April 2024 arXiv papers — page 37

Konstantin Yakovlev, Anton Andreychuk, Roni Stern

Multi-agent pathfinding (MAPF) is the problem of finding a set of conflict-free paths for a set of agents. Typically, the agents' moves are limited to a pre-defined graph of possible locations and allowed transitions between them, e.g. a 4-neighborhood grid. We explore how to solve MAPF problems when each agent can move between any pair of possible locations

Lukas F. Bröring, Anna M. Viergever

We compute the quadratic Euler characteristic of the symmetric powers of a smooth, projective curve over any field $k$ that is not of characteristic two, using the Motivic Gauss-Bonnet Theorem of Levine-Raksit. As an application, we show over a field of characteristic zero that the power structure on the Grothendieck-Witt ring introduced by Pajwani-P\'al com

Yan Li

In this paper, we consider the well-posedness theory of two-dimensional compressible subsonic jet flows for steady full Euler system with general vorticity. Inspired by the analysis in arXiv:2006.05672, we show that the stream function formulation for such system admits a variational structure. Then the existence and uniqueness of a smooth subsonic jet flow

Qi Cao, Yulin Shao, Fan Yang, Octavia A. Dobre

This paper explores the distributed broadcast problem within the context of network communications, a critical challenge in decentralized information dissemination. We put forth a novel hypergraph-based approach to address this issue, focusing on minimizing the number of broadcasts to ensure comprehensive data sharing among all network users. The key contrib

An Yan, Zhengyuan Yang, Junda Wu, Wanrong Zhu

Set-of-Mark (SoM) Prompting unleashes the visual grounding capability of GPT-4V, by enabling the model to associate visual objects with tags inserted on the image. These tags, marked with alphanumerics, can be indexed via text tokens for easy reference. Despite the extraordinary performance from GPT-4V, we observe that other Multimodal Large Language Models

Jiawei Li, Didier Sornette, Zhongliang Wu, Jiancang Zhuang

The Epidemic-Type Aftershock Sequences (ETAS) model and its variants effectively capture the space-time clustering of seismicity, setting the standard for earthquake forecasting. Accurate unbiased ETAS calibration is thus crucial. But we identify three sources of bias, (i) boundary effects, (ii) finite-size effects, and (iii) censorship, which are often over

Tan-Zheng Wu, Yu-Wei Yu, Ying-Ying Gan, Hai-Ming Zhang

The radio galaxy PKS 1007+142 is classified as a compact steep-spectrum source (CSS) and belongs to the class of young Active Galactic Nuclei (AGNs). In this paper, we investigate the $\gamma$-ray emission from this CSS by conducting a comprehensive analysis of the 15 yr Fermi Large Area Telescope (Fermi-LAT) observation data. The Fermi-LAT latest Source Cat

Maarten Golterman, Yigal Shamir

We apply the Grabowska-Kaplan framework, originally proposed for lattice chiral gauge theories, to QCD. We show that the resulting theory contains a conserved and gauge invariant singlet axial current, both on the lattice and in the continuum limit. This must give rise to a difference with QCD, with the simplest possibility being a superfluous Nambu-Goldston

Xinxin Fan, Lin Liu, Haoran Zhang

The use of multimodal data in assisted diagnosis and segmentation has emerged as a prominent area of interest in current research. However, one of the primary challenges is how to effectively fuse multimodal features. Most of the current approaches focus on the integration of multimodal features while ignoring the correlation and consistency between differen

Kenji Koide, Shuji Oishi, Masashi Yokozuka, Atsuhiko Banno

This paper presents a 6-DoF range-based Monte Carlo localization method with a GPU-accelerated Stein particle filter. To update a massive amount of particles, we propose a Gauss-Newton-based Stein variational gradient descent (SVGD) with iterative neighbor particle search. This method uses SVGD to collectively update particle states with gradient and neighbo

Yukai Zhou, Jian Lou, Zhijie Huang, Zhan Qin

Ensuring the safety alignment of Large Language Models (LLMs) is critical for generating responses consistent with human values. However, LLMs remain vulnerable to jailbreaking attacks, where carefully crafted prompts manipulate them into producing toxic content. One category of such attacks reformulates the task as an optimization problem, aiming to elicit

Prasanta Chowdhury, Mohamad Numan, Shuvankar Gupta, Souvik Chatterjee

The effect of carrier localization due to electron-electron interaction in anomalous Hall effect is elusive and there are contradictory results in the literature. To address the issue, we report here the detailed transport study including the Hall measurements on $\beta$-Mn type cubic compound Co$_7$Zn$_7$Mn$_6$ with chiral crystal structure, which lacks glo

Kabir Ahuja, Vidhisha Balachandran, Madhur Panwar, Tianxing He

Transformers trained on natural language data have been shown to learn its hierarchical structure and generalize to sentences with unseen syntactic structures without explicitly encoding any structural bias. In this work, we investigate sources of inductive bias in transformer models and their training that could cause such generalization behavior to emerge.

