaoyi Liu, Yichen Cai

The channel is one of the five critical components of a communication system, and its ergodic capacity is based on all realizations of statistic channel model. This statistical paradigm has successfully guided the design of mobile communication systems from 1G to 5G. However, this approach relies on offline channel measurements in specific environments, and

Francesco Parente

Chu spaces and Chu transforms were first investigated in category theory by Barr and Chu in 1979. In 2000 van Benthem shifted to the model-theoretic point of view by isolating a class of infinitary two-sorted properties, the flow formulas, which are preserved by all Chu transforms. D\v{z}amonja and V\"a\"an\"anen in 2021 considered a special kind of Chu tran

Prajwal Kailas, Max Homilius, Rahul C. Deo, Calum A. MacRae

Accurate diagnostic coding of medical notes is crucial for enhancing patient care, medical research, and error-free billing in healthcare organizations. Manual coding is a time-consuming task for providers, and diagnostic codes often exhibit low sensitivity and specificity, whereas the free text in medical notes can be a more precise description of a patient

Sudhanshu Shekhar, Bhabani Prasad Mandal, Anirban Dutta

In this article, we employ the transfer matrix method to investigate relativistic particles in super-periodic potentials (SPPs) of arbitrary order $n \in I^{+}$. We calculate the reflection and transmission probabilities for spinless Klein particles encountering rectangular potential barriers with super-periodic repetition. It is found that spinless relativi

Mengde Han, Tianqing Zhu, Lefeng Zhang, Huan Huo

Vertical Federated Learning (VFL) offers a novel paradigm in machine learning, enabling distinct entities to train models cooperatively while maintaining data privacy. This method is particularly pertinent when entities possess datasets with identical sample identifiers but diverse attributes. Recent privacy regulations emphasize an individual's \emph{right

Xiaoxuan Sun, Yue Yao, Xiaoye Wang, Pochun Li

With the rapid development of artificial intelligence technology, its application in the optimization of complex computer systems is becoming more and more extensive. Edge computing is an efficient distributed computing architecture, and the health status of its nodes directly affects the performance and reliability of the entire system. In view of the lack

B. Shuriya, S. Vimal Kumar, K. Bagyalakshmi

In this paper, we introduce the Fully Homomorphic Integrity Model (HIM), a novel approach designed to enhance security, efficiency, and reliability in encrypted data processing, primarily within the health care industry. HIM addresses the key challenges that noise accumulation, computational overheads, and data integrity pose during homomorphic operations. O

Gilbert J. Groenewald, Sanne ter Horst, Hugo J. Woerdeman

After a review of the reproducing kernel Banach space framework and semi-inner products, we apply the techniques to the setting of Hardy spaces $H^p$ and Bergman spaces $A^p$, $1<p<\infty$, on the unit ball in $\mathbb{C}^n$, as well as the Hardy space on the polydisk and half-space. In particular, we show how the framework leads to a procedure to find a min

Purity Mugambi, Alexandra Meliou, Madalina Fiterau

A crucial step in cohort studies is to extract the required cohort from one or more study datasets. This step is time-consuming, especially when a researcher is presented with a dataset that they have not previously worked with. When the cohort has to be extracted from multiple datasets, cohort extraction can be extremely laborious. In this study, we present

Siyuan Liang, Jiajun Gong, Tianmeng Fang, Aishan Liu

Website fingerprinting (WF) attacks, which covertly monitor user communications to identify the web pages they visit, pose a serious threat to user privacy. Existing WF defenses attempt to reduce attack accuracy by disrupting traffic patterns, but attackers can retrain their models to adapt, making these defenses ineffective. Meanwhile, their high overhead l

Jagadish Chandra Mahato, Anupam Roy, Rajib Batabyal, Debolina Das

$Ar^+$ ion has been used regularly for the cleaning of semiconductor, metal surfaces for epitaxial nanostructures growth. We have investigated the effect of low-energy $Ar^+$ ion sputtering and subsequent annealing on the $Si(111)-(7\times7)$ surfaces under ultrahigh vacuum (UHV) condition. Using $in-situ$ scanning tunnelling microscopy (STM) we have compare

Nidhi, K. Sreenadh

In this paper we study the existence and regularity results of normalized solutions to the following quasilinear elliptic Choquard equation with critical Sobolev exponent and mixed diffusion type operators: \begin{equation*} \begin{array}{rcl} -\Delta_p u+(-\Delta_p)^su & = & \lambda |u|^{p-2}u +|u|^{p^*-2}u+ \mu(I_{\alpha}*|u|^q)|u|^{q-2}u\;\;\text{in } \ma

