November 2024 arXiv papers — page 91

Yachao Yang, Yanfeng Sun, Jipeng Guo, Junbin Gao

Graph Neural Networks (GNNs) have excelled in handling graph-structured data, attracting significant research interest. However, two primary challenges have emerged: interference between topology and attributes distorting node representations, and the low-pass filtering nature of most GNNs leading to the oversight of valuable high-frequency information in gr

Jongmin Park, Seunghoon Han, Jong-Ryul Lee, Sungsu Lim

To leverage the complex structures within heterogeneous graphs, recent studies on heterogeneous graph embedding use a hyperbolic space, characterized by a constant negative curvature and exponentially increasing space, which aligns with the structural properties of heterogeneous graphs. However, despite heterogeneous graphs inherently possessing diverse powe

Yucong Meng, Zhiwei Yang, Minghong Duan, Yonghong Shi

Magnetic resonance imaging (MRI) is a crucial tool for clinical diagnosis while facing the challenge of long scanning time. To reduce the acquisition time, fast MRI reconstruction aims to restore high-quality images from the undersampled k-space. Existing methods typically train deep learning models to map the undersampled data to artifact-free MRI images. H

Jack Yarndley, Harry Holt, Roberto Armellin

The optimal design of multi-target rendezvous and flyby missions is challenging due to the combination of traditional spacecraft trajectory optimization and high-dimensional combinatorial problems. This often requires large-scale global search techniques or simplified approximations that rely on manual tuning to be performant. While global search techniques

Xu Xiao, Jiacheng Ding, XiaoLin Luo, Sun Ke Lan

We propose a UNet-based deep learning model to reconstruct the real-space dark matter (DM) velocity field from the redshift-space distribution of sparse DM halos. Using various statistical measures, we show that the reconstructed velocity components--including velocity magnitude, momentum, and divergence--closely match the ground truth, achieving better than

Roberto Armellin, Andrea Bellome, Xiaoyu Fu, Harry Holt

We present the solution approach developed by the team `TheAntipodes' during the 12th edition of the Global Trajectory Optimization Competition (GTOC 12). An overview of the approach is as follows: (1) generate asteroid subsets, (2) chain building with beam search, (3) convex low-thrust trajectory optimization, (4) manual refinement of rendezvous times, and

Hari Chapagain

Feynman's modification to electrodynamics and its application to the calculation of self-energy of a free spin-$\frac{1}{2}$ particle, appearing in his 1948 Physical Review paper, is shown to be applicable for the self-energy calculation of a free spin-0 particle as well. Feynman's modification to electrodynamics is shown to be equivalent to a Hamiltonian ap

Jinxing Zhou, Dan Guo, Ruohao Guo, Yuxin Mao

The Audio-Visual Event Localization (AVEL) task aims to temporally locate and classify video events that are both audible and visible. Most research in this field assumes a closed-set setting, which restricts these models' ability to handle test data containing event categories absent (unseen) during training. Recently, a few studies have explored AVEL in an

Thai Bui, Hoa T. Vu

We study the maximum set coverage problem in the massively parallel model. In this setting, $m$ sets that are subsets of a universe of $n$ elements are distributed among $m$ machines. In each round, these machines can communicate with each other, subject to the memory constraint that no machine may use more than $\tilde{O}(n)$ memory. The objective is to fin

Yeping Wang, Shihao Yang

Physics-Informed Neural Networks (PINNs) solve forward PDEs by minimizing residual losses from the governing equations with initial and boundary conditions, but they often struggle with discontinuities such as shocks. In contrast, finite volume methods (FVM) handle discontinuities by enforcing integral conservation, which admits weak solutions. Motivated by

Mehdi Neshat, Michael Phipps, Nikhil Jha, Danial Khojasteh

Over an extensive duration, administrators and clinicians have endeavoured to predict Emergency Department (ED) visits with precision, aiming to optimise resource distribution. Despite the proliferation of diverse AI-driven models tailored for precise prognostication, this task persists as a formidable challenge, besieged by constraints such as restrained ge

Therese Biedl, Stephane Durocher, Debajyoti Mondal, Rahnuma Islam Nishat

A conforming partition of a rectilinear n-gon P (possibly with holes) is a partition of P into rectangles without using Steiner points (i.e., all corners of all rectangles must lie on the boundary of P). The stabbing number of such a partition is the maximum number of rectangles intersected by an axis-aligned segment lying in the interior of P. In this paper

Stephen P. Glasby

We give a complete and irredundant list of the finite groups $G$ for which Aut$(G)$, acting naturally on $G$, has precisely $3$ orbits. There are 7 infinite families: one abelian, one non-nilpotent, three families of non-abelian $2$-groups and two families of non-abelian $p$-groups with $p$ odd. The non-abelian $2$-group examples were first classified by Bor