Yuanchen Bei, Sheng Zhou, Jinke Shi, Yao Ma

Unsupervised graph anomaly detection aims at identifying rare patterns that deviate from the majority in a graph without the aid of labels, which is important for a variety of real-world applications. Recent advances have utilized Graph Neural Networks (GNNs) to learn effective node representations by aggregating information from neighborhoods. This is motiv

Sri Harsha Dumpala, Aman Jaiswal, Chandramouli Sastry, Evangelos Milios

Despite their remarkable successes, state-of-the-art language models face challenges in grasping certain important semantic details. This paper introduces the VISLA (Variance and Invariance to Semantic and Lexical Alterations) benchmark, designed to evaluate the semantic and lexical understanding of language models. VISLA presents a 3-way semantic (in)equiva

Chunyu Xuan, Yazhe Niu, Yuan Pu, Shuai Hu

Monte Carlo Tree Search (MCTS)-based algorithms, such as MuZero and its derivatives, have achieved widespread success in various decision-making domains. These algorithms employ the reanalyze process to enhance sample efficiency from stale data, albeit at the expense of significant wall-clock time consumption. To address this issue, we propose a general appr

Jaeseong You, Minseop Park, Kyunggeun Lee, Seokjun An

This paper investigates three different parameterizations of asymmetric uniform quantization for quantization-aware training: (1) scale and offset, (2) minimum and maximum, and (3) beta and gamma. We perform a comprehensive comparative analysis of these parameterizations' influence on quantization-aware training, using both controlled experiments and real-wo

Ulysse Pavloff, Yackolley Amoussou-Genou, Sara Tucci-Piergiovanni

In May 2023, the Ethereum blockchain experienced its first inactivity leak, a mechanism designed to reinstate chain finalization amid persistent network disruptions. This mechanism aims to reduce the voting power of validators who are unreachable within the network, reallocating this power to active validators. This paper investigates the implications of the

Binghui Zou, Chunjie Cao, Longjuan Wang, Yinan Cheng

Malware can greatly compromise the integrity and trustworthiness of information and is in a constant state of evolution. Existing feature fusion-based detection methods generally overlook the correlation between features. And mere concatenation of features will reduce the model's characterization ability, lead to low detection accuracy. Moreover, these metho

Ou Deng, Shoji Nishimura, Atsushi Ogihara, Qun Jin

This study proposes Evolutionary Causal Discovery (ECD) for causal discovery that tailors response variables, predictor variables, and corresponding operators to research datasets. Utilizing genetic programming for variable relationship parsing, the method proceeds with the Relative Impact Stratification (RIS) algorithm to assess the relative impact of predi

David T. Stephen, Oliver Hart

In the era of noisy, intermediate-scale quantum (NISQ) devices, the efficient preparation of many-body resource states is a task of paramount importance. In this paper we focus on the deterministic preparation of matrix-product states (MPS) in constant depth by utilizing measurements and classical communication to fuse smaller states into larger ones. We pla

Cong Wu, Xiao-Jun Wu, Tianyang Xu, Josef Kittler

Pooling is a crucial operation in computer vision, yet the unique structure of skeletons hinders the application of existing pooling strategies to skeleton graph modelling. In this paper, we propose an Improved Graph Pooling Network, referred to as IGPN. The main innovations include: Our method incorporates a region-awareness pooling strategy based on struct

Chananchida Sang-aram, Robin Browaeys, Ruth Seurinck, Yvan Saeys

Ligand-receptor interactions constitute a fundamental mechanism of cell-cell communication and signaling. NicheNet is a well-established computational tool that infers ligand-receptor interactions that potentially regulate gene expression changes in receiver cell populations. Whereas the original publication delves into the algorithm and validation, this pap

Zhichao Liang, Yinuo Zhang, Jushen Wu, Quanying Liu

The human brain receives complex inputs when performing cognitive tasks, which range from external inputs via the senses to internal inputs from other brain regions. However, the explicit inputs to the brain during a cognitive task remain unclear. Here, we present an input identification framework for reverse engineering the control nodes and the correspondi

Minrui Xu, Dusit Niyato, Jiawen Kang, Zehui Xiong

Generative AI (GAI) can enhance the cognitive, reasoning, and planning capabilities of intelligent modules in the Internet of Vehicles (IoV) by synthesizing augmented datasets, completing sensor data, and making sequential decisions. In addition, the mixture of experts (MoE) can enable the distributed and collaborative execution of AI models without performa

Jia Tian, Tengzhou Lai, Farzad Omidi

In this study, we examine the modular transformations of the (root-)$\text{T}\overline{\text{T}}$ deformed torus partition function of a two-dimensional CFT (with a gravitational anomaly) from the holographic perspective by computing the on-shell actions of various saddle solutions of the dual gravity theories.

Yu Feng, Qingyou He, Jian-Guo Liu, Zhennan Zhou

We consider a Hele-Shaw model that describes tumor growth subject to nutrient supply. The model is derived by taking the incompressible limit of porous medium type equations, and the boundary instability of this model was recently studied in \cite{feng2022tumor} using asymptotic analysis. In this paper, we further prove the existence of nonsymmetric travelin

Takashi Goda

In this short note, we prove that the one-dimensional Kronecker sequence $i\alpha \bmod 1, i=0,1,2,\ldots,$ is quasi-uniform if and only if $\alpha$ is a badly approximable number. Our elementary proof relies on a result on the three-gap theorem for Kronecker sequences due to Halton (1965).

Matthew Otten, Byeol Kang, Dmitry Fedorov, Anouar Benali

As quantum hardware continues to improve, more and more application scientists have entered the field of quantum computing. However, even with the rapid improvements in the last few years, quantum devices, especially for quantum chemistry applications, still struggle to perform calculations that classical computers could not calculate. In lieu of being able

Shi-Yu Xia, Wenxuan Zhu, Xu Yang, Xin Geng

In practice, we usually need to build variable-sized models adapting for diverse resource constraints in different application scenarios, where weight initialization is an important step prior to training. The Learngene framework, introduced recently, firstly learns one compact part termed as learngene from a large well-trained model, after which learngene i

Andrew J. Levan, Peter G. Jonker, Andrea Saccardi, Daniele Bjørn Malesani

The nature of the minute-to-hour long Fast X-ray Transients (FXTs) localised by telescopes such as Chandra, Swift, and XMM-Newton remains mysterious, with numerous models suggested for the events. Here, we report multi-wavelength observations of EP240315a, a 1600 s long transient detected by the Einstein Probe, showing it to have a redshift of z=4.859. We me

Josh Alman, Ran Duan, Virginia Vassilevska Williams, Yinzhan Xu

We present a new improvement on the laser method for designing fast matrix multiplication algorithms. The new method further develops the recent advances by [Duan, Wu, Zhou FOCS 2023] and [Vassilevska Williams, Xu, Xu, Zhou SODA 2024]. Surprisingly the new improvement is achieved by incorporating more asymmetry in the analysis, circumventing a fundamental to

Zhijie Rao, Jingcai Guo, Xiaocheng Lu, Jingming Liang

Zero-shot learning has consistently yielded remarkable progress via modeling nuanced one-to-one visual-attribute correlation. Existing studies resort to refining a uniform mapping function to align and correlate the sample regions and subattributes, ignoring two crucial issues: 1) the inherent asymmetry of attributes; and 2) the unutilized channel informatio

Shivam Bhargava, Nagaiah Chamakuri

This study introduces a computational approach leveraging Physics-Informed Neural Networks (PINNs) for the efficient computation of arterial blood flows, particularly focusing on solving the incompressible Navier-Stokes equations by using the domain decomposition technique. Unlike conventional computational fluid dynamics methods, PINNs offer advantages by e

Yongxu Jin, Dalton Omens, Zhenglin Geng, Joseph Teran

Since loose-fitting clothing contains dynamic modes that have proven to be difficult to predict via neural networks, we first illustrate how to coarsely approximate these modes with a real-time numerical algorithm specifically designed to mimic the most important ballistic features of a classical numerical simulation. Although there is some flexibility in th

Aravind Kumar N, Hari Krishna S, Anita Angeline A

This paper presents a comprehensive exploration of Fast Fourier Transform (FFT) and linear convolution implementations, integrating both conventional methods and novel approaches leveraging the Bit Slicing Multiplier (BSM) technique. The Bit Slicing Multiplier utilizes Look-Up Tables (LUTs) to execute bitwise operations in parallel, offering efficient arithm

Xiang He, Weiye Song, Yiming Wang, Fabio Poiesi

Automatic retinal layer segmentation with medical images, such as optical coherence tomography (OCT) images, serves as an important tool for diagnosing ophthalmic diseases. However, it is challenging to achieve accurate segmentation due to low contrast and blood flow noises presented in the images. In addition, the algorithm should be light-weight to be depl

