December 2024 arXiv papers — page 110

Anand Krishna, Philips George John, Adarsh Barik, Vincent Y. F. Tan

In this work, we extend the concept of the $p$-mean welfare objective from social choice theory (Moulin 2004) to study $p$-mean regret in stochastic multi-armed bandit problems. The $p$-mean regret, defined as the difference between the optimal mean among the arms and the $p$-mean of the expected rewards, offers a flexible framework for evaluating bandit alg

Dongning Liu, Zhanping Jin, Jingyuan Liu, Xiaotong Zou

Quantum teleportation is a crucial function in quantum networks. The implementation of photonic quantum teleportation could be highly simplified by quantum photonic circuits. To extend chip-to-chip teleportation distance, more effort is needed on both chip design and system implementation. In this work, we demonstrate a chip-to-chip photonic quantum teleport

Zhangbin Li, Jinxing Zhou, Jing Zhang, Shengeng Tang

Answering questions related to audio-visual scenes, i.e., the AVQA task, is becoming increasingly popular. A critical challenge is accurately identifying and tracking sounding objects related to the question along the timeline. In this paper, we present a new Patch-level Sounding Object Tracking (PSOT) method. It begins with a Motion-driven Key Patch Trackin

Yu Chen, Shuai Zheng, Nianyi Wang, Menglong Jin

Fluid simulation is an important research topic in computer graphics (CG) and animation in video games. Traditional methods based on Navier-Stokes equations are computationally expensive. In this paper, we treat fluid motion as point cloud transformation and propose the first neural network method specifically designed for efficient and robust fluid simulati

Aaron Pim, Tristan Pryer, Alex Trenam

This work addresses an optimal control problem constrained by a degenerate kinetic equation of parabolic-hyperbolic type. Using a hypocoercivity framework we establish the well-posedness of the problem and demonstrate that the optimal solutions exhibit a hypocoercive decay property, ensuring stability and robustness. Building on this framework, we develop a

Ulrich Bunke

In this note we give a simple argument for the fact that the coarse assembly map for a strong coarse homology theory with weak transfers and a bornological coarse space of weakly finite homotopical asymptotic dimension is a phantom equivalence.

Chaitanya Kirti, Ayon Chattopadhyay, Ashish Anand, Prithwijit Guha

Event extraction is an important natural language processing (NLP) task of identifying events in an unstructured text. Although a plethora of works deal with event extraction from new articles, clinical text etc., only a few works focus on event extraction from literary content. Detecting events in short stories presents several challenges to current systems

Bundit Laekhanukit

The Directed Steiner Tree (DST) problem is defined on a directed graph $G=(V,E)$, where we are given a designated root vertex $r$ and a set of $k$ terminals $K \subseteq V \setminus {r}$. The goal is to find a minimum-cost subgraph that provides directed $r \rightarrow t$ paths for all terminals $t \in K$. The approximability of DST has long been a central o

Zhuoran Qiao, Feizhi Ding, Thomas Dresselhaus, Mia A. Rosenfeld

Structure determination is essential to a mechanistic understanding of diseases and the development of novel therapeutics. Machine-learning-based structure prediction methods have made significant advancements by computationally predicting protein and bioassembly structures from sequences and molecular topology alone. Despite substantial progress in the fiel

Jiarun Liu, Jia Hao, Chunhong Zhang, Zheng Hu

The rapid advancement of autonomous web navigation has significantly benefited from grounding pretrained Large Language Models (LLMs) as agents. However, current research has yet to fully leverage the redundancy of HTML elements for contrastive training. This paper introduces a novel approach to LLM-based web navigation tasks, called Web Element Preference O

Haorong Han, Jidong Yuan, Chixuan Wei, Zhongyang Yu

Consistency regularization and pseudo-labeling have significantly advanced semi-supervised learning (SSL). Prior works have effectively employed Mixup for consistency regularization in SSL. However, our findings indicate that applying Mixup for consistency regularization may degrade SSL performance by compromising the purity of artificial labels. Moreover, m

Ettore Vittone, Georgios Provatas, Karla Ivanković Nizić, Milko Jaksic

The ion beam-induced charge (IBIC) analysis of a commercial silicon photodiode configured as a Position Sensitive Detector (PSD) for energetic charged particles is the subject of this report. Although the photodiode is designed for detecting the position of incident light and optimized for use in the UV region, we present evidence that it also performs well

Jingyu Zhang, Yilei Wang, Lang Qian, Peng Sun

As a potential application of Vehicle-to-Everything (V2X) communication, multi-agent collaborative perception has achieved significant success in 3D object detection. While these methods have demonstrated impressive results on standard benchmarks, the robustness of such approaches in the face of complex real-world environments requires additional verificatio

