October 2023 arXiv papers — page 112

Yueqi Ma, Minjie Chen, Xiang Li

While generating better negative samples for contrastive learning has been widely studied in the areas of CV and NLP, very few work has focused on graph-structured data. Recently, Mixup has been introduced to synthesize hard negative samples in graph contrastive learning (GCL). However, due to the unsupervised learning nature of GCL, without the help of soft

Henri Lombardi, Claude Quitté

The Traverso-Swan theorem says that a reduced ring A is seminormal if and only if the natural morphism from Pic(A) to Pic(A[X]) is an isomorphism. We give here all the details needed to understand the elementary constructive proof for this result given by Thierry Coquand in the paper: On seminormality. J. Algebra 305, no. 1-3, 577-584, (2006). In this new ve

Boan Liu, Liang Ding, Li Shen, Keqin Peng

The Mixture of Experts (MoE) has emerged as a highly successful technique in deep learning, based on the principle of divide-and-conquer to maximize model capacity without significant additional computational cost. Even in the era of large-scale language models (LLMs), MoE continues to play a crucial role, as some researchers have indicated that GPT-4 adopts

Yulei Qin, Xingyu Chen, Yunhang Shen, Chaoyou Fu

Webly supervised learning has attracted increasing attention for its effectiveness in exploring publicly accessible data at scale without manual annotation. However, most existing methods of learning with web datasets are faced with challenges from label noise, and they have limited assumptions on clean samples under various noise. For instance, web images r

Wangyu Wu, Tianhong Dai, Xiaowei Huang, Fei Ma

Weakly-supervised semantic segmentation (WSSS), which aims to train segmentation models solely using image-level labels, has achieved significant attention. Existing methods primarily focus on generating high-quality pseudo labels using available images and their image-level labels. However, the quality of pseudo labels degrades significantly when the size o

Youngtack Oh, Minseok Seo, Doyi Kim, Junghoon Seo

Climate change has led to an increased frequency of natural disasters such as floods and cyclones. This emphasizes the importance of effective disaster monitoring. In response, the remote sensing community has explored change detection methods. These methods are primarily categorized into supervised techniques, which yield precise results but come with high

Qing-lin Dong, Xiang-ying Xing

The origin of organelles (mitochondrion, chloroplast and nucleus) remains enigmatic. The endosymbiotic hypothesis that chloroplasts, mitochondria and nuclei descend from the endosymbiotic cyanobacterium, bacterium and archaebacterium respectively is dominant yet uncompelling, while our discovery of de novo organelle biogenesis in the cyanobacterium TDX16 tha

David C. Jeong, Tianma Shen, Hongji Liu, Raghav Kapoor

Emotion detection presents challenges to intelligent human-robot interaction (HRI). Foundational deep learning techniques used in emotion detection are limited by information-constrained datasets or models that lack the necessary complexity to learn interactions between input data elements, such as the the variance of human emotions across different contexts

Jinwoo Seo, Soora Choi, Eungyeom Ha, Beomjune Kim

The face classification system is an important tool for recognizing personal identity properly. This paper introduces a new Large-Scale Korean Influencer Dataset named KoIn. Our presented dataset contains many real-world photos of Korean celebrities in various environments that might contain stage lighting, backup dancers, and background objects. These vario

Sumedh Rasal, Sanjay Kumar Boddhu

This paper introduces an innovative approach to road network generation through the utilization of a multi-modal Large Language Model (LLM). Our model is specifically designed to process aerial images of road layouts and produce detailed, navigable road networks within the input images. The core innovation of our system lies in the unique training methodolog

Huanhuan Ma, Weizhi Xu, Yifan Wei, Liuji Chen

Fact verification aims to automatically probe the veracity of a claim based on several pieces of evidence. Existing works are always engaging in accuracy improvement, let alone explainability, a critical capability of fact verification systems. Constructing an explainable fact verification system in a complex multi-hop scenario is consistently impeded by the

Enric Boix-Adsera, Omid Saremi, Emmanuel Abbe, Samy Bengio

We investigate the capabilities of transformer models on relational reasoning tasks. In these tasks, models are trained on a set of strings encoding abstract relations, and are then tested out-of-distribution on data that contains symbols that did not appear in the training dataset. We prove that for any relational reasoning task in a large family of tasks,

Marko Sterbentz, Cameron Barrie, Donna Hooshmand, Shubham Shahi

The principal goal of data science is to derive meaningful information from data. To do this, data scientists develop a space of analytic possibilities and from it reach their information goals by using their knowledge of the domain, the available data, the operations that can be performed on those data, the algorithms/models that are fed the data, and how a

Mitsuo Higaki, Ryoma Horiuchi

We consider the three-dimensional steady Navier-Stokes system in the exterior of an infinite cylinder under the action of an external force. We construct solutions in the class of vertically uniform flows which vanish at horizontal infinity. More precisely, for a boundary datum determined by a rotating flow and a suction flow, and for a small force of the fo

Xu Liu, Junfeng Hu, Yuan Li, Shizhe Diao

Multivariate time series forecasting plays a pivotal role in contemporary web technologies. In contrast to conventional methods that involve creating dedicated models for specific time series application domains, this research advocates for a unified model paradigm that transcends domain boundaries. However, learning an effective cross-domain model presents

Hideyuki Miura, Jin Takahashi

We address the critical norm blow-up problem for the nonlinear heat equation $u_t-\Delta u=|u|^{p-1}u$ in $\mathbf{R}^n\times(0,T)$. In the supercritical range $p>(n+2)/(n-2)$, we prove that if the maximal existence time $T$ is finite, then $\lim_{t\to T}\|u(\cdot,t)\|_{L^{n(p-1)/2}(\mathbf{R}^n)} =\infty$ without assuming extra conditions such as radial sym

Yifeng Xiong, Fan Liu, Kai Wan, Weijie Yuan

Sensing and communications (S&C) have been historically developed in parallel. In recent decade, they have been evolving from separation to integration, giving rise to the integrated sensing and communications (ISAC) paradigm, that has been recognized as one of the six key 6G usage scenarios. Despite the plethora of research works dedicated to ISAC signal pr

Jia Li, Ge Li, Chongyang Tao, Jia Li

Large language models (LLMs) have shown impressive in-context learning (ICL) ability in code generation. LLMs take a prompt consisting of requirement-code examples and a new requirement as input, and output new programs. Existing studies have found that ICL is highly dominated by the examples and thus arises research on example selection. However, existing a

Dianbo Ma, Jianqiang Xiao, Ziyan Gao, Satoshi Yamane

In this work, we propose a novel staged depthwise correlation and feature fusion network, named DCFFNet, to further optimize the feature extraction for visual tracking. We build our deep tracker upon a siamese network architecture, which is offline trained from scratch on multiple large-scale datasets in an end-to-end manner. The model contains a core compon

Yuxing Cheng, Jianzhong Lu, Min Li, Xing Wu

In this paper, we focus on zero-filter limit problem for the Camassa-Holm equation in the more general Besov spaces. We prove that the solution of the Camassa-Holm equation converges strongly in $L^\infty(0,T;B^s_{2,r}(\R))$ to the inviscid Burgers equation as the filter parameter $\alpha$ tends to zero with the given initial data $u_0\in B^s_{2,r}(\R)$. Mor

Ishmeet Matharoo

This research paper presents a thorough economic analysis of Bitcoin and its impact. We delve into fundamental principles, and technological evolution into a prominent decentralized digital currency. Analysing Bitcoin's economic dynamics, we explore aspects such as transaction volume, market capitalization, mining activities, and macro trends. Moreover, we i

Ziqiang Li, Pengfei Xia, Hong Sun, Yueqi Zeng

As the number of parameters in Deep Neural Networks (DNNs) scales, the thirst for training data also increases. To save costs, it has become common for users and enterprises to delegate time-consuming data collection to third parties. Unfortunately, recent research has shown that this practice raises the risk of DNNs being exposed to backdoor attacks. Specif

Guanqi Liang, Di Wu, Yuxiao Tu, Tin Lun Lam

The intrinsic modularity and reconfigurability of modular reconfigurable robots (MRR) confer advantages such as versatility, fault tolerance, and economic efficacy, thereby showcasing considerable potential across diverse applications. The continuous evolution of the technology landscape and the emergence of diverse conceptual designs have generated multiple

Nicholas Boucher, Ross Anderson

Ensuring the security of software supply chains requires reliable identification of upstream dependencies. We present the Automatic Bill of Materials, or ABOM, a technique for embedding dependency metadata in binaries at compile time. Rather than relying on developers to explicitly enumerate dependency names and versions, ABOM embeds a hash of each distinct

V. Ryzhii, M. S. Shur, M. Ryzhii, V. Mitin

We propose and analyze the terahertz (THz) bolometric vector detectors based on the graphene-channel field-effect transistors (GC-FET) with the black-P gate barrier layer or with the composite b-BN/black-P/b-BN gate layer. The phase difference between the signal received by the FET source and drain substantially affects the plasmonic resonances. This results

Arvind Ayyer, James B. Martin

We introduce and study a natural multispecies variant of the inhomogeneous PushTASEP with site-dependent rates on the finite ring. We show that the stationary distribution of this process is proportional to the ASEP polynomials at $q = 1$ and $t = 0$. This is done by constructing a multiline process which projects to the multispecies PushTASEP, and identifyi

Yangchao Wu, Tian Yu Liu, Hyoungseob Park, Stefano Soatto

Unsupervised depth completion and estimation methods are trained by minimizing reconstruction error. Block artifacts from resampling, intensity saturation, and occlusions are amongst the many undesirable by-products of common data augmentation schemes that affect image reconstruction quality, and thus the training signal. Hence, typical augmentations on imag

Hiroaki Kouno, Kouji Kashiwa

A simple phenomenological hybrid hadron-quark model with effective volume effects of baryons and chiral dynamics is investigated. The hybrid EoS naturally connects the low density baryonic matter with the high density quark matter. In the intermediate region, the phase which can not be regarded as pure hadron matter or pure quark matter appears. In this mode

Chung-Hao Chen, Zong-Rou Jiang, Tzay-Ming Hong

It was recently established that dogs share the same lapping technique as cats by flicking their tongue against the water surface and then yanking it back, dragging up a column of water. This liquid column appears frequently in daily life and industrial applications, such as walking through a puddle and roller printing. While governed by the Navier-Stokes eq

Ni Putu Intan Maharani, Yoga Yustiawan, Fauzy Caesar Rochim, Ayu Purwarianti

BERT and IndoBERT have achieved impressive performance in several NLP tasks. There has been several investigation on its adaption in specialized domains especially for English language. We focus on financial domain and Indonesian language, where we perform post-training on pre-trained IndoBERT for financial domain using a small scale of Indonesian financial

S. Mubeen, I. Aslam, Ghazi S. Khammash, Saralees Nadarajah

Recently, extensions of gamma and beta functions have been studied by many researchers due to their nice properties and variety of applications in different fields of science. The aim of this note is to investigate generalized inequalities associated with extended beta and gamma functions

Barath Kumar JK, Aswadh Khumar G S

Over the past few years, the division of gait phases has emerged as a complex area of research that carries significant importance for various applications in the field of gait technologies. The accurate partitioning of gait phases plays a crucial role in advancing these applications. Researchers have been exploring a range of sensors that can be employed to

Zhexiong Liu, Mohamed Elaraby, Yang Zhong, Diane Litman

This paper presents an overview of the ImageArg shared task, the first multimodal Argument Mining shared task co-located with the 10th Workshop on Argument Mining at EMNLP 2023. The shared task comprises two classification subtasks - (1) Subtask-A: Argument Stance Classification; (2) Subtask-B: Image Persuasiveness Classification. The former determines the s

Angshuman Khan, Surajit Sur, Chiradeep Mukherjee, Aninda Sankar Sukla

Quantum dot Cellular Automata (QCA) is the emerging area in the field of nanotechnology. Inverter is a fundamental logic primitive in QCA. Molecular, semiconductor, magnetic, and metallic QCA are main methodology in the fabrication of quantum cell. While all types of QCA work on room temperature, metallic one is not suitable in normal temperature. So tempera

Barath Kumar JK, Aswadh Khumar G S

Accurate and rapid detection of gait phases is of utmost importance in achieving optimal performance of powered lower-limb prostheses and exoskeletons. With the increasing versatility and complexity of these robotic systems, there is a growing need to enhance the performance of gait detection algorithms. The development of reliable and functional gait detect

Yuan Cai, Bin Han, Na Zhao

We prove the global existence of the smooth solutions near equilibrium to the Cauchy problem of the incompressible non-resistive magnetohydrodynamic equations in the whole three dimensional space under some admissible condition. The result has been obtained by Xu and Zhang (SIAM J. Math. Anal. 47: 26--65, 2015) in anisotropic Besov space framework. In this p

Barath Kumar JK, Aswadh Khumar G S

This paper focuses on the analysis of human gait cycle dynamics and presents a mathematical model to determine the torque exerted on the lower limb joints throughout the complete gait cycle, including its various phases. The study involved a healthy subject who participated in a series of initial walking experiments. The development of a mathematical model t

Harun Al Rashid, Dheeraj Kumar Singh

In the half-filled one-orbital Hubbard model on a square lattice, we study the effect of next-nearest neighbor hopping on the single-particle spectral function at finite temperature using an exact-diagonalization + Monte-Carlo based approach to the simulation process. We find that the pseudogap-like dip, existing in the density of states in between the N\'{e

Haoyuan Sun, Navid Azizan, Akash Srivastava, Hao Wang

When machine learning models are trained on synthetic data and then deployed on real data, there is often a performance drop due to the distribution shift between synthetic and real data. In this paper, we introduce a new ensemble strategy for training downstream models, with the goal of enhancing their performance when used on real data. We generate multipl

Aswadh Khumar G S, Barath Kumar JK

In this study, a gait phase classification method based on SVM multiclass classification is introduced, with a focus on the precise identification of the stance and swing phases, which are further subdivided into seven phases. Data from individual IMU sensors, such as Shank Acceleration X, Y, Z, Shank Gyro X, and Knee Angles, are used as features in this cla

Youbang Sun, Tao Liu, Ruida Zhou, P. R. Kumar

This work studies an independent natural policy gradient (NPG) algorithm for the multi-agent reinforcement learning problem in Markov potential games. It is shown that, under mild technical assumptions and the introduction of the \textit{suboptimality gap}, the independent NPG method with an oracle providing exact policy evaluation asymptotically reaches an

Zhihua Zhong, Jingsen Zhu, Yuxin Dai, Chuankun Zheng

The workload of real-time rendering is steeply increasing as the demand for high resolution, high refresh rates, and high realism rises, overwhelming most graphics cards. To mitigate this problem, one of the most popular solutions is to render images at a low resolution to reduce rendering overhead, and then manage to accurately upsample the low-resolution r

Yuyang Bai, Shangbin Feng, Vidhisha Balachandran, Zhaoxuan Tan

Large language models (LLMs) demonstrate remarkable performance on knowledge-intensive tasks, suggesting that real-world knowledge is encoded in their model parameters. However, besides explorations on a few probing tasks in limited knowledge domains, it is not well understood how to evaluate LLMs' knowledge systematically and how well their knowledge abilit

Hang Chen

Let $(M^m,g)$ be an $m$-dimensional closed Riemannian manifold with non-negative sectional curvatures, $m\ge 3$. We define a conformal invariant and prove that, if the conformal invariant is bounded from above by a constant depending only on $m$, then there are no closed $n$-dimensional stable minimal submanifolds in $M$ for all $\xi(m)\le n\le m-2$, where $

Rahul Jain, Anoushka Saha, Gourav Daga, Durba Bhattacharya

Type 2 diabetes mellitus represents a prevalent and widespread global health concern, necessitating a comprehensive assessment of its risk factors. This study aimed towards learning whether there is any differential impact of age, Lifestyle, BMI and Waist to height ratio on the risk of Type 2 diabetes mellitus in males and females in Kolkata, West Bengal, In

Nitish Deshpande, Miguel R. Castellanos, Saeed R. Khosravirad, Jinfeng Du

Impedance-matching networks affect power transfer from the radio frequency (RF) chains to the antennas. Their design impacts the signal to noise ratio (SNR) and the achievable rate. In this paper, we maximize the information-theoretic achievable rate of a multiple-input-single-output (MISO) system with wideband matching constraints. Using a multiport circuit

S. D. Liles, D. J. Halverson, Z. Wang, A. Shamim

Holes in silicon quantum dots are promising for spin qubit applications due to the strong intrinsic spin-orbit coupling. The spin-orbit coupling produces complex hole-spin dynamics, providing opportunities to further optimize spin qubits. Here, we demonstrate a singlet-triplet qubit using hole states in a planar metal-oxide-semiconductor double quantum dot.

Qiankun Cheng, Rongen Dong, Wenlong Cai, Ruiqi Liu

Due to its ability of overcoming the impact of double-fading effect, active intelligent reflecting surface (IRS) has attracted a lot of attention. Unlike passive IRS, active IRS should be supplied by power, thus adjusting power between base station (BS) and IRS having a direct impact on the system rate performance. In this paper, the active IRS-aided network

Zhuofeng Wu, Chaowei Xiao, VG Vinod Vydiswaran

In this paper, we propose a hierarchical contrastive learning framework, HiCL, which considers local segment-level and global sequence-level relationships to improve training efficiency and effectiveness. Traditional methods typically encode a sequence in its entirety for contrast with others, often neglecting local representation learning, leading to challe

Jonathan Cohen

Let $F$ be a non-archimedean local field of characteristic zero and $(\pi, V)$ a depth zero, irreducible, supercuspidal representation of $GSp(4, F)$. We calculate the dimensions of the spaces of Klingen-invariant vectors in $V$ of level $\mathfrak{p}^n$ for all $n\geq 0 $.

Yuxiu Lin, Hui Liu, Ren Wang, Qiang Guo

Recent multi-view subspace clustering achieves impressive results utilizing deep networks, where the self-expressive correlation is typically modeled by a fully connected (FC) layer. However, they still suffer from two limitations. i) The parameter scale of the FC layer is quadratic to sample numbers, resulting in high time and memory costs that significantl

Haijun Yu, Itzhak Fouxon, Jianchun Wang, Xiangru Li

We study Lyapunov exponents of tracers in compressible homogeneous isotropic turbulence at different turbulent Mach number $M_t$ and Taylor-scale Reynolds number $Re_\lambda$. We demonstrate that statistics of finite-time Lyapunov exponents have the same form as in incompressible flow due to density-velocity coupling. Modulus of the smallest Lyapunov exponen

Fanghua Ye, Meng Fang, Shenghui Li, Emine Yilmaz

Query rewriting plays a vital role in enhancing conversational search by transforming context-dependent user queries into standalone forms. Existing approaches primarily leverage human-rewritten queries as labels to train query rewriting models. However, human rewrites may lack sufficient information for optimal retrieval performance. To overcome this limita

Jaewoo Park, Sugil Lee, Jongeun Lee

Recently DRAM-based PIMs (processing-in-memories) with unmodified cell arrays have demonstrated impressive performance for accelerating AI applications. However, due to the very restrictive hardware constraints, PIM remains an accelerator for simple functions only. In this paper we propose NTT-PIM, which is based on the same principles such as no modificatio

Hien Bui, Michael Posa

In contact-rich tasks, the hybrid, multi-modal nature of contact dynamics poses great challenges in model representation, planning, and control. Recent efforts have attempted to address these challenges via data-driven methods, learning dynamical models in combination with model predictive control. Those methods, while effective, rely solely on minimizing fo

Sean Richardson

We consider functions $f: \mathbb{Z} \to \mathbb{R}$ and kernels $u: \{-n, \cdots, n\} \to \mathbb{R}$ normalized by $\sum_{\ell = -n}^{n} u(\ell) = 1$, making the convolution $u \ast f$ a "smoother" local average of $f$. We identify which choice of $u$ most effectively smooths the second derivative in the following sense. For each $u$, basic Fourier analysi

Jorge I. Poveda

We introduce new sufficient conditions for verifying stability and recurrence properties in singularly perturbed stochastic hybrid dynamical systems. Specifically, we focus on hybrid systems with deterministic continuous-time dynamics that exhibit multiple time scales and are modeled by constrained differential inclusions, as well as discrete-time dynamics m

Zhenyi Liao, Zhijie Deng

Leveraging pre-trained conditional diffusion models for video editing without further tuning has gained increasing attention due to its promise in film production, advertising, etc. Yet, seminal works in this line fall short in generation length, temporal coherence, or fidelity to the source video. This paper aims to bridge the gap, establishing a simple and

Huhe Han

Let $\gamma: S^n\to \mathbb{R}_+$ be a convex integrand and $\mathcal{W}_\gamma$ be the Wulff shape of $ \gamma$. Apex point naturally arise in non-smooth Wulff shape, in particular, vertex of convex polytope. %Let $P\in S^n$. In this paper, we study the behavior of convex integrand around apex point of its Wulff shape. We prove that $\gamma(P)$ is locally m

Alexandre Salas-Bernárdez, Michael Borinsky, Zeno Capatti, Eric Laenen

Flow Oriented Perturbation Theory (FOPT) is a novel approach to Feynman diagrams based on the coordinate (position) space description of Quantum Field Theories (QFT). FOPT offers interesting features regarding the computation of higher-loop Feynman amplitudes such as combinatorial and canonical Feynman rules, explicit infrared singularity factorization on a

Haitian Yang, Wen-An Yong

This paper is concerned with boundary stabilization of two-dimensional hyperbolic systems of partial differential equations. By adapting the Lyapunov function previously proposed by the second author for linearized hyperbolic systems with relaxation structure, we derive certain control laws so that the corresponding solutions decay exponentially in time. The

Yang Yu, Qi Liu, Kai Zhang, Yuren Zhang

User modeling, which aims to capture users' characteristics or interests, heavily relies on task-specific labeled data and suffers from the data sparsity issue. Several recent studies tackled this problem by pre-training the user model on massive user behavior sequences with a contrastive learning task. Generally, these methods assume different views of the

Zeyu Zhang, Shuyan Wan, Sijie Wang, Xianda Zheng

Signed Graph Neural Networks (SGNNs) are vital for analyzing complex patterns in real-world signed graphs containing positive and negative links. However, three key challenges hinder current SGNN-based signed graph representation learning: sparsity in signed graphs leaves latent structures undiscovered, unbalanced triangles pose representation difficulties f

Attila Bérczes, Yann Bugeaud, Kálmán Győry, Jorge Mello

Let $f$ be a polynomial with coefficients in the ring $O_S$ of $S$-integers of a number field $K$, $b$ a non-zero $S$-integer, and $m$ an integer $\ge 2$. We consider the equation $( \star )$: $f(x) = b y^m$ in $x,y \in O_S$. Under the well-known LeVeque condition, we give fully explicit upper bounds in terms of $K, S, f, m$ and the $S$-norm of $b$ for the h

Mariko Kubo, Tohru Nagao, Hisakazu Uchiyama, Takuji Yamashita

Many massive quiescent galaxies have been discovered at $z>2$ thanks to multi-wavelength deep and wide surveys, however, substantial deep near-infrared spectroscopic observations are needed to constrain their star-formation histories statistically. Here, we present a new technique to select quiescent galaxies with a short quenching timescale ($\leq0.1$ Gyr)

Matias D. Cattaneo, Jason M. Klusowski, William G. Underwood

Random forests are popular methods for regression and classification analysis, and many different variants have been proposed in recent years. One interesting example is the Mondrian random forest, in which the underlying constituent trees are constructed via a Mondrian process. We give precise bias and variance characterizations, along with a Berry-Esseen-t

Haochen Lei, Yan Li, Hongyuan Cao

Identifying signals that replicate across multiple studies is essential for establishing robust scientific evidence, yet existing methods for high-dimensional replicability analysis either rely on restrictive modeling assumptions, are limited to two-study settings, or lack statistical power. We propose a general empirical Bayes framework for multi-study repl

Muhammad Baqer Mollah, Honggang Wang, Mohammad Ataul Karim, Hua Fang

Connected and autonomous vehicles (CAVs) will revolutionize tomorrow's intelligent transportation systems, being considered promising to improve transportation safety, traffic efficiency, and mobility. In fact, envisioned use cases of CAVs demand very high throughput, lower latency, highly reliable communications, and precise positioning capabilities. The av

Pooria Namyar, Behnaz Arzani, Srikanth Kandula, Santiago Segarra

We consider the max-min fair resource allocation problem. The best-known solutions use either a sequence of optimizations or waterfilling, which only applies to a narrow set of cases. These solutions have become a practical bottleneck in WAN traffic engineering and cluster scheduling, especially at larger problem sizes. We improve both approaches: (1) we sho

Yue-Zhou Li

We review the effective field theory (EFT) bootstrap by formulating it as an infinite-dimensional semidefinite program (SDP), built from the crossing symmetric sum rules and the S-matrix primal ansatz. We apply the program to study the large-$N$ chiral perturbation theory ($\chi$PT) and observe excellent convergence of EFT bounds between the dual (rule-out)

Julius Ross, David Witt Nyström

We describe the harmonic interpolation of convex bodies, and prove a strong form of the Brunn-Minkowski inequality and characterize its equality case. As an application we improve a theorem of Berndtsson on the volume of slices of a pseudoconvex domain. We furthermore apply this to prove subharmonicity of the expected absolute value of the determinant of a m

Shuwen Yang, Anran Wu, Xingjiao Wu, Luwei Xiao

Pre-trained multimodal models have achieved significant success in retrieval-based question answering. However, current multimodal retrieval question-answering models face two main challenges. Firstly, utilizing compressed evidence features as input to the model results in the loss of fine-grained information within the evidence. Secondly, a gap exists betwe

Nikhil Byrapuram, Hwiseo, Choi, Adam Ge

We study the maximum number of quads among $\ell$ cards from an EvenQuads deck of size $2^n$. This corresponds to enumerating quadruples of integers in the range $[0,\ell-1]$ such that their bitwise XOR is zero. In this paper, we conjecture a formula that calculates the maximum number of quads among $\ell$ cards.

