May 2024 arXiv papers — page 44

Andrii Trelin, Sophie Kussauer, Paul Weinbrenner, Anja Clasen

There is considerable evidence that action potentials are accompanied by "intrinsic optical signals", such as a nanometer-scale motion of the cell membrane. Here we present ChiSCAT, a technically simple imaging scheme that detects such signals with interferometric sensitivity. ChiSCAT combines illumination by a {\bf ch}aotic speckle pattern and interferometr

Zerun Wang, Jiafeng Mao, Liuyu Xiang, Toshihiko Yamasaki

Semi-supervised learning (SSL) can improve model performance by leveraging unlabeled images, which can be collected from public image sources with low costs. In recent years, synthetic images have become increasingly common in public image sources due to rapid advances in generative models. Therefore, it is becoming inevitable to include existing synthetic i

Lucas Jarnac, Yoan Chabot, Miguel Couceiro

Knowledge Graphs (KGs) are a major asset for companies thanks to their great flexibility in data representation and their numerous applications, e.g., vocabulary sharing, Q/A or recommendation systems. To build a KG it is a common practice to rely on automatic methods for extracting knowledge from various heterogeneous sources. But in a noisy and uncertain w

Kai Zheng, Qilong Feng, Yaohang Li, Qichang Zhao

Complex networks, which are the abstractions of many real-world systems, present a persistent challenge across disciplines for people to decipher their underlying information. Recently, hyperbolic geometry of latent spaces has gained traction in network analysis, due to its ability to preserve certain local intrinsic properties of the nodes. In this study, w

Dan J. Hill

The emergence of localised radial patterns from a Turing instability has been well studied in two and three dimensional settings and predicted for higher spatial dimensions. We prove the existence of localised $(n+1)$-dimensional radial patterns in general two-component reaction-diffusion systems near a Turing instability, where $n>0$ is taken to be a contin

Veasna Chaya, Ate Poortinga, Keo Nimol, Se Sokleap

Cambodia's agricultural landscape is rapidly transforming, particularly in the cashew sector. Despite the country's rapid emergence and ambition to become the largest cashew producer, comprehensive data on plantation areas and the environmental impacts of this expansion are lacking. This study addresses the gap in detailed land use data for cashew plantation

Guan Wang, Zhimin Li, Qingchao Chen, Yang Liu

Dynamic Scene Graph Generation (DSGG) focuses on identifying visual relationships within the spatial-temporal domain of videos. Conventional approaches often employ multi-stage pipelines, which typically consist of object detection, temporal association, and multi-relation classification. However, these methods exhibit inherent limitations due to the separat

Francesco Montagna, Max Cairney-Leeming, Dhanya Sridhar, Francesco Locatello

Supervised learning for causal discovery from observational data often achieves competitive performance despite seemingly avoiding the explicit assumptions that traditional methods require for identifiability. In this work, we analyze CSIvA (Ke et al., 2023) on bivariate causal models, a transformer architecture for amortized inference promising to train on

Butian Xiong, Xiaoyu Ye, Tze Ho Elden Tse, Kai Han

With the emergence of Gaussian Splats, recent efforts have focused on large-scale scene geometric reconstruction. However, most of these efforts either concentrate on memory reduction or spatial space division, neglecting information in the semantic space. In this paper, we propose a novel method, named SA-GS, for fine-grained 3D geometry reconstruction usin

Friedemann Zenke, Axel Laborieux

Humans and animals learn throughout life. Such continual learning is crucial for intelligence. In this chapter, we examine the pivotal role plasticity mechanisms with complex internal synaptic dynamics could play in enabling this ability in neural networks. By surveying theoretical research, we highlight two fundamental enablers for continual learning. First

H. Shi, F. Ringe, D. Wang, O. Moran

The multiferroic properties of TbMnO3 demonstrate high versatility under applied pressure, making the material potentially suitable for use in flexible electronics. Here, we report on the preparation of elastic freestanding TbMnO3 membranes with dominant (001) or (010) crystallographic out-of-plane orientation. Membranes with thickness of 20 nm display ortho

Ignatios Antoniadis, Daniele Bielli, Auttakit Chatrabhuti, Hiroshi Isono

We study the problem of false vacuum decay in arbitrary dimensions, in the presence of gravity, and compute the transition probability within the thin-wall approximation, generalising the results of Coleman and de Luccia. In the particular case of one compact dimension, we present explicit formulae for the Euclidean Bounce configuration that drives the trans