Jian Li, Siwang Zhou

Image rescaling (IR) seeks to determine the optimal low-resolution (LR) representation of a high-resolution (HR) image to reconstruct a high-quality super-resolution (SR) image. Typically, HR images with resolutions exceeding 2K possess rich information that is unevenly distributed across the image. Traditional image rescaling methods often fall short becaus

Zhuyang Xie, Yan Yang, Yankai Yu, Jie Wang

Dense video captioning aims to detect and describe all events in untrimmed videos. This paper presents a dense video captioning network called Multi-Concept Cyclic Learning (MCCL), which aims to: (1) detect multiple concepts at the frame level, using these concepts to enhance video features and provide temporal event cues; and (2) design cyclic co-learning b

Yutian Lei, Luping Ji, Pei Liu

Out-of-distribution (OOD) detection is indispensable for deploying reliable machine learning systems in real-world scenarios. Recent works, using auxiliary outliers in training, have shown good potential. However, they seldom concern the intrinsic correlations between in-distribution (ID) and OOD data. In this work, we discover an obvious correlation that OO

Tsuyoshi Suehara, Koh Takeuchi, Hisashi Kashima, Satoshi Oyama

Mechanism design, a branch of economics, aims to design rules that can autonomously achieve desired outcomes in resource allocation and public decision making. The research on mechanism design using machine learning is called automated mechanism design or mechanism learning. In our research, we constructed a new network based on the existing method for singl

Quan-Sheng Zeng, Yunheng Li, Daquan Zhou, Guanbin Li

Open-vocabulary image segmentation has been advanced through the synergy between mask generators and vision-language models like Contrastive Language-Image Pre-training (CLIP). Previous approaches focus on generating masks while aligning mask features with text embeddings during training. In this paper, we observe that relying on generated low-quality masks

Minjun Kim, Minjee Kim, Jinhoon Jeong

Generative models trained on multi-institutional datasets can provide an enriched understanding through diverse data distributions. However, training the models on medical images is often challenging due to hospitals' reluctance to share data for privacy reasons. Federated learning(FL) has emerged as a privacy-preserving solution for training distributed dat

Takumi Shimoda, Alex Fukunaga

Parallelization of non-admissible search algorithms such as GBFS poses a challenge because straightforward parallelization can result in search behavior which significantly deviates from sequential search. Previous work proposed PUHF, a parallel search algorithm which is constrained to only expand states that can be expanded by some tie-breaking strategy for

Shasha Yu, Qinchen Zhang, Yuwei Zhao

This project aims to predict short-term and long-term upward trends in the S&P 500 index using machine learning models and feature engineering based on the "101 Formulaic Alphas" methodology. The study employed multiple models, including Logistic Regression, Decision Trees, Random Forests, Neural Networks, K-Nearest Neighbors (KNN), and XGBoost, to identify

Wei Dai, Kai Hwang, Jicong Fan

Unsupervised anomaly detection (UAD) plays an important role in modern data analytics and it is crucial to provide simple yet effective and guaranteed UAD algorithms for real applications. In this paper, we present a novel UAD method for tabular data by evaluating how much noise is in the data. Specifically, we propose to learn a deep neural network from the

DAMPE Collaboration, F. Alemanno, C. Altomare, Q. An

Secondary cosmic ray fluxes are important probes of the propagation and interaction of high-energy particles in the Galaxy. Recent measurements of primary and secondary cosmic ray nuclei have revealed unexpected spectral features that demand a deeper understanding. In this work we report the direct measurement of the cosmic ray boron spectrum from 10 GeV/n t

Shuo Wang, Issei Sato

Induction head mechanism is a part of the computational circuits for in-context learning (ICL) that enable large language models (LLMs) to adapt to new tasks without fine-tuning. Most existing work explains the training dynamics behind acquiring such a powerful mechanism. However, the model's ability to coordinate in-context information over long contexts an

Sucheng Ren, Xiaomeng Li

Vision Transformer shows great superiority in medical image segmentation due to the ability in learning long-range dependency. For medical image segmentation from 3D data, such as computed tomography (CT), existing methods can be broadly classified into 2D-based and 3D-based methods. One key limitation in 2D-based methods is that the intra-slice information

