May 2024 arXiv papers — page 16

Zhihao Chang, Linzhu Yu, Huan Li, Sai Wu

Similarity search is a fundamental but expensive operator in querying trajectory data, due to its quadratic complexity of distance computation. To mitigate the computational burden for long trajectories, neural networks have been widely employed for similarity learning and each trajectory is encoded as a high-dimensional vector for similarity search with lin

Shi Mu, Chen Li, Xiang Li, Shunpan Liang

Existing works based on molecular knowledge neglect the 3D geometric structure of molecules and fail to learn the high-dimensional information of medications, leading to structural confusion. Additionally, it does not extract key substructures from a single patient visit, resulting in the failure to identify medication molecules suitable for the current pati

Hiroaki Sasaki

Identifiability of statistical models is a key notion in unsupervised representation learning. Recent work of nonlinear independent component analysis (ICA) employs auxiliary data and has established identifiable conditions. This paper proposes a statistical model of two latent vectors with single auxiliary data generalizing nonlinear ICA, and establishes va

Lindsey van der Aalst, Jan Bouwe van den Berg, Jean-Philippe Lessard

In the dynamics generated by the suspension bridge equation, traveling waves are an essential feature. The existing literature focuses primarily on the idealized one-dimensional case, while traveling structures in two spatial dimensions have only been studied via numerical simulations. We use computer-assisted proof methods based on a Newton-Kantorovich type

Muzhi Han, Yifeng Zhu, Song-Chun Zhu, Ying Nian Wu

Learning abstract state representations and knowledge is crucial for long-horizon robot planning. We present InterPreT, an LLM-powered framework for robots to learn symbolic predicates from language feedback of human non-experts during embodied interaction. The learned predicates provide relational abstractions of the environment state, facilitating the lear

Jacques Suray, Jan H. Klemmer, Juliane Schmüser, Sascha Fahl

Extending knowledge by identifying and investigating valuable research questions and problems is a core function of research. Research publications often suggest avenues for future work to extend and build upon their results. Considering these suggestions can contribute to developing research ideas that build upon previous work and produce results that tie i

Sungchul Hong, Seunghwan An, Jong-June Jeon

Recent advances in a generative neural network model extend the development of data augmentation methods. However, the augmentation methods based on the modern generative models fail to achieve notable performance for class imbalance data compared to the conventional model, Synthetic Minority Oversampling Technique (SMOTE). We investigate the problem of the

Shuxiong Zhang

Let $\{X_t\}_{t\geq 0 }$ be a $d$-dimensional supercritical super-Brownian motion started from the origin with branching mechanism $\psi$. Denote by $R_t:=\inf\{r>0:X_s(\{x\in \mathbb{R}^d:|x|\geq r\})=0,~\forall~0\leq s\leq t\}$ the radius of the minimal ball (centered at the origin) containing the range of $\{X_s\}_{s\geq 0 }$ up to time $t$. In \cite{Pins

Mohammed Abdulsalam Alsofiani

Building Information Modeling (BIM) is a crucial technology in the construction industry, offering benefits such as enhanced collaboration, real-time decision-making, and significant cost and time savings. Despite its advantages, BIM adoption faces numerous barriers. This study aims to create a reliable tool to assess the Rate of BIM Adoption (RBA), drawing

Marta Buetas Arcas, Richard Osuala, Karim Lekadir, Oliver Díaz

Artificial Intelligence (AI) has emerged as a valuable tool for assisting radiologists in breast cancer detection and diagnosis. However, the success of AI applications in this domain is restricted by the quantity and quality of available data, posing challenges due to limited and costly data annotation procedures that often lead to annotation shifts. This s

Nikolai G. Khlebtsov, Sergey V. Zarkov

Biomedical applications of plasmonic nanoparticle conjugates need control over their optical properties modulated by surface coating with stabilizing or targeting molecules often attached to or embedded in the secondary functionalization shell, such as silica. Although current numerical techniques can simulate the plasmonic response of such structures, it is

Yuchen He, Zichun Ye, Chihao Zhang

We study the stochastic multi-armed bandit problem in the $P$-pass streaming model. In this problem, the $n$ arms are present in a stream and at most $m<n$ arms and their statistics can be stored in the memory. We give a complete characterization of the optimal regret in terms of $m, n$ and $P$. Specifically, we design an algorithm with $\tilde O\left((n-m)^

Wenxuan Liu, Sai Qian Zhang

Diffusion Transformers (DiTs) have recently gained substantial attention in both industrial and academic fields for their superior visual generation capabilities, outperforming traditional diffusion models that use U-Net. However,the enhanced performance of DiTs also comes with high parameter counts and implementation costs, seriously restricting their use o