Zejun Li, Ruipu Luo, Jiwen Zhang, Minghui Qiu

While large multi-modal models (LMMs) have exhibited impressive capabilities across diverse tasks, their effectiveness in handling complex tasks has been limited by the prevailing single-step reasoning paradigm. To this end, this paper proposes VoCoT, a multi-step Visually grounded object-centric Chain-of-Thought reasoning framework tailored for inference wi

Nils Philipp Walter, Linara Adilova, Jilles Vreeken, Michael Kamp

Flatness of the loss surface not only correlates positively with generalization, but is also related to adversarial robustness since perturbations of inputs relate non-linearly to perturbations of weights. In this paper, we empirically analyze the relation between adversarial examples and relative flatness with respect to the parameters of one layer. We obse

Hongyaoxing Gu

Matrix multiplication computation acceleration has been a research hotspot across various domains. Due to the characteristics of some applications, approximate matrix multiplication can achieve significant performance improvements without losing much precision. In this paper, we propose LRAMM - a high-performance matrix multiplication approximation algorithm

Allen Lobo, Vinod Kumar Sayal

The kinetic analyses of many-particle soft matter often employ many simulation studies of various physical phenomena which supplement the experimental limitations or compliment the theoretical findings of the study. Such simulations are generally conducted by the numerical integration techniques of the governing equations. In the typical case of collisionles

Thao Nguyen, Matthew Wallingford, Sebastin Santy, Wei-Chiu Ma

Massive web-crawled image-text datasets lay the foundation for recent progress in multimodal learning. These datasets are designed with the goal of training a model to do well on standard computer vision benchmarks, many of which, however, have been shown to be English-centric (e.g., ImageNet). Consequently, existing data curation techniques gravitate toward

Changwen Li, Joseph Sifakis, Rongjie Yan, Jian Zhang

Simulation-based testing remains the main approach for validating Autonomous Driving Systems. We propose a rigorous test method based on breaking down scenarios into simple ones, taking into account the fact that autopilots make decisions according to traffic rules whose application depends on local knowledge and context. This leads us to consider the autopi

Xiaoming Kan, Fredrik Hedenus, Lina Reichenberg

The One Sun One World One Grid (OSOWOG) initiative advocates the development of a global Super grid for sharing renewable energy, especially solar energy. This study evaluates the economic benefits of such a Super grid, which connects six large regions spanning from Australia to the US, utilizing a detailed energy system optimization model and considering he

H. Nick Zinat Matin, Y. Yeo, X. Gong, M. L. Delle Monache

In this paper, we propose an integrated dynamical model of Connected and Automated Vehicles (CAVs) which incorporates CAV technologies and a microscopic car-following model to improve safety, efficiency and convenience. We rigorously investigate the analytical properties such as well-posedness, maximum principle, perturbation and stability of the proposed mo

Donatella Darsena, Giacinto Gelli, Ivan Iudice

In this paper, we present a first attempt to incorporate in the GNU Radio ecosystem a tool called CycloDSP, devoted to the analysis of complex-valued cyclostationary signals. Such signals are ubiquitous in communication and signal processing, exhibiting periodic or almost periodic statistics that are characterized by a countable set of cycle frequencies, whi

Khalil Zakeri, Ryan Roemer, Ke Zou

The origin of superconductivity in the FeSe monolayer on SrTiO$_3$ remains one of the unresolved mysteries in condensed-matter physics. Here by investigation of the temperature evolution of the dynamic charge response of FeSe/SrTiO$_3$ we infer that the response of the monolayer itself is nearly temperature independent. This indicates a constant Fermi surfac

Léore Bensabath, Mathis Petrovich, Gül Varol

We provide results of our study on text-based 3D human motion retrieval and particularly focus on cross-dataset generalization. Due to practical reasons such as dataset-specific human body representations, existing works typically benchmarkby training and testing on partitions from the same dataset. Here, we employ a unified SMPL body format for all datasets

Gal Yona, Roee Aharoni, Mor Geva

We posit that large language models (LLMs) should be capable of expressing their intrinsic uncertainty in natural language. For example, if the LLM is equally likely to output two contradicting answers to the same question, then its generated response should reflect this uncertainty by hedging its answer (e.g., "I'm not sure, but I think..."). We formalize f

Jaewoo Lee, Sujin Yun, Taeyoung Yun, Jinkyoo Park

Offline Reinforcement Learning (Offline RL) presents challenges of learning effective decision-making policies from static datasets without any online interactions. Data augmentation techniques, such as noise injection and data synthesizing, aim to improve Q-function approximation by smoothing the learned state-action region. However, these methods often fal