Hengyue Zhang, Martin Bureau, Mark D. Smith, Michele Cappellari

The mass ($M_\mathrm{BH}$) of a supermassive black hole (SMBH) can be measured using spatially-resolved kinematics of the region where the SMBH dominates gravitationally. The most reliable measurements are those that resolve the smallest physical scales around the SMBHs. We consider here three metrics to compare the physical scales probed by kinematic tracer

Hongyuan Li, Ziyu Xiang, Tianle Wang, Mit H. Naik

One-dimensional (1D) interacting electrons are often described as a Luttinger liquid1-4 having properties that are intrinsically different from Fermi liquids in higher dimensions5,6. 1D electrons in materials systems exhibit exotic quantum phenomena that can be tuned by both intra- and inter-1D-chain electronic interactions, but their experimental characteri

S. Guo, T. An, Y. Liu, Y. Sotnikova

Context : Understanding the mechanisms that launch and shape powerful relativistic jets from supermassive black holes (SMBHs) in high-redshift active galactic nuclei (AGN) is crucial for probing the co-evolution of SMBHs and galaxies over cosmic time. Aims :We study the high-redshift ($z=3.396$) blazar OH~471 to explore the jet launching mechanism in the ear

Tiemo Landes, Brian J. Smith, Michael G. Raymer

We closely replicated and extended a recent experiment ("Spatial properties of entangled two-photon absorption," Phys. Rev. Lett. 129, 183601, 2022) that reportedly observed enhancement of two-photon absorption rates in molecular samples by using time-frequency-entangled photon pairs, and we found that in the low-flux regime, where such enhancement is theore

Stephen Bothwell, Brian DuSell, David Chiang, Brian Krostenko

Computational historical linguistics seeks to systematically understand processes of sound change, including during periods at which little to no formal recording of language is attested. At the same time, few computational resources exist which deeply explore phonological and morphological connections between proto-languages and their descendants. This is p

Maoyang Xiang, Emil Goh, T. Hui Teo

The escalating complexity of modern digital systems has imposed significant challenges on integrated circuit (IC) design, necessitating tools that can simplify the IC design flow. The advent of Large Language Models (LLMs) has been seen as a promising development, with the potential to automate the generation of Hardware Description Language (HDL) code, ther

Peter Vadasz

Gravitational waves are being shown to derive directly from Newtonian dynamics for a continuous mass distribution, e g compressible fluids or equivalent. It is shown that the equations governing a continuous mass distribution, i e the inviscid Navier Stokes equations for a general variable gravitational field g(t,x), are equivalent to a form identical to Max

John Iacono, Piotr Micek, Pat Morin, Bruce Reed

An $\ell$-vertex-ranking of a graph $G$ is a colouring of the vertices of $G$ with integer colours so that in any connected subgraph $H$ of $G$ with diameter at most $\ell$, there is a vertex in $H$ whose colour is larger than that of every other vertex in $H$. The $\ell$-vertex-ranking number, $\chi_{\ell-\mathrm{vr}}(G)$, of $G$ is the minimum integer $k$

Minglu Zhao, Dehong Xu, Tao Gao

Attention is a cornerstone of human cognition that facilitates the efficient extraction of information in everyday life. Recent developments in artificial intelligence like the Transformer architecture also incorporate the idea of attention in model designs. However, despite the shared fundamental principle of selectively attending to information, human atte

Sifan Long, Linbin Wang, Zhen Zhao, Zichang Tan

Prompt learning has become the most effective paradigm for adapting large pre-trained vision-language models (VLMs) to downstream tasks. Recently, unsupervised prompt tuning methods, such as UPL and POUF, directly leverage pseudo-labels as supervisory information to fine-tune additional adaptation modules on unlabeled data. However, inaccurate pseudo labels

Eva-Maria Hekkelman, Edward McDonald, Teun D. H. van Nuland

We push the definition of multiple operator integrals (MOIs) into the realm of unbounded operators, using the pseudodifferential calculus from the works of Connes and Moscovici, Higson, and Guillemin. This in particular provides a natural language for operator integrals in noncommutative geometry. For this purpose, we develop a functional calculus for these

Hyukjung Kim, Ewan D. Stewart, Heeseung Zoe

We investigate the stability of the electroweak vacuum with respect to vacuum tunneling to the Komatsu vacuum, which exists when $m_L^2 + m_{H_u}^2<0$, in the cMSSM. Employing the numerical tools SARAH, SPheno and CosmoTransitions, we scan and constrain the parameter space of the cMSSM up to 10 TeV. Regions excluded due to having a vacuum tunneling half-life

Changjuan Ran, Yeting Guo, Fang Liu, Shenglan Cui

The unique artistic style is crucial to artists' occupational competitiveness, yet prevailing Art Commission Platforms rarely support style-based retrieval. Meanwhile, the fast-growing generative AI techniques aggravate artists' concerns about releasing personal artworks to public platforms. To achieve artistic style-based retrieval without exposing personal

Sandeep Kumar

This paper presents the calculation of elastic properties of three-dimensional Yukawa or dust crystals using molecular dynamics (MD) simulations. The elastic properties are computed by deforming (compressing/expanding) the dust crystals along different directions. The stress and strain of the deformed crystal are used to calculate elastic properties. The bul

Henrik Jacobsen, Marek Barthkowiak, Tobias Weber, Uwe Stuhr

Here we report on an elastic and inelastic neutron scattering study addressing the effect of electric fields on quantum paraelectric SrTiO3. Our elastic scattering results find small changes as a function of field in a superlattice reflection that sample the octahedral rotations, which is indicative of only weak coupling of octahedral rotation and electric p

Zhensu Sun, Xiaoning Du, Zhou Yang, Li Li

Artificial Intelligence (AI) models have emerged as another important audience for programming languages alongside humans and machines, as we enter the era of large language models (LLMs). LLMs can now perform well in coding competitions and even write programs like developers to solve various tasks, including mathematical problems. However, the grammar and

Xiaomin Yu, Yezhaohui Wang, Yanfang Chen, Zhen Tao

In recent years, generative artificial intelligence models, represented by Large Language Models (LLMs) and Diffusion Models (DMs), have revolutionized content production methods. These artificial intelligence-generated content (AIGC) have become deeply embedded in various aspects of daily life and work. However, these technologies have also led to the emerg

Paolo Bertozzini, Wanchalerm Sucpikarnon, Apimook Watcharangkool

We investigate the notion of subsystem in the framework of spectral triple as a generalized notion of noncommutative submanifold. In the case of manifolds, we consider several conditions on Dirac operators which turn embedded submanifolds into isometric submanifolds. We then suggest a definition of spectral subtriple based on the notion of submanifold algebr

Zitong Huang, Ze Chen, Bowen Dong, Chaoqi Liang

Model Weight Averaging (MWA) is a technique that seeks to enhance model's performance by averaging the weights of multiple trained models. This paper first empirically finds that 1) the vanilla MWA can benefit the class-imbalanced learning, and 2) performing model averaging in the early epochs of training yields a greater performance improvement than doing t

Yun-Tong Yang, Fu-Zhou Chen, Hong-Gang Luo

Exploring the influence of frustration on the phases and related phase transitions in condensed matter physics is of fundamental importance in uncovering the role played by frustration. In the two-dimensional square lattice, a minimal frustration has been formulated in 1981 as the Shastry-Sutherland (SS) model described by competitions between the nearest-ne

Bubai Manna, Bodhayan Roy, Vorapong Suppakitpaisarn

We examine the dynamic network flow problem under the assumption that the flow consists of discrete units. The dynamic network flow problem is commonly addressed in the context of developing evacuation plans, where the flow is typically treated as a continuous quantity. However, real-world scenarios often involve moving groups, such as families, as single un

Hiroyuki Hanada, Satoshi Akahane, Tatsuya Aoyama, Tomonari Tanaka

In this study, we propose a method Distributionally Robust Safe Screening (DRSS), for identifying unnecessary samples and features within a DR covariate shift setting. This method effectively combines DR learning, a paradigm aimed at enhancing model robustness against variations in data distribution, with safe screening (SS), a sparse optimization technique

Cheng Du, Yi Jiang

Sequences with low aperiodic autocorrelation sidelobes have been extensively researched in literatures. With sufficiently low integrated sidelobe level (ISL), their power spectrums are asymptotically flat over the whole frequency domain. However, for the beam sweeping in the massive multi-input multi-output (MIMO) broadcast channels, the flat spectrum should

Rahmat Adesunkanmi, Balaji Sesha Srikanth Pokuri, Ratnesh Kumar

In many real-world applications where the system dynamics has an underlying interdependency among its variables (such as power grid, economics, neuroscience, omics networks, environmental ecosystems, and others), one is often interested in knowing whether the past values of one time series influences the future of another, known as Granger causality, and the

Hedda Cohen Indelman, Elay Dahan, Angeles M. Perez-Agosto, Carmit Shiran

Despite the remarkable success of deep learning in medical imaging analysis, medical image segmentation remains challenging due to the scarcity of high-quality labeled images for supervision. Further, the significant domain gap between natural and medical images in general and ultrasound images in particular hinders fine-tuning models trained on natural imag

Davide Bianchi, Florian Bossmann, Wenlong Wang, Mingming Liu

We introduce a data-adaptive inversion method that integrates classical or deep learning-based approaches with iterative graph Laplacian regularization, specifically targeting acoustic impedance inversion - a critical task in seismic exploration. Our method initiates from an impedance estimate derived using either traditional inversion techniques or neural n

Jiamin Wu, Kenkun Liu, Han Gao, Xiaoke Jiang

Recently, Gaussian splatting has demonstrated significant success in novel view synthesis. Current methods often regress Gaussians with pixel or point cloud correspondence, linking each Gaussian with a pixel or a 3D point. This leads to the redundancy of Gaussians being used to overfit the correspondence rather than the objects represented by the 3D Gaussian

Mingyu Yang, Wentao Li, Jiabao Jin, Xiaoyao Zhong

Approximate K Nearest Neighbor (AKNN) search in high-dimensional spaces is a critical yet challenging problem. In AKNN search, distance computation is the core task that dominates the runtime. Existing approaches typically use approximate distances to improve computational efficiency, often at the cost of reduced search accuracy. To address this issue, the s

Sophie Libkind, David I. Spivak

Interviews run on people, programs run on operating systems, voting schemes run on voters, games run on players. Each of these is an example of the abstraction pattern runs on matter. Pattern determines the decision tree that governs how a situation can unfold, while matter responds with decisions at each juncture. In this article, we will give a straightfor

Yilin Chen, Weijun Xu

We develop a variant of the para-controlled distributions framework based on operator dependent (generalised) Besov spaces. These spaces were introduced by Kerkyacharian-Petrushev ([KP15]). In contrast to the Fourier decomposition in the classical setting, they are built from spectral decomposition of general self-adjoint elliptic operators. A major differen

Marcie Mun, Emily Wisnioski, Andrew J. Battisti, J. Trevor Mendel

Using adaptive optics with the Multi-Unit Spectroscopic Explorer (MUSE) on the Very Large Telescope (VLT), the Middle Ages Galaxy Properties with Integral Field Spectroscopy (MAGPI) survey allows us to study the spatially resolved Universe at a crucial time of ~4 Gyr ago ($z$ ~ 0.3) when simulations predict the greatest diversity in evolutionary pathways for

Yi Han, Julien M. Hendrickx, Ge Chen, Wenjun Mei

Simple yet predictive mathematical models are essential for mechanistic understanding of opinion evolution in social groups. The weighted-median mechanism has recently been proposed as a well-founded alternative to conventional DeGroot-type opinion dynamics. However, the original weighted-median model excludes compromise behavior, as individuals directly ado

Y. S. J. Liang, Darren C. Ong

In this work, we introduce a class of fractal subsets of $[0,1]$ corresponding to the aperiodically ordered metallic means sequences. We find simple formulas for the fractal dimension for these fractals.

Gabriel Kulp, Andrew Ensinger, Lizhong Chen

Tensors play a vital role in machine learning (ML) and often exhibit properties best explored while maintaining high-order. Efficiently performing ML computations requires taking advantage of sparsity, but generalized hardware support is challenging. This paper introduces FLAASH, a flexible and modular accelerator design for sparse tensor contraction that ac

Sergio Cecotti

The Weil correspondence states that the datum of a Seiberg-Witten differential is equivalent to an algebraic group extension of the integrable system associated to the Seiberg-Witten geometry. Remarkably this group extension represents quantum consistent couplings for the $\mathcal{N}=2$ QFT if and only if the extension is anti-affine in the algebro-geometri

Ryan E. G. Bushling, Jacob B. Fiedler

Let $E \subseteq \mathbb{R}^n$ be a union of line segments and $F \subseteq \mathbb{R}^n$ the set obtained from $E$ by extending each line segment in $E$ to a full line. Keleti's line segment extension conjecture posits that the Hausdorff dimension of $F$ should equal that of $E$. Working in $\mathbb{R}^2$, we use effective methods to prove a strong packing

Xiangyun Ding, Yan Gu, Yihan Sun

The idea of dynamic programming (DP), proposed by Bellman in the 1950s, is one of the most important algorithmic techniques. However, in parallel, many fundamental and sequentially simple problems become more challenging, and open to a (nearly) work-efficient solution (i.e., the work is off by at most a polylogarithmic factor over the best sequential solutio

