November 2024 arXiv papers — page 25

Davood Mahdavian Yekta

In this letter, we study a holographic diffeomorphism invariant higher-derivative extension of Bergshoeff-Hohm-Townsend (BHT) cosmological gravity in the context of Wald's formalism. We calculate the entropy, mass and angular momentum of warped anti-de Sitter (WAdS$_3$) black holes in ghost-free BHT massive gravity and its extension using the covariant phase

Zhang Cheng, Yanxia Wang

Using lightweight models as backbone networks in gaze estimation tasks often results in significant performance degradation. The main reason is that the number of feature channels in lightweight networks is usually small, which makes the model expression ability limited. In order to improve the performance of lightweight models in gaze estimation tasks, a ne

Xiaokai Cao, Wenjin Mo, Zhenyu He, Changdong Wang

With the development of the internet, recommending interesting products to users has become a highly valuable research topic for businesses. Recommendation systems play a crucial role in addressing this issue. To prevent the leakage of each user's (client's) private data, Federated Recommendation Systems (FedRec) have been proposed and widely used. However,

Rahul Pandey, Ziwei Zhu, Hemant Purohit

Effective labeled data collection plays a critical role in developing and fine-tuning robust streaming analytics systems. However, continuously labeling documents to filter relevant information poses significant challenges like limited labeling budget or lack of high-quality labels. There is a need for efficient human-in-the-loop machine learning (HITL-ML) d

Roberto Albarran García, Martha Alvarez-Ramírez, Hildeberto Jardón-Kojakhmetov

We study a predator-prey system with a generalist Leslie-Gower predator, a functional Holling type II response, and a weak Allee effect on the prey. The prey's population often grows much faster than its predator, allowing us to introduce a small time scale parameter $\varepsilon$ that relates the growth rates of both species, giving rise to a slow-fast syst

Ryan Kellermann, Alessandro Barone, Ahmed Elgaziari, Shoji Hashimoto

We report on the calculation of the inclusive semileptonic decay of the $D_s$ meson on the lattice. We simulate the $D_s \rightarrow X_s\ell\nu_\ell$ process with M\"obius domain-wall charm and strange quarks, whose masses are approximately tuned to their physical values. Our simulations cover the whole kinematical region. The focus of this work is to presen

Liang Li, Lu Guo, K. Godbey, A. S. Umar

We employ the microscopic time-dependent Hartree-Fock (TDHF) theory to study the 48Ca+249Bk and 48Ti+238U systems, taking into account the dependence on orientation for deformed nuclei and full range of impact parameters. By analyzing fragment distributions of neutron and proton numbers, we assess the influence of different isoscalar and isovector tensor cou

Jin Chen, Qiang Jia

We use the 3D SymTFT approach to study the generalized symmetries and partition functions of 2D CFTs in various orbifolded and fermionic phases. These phases can be realized by the sandwich construction in the associated 3D SymTFTs with different gaped boundaries that encode the data of symmetries in the 2D CFTs. We demonstrate that the gaped boundaries can

Ol'ga V. Sipacheva, Aleksandr A. Solonkov

A brief introduction to universal algebra and the theory of topological algebras, their varieties, and free topological algebras is presented. Free topological Mal'tsev algebras are studied. Their properties, relationship with topological groups and heaps, and the problem of direct limit decomposition are considered.

Yi Ren, Ruge Xu, Xinfei Guo, Weikang Qian

A widely-used technique in designing energy-efficient deep neural network (DNN) accelerators is quantization. Recent progress in this direction has reduced the bitwidths used in DNN down to 2. Meanwhile, many prior works apply approximate multipliers (AppMuls) in designing DNN accelerators to lower their energy consumption. Unfortunately, these works still a

Jonathan Soriano, Srinath Saikrishnan, Vikram Seenivasan, Bernie Boscoe

In this work, we explore methods to improve galaxy redshift predictions by combining different ground truths. Traditional machine learning models rely on training sets with known spectroscopic redshifts, which are precise but only represent a limited sample of galaxies. To make redshift models more generalizable to the broader galaxy population, we investiga

Shaoqi Hou, Zhi-Chao Zhao, Zhoujian Cao, Zong-Hong Zhu

The gravitational memory effect manifests gravitational nonlinearity, degenerate vacua, and asymptotic symmetries; its detection is considered challenging. We propose using the space-borne interferometer to detect memory signals from stellar-mass binary black holes (BBHs), typically targeted by ground-based detectors. We use DECIGO detector as an example. Ov

Sam Carey

The study of neutrino-nucleus scattering processes is important for the new generation neutrino experiments for better understanding of the neutrino oscillation phenomenon. A significant source of uncertainty in the cross-section comes from limitations in our knowledge of nucleon and nuclear effects in the scattering process. Here we present a fully analytic

Stefan Vogl, Xun-Jie Xu

Supