April 2024 arXiv papers — page 188

Aritra Biswas, Sébastien Descotes-Genon, Joaquim Matias, Gilberto Tetlalmatzi-Xolocotzi

We study penguin-mediated $\bar{B}_{d(s)}\to\bar{K}^{*0}(K^{*0})\phi$ transitions, proposing a new optimised observable $L_{K^*\phi}$ from the ratio of the corresponding branching ratios of these decays, with limited hadronic uncertainties and enhanced New Physics sensitivity. It deviates by $1.48\sigma$ between its experimental value and SM determination wi

Weijun Zhang, Zhitao Zhang

In this paper, we are concerned with the monotonic and symmetric properties of convex solutions Monge-Amp\`ere systems for instance, considering \begin{equation*} \det(D^2u^i)=f^i(x,{\bf u},\nabla u^i), \ 1\leq i\leq m, \end{equation*} over unbounded domains of various cases, including the whole spaces $\mathbb{R}^n$, the half spaces $\mathbb{R}^n_+$ and the

Mingxin Yu, Chenning Yu, M-Mahdi Naddaf-Sh, Devesh Upadhyay

Sampling-based motion planning methods for manipulators in crowded environments often suffer from expensive collision checking and high sampling complexity, which make them difficult to use in real time. To address this issue, we propose a new generalizable control barrier function (CBF)-based steering controller to reduce the number of samples needed in a s

Xin Li

This paper introduces a unifying framework that links the Context-Content Uncertainty Principle (CCUP) with optimal transport (OT) via primal-dual inference. We propose that cognitive representations are not static encodings but active dual constraints that shape feasible manifolds for learning and inference. Cognition is formalized as the dynamic alignment

Anuja Tayal, Barbara Di Eugenio, Devika Salunke, Andrew D. Boyd

We propose a dialogue system that enables heart failure patients to inquire about salt content in foods and help them monitor and reduce salt intake. Addressing the lack of specific datasets for food-based salt content inquiries, we develop a template-based conversational dataset. The dataset is structured to ask clarification questions to identify food item

A. Circiello, A. McDaniel, A. Drlica-Wagner, C. Karwin

Recent results from numerical simulations and models of galaxy formation suggest that recently discovered ultra-faint compact stellar systems (UFCSs) in the halo of the Milky Way (MW) may be some of the smallest and faintest galaxies. If this is the case, these systems would be attractive targets for indirect searches of weakly interacting massive particle (

Friedrich Knop

Let $X=G/H$ be a spherical variety over an algebraically closed field of characteristic $p\ge0$. We compute the $p'$-parts of $\pi_0(H)$ and $\pi_1(X)$ from the spherical system of $X$.

Hongwei Zheng, Linyuan Zhou, Han Li, Jinming Su

Data mixing methods play a crucial role in semi-supervised learning (SSL), but their application is unexplored in long-tailed semi-supervised learning (LTSSL). The primary reason is that the in-batch mixing manner fails to address class imbalance. Furthermore, existing LTSSL methods mainly focus on re-balancing data quantity but ignore class-wise uncertainty

Adrian Langer

We study contact structures on smooth complex projective varieties with a simple normal crossing divisor, generalizing some well-known results concerning the non-logarithmic case. In particular, we describe the structure of elementary log contractions of such log varieties and we construct the corresponding contact structures.

Ruiqi Zheng, Liang Qu, Tong Chen, Kai Zheng

To make room for privacy and efficiency, the deployment of many recommender systems is experiencing a shift from central servers to personal devices, where the federated recommender systems (FedRecs) and decentralized collaborative recommender systems (DecRecs) are arguably the two most representative paradigms. While both leverage knowledge (e.g., gradients

Michiel P. Bron, Peter G. M. van der Heijden, Ad J. Feelders, Arno P. J. M. Siebes

Technology-Assisted Review (TAR) aims to reduce the human effort required for screening processes such as abstract screening for systematic literature reviews. Human reviewers label documents as relevant or irrelevant during this process, while the system incrementally updates a prediction model based on the reviewers' previous decisions. After each model up

Carter Lee Rhea, Julie Hlavacek-Larrondo, Justine Giroux, Auriane Thilloy

In astronomy, spectroscopy consists of observing an astrophysical source and extracting its spectrum of electromagnetic radiation. Once extracted, a model is fit to the spectra to measure the observables, leading to an understanding of the underlying physics of the emission mechanism. One crucial, and often overlooked, aspect of this model is the background

Wenrui Li, Xiaopeng Hong, Ruiqin Xiong, Xiaopeng Fan

Temporal video grounding (TVG) is a critical task in video content understanding, requiring precise alignment between video content and natural language instructions. Despite significant advancements, existing methods face challenges in managing confidence bias towards salient objects and capturing long-term dependencies in video sequences. To address these

Gabor Lippner, Yujia Shi

We quantify the effect of weighted loops at the source and target nodes of a graph on the strength of quantum state transfer between these vertices. We give lower bounds on loop weights that guarantee strong transfer fidelity that works for any graph where this protocol is feasible. By considering local spectral symmetry, we show that the required weight siz

Jinzi Mac Huang

The continental plates of Earth are known to drift over a geophysical timescale, and their interactions have lead to some of the most spectacular geoformations of our planet while also causing natural disasters such as earthquakes and volcanic activity. Understanding the dynamics of interacting continental plates is thus significant. In this work, we present

Qianyu Guo, Ziqing Yu, Jiaming Fu, Yawen Lu

Robotic grippers are receiving increasing attention in various industries as essential components of robots for interacting and manipulating objects. While significant progress has been made in the past, conventional rigid grippers still have limitations in handling irregular objects and can damage fragile objects. We have shown that soft grippers offer defo

Rumeng Li, Xun Wang, Hong Yu

To address the limitations of Large Language Models (LLMs) in the International Classification of Diseases (ICD) coding task, where they often produce inaccurate and incomplete prediction results due to the high-dimensional and skewed distribution of the ICD codes, and often lack interpretability and reliability as well. We introduce an innovative multi-agen

N. Bijas, Chakali Eswaraiah, Panigrahy Sandhyarani, Jessy Jose

Dust polarisation observations at optical wavelengths help understand the dust grain properties and trace the plane-of-the-sky component of the magnetic field. In this study, we make use of published optical polarisation data acquired with AIMPOL along with distances ($d$) and extinction ($A_{\mathrm{V}}$) data. We study the variation of polarisation efficie

Maxwell H. Wang, Jukka-Pekka Onnela

When modeling the dynamics of infectious disease, the incorporation of contact network information allows for the capture of the non-randomness and heterogeneity of realistic contact patterns. Oftentimes, it is assumed that the underlying contact pattern is known with perfect certainty. However, in realistic settings, the observed data often serves as an imp

Jiarui Meng, Haijie Li, Yanmin Wu, Qiankun Gao

3D Gaussian Splatting (3DGS) has significantly advanced 3D scene reconstruction and novel view synthesis. However, like Neural Radiance Fields (NeRF), 3DGS struggles with accurately modeling physical reflections, particularly in mirrors, leading to incorrect reconstructions and inconsistent reflective properties. To address this challenge, we introduce Mirro

Yilin Wen, Yi Guo, Zechun Hu, Gabriela Hug

Uncertainty modeling has become increasingly important in power system decision-making. The widely-used tractable uncertainty modeling method-chance constraints with Conditional Value at Risk (CVaR) approximation, can be overconservative and even turn an originally feasible problem into an infeasible one. This paper proposes a new approximation method for mu

Longfei Han, Qiuyu Xu, Klaus Kefferpütz, Ying Lu

4D mmWave radar sensors are suitable for roadside perception in city-scale Intelligent Transportation Systems (ITS) due to their long sensing range, weatherproof functionality, simple mechanical design, and low manufacturing cost. In this work, we investigate radar-based ITS for scalable traffic analysis. Localization of these radar sensors at city scale is

Haoran Li, Junqi Liu, Zexian Wang, Shiyuan Luo

The modeling of environmental ecosystems plays a pivotal role in the sustainable management of our planet. Accurate prediction of key environmental variables over space and time can aid in informed policy and decision-making, thus improving people's livelihood. Recently, deep learning-based methods have shown promise in modeling the spatial-temporal relation

Bing Xiao, Haichao Zhang, Shijie Zhao, Lu Cao

This brief gives a set of unified Lyapunov stability conditions to guarantee the predefined-time/finite-time stability of a dynamical systems. The derived Lyapunov theorem for autonomous systems establishes equivalence with existing theorems on predefined-time/finite-time stability. The findings proposed herein develop a nonsingular sliding mode control fram

Nan Zhou, Zheng Ma

In this paper, we put forward a neural network framework to solve the nonlinear hyperbolic systems. This framework, named relaxation neural networks(RelaxNN), is a simple and scalable extension of physics-informed neural networks(PINN). It is shown later that a typical PINN framework struggles to handle shock waves that arise in hyperbolic systems' solutions