Daniel Severo

This thesis deals with the problem of communicating and storing non-sequential data. We investigate this problem through the lens of lossless source coding, also sometimes referred to as lossless compression, from both an algorithmic and information-theoretic perspective. Lossless compression algorithms typically preserve the ordering in which data points ar

Ruiyang Zhang, Hu Zhang, Zhedong Zheng

Given the higher information load processed by large vision-language models (LVLMs) compared to single-modal LLMs, detecting LVLM hallucinations requires more human and time expense, and thus rise a wider safety concerns. In this paper, we introduce VL-Uncertainty, the first uncertainty-based framework for detecting hallucinations in LVLMs. Different from mo

Serena Dipierro, Mateusz Kwaśnicki, Jack Thompson, Enrico Valdinoci

We revisit a Harnack inequality for antisymmetric functions that has been recently established for the fractional Laplacian and we extend it to more general nonlocal elliptic operators. The new approach to deal with these problems that we propose in this paper leverages Bochner's relation, allowing one to relate a one-dimensional Fourier transform of an odd

Justin Whitehouse, Ben Chugg, Diego Martinez-Taboada, Aaditya Ramdas

We consider estimating the shared mean of a sequence of heavy-tailed random variables taking values in a Banach space. In particular, we revisit and extend a simple truncation-based mean estimator first proposed by Catoni and Giulini. While existing truncation-based approaches require a bound on the raw (non-central) second moment of observations, our result

Elsiddig Awadelkarim, Neil K. Chada, Ajay Jasra

We consider the development of unbiased estimators, to approximate the stationary distribution of Mckean-Vlasov stochastic differential equations (MVSDEs). These are an important class of processes, which frequently appear in applications such as mathematical finance, biology and opinion dynamics. Typically the stationary distribution is unknown and indeed o

Matthew Giesler, Sizheng Ma, Keefe Mitman, Naritaka Oshita

Using high-accuracy numerical relativity waveforms, we confirm the presence of numerous overtones of the $\ell=2$, $m=2$ quasinormal mode early in the ringdown of binary black hole mergers. We do this by demonstrating the stability of the mode amplitudes at different fit times, ruling out the possibility that a linear superposition of modes unphysically fits

Oliver Watt-Meyer, Brian Henn, Jeremy McGibbon, Spencer K. Clark

Existing machine learning models of weather variability are not formulated to enable assessment of their response to varying external boundary conditions such as sea surface temperature and greenhouse gases. Here we present ACE2 (Ai2 Climate Emulator version 2) and its application to reproducing atmospheric variability over the past 80 years on timescales fr

Chengbo Zhang

Efficient management of on-demand delivery systems is essential for modern urban logistics, especially in densely populated cities with complex spatial layouts. This study introduces a novel, computer-supported cooperative framework that utilizes Infomap-based hierarchical community detection to analyze spatial multilevel clustering patterns. The experiment

Keer Lu, Keshi Zhao, Zhuoran Zhang, Zheng Liang

As demonstrated by the proprietary Large Language Models (LLMs) such as GPT and Claude series, LLMs have the potential to achieve remarkable proficiency across a wide range of domains, including law, medicine, finance, science, code, etc., all within a single model. These capabilities are further augmented during the Supervised Fine-Tuning (SFT) phase. Despi

Thanh V. T. Tran, Nhat Khang Ngo, Viet Anh Nguyen, Truong Son Hy

Latent space optimization (LSO) is a powerful method for designing discrete, high-dimensional biological sequences that maximize expensive black-box functions, such as wet lab experiments. This is accomplished by learning a latent space from available data and using a surrogate model to guide optimization algorithms toward optimal outputs. However, existing

Linlin Tan, Haishan Wu

Mangroves play a crucial role in maintaining coastal ecosystem health and protecting biodiversity. Therefore, continuous mapping of mangroves is essential for understanding their dynamics. Earth observation imagery typically provides a cost-effective way to monitor mangrove dynamics. However, there is a lack of regional studies on mangrove areas in the UAE.