Ruijie Lu, Yixin Chen, Junfeng Ni, Baoxiong Jia

Repurposing pre-trained diffusion models has been proven to be effective for NVS. However, these methods are mostly limited to a single object; directly applying such methods to compositional multi-object scenarios yields inferior results, especially incorrect object placement and inconsistent shape and appearance under novel views. How to enhance and system

Nobuo Namura, Sho Takemori

Real-world optimization problems often involve complex objective functions with costly evaluations. While Bayesian optimization (BO) with Gaussian processes is effective for these challenges, it suffers in high-dimensional spaces due to performance degradation from limited function evaluations. To overcome this, simplification techniques like dimensionality

Zaifu Zhan, Rui Zhang

To efficiently select optimal dataset combinations for enhancing multi-task learning (MTL) performance in large language models, we proposed a novel framework that leverages a neural network to predict the best dataset combinations. The framework iteratively refines the selection, greatly improving efficiency, while being model-, dataset-, and domain-indepen

Gang Tao

This paper develops some extensions to the work of [1] which studied the continuous-time adaptive output tracking control schemes with the reference output signal generated from an unknown reference model system. The presented extensions include adaptive control schemes with reference model system uncertainties for single-input single-output (SISO) discrete-

Xiechi Zhang, Shunfan Zheng, Linlin Wang, Gerard de Melo

As multimodal large language models (MLLMs) gain prominence in the medical field, the need for precise evaluation methods to assess their effectiveness has become critical. While benchmarks provide a reliable means to evaluate the capabilities of MLLMs, traditional metrics like ROUGE and BLEU employed for open domain evaluation only focus on token overlap an

Maria Efimovich, Jayden Lim, Vedant Mehta, Ethan Poon

Classifying chest radiographs is a time-consuming and challenging task, even for experienced radiologists. This provides an area for improvement due to the difficulty in precisely distinguishing between conditions such as pleural effusion, pneumothorax, and pneumonia. We propose a novel transfer learning model for multi-label lung disease classification, uti

Bikram Khanal, Pablo Rivas

Quantum machine learning offers a transformative approach to solving complex problems, but the inherent noise hinders its practical implementation in near-term quantum devices. This obstacle makes it difficult to understand the generalizability of quantum circuit models. Designing robust quantum machine learning models under noise requires a principled under

Yiping Zhang, Yuntao Shou, Wei Ai, Tao Meng

With the recent advances in computer vision, age estimation has significantly improved in overall accuracy. However, owing to the most common methods do not take into account the class imbalance problem in age estimation datasets, they suffer from a large bias in recognizing long-tailed groups. To achieve high-quality imbalanced learning in long-tailed group

Gangqiang Hu, Jianfeng Lu, Jianmin Han, Shuqin Cao

Due to the sensitivity of data, Federated Learning (FL) is employed to enable distributed machine learning while safeguarding data privacy and accommodating the requirements of various devices. However, in the context of semi-decentralized FL, clients' communication and training states are dynamic. This variability arises from local training fluctuations, he

Liang Chen, Zekun Wang, Shuhuai Ren, Lei Li

Building on the foundations of language modeling in natural language processing, Next Token Prediction (NTP) has evolved into a versatile training objective for machine learning tasks across various modalities, achieving considerable success. As Large Language Models (LLMs) have advanced to unify understanding and generation tasks within the textual modality

Zhiying Xu, Minlan Yu, Francis Y. Yan

Efficient resource allocation is essential in cloud systems to facilitate resource sharing among tenants. However, the growing scale of these optimization problems have outpaced commercial solvers commonly employed in production. To accelerate resource allocation, prior approaches either customize solutions for narrow domains or impose workload-specific assu

Duc-Cuong Dang, Aneta Neumann, Frank Neumann, Andre Opris

Quality diversity (QD) algorithms have shown to provide sets of high quality solutions for challenging problems in robotics, games, and combinatorial optimisation. So far, theoretical foundational explaining their good behaviour in practice lack far behind their practical success. We contribute to the theoretical understanding of these algorithms and study t

Tomohiro Yoshitake, Megumi Shidatsu, Yoshihiro Ueda, Daisaku Nogami

To understand the evolution of global accretion disk structure in the ``rebrightening'' phase of MAXI J1820$+$070, we perform a comprehensive analysis of its near infrared/optical/UV to X-ray spectral energy distribution (SED) utilizing data obtained by OISTER, Las Cumbres Observatory (LCO), Swift, NICER, and NuSTAR in 2019. Optical spectra observed with Sei