S. K. Kadam, Sameer Salunkhe, N. D. Vagshette, Surajit Paul

Spiral structures and cold fronts in X-rays are frequently observed in cool core galaxy clusters. However, studies on radio mini-haloes associated with such spirals and their physical connections are rare. Here, we present the detection of an extended diffuse radio emission entrained in the X-ray spiral structure in a known cool core cluster Abell 795 (A795)

Andrea Bacciu, Enrico Palumbo, Andreas Damianou, Nicola Tonellotto

Query recommendation systems are ubiquitous in modern search engines, assisting users in producing effective queries to meet their information needs. However, these systems require a large amount of data to produce good recommendations, such as a large collection of documents to index and query logs. In particular, query logs and user data are not available

Olivier Mousis, Thibault Cavalié, Jonathan I. Lunine, Kathleen E. Mandt

The exploration of carbon-to-oxygen ratios has yielded intriguing insights into the composition of close-in giant exoplanets, giving rise to a distinct classification: carbon-rich planets, characterized by a carbon-to-oxygen ratio $\ge$ 1 in their atmospheres, as opposed to giant planets exhibiting carbon-to-oxygen ratios close to the protosolar value. In co

Abhinav Agrawal, Justin Domke

Predictive posterior densities (PPDs) are of interest in approximate Bayesian inference. Typically, these are estimated by simple Monte Carlo (MC) averages using samples from the approximate posterior. We observe that the signal-to-noise ratio (SNR) of such estimators can be extremely low. An analysis for exact inference reveals SNR decays exponentially as t

Ron Keuth, Lasse Hansen, Maren Balks, Ronja Jäger

Purpose: Semantic segmentation and landmark detection are fundamental tasks of medical image processing, facilitating further analysis of anatomical objects. Although deep learning-based pixel-wise classification has set a new-state-of-the-art for segmentation, it falls short in landmark detection, a strength of shape-based approaches. Methods: In this work,

Boming Zhao, Yuan Li, Ziyu Sun, Lin Zeng

Forecasting future scenarios in dynamic environments is essential for intelligent decision-making and navigation, a challenge yet to be fully realized in computer vision and robotics. Traditional approaches like video prediction and novel-view synthesis either lack the ability to forecast from arbitrary viewpoints or to predict temporal dynamics. In this pap

Chong Li, Wen Yang, Jiajun Zhang, Jinliang Lu

Large language models respond well in high-resource languages like English but struggle in low-resource languages. It may arise from the lack of high-quality instruction following data in these languages. Directly translating English samples into these languages can be a solution but unreliable, leading to responses with translation errors and lacking langua

Hyemin Ahn

We hypothesize dance as a motion that forms a visual rhythm from music, where the visual rhythm can be perceived from an optical flow. If an agent can recognize the relationship between visual rhythm and music, it will be able to dance by generating a motion to create a visual rhythm that matches the music. Based on this, we propose a framework for any kind

Rukmini Dey, Anantadulal Paul, Rahul Kumar Singh

This paper establishes an interesting connection between the family of CMC surfaces of revolution in $\mathbb E_1^3$ and some specific families of elliptic curves. As a consequence of this connection, we show in the class of spacelike CMC surfaces of revolution in the $\mathbb E_1^3$, only spacelike cylinders and standard hyperboloids are algebraic. We also

Hans Bekaert, Wim Van Dooren, Hans Van Winckel, Markus Poessel

In the context of the European Erasmus+ project Teaching ASTronomy at the Educational level (TASTE), we investigated the extent to which a learning module at school and a set of activities during a planetarium visit help students to gain insight in the Apparent Motion of the Sun and Stars. Therefore, we have set up a two treatment study with a pretest postte

Jiatong Li, Renjun Hu, Kunzhe Huang, Yan Zhuang

Expert-designed close-ended benchmarks are indispensable in assessing the knowledge capacity of large language models (LLMs). Despite their widespread use, concerns have mounted regarding their reliability due to limited test scenarios and an unavoidable risk of data contamination. To rectify this, we present PertEval, a toolkit devised for in-depth probing

Turdebek N. Bekjan

Let $\mathcal{M}$ be a $\sigma$-finite von Neumann algebra, equipped with a normal faithful state $\varphi$, and let $\mathcal{A}$ be a maximal subdiagonal subalgebra of $\mathcal{M}$. We have proved that for $0< p<1$, $H^p(\mathcal{A})$ is independent of $\varphi$. Furthermore, in the case that $\mathcal{A}$ is a type 1 subdiagonal subalgebra, we have exten