Kushal Ramkumar, Wanling Cai, John McCarthy, Gavin Doherty

Security attacks are rising, as evidenced by the number of reported vulnerabilities. Among them, unknown attacks, including new variants of existing attacks, technical blind spots or previously undiscovered attacks, challenge enduring security. This is due to the limited number of techniques that diagnose these attacks and enable the selection of adequate se

Shubhi Bansal, Mohit Kumar, Chandravardhan Singh Raghaw, Nagendra Kumar

Social media users articulate their opinions on a broad spectrum of subjects and share their experiences through posts comprising multiple modes of expression, leading to a notable surge in such multimodal content on social media platforms. Nonetheless, accurately forecasting the popularity of these posts presents a considerable challenge. Prevailing methodo

Xiangyu Pi, Lipeng Zhu, Haobin Mao, Zhenyu Xiao

The effective utilization of unlicensed spectrum is regarded as an important direction to enable the massive access and broad coverage for next-generation wireless local area network (WLAN). Due to the crowded spectrum occupancy and dense user terminals (UTs), the conventional fixed antenna (FA)-based access points (APs) face huge challenges in realizing mas

Zi-Xuan Zhao, Song He, Hao Ouyang, Hong-an Zeng

We study the relative R\'enyi entropy (RRE) under local quenches in two-dimensional conformal field theories (CFTs), focusing on rational CFTs (RCFTs) and holographic CFTs. In RCFTs, the RRE evolves as a monotonic function over time, depending on finite-dimensional matrices. It is sometimes symmetric, prompting an exploration of its relation to the trace squ

Lianqing Zheng, Long Yang, Qunshu Lin, Wenjin Ai

The rapid advancement of deep learning has intensified the need for comprehensive data for use by autonomous driving algorithms. High-quality datasets are crucial for the development of effective data-driven autonomous driving solutions. Next-generation autonomous driving datasets must be multimodal, incorporating data from advanced sensors that feature exte

Yi-Da Li, Qing Wang

We propose a new method to calculate perturbatively the isospectral Hermitian theory for the $\mathcal{PT}$-symmetric $i\phi^3$ quantum field theory in $d$ dimensions, whose result is local. The result of the new method in $1$ dimension reproduces our previous result in the $ix^3$ quantum mechanics, and the new method can be seen as a generalization of our p

Tomohiro C. Yoshida, Hideko Nomura, Takashi Tsukagoshi, Kiyoaki Doi

Planetary bodies are formed by coagulation of solid dust grains in protoplanetary disks. Therefore, it is crucial to constrain the physical and chemical properties of the dust grains. In this study, we measure the dust albedo at mm-wavelength, which depends on dust properties at the disk midplane. Since the albedo and dust temperature are generally degenerat

Wenjun Huang, Jianguo Hu

The Long Short-Term Memory (LSTM) networks have traditionally faced challenges in scaling and effectively capturing complex dependencies in visual tasks. The xLSTM architecture has emerged to address these limitations, incorporating exponential gating and a parallel matrix memory structure to enhance performance and scalability. Despite these advancements, t

Kirill Boguslavski, Paul Hotzy, David I. Müller

The complex Langevin (CL) method shows significant potential in addressing the numerical sign problem. Nonetheless, it often produces incorrect results when used without any stabilization techniques. Leveraging insights from previous research that links Lefschetz thimbles and CL, we explore a strategy to regularize the CL method to address this issue of inco

Roman V. Buniy, Thomas W. Kephart

We provide an in-depth study of tripartite entanglement of qudits. We start with a short review of tripartite entanglement invariants, prove a theorem about the complete list of all allowed values of three (out of the total of four) such invariants, and give several bounds on the allowed values of the fourth invariant. After introducing several operations on

Majid Azizi, Maryam Soleymaninia, Hadi Hashamipour, Maral Salajegheh

We present new parton distribution functions (PDFs) at next-to-leading order (NLO) and next-to-next-to-leading order (NNLO) in perturbative QCD, derived from a comprehensive global QCD analysis of high-precision data sets from combined HERA deep-inelastic scattering (DIS), the Tevatron, and the Large Hadron Collider (LHC). To improve constraints on quark fla

Tao Wu, Chuhao Zhou, Yen Heng Wong, Lin Gu

The rapid advancement of Vision-Language Models (VLMs) has significantly advanced the development of Embodied Question Answering (EQA), enhancing agents' abilities in language understanding and reasoning within complex and realistic scenarios. However, EQA in real-world scenarios remains challenging, as human-posed questions often contain noise that can inte