Vishvesha K. Sridhar, Yanzhu Chen, Bryan Gard, Edwin Barnes

Quantum computing may provide advantage in solving classical optimization problems. One promising algorithm is the quantum approximate optimization algorithm (QAOA). There have been many proposals for improving this algorithm, such as using an initial state informed by classical approximation solutions. A variation of QAOA called ADAPT-QAOA constructs the an

Xuesong Wang, Yi Zhou, Dongsheng Zhang

The escalating demand for high-speed and high-precision machining in machine tool feed system has brought to the forefront the challenge of its design method. Currently, existing methodologies struggle to ascertain compliance with dynamic performance requirements during the design phase, often resulting in either excessive or insufficient design. Therefore,

Md Sakib Hasan, Catherine D. Schuman, Zhongyang Zhang, Tauhidur Rahman

Neuromorphic Computing promises orders of magnitude improvement in energy efficiency compared to traditional von Neumann computing paradigm. The goal is to develop an adaptive, fault-tolerant, low-footprint, fast, low-energy intelligent system by learning and emulating brain functionality which can be realized through innovation in different abstraction laye

Hao-Fang Cheng, Yi-Ching Ho, Cheng-Wei Chen

Robotic technologies are becoming increasingly popular in dentistry due to the high level of precision required in delicate dental procedures. Most dental robots available today are designed for implant surgery, helping dentists to accurately place implants in the desired position and depth. In this paper, we introduce the DentiBot, the first robot specifica

Xinyu Lian, Yinfang Chen, Runxiang Cheng, Jie Huang

Misconfigurations are major causes of software failures. Existing practices rely on developer-written rules or test cases to validate configurations, which are expensive. Machine learning (ML) for configuration validation is considered a promising direction, but has been facing challenges such as the need of large-scale field data and system-specific models.

Anindya Sarkar, Nathan Jacobs, Yevgeniy Vorobeychik

Visual active search (VAS) has been proposed as a modeling framework in which visual cues are used to guide exploration, with the goal of identifying regions of interest in a large geospatial area. Its potential applications include identifying hot spots of rare wildlife poaching activity, search-and-rescue scenarios, identifying illegal trafficking of weapo

Qi Heng Ho, Tyler Becker, Benjamin Kraske, Zakariya Laouar

Many sequential decision problems involve optimizing one objective function while imposing constraints on other objectives. Constrained Partially Observable Markov Decision Processes (C-POMDP) model this case with transition uncertainty and partial observability. In this work, we first show that C-POMDPs violate the optimal substructure property over success

David Liu, Jackie Baek, Tina Eliassi-Rad

We study the fairness of dimensionality reduction methods for recommendations. We focus on the fundamental method of principal component analysis (PCA), which identifies latent components and produces a low-rank approximation via the leading components while discarding the trailing components. Prior works have defined notions of "fair PCA"; however, these de

Kuan Xu, Li Shen, Lindong Liu

Column generation (CG) is a vital method to solve large-scale problems by dynamically generating variables. It has extensive applications in common combinatorial optimization, such as vehicle routing and scheduling problems, where each iteration step requires solving an NP-hard constrained shortest path problem. Although some heuristic methods for accelerati

Adam Winnifrith, Carlos Outeiral, Brian Hie

Engineering new molecules with desirable functions and properties has the potential to extend our ability to engineer proteins beyond what nature has so far evolved. Advances in the so-called "de novo" design problem have recently been brought forward by developments in artificial intelligence. Generative architectures, such as language models and diffusion

Sung-Soo Byun, Yong-Woo Lee

We consider the elliptic Ginibre matrices in the orthogonal symmetry class that interpolates between the real Ginibre ensemble and the Gaussian orthogonal ensemble. We obtain the finite size corrections of the real eigenvalue densities in both the global and edge scaling regimes, as well as in both the strong and weak non-Hermiticity regimes. Our results ext

Navar Medeiros M. Nascimento, Pedro Cavalcante de Sousa Junior, Pedro Yuri Rodrigues Nunes, Suane Pires Pinheiro da Silva

We introduce a novel technique called ShapedNet to enhance body composition assessment. This method employs a deep neural network capable of estimating Body Fat Percentage (BFP), performing individual identification, and enabling localization using a single photograph. The accuracy of ShapedNet is validated through comprehensive comparisons against the gold

Juan E. Arias-Martinez, Hamlin Wu, Martin Head-Gordon

We formulate a one-center non-orthogonal configuration interaction singles (1C-NOCIS) theory for the computation of core excited states of an initial singlet state with two unpaired electrons. This model, which we refer to as 1C-NOCIS two-electron open-shell (2eOS), is appropriate for computing the K-edge near-edge X-ray absorption spectra (NEXAS) of the val

Yukiho Kobayashi, Arnau Aguasca-Cabot, María Isabel Bernardos Martín, David Green

Novae are luminous explosions in close binaries which host a white dwarf and a companion donor star. They are triggered by a thermonuclear runaway when the white dwarf accretes a critical amount of matter from the secondary. Though novae are established as high-energy gamma-ray emitters through observations by the Fermi Large Area Telescope (LAT), the origin