Yuzhou Fang, Chao Zhang

This paper is devoted to studying the weak Harnack inequalities for nonlocal double phase functionals by using expansion of positivity, whose prototype is $$ \iint_{\mathbb{R}^n\times\mathbb{R}^n} \left(\frac{|u(x)-u(y)|^p}{|x-y|^{n+sp}}+a(x,y)\frac{|u(x)-u(y)|^q}{|x-y|^{n+tq}}\right) \,dxdy $$ with $a\ge0$ and $0<s\le t<1<p\le q$. The core of our approach i

Chengwei Dai, Kun Li, Wei Zhou, Songlin Hu

As Large Language Models (LLMs) scale up and gain powerful Chain-of-Thoughts (CoTs) reasoning abilities, practical resource constraints drive efforts to distill these capabilities into more compact Smaller Language Models (SLMs). We find that CoTs consist mainly of simple reasoning forms, with a small proportion ($\approx 4.7\%$) of key reasoning steps that

Dayang Liang, Jinyang Lai, Yunlong Liu

How to improve the ability of scene representation is a key issue in vision-oriented decision-making applications, and current approaches usually learn task-relevant state representations within visual reinforcement learning to address this problem. While prior work typically introduces one-step behavioral similarity metrics with elements (e.g., rewards and

Yong-Qiang Mao, Hanbo Bi, Xuexue Li, Kaiqiang Chen

Thanks to the application of deep learning technology in point cloud processing of the remote sensing field, point cloud segmentation has become a research hotspot in recent years, which can be applied to real-world 3D, smart cities, and other fields. Although existing solutions have made unprecedented progress, they ignore the inherent characteristics of po

Belle, Belle II Collaborations, :, I. Adachi

We report the result of a search for the rare decay $B^{0} \to \gamma \gamma$ using a combined dataset of $753\times10^{6}$ $B\bar{B}$ pairs collected by the Belle experiment and $387\times10^{6}$ $B\bar{B}$ pairs collected by the Belle II experiment from decays of the $\rm \Upsilon(4S)$ resonance produced in $e^{+}e^{-}$ collisions. A simultaneous fit to th

Han Feng, Wenchao Ma, Quankai Gao, Xianwei Zheng

Estimating 3D full-body avatars from AR/VR devices is essential for creating immersive experiences in AR/VR applications. This task is challenging due to the limited input from Head Mounted Devices, which capture only sparse observations from the head and hands. Predicting the full-body avatars, particularly the lower body, from these sparse observations pre

Anagh Venneti, Sakshi Gautam, Sarmistha Banik, B. K. Agrawal

We obtain posterior distribution of equations of state (EOSs) across a broad range of density by imposing explicitly the constraints from precisely measured fundamental properties of finite nuclei, in combination with the experimental data from heavy-ion collisions and the astrophysical observations of radius, tidal deformability and minimum-maximum mass of

Zixian Guo, Ming Liu, Zhilong Ji, Jinfeng Bai

Mastering a skill generally relies on both hands-on experience from doers and insightful, high-level guidance by mentors. Will this strategy also work well for solving complex non-convex optimization problems? Here, a common gradient-based optimizer acts like a disciplined doer, making locally optimal updates at each step. Large Language Models (LLMs) can al

Yuqing Xiong

This paper provides some new approaches to MPI implementations to improve MPI performance. These approaches include dynamically composable libraries, reducing average layer numbers of MPI libraries, and a single entity of MPI-network, MPI-protocol, and MPI.

Yi-Ning Zhao, Lin-Shan Chen, Lingxin Kong, Chong Wang

By forming measurement matrices with the Kronecker product of two random matrices, image encryption in computational ghost imaging is investigated. The two-dimensional images are conveniently reconstructed with the pseudo-inverse matrices of the two random matrices. To suppress the noise, the method of truncated singular value decomposition can be applied to

Shaohua Wang, Xing Xie, Yong Li, Danhuai Guo

This report focuses on spatial data intelligent large models, delving into the principles, methods, and cutting-edge applications of these models. It provides an in-depth discussion on the definition, development history, current status, and trends of spatial data intelligent large models, as well as the challenges they face. The report systematically elucid

Yutong Chen, Jiandong Gao, Ji Wu

In this paper, we investigate dynamic feature selection within multivariate time-series scenario, a common occurrence in clinical prediction monitoring where each feature corresponds to a bio-test result. Many existing feature selection methods fall short in effectively leveraging time-series information, primarily because they are designed for static data.