Chao-Hsiang Sheu

We investigate the trans-series structure of a quantum mechanical system originating from a Lie-algebraic K\"ahler sigma model with multiple right-handed chiral fermions, extending previous results for the standard onecomplex projective ($\mathbb{CP}^1$) model [1],[2] to its deformed counterpart. We identify and analyze saddle point solutions and examine the

Yuanfan Zheng, Jinlin Wu, Wuyang Li, Zhen Chen

Domain Adaptive Object Detection (DAOD) transfers knowledge from a labeled source domain to an unannotated target domain under closed-set assumption. Universal DAOD (UniDAOD) extends DAOD to handle open-set, partial-set, and closed-set domain adaptation. In this paper, we first unveil two issues: domain-private category alignment is crucial for global-level

Tomohiro Yoshitake, Megumi Shidatsu, Yoshihiro Ueda, Shin Mineshige

We report the results of quasi-simultaneous multiwavelength (near-infrared, optical, UV, and X-ray) observations of the Galactic X-ray black hole binary MAXI J1820+070 performed in 2019 May 10-13, $\sim 60$ days after the onset of the first rebrightening phase. It showed a much larger optical-to-X-ray luminosity ratio ($\sim 8$) than in the initial outburst

Yuning Han, Bingyin Zhao, Rui Chu, Feng Luo

Recent studies show that diffusion models (DMs) are vulnerable to backdoor attacks. Existing backdoor attacks impose unconcealed triggers (e.g., a gray box and eyeglasses) that contain evident patterns, rendering remarkable attack effects yet easy detection upon human inspection and defensive algorithms. While it is possible to improve stealthiness by reduci

Akshay Singh, Damien Bégué, Asaf Pe'er

We studied magnetically arrested disks (MAD) around rotating black holes (BH), under the influence of radiative cooling. We introduce a critical value of the mass accretion rate $\dot M_{\rm crit}$ for which the cooling by the synchrotron process efficiently radiates the thermal energy of the disk. We find $\dot M_{\rm crit} \approx 10^{-5.5} \dot M_{\rm Edd

Ahmed Rashed

The study investigates flavor-changing neutral current (FCNC) decays of $B$ and $K$ mesons in the context of a dark $U(1)_D$ model with a dark photon/dark $Z$ mass between 10 MeV and 2 GeV. While the model improves the fit to certain decay distributions, such as $B \to K^{(*)} \ell^+ \ell^- $ and $ B_s \to \phi \mu^+ \mu^- $, it is ruled out by stringent exp

Ahmed Rashed

The process $\Lambda_b \rightarrow \Lambda_c \ell^- \bar{\nu}_\ell$ serves as a tool for exploring new physics, with contributions from scalar, vector, and tensor hadronic currents in various models. These form factors are derived from the quark model or lattice QCD. This work introduces a C-code for efficiently reading lattice QCD form factors for these cur

Monique Cockram, Noelia Martinez Rey

Event-based sensors detect only changes in brightness across a scene, with each pixel producing an asynchronous stream of spatial-temporal data, rather than recording frames of overall illumination such as a traditional frame-based sensor. This is advantageous for implementing into a wavefront sensor, which benefits from high temporal resolution and high dyn

Delong Zhang, Qiwei Huang, Yuanliu Liu, Yang Sun

Image-based virtual try-on is challenging since the generated image should fit the garment to model images in various poses and keep the characteristics and details of the garment simultaneously. A popular research stream warps the garment image firstly to reduce the burden of the generation stage, which relies highly on the performance of the warping module

Alberto Silvio Chiappa, Briti Gangopadhyay, Zhao Wang, Shingo Takamatsu

Online advertising has become one of the most successful business models of the internet era. Impression opportunities are typically allocated through real-time auctions, where advertisers bid to secure advertisement slots. Deciding the best bid for an impression opportunity is challenging, due to the stochastic nature of user behavior and the variability of

Jianhui Huang, Wenqiang Li, Harry Zheng

This paper investigates a novel class of mean field games involving a major agent and numerous minor agents, where the agents' functionals are recursive with nonlinear backward stochastic differential equation (BSDE) representations. We term these games "recursive major-minor" (RMM) problems. Our RMM modeling is quite general, as it employs empirical (state,