Abhijit Sinha, Jackson Diodati, Narayanan Menon, Shubha Tewari

Ensuring a smooth rate of efflux of particles from an outlet without unpredictable clogging events is crucial in processing powders and grains. We show by experiments and simulations that an obstacle placed near the outlet can greatly suppress clog formation in a 3-dimensional granular flow; this counterintuitive phenomenon had previously been demonstrated i

M. L. Allen, P. L. Biermann, A. Chieffi, R. Chini

In Radio Super Novae (RSNe) a magnetic field of $(B \, \times \, r) \, = \, 10^{16.0 \pm 0.12} \, {\rm Gauss \, \times \, cm}$ is observed; these are the same numbers for Blue Super Giant (BSG) star explosions as for Red Super Giant (RSG) star explosions, despite their very different wind properties. The EHT data for M87 as well for low power radio galaxies

Yonghuang Wu, Xuan Xie, Xinyuan Niu, Chengqian Zhao

Pathology computing has dramatically improved pathologists' workflow and diagnostic decision-making processes. Although computer-aided diagnostic systems have shown considerable value in whole slide image (WSI) analysis, the problem of multi-classification under sample imbalance remains an intractable challenge. To address this, we propose learning fine-grai

Juan Manuel Lorenzo-Naveiro, Alberto Rodríguez-Vázquez

In this article we classify totally geodesic submanifolds of homogeneous nearly Kähler 6-manifolds, and of the G2-cones over these 6-manifolds. To this end, we develop new techniques for the study of totally geodesic submanifolds of analytic Riemannian manifolds, naturally reductive homogeneous spaces, and Riemannian cones. In particular, we obtain an exampl

Seonghyeon Moon, Haein Kong, Muhammad Haris Khan, Mubbasir Kapadia

Few-shot segmentation (FSS) aims to segment the target object in a query image using only a small set of support images and masks. Therefore, having strong prior information for the target object using the support set is essential for guiding the initial training of FSS, which leads to the success of few-shot segmentation in challenging cases, such as when t

Cameron Morin, Matti Marttinen Larsson

Much linguistic research relies on annotated datasets of features extracted from text corpora, but the rapid quantitative growth of these corpora has created practical difficulties for linguists to manually annotate large data samples. In this paper, we present a replicable, supervised method that leverages large language models for assisting the linguist in

Qian Chang, Xia Li, Xiufeng Cheng, Runsong Jia

In this paper, we propose Graph Retention Networks (GRNs) as a unified architecture for deep learning on dynamic graphs. The GRN extends the concept of retention into dynamic graph data as graph retention, equipping the model with three key computational paradigms: parallelizable training, low-cost $\mathcal{O}(1)$ inference, and long-term chunkwise training

Xiao-Hang Jiang, Hui-Peng Du, Yang Ai, Ye-Xin Lu

This paper proposes ESTVocoder, a novel excitation-spectral-transformed neural vocoder within the framework of source-filter theory. The ESTVocoder transforms the amplitude and phase spectra of the excitation into the corresponding speech amplitude and phase spectra using a neural filter whose backbone is ConvNeXt v2 blocks. Finally, the speech waveform is r

Ngo T. Que, Do T. Nga, Anh D. Phan, Le M. Tu

We present a theoretical approach, for the first time, to investigate optical and photothermal properties of high-entropy alloy nanoparticles with a focus on FeCoNi-based alloys. We systematically analyze the absorption spectra of spherical nanoparticles composed of pure metals and alloys in various surrounding media. Through comparison with experimental dat

Hechuan Wen, Tong Chen, Guanhua Ye, Li Kheng Chai

Causal effect estimation (CEE) provides a crucial tool for predicting the unobserved counterfactual outcome for an entity. As CEE relaxes the requirement for ``perfect'' counterfactual samples (e.g., patients with identical attributes and only differ in treatments received) that are impractical to obtain and can instead operate on observational data, it is u

Sneha Sudhir Shetiya, Vikas Vyas, Shreyas Renukuntla

This paper explains how traditional centralized architectures are transitioning to distributed zonal approaches to address challenges in scalability, reliability, performance, and cost-effectiveness. The role of edge computing and neural networks in enabling sophisticated sensor fusion and decision-making capabilities for autonomous vehicles is examined. Add

Xinyu Gao

We establish $L^{p_1}(\mathbb R^d) \times \cdots \times L^{p_n}(\mathbb R^d) \rightarrow L^r(\mathbb R^d)$ bounds for spherical averaging operators $\mathcal A^n$ in dimensions $d \geq 2$ for indices $1\le p_1,\dots , p_n\le \infty$ and $\frac{1}{p_1}+\cdots +\frac{1}{p_n}=\frac{1}{r}$. We obtain this result by first showing that $\mathcal A^n$ maps $L^1 \ti

Xingming Long, Jie Zhang, Shiguang Shan, Xilin Chen

The primary goal of out-of-distribution (OOD) detection tasks is to identify inputs with semantic shifts, i.e., if samples from novel classes are absent in the in-distribution (ID) dataset used for training, we should reject these OOD samples rather than misclassifying them into existing ID classes. However, we find the current definition of "semantic shift"