Guolin Qin, Jie Wan

In this paper, we consider the existence of concentrated helical vortices of 3D incompressible Euler equations with swirl. First, without the assumption of the orthogonality condition, we derive a 2D vorticity-stream formulation of 3D incompressible Euler equations under helical symmetry. Then based on this system, we deduce a non-autonomous second order sem

Rundong Fang, Ji-Heng Guo, Jia Liu, Xiao-Ping Wang

We investigate a fifth force mediated by a light vector boson that couples to lepton spins, characterized by axial-vector couplings to leptons and vector couplings to nucleons. This interaction generates a potential proportional to the inner product of the lepton spin vector and the nucleon-lepton relative velocity vector, a feature extensively explored with

Xuan Liu, Yilin Song, Jiqiang Zheng

In this paper, we investigate the global well-posedness and scattering theory for the defocusing nonlinear Schr\"odinger equation $iu_t + \Delta_\Omega u = |u|^\alpha u$ in the exterior domain $\Omega$ of a smooth, compact and strictly convex obstacle in $\mathbb{R}^3$. It is conjectured that in Euclidean space, if the solution has a prior bound in the criti

Jianfeng Li, Jiawen Zhang, Feng Wang, Lianbo Ma

One-shot methods have significantly advanced the field of neural architecture search (NAS) by adopting weight-sharing strategy to reduce search costs. However, the accuracy of performance estimation can be compromised by co-adaptation. Few-shot methods divide the entire supernet into individual sub-supernets by splitting edge by edge to alleviate this issue,

Hui Li, Kedan Li, Jiaqing Lv, Yuanshao Liang

Since its inception in the 1960s, the internet has profoundly transformed human life. However, its original design now struggles to meet the evolving demands of modern society. Three primary defects have emerged: First, the concentration of power among a few dominant entities has intensified international conflicts and widened the technological divide. Secon

Falong Tan, Jie Liu, Heng Peng, Lixing Zhu

This paper proposes a novel two-step strategy for testing the goodness-of-fit of parametric regression models in ultra-high dimensional sparse settings, where the predictor dimension far exceeds the sample size. This regime usually renders existing goodness-of-fit tests for regressions infeasible, primarily due to the curse of dimensionality or their relianc

Ji-jun Park, Soo-joon Choi

Video captioning is a critical task in the field of multimodal machine learning, aiming to generate descriptive and coherent textual narratives for video content. While large vision-language models (LVLMs) have shown significant progress, they often struggle to capture the causal and temporal dynamics inherent in complex video sequences. To address this limi

Jinzheng Li, Jingshu Zhang, Hongguang Li, Yiqing Shen

Financial decision-making requires processing vast amounts of real-time information while understanding their complex temporal relationships. While traditional search engines excel at providing real-time information access, they often struggle to comprehend sophisticated user intentions and contextual nuances. Conversely, Large Language Models (LLMs) demonst

Jinrong Zhang, Penghui Wang, Chunxiao Liu, Wei Liu

To break through the limitations of pre-training models on fixed categories, Open-Set Object Detection (OSOD) and Open-Set Segmentation (OSS) have attracted a surge of interest from researchers. Inspired by large language models, mainstream OSOD and OSS methods generally utilize text as a prompt, achieving remarkable performance. Following SAM paradigm, some

Cong Wan, Xiangyang Luo, Hao Luo, Zijian Cai

Visual generation has witnessed remarkable progress in single-image tasks, yet extending these capabilities to temporal sequences remains challenging. Current approaches either build specialized video models from scratch with enormous computational costs or add separate motion modules to image generators, both requiring learning temporal dynamics anew. We ob

Shibaranjani Dasgupta, Chandan Maity, Somdip Mukherjee, Rohan Singh

Large language models (LLMs) are powerful but resource intensive, limiting accessibility. HITgram addresses this gap by offering a lightweight platform for n-gram model experimentation, ideal for resource-constrained environments. It supports unigrams to 4-grams and incorporates features like context sensitive weighting, Laplace smoothing, and dynamic corpus

Sergei V. Kozyrev, Ilya A Lopatin, Alexander N Pechen

While there are many works on the applications of machine learning, not so many of them are trying to understand the theoretical justifications to explain their efficiency. In this work, overfitting control (or generalization property) in machine learning is explained using analogies from physics and biology. For stochastic gradient Langevin dynamics, we sho