Xiao Teng, Long Lan, Dingyao Chen, Kele Xu

Unsupervised visible-infrared person re-identification (USL-VI-ReID) is of great research and practical significance yet remains challenging due to the absence of annotations. Existing approaches aim to learn modality-invariant representations in an unsupervised setting. However, these methods often encounter label noise within and across modalities due to s

Wo Long, Victor Xiao

The residuals in factor models prevalent in asset pricing presents opportunities to exploit the mis-pricing from unexplained cross-sectional variation for arbitrage. We performed a replication of the methodology of Guijarro-Ordonez et al. (2019) (G-P-Z) on Deep Learning Statistical Arbitrage (DLSA), originally applied to U.S. equity data from 1998 to 2016, u

Mohamed Basem, Islam Oshallah, Baraa Hikal, Ali Hamdi

Understanding the deep meanings of the Qur'an and bridging the language gap between modern standard Arabic and classical Arabic is essential to improve the question-and-answer system for the Holy Qur'an. The Qur'an QA 2023 shared task dataset had a limited number of questions with weak model retrieval. To address this challenge, this work updated the origina

Dylan M. Asmar, Mykel J. Kochenderfer

Decentralized partially observable Markov decision processes with communication (Dec-POMDP-Com) provide a framework for multiagent decision making under uncertainty, but the NEXP-complete complexity for finite-horizon problems renders solutions intractable in general. While sharing actions and observations can reduce the complexity to PSPACE-complete, we pro

Yi Gao

We investigate the superconducting pairing symmetry in pressurized La$_3$Ni$_2$O$_7$ based on a bilayer two-orbital model. There are two symmetric bands $\alpha$ and $\gamma$, as well as two antisymmetric ones $\beta$ and $\delta$. It is found that the $\gamma$ band induces considerable ferromagnetic spin fluctuation and prefers an odd-frequency, $s$-wave sp

Qi Zhang, Zhouhang Luo, Tao Yu, Hui Huang

View transformation robustness (VTR) is critical for deep-learning-based multi-view 3D object reconstruction models, which indicates the methods' stability under inputs with various view transformations. However, existing research seldom focused on view transformation robustness in multi-view 3D object reconstruction. One direct way to improve the models' VT

Chandan K Reddy, Parshin Shojaee

Scientific discovery is a complex cognitive process that has driven human knowledge and technological progress for centuries. While artificial intelligence (AI) has made significant advances in automating aspects of scientific reasoning, simulation, and experimentation, we still lack integrated AI systems capable of performing autonomous long-term scientific

Amir Kazemi-Moridani, Andrew J. Baker, Marc Verheijen, Eric Gawiser

We present measurements of the neutral atomic hydrogen (HI) mass function (HIMF) and cosmic HI density ($\Omega_{\rm HI}$) at $0 \leq z \leq 0.088$ from the Looking at the Distant Universe with MeerKAT Array (LADUMA) survey. Using LADUMA Data Release 1 (DR1), we analyze the HIMF via a new "recovery matrix" (RM) method that we benchmark against a more traditi

Bruce D. Popp

J. Willard Gibbs published a book in 1902 on statistical mechanics that quickly received significant attention from his contemporaries because of the reputation that he had secured with his prior work on thermodynamics. People reading Gibbs's book were often familiar with Ludwig Boltzmann's work on the kinetic theory of gases. This article looks at the publi

Jialu Wang, Chengbin Xu, Fang Zhang

In this paper, we study the dispersive decay estimates for solution to the $3\mathrm{D}$ energy-critical nonlinear Schr\"odinger equation with an inverse-square operator $\mathcal{L}_a$ where the operator is denoted by $\mathcal{L}_{a}:=-\Delta+\frac{a}{|x|^2}$ with the constant $a\geq0$. Inspired by the work of \cite{KMVZZ1,K}, we first establish that the s

Namhyuk Ahn, KiYoon Yoo, Wonhyuk Ahn, Daesik Kim

Recent advancements in diffusion models revolutionize image generation but pose risks of misuse, such as replicating artworks or generating deepfakes. Existing image protection methods, though effective, struggle to balance protection efficacy, invisibility, and latency, thus limiting practical use. We introduce perturbation pre-training to reduce latency an

Farid Ablayev, Nailya Salikhova, Marat Ablayev

In this work, we present a quantum query algorithm for searching a word of length $m$ in an unsorted dictionary of size $n$. The algorithm uses $O(\sqrt{n})$ queries (Grover operators), like previously known algorithms. What is new is that the algorithm is based on the quantum fingerprinting-hashing technique, which (a) provides a first level of amplitude am