Qin Yuan, Chunlei Li, Xiangyong Zeng, Tor Helleseth

Nonlinear complexity is an important measure for assessing the randomness of sequences. In this paper we investigate how circular shifts affect the nonlinear complexities of finite-length binary sequences and then reveal a more explicit relation between nonlinear complexities of finite-length binary sequences and their corresponding periodic sequences. Based

Praveen Kumar Ranjan, Abhinav Sinha, Yongcan Cao

In this paper, we address the problem of enclosing an arbitrarily moving target in three dimensions by a single pursuer while ensuring the pursuer's safety by preventing collisions with the target. The proposed guidance strategy steers the pursuer to a safe region of space surrounding and excluding the target, allowing it to maintain a certain distance from

Russell Avdek, Zhengyi Zhou

We prove that Bourgeois' contact structures on $M \times \mathbb{T}^{2}$ determined by the supporting open books of a contact manifold $(M, \xi)$ are always tight. The proof is based on a contact homology computation leveraging holomorphic foliations and Kuranishi structures.

Entong He, Yuxiang Yang, Chenshu Wu

Remarkable advances have been achieved in localization techniques in past decades, rendering it one of the most important technologies indispensable to our daily lives. In this paper, we investigate a novel localization approach for future computing by presenting QuERLoc, the first study on localization using quantum-enhanced ranging. By fine-tuning the evol

Ning-Ning Guo, Jian-Fu Zhang, Hua-Ping Xiao, Jungyeon Cho

Since synchrotron polarization fluctuations are related to the fundamental properties of the magnetic field, we propose the polarization intensity variance to measure the Galactic interstellar medium (ISM) magnetization. We confirm the method's applicability by comparing it with the polarization angle dispersion and its reliability by measuring the underlyin

Michikuni Eguchi, Mai Nishimura, Shigeo Yoshida, Takefumi Hiraki

Robot swarms hold immense potential for performing complex tasks far beyond the capabilities of individual robots. However, the challenge in unleashing this potential is the robots' limited sensory capabilities, which hinder their ability to detect and adapt to unknown obstacles in real-time. To overcome this limitation, we introduce a novel robot swarm cont

Wenlong Zhao, Debanjan Mondal, Niket Tandon, Danica Dillion

The awareness of multi-cultural human values is critical to the ability of language models (LMs) to generate safe and personalized responses. However, this awareness of LMs has been insufficiently studied, since the computer science community lacks access to the large-scale real-world data about multi-cultural values. In this paper, we present WorldValuesBen

Xiaoling Zhou, Wei Ye, Zhemg Lee, Rui Xie

Data augmentation plays a pivotal role in enhancing and diversifying training data. Nonetheless, consistently improving model performance in varied learning scenarios, especially those with inherent data biases, remains challenging. To address this, we propose to augment the deep features of samples by incorporating their adversarial and anti-adversarial per

Haomiao Ni, Bernhard Egger, Suhas Lohit, Anoop Cherian

Text-conditioned image-to-video generation (TI2V) aims to synthesize a realistic video starting from a given image (e.g., a woman's photo) and a text description (e.g., "a woman is drinking water."). Existing TI2V frameworks often require costly training on video-text datasets and specific model designs for text and image conditioning. In this paper, we prop

Gehui Chen, Guan'an Wang, Xiaowen Huang, Jitao Sang

Existing works have made strides in video generation, but the lack of sound effects (SFX) and background music (BGM) hinders a complete and immersive viewer experience. We introduce a novel semantically consistent v ideo-to-audio generation framework, namely SVA, which automatically generates audio semantically consistent with the given video content. The fr

Zhiwei Dong, Xi Zhu, Xiya Cao, Ran Ding

Lane detection has made significant progress in recent years, but there is not a unified architecture for its two sub-tasks: 2D lane detection and 3D lane detection. To fill this gap, we introduce B\'{e}zierFormer, a unified 2D and 3D lane detection architecture based on B\'{e}zier curve lane representation. B\'{e}zierFormer formulate queries as B\'{e}zier c

Bowen Liu, Maolin Dai, Takuma Shirahata, Yifan Ma

Repetition-rate-scanned lasers are practical in accordion frequency comb generation that serves as a variable gearbox connecting optical and radio wave domains. Rapid and wide-range scanned repetition rate can benefit versatile purposes, however scanning robustness remains unsecured that typically requires complicated feedback loops. Recently, multiplexed la

Haoyuan Li, Qi Hu, Binjia Zhou, You Yao

Visible-infrared image pairs provide complementary information, enhancing the reliability and robustness of object detection applications in real-world scenarios. However, most existing methods face challenges in maintaining robustness under complex weather conditions, which limits their applicability. Meanwhile, the reliance on attention mechanisms in modal