Saikat Chakraborty, Peter K. S. Dunsby, Rituparno Goswami, Amare Abebe

We present a dynamical system formulation for inhomogeneous LRS-II spacetimes using the covariant 1+1+2 decomposition approach. Our approach describes the LRS-II dynamics from the point of view of a comoving observer. Promoting the covariant radial derivatives of the covariant dynamical quantities to new dynamical variables and utilizing the commutation rela

Amir Faghihi, Mohammadreza Fathollahi, Roozbeh Rajabi

Today, skin cancer is considered as one of the most dangerous and common cancers in the world which demands special attention. Skin cancer may be developed in different types; including melanoma, actinic keratosis, basal cell carcinoma, squamous cell carcinoma, and Merkel cell carcinoma. Among them, melanoma is more unpredictable. Melanoma cancer can be diag

Zhenyu Liang, Tao Jiang, Kebin Sun, Ran Cheng

Evolutionary multiobjective optimization has witnessed remarkable progress during the past decades. However, existing algorithms often encounter computational challenges in large-scale scenarios, primarily attributed to the absence of hardware acceleration. In response, we introduce a Tensorized Reference Vector Guided Evolutionary Algorithm (TensorRVEA) for

Casey Kennington, Malihe Alikhani, Heather Pon-Barry, Katherine Atwell

The ability to interact with machines using natural human language is becoming not just commonplace, but expected. The next step is not just text interfaces, but speech interfaces and not just with computers, but with all machines including robots. In this paper, we chronicle the recent history of this growing field of spoken dialogue with robots and offer t

Vivian Liu, Yiqiao Yin

Prominent works in the field of Natural Language Processing have long attempted to create new innovative models by improving upon previous model training approaches, altering model architecture, and developing more in-depth datasets to better their performance. However, with the quickly advancing field of NLP comes increased greenhouse gas emissions, posing

Chull Hwan Song, Taebaek Hwang, Jooyoung Yoon, Shunghyun Choi

Vision-language models (VLMs) have made significant strides in cross-modal understanding through large-scale paired datasets. However, in fashion domain, datasets often exhibit a disparity between the information conveyed in image and text. This issue stems from datasets containing multiple images of a single fashion item all paired with one text, leading to

Qiping Lai, Chen Shen, Dongsheng Li

The significant electrical distance between wind power collection points and the main grid poses challenges for weak grid-connected wind power systems. A new type of voltage oscillation phenomenon induced by repeated low voltage ride-through (LVRT) of the wind turbine has been observed, threatening the safe and stable operation of such power systems. Therefo

Michael S. Lee, Reid Simmons, Henny Admoni

Demonstrations are a powerful way of increasing the transparency of AI policies. Though informative demonstrations may be selected a priori through the machine teaching paradigm, student learning may deviate from the preselected curriculum in situ. This paper thus explores augmenting a curriculum with a closed-loop teaching framework inspired by principles f

Hu Yu, Hao Luo, Fan Wang, Feng Zhao

The correspondence between input text and the generated image exhibits opacity, wherein minor textual modifications can induce substantial deviations in the generated image. While, text embedding, as the pivotal intermediary between text and images, remains relatively underexplored. In this paper, we address this research gap by delving into the text embeddi

Zelin He, Ying Sun, Jingyuan Liu, Runze Li

The main challenge that sets transfer learning apart from traditional supervised learning is the distribution shift, reflected as the shift between the source and target models and that between the marginal covariate distributions. In this work, we tackle model shifts in the presence of covariate shifts in the high-dimensional regression setting. Specificall

Tony Liimatainen, Mikko Salo

We give examples on the use of the Stone-Weierstrass theorem in inverse problems. We show uniqueness in the linearized Calder\'on problem on holomorphically separable K\"ahler manifolds, and in the Calder\'on problem for nonlinear equations on conformally transversally anisotropic manifolds. We also study the holomorphic separability condition in terms of pl

Jialou Wang, Manli Zhu, Yulei Li, Honglei Li

Localization plays a crucial role in enhancing the practicality and precision of VQA systems. By enabling fine-grained identification and interaction with specific parts of an object, it significantly improves the system's ability to provide contextually relevant and spatially accurate responses, crucial for applications in dynamic environments like robotics

Hongyu Zhang, Maoran Zhu, Qi Cai, Yuanxin Wu

This paper proposes an innovative state estimation method for visual-inertial fusion based on Chebyshev polynomial optimization. Specifically, the pose is modeled as a Chebyshev polynomial of a certain order, and its time derivatives are used to calculate linear acceleration and angular velocity, which, along with inertial measurements, constitute dynamic co