Mitsuhiro Fujikawa, Yohei Akimoto, Jun Sakuma, Kazuto Fukuchi

Transfer learning enhances prediction accuracy on a target distribution by leveraging data from a source distribution, demonstrating significant benefits in various applications. This paper introduces a novel dissimilarity measure that utilizes vicinity information, i.e., the local structure of data points, to analyze the excess error in classification under

Haojun Wang, Kun Liu, Baojia Li, Emilia Fridman

This paper is concerned with the security problem for interconnected systems, where each subsystem is required to detect local attacks using locally available information and the information received from its neighboring subsystems. Moreover, we consider that there exists an additional eavesdropper being able to infer the private information by eavesdropping

Beibei Liu, Lisa Piccirillo

We provide new examples of 3-manifolds with weight one fundamental group and the same integral homology as the lens space $L(2k,1)$ which are not surgery on any knot in the three-sphere. Our argument uses Furuta's 10/8-theorem, and is simple and combinatorial to apply.

Alexander Stotsky

New Kaczmarz algorithms with rank two gain update, extended orthogonality property and forgetting mechanism which includes both exponential and instantaneous forgetting (implemented via a proper choice of the forgetting factor and the window size) are introduced and associated in this report with well-known Kaczmarz algorithms with rank one update.

Yuki Iwamoto, Ken Kaneiwa

Rule-induction models have demonstrated great power in the inductive setting of knowledge graph completion. In this setting, the models are tested on a knowledge graph entirely composed of unseen entities. These models learn relation patterns as rules by utilizing subgraphs. Providing the same inputs with different rules leads to differences in the model's p

Filip Postepski, Grzegorz M. Wojcik, Krzysztof Wrobel, Andrzej Kawiak

The Guided Imagery technique is reported to be used by therapists all over the world in order to increase the comfort of patients suffering from a variety of disorders from mental to oncology ones and proved to be successful in numerous of ways. Possible support for the therapists can be estimation of the time at which subject goes into deep relaxation. This

Jishu Zhao, Xi Wang, Jinlong Lei

This paper focus on investigating the distributed Riemannian stochastic optimization problem on the Stiefel manifold for multi-agent systems, where all the agents work collaboratively to optimize a function modeled by the average of their expectation-valued local costs. Each agent only processes its own local cost function and communicate with neighboring ag

Safa Alver, Ali Rahimi-Kalahroudi, Doina Precup

In neuroscience, one of the key behavioral tests for determining whether a subject of study exhibits model-based behavior is to study its adaptiveness to local changes in the environment. In reinforcement learning, however, recent studies have shown that modern model-based agents display poor adaptivity to such changes. The main reason for this is that moder

Tymon Frelik

We study the geometry associated with the kinematics of a planar robot known as the "three-segment snake," whose velocity distribution belongs to a class of (2,3,5) distributions. We discover that, under certain assumptions on its construction parameters, the snake may be endowed with a CR structure of CR dimension 1 and real codimension 3. We solve the asso

Eun Jung Chung, Chang Won Lee, Shinyoung Kim, Mario Tafalla

We present 850~$\mu$m linear polarization and C$^{18}$O~(3-2) and $^{13}$CO~(3-2) molecular line observations toward the filaments (F13 and F13S) in the Cocoon Nebula (IC~5146) using the JCMT POL-2 and HARP instruments. F13 and F13S are found to be thermally supercritical with identified dense cores along their crests. Our findings include that the polarizat

Antonio E. Porreca

Many unconventional computing models, including some that appear to be quite different from traditional ones such as Turing machines, happen to characterise either the complexity class P or PSPACE when working in deterministic polynomial time (and in the maximally parallel way, where this applies). We discuss variants of cellular automata and membrane system

Yunfeng Shi, W. -M. Wang

We establish large sets of Anderson localized states for the quasi-periodic nonlinear Schr\"odinger equation on $\mathbb Z^d$, thus extending Anderson localization from the linear (cf. Bourgain [Geom. Funct. Anal., 17(3):682--706, 2007]) to a nonlinear setting, and the random (cf. Bourgain-Wang [J. Eur. Math. Soc., 10(1):1--45, 2008]) to a deterministic sett

Liang Shi, Jie Zhang, Shiguang Shan

Text-to-image diffusion models, such as Stable Diffusion, generate highly realistic images from text descriptions. However, the generation of certain content at such high quality raises concerns. A prominent issue is the accurate depiction of identifiable facial images, which could lead to malicious deepfake generation and privacy violations. In this paper,

Zhoujie Ding, Ken Ziyu Liu, Pura Peetathawatchai, Berivan Isik

Low-rank adaptation of large models, particularly LoRA, has gained traction due to its computational efficiency. This efficiency, contrasted with the prohibitive costs of full-model fine-tuning, means that practitioners often turn to LoRA and sometimes without a complete understanding of its ramifications. In this study, we focus on fairness and ask whether

Jiwei Jia, Young Ju Lee, Ruitong Shan

In this paper, we present a new framework how a PDE with constraints can be formulated into a sequence of PDEs with no constraints, whose solutions are convergent to the solution of the PDE with constraints. This framework is then used to build a novel finite neuron method to solve the 2nd order elliptic equations with the Dirichlet boundary condition. Our a

A. V. Samokish, V. E. Egorushkin

We demonstrated the analogy between Economics and Gauge Theory of Plasticity and used it to describe the relationship between money supply and inflation at the economic market. The received equations of economical dynamics in phase space are similar to the plasticity equations and economic variables - choice, competition and profit correspond to the state of

Yiqin Wang, Chong Han, Shu Sun, Jianhua Zhang

Technologies like ultra-massive multiple-input-multiple-output (UM-MIMO) and reconfigurable intelligent surfaces (RISs) are of special interest to meet the key performance indicators of future wireless systems including ubiquitous connectivity and lightning-fast data rates. One of their common features, the extremely large-scale antenna array (ELAA) systems

Fedor Bakharev, Sergey Matveenko

The spectral properties of the restricted fractional Dirichlet Laplacian in ${\sf V}$-shaped waveguides are studied. The continuous spectrum for such domains with cylindrical outlets is known to occupy the ray $[\Lambda_\dagger, +\infty)$ with the threshold corresponding to the smallest eigenvalue of the cross-sectional problems. In this work the presence of

Deepshikha

Frames are the most natural generalization of orthonormal bases that allow the inclusion of redundant systems. In this article, we introduce the concept of frames generated by graphs in finite-dimensional spaces and study their properties. Let $G$ be a simple graph of $n$ vertices with Laplacian matrix $L$. We define the notions of $G(n,k)$-frames and $L_G(n

Beomjun Choi, Pei-Ken Hung

R. Thom's gradient conjecture states that if a gradient flow of an analytic function converges to a limit, it does so along a unique limiting direction. In this paper, we extend and settle this conjecture in the context of infinite dimensional problems. Building on the foundational works of {\L}ojasiewicz, L. Simon, and the resolution of the conjecture for f

Haohan Weng, Yikai Wang, Tong Zhang, C. L. Philip Chen

Generating compact and sharply detailed 3D meshes poses a significant challenge for current 3D generative models. Different from extracting dense meshes from neural representation, some recent works try to model the native mesh distribution (i.e., a set of triangles), which generates more compact results as humans crafted. However, due to the complexity and

Y. H. Shao, S. Y. Chen, H. Z. Yang, F. Xi

Time encoding machine (TEM) is a biologically-inspired scheme to perform signal sampling using timing. In this paper, we study its application to the sampling of bandpass signals. We propose an integrate-and-fire TEM scheme by which the in-phase (I) and quadrature (Q) components are extracted through reconstruction. We design the TEM according to the signal

Anran Liu, Cheng Lin, Yuan Liu, Xiaoxiao Long

Recently, the emergence of diffusion models has opened up new opportunities for single-view reconstruction. However, all the existing methods represent the target object as a closed mesh devoid of any structural information, thus neglecting the part-based structure, which is crucial for many downstream applications, of the reconstructed shape. Moreover, the

Zhen Zhao, Dunbing Tang, Changchun Liu, Liping Wang

As customer demand for multi-variety and small-batch production increases, dynamic disturbances place greater demands on manufacturing systems. To address such challenges, researchers proposed the multi-agent manufacturing system. However, conventional agent negotiation typically relies on pre-defined and fixed heuristic rules, which are ill-suited to managi

Jiaqi Tang, Hao Lu, Ruizheng Wu, Xiaogang Xu

Video Anomaly Detection (VAD) systems can autonomously monitor and identify disturbances, reducing the need for manual labor and associated costs. However, current VAD systems are often limited by their superficial semantic understanding of scenes and minimal user interaction. Additionally, the prevalent data scarcity in existing datasets restricts their app

Tiia-Maria Pasanen, Jouni Helske, Tarmo Ketola

Real world spatio-temporal datasets, and phenomena related to them, are often challenging to visualise or gain a general overview of. In order to summarise information encompassed in such data, we combine two well known statistical modelling methods. To account for the spatial dimension, we use the intrinsic modification of the conditional autoregression, an