Apurva Kalia, Yan Zhou Chen, Dilip Krishnan, Soha Hassoun

Motivation: A major challenge in metabolomics is annotation: assigning molecular structures to mass spectral fragmentation patterns. Despite recent advances in molecule-to-spectra and in spectra-to-molecular fingerprint prediction (FP), annotation rates remain low. Results: We introduce in this paper a novel paradigm (JESTR) for annotation. Unlike prior appr

Yu-Chu Lin, Haitao Wang, Kung-Chien Wu

We study the quantitative pointwise behavior of solutions to the Boltzmann equation for hard potentials and Maxwellian molecules, which generalize the hard sphere case introduced by Liu-Yu in 2004 (Comm. Pure Appl. Math. 57:1543-1608, 2004). The large time behavior of the solution is dominated by fluid structures, similar to the hard sphere case. However, un

Zhao Wang, Briti Gangopadhyay, Mengjie Zhao, Shingo Takamatsu

Current keyword decision-making in sponsored search advertising relies on large, static datasets, limiting the ability to automatically set up keywords and adapt to real-time KPI metrics and product updates that are essential for effective advertising. In this paper, we propose On-the-fly Keyword Generation (OKG), an LLM agent-based method that dynamically m

Tianyi Yan, Dongming Wu, Wencheng Han, Junpeng Jiang

Autonomous driving evaluation requires simulation environments that closely replicate actual road conditions, including real-world sensory data and responsive feedback loops. However, many existing simulations need to predict waypoints along fixed routes on public datasets or synthetic photorealistic data, \ie, open-loop simulation usually lacks the ability

Jingxuan Wei, Cheng Tan, Qi Chen, Gaowei Wu

We introduce the task of text-to-diagram generation, which focuses on creating structured visual representations directly from textual descriptions. Existing approaches in text-to-image and text-to-code generation lack the logical organization and flexibility needed to produce accurate, editable diagrams, often resulting in outputs that are either unstructur

Palak Sood, Chengyang He, Divyanshu Gupta, Yue Ning

Mental health support in colleges is vital in educating students by offering counseling services and organizing supportive events. However, evaluating its effectiveness faces challenges like data collection difficulties and lack of standardized metrics, limiting research scope. Student feedback is crucial for evaluation but often relies on qualitative analys

Xibo Sun, Jiarui Fang, Aoyu Li, Jinzhe Pan

The increased model capacity of Diffusion Transformers (DiTs) and the demand for generating higher resolutions of images and videos have led to a significant rise in inference latency, impacting real-time performance adversely. While prior research has highlighted the presence of high similarity in activation values between adjacent diffusion steps (referred

Guilherme Limberg

The discovery of fast moving stars in the Milky Way's most massive globular cluster, $\omega$Centauri ($\omega$Cen), has provided strong evidence for an intermediate-mass black hole (IMBH) inside of it. However, $\omega$Cen is known to be the stripped nuclear star cluster (NSC) of an ancient, now-destroyed, dwarf galaxy. The best candidate to be the original

Max Lovitt, Haotian Ma, Song Wang, Yifan Peng

With social media communities increasingly becoming places where suicidal individuals post and congregate, natural language processing presents an exciting avenue for the development of automated suicide risk assessment systems. However, past efforts suffer from a lack of labeled data and class imbalances within the available labeled data. To accommodate thi

Jie Ju, Bo Zhang, Wenyuan Cui, ZhenYan Huo

Blue horizontal-branch (BHB) stars are crucial for studying the structure of the Galactic halo. Accurate atmospheric parameters of BHB stars are essential for investigating the formation and evolution of the Galaxy. In this work, a data-driven technique named stellar label machine (SLAM) is used to estimate the atmospheric parameters of Large Sky Area Multi-

Onur Vural, Shah Muhammad Hamdi, Soukaina Filali Boubrahimi

In heliophysics research, predicting solar flares is crucial due to their potential to impact both space-based systems and Earth's infrastructure substantially. Magnetic field data from solar active regions, recorded by solar imaging observatories, are transformed into multivariate time series to enable solar flare prediction using temporal window-based anal

Yuichi Goto, Marc Hallin

Time-reversibility is a crucial feature of many time series models, while time-irreversibility is the rule rather than the exception in real-life data. Testing the null hypothesis of time-reversibilty, therefore, should be an important step preliminary to the identification and estimation of most traditional time-series models. Existing procedures, however,

Son T. Luu, Hiep Nguyen, Trung Vo, Le-Minh Nguyen

In this paper, we propose ZeFaV - a zero-shot based fact-checking verification framework to enhance the performance on fact verification task of large language models by leveraging the in-context learning ability of large language models to extract the relations among the entities within a claim, re-organized the information from the evidence in a relational