Ting Li, Jianshu Chao, Deyu An

Unsupervised Domain Adaptation (UDA) refers to the method that utilizes annotated source domain data and unlabeled target domain data to train a model capable of generalizing to the target domain data. Domain discrepancy leads to a significant decrease in the performance of general network models trained on the source domain data when applied to the target d

Nico Schiavone, Xingyu Li

Foundation models contain a wealth of information from their vast number of training samples. However, most prior arts fail to extract this information in a precise and efficient way for small sample sizes. In this work, we propose a framework utilizing reinforcement learning as a control for foundation models, allowing for the granular generation of small,

Dehao Zhao

Symmetric teleparallel gravity (STG) is a gravity theory which takes non-metricity tensor to describe gravity effects. In the STG framework, we study the conformal equivalent scalar-tensor theory of f(Q) model and calculate the cosmological linear perturbations of the conformal transformed action. We confirm the result already present in references that f(Q)

Dean Alvin L. Pablico, John Jaykel P. Magadan, Carl Anthony L. Arguelles, Eric A. Galapon

The construction of time of arrival (TOA) operators canonically conjugate to the system Hamiltonian entails finding the solution of a specific second-order partial differential equation called the time kernel equation (TKE). In this paper, we provide an exact analytic solution of the TKE for a special class of potentials satisfying a specific separability co

Yu Jiang, Jie Liang, Fuchen Ma, Yuanliang Chen

Fuzzing, a widely-used technique for bug detection, has seen advancements through Large Language Models (LLMs). Despite their potential, LLMs face specific challenges in fuzzing. In this paper, we identified five major challenges of LLM-assisted fuzzing. To support our findings, we revisited the most recent papers from top-tier conferences, confirming that t

Ao Xiang, Jingyu Zhang, Qin Yang, Liyang Wang

With the development and widespread application of digital image processing technology, image splicing has become a common method of image manipulation, raising numerous security and legal issues. This paper introduces a new splicing image detection algorithm based on the statistical characteristics of natural images, aimed at improving the accuracy and effi

Liexin Cheng, Xue Cheng, Xianhua Peng

The Chicago Board Options Exchange Volatility Index (VIX) is calculated from SPX options and derivatives of VIX are also traded in market, which leads to the so-called ``consistent modeling" problem. This paper proposes a time-changed L\'evy model for log price with a composite change of time structure to capture both features of the implied SPX volatility a

Saranya Krishnamoorthy, Ayush Singh, Shabnam Tafreshi

Electronic health records (EHR) even though a boon for healthcare practitioners, are growing convoluted and longer every day. Sifting around these lengthy EHRs is taxing and becomes a cumbersome part of physician-patient interaction. Several approaches have been proposed to help alleviate this prevalent issue either via summarization or sectioning, however,

Xin Lü, Shengli Tan

Let $\mathcal F$ be a foliation on a smooth projective surface $S$ over the complex number $\mathbb{C}$. We introduce three birational non-negative invariants $c_1^2(\mathcal F)$, $c_2(\mathcal F)$ and $\chi(\mathcal F)$, called the Chern numbers. If the foliation $\mathcal F$ is not of general type, the first Chern number $c_1^2(\mathcal F)=0$, and $c_2(\ma

Arman Maesumi, Dylan Hu, Krishi Saripalli, Vladimir G. Kim

Procedural noise is a fundamental component of computer graphics pipelines, offering a flexible way to generate textures that exhibit "natural" random variation. Many different types of noise exist, each produced by a separate algorithm. In this paper, we present a single generative model which can learn to generate multiple types of noise as well as blend b

Shun Ohkubo

Let $K$ be a complete non-archimedean valuation field of characteristic $0$, with non-trivial valuation, equipped with (possibly multiple) commuting bounded derivations. We prove a decomposition theorem for finite differential modules over $K$, where decompositions regarding the extrinsic subsidiary $\partial$-generic radii of convergence in the sense of Ked

Hantaek Bae, Jinwook Jung, Jaeyong Shin

In this paper, we derive decay rates for solutions to the incompressible Navier-Stokes equations and Hall-magnetohydrodynamic equations. We first improve the decay rate of weak solutions to these equations by refining the Fourier splitting method with initial data in the space of pseudo-measures. Additionally, we investigate these equations with initial data

Yiran Guo, Wei Chen, Jialong Xu, Lun Li

The design of precoding plays a crucial role in achieving a high downlink sum-rate in multiuser multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems. In this correspondence, we propose a deep learning based joint CSI feedback and multiuser precoding method in frequency division duplex systems, aiming at maximizing t