Pronit Raj, Chandrashekhar Kumar, Harshit Shekhar, Amit Kumar

In today's digital world, streaming platforms offer a vast array of movies, making it hard for users to find content matching their preferences. This paper explores integrating real time data from popular movie websites using advanced HTML scraping techniques and APIs. It also incorporates a recommendation system trained on a static Kaggle dataset, enhancing

Fengshuo Bai, Runze Liu, Yali Du, Ying Wen

Evaluating deep reinforcement learning (DRL) agents against targeted behavior attacks is critical for assessing their robustness. These attacks aim to manipulate the victim into specific behaviors that align with the attacker's objectives, often bypassing traditional reward-based defenses. Prior methods have primarily focused on reducing cumulative rewards;

Xiaoyan Yu, Yifan Wei, Shuaishuai Zhou, Zhiwei Yang

The vast, complex, and dynamic nature of social message data has posed challenges to social event detection (SED). Despite considerable effort, these challenges persist, often resulting in inadequately expressive message representations (ineffective) and prolonged learning durations (inefficient). In response to the challenges, this work introduces an unsupe

Rubén Blasco-García, María Cumplido, Derek F. Holt, Rose Morris-Wright

We prove that the word problem in an Artin group G based on a diagram without A_3 or B_3 subdiagrams can be solved using a system of length preserving rewrite rules which, together with free reduction, can be used to reduce any word over the standard generators of G to a geodesic word in G in quadratic time. This result builds on work of Holt and Rees, and o

Haonan Gu

This paper first introduces the concept of p-adic number and field. Then it develops the p-adic integration and applied it to solve p-adic Schrodinger equations.

Naotoshi Fujihara, Naoyuki Koike

In this paper, we study the graphical mean curvature flow in a warped product $_r G/K \times I$, where $G/K$ is a symmetric space of compact type, $I$ is an open interval, and $r$ is a smooth positive function on $I$. If the initial hypersurface is $K$-equivariant, then the $K$-equivariance is preserved along the mean curvature flow. Here, we note that isotr

Tulashi Prasad Joshi, Amrendra Kumar Yadav, Arjun Chhetri, Suraj Agrahari

Online shopping has revolutionized the retail industry, providing customers with convenience and accessibility. However, customers often hesitate to purchase wearable products such as watches, jewelry, glasses, shoes, and clothes due to the lack of certainty regarding fit and suitability. This leads to significant return rates, causing problems for both cust

Derrick Quinn, Mohammad Nouri, Neel Patel, John Salihu

An evolving solution to address hallucination and enhance accuracy in large language models (LLMs) is Retrieval-Augmented Generation (RAG), which involves augmenting LLMs with information retrieved from an external knowledge source, such as the web. This paper profiles several RAG execution pipelines and demystifies the complex interplay between their retrie

Roopa Bhat, Lord Crawford, Nicole Hong

For many, it can be intimidating or even impossible to seek professional medical help if they have symptoms of an illness. As such, some people approach platforms like Reddit or Quora for a community-based conversation in an attempt to diagnose themselves. In this paper, we unearth what motivates people to share personal health information on these platforms

Yi Hao Puah, Anh Tu Ngo, Nandish Chattopadhyay, Anupam Chattopadhyay

Adoption of machine learning models across industries have turned Neural Networks (DNNs) into a prized Intellectual Property (IP), which needs to be protected from being stolen or being used without authorization. This topic gave rise to multiple watermarking schemes, through which, one can establish the ownership of a model. Watermarking using backdooring i

Nozomi Nakatsuyama, Masatomo Takahashi

We consider mixed types of not only regular curves but also curves with singular points in the Lorentz-Minkowski 3-space. In order to consider mixed type of curves with singular points, we consider the lightcone frame and lightcone framed curves. By using lightcone frame, we can consider Bertrand types for lightcone framed curves, so-called Bertrand lightcon

Yuhao Wang, Xuehu Liu, Tianyu Yan, Yang Liu

Multi-modal object Re-IDentification (ReID) aims to retrieve specific objects by utilizing complementary image information from different modalities. Recently, large-scale pre-trained models like CLIP have demonstrated impressive performance in traditional single-modal object ReID tasks. However, they remain unexplored for multi-modal object ReID. Furthermor

Zexuan Fan, Sunchun Zhou, Hengye Yang, Junyi Cai

This paper introduces a comprehensive planning and navigation framework that address these limitations by integrating semantic mapping, adaptive coverage planning, dynamic obstacle avoidance and precise trajectory tracking. Our framework begins by generating panoptic occupancy local semantic maps and accurate localization information from data aligned betwee