Xin-Qi Luo, Wei Xia

Let $p$ be an odd prime. For any $b,c\in\mathbb{Z}$, Z.-W. Sun introduced the new-type determinant $$D_p(b,c)=|(i^2+bij+cj^2)^{p-2}|_{1\leqslant i,j\leqslant p-1},$$ and studied its arithmetic properties. In this paper we mainly prove that $$\left(\frac{D_p(b,1)}{p}\right)=\left(\frac{2b}{p}\right)$$ when $(\frac{b^2-4}{p})=-1$ and $p\equiv1\pmod 4$. As an a

Koki Endo, Shuhei Tsuchida, Tsukasa Fukusato, Takeo Igarashi

Segmenting dance video into short movements is a popular way to easily understand dance choreography. However, it is currently done manually and requires a significant amount of effort by experts. That is, even if many dance videos are available on social media (e.g., TikTok and YouTube), it remains difficult for people, especially novices, to casually watch

Feng Chen, Zhen Yang, Bohan Zhuang, Qi Wu

We present a novel task called online video editing, which is designed to edit \textbf{streaming} frames while maintaining temporal consistency. Unlike existing offline video editing assuming all frames are pre-established and accessible, online video editing is tailored to real-life applications such as live streaming and online chat, requiring (1) fast con

Xuan-Bac Nguyen, Hoang-Quan Nguyen, Hugh Churchill, Samee U. Khan

Although quantum machine learning has been introduced for a while, its applications in computer vision are still limited. This paper, therefore, revisits the quantum visual encoding strategies, the initial step in quantum machine learning. Investigating the root cause, we uncover that the existing quantum encoding design fails to ensure information preservat

Jianchun Chu, Man-Chun Lee, Jintian Zhu

The classical Llarull theorem states that a smooth metric on $n$-sphere cannot have scalar curvature no less than $n(n-1)$ and dominate the standard spherical metric at the same time unless it is the standard spherical metric. In this work, we prove that Llarull's rigidity theorem holds for $L^{\infty}$ metrics on spheres with finitely many points punctured.

Thong Thanh Nguyen, Zhiyuan Hu, Xiaobao Wu, Cong-Duy T Nguyen

Seeking answers effectively for long videos is essential to build video question answering (videoQA) systems. Previous methods adaptively select frames and regions from long videos to save computations. However, this fails to reason over the whole sequence of video, leading to sub-optimal performance. To address this problem, we introduce a state space layer

Xuan-Bac Nguyen, Hoang-Quan Nguyen, Samuel Yen-Chi Chen, Samee U. Khan

Unsupervised vision clustering, a cornerstone in computer vision, has been studied for decades, yielding significant outcomes across numerous vision tasks. However, these algorithms involve substantial computational demands when confronted with vast amounts of unlabeled data. Conversely, quantum computing holds promise in expediting unsupervised algorithms w

Yuan Tao, Huifu Xu

Bayesian game is a strategic decision-making model where each player's type parameter characterizing its own objective is private information: each player knows its own type but not its rivals' types, and Bayesian Nash equilibrium (BNE) is an outcome of this game where each player makes a strategic optimal decision according to its own type under the Nash co

Ashok Mondal

A 2D dynamic model is utilized to investigate star formation in rotating filamentary molecular clouds (FMCs) amidst magnetic fields. The study reveals that the emergence of field stars is possible under both weak and strong magnetic fields due to the presence of low-density structures. The presence of a strong rotation in a strongly magnetized FMC Plays a cr

Jared Marx-Kuo

We pose the isospectral problem for the $p$-widths: Is a riemannian manifold $(M^n, g)$ uniquely determined by its $p$-widths, $\{\omega_p(M,g)\}_{p=1}^{\infty}$? We construct many counterexamples on $S^2$ using Zoll metrics and the fact that geodesic $p$-widths are given by unions of immersed geodesics.

Yuxing Duan, Shihan Peng, Lin Zhu, Wei Zhang

Event camera has significant advantages in capturing dynamic scene information while being prone to noise interference, particularly in challenging conditions like low threshold and low illumination. However, most existing research focuses on gentle situations, hindering event camera applications in realistic complex scenarios. To tackle this limitation and

Longwen Zhang, Ziyu Wang, Qixuan Zhang, Qiwei Qiu

In the realm of digital creativity, our potential to craft intricate 3D worlds from imagination is often hampered by the limitations of existing digital tools, which demand extensive expertise and efforts. To narrow this disparity, we introduce CLAY, a 3D geometry and material generator designed to effortlessly transform human imagination into intricate 3D d

Henry Liu

An edge-coloured cycle is rainbow if the edges have distinct colours. Let $G$ be a graph such that any $k$ vertices lie in a cycle of $G$. The $k$-rainbow cycle index of $G$, denoted by $crx_k(G)$, is the minimum number of colours required to colour the edges of $G$ such that, for every set $S$ of $k$ vertices in $G$, there exists a rainbow cycle in $G$ cont

