December 2024 arXiv papers — page 193

Yasser Khalafaoui, Basarab Matei, Martino Lovisetto, Nistor Grozavu

Recently, deep matrix factorization has been established as a powerful model for unsupervised tasks, achieving promising results, especially for multi-view clustering. However, existing methods often lack effective feature selection mechanisms and rely on empirical hyperparameter selection. To address these issues, we introduce a novel Deep Matrix Factorizat

Yao Lyu, Xiangteng Zhang, Shengbo Eben Li, Jingliang Duan

Training deep reinforcement learning (RL) agents necessitates overcoming the highly unstable nonconvex stochastic optimization inherent in the trial-and-error mechanism. To tackle this challenge, we propose a physics-inspired optimization algorithm called relativistic adaptive gradient descent (RAD), which enhances long-term training stability. By conceptual

Francesco Cauteruccio, Enrico Corradini, Luca Virgili

Advent of Code (AoC from now on) is a popular coding challenge requiring to solve programming puzzles for a variety of skill sets and levels. AoC follows the advent calendar, therefore it is an annual challenge that lasts for 25 days. AoC participants usually post their solutions on social networks and discuss them online. These challenges are interesting to

Roberto Pereira, Cristian J. Vaca-Rubio, Luis Blanco

Federated Learning (FL) has emerged as a solution for distributed model training across decentralized, privacy-preserving devices, but the different energy capacities of participating devices (system heterogeneity) constrain real-world implementations. These energy limitations not only reduce model accuracy but also increase dropout rates, impacting on conve

Mikhail Tuzhilin

Relations between average shortest path length and average clustering coefficient, radiality, closeness and stress centralities were obtained for simple graphs.

Alexandre Thorel

We study a transmission problem, in population dynamics, between two juxtaposed habitats. In each habitat, we consider a generalized diffusion equation composed by the Laplace operator and a biharmonic term. We consider that the coefficients in front of each term could be negative or null. Using semigroups theory and functional calculus, we give some relatio

Qing Zhang, Jinguang Tong, Jing Zhang, Jie Hong

Despite recent advances in text-to-3D generation techniques, current methods often suffer from geometric inconsistencies, commonly referred to as the Janus Problem. This paper identifies the root cause of the Janus Problem: viewpoint generation bias in diffusion models, which creates a significant gap between the actual generated viewpoint and the expected o

David Levin, José M. Ramón, Juan Ruiz-Alvarez, Dionisio F. Yáñez

Shepard method is a fast algorithm that has been classically used to interpolate scattered data in several dimensions. This is an important and well-known technique in numerical analysis founded in the main idea that data that is far away from the approximation point should contribute less to the resulting approximation. Approximating piecewise smooth functi

Lei Yu, Hongyang Chen, Jingsong Lv, Linyao Yang

Graph Transformers (GTs) have demonstrated significant advantages in graph representation learning through their global attention mechanisms. However, the self-attention mechanism in GTs tends to neglect the inductive biases inherent in graph structures, making it chanllenging to effectively capture essential structural information. To address this issue, we

Bektur Murzaliev, Mikhail Katsnelson, Mikhail Titov

Broken inversion symmetry of magnetic lattice is normally described by Lifshitz invariants in micromagnetic energy functional. Three exceptions are the lattices with T$_\textrm{d}$, C$_\textrm{3h}$ and D$_\textrm{3h}$ point group symmetries. The inversion symmetry breaking of the corresponding magnets is described by more complex 4-spin chiral invariants tha

Jean Bertin

This article introduces an innovative Retrieval Augmented Generation approach to similarity search. The proposed method uses a generative model to capture nuanced semantic information and retrieve similarity scores based on advanced context understanding. The study focuses on the BIOSSES dataset containing 100 pairs of sentences extracted from the biomedical

Pubudu L. Indrasiri, Bipasha Kashyap, Chandima Kolambahewage, Bahareh Nakisa

Emotion recognition is significantly enhanced by integrating multimodal biosignals and IMU data from multiple domains. In this paper, we introduce a novel multi-scale attention-based LSTM architecture, combined with Squeeze-and-Excitation (SE) blocks, by leveraging multi-domain signals from the head (Meta Quest Pro VR headset), trunk (Equivital Vest), and pe

Junyuan Wang, Tianyao Wu, Ouyang Zhou, Yaping Zhu

Clustered cell-free networking, which dynamically partitions the whole network into nonoverlapping subnetworks, has been recently proposed to mitigate the cell-edge problem in cellular networks. However, prior works only focused on optimizing clustered cell-free networking in static scenarios with fixed users. This could lead to a large number of handovers i

Jinghong Lin, Yiming Ma, Xiaomeng Xu

We prove a connection formula for the basic hypergeomtric function ${}_n\varphi_{n-1}\left( a_1,...,a_{n-1},0; b_1,...,b_{n-1} ; q, z\right)$ by using the $q$-Borel resummation. As an application, we compute $q$-Stokes matrices of a special confluent hypergeometric $q$-difference system with an irregular singularity. We show that by letting $q\rightarrow 1$,

Jaehyun Choi, Junwon Ko, Dong-Jae Lee, Junmo Kim

Open compound domain adaptation (OCDA) is a practical domain adaptation problem that consists of a source domain, target compound domain, and unseen open domain. In this problem, the absence of domain labels and pixel-level segmentation labels for both compound and open domains poses challenges to the direct application of existing domain adaptation and gene

Changzhi Zhou, Dandan Song, Yuhang Tian, Zhijing Wu

Recently, Large Language Models (LLMs) have garnered increasing attention in the field of natural language processing, revolutionizing numerous downstream tasks with powerful reasoning and generation abilities. For example, In-Context Learning (ICL) introduces a fine-tuning-free paradigm, allowing out-of-the-box LLMs to execute downstream tasks by analogy le

Qiang Huo

Tsukamoto (2022) introduced the notion of Bedford-McMullen carpet system, a subsystem of $([0,1]^{\mathbb{N}}\times[0,1]^{\mathbb{N}},shift)$ whose metric mean dimension and mean Hausdorff dimension does not coincide in general. The aim of this paper is to develop the mean dimension theory for Bedford-McMullen sponge system, which is a subsystem of $(([0,1]^

Jebran Khan, Kashif Ahmad, Senthil Kumar Jagatheesaperumal, Nasir Ahmad

In the modern world, our cities and societies face several technological and societal challenges, such as rapid urbanization, global warming & climate change, the digital divide, and social inequalities, increasing the need for more sustainable cities and societies. Addressing these challenges requires a multifaceted approach involving all the stakeholders,

Masaki Fukuda, Shun K. Kobayashi, Kento Watanabe, Kazuya Yonekura

Heterotic string theory has nonsupersymmetric branes whose existence is suggested by the cobordism conjecture. We numerically construct static, spherically symmetric, and asymptotically flat black brane solutions in ten-dimensional heterotic superstring theories for 0- and 4-branes. These branes carry charges that are measured by Chern classes on the sphere

BG Mbewe, RR Mekuria, S Sahlu, A Abebe

In this paper, we study the viscous fluid cosmological model that when certain conditions are invoked mimics the $\Lambda$CDM model. The background equations governing the evolution of viscous interacting fluids in a multifluid system are derived. The Markov Chain Monte Carlo (MCMC) simulation is applied to constrain the best-fit cosmological parameters with

Daniel Siegismund, Mario Wieser, Stephan Heyse, Stephan Steigele

Deep Neural Networks (DNNs) have shown remarkable success in various computer vision tasks. However, their black-box nature often leads to difficulty in interpreting their decisions, creating an unfilled need for methods to explain the decisions, and ultimately forming a barrier to their wide acceptance especially in biomedical applications. This work introd

Davide Martinenghi

Several indicators have been recently proposed for measuring various characteristics of the tuples of a dataset -- particularly, the so-called skyline tuples, i.e., those that are not dominated by other tuples. Numeric indicators are very important as they may, e.g., provide an additional criterion to be used to rank skyline tuples and focus on a subset ther

Lillian Muyama, Estelle Lu, Geoffrey Cheminet, Jacques Pouchot

Clinical diagnostic guidelines outline the key questions to answer to reach a diagnosis. Inspired by guidelines, we aim to develop a model that learns from electronic health records to determine the optimal sequence of actions for accurate diagnosis. Focusing on anemia and its sub-types, we employ deep reinforcement learning (DRL) algorithms and evaluate the

Daisuke Nakamura, Mu-Kun Lee, Kosuke Karube, Masahito Mochizuki

Chiral magnets under broken time-reversal symmetry can give rise to rectification of moving electrons, called nonreciprocal transport. Several mechanisms, such as the spin-fluctuation-induced chiral scattering and asymmetry in the electronic band dispersion with and without the relativistic spin-orbit interaction, have been proposed, but clear identification

Wenxuan Wang, Chenglei Wang, Huihui Qi, Menghao Ye

With the wide application of deep neural network models in various computer vision tasks, there has been a proliferation of adversarial example generation strategies aimed at deeply exploring model security. However, existing adversarial training defense models, which rely on single or limited types of attacks under a one-time learning process, struggle to a

Nguyen Van Thu, Pham Duy Thanh, Lo Thi Thuy

This study utilizes the Cornwall-Jackiw-Tomboulis effective action approach combined with variational perturbation theory to investigate the relative shift in the transition temperature of a homogeneous, repulsive, weakly interacting Bose gas compared to that of an ideal Bose gas. By applying both the one-loop and self-consistent Popov approximations, the un

Wenjing Jiang, Jin Yan, Sachin S. Sapatnekar

As circuit designs become more intricate, obtaining accurate performance estimation in early stages, for effective design space exploration, becomes more time-consuming. Traditional logic optimization approaches often rely on proxy metrics to approximate post-mapping performance and area. However, these proxies do not always correlate well with actual post-m

Zhiyuan Chen, Fan Lu, Guo Yu, Bin Li

Tracking the 6DoF pose of unknown objects in monocular RGB video sequences is crucial for robotic manipulation. However, existing approaches typically rely on accurate depth information, which is non-trivial to obtain in real-world scenarios. Although depth estimation algorithms can be employed, geometric inaccuracy can lead to failures in RGBD-based pose tr

Jan Kadel, August See, Ritwik Sinha, Mathias Fischer

Bots constitute a significant portion of Internet traffic and are a source of various issues across multiple domains. Modern bots often become indistinguishable from real users, as they employ similar methods to browse the web, including using real browsers. We address the challenge of bot detection in high-traffic scenarios by analyzing three distinct detec

Indronil Bhattacharjee, Al-Mahmud, Tareq Mahmud

Diabetic Retinopathy is one of the most familiar diseases and is a diabetes complication that affects eyes. Initially, diabetic retinopathy may cause no symptoms or only mild vision problems. Eventually, it can cause blindness. So early detection of symptoms could help to avoid blindness. In this paper, we present some experiments on some features of diabeti

Maximilian Schenke, Shalbus Bukarov

The discipline of automatic control is making increased use of concepts that originate from the domain of machine learning. Herein, reinforcement learning (RL) takes an elevated role, as it is inherently designed for sequential decision making, and can be applied to optimal control problems without the need for a plant system model. To advance education of c

Youquan Xian, Xueying Zeng, Duancheng Xuan, Danping Yang

Blockchain smart contracts have catalyzed the development of decentralized applications across various domains, including decentralized finance. However, due to constraints in computational resources and the prevalence of data silos, current smart contracts face significant challenges in fully leveraging the powerful capabilities of Large Language Models (LL

Sepand Dyanatkar, Angran Li, Alexander Dungate

Climate change's destruction of marine biodiversity is threatening communities and economies around the world which rely on healthy oceans for their livelihoods. The challenge of applying computer vision to niche, real-world domains such as ocean conservation lies in the dynamic and diverse environments where traditional top-down learning struggle with long-

Jingyu Gong, Chong Zhang, Fengqi Liu, Ke Fan

Scene-aware motion synthesis has been widely researched recently due to its numerous applications. Prevailing methods rely heavily on paired motion-scene data, while it is difficult to generalize to diverse scenes when trained only on a few specific ones. Thus, we propose a unified framework, termed Diffusion Implicit Policy (DIP), for scene-aware motion syn

Pei Tang, Jingtao Guo, Ivan Wang-Hei Ho

Traditional global positioning systems often underperform indoors, whereas Wi-Fi has become an effective medium for various radio sensing services. Specifically, utilizing channel state information (CSI) from Wi-Fi networks provides a non-contact method for precise indoor positioning; yet, accurately interpreting the complex CSI matrix to develop a reliable

Mingzhe Zheng, Yongqi Xu, Haojian Huang, Xuran Ma

Current video generation models excel at short clips but fail to produce cohesive multi-shot narratives due to disjointed visual dynamics and fractured storylines. Existing solutions either rely on extensive manual scripting/editing or prioritize single-shot fidelity over cross-scene continuity, limiting their practicality for movie-like content. We introduc

Marta Victoria, Zhe Zhang, Gorm B. Andresen, Parisa Rahdan

Energy communities are promoted in the European legislation as a strategy to enable citizen participation in the energy transition. Solar photovoltaic (PV) systems, due to their distributed nature, present an opportunity to create such communities. At Aarhus University (Denmark), we have established an energy community consisting of a 98-kW rooftop solar PV

Koustav Banerjee, Peter Paule, Cristian-Silviu Radu, Carsten Schneider

Let $p(n)$ denote the partition function. In this paper our main goal is to derive an asymptotic expansion up to order $N$ (for any fixed positive integer $N$) along with estimates for error bounds for the shifted quotient of the partition function, namely $p(n+k)/p(n)$ with $k\in \mathbb{N}$, which generalizes a result of Gomez, Males, and Rolen. In order t

Kenta Watanabe

Let $X$ be a K3 surface, let $C$ be a smooth curve of genus $g$ on $X$, and let $A$ be a base point free and primitive line bundle $g_d^r$ on $C$ with $d\geq4$ and $r\geq\sqrt{\frac{d}{2}}$. In this paper, we prove that if $g>2d-3+(r-1)^2$, then there exists a line bundle $N$ on $X$ which is adapted to $|C|$ such that $|A|$ is contained in the linear system

Shuai Lu, Min Du, P. Victor Debattista

Bars are important in the secular evolution of galaxies. This study is aimed at exploring the reasons why some galaxies have bars at redshift $z=0$ while others do not. We use ellipse fitting to measure the properties and evolution of bars in the IllustrisTNG cosmological simulation. By using the K-S two-sample test and tracing their evolutionary changes, we

Miroslav Purkrabek, Jiri Matas

Current Human Pose Estimation methods have achieved significant improvements. However, state-of-the-art models ignore out-of-image keypoints and use uncalibrated heatmaps as keypoint location representations. To address these limitations, we propose ProbPose, which predicts for each keypoint: a calibrated probability of keypoint presence at each location in

Sankaran P. G., Sunoj S. M., Pavithra Hariharan

Information generating functions have been used for generating various entropy and divergence measures. In the present work, we introduce quantile based relative information generating function and study its properties. The proposed generating function provides well-known Kullback-Leibler divergence measure. The quantile based relative information generating

Da Ma, Lu Chen, Situo Zhang, Yuxun Miao

The rapid expansion of context window sizes in Large Language Models~(LLMs) has enabled them to tackle increasingly complex tasks involving lengthy documents. However, this progress comes at the cost of a substantial increase in memory usage during inference, primarily due to the linear growth of the key-value~(KV) cache. Existing KV cache compression method

Pengjie Zhou, Haoyu Wei, Huiming Zhang

Reinforcement Learning (RL) is a widely researched area in artificial intelligence that focuses on teaching agents decision-making through interactions with their environment. A key subset includes stochastic multi-armed bandit (MAB) and continuum-armed bandit (SCAB) problems, which model sequential decision-making under uncertainty. This review outlines the

Javier Ureña Santiago, Thomas Ströhle, Antonio Rodríguez-Sánchez, Ruth Breu

Microorganism enumeration is an essential task in many applications, such as assessing contamination levels or ensuring health standards when evaluating surface cleanliness. However, it's traditionally performed by human-supervised methods that often require manual counting, making it tedious and time-consuming. Previous research suggests automating this tas

Jing Zeng, Qi Ye, Tianle Liu, Yang Xu

Implicit neural representations and 3D Gaussian splatting (3DGS) have shown great potential for scene reconstruction. Recent studies have expanded their applications in autonomous reconstruction through task assignment methods. However, these methods are mainly limited to single robot, and rapid reconstruction of large-scale scenes remains challenging. Addit

Hideaki Hara, Yuki Miyamoto, Junseok Han, Riku Omoto

We conduct a theoretical study to understand the periodic superradiance observed in an Er:YSO crystal. First, we construct a model based on the Maxwell-Bloch equations for a reduced level system, a pair of superradiance states and a population reservoir state. Analysis of the eigenvalues of the linearized differential equations shows that periodic superradia

Parth Saxena, Pratham Saxena, Adarsh Sowcar, Sreeharsha Angara

As the frequency and severity of climate-related events such as droughts, floods, and water scarcity continue to escalate, accurate rainfall monitoring becomes increasingly critical. This paper covers various industry methods of measuring rainfall as well as our own ground pluviometer system. Our system consists of an inexpensive static rain gauge that can o

Sukhdeep Singh Gill, Somnath Bharadwaj, Sk. Saiyad Ali, Khandakar Md Asif Elahi

Considering radio-interferometric observations, we present a fast and efficient estimator to compute the binned angular bispectrum (ABS) from gridded visibility data. The estimator makes use of Fast Fourier Transform (FFT) techniques to compute the bispectrum covering all possible triangle shapes and sizes. Here, we present the formalism of the estimator and

Yushuai Ji, Zepeng Liu, Sheng Wang, Yuan Sun

The k-means algorithm can simplify large-scale spatial vectors, such as 2D geo-locations and 3D point clouds, to support fast analytics and learning. However, when processing large-scale datasets, existing k-means algorithms have been developed to achieve high performance with significant computational resources, such as memory and CPU usage time. These algo

Wei-An Chen, Ya-Wen Tang, Seamus D. Clarke

Recent high-resolution observations at millimeter (mm) and sub-mm reveal a diverse spatial distribution for sub-pc scale dense cores within star-forming regions, ranging from clustered to aligned arrangements. To address the increasing volume of observational and simulation data, we introduce "alignment parameters" as a quantitative and reproducible method t

Sanjay Chakraborty

The working environment in healthcare analytics is transforming with the emergence of healthcare 5.0 and the advancements in quantum neural networks. In addition to analyzing a comprehensive set of case studies, we also review relevant literature from the fields of quantum computing applications and smart healthcare analytics, focusing on the implications of

Fnu Neha, Deepshikha Bhati, Deepak Kumar Shukla, Sonavi Makarand Dalvi

Medical imaging is essential in healthcare to provide key insights into patient anatomy and pathology, aiding in diagnosis and treatment. Non-invasive techniques such as X-ray, Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and Ultrasound (US), capture detailed images of organs, tissues, and abnormalities. Effective analysis of these images requ

Kazuto Nakashima, Xiaowen Liu, Tomoya Miyawaki, Yumi Iwashita

Building LiDAR generative models holds promise as powerful data priors for restoration, scene manipulation, and scalable simulation in autonomous mobile robots. In recent years, approaches using diffusion models have emerged, significantly improving training stability and generation quality. Despite their success, diffusion models require numerous iterations

Zhongnian Li, Meng Wei, Peng Ying, Xinzheng Xu

Learning from Multi-Positive and Unlabeled (MPU) data has gradually attracted significant attention from practical applications. Unfortunately, the risk of MPU also suffer from the shift of minimum risk, particularly when the models are very flexible as shown in Fig.\ref{moti}. In this paper, to alleviate the shifting of minimum risk problem, we propose an E

Jin Huang, Pengfei Chen, Guangba Yu, Yilun Wang

Serverless becomes popular as a novel computing paradigms for cloud native services. However, the complexity and dynamic nature of serverless applications present significant challenges to ensure system availability and performance. There are many root cause analysis (RCA) methods for microservice systems, but they are not suitable for precise modeling serve

Muhammad Zaki Almuzakki, Bayu Jayawardhana, Aneel Tanwani, Antonis I. Vakis

This paper studies stabilization of linear time-invariant (LTI) systems when control actions can only be realized in finitely many directions where it is possible to actuate uniformly or logarithmically extended positive scaling factors in each direction. Furthermore, a nearest-action selection approach is used to map the continuous measurements to a realiza

Jungwon Park, Jungmin Ko, Dongnam Byun, Jangwon Suh

Recent text-to-image diffusion models leverage cross-attention layers, which have been effectively utilized to enhance a range of visual generative tasks. However, our understanding of cross-attention layers remains somewhat limited. In this study, we introduce a mechanistic interpretability approach for diffusion models by constructing Head Relevance Vector

Pritam Sarkar

A measure of how sensitive the entanglement entropy is in a quantum system, has been proposed and its information geometric origin is discussed. It has been demonstrated for two exactly solvable spin systems, that thermodynamic criticality is directly \textit{indicated} by finite size scaling of the global maxima and turning points of the susceptibility of e

Sourav Chakraborty, Eldar Fischer, Arijit Ghosh, Amit Levi

The Huge Object model of property testing [Goldreich and Ron, TheoretiCS 23] concerns properties of distributions supported on $\{0,1\}^n$, where $n$ is so large that even reading a single sampled string is unrealistic. Instead, query access is provided to the samples, and the efficiency of the algorithm is measured by the total number of queries that were m

Jikai Wang, Huan Zheng, Jianbing Shen

Lightweight image super-resolution (SR) methods aim at increasing the resolution and restoring the details of an image using a lightweight neural network. However, current lightweight SR methods still suffer from inferior performance and unpleasant details. Our analysis reveals that these methods are hindered by constrained feature diversity, which adversely

Muhammad Islam, Niroshinie Fernando, Seng W. Loke, Azadeh Ghari Neiat

We propose Blockchain-enabled Device-enhanced Multi-access Edge Computing (BdMEC). BdMEC extends the Honeybee framework for on-demand resource pooling with blockchain technology to ensure trust, security, and accountability among devices (even when they are owned by different parties). BdMEC mitigates risks from malicious devices by making computations trace

N. K. Porayko, P. Usynina, J. Terol-Calvo, J. Martin Camalich

Ultra-light axion-like particles (ALPs) can be a viable solution to the dark matter problem. The scalar field associated with ALPs, coupled to the electromagnetic field, acts as an active birefringent medium, altering the polarisation properties of light through which it propagates. In particular, oscillations of the axionic field induce monochromatic variat

Tomohiro Nishiyama, Yoshihiko Hasegawa

Open quantum systems are governed by both unitary and non-unitary dynamics, with dissipation arising from the latter. Traditional quantum divergence measures, such as quantum relative entropy, fail to account for the non-unitary oriented dissipation as the divergence is positive even between unitarily connected states. We introduce a framework for quantifyin

Zhongnian Li, Meng Wei, Peng Ying, Tongfeng Sun

Annotating data for sensitive labels (e.g., disease, smoking) poses a potential threats to individual privacy in many real-world scenarios. To cope with this problem, we propose a novel setting to protect privacy of each instance, namely learning from concealed labels for multi-class classification. Concealed labels prevent sensitive labels from appearing in

Xiao Xue, Shi Dai, Hoang Nhan Luu, Tao Liu

We conduct the first-ever Pulsar Polarization Array (PPA) analysis to detect the ultralight Axion-Like Dark Matter (ALDM) using the polarization data of 22 millisecond pulsars from the third data release of Parkes Pulsar Timing Array. As one of the major dark matter candidates, the ultralight ALDM exhibits a pronounced wave nature on astronomical scales and

KJ Hepworth

Big data visualization - the visual-spatial display of quantitative information culled from huge data sets - is now firmly embedded within the everyday experiences of people across the globe, yet scholarship on it remains surprisingly small. Within this literature, critical theorizations of big data visualizations are rare, as digital positivist perspectives

Quanjiang Guo, Yihong Dong, Ling Tian, Zhao Kang

Despite the recent success of two-stage prototypical networks in few-shot named entity recognition (NER), challenges such as over/under-detected false spans in the span detection stage and unaligned entity prototypes in the type classification stage persist. Additionally, LLMs have not proven to be effective few-shot information extractors in general. In thi

Yizhe Liu, Qinshu Li, Fang Liu, Xinqiang Wang

Non-Fourier thermal transports have drawn significant attention for decades. Among them, the frequency dependent thermal conductivity has been extensively explored by pump-probe techniques, such as time-domain thermoreflectance, which is employed to probe the spectra of phonon mean free paths. However, previous studies on silicon have not exhibited apparent

I. T. Habibullin, A. R. Khakimova

The duality between a class of the Davey-Stewartson type coupled systems and a class of two-dimensional Toda type lattices is discussed. A new coupled system related to the recently found lattice is presented. A method for eliminating nonlocalities in coupled systems by virtue of special finite reductions of the lattices is suggested. An original algorithm f

Qisen Wang, Yifan Zhao, Jiawei Ma, Jia Li

Novel view synthesis under sparse views has been a long-term important challenge in 3D reconstruction. Existing works mainly rely on introducing external semantic or depth priors to supervise the optimization of 3D representations. However, the diffusion model, as an external prior that can directly provide visual supervision, has always underperformed in sp

Yeji Lee, Vineeth K. Bandari, John S. McCaskill, Pranathi Adluri

Modular microrobotics can potentially address many information-intensive microtasks in medicine, manufacturing and the environment. However, surface area has limited the natural powering, communication, functional integration, and self-assembly of smart mass-fabricated modular robotic devices at small scales. We demonstrate the integrated self-folding and se

Christopher Schwanke

We develop a functional calculus on Archimedean vector lattices for semicontinuous positively homogeneous real-valued functions defined on $\R^n$ which are bounded on the unit sphere. It is further shown that this semicontinuous Archimedean vector lattice functional calculus extends the existing continuous Archimedean vector lattice functional calculus by Bu

Advait Chandorkar

This paper presents a physics-informed deep learning approach for predicting the replicator equation, allowing accurate forecasting of population dynamics. This methodological innovation allows us to derive governing differential or difference equations for systems that lack explicit mathematical models. We used the SINDy model first introduced by Fasel, Kai

I. T. Habibullin, A. R. Khakimova

It is known that there is a duality between the Davey--Stewartson type coupled systems and a class of integrable two--dimensional Toda type lattices. More precisely, the coupled systems are generalized symmetries for the lattices and the lattices can be interpreted as dressing chains for the systems. In our recent study we have found a novel lattice which ap

Zixuan Hu, Yongxian Wei, Li Shen, Chun Yuan

Large Language Models (LLMs) such as ChatGPT demonstrate strong few-shot adaptability without requiring fine-tuning, positioning them ideal for data-limited and real-time applications. However, this adaptability has not yet been replicated in current Visual Foundation Models (VFMs), which require explicit fine-tuning with sufficient tuning data. Besides, the

Olga Muñoz, Antonio Pascual-Iserte, Guillermo San Arranz

In this paper, we address the design of multi-user multiple-input single-output (MU-MISO) precoders for indoor visible light communication (VLC) systems. The goal is to minimize the transmitted optical power per light emitting diode (LED) under imperfect channel state information (CSI) at the transmitter side. Robust precoders for imperfect CSI available in

Xingyue Qian, Chen Nie, Zhezhi He, Weikang Qian

Single instruction, multiple data (SIMD) is a popular design style of in-memory computing (IMC) architectures, which enables memory arrays to perform logic operations to achieve low energy consumption and high parallelism. To implement a target function on the data stored in memory, the function is first transformed into a netlist of the supported logic oper

Ilan Doron-Arad, Ariel Kulik, Hadas Shachnai

The $\ell$-matroid intersection ($\ell$-MI) problem asks if $\ell$ given matroids share a common basis. Already for $\ell = 3$, notable canonical NP-complete special cases are $3$-Dimensional Matching and Hamiltonian Path on directed graphs. However, while these problems admit exponential-time algorithms that improve the simple brute force, the fastest known

Ghanesh Narasimhan, Dennice F. Gayme, Charles Meneveau

Analytical wake models provide a computationally efficient means to predict velocity distributions in wind turbine wakes in the atmospheric boundary layer (ABL). Most existing models are developed for neutral atmospheric conditions and correspondingly neglect the effects of buoyancy and Coriolis forces that lead to veer, i.e. changes in the wind direction wi

Ayan Banerjee, Sandeep K. S. Gupta

Recovering a physics-driven model, i.e. a governing set of equations of the underlying dynamical systems, from the real-world data has been of recent interest. Most existing methods either operate on simulation data with unrealistically high sampling rates or require explicit measurements of all system variables, which is not amenable in real-world deploymen

Renan A. Rojas-Gomez, Minh N. Do

State-of-the-art Style Transfer methods often leverage pre-trained encoders optimized for discriminative tasks, which may not be ideal for image synthesis. This can result in significant artifacts and loss of photorealism. Motivated by the ability of multiscale geometric image representations to capture fine-grained details and global structure, we propose G

Liang-Wen Ji, Wu-Zhang Yang, Yi-Ming Lu, Jia-Yi Lu

We report Ni-doping effect on the magnetic and electronic properties of thiospinel Co$_{1-x}$Ni$_x$[Co$_{0.3}$Ir$_{1.7}$]S$_4$ (0 $\leq x \leq$ 1). The parent compound Co[Co$_{0.3}$Ir$_{1.7}$]S$_4$ exhibits antiferromagnetic order below $T_\mathrm{N} \sim$ 292 K within the $A$-site diamond sublattice, along with a narrow charge-transfer gap. Upon Ni doping,

Xingyue Qian, Chenyang Lv, Zhezhi He, Weikang Qian

In-memory computing (IMC) with single instruction multiple data (SIMD) setup enables memory to perform operations on the stored data in parallel to achieve high throughput and energy saving. To instruct a SIMD IMC hardware to compute a function, a logic compiler is needed that involves two steps: logic synthesis and scheduling. Logic synthesis transforms the

Yaxin Liang, Xinshi Li, Xin Huang, Ziqi Zhang

This study proposes an automated data mining framework based on autoencoders and experimentally verifies its effectiveness in feature extraction and data dimensionality reduction. Through the encoding-decoding structure, the autoencoder can capture the data's potential characteristics and achieve noise reduction and anomaly detection, providing an efficient

Zhibo Yang, Jun Tang, Zhaohai Li, Pengfei Wang

Large Multimodal Models (LMMs) have demonstrated impressive performance in recognizing document images with natural language instructions. However, it remains unclear to what extent capabilities in literacy with rich structure and fine-grained visual challenges. The current landscape lacks a comprehensive benchmark to effectively measure the literate capabil

Chander Mohan Bishnoi, Sanjay Mishra

In this paper, we investigate various cardinal properties of the space $Q_{C}X$ of all real-valued quasicontinuous functions on the topological space $X$, under the topology of uniform convergence on compact subsets. It begins by examining the relationship between tightness and other properties in the context of the space $X$, highlighting results such as th