Chloé Arson, Dominic Balog-Way, Koenraad Beckers, Wayne Bezner-Kerr

To review the lessons learnt from recent deep geothermal case studies and plan strategically the research, development, regulation, and communication work required for the implementation of an Enhanced Geothermal System (EGS) at Cornell University, a group of engineers and scholars convened a two-day workshop on the Ithaca campus, on October 23-24, 2024. The

Adam Bethell, Ravi Garg, Ian Reid

Estimating the 6D pose and 3D size of an object from an image is a fundamental task in computer vision. Most current approaches are restricted to specific instances with known models or require ground truth depth information or point cloud captures from LIDAR. We tackle the harder problem of pose estimation for category-level objects from a single RGB image.

Tony Haoran Feng, Andrew Luxton-Reilly, Burkhard C. Wünsche, Paul Denny

Generative Artificial Intelligence (GenAI) offers numerous opportunities to revolutionise teaching and learning in Computing Education (CE). However, educators have expressed concerns that students may over-rely on GenAI and use these tools to generate solutions without engaging in the learning process. While substantial research has explored GenAI use in CE

Liyu Zhang, Weiqi Wang, Tianqing Fang, Yangqiu Song

Knowledge Editing (KE) aims to adjust a Large Language Model's (LLM) internal representations and parameters to correct inaccuracies and improve output consistency without incurring the computational expense of re-training the entire model. However, editing commonsense knowledge still faces difficulties, including limited knowledge coverage in existing resou

Junjie Lin, Jian Zhao, Lin Liu, Yue Deng

Traditionally, AI development for two-player zero-sum games has relied on two primary techniques: decision trees and reinforcement learning (RL). A common approach involves using a fixed decision tree as one player's strategy while training an RL agent as the opponent to identify vulnerabilities in the decision tree, thereby improving its strategic strength

Linqin Wang, Yaping Liu, Zhengtao Yu, Shengxiang Gao

With the rapid advancement of large language models (LLMs), discrete speech representations have become crucial for integrating speech into LLMs. Existing methods for speech representation discretization rely on a predefined codebook size and Euclidean distance-based quantization. However, 1) the size of codebook is a critical parameter that affects both cod

Imane Benchouk, Lateef Jolaoso, Khadra Nachi, Alain Zemkoho

We consider a smooth pessimistic bilevel optimization problem, where the lower-level problem is convex and satisfies the Slater constraint qualification. These assumptions ensure that the Karush-Kuhn-Tucker (KKT) reformulation of our problem is well-defined. We then introduce and study the (i) Scholtes, (ii) Lin and Fukushima, (iii) Kadrani, Dussault and Ben

Junlin Julian Jiang, Xin Li

This paper proposes a look ahead text understanding problem with look ahead section identification (LASI) as an example. This problem may appear in generative AI as well as human interactions, where we want to understand the direction of a developing text or conversation. We tackle the problem using transformer-based LLMs. We show that LASI is more challengi

Shigeki Akiyama, Emily R. Korfanty, Yanli Xu

We construct new Delone sets associated with badly approximable numbers which are expected to have rotationally invariant diffraction. We optimize the discrepancy of corresponding tile orientations by investigating the linear equation $x+y+z=1$ where $\pi x$, $\pi y$, $\pi z$ are three angles of a triangle used in the construction and $x$, $y$, $z$ are badly

Akshita Jha, Sanchit Kabra, Chandan K. Reddy

Recent studies have shown that generative language models often reflect and amplify societal biases in their outputs. However, these studies frequently conflate observed biases with other task-specific shortcomings, such as comprehension failure. For example, when a model misinterprets a text and produces a response that reinforces a stereotype, it becomes d

Malcolm Bogroff, Gabriel Cowley, Ariel Nicastro, David Levy

Cathodoluminescence microscopy is now a well-established and powerful tool for probing the photonic properties of nanoscale materials, but in many cases, nanophotonic materials are easily damaged by the electron-beam doses necessary to achieve reasonable cathodoluminescence signal-to-noise ratios. Two-dimensional materials have proven particularly susceptibl