Ayo Aitokhuehi, Benjamin Braiman, David Owen Horace Cutler, Tamás Darvas

Given a compact K\"ahler manifold $(X,\omega)$, due to the work of Darvas-Di Nezza-Lu, the space of singularity types of $\omega$-psh functions admits a natural pseudo-metric $d_\mathcal S$ that is complete in the presence of positive mass. When restricted to model singularity types, this pseudo-metric is a bona fide metric. In case of the projective space,

Karthik Gururangan, Jun Shen, Piotr Piecuch

We report the first study using active-orbital-based and adaptive CC($P$;$Q$) approaches to describe excited electronic states. These CC($P$;$Q$) methodologies are applied, alongside their completely renormalized (CR) coupled-cluster (CC) and equation-of-motion (EOM) CC counterparts, to recover the ground- and excited-state potential cuts of the water molecu

Peng Fang, Wei Wang, Ruofeng Tong, Hailong Li

Computing maximum/minimum distances between 3D meshes is crucial for various applications, i.e., robotics, CAD, VR/AR, etc. In this work, we introduce a highly parallel algorithm (gDist) optimized for Graphics Processing Units (GPUs), which is capable of computing the distance between two meshes with over 15 million triangles in less than 0.4 milliseconds (F

Zhixin Chen, Jie-Ren Deng, Mengyun Wang, Nikolaos Farmakidis

Interferometry has underpinned a century of discoveries, ranging from the disproval of the ether theory to the detection of gravitational waves, offering insights into wave dynamics with unrivalled precision through the measurement of phase relationships. In electronics, phase-sensitive measurements can probe the nature of transmissive topological and quantu

Akash Kumar, Mikhail Belkin, Parthe Pandit

Recent advances in machine learning have led to increased interest in reproducing kernel Banach spaces (RKBS) as a more general framework that extends beyond reproducing kernel Hilbert spaces (RKHS). These works have resulted in the formulation of representer theorems under several regularized learning schemes. However, little is known about an optimization

Lidan Zhao, Zhanjing Tao, Min Zhang

In this paper, we propose a new well-balanced fifth-order finite volume WENO method for solving one- and two-dimensional shallow water equations with bottom topography. The well-balanced property is crucial to the ability of a scheme to simulate perturbation waves over the ``lake-at-rest'' steady state such as waves on a lake or tsunami waves in the deep oce

Gwangseok Han, Wonbin Kweon, Minsoo Kim, Hwanjo Yu

Diversity control is an important task to alleviate bias amplification and filter bubble problems. The desired degree of diversity may fluctuate based on users' daily moods or business strategies. However, existing methods for controlling diversity often lack flexibility, as diversity is decided during training and cannot be easily modified during inference.

Andreas Prohl, Yanqing Wang

This paper investigates numerical methods for solving stochastic linear quadratic (SLQ) optimal control problems governed by stochastic partial differential equations (SPDEs). Two distinct approaches, the open-loop and closed-loop ones, are developed to ensure convergence rates in the fully discrete setting. The open-loop approach, utilizing the finite eleme

Ilias Diakonikolas, Lisheng Ren, Nikos Zarifis

We study the problem of PAC learning halfspaces in the reliable agnostic model of Kalai et al. (2012). The reliable PAC model captures learning scenarios where one type of error is costlier than the others. Our main positive result is a new algorithm for reliable learning of Gaussian halfspaces on $\mathbb{R}^d$ with sample and computational complexity $$d^{

KOTO Collaboration, J. K. Ahn, M. Farriagton, M. Gonzalez

We performed a search for the $K_L \to \pi^{0} \nu \bar{\nu}$ decay using the data taken in 2021 at the J-PARC KOTO experiment. With newly installed counters and new analysis method, the expected background was suppressed to $0.252\pm0.055_{\mathrm{stat}}$$^{+0.052}_{-0.067}$$_{\mathrm{syst}}$. With a single event sensitivity of $(9.33 \pm 0.06_{\rm stat} \p

Xin-Xin Qi, Hao Sun

We consider a two-component scalar dark matter model in this work, where the scalars are stabilized by extra $Z_2 \times Z'_2$ symmetry. To guarantee the stability of the vacuum, we consider the copositive criteria and different choices of the signs of the couplings will contribute to different viable parameter spaces. Based on the copositive criteria, we sy

Yingjie Zhou, Zicheng Zhang, Jiezhang Cao, Jun Jia

Artificial Intelligence (AI) has demonstrated significant capabilities in various fields, and in areas such as human-computer interaction (HCI), embodied intelligence, and the design and animation of virtual digital humans, both practitioners and users are increasingly concerned with AI's ability to understand and express emotion. Consequently, the question