Yihe Deng, Pan Lu, Fan Yin, Ziniu Hu

Large vision language models (LVLMs) integrate large language models (LLMs) with pre-trained vision encoders, thereby activating the perception capability of the model to understand image inputs for different queries and conduct subsequent reasoning. Improving this capability requires high-quality vision-language data, which is costly and labor-intensive to

Z. Moldabekov, J. Vorberger, T. Dornheim

Energy functionals serve as the basis for different models and methods in quantum and classical many-particle physics. Arguably, one of the most successful and widely used approaches in material science at both ambient and extreme conditions is density functional theory (DFT). Various flavors of DFT methods are being actively used to study material propertie

Kaixuan Huang, Xudong Guo, Mengdi Wang

Speculative decoding reduces the inference latency of a target large language model via utilizing a smaller and faster draft model. Its performance depends on a hyperparameter K -- the candidate length, i.e., the number of candidate tokens for the target model to verify in each round. However, previous methods often use simple heuristics to choose K, which m

S. Alekhin, S. Amoroso, L. Buonocore, A. Huss

We compute differential distributions for Drell-Yan processes at the LHC and the Tevatron colliders at next-to-next-to-leading order in perturbative QCD, including fiducial cuts on the decay leptons in the final state. The comparison of predictions obtained with four different codes shows excellent agreement, once linear power corrections from the fiducial c

Rongbiao Wang, JungHo Lee, Lek-Heng Lim

We extend several celebrated methods in classical analysis for summing series of complex numbers to series of complex matrices. These include the summation methods of Abel, Borel, Ces\'aro, Euler, Lambert, N\"orlund, and Mittag-Leffler, which are frequently used to sum scalar series that are divergent in the conventional sense. One feature of our matrix exte

Alessandro Sanvito, Andrea Ramazzina, Stefanie Walz, Mario Bijelic

No augmented application is possible without animated humanoid avatars. At the same time, generating human replicas from real-world monocular hand-held or robotic sensor setups is challenging due to the limited availability of views. Previous work showed the feasibility of virtual avatars but required the presence of 360 degree views of the targeted subject.

Fenghao Dong, Yang He, Yutong Liang, Zirui Liu

The challenge of estimating similarity between sets has been a significant concern in data science, finding diverse applications across various domains. However, previous approaches, such as MinHash, have predominantly centered around hashing techniques, which are well-suited for sets but less naturally adaptable to multisets, a common occurrence in scenario

Cailing Chen, Zheng Zheng, Chao-Wei Tsai, Sihan Jiao

Recent sub-millimeter dust thermal emission observations have unveiled a significant number of inter-arm massive molecular clouds in M31.However,the effectiveness of this technique is limited to its sensitivity,making it challenging to study more distant galaxies.This study introduces an alternative approach,utilizing optical extinctions derived from space-b

Yujia Liu, Tong Bu, Jianhao Ding, Zecheng Hao

Spiking Neural Networks (SNNs) have attracted great attention for their energy-efficient operations and biologically inspired structures, offering potential advantages over Artificial Neural Networks (ANNs) in terms of energy efficiency and interpretability. Nonetheless, similar to ANNs, the robustness of SNNs remains a challenge, especially when facing adve

Woorak Choi, Martin Bureau, Lijie Liu, Michele Cappellari

NGC~613 is a nearby barred spiral galaxy with a nuclear ring. Exploiting high spatial resolution ($\approx20$ pc) Atacama Large Millimeter/sub-millimeter Array $^{12}$CO(1-0) observations, we study the giant molecular clouds (GMCs) in the nuclear ring and its vicinity, identifying $158$ spatially- and spectrally-resolved GMCs. The GMC sizes ($R_{\mathrm{c}}$

Jia Li, Lijie Hu, Zhixian He, Jingfeng Zhang

With the advancement of image-to-image diffusion models guided by text, significant progress has been made in image editing. However, a persistent challenge remains in seamlessly incorporating objects into images based on textual instructions, without relying on extra user-provided guidance. Text and images are inherently distinct modalities, bringing out di

Haoxing Chen, Yan Hong, Zizheng Huang, Zhuoer Xu

Recently, video generation techniques have advanced rapidly. Given the popularity of video content on social media platforms, these models intensify concerns about the spread of fake information. Therefore, there is a growing demand for detectors capable of distinguishing between fake AI-generated videos and mitigating the potential harm caused by fake infor