Mark Bajo, Haruka Fukukawa, Ryuji Morita, Yuma Ogasawara

This study explores fine-tuning multilingual ASR (Automatic Speech Recognition) models, specifically OpenAI's Whisper-Tiny, to improve performance in Japanese. While multilingual models like Whisper offer versatility, they often lack precision in specific languages. Conversely, monolingual models like ReazonSpeech excel in language-specific tasks but are les

Manan Suri, Puneet Mathur, Franck Dernoncourt, Kanika Goswami

Understanding information from a collection of multiple documents, particularly those with visually rich elements, is important for document-grounded question answering. This paper introduces VisDoMBench, the first comprehensive benchmark designed to evaluate QA systems in multi-document settings with rich multimodal content, including tables, charts, and pr

Juncheng Wang, Bingjie Yan, Yituo Liu

We consider online convex optimization with time-varying constraints and conduct performance analysis using two stringent metrics: dynamic regret with respect to the online solution benchmark, and hard constraint violation that does not allow any compensated violation over time. We propose an efficient algorithm called Constrained Online Learning with Doubly

Yiheng Lin, Yihan Hu, Chenyi Zhang, Ting Liu

Transformer-based models have recently achieved outstanding performance in image matting. However, their application to high-resolution images remains challenging due to the quadratic complexity of global self-attention. To address this issue, we propose MEMatte, a \textbf{m}emory-\textbf{e}fficient \textbf{m}atting framework for processing high-resolution i

Jinrui Gou, Yifan Liu, Minghao Shao, Torsten Suel

Top-k threshold estimation is the problem of estimating the score of the k-th highest ranking result of a search query. A good estimate can be used to speed up many common top-k query processing algorithms, and thus a number of researchers have recently studied the problem. Among the various approaches that have been proposed, quantile methods appear to give

Zhiying Wang, Gang Sun, Yuhui Wang, Hongfang Yu

The Space-Air-Ground Integrated Network (SAGIN) framework is a crucial foundation for future networks, where satellites and aerial nodes assist in computational task offloading. The low-altitude economy, leveraging the flexibility and multifunctionality of Unmanned Aerial Vehicles (UAVs) in SAGIN, holds significant potential for development in areas such as

Fayzah Alshammari, Yunpeng Luo, Qi Alfred Chen

Indoor Delivery Robots (IDRs) play a vital role in the upcoming fourth industrial revolution, autonomously navigating and transporting items within indoor environments. In this work, we thus aim to conduct the first security analysis of the IDR systems considering both cyber- and physical-layer attack surface and domain-specific attack goals across security,

Wojciech Młotkowski, Nobuaki Obata

Motivated by the product formula of the Chebyshev polynomials of the second kind $U_n(x)$, we newly introduce the partial Chebyshev polynomials $U^{\mathrm{e}}_n(x)$ and $U^{\mathrm{o}}_n(x)$ and derive their basic properties, relations to the classical Chebyshev polynomials, and new factorization formulas for $U_n(x)$. In order to calculate the quadratic em

Ruddarraju Amrutha, Pratyusha Chattopadhyay

V.I. Kopeiko proved that over a euclidean ring, the symplectic group defined with respect to the standard skew-symmetric matrix is same as the elementary symplectic group. Here we generalise the result of Kopeiko for a symplectic group defined with respect to any invertible skew-symmetric matrix of Pfaffian one.

Xin Zheng, Lei Guo

It is well-known that saturated output observations are prevalent in various practical systems and that the $\ell_1$-norm is more robust than the $\ell_2$-norm-based parameter estimation. Unfortunately, adaptive identification based on both saturated observations and the $\ell_1$-optimization turns out to be a challenging nonlinear problem, and has rarely be

Junliang Li, Kai Ye, Haolan Kang, Mingxuan Liang

In recent years, as robotics has advanced, human-robot collaboration has gained increasing importance. However, current robots struggle to fully and accurately interpret human intentions from voice commands alone. Traditional gripper and suction systems often fail to interact naturally with humans, lack advanced manipulation capabilities, and are not adaptab

A. Farren, S. R. Stroberg

Robustly quantifying the uncertainty in the isospin-related theoretical correction $\delta_C$ to superallowed beta decay rates is vital for a correct assessment of CKM unitarity. To this end, we identify the sources of artificial or \textit{spurious} isospin symmetry breaking introduced by the IMSRG many-body framework at a computational level and provide re

Huy Chau, Duy Nguyen, Thai Nguyen

In a reinforcement learning (RL) framework, we study the exploratory version of the continuous time expected utility (EU) maximization problem with a portfolio constraint that includes widely-used financial regulations such as short-selling constraints and borrowing prohibition. The optimal feedback policy of the exploratory unconstrained classical EU proble