Zi-Qi Wang, Xiao-Li Huang, En-Wei Liang

The radiation physics of gamma-ray bursts (GRBs) remains an open question. Based on the simulation analysis and recent observations, it was proposed that GRB jets are composed of a narrow ultra-relativistic core surrounded by a wide sub-relativistic cocoon. We show that emission from the synchrotron radiations and the synchrotron self-Compton (SSC) process o

Sami Arja, Alexandre Marcireau, Nicholas Owen Ralph, Saeed Afshar

Event cameras capture sparse, asynchronous brightness changes which offer high temporal resolution, high dynamic range, low power consumption, and sparse data output. These advantages make them ideal for Space Situational Awareness, particularly in detecting resident space objects moving within a telescope's field of view. However, the output from event came

Yu-Fei Shi, Yang Ai, Ye-Xin Lu, Hui-Peng Du

Assessing the naturalness of speech using mean opinion score (MOS) prediction models has positive implications for the automatic evaluation of speech synthesis systems. Early MOS prediction models took the raw waveform or amplitude spectrum of speech as input, whereas more advanced methods employed self-supervised-learning (SSL) based models to extract seman

Qianwen Lu, Xingchao Yang, Takafumi Taketomi

The advancement of makeup transfer, editing, and image encoding has demonstrated their effectiveness and superior quality. However, existing makeup works primarily focus on low-dimensional features such as color distributions and patterns, limiting their versatillity across a wide range of makeup applications. Futhermore, existing high-dimensional latent enc

Taowen Wang, Cheng Han, James Chenhao Liang, Wenhao Yang

Recently in robotics, Vision-Language-Action (VLA) models have emerged as a transformative approach, enabling robots to execute complex tasks by integrating visual and linguistic inputs within an end-to-end learning framework. Despite their significant capabilities, VLA models introduce new attack surfaces. This paper systematically evaluates their robustnes

Liying Chen, Ou Deng, Ting Fang, Mei Chen

Objective: Systemic lupus erythematosus (SLE) is a complex autoimmune disease characterized by unpredictable flares. This study aimed to develop a novel proteomics-based risk prediction model specifically for Asian SLE populations to enhance personalized disease management and early intervention. Methods: A longitudinal cohort study was conducted over 48 wee

Congcong Liu, Zhengshuo Li

Aggregators of distributed energy resources are increasingly encouraged to participate in wholesale market bidding. However, the delivery of the power they are awarded can result in over-voltage or congestion issues within the distribution network (DN). The opportunity to lease energy storage from the utility that manages the DN provides the aggregator with

Rahul Arulkumaran, Suyash Kumar, Shikha Tomar, Manideep Gongalla

Cryptocurrencies are highly volatile financial instruments with more and more new retail investors joining the scene with each passing day. Bitcoin has always proved to determine in which way the rest of the cryptocurrency market is headed towards. As of today Bitcoin has a market dominance of close to 50 percent. Bull and bear phases in cryptocurrencies are

Xiaoyang Xie, Zhanjing Tao, Chunhai Jiao, Min Zhang

In this paper, we develop a simple, efficient, and fifth-order finite difference interpolation-based Hermite WENO (HWENO-I) scheme for one- and two-dimensional hyperbolic conservation laws. We directly interpolate the solution and first-order derivative values and evaluate the numerical fluxes based on these interpolated values. We do not need the split of t

Alexander S. Dellios, Margaret D. Reid, Peter D. Drummond

An important challenge with the current generation of noisy, large-scale quantum computers is the question of validation. Does the hardware generate correct answers? If not, what are the errors? This issue is often combined with questions of computational advantage, but it is a fundamentally distinct issue. In current experiments, complete validation of the

Hussel Suriyaarachchi, Paul Denny, Suranga Nanayakkara

Productive Failure (PF) is a learning approach where students initially tackle novel problems targeting concepts they have not yet learned, followed by a consolidation phase where these concepts are taught. Recent application in STEM disciplines suggests that PF can help learners develop more robust conceptual knowledge. However, empirical validation of PF f

Jewel A. Abbate, Yufan Xu, Tobias Vogt, Susanne Horn

Convection in planets and stars is predicted to occur in the "ultimate regime'' of diffusivity-free, rapidly rotating turbulence, in which flows are characteristically unaffected by viscous and thermal diffusion. Boundary layer diffusion, however, has historically hindered experimental study of this regime. Here, we utilize the boundary-independent oscillato

Hansen Shieh, Michael Yang, Ashley Yu

In this paper, we introduce the dotted pattern-avoiding map $s_{\dot{\tau}}$, which avoids the dotted pattern $\dot{\tau}$ instead of descents as West's stack-sorting map $s$ does. We also extend the pattern-avoiding machine, which is composed of a $\sigma$ avoiding map and West's stack-sorting map $s$, to the dotted pattern-avoiding machine. In this paper,

Yunze Luo, Yuezihan Jiang, Yinjie Jiang, Gaode Chen

With the rise of e-commerce and short videos, online recommender systems that can capture users' interests and update new items in real-time play an increasingly important role. In both online and offline recommendation systems, the cold-start problem caused by interaction sparsity has been impacting the effectiveness of recommendations for cold-start items.