Amarnath Gupta, Shweta Purawat, Subhasis Dasgupta, Pratyush Karmakar

Experimental materials science is experiencing significant growth due to automated experimentation and AI techniques. Integrated autonomous platforms are emerging, combining generative models, robotics, simulations, and automated systems for material synthesis. However, two major challenges remain: democratizing access to these technologies and creating acce

Wenhao Yang, Yibo Wang, Peng Zhao, Lijun Zhang

To address the uncertainty in function types, recent progress in online convex optimization (OCO) has spurred the development of universal algorithms that simultaneously attain minimax rates for multiple types of convex functions. However, for a $T$-round online problem, state-of-the-art methods typically conduct $O(\log T)$ projections onto the domain in ea

Seungbeom Hong, Ilmun Kim, Jun Song

In this work, we develop a new theory and method for sufficient dimension reduction (SDR) in single-index models, where SDR is a sub-field of supervised dimension reduction based on conditional independence. Our work is primarily motivated by the recent introduction of the Hellinger correlation as a dependency measure. Utilizing this measure, we develop a me

Duhun Hwang, Suhyun Kang, Moonjung Eo, Jimyeong Kim

The objective of Domain Generalization (DG) is to devise algorithms and models capable of achieving high performance on previously unseen test distributions. In the pursuit of this objective, average measure has been employed as the prevalent measure for evaluating models and comparing algorithms in the existing DG studies. Despite its significance, a compre

Hyungryul Baik, Junseok Kim

We study the acylindrical hyperbolicity of the outer automorphism group of a right-angled Artin group $A_\Gamma$. When the defining graph $\Gamma$ has no SIL-pair (separating intersection of links), we obtain a necessary and sufficient condition for $\mathrm{Out}(A_\Gamma)$ to be acylindrically hyperbolic. As a corollary, if $\Gamma$ is a random connected gr

Lavanya Prahallad, Radhika Mamidi

Gender bias in machine translation (MT) sys- tems poses a significant challenge to achieving accurate and inclusive translations. This paper examines gender bias in machine translation systems for languages such as Telugu and Kan- nada from the Dravidian family, analyzing how gender inflections affect translation accuracy and neutrality using Google Translat

SNO+ Collaboration, :, A. Allega, M. R. Anderson

The SNO+ collaboration reports its first spectral analysis of long-baseline reactor antineutrino oscillation using 114 tonne-years of data. Fitting the neutrino oscillation probability to the observed energy spectrum yields constraints on the neutrino mass-squared difference $\Delta m^2_{21}$. In the ranges allowed by previous measurements, the best-fit $\De

Dena F. Mujtaba, Nihar R. Mahapatra

The recruitment process significantly impacts an organization's performance, productivity, and culture. Traditionally, human resource experts and industrial-organizational psychologists have developed systematic hiring methods, including job advertising, candidate skill assessments, and structured interviews to ensure candidate-organization fit. Recently, re

Raj Kumar Nayak

In this article, we establish an improvement of the Cauchy-Schwarz inequality. Let $x, y \in \mathcal{H},$ and let $f: (0,1) \rightarrow \mathbb{R}^+$ be a well-defined function, where $\mathbb{R}^+$ denote the set of all positive real numbers. Then \[|\langle x, y \rangle|^2 \leq \frac{f(t)}{1+f(t)} \|x\|^2 \|y\|^2 + \frac{1}{1+ f(t)} |\langle x, y \rangle

Yan Yang, Bin Gao, Ya-xiang Yuan

Bilevel reinforcement learning (RL), which features intertwined two-level problems, has attracted growing interest recently. The inherent non-convexity of the lower-level RL problem is, however, to be an impediment to developing bilevel optimization methods. By employing the fixed point equation associated with the regularized RL, we characterize the hyper-g

Heide Narnhofer

We study the behaviour of continuous automorphism groups of quantum spin systems on the lattice. Whereas the shift is norm asymptotically abelian continuous automorphism groups can lead only to delocalization but not to norm asymptotic abelianess. As a consequence the shift does not allow a continuous extension.

Qizao Wang, Xuelin Qian, Bin Li, Xiangyang Xue

In real-world scenarios, person Re-IDentification (Re-ID) systems need to be adaptable to changes in space and time. Therefore, the adaptation of Re-ID models to new domains while preserving previously acquired knowledge is crucial, known as Lifelong person Re-IDentification (LReID). Advanced LReID methods rely on replaying exemplars from old domains and app

Wenjing Xie, Juxin Niu, Chun Jason Xue, Nan Guan

While large language models (LLMs) have been used for automated grading, they have not yet achieved the same level of performance as humans, especially when it comes to grading complex questions. Existing research on this topic focuses on a particular step in the grading procedure: grading using predefined rubrics. However, grading is a multifaceted procedur