Ilya Mandel, Ryosuke Hirai, Lewis Picker

We describe some of our group's recent work on common envelopes. Our goal is to understand the onset and outcomes of dynamically unstable mass transfer, including the properties of the binaries left behind and the outflows during the common envelope stage. We have also started thinking about light curves of common envelope events. During a talk at StanFest,

Li Ni, Zhou Xie, Yiwen Zhang, Wenjian Luo

Real-world networks are often constructed from different sources or domains, including various types of entities and diverse relationships between networks, thus forming multi-domain networks. A single network typically fails to capture the complete graph structure and the diverse relationships among multiple networks. Consequently, leveraging multiple netwo

Jihwan Oh, Jeonghwan Choi, Nicole Hee-Yeon Kim, Taewon Yun

Training automatic summary fact verifiers often faces the challenge of a lack of human-labeled data. In this paper, we explore alternative way of leveraging Large Language Model (LLM) generated feedback to address the inherent limitation of using human-labeled data. We introduce FineSumFact, a large-scale dataset containing fine-grained factual feedback on s

Karthik Gururangan, Achintya Kumar Dutta, Piotr Piecuch

The double ionization potential (DIP) equation-of-motion (EOM) coupled-cluster (CC) method with a full treatment of 4-hole-2-particle (4$h$-2$p$) correlations and triply excited clusters, abbreviated as DIP-EOMCCSDT(4$h$-2$p$), and its approximate form called DIP-EOMCCSD(T)(a)(4$h$-2$p$) have been formulated and implemented in the open-source CCpy package av

Dupati Srikar Chandra, P. K. Srijith, Dana Rezazadegan, Chris McCarthy

Continual learning allows the system to learn and adapt to new tasks while retaining the knowledge acquired from previous tasks. However, deep learning models suffer from catastrophic forgetting of knowledge learned from earlier tasks while learning a new task. Moreover, retraining large models like transformers from scratch for every new task is costly. An

Zhipeng Deng

We present a general solution and formulation framework to Bellman's lost-in-a-forest problem. The forest boundary is known and may take any shape. The starting point and the orientation are unspecified. We convert the problem into translation and rotation of the forest boundary. This transformation allows us to formulate this problem as a constrained minimi

Baljinder Singh Heera, Shrinivas Petale, Yatindra Nath Singh, Suresh Subramaniam

The implementation of 5G and the future deployment of 6G necessitate the utilization of optical networks that possess substantial capacity and exhibit minimal latency. The dynamic arrival and departure of connection requests in optical networks result in particular central links experiencing more traffic and congestion than non-central links. The occurrence

Youngwon Lee, Seung-won Hwang, Daniel Campos, Filip Graliński

Retrieval-augmented generation (RAG) has emerged as a popular approach to steering the output of a large language model (LLM) by incorporating retrieved contexts as inputs. However, existing work observed the generator bias, such that improving the retrieval results may negatively affect the outcome. In this work, we show such bias can be mitigated, from inf

Bohan Wu, Eli N. Weinstein, Sohrab Salehi, Yixin Wang

Parametric Bayesian modeling offers a powerful and flexible toolbox for machine learning. Yet the model, however detailed, may still be wrong, and this can make inferences untrustworthy. In this paper we introduce a new class of semiparametric corrections for parametric Bayesian models, when the target of inference is a functional of the true data distributi

Guanyu Nie, Vaneet Aggarwal, Christopher John Quinn

In this paper, we present the first sublinear $\alpha$-regret bounds for online $k$-submodular optimization problems with full-bandit feedback, where $\alpha$ is a corresponding offline approximation ratio. Specifically, we propose online algorithms for multiple $k$-submodular stochastic combinatorial multi-armed bandit problems, including (i) monotone funct

Deng Siqin, Zhou Xiaoyi

Vision Transformers (ViTs) have achieved record-breaking performance in various visual tasks. However, concerns about their robustness against backdoor attacks have grown. Backdoor attacks involve associating a specific trigger with a target label, causing the model to predict the attacker-specified label when the trigger is present, while correctly identify

Haoyu Jiang, Zhi-Qi Cheng, Gabriel Moreira, Jiawen Zhu

Universal Cross-Domain Retrieval (UCDR) retrieves relevant images from unseen domains and classes without semantic labels, ensuring robust generalization. Existing methods commonly employ prompt tuning with pre-trained vision-language models but are inherently limited by static prompts, reducing adaptability. We propose UCDR-Adapter, which enhances pre-train

Yusuke Akamatsu, Akinori F. Ebihara, Terumi Umematsu

Blood pressure (BP) measurement is crucial for daily health assessment. Remote photoplethysmography (rPPG), which extracts pulse waves from face videos captured by a camera, has the potential to enable convenient BP measurement without specialized medical devices. However, there are various uncertainties in BP estimation using rPPG, leading to limited estima

Vivek Gupta, Keith Bannister, Chris Flynn, Clancy James

Searches for impulsive, astrophysical transients are often highly computationally demanding. A notable example is the dedispersion process required for performing blind searches for Fast Radio Bursts (FRBs) in radio telescope data. We introduce a novel approach - Efficient Summation of Arbitrary Masks (ESAM) - which efficiently computes 1-D convolution of ma

Christo Aluckal, Roopesh Vinodh Kumar Lal, Sean Courtney, Yash Turkar

Developing excavation autonomy is challenging given the environments where excavators operate, the complexity of physical interaction and the degrees of freedom of operation of the excavator itself. Simulation is a useful tool to build parts of the autonomy without the complexity of experimentation. Traditional excavator simulators are geared towards high fi

Belle, Belle II Collaborations, :, I. Adachi

Using data samples of 983.0~$\rm fb^{-1}$ and 427.9~$\rm fb^{-1}$ accumulated with the Belle and Belle~II detectors operating at the KEKB and SuperKEKB asymmetric-energy $e^+e^-$ colliders, singly Cabibbo-suppressed decays $\Xi_c^{+} \to pK_{S}^{0}$, $\Xi_c^+ \to \Lambda \pi^+$, and $\Xi_c^+ \to \Sigma^{0} \pi^+$ are observed for the first time. The ratios o

Ling Yan, Pei Zhang, Yanli Wang, Zhennan Zhou

In this paper, we focus on efficiently and flexibly simulating the Fokker-Planck equation associated with the Nonlinear Noisy Leaky Integrate-and-Fire (NNLIF) model, which reflects the dynamic behavior of neuron networks. We apply the Galerkin spectral method to discretize the spatial domain by constructing a variational formulation that satisfies complex bo

Sukai Huang, Trevor Cohn, Nir Lipovetzky

The capability of Large Language Models (LLMs) to plan remains a topic of debate. Some critics argue that strategies to boost LLMs' reasoning skills are ineffective in planning tasks, while others report strong outcomes merely from training models on a planning corpus. This study reassesses recent strategies by developing an end-to-end LLM planner and employ

Chenghui Yu, Peiyi Li, Haoze Wu, Yiri Wen

Reducing negative user experiences is essential for the success of recommendation platforms. Exposing users to inappropriate content could not only adversely affect users' psychological well-beings, but also potentially drive users away from the platform, sabotaging the platform's long-term success. However, recommendation algorithms tend to weigh more heavi

Chi Zhang, Jiajun Song, Siyu Li, Yitao Liang

Mathematics olympiads are prestigious competitions, with problem proposing and solving highly honored. Building artificial intelligence that proposes and solves olympiads presents an unresolved challenge in automated theorem discovery and proving, especially in geometry for its combination of numerical and spatial elements. We introduce TongGeometry, a Eucli

Bimpe Ayoola, Miikka Kuutila, Rina R. Wehbe, Paul Ralph

Sustainable software development involves creating software in a manner that meets present goals without undermining our ability to meet future goals. In a software engineering context, sustainability has at least four dimensions: ecological, economic, social, and technical. No interventions for improving social sustainability in software engineering have be

Kiarash Banihashem, Diptarka Chakraborty, Shayan Chashm Jahan, Iman Gholami

Selecting representatives based on voters' preferences is a fundamental problem in social choice theory. While cardinal utility functions offer a detailed representation of preferences, ordinal rankings are often the only available information due to their simplicity and practical constraints. The metric distortion framework addresses this issue by modeling

Jiahe Ling

This study investigates the variability of the Central England Temperature (CET) series in relation to the North Atlantic Oscillation (NAO) and the Pacific Decadal Oscillation (PDO) using advanced time series modeling techniques. Leveraging the world's longest continuous instrumental temperature dataset (1723-2023), this research applies ARIMA and ARIMAX mod

Ashley Kline, Abirami Elangovan, Dominique Escandon, Scott Wade

The use of Unmanned Aerial Vehicles (UAVs) for aerial tasks and environmental manipulation is increasingly desired. This can be demonstrated via art tasks. This paper presents the development of Magnasketch, capable of translating image inputs into art on a magnetic drawing board via a Bitcraze Crazyflie 2.0 quadrotor. Optimal trajectories were generated usi

Renqiang Luo, Huafei Huang, Ivan Lee, Chengpei Xu

Recent studies have highlighted significant fairness issues in Graph Transformer (GT) models, particularly against subgroups defined by sensitive features. Additionally, GTs are computationally intensive and memory-demanding, limiting their application to large-scale graphs. Our experiments demonstrate that graph partitioning can enhance the fairness of GT m

Lee-Che Lin, Seng Ghee Tan, Ching-Ray Chang, Shih-Jye Sun

We propose a nested quantum dot structure for improved control of entanglement induced by the Heisenberg exchange between an electron and a qubit with relative motion. The entanglement is quantified by the mutual information (MI). The electron, initially prepared in the ground state, generally produces greater entanglement when excited to the scattering stat

Amir Kalev, Itay Hen

Quantum simulation is a foundational application for quantum computers, projected to offer insights into complex quantum systems beyond the reach of classical computation. However, with the exception of Trotter-based methods, which suffer from suboptimal scaling with respect to simulation precision, existing simulation techniques are, for the most part, too

Yanda Wu, Stefano Profumo

If a cosmological first-order phase transition occurs sufficiently slowly, delayed vacuum decay may lead to the formation of primordial black holes. Here we consider a simple model as a case study of how the abundance of the produced black holes depends on the model's input parameters. We demonstrate, using both numerical and analytical arguments and methods

Chandra Kundu, Abiy Tasissa, HanQin Cai

This paper addresses the problem of estimating the positions of points from distance measurements corrupted by sparse outliers. Specifically, we consider a setting with two types of nodes: anchor nodes, for which exact distances to each other are known, and target nodes, for which complete but corrupted distance measurements to the anchors are available. To

Jingyang Li, Kuangyu Ding, Kim-Chuan Toh, Pan Zhou

Preconditioned stochastic optimization algorithms, exemplified by Shampoo, outperform first-order optimizers by offering theoretical convergence benefits and practical gains in large-scale neural network training. However, they incur substantial memory overhead due to the storage demands of non-diagonal preconditioning matrices. To address this, we introduce

Kenneth Chan, Gary Charness, Chetan Dave, J. Lucas Reddinger

We experimentally investigate how confidence over multiple priors affects belief updating. Theory predicts that the average Bayesian posterior is unaffected by confidence over multiple priors if average priors are the same. We manipulate confidence by varying the time subjects view a black-and-white grid, the proportion representing the prior in a Bernoulli

Weinan Wang

This paper explores the possibility of the existence of dark photons within the visible light range and provides evidence for their existence through a thought experiment. A new model of dark photons is established based on extensive theoretical research, forming a comprehensive theory of dark photons. This theory provides a reasonable explanation for certai

Yunfei Hong, Junkai Deng, Yang Yang, Ri He

Ferroelectric domain structures, separated by domain walls, often display unconventional physics and hold significant potential for applications in nano-devices. Most naturally growth domain walls are charge-neutral to avoid increased electrostatic energy, while the intrinsically stable charged 180{\deg} domain walls in Bi monolayer challenged this conventio

Kaichen Xu, Qilong Wu, Yan Lu, Yinan Zheng

The detection of anomalous tissue regions (ATRs) within affected tissues is crucial in clinical diagnosis and pathological studies. Conventional automated ATR detection methods, primarily based on histology images alone, falter in cases where ATRs and normal tissues have subtle visual differences. The recent spatial transcriptomics (ST) technology profiles g

Jinzong Dong, Zhaohui Jiang, Dong Pan, Haoyang Yu

Confidence calibration of classification models is a technique to estimate the true posterior probability of the predicted class, which is critical for ensuring reliable decision-making in practical applications. Existing confidence calibration methods mostly use statistical techniques to estimate the calibration curve from data or fit a user-defined calibra

Lirong Wu, Haitao Lin, Yufei Huang, Zhangyang Gao

Antibodies are Y-shaped proteins that protect the host by binding to specific antigens, and their binding is mainly determined by the Complementary Determining Regions (CDRs) in the antibody. Despite the great progress made in CDR design, existing computational methods still encounter several challenges: 1) poor capability of modeling complex CDRs with long

Ashish Kumar, Jilaun Zhang, Saeid Tizpaz-Niari, Gang Tan

Despite the crucial need for formal safety and security verification of programs, discovering loop invariants remains a significant challenge. Static analysis is a primary technique for inferring loop invariants but often relies on substantial assumptions about underlying theories. Data-driven methods supported by dynamic analysis and machine learning algori