Mohammed Osman

We obtain a tail bound for the least non-zero singular value of $A-z$ when $A$ is a random matrix and $z$ is an eigenvalue of $A$ in a neighbourhood of a given point $z_0$ in the bulk of the spectrum. The argument relies on a resolvent comparison and a tail bound for Gauss-divisible matrices. The latter can be obtained by the method of partial Schur decompos

Hongtao Xv, Yaohua Sun, Yafei Zhao, Mugen Peng

Cooperative positioning with multiple low earth orbit (LEO) satellites is promising in providing location-based services and enhancing satellite-terrestrial communication. However, positioning accuracy is greatly affected by inter-beam interference and satellite-terrestrial topology geometry. To select the best combination of satellites from visible ones and

Parker Seegmiller, Joseph Gatto, Omar Sharif, Madhusudan Basak

Large language models (LLMs) have been shown to be proficient in correctly answering questions in the context of online discourse. However, the study of using LLMs to model human-like answers to fact-driven social media questions is still under-explored. In this work, we investigate how LLMs model the wide variety of human answers to fact-driven questions po

Georgy Gordeev, Sören Wasserroth, Han Li, Ado Jorio

Dielectric screening plays a vital role for the physical properties in the nanoscale and also alters our ability to detect and characterize nanomaterials by optical techniques. We study the dielectric screening inside of carbon nanotubes and how it changes electromagnetic fields and many-body effects for encapsulated nanostructures. First, we show that the l

Huan Zhang, Yifan Chen, Eric Vanden-Eijnden, Benjamin Peherstorfer

Sequential-in-time methods solve a sequence of training problems to fit nonlinear parametrizations such as neural networks to approximate solution trajectories of partial differential equations over time. This work shows that sequential-in-time training methods can be understood broadly as either optimize-then-discretize (OtD) or discretize-then-optimize (Dt

Lei Han, Xuming Luo, Yingqian Xu, Hua Bai

Electrical-controllable antiferromagnet tunnel junction is a key goal in spintronics, holding immense promise for ultra-dense and ultra-stable antiferromagnetic memory with high processing speed for modern information technology. Here, we have advanced towards this goal by achieving an electrical-controllable antiferromagnet-based tunnel junction of Pt/Co/Pt

Han Cai, Muyang Li, Zhuoyang Zhang, Qinsheng Zhang

We present Condition-Aware Neural Network (CAN), a new method for adding control to image generative models. In parallel to prior conditional control methods, CAN controls the image generation process by dynamically manipulating the weight of the neural network. This is achieved by introducing a condition-aware weight generation module that generates conditi

Samantha Linn, Sean D. Lawley

Many physical phenomena are modeled as stochastic searchers looking for targets. In these models, the probability that a searcher finds a particular target, its so-called hitting probability, is often of considerable interest. In this work we determine hitting probabilities for stochastic search processes conditioned on being faster than a random short time.

Amol Khanna, Edward Raff, Nathan Inkawhich

Linear models are ubiquitous in data science, but are particularly prone to overfitting and data memorization in high dimensions. To guarantee the privacy of training data, differential privacy can be used. Many papers have proposed optimization techniques for high-dimensional differentially private linear models, but a systematic comparison between these me

Kyuhee Kim, Surin Lee, Sangah Lee

In this paper, we present KoCoNovel, a novel character coreference dataset derived from Korean literary texts, complete with detailed annotation guidelines. Comprising 178K tokens from 50 modern and contemporary novels, KoCoNovel stands as one of the largest public coreference resolution corpora in Korean, and the first to be based on literary texts. KoCoNov

Jianqiao Zheng, Xueqian Li, Simon Lucey

The training of vision transformer (ViT) networks on small-scale datasets poses a significant challenge. By contrast, convolutional neural networks (CNNs) have an architectural inductive bias enabling them to perform well on such problems. In this paper, we argue that the architectural bias inherent to CNNs can be reinterpreted as an initialization bias with

Hongshun Yao, Yu-Ao Chen, Erdong Huang, Kaichu Chen

Quantum state purification is crucial in quantum communication and computation, aiming to recover a purified state from multiple copies of an unknown noisy state. This work introduces a general state purification framework designed to achieve the highest fidelity with a specified probability and characterize the associated trade-offs. For i.i.d. quantum stat

Zachariah B. Etienne

To fully unlock the scientific potential of upcoming gravitational wave (GW) interferometers, numerical relativity (NR) simulation accuracy will need to be greatly enhanced. We present three infrastructure-agnostic improvements to the moving-puncture approach for binary black hole (BBH) simulations, aimed at greatly reducing constraint violation and improvin

Dongxu Chang, Qingqing Peng, Zhiming Ma, Guanghui Wang

In this study, the performance of generalized low-density parity-check (GLDPC) codes under the a posteriori probability (APP) decoder is analyzed. We explore the concentration, symmetry, and monotonicity properties of GLDPC codes under the APP decoder, extending the applicability of density evolution to GLDPC codes. On the binary memoryless symmetric channel

Jonathan Pan, Swee Liang Wong, Xin Wei Chia, Yidi Yuan

Large Language Models (LLMs) have been applied to automate cyber security activities and processes including cyber investigation and digital forensics. However, the use of such models for cyber investigation and digital forensics should address accountability and security considerations. Accountability ensures models have the means to provide explainable rea

Jack H. Koolen, Mamoon Abdullah, Brhane Gebremichel, Jae-Ho Lee

Let $\Gamma$ be a graph with diameter at least two. Then $\Gamma$ is said to be $1$-homogeneous (in the sense of Nomura) whenever for every pair of adjacent vertices $x$ and $y$ in $\Gamma$, the distance partition of the vertex set of $\Gamma$ with respect to both $x$ and $y$ is equitable, and the parameters corresponding to equitable partitions are independ

Yang Liu, He Guan, Chuanchen Luo, Lue Fan

The advancement of real-time 3D scene reconstruction and novel view synthesis has been significantly propelled by 3D Gaussian Splatting (3DGS). However, effectively training large-scale 3DGS and rendering it in real-time across various scales remains challenging. This paper introduces CityGaussian (CityGS), which employs a novel divide-and-conquer training a

Soumia Mamdouhi

Recently, many authors have embraced the study of certain properties of modules such as projectivity, injectivity and flatness from an alternative point of view. This way, Durgun has introduced absolutely pure domains of modules as a mean to gauge the extent of the flatness of any given module (so not just to determine whether or not the module is flat). In

Ashish Rana, Michael Oesterle, Jannik Brinkmann

For multi-agent reinforcement learning systems (MARLS), the problem formulation generally involves investing massive reward engineering effort specific to a given problem. However, this effort often cannot be translated to other problems; worse, it gets wasted when system dynamics change drastically. This problem is further exacerbated in sparse reward scena

Prakash Chandra Chhipa, Kanjar De, Meenakshi Subhash Chippa, Rajkumar Saini

The challenge of Out-Of-Distribution (OOD) robustness remains a critical hurdle towards deploying deep vision models. Vision-Language Models (VLMs) have recently achieved groundbreaking results. VLM-based open-vocabulary object detection extends the capabilities of traditional object detection frameworks, enabling the recognition and classification of object

Artemy Kolchinsky

In a well-known paper, Jeremy England derived a bound on the free energy dissipated by a self-replicating system [England, "Statistical physics of self-replication", The Journal of Chemical Physics, 2013]. This bound is usually interpreted as a universal relationship that connects thermodynamic dissipation to replicator per-capita decay and growth rates. We

Bohao Yang, Kun Zhao, Dong Liu, Chen Tang

Automatic open-domain dialogue evaluation has attracted increasing attention, yet remains challenging due to the complexity of assessing response appropriateness. Traditional evaluation metrics, typically trained with true positive and randomly selected negative responses, tend to assign higher scores to responses that share greater content similarity with c

J. Rika Simon, Dmitrii Maksimov, Christian Lotze, Paul Wiechers

Sulphur vacancies in MoS$_2$ on Au(111) have been shown to be negatively charged as reflected by a Kondo resonance. Here, we use scanning tunneling microscopy to show that these vacancies serve as anchoring sites for thiol-based molecules (CF$_3$-3P-SH) with two distinct reaction products, one of them showing a Kondo resonance. Based on comparisons with dens

Yulin Chen, Guoheng Huang, Kai Huang, Zijin Lin

Accurate segmentation of lesion regions is crucial for clinical diagnosis and treatment across various diseases. While deep convolutional networks have achieved satisfactory results in medical image segmentation, they face challenges such as loss of lesion shape information due to continuous convolution and downsampling, as well as the high cost of manually

Eduardo Moya-Lasheras, Edgar Ramirez-Laboreo, Carlos Sagues

The impact forces during switching operations of short-stroke actuators may cause bouncing, audible noise and mechanical wear. The application of soft-landing control strategies to these devices aims at minimizing the impact velocities of their moving components to ultimately improve their lifetime and performance. In this paper, a novel approach for soft-la

Jaime E. Forero-Romero, Alejandro Palomino, Felipe Leonardo Gómez-Cortés, Xiao-Dong Li

This paper introduces ASTRA (Algorithm for Stochastic Topological RAnking), a new method for classifying galaxies into cosmic web structures -- voids, sheets, filaments, and knots -- specifically designed for large spectroscopic surveys. ASTRA operates on observed galaxy positions and a corresponding random catalog, generating probabilistic cosmic web classi

Hyeongmin Lee, Kyoungkook Kang, Jungseul Ok, Sunghyun Cho

Recent image tone adjustment (or enhancement) approaches have predominantly adopted supervised learning for learning human-centric perceptual assessment. However, these approaches are constrained by intrinsic challenges of supervised learning. Primarily, the requirement for expertly-curated or retouched images escalates the data acquisition expenses. Moreove

Ajay Devda, Akshay Sunil, Murthy R, B Deepthi

Forecasting rainfall in tropical areas is challenging due to complex atmospheric behaviour, elevated humidity levels, and the common presence of convective rain events. In the Indian context, the difficulty is further exacerbated because of the monsoon intra seasonal oscillations, which introduce significant variability in rainfall patterns over short period

Wen-Jie Shu, Hong-Xia Dou, Rui Wen, Xiao Wu

Pansharpening aims to enhance remote sensing image (RSI) quality by merging high-resolution panchromatic (PAN) with multispectral (MS) images. However, prior techniques struggled to optimally fuse PAN and MS images for enhanced spatial and spectral information, due to a lack of a systematic framework capable of effectively coordinating their individual stren

Xiang Ji, Haiyang Jiang, Yinqiang Zheng

Motion blur is a frequently observed image artifact, especially under insufficient illumination where exposure time has to be prolonged so as to collect more photons for a bright enough image. Rather than simply removing such blurring effects, recent researches have aimed at decomposing a blurry image into multiple sharp images with spatial and temporal cohe

Akinari Hoshi, Kazuki Kanai, Aiichi Yamasaki

Let $k$ be a field, $T$ be an algebraic $k$-torus, $X$ be a smooth $k$-compactification of $T$ and ${\rm Pic}\,\overline{X}$ be the Picard group of $\overline{X}=X\times_k\overline{k}$ where $\overline{k}$ is a fixed separable closure of $k$. Hoshi, Kanai and Yamasaki [HKY22], [HKY23] determined $H^1(k,{\rm Pic}\, \overline{X})$ for norm one tori $T=R^{(1)}_

William H. Clark, Joseph M. Ernst, Robert W. McGwier

Cognitive Radios (CRs) build upon Software Defined Radios (SDRs) to allow for autonomous reconfiguration of communication architectures. In recent years, CRs have been identified as an enabler for Dynamic Spectrum Access (DSA) applications in which secondary users opportunistically share licensed spectrum. A major challenge for DSA is accurately characterizi

M. Iskin

Considering a spin-up and a spin-down fermion in a generic tight-binding lattice with a multi-site basis, we investigate the two-body problem using a multiband extended-Hubbard model with finite-ranged hopping and interaction parameters. We derive a linear eigenvalue problem for the entire two-body spectrum, alongside a nonlinear eigenvalue problem for the b

Chang Che, Haotian Zheng, Zengyi Huang, Wei Jiang

The article explores the intersection of computer vision technology and robotic control, highlighting its importance in various fields such as industrial automation, healthcare, and environmental protection. Computer vision technology, which simulates human visual observation, plays a crucial role in enabling robots to perceive and understand their surroundi

Scott Armstrong, Ahmed Bou-Rabee, Tuomo Kuusi

We consider the long-time behavior of a diffusion process on $\mathbb{R}^d$ advected by a stationary random vector field which is assumed to be divergence-free, dihedrally symmetric in law and have a log-correlated potential. A special case includes $\nabla^\perp$ of the Gaussian free field in two dimensions. We show the variance of the diffusion process at

Rob H. Bemthuis, Ruben R. Govers, Amin Asadi

Agent-based simulation (ABS) models are potent tools for analyzing complex systems. However, understanding and validating ABS models can be a significant challenge. To address this challenge, cutting-edge data-driven techniques offer sophisticated capabilities for analyzing the outcomes of ABS models. One such technique is process mining, which encompasses a

Zezhuang Hao

The flat/CFT dictionary between the bulk gravitational theory and boundary conformal field theory is systematically developed in this paper. Asymptotically flat spacetime is built up by asymptotically AdS hyperboloid slices in terms of Fefferman Graham coordinates together with soft modes propagating between different slices near the null boundary. Then we c

Pietro Bonazzi, Marie-Julie Rakatosaona, Marco Cannici, Federico Tombari

Existing deep learning methods for the reconstruction and denoising of point clouds rely on small datasets of 3D shapes. We circumvent the problem by leveraging deep learning methods trained on billions of images. We propose a method to reconstruct point clouds from few images and to denoise point clouds from their rendering by exploiting prior knowledge dis

Yassine Hamdi, Aaron B. Wagner, Deniz Gündüz

In image compression, with recent advances in generative modeling, the existence of a trade-off between the rate and the perceptual quality (realism) has been brought to light, where the realism is measured by the closeness of the output distribution to the source. It has been shown that randomized codes can be strictly better under a number of formulations.

Tong Hui, Stefan Rucareanu, Esteban Zamora, Simone D'Angelo

Aerial manipulators are increasingly used in contact-based industrial applications, where tasks like drilling and pushing require platforms to exert significant forces in multiple directions. To enhance force generation capabilities, various approaches, such as thrust vectoring and perching, have been explored. In this article, we introduce a novel approach

Abdullah H Alqahtani

Network attacks have became increasingly more sophisticated and stealthy due to the advances in technologies and the growing sophistication of attackers. Advanced Persistent Threats (APTs) are a type of attack that implement a wide range of strategies to evade detection and be under the defence radar. Software Defined Network (SDN) is a network paradigm that

Igor Burban, Semyon Klevtsov

The goal of this paper is to give an explicit computation of the curvature of the magnetic vector bundle of the multi-layer model of the fractional quantum Hall effect on a torus. We also obtain concrete formulae for the norms of the corresponding wave functions arising in such models.

Manish Patel, Debasish Chaudhuri

In this study, we investigate the behavior of inertial active Brownian particles in a $d$-dimensional harmonic trap in the presence of translational diffusion. While the solution of the Fokker-Planck equation is generally challenging, it can be utilized to compute the exact time evolution of all time-dependent dynamical moments using a Laplace transform appr

Xiping Sun, Jing Chen, Cong Wu, Kun He

Voice authentication has been widely used on smartphones. However, it remains vulnerable to spoofing attacks, where the attacker replays recorded voice samples from authentic humans using loudspeakers to bypass the voice authentication system. In this paper, we present MagLive, a robust voice liveness detection scheme designed for smartphones to mitigate suc

Guanheng Chen

A prequantization bundle is a negative circle bundle over a symplectic surface together with a contact form induced by a S1-invariant connection. Given a symplectically aspherical symplectic filling of a prequantization bundle satisfying certain topological conditions, suppose that a version of symplectic capacity of the symplectic filling is finite. Then, w

Jaemin Kim, Yohan Na, Kangmin Kim, Sang Rak Lee

Recently, sentiment-aware pre-trained language models (PLMs) demonstrate impressive results in downstream sentiment analysis tasks. However, they neglect to evaluate the quality of their constructed sentiment representations; they just focus on improving the fine-tuning performance, which overshadows the representation quality. We argue that without guarante

Azad Ghaffari, Tiago Roux Oliveira

A second-order Newton-based extremum seeking (SONES) algorithm is presented to estimate directional inflection points for multivariable static maps. The design extends the first-order Newton-based extremum seeking algorithm that drives the system toward its peak point. This work provides perturbation matrices to estimate the second- and third-order derivativ

Zihao Wang, Yingyu Yang, Yuzhou Chen, Tingting Yuan

Cross-modality image segmentation aims to segment the target modalities using a method designed in the source modality. Deep generative models can translate the target modality images into the source modality, thus enabling cross-modality segmentation. However, a vast body of existing cross-modality image translation methods relies on supervised learning. In

Zihan Guan, Mengxuan Hu, Sheng Li, Anil Vullikanti

Diffusion models are vulnerable to backdoor attacks, where malicious attackers inject backdoors by poisoning certain training samples during the training stage. This poses a significant threat to real-world applications in the Model-as-a-Service (MaaS) scenario, where users query diffusion models through APIs or directly download them from the internet. To m

Seongmin Lee, Zijie J. Wang, Aishwarya Chakravarthy, Alec Helbling

While large language models (LLMs) have shown remarkable capability to generate convincing text across diverse domains, concerns around its potential risks have highlighted the importance of understanding the rationale behind text generation. We present LLM Attributor, a Python library that provides interactive visualizations for training data attribution of