Tianshu Wang, Xiaoyang Chen, Hongyu Lin, Xuanang Chen

Entity matching (EM) is a critical step in entity resolution (ER). Recently, entity matching based on large language models (LLMs) has shown great promise. However, current LLM-based entity matching approaches typically follow a binary matching paradigm that ignores the global consistency among record relationships. In this paper, we investigate various meth

Dongxu Liu, Nhung Nguyen, Tinh Quoc Bui, Luka Pocivavsek

Fracture resistance of blood clots plays a crucial role in physiological hemostasis and pathological thromboembolism. Although recent experimental and computational studies uncovered the poro-viscoelastic property of blood clots and its connection to the time-dependent deformation behavior, the effect of these time-dependent processes on clot fracture and th

Bobby Yan, Alexander J. Root, Trevor Gale, David Broman

The rapid growth in the size of deep learning models strains the capabilities of traditional dense computation paradigms. Leveraging sparse computation has become increasingly popular for training and deploying large-scale models, but existing deep learning frameworks lack extensive support for sparse operations. To bridge this gap, we introduce Scorch, a li

Antonino Ficarra, Pedro Macias Marques

Let $K$ be a field, $I\subset R=K[x_1,\dots,x_n]$ and $J\subset T=K[y_1,\dots,y_m]$ be graded ideals. Set $S=R\otimes_KT$ and let $L=IS+JS$. The behaviour of the $\text{v}$-function $\text{v}(L^k)$ in terms of the $\text{v}$-functions $\text{v}(I^k)$ and $\text{v}(J^k)$ is investigated. When $I$ and $J$ are monomial ideals, we describe $\text{v}(L^k)$, givin

Mikhail Dektiarev, Nikolay Vereshchagin

Half-duplex communication complexity with adversary was defined in [Hoover, K., Impagliazzo, R., Mihajlin, I., Smal, A. V. Half-Duplex Communication Complexity, ISAAC 2018.] Half-duplex communication protocols generalize classical protocols defined by Andrew Yao in [Yao, A. C.-C. Some Complexity Questions Related to Distributive Computing (Preliminary Report

Ze Cheng, Zhongkai Hao, Xiaoqiang Wang, Jianing Huang

For partial differential equations on domains of arbitrary shapes, existing works of neural operators attempt to learn a mapping from geometries to solutions. It often requires a large dataset of geometry-solution pairs in order to obtain a sufficiently accurate neural operator. However, for many industrial applications, e.g., engineering design optimization

Xuetao Li, Yuxia Zhang, Cailean Osborne, Minghui Zhou

Open source software (OSS) has been playing a fundamental role in not only information technology but also our social lives. Attracted by various advantages of OSS, increasing commercial companies take extensive participation in open source development and have had a broad impact. This paper provides a comprehensive systematic literature review (SLR) of exis

Wangyang Ying, Dongjie Wang, Xuanming Hu, Yuanchun Zhou

Feature transformation is to derive a new feature set from original features to augment the AI power of data. In many science domains such as material performance screening, while feature transformation can model material formula interactions and compositions and discover performance drivers, supervised labels are collected from expensive and lengthy experim

Kai Ma, Shao-Feng Ge, Lin-Yun He, Ning Zhou

Instead of the energy recoil signal at direct detection experiments, dark fermion appears as missing energy at hadron colliders. For a fermionc dark sector particle that coupled with quarks and neutrino via absorption operators, its production at collider is accompanied by an invisible neutrino. We study in details the mono-$X$ (photon, jet, and $Z$) product

Dongbin Kim, Jinseong Park, Jaewook Lee, Hoki Kim

Time series forecasting is crucial for applications across multiple domains and various scenarios. Although Transformer models have dramatically advanced the landscape of forecasting, their effectiveness remains debated. Recent findings have indicated that simpler linear models might outperform complex Transformer-based approaches, highlighting the potential

Yidong Ouyang, Liyan Xie, Hongyuan Zha, Guang Cheng

Diffusion models, a specific type of generative model, have achieved unprecedented performance in recent years and consistently produce high-quality synthetic samples. A critical prerequisite for their notable success lies in the presence of a substantial number of training samples, which can be impractical in real-world applications due to high collection c

Mohammed Abdulsalam Alsofiani

The current study presents a comprehensive review of the benefits and barriers associated with the adoption of Building Information Modeling (BIM) in infrastructure projects, focusing on the period from 2013 to 2023. The research explores the manifold advantages offered by BIM, spanning the entire project life cycle, including planning, design, construction,