Yonghwa Cho, Jinhyung Park

The non-isomorphic locus of a general projection from an embedded smooth projective variety to a hypersurface moves in a linear system of an effective divisor which we call the double point divisor. David Mumford proved that the double point divisor from outer projection is always base point free, and Bo Ilic proved that it is ample except for a Roth variety

Markus Kriener, Takashi Koretsune, Ryotaro Arita, Yoshinori Tokura

Topological materials attract a considerable research interest because of their characteristic band structure giving rise to various new phenomena in quantum physics. Beside this, they are tempting from a functional materials point of view: Topological materials bear potential for an enhanced thermoelectric efficiency because they possess the required ingred

Miguel Jiménez-Redondo, Olli Sipilä, Pavol Jusko, Paola Caselli

The rate coefficients of various isotopic variations of the H2+ + H2 and H3+ + H2 reactions in the 10-250 K temperature range were measured using a cryogenic 22 pole radio frequency ion trap. The processes involving diatomic ions were found to behave close to the Langevin rate, whereas temperature-dependent rate coefficients were obtained for the four isotop

Cui Zhang, Maoxin Ji, Qiong Wu, Pingyi Fan

In the Internet of Vehicles (IoV), Age of Information (AoI) has become a vital performance metric for evaluating the freshness of information in communication systems. Although many studies aim to minimize the average AoI of the system through optimized resource scheduling schemes, they often fail to adequately consider the queue characteristics. Moreover, t

Luoxuan Weng, Yinghao Tang, Yingchaojie Feng, Zhuo Chang

Business intelligence (BI) transforms large volumes of data within modern organizations into actionable insights for informed decision-making. Recently, large language model (LLM)-based agents have streamlined the BI workflow by automatically performing task planning, reasoning, and actions in executable environments based on natural language (NL) queries. H

Hai-Cheng Feng, Sha-Sha Li, J. M. Bai, H. T. Liu

Current reverberation mapping (RM) studies primarily focus on single emission lines, particularly the \hb\ line, which may not fully reveal the geometry and kinematic properties of the broad-line region (BLR). To overcome this limitation, we conducted multiline RM observations on two highly variable active galactic nuclei (AGNs), KUG 1141+371 and UGC 3374, u

B. X. Gao, M. Lyu, L. Y. Cao, L. Wang

The interplay between nontrivial topology, magnetism and strong correlation has generated considerable research interest in condensed matter physics. The topological RAlX (R = rare earth ; X = Si and Ge) family has provided an excellent platform for exploring these complex interactions. Here, we performed infrared spectroscopy measurements on the ferromagnet

Tuowei Wang, Kun Li, Donglin Bai, Fusong Ju

AI infrastructures, predominantly GPUs, have delivered remarkable performance gains for deep learning. Conversely, scientific computing, exemplified by quantum chemistry systems, suffers from dynamic diversity, where computational patterns are more diverse and vary dynamically, posing a significant challenge to sponge acceleration off GPUs. In this paper, we

Jinzhi Zhang, Feng Xiong, Mu Xu

Autoregressive transformers have revolutionized high-fidelity image generation. One crucial ingredient lies in the tokenizer, which compresses high-resolution image patches into manageable discrete tokens with a scanning or hierarchical order suitable for large language models. Extending these tokenizers to 3D generation, however, presents a significant chal

Junsu Jang, Florian Meyer

Navigation and source localization in the undersea environment are challenged by the absence of a ubiquitous positioning system. Passive acoustic ranging offers a valuable means of obtaining location information underwater. We present a range estimation method based on waveguide invariant (WI) theory, using ship noise recorded by a hydrophone as an acoustic

Tyler Chamberlain

The secular manifold $\Sigma_G$ and its singularities are intimately related to the spectra of metric graphs $(G,\ell)$. In this paper, we present a complete description of the singular locus for tree graphs, and confirm that it agrees with a conjecture of Colin de Verdi\`ere. We also discuss numerous applications toward studying the behavior of metric tree

Sanjay Mishra, Pankaj Pandey, Sreeram Ravindran

In this paper, we find necessary and sufficient conditions for countable fan tightness and countable strong fan tightness of the space (briefly, $C_{p}(X,G)$) of all group-valued continuous functions endowed with the topology of pointwise convergence in term of Menger property and Rothberger property respectively. Furthermore, we establish a relationship bet

Pritesh Prakash, Anoop Kumar Rai

Aging presents a significant challenge in face recognition, as changes in skin texture and tone can alter facial features over time, making it particularly difficult to compare images of the same individual taken years apart, such as in long-term identification scenarios. Transformer networks have the strength to preserve sequential spatial relationships cau