Anastasios Tsiamis, Mohamed Abdalmoaty, Roy S. Smith, John Lygeros

We study non-parametric frequency-domain system identification from a finite-sample perspective. We assume an open loop scenario where the excitation input is periodic and consider the Empirical Transfer Function Estimate (ETFE), where the goal is to estimate the frequency response at certain desired (evenly-spaced) frequencies, given input-output samples. W

Luxi He, Mengzhou Xia, Peter Henderson

Current Large Language Models (LLMs), even those tuned for safety and alignment, are susceptible to jailbreaking. Some have found that just further fine-tuning an aligned model with benign data (i.e., data without harmful content) surprisingly leads to substantial degradation in safety. We delve into the data-centric aspects of why benign fine-tuning inadver

Bo-Yang Wang, Yi-Fan Wang, Zi-He An, Li-Yang Xie

We report on the observation of a conspicuous loss in an ultracold mixture of $^{7}$Li and $^{88}$Sr atoms confined in a far-off-resonance optical dipole trap. We attribute the trap loss to the three-body inelastic Li-Sr-Sr collision and extract the corresponding three-body recombination coefficient $K_3$ at $T\sim 18.5,45,70,600$ $\mu K$. The measured three

Wen-Chuang Shang, Yi-Ning Han, Shimpei Endo, Chao Gao

We investigate the topological properties of a two-chain quantum ladder with uneven legs, i.e. the two chains differ in their periods by a factor of two. Such an uneven ladder presents rich band structures classified by the closure of either direct or indirect bandgaps. It also provides opportunities to explore fundamental concepts concerning band topology a

Nausheen Mohammed, Akash Lal, Aseem Rastogi, Subhajit Roy

Memory safety violations in low-level code, written in languages like C, continues to remain one of the major sources of software vulnerabilities. One method of removing such violations by construction is to port C code to a safe C dialect. Such dialects rely on programmer-supplied annotations to guarantee safety with minimal runtime overhead. This porting,

Martin Bauer

Flavour-violating decays are some of the most sensitive probes for New Physics with masses below the B meson threshold. In this talk I review the sensitivity to axion-like particles, hidden photons and heavy neutral leptons assuming minimal models where these are the only particle in addition to the Standard Model, respectively.

Maxim Nikolaev, Mikhail Kuznetsov, Dmitry Vetrov, Aibek Alanov

Our paper addresses the complex task of transferring a hairstyle from a reference image to an input photo for virtual hair try-on. This task is challenging due to the need to adapt to various photo poses, the sensitivity of hairstyles, and the lack of objective metrics. The current state of the art hairstyle transfer methods use an optimization process for d

Shiwang Ma

We study the non-existence and multiplicity of positive solutions of the nonlinear Choquard type equation $$ -\Delta u+ \varepsilon u=(I_\alpha \ast |u|^{p})|u|^{p-2}u+ |u|^{q-2}u, \quad {\rm in} \ \mathbb R^N, \qquad (P_\varepsilon)$$ where $N\ge 3$ is an integer, $p\in [\frac{N+\alpha}{N}, \frac{N+\alpha}{N-2}]$, $q\in (2,\frac{2N}{N-2}]$, $I_\alpha$ is th

John Southworth

OO Peg is a detached eclipsing binary system containing two late-A-type stars in a circular orbit with a period of 2.985 d. Using published spectroscopic results and a light curve from the Transiting Exoplanet Survey Satellite (TESS) we determine their masses to be 1.69 +/- 0.09 and 1.74 +/- 0.06 Msun, and their radii to be 2.12 +/- 0.03 and 1.91 +/- 0.03 Rs

Stefan Goessner

The mathematical theory underlying Hamiltonian mechanics is called symplectic geometry. So symplectic geometry arose from the roots of mechanics and is seen as one of the most valuable links between physics and mathematics today. Symplectic geometry in its simplest possible case is the geometry of the plane. Surprise is, we can hardly find relevant publicati

Sarah H. Q. Li, Florian Dörfler

Motivated by how forecast errors exacerbate order fluctuations in supply chains, we leverage robust feedback controller synthesis to characterize, compute, and minimize the worst-case order fluctuation experienced by an individual supply chain vendor. Assuming bounded forecast errors and demand fluctuations, we model forecast error and demand fluctuations as

Xu Yang, Changxing Ding, Zhibin Hong, Junhao Huang

Image-based virtual try-on is an increasingly important task for online shopping. It aims to synthesize images of a specific person wearing a specified garment. Diffusion model-based approaches have recently become popular, as they are excellent at image synthesis tasks. However, these approaches usually employ additional image encoders and rely on the cross