Xingqun Qi, Hengyuan Zhang, Yatian Wang, Jiahao Pan

Deriving co-speech 3D gestures has seen tremendous progress in virtual avatar animation. Yet, the existing methods often produce stiff and unreasonable gestures with unseen human speech inputs due to the limited 3D speech-gesture data. In this paper, we propose CoCoGesture, a novel framework enabling vivid and diverse gesture synthesis from unseen human spee

Ziying Song, Hongyu Pan, Feiyang Jia, Yongchang Zhang

In the field of 3D object detection tasks, fusing heterogeneous features from LiDAR and camera sensors into a unified Bird's Eye View (BEV) representation is a widely adopted paradigm. However, existing methods often suffer from imprecise sensor calibration, leading to feature misalignment in LiDAR-camera BEV fusion. Moreover, such inaccuracies cause errors

Maxim Gurevich

We obtain two explicit formulas for the full local character expansion of any irreducible representation of a p-adic general linear group in principal blocks. The first, generalizing previous work of the author on the Iwahori-spherical case, expresses the expansion in terms of dimensions of degenerate Whittaker models. The second gives a closed expression in

Zihan Liu, Yupeng Hou, Julian McAuley

Multi-behavior sequential recommendation (MBSR) aims to incorporate behavior types of interactions for better recommendations. Existing approaches focus on the next-item prediction objective, neglecting the value of integrating the target behavior type into the learning objective. In this paper, we propose MBGen, a novel Multi-Behavior sequential Generative

Valentin Wössner, Oliver M. Drozdowski, Falko Ziebert, Ulrich S. Schwarz

Migration of animal cells is based on the interplay between actin polymerization at the front, adhesion along the cell-substrate interface, and actomyosin contractility at the back. Active gel theory has been used before to demonstrate that actomyosin contractility is sufficient for polarization and self-sustained cell migration in the absence of external cu

Yichi Zhang, Zhuo Chen, Lingbing Guo, Yajing Xu

Learning high-quality multi-modal entity representations is an important goal of multi-modal knowledge graph (MMKG) representation learning, which can enhance reasoning tasks within the MMKGs, such as MMKG completion (MMKGC). The main challenge is to collaboratively model the structural information concealed in massive triples and the multi-modal features of

Tianhang Wang, Fan Lu, Zehan Zheng, Zhijun Li

Collaborative perception is dedicated to tackling the constraints of single-agent perception, such as occlusions, based on the multiple agents' multi-view sensor inputs. However, most existing works assume an ideal condition that all agents' multi-view cameras are continuously available. In reality, cameras may be highly noisy, obscured or even failed during

Jiaping Xiao, Phumrapee Pisutsin, Cheng Wen Tsao, Mir Feroskhan

UAV tracking and pose estimation plays an imperative role in various UAV-related missions, such as formation control and anti-UAV measures. Accurately detecting and tracking UAVs in a 3D space remains a particularly challenging problem, as it requires extracting sparse features of micro UAVs from different flight environments and continuously matching corres

Maximilian Köhler, Timo Neumeier, Malte. A. Peter, Daniel Peterseim

This paper presents an efficient algorithm for the approximation of the rank-one convex hull in the context of nonlinear solid mechanics. It is based on hierarchical rank-one sequences and simultaneously provides first and second derivative information essential for the calculation of mechanical stresses and the computational minimization of discretized ener

Dehong Xu, Ruiqi Gao, Wen-Hao Zhang, Xue-Xin Wei

This paper investigates the conformal isometry hypothesis as a potential explanation for the hexagonal periodic patterns in grid cell response maps. We posit that grid cell activities form a high-dimensional vector in neural space, encoding the agent's position in 2D physical space. As the agent moves, this vector rotates within a 2D manifold in the neural s

Christoph Lehrenfeld, Paul Stocker, Maximilian Zienecker

In this work we compare crucial parameters for efficiency of different finite element methods for solving partial differential equations (PDEs) on polytopal meshes. We consider the Virtual Element Method (VEM) and different Discontinuous Galerkin (DG) methods, namely the Hybrid DG and Trefftz DG methods. The VEM is a conforming method, that can be seen as a

Lujun Wei, Yiyang Zhang, Fei Huang, Jiajv Yang

The aim of voltage control of magnetism is to reduce the power consumption of spintronic devices. For a spin valve, the magnetization directions of two ferromagnetic layers determine the giant magnetoresistance magnitude. However, achieving all-voltage manipulation of the magnetization directions between parallel and antiparallel states is a significant chal

Yu. L. Bolotin, V. V. Yanovsky

Quantum gravitational effects, on the one hand, lead to a limitation in the accuracy of measuring spatial and time intervals, and, on the other hand, they generate a discrete of spacetime structure (quantum foam). The common source of both measurement limitations and discreteness of space-time are quantum fluctuations, so their characteristics must be relate

Joongwon Lee, Wonho Zhung, Jisu Seo, Woo Youn Kim

Recent remarkable advancements in geometric deep generative models, coupled with accumulated structural data, enable structure-based drug design (SBDD) using only target protein information. However, existing models often struggle to balance multiple objectives, excelling only in specific tasks. BInD, a diffusion model with knowledge-based guidance, is intro

Yunqi Zhang, Songda Li, Chunyuan Deng, Luyi Wang

Gender bias in vision-language models (VLMs) can reinforce harmful stereotypes and discrimination. In this paper, we focus on mitigating gender bias towards vision-language tasks. We identify object hallucination as the essence of gender bias in VLMs. Existing VLMs tend to focus on salient or familiar attributes in images but ignore contextualized nuances. M

Xuetong Li, Jing Zhou, Hansheng Wang

We study here a Gaussian Mixture Model (GMM) with rare events data. In this case, the commonly used Expectation-Maximization (EM) algorithm exhibits extremely slow numerical convergence rate. To theoretically understand this phenomenon, we formulate the numerical convergence problem of the EM algorithm with rare events data as a problem about a contraction o

Jingguo Liu, Yijun Xu, Shigang Li, Jianfeng Li

Disconnectivity and distortion are the two problems which must be coped with when processing 360 degrees equirectangular images. In this paper, we propose a method of estimating the depth of monocular panoramic image with a teacher-student model fusing equirectangular and spherical representations. In contrast with the existing methods fusing an equirectangu

Youn Kil Jung, Kyu-Ha Hwang, Hongjing Yang, Andrew Gould

We report a free-floating planet (FFP) candidate identified from the analysis of the microlensing event KMT-2023-BLG-2669. The lensing light curve is characterized by a short duration $(\lesssim 3\,{\rm days})$ and a small amplitude $(\lesssim 0.7\,{\rm mag})$. From the analysis, we find the Einstein timescale of $t_{\rm E} \backsimeq 0.33\,{\rm days}$ and t

Haoyan Yang, Yixuan Wang, Xingyin Xu, Hanyuan Zhang

The study explores mitigating overconfidence bias in LLMs to improve their reliability. We introduce a knowledge transfer (KT) method utilizing chain of thoughts, where "big" LLMs impart knowledge to "small" LLMs via detailed, sequential reasoning paths. This method uses advanced reasoning of larger models to fine-tune smaller models, enabling them to produc

Jin Bong Lee, Jinsol Seo

In this paper, we investigate $L^p$ bounds of maximal Fourier multiplier operators with dilation of fractional dimensions. For Fourier multipliers, we suggest a criterion related to dimensions of dilation sets which guarantees $L^p$ bounds of the maximal operators for each $p$. Our criterion covers Mikhlin-type multipliers, multipliers with limited decay, an

Zhihao Liu, Xianliang Yang, Zichuan Liu, Yifan Xia

Multi-agent reinforcement learning (MARL) is employed to develop autonomous agents that can learn to adopt cooperative or competitive strategies within complex environments. However, the linear increase in the number of agents leads to a combinatorial explosion of the action space, which may result in algorithmic instability, difficulty in convergence, or en

Mohit Panwar, Pankaj Jain, Amitesh Omar

The signal of dipole anisotropy in quasar number counts is studied using the CatWISE2020 catalog in various color bins. It is found that the dipole signal differs significantly in two color bins, namely, $1.1>W1-W2\ge 0.8$ and $1.4>W1-W2>1.1$. The color bin $1.4>W1-W2>1.1$ appears strongly contaminated, with possibly Galactic contributions and is unreliable

Sirui Xie, Zhisheng Xiao, Diederik P Kingma, Tingbo Hou

While diffusion models can learn complex distributions, sampling requires a computationally expensive iterative process. Existing distillation methods enable efficient sampling, but have notable limitations, such as performance degradation with very few sampling steps, reliance on training data access, or mode-seeking optimization that may fail to capture th

Mingqing Xiao, Yixin Zhu, Di He, Zhouchen Lin

Spiking neural networks (SNNs) are investigated as biologically inspired models of neural computation, distinguished by their computational capability and energy efficiency due to precise spiking times and sparse spikes with event-driven computation. A significant question is how SNNs can emulate human-like graph-based reasoning of concepts and relations, es