Cheng-Zhen Wang, Ulrich Kuhl, Adin Dowling, Holger Schanz

Bound states in the continuum (BICs) defy conventional wisdom that assumes a spectral separation between propagating waves, that carry energy away, and spatially localized waves corresponding to discrete frequencies. They can be described as resonance states with infinite lifetime, i.e., leaky modes with zero leakage. The advent of metamaterials and nanophot

Ayush Rai, Shaoshuai Mou

This paper provides a solution to the problem of safe region formation control with reference velocity tracking for a second-order multi-agent system without velocity measurements. Safe region formation control is a control problem where the agents are expected to attain the desired formation while reaching the target region and simultaneously ensuring colli

Alexander Ororbia, Mary Alexandria Kelly

Over the last few years, large neural generative models, capable of synthesizing semantically rich passages of text or producing complex images, have recently emerged as a popular representation of what has come to be known as ``generative artificial intelligence'' (generative AI). Beyond opening the door to new opportunities as well as challenges for the do

Ankitha Sudarshan, Vinay Samuel, Parth Patwa, Ibtihel Amara

Automatic Speech Recognition (ASR) has witnessed a profound research interest. Recent breakthroughs have given ASR systems different prospects such as faithfully transcribing spoken language, which is a pivotal advancement in building conversational agents. However, there is still an imminent challenge of accurately discerning context-dependent words and phr

Shi Chen, Ming Jiang, Qi Zhao

In recent years, deep saliency models have made significant progress in predicting human visual attention. However, the mechanisms behind their success remain largely unexplained due to the opaque nature of deep neural networks. In this paper, we present a novel analytic framework that sheds light on the implicit features learned by saliency models and provi

Fedor V. Fomin, Petr A. Golovach, Danil Sagunov, Kirill Simonov

According to the classic Chv{\'{a}}tal's Lemma from 1977, a graph of minimum degree $\delta(G)$ contains every tree on $\delta(G)+1$ vertices. Our main result is the following algorithmic "extension" of Chv\'{a}tal's Lemma: For any $n$-vertex graph $G$, integer $k$, and a tree $T$ on at most $\delta(G)+k$ vertices, deciding whether $G$ contains a subgraph is

Simon Foucart, Chunyang Liao

Optimal recovery is a mathematical framework for learning functions from observational data by adopting a worst-case perspective tied to model assumptions on the functions to be learned. Working in a finite-dimensional Hilbert space, we consider model assumptions based on approximability and observation inaccuracies modeled as additive errors bounded in $\el

Jiachen Li, Qiaozi Gao, Michael Johnston, Xiaofeng Gao

Prompt-based learning has been demonstrated as a compelling paradigm contributing to large language models' tremendous success (LLMs). Inspired by their success in language tasks, existing research has leveraged LLMs in embodied instruction following and task planning. In this work, we tackle the problem of training a robot to understand multimodal prompts,

Razan Baltaji, Sourya Basu, Lav R. Varshney

Constructing model-agnostic group equivariant networks, such as equitune (Basu et al., 2023b) and its generalizations (Kim et al., 2023), can be computationally expensive for large product groups. We address this problem by providing efficient model-agnostic equivariant designs for two related problems: one where the network has multiple inputs each with pot

Jiawei Ruan, Y. -H. Chan, Steven G. Louie

Excitons are vital in the photophysics of materials, especially in low-dimensional systems. The conceptual and quantitative understanding of excitonic effects in nonlinear optical (NLO) processes is more challenging compared to linear ones. Here, we present an ab initio approach to second-order NLO responses, incorporating excitonic effects, that employs an

Yingze Hou, Yousef Oleyaeimotlagh, Rahul Mishra, Hoda Bidkhori

Optimal algorithms are developed for robust detection of changes in non-stationary processes. These are processes in which the distribution of the data after change varies with time. The decision-maker does not have access to precise information on the post-change distribution. It is shown that if the post-change non-stationary family has a distribution that

Chang Lu, Chandan K. Reddy, Ping Wang, Yue Ning

Automatic coding of International Classification of Diseases (ICD) is a multi-label text categorization task that involves extracting disease or procedure codes from clinical notes. Despite the application of state-of-the-art natural language processing (NLP) techniques, there are still challenges including limited availability of data due to privacy constra

Murat Isik, Malvin Nkomo, Anup Das, Kapil R. Dandekar

Sixth-generation (6G) communication systems are poised to accommodate high data-rate wireless communication services in highly dynamic channels, with applications including high-speed trains, unmanned aerial vehicles, and intelligent transportation systems. Orthogonal frequency-division multiplexing (OFDM) modulation suffers from performance degradation in s