Jin-Cheng Jhang, Tao Tu, Fu-En Wang, Ke Zhang

The field of indoor monocular 3D object detection is gaining significant attention, fueled by the increasing demand in VR/AR and robotic applications. However, its advancement is impeded by the limited availability and diversity of 3D training data, owing to the labor-intensive nature of 3D data collection and annotation processes. In this paper, we present

Shi-Yao Shao, Qing Li, Li-Hua Zhang, Bang Liu

We describe a three-dimensional (3D) magneto-optical trap (MOT) capable of simultaneously capturing 85Rb and 133Cs atoms. Unlike conventional setups, our system utilizes two separate laser systems that are combined before entering the vacuum chamber, enabling the simultaneous trapping of two different atomic species. Additionally, in our 3D MOT configuration

Xing Lei, Xuetao Zhang, Donglin Wang

Recently, a state-of-the-art family of algorithms, known as Goal-Conditioned Weighted Supervised Learning (GCWSL) methods, has been introduced to tackle challenges in offline goal-conditioned reinforcement learning (RL). GCWSL optimizes a lower bound of the goal-conditioned RL objective and has demonstrated outstanding performance across diverse goal-reachin

Rui Liu, Shuwei He, Yifan Hu, Haizhou Li

Visual Text-to-Speech (VTTS) aims to take the environmental image as the prompt to synthesize the reverberant speech for the spoken content. The challenge of this task lies in understanding the spatial environment from the image. Many attempts have been made to extract global spatial visual information from the RGB space of an spatial image. However, local a

Milad Soltany, Farhad Pourpanah, Mahdiyar Molahasani, Michael Greenspan

In this paper, we propose a novel approach, Federated Domain Generalization with Label Smoothing and Balanced Decentralized Training (FedSB), to address the challenges of data heterogeneity within a federated learning framework. FedSB utilizes label smoothing at the client level to prevent overfitting to domain-specific features, thereby enhancing generaliza

TianZhu Liu, BangYan Hu, YanFeng Gu, Xian Li

Multispectral point cloud (MPC) captures 3D spatial-spectral information from the observed scene, which can be used for scene understanding and has a wide range of applications. However, most of the existing classification methods were extensively tested on indoor datasets, and when applied to outdoor datasets they still face problems including sparse labele

Stephen S. -T. Yau, Hao Zuo, Huaiqing Zuo

The notion of the Yau sequence was introduced by Tomaru, as an attempt to extend Yau's elliptic sequence for (weakly) elliptic singularities to normal surface singularities of higher fundamental genera. In this paper, we obtain the canonical cycle using the Yau cycle for certain surface singularities of degree two. Furthermore, we obtain a formula of arithme

Laia Coronas Sala, Parfait Atchade-Adelemou

Quantum Computing (QC) offers outstanding potential for molecular characterization and drug discovery, particularly in solving complex properties like the Ground State Energy (GSE) of biomolecules. However, QC faces challenges due to computational noise, scalability, and system complexity. This work presents a hybrid framework combining Machine Learning (ML)

Qiang Ding, Lvzhou Luo, Yixuan Cao, Ping Luo

To assist humans in efficiently validating RAG-generated content, developing a fine-grained attribution mechanism that provides supporting evidence from retrieved documents for every answer span is essential. Existing fine-grained attribution methods rely on model-internal similarity metrics between responses and documents, such as saliency scores and hidden

Robert B. Parker, Oscar Dowson, Nicole LoGiudice, Manuel Garcia

We compare full-space, reduced-space, and gray-box formulations for representing trained neural networks in nonlinear constrained optimization problems. We test these formulations on a transient stability-constrained, security-constrained alternating current optimal power flow (SCOPF) problem where the transient stability criteria are represented by a traine

Wentao Yu, Shuo Chen, Yongxin Tong, Tianlong Gu

Heterogeneity is a fundamental and challenging issue in federated learning, especially for the graph data due to the complex relationships among the graph nodes. To deal with the heterogeneity, lots of existing methods perform the weighted federation based on their calculated similarities between pairwise clients (i.e., subgraphs). However, their inter-subgr

Tokuro Hata, Hiroki Mitani, Hidetaka Uchiyama, Takafumi Akiho

Quantum antidots (QAD) are attractive for manipulating quasiparticles in quantum Hall (QH) systems. Here, we form a QAD in the integer and fractional QH regimes at nominal Landau-level filling factor $\nu$ = 2, 1, and 2/3 using a submicron pillar gate with an airbridge connection. After confirming the required conditions for a fully depleted QAD, we analyze