Betty Shea, Mark Schmidt

The value of second-order methods lies in the use of curvature information. Yet, this information is costly to extract and once obtained, valuable negative curvature information is often discarded so that the method is globally convergent. This limits the effectiveness of second-order methods in modern machine learning. In this paper, we show that second-ord

Haoxing Chen, Zizheng Huang, Yan Hong, Yanshuo Wang

Pre-trained vision-language models provide a robust foundation for efficient transfer learning across various downstream tasks. In the field of video action recognition, mainstream approaches often introduce additional modules to capture temporal information. Although the additional modules increase the capacity of model, enabling it to better capture video-

Piyush Bagad, Makarand Tapaswi, Cees G. M. Snoek, Andrew Zisserman

We study the connection between audio-visual observations and the underlying physics of a mundane yet intriguing everyday activity: pouring liquids. Given only the sound of liquid pouring into a container, our objective is to automatically infer physical properties such as the liquid level, the shape and size of the container, the pouring rate and the time t

Yiwei Wang, Tao Yang, Hailin Huang, Tianjie Zou

This paper presents a data-driven electrical machine design (EMD) framework using wound-rotor synchronous generator (WRSG) as a design example. Unlike traditional preliminary EMD processes that heavily rely on expertise, this framework leverages an artificial-intelligence based expert database, to provide preliminary designs directly from user specifications

Yan Sun, Ji Yang, Shaobo Zhang, Qing-Zeng Yan

Based on 32162 molecular clouds from the Milky Way Imaging Scroll Painting project, we obtain new face-on molecular gas maps of the northern outer Galaxy. The total molecular gas surface density map reveals three segments of spirals, extending 16-43 kiloparsecs in length. The Perseus and Outer arms stand out prominently, appearing as quasi-continuous structu

Tiancheng Lin, Jinglei Zhang, Yi Xu, Kai Chen

Context-aware methods have achieved remarkable advancements in supervised scene text recognition by leveraging semantic priors from words. Considering the heterogeneity of text and background in STR, we propose that such contextual priors can be reinterpreted as the relations between textual elements, serving as effective self-supervised labels for represent

Bibek Gupta, Eric Sihite, Alireza Ramezani

Dynamic morphing wing flights present significant challenges in accurately estimating external forces due to complex interactions between aerodynamics, rapid wing movements, and external disturbances. Traditional force estimation methods often struggle with unpredictable disturbances like wind gusts or unmodeled impacts that can destabilize flight in real-wo

Shiyi Cao, Shu Liu, Tyler Griggs, Peter Schafhalter

Efficient deployment of large language models, particularly Mixture of Experts (MoE), on resource-constrained platforms presents significant challenges, especially in terms of computational efficiency and memory utilization. The MoE architecture, renowned for its ability to increase model capacity without a proportional increase in inference cost, greatly re

Kaushik Venkatesh Krishnamurthy, Chenghao Wang, Shreyansh Pitroda, Eric Sihite

Legged-aerial multimodal robots can make the most of both legged and aerial systems. In this paper, we propose a control framework that bypasses heavy onboard computers by using an optimization-free Explicit Reference Governor that incorporates external thruster forces from an attitude controller. Ground reaction forces are maintained within friction cone co

Ellison B. Weiner, Irene Dankwa-Mullan, William A. Nelson, Saeed Hassanpour

Artificial intelligence (AI) has rapidly transformed various sectors, including healthcare, where it holds the potential to revolutionize clinical practice and improve patient outcomes. However, its integration into medical settings brings significant ethical challenges that need careful consideration. This paper examines the current state of AI in healthcar

Lei Fu, Xuanyou Li

We define the hypergeometric exponential sum associated to a family of representations of a reductive group over a finite field. We introduce the hypergeometric $\ell$-adic sheaf to describe the hypergeometric exponential sum. Motivated by the definition of the hypergeometric sheaf, we introduce the hypergeometric $\mathcal D$-module, prove it is holonomic a

Jaewoo Heo, George Hu, Zeyu Wang, Serena Yeung-Levy

Human Mesh Recovery (HMR) is an important yet challenging problem with applications across various domains including motion capture, augmented reality, and biomechanics. Accurately predicting human pose parameters from a single image remains a challenging 3D computer vision task. In this work, we introduce DeforHMR, a novel regression-based monocular HMR fra