Ashu Kushwaha, Teruaki Suyama

The presence of magnetic fields in the early universe affects the cosmological processes, leading to the distinct signature, which allows constraining their properties and the genesis mechanisms. In this study, we revisit the method to constrain the amplitude of the magnetic fields on small scales in the radiation-dominated era from the abundance of primordi

Xiao-Yong Jin

We construct neural networks that work for any Lie group and maintain gauge covariance, enabling smooth, invertible gauge field transformations. We implement these transformations for 4D SU(3) lattice gauge fields and explore their use in HMC. We focus on developing loss functions and optimizing the transformations. We show the effects on HMC's molecular dyn

Minsun Kim, SeonGyeom Kim, Suyoun Lee, Yoosang Yoon

While ChatGPT has significantly impacted education by offering personalized resources for students, its integration into educational settings poses unprecedented risks, such as inaccuracies and biases in AI-generated content, plagiarism and over-reliance on AI, and privacy and security issues. To help teachers address such risks, we conducted a two-phase ite

Tianyu Chen, Zhendong Wang, Mingyuan Zhou

Offline reinforcement learning (RL) leverages pre-collected datasets to train optimal policies. Diffusion Q-Learning (DQL), introducing diffusion models as a powerful and expressive policy class, significantly boosts the performance of offline RL. However, its reliance on iterative denoising sampling to generate actions slows down both training and inference

Xuan Wu, Hongxiang Li, Yuanjiang Luo, Xuxin Cheng

Sign language video retrieval plays a key role in facilitating information access for the deaf community. Despite significant advances in video-text retrieval, the complexity and inherent uncertainty of sign language preclude the direct application of these techniques. Previous methods achieve the mapping between sign language video and text through fine-gra

Haitao Cao, Baoping Cheng, Qiran Pu, Haocheng Zhang

Parametric 3D models have enabled a wide variety of computer vision and graphics tasks, such as modeling human faces, bodies and hands. In 3D face modeling, 3DMM is the most widely used parametric model, but can't generate fine geometric details solely from identity and expression inputs. To tackle this limitation, we propose a neural parametric model named

Rui-Jie Zhu, Ziqing Wang, Leilani Gilpin, Jason K. Eshraghian

Autonomous driving demands an integrated approach that encompasses perception, prediction, and planning, all while operating under strict energy constraints to enhance scalability and environmental sustainability. We present Spiking Autonomous Driving (SAD), the first unified Spiking Neural Network (SNN) to address the energy challenges faced by autonomous d

Jingwei Sun, Zhixu Du, Yiran Chen

Large language models (LLMs) have demonstrated remarkable proficiency in a range of natural language processing tasks. Once deployed, LLMs encounter users with personalized factual knowledge, and such personalized knowledge is consistently reflected through users' interactions with the LLMs. To enhance user experience, real-time model personalization is esse

Xiaohui Zhang, Eric C Landsness, Lindsey M Brier, Wei Chen

Wide-field calcium imaging (WFCI) that records neural calcium dynamics allows for identification of functional brain networks (FBNs) in mice that express genetically encoded calcium indicators. Estimating FBNs from WFCI data is commonly achieved by use of seed-based correlation (SBC) analysis and independent component analysis (ICA). These two methods are co

Xiaofeng Cong, Yu Zhao, Jie Gui, Junming Hou

Underwater image enhancement (UIE) presents a significant challenge within computer vision research. Despite the development of numerous UIE algorithms, a thorough and systematic review is still absent. To foster future advancements, we provide a detailed overview of the UIE task from several perspectives. Firstly, we introduce the physical models, data cons

Jimmy Dani, Kalyan Nakka, Nitesh Saxena

Indistinguishability is a fundamental principle of cryptographic security, crucial for securing data transmitted between Internet of Things (IoT) devices. This principle ensures that an attacker cannot distinguish between the encrypted data, also known as ciphertext, and random data or the ciphertexts of the two messages encrypted with the same key. This res

Hongbin Lin, Yifan Zhang, Shuaicheng Niu, Shuguang Cui

Monocular 3D object detection (Mono 3Det) aims to identify 3D objects from a single RGB image. However, existing methods often assume training and test data follow the same distribution, which may not hold in real-world test scenarios. To address the out-of-distribution (OOD) problems, we explore a new adaptation paradigm for Mono 3Det, termed Fully Test-tim

Matt Jones, Peter Chang, Kevin Murphy

We propose a novel approach to sequential Bayesian inference based on variational Bayes (VB). The key insight is that, in the online setting, we do not need to add the KL term to regularize to the prior (which comes from the posterior at the previous timestep); instead we can optimize just the expected log-likelihood, performing a single step of natural grad