Runzhao Yang, Yinda Chen, Zhihong Zhang, Xiaoyu Liu

In the field of medical image compression, Implicit Neural Representation (INR) networks have shown remarkable versatility due to their flexible compression ratios, yet they are constrained by a one-to-one fitting approach that results in lengthy encoding times. Our novel method, ``\textbf{UniCompress}'', innovatively extends the compression capabilities of

Zhoujie Fu, Jiacheng Wei, Wenhao Shen, Chaoyue Song

In this work, we introduce a novel approach for creating controllable dynamics in 3D-generated Gaussians using casually captured reference videos. Our method transfers the motion of objects from reference videos to a variety of generated 3D Gaussians across different categories, ensuring precise and customizable motion transfer. We achieve this by employing

Zhihang Song, Dingyi Yao, Ruibo Ming, Lihui Peng

Multi-modal object detection in autonomous driving has achieved great breakthroughs due to the usage of fusing complementary information from different sensors. The calibration in fusion between sensors such as LiDAR and camera was always supposed to be precise in previous work. However, in reality, calibration matrices are fixed when the vehicles leave the

Yinda Chen, Haoyuan Shi, Xiaoyu Liu, Te Shi

Neuron segmentation from electron microscopy (EM) volumes is crucial for understanding brain circuits, yet the complex neuronal structures in high-resolution EM images present significant challenges. EM data exhibits unique characteristics including high noise levels, anisotropic voxel dimensions, and ultra-long spatial dependencies that make traditional vis

Aleena Philip, Deepika Baweja

In this paper, we study the notion of mid summability in a general setting using the duality theory of sequence spaces. We define the vector valued sequence space $\lambda^{mid}(X)$ corresponding to a Banach space $X$ and sequence space $\lambda$. We prove that $\lambda^{mid}(\cdot)$ can be placed in a chain with the vector valued sequence spaces $\lambda^{s

Chenyu Zheng, Wei Huang, Rongzhen Wang, Guoqiang Wu

Autoregressively trained transformers have brought a profound revolution to the world, especially with their in-context learning (ICL) ability to address downstream tasks. Recently, several studies suggest that transformers learn a mesa-optimizer during autoregressive (AR) pretraining to implement ICL. Namely, the forward pass of the trained transformer is e

Peter Stano, Daniel Loss

We theoretically investigate heavy-hole--light-hole mixing in two-dimensional hole gases (2DHG). We restrict our analysis to the zone center, appropriate for the low-density regime, which leads to a simple description, analytical results, and physical insights. We identify two different types of hole-Hamiltonian terms concerning mixing. The first type change

Yogev Bar-On, Yishay Mansour

We introduce a novel online learning framework that unifies and generalizes pre-established models, such as delayed and corrupted feedback, to encompass adversarial environments where action feedback evolves over time. In this setting, the observed loss is arbitrary and may not correlate with the true loss incurred, with each round updating previous observat

Brian Barch

Local non-Hermitian (NH) quantum systems generically exhibit breakdown of Lieb-Robinson (LR) bounds, motivating study of whether new locality measures might shed light not seen by existing measures. In this paper we extend the standard connected correlation function (CC) to NH systems in a form that recovers locality. Additionally, we use the metric formalis

David I. Ketcheson, Abhijit Biswas

We present a framework for constructing a first-order hyperbolic system whose solution approximates that of a desired higher-order evolution equation. Constructions of this kind have received increasing interest in recent years, and are potentially useful as either analytical or computational tools for understanding the corresponding higher-order equation. W

Hanxue Ding, Shaoyi Xu, Ziheng Xu, Rongtao Xu

In order to achieve stable and reliable industrial manufacturing, wireless networks must meet the stringent communication requirements of industrial automation, particularly the need for deterministic low latency communication. The limited wireless resources and time-varying fading channel contribute to the random fluctuations of transmission delay, making i

Liya Jess Kurian, Chithra A.

In this paper, we define two operations, neighbourhood m-splitting hypergraph $NS_m(\mathscr{G}^*)$ and non-neighbourhood splitting hypergraph $NNS(\mathscr{G}^*)$, and obtain several properties of their adjacency spectrum. We also estimate the energies of $NS_m(\mathscr{G}^*)$ and $NNS(\mathscr{G}^*)$. Moreover, we introduce two new join operations on $k$-u

Guillermo Pineda-Villavicencio, Jie Wang, David Yost

We study the existence and structure of $d$-polytopes for which the number $f_1$ of edges is small compared to the number $f_0$ of vertices. Our results are more elegantly expressed in terms of the excess degree of the polytope, defined as $2f_1-df_0$. We show that the excess degree of a $d$-polytope cannot lie in the range $[d+3,2d-7]$, complementing the kn