Kanishka Tyagi, Chinmay Rane, Ketaki Vaidya, Jeshwanth Challgundla

This study presents a comparative analysis of two objective functions, Mean Squared Error (MSE) and Softmax Cross-Entropy (SCE) for neural network classification tasks. While SCE combined with softmax activation is the conventional choice for transforming network outputs into class probabilities, we explore an alternative approach using MSE with sigmoid acti

Jean-Baptiste Jolly, Kirsten Knudsen, Nicolas Laporte, Andrea Guerrero

Understanding the dust content of galaxies, its evolution with redshift and its relationship to stars and star formation is fundamental for our understanding of galaxy evolution. Using the ALMA Lensing Cluster Survey (ALCS) wide-area band-6 continuum dataset ($\sim\,$110 arcmin$^2$ across 33 lensing clusters), we aimed at constraining the dust mass evolution

Zheng Hui, Zhaoxiao Guo, Hang Zhao, Juanyong Duan

Effective toxic content detection relies heavily on high-quality and diverse data, which serve as the foundation for robust content moderation models. Synthetic data has become a common approach for training models across various NLP tasks. However, its effectiveness remains uncertain for highly subjective tasks like hate speech detection, with previous rese

Akash Ratheesh, Vincent Pacelli, Augustinos D. Saravanos, Evangelos A. Theodorou

This paper presents a novel algorithm for solving distribution steering problems featuring nonlinear dynamics and chance constraints. Covariance steering (CS) is an emerging methodology in stochastic optimal control that poses constraints on the first two moments of the state distribution -- thereby being more tractable than full distributional control. Neve

Urban Senica, Michael A. Schreiber, Paolo Micheletti, Mattias Beck

In a laser, the control of its spectral emission depends on the physical dimensions of the optical resonator, limiting it to a set of discrete cavity modes at specific frequencies. Here, we overcome this fundamental limit by demonstrating a monolithic semiconductor laser with a continuously tunable repetition rate from 4 up to 16 GHz, by employing a microwav

Bruno F. F. Gonçalves, Isabel S. Labouriau, Alexandre A. P. Rodrigues

In this article, we study the FitzHugh-Nagumo $(1,1)$--fast-slow system where the vector fields associated to the slow/fast equations come from the reduction of the Hodgin-Huxley model for the nerve impulse. After deriving dynamical properties of the singular and regular cases, we perform a bifurcation analysis and we investigate how the parameters (of the a

Sasan Amariamir, Janine George, Philipp Benner

Material discovery is a cornerstone of modern science, driving advancements in diverse disciplines from biomedical technology to climate solutions. Predicting synthesizability, a critical factor in realizing novel materials, remains a complex challenge due to the limitations of traditional heuristics and thermodynamic proxies. While stability metrics such as

Zheng Liu, Yuan Jiang, Yumeng Li, Pingfeng Wang

With the popularity of electric vehicles, the demand for lithium-ion batteries is increasing. Temperature significantly influences the performance and safety of batteries. Battery thermal management systems can effectively control the temperature of batteries; therefore, the performance and safety can be ensured. However, the development process of battery t

Agus L. Soenjaya, Thanh Tran

We propose some finite element schemes to solve a class of fourth-order nonlinear PDEs, which include the vector-valued Landau--Lifshitz--Baryakhtar equation, the Swift--Hohenberg equation, and various Cahn--Hilliard-type equations with source and convection terms, among others. The proposed numerical methods include a spatially semi-discrete scheme and two

Allen Knutson, Paul Zinn-Justin

We recall the lower-upper varieties from [Knutson '05] and give a formula for their equivariant cohomology classes, as a sum over generic pipe dreams. We recover as limits the classic and bumpless pipe dream formulae for double Schubert polynomials. As a byproduct, we obtain a formula for the degree of the $n$th commuting variety as a sum of powers of 2. Gen

Alexandru Manea, Claude Pruneau, Diana Catalina Brandibur, Andrea Danu

Balance functions have been regarded in the past as a method of investigating the late-stage hadronization found in the presence of a strongly-coupled medium. They are also used to constrain mechanisms of particle production in large and small collision systems. Measurements of charge balance functions for inclusive and identified particle pairs are reported

Martin Andrews

Excellent progress has been made recently in solving ARC Challenge problems. However, it seems that new techniques may be required to push beyond 60% accuracy. Even commercial Large Language Models (LLMs) struggle to 'understand' many of the problems (when given the input and output grids), which makes discovering solutions by LLM-lead program search somewha