Xiaomin Chen, Chuan Li

Coronal mass ejections (CMEs) drive powerful shocks and thereby accelerate solar energetic particles (SEPs) as they propagate from the corona into interplanetary space. Here we present the processes of three-stage particle acceleration by a CME-driven shock detected by the in situ spacecraft--Parker Solar Probe (PSP) on 2022 August 27. The onset of SEPs is p

Amartya Banerjee, Harlin Lee, Nir Sharon, Caroline Moosmüller

Capturing data from dynamic processes through cross-sectional measurements is seen in many fields, such as computational biology. Trajectory inference deals with the challenge of reconstructing continuous processes from such observations. In this work, we propose methods for B-spline approximation and interpolation of point clouds through consecutive averagi

Haodi He, Colton Stearns, Adam W. Harley, Leonidas J. Guibas

Large-scale vision foundation models such as Segment Anything (SAM) demonstrate impressive performance in zero-shot image segmentation at multiple levels of granularity. However, these zero-shot predictions are rarely 3D-consistent. As the camera viewpoint changes in a scene, so do the segmentation predictions, as well as the characterizations of "coarse" or

Matthew Avaylon, Ryan Ly, Andrew Tritt, Benjamin Dichter

Across many domains, large swaths of digital assets are being stored across distributed data repositories, e.g., the DANDI Archive [8]. The distribution and diversity of these repositories impede researchers from formally defining terminology within experiments, integrating information across datasets, and easily querying, reusing, and analyzing data that fo

Zhaoxi Zhang, Xiaomei Zhang, Yanjun Zhang, Leo Yu Zhang

The Large Language Model (LLM) watermark is a newly emerging technique that shows promise in addressing concerns surrounding LLM copyright, monitoring AI-generated text, and preventing its misuse. The LLM watermark scheme commonly includes generating secret keys to partition the vocabulary into green and red lists, applying a perturbation to the logits of to

Fei Peng

Building on Olander's work on algebraic spaces, we prove Orlov's representability theorem relating fully faithful functors and Fourier--Mukai transforms between the bounded derived category of coherent sheaves to the case of smooth, proper, and tame algebraic stacks. This extends previous results of Kawamata for Deligne--Mumford stacks with generically trivi

Aisha Urooj Khan, John Garrett, Tyler Bradshaw, Lonie Salkowski

A visual-language model (VLM) pre-trained on natural images and text pairs poses a significant barrier when applied to medical contexts due to domain shift. Yet, adapting or fine-tuning these VLMs for medical use presents considerable hurdles, including domain misalignment, limited access to extensive datasets, and high-class imbalances. Hence, there is a pr

Feng Shao, Dongyi Wei, Zhifei Zhang

In this paper, we consider the defocusing nonlinear wave equation $-\partial_t^2u+\Delta u=|u|^{p-1}u$ in $\mathbb R\times \mathbb R^d$. Building on our companion work ({\it \small Self-similar imploding solutions of the relativistic Euler equations}), we prove that for $d=4, p\geq 29$ and $d\geq 5, p\geq 17$, there exists a smooth complex-valued solution th

Masatoshi Uehara, Yulai Zhao, Ehsan Hajiramezanali, Gabriele Scalia

AI-driven design problems, such as DNA/protein sequence design, are commonly tackled from two angles: generative modeling, which efficiently captures the feasible design space (e.g., natural images or biological sequences), and model-based optimization, which utilizes reward models for extrapolation. To combine the strengths of both approaches, we adopt a hy

Ankush Gajanan Arudkar, Bernard J. E. Evans

Accurate detection of colorectal cancer and early prevention heavily rely on precise polyp identification during gastrointestinal colonoscopy. Due to limited data, many current state-of-the-art deep learning methods for polyp segmentation often rely on post-processing of masks to reduce noise and enhance results. In this study, we propose an approach that in

Haodong Xiang, Xinghui Li, Kai Cheng, Xiansong Lai

Embodied intelligence requires precise reconstruction and rendering to simulate large-scale real-world data. Although 3D Gaussian Splatting (3DGS) has recently demonstrated high-quality results with real-time performance, it still faces challenges in indoor scenes with large, textureless regions, resulting in incomplete and noisy reconstructions due to poor

Yutao Zhu, Zhaoheng Huang, Zhicheng Dou, Ji-Rong Wen

Retrieval-augmented generation (RAG) is a promising way to improve large language models (LLMs) for generating more factual, accurate, and up-to-date content. Existing methods either optimize prompts to guide LLMs in leveraging retrieved information or directly fine-tune LLMs to adapt to RAG scenarios. Although fine-tuning can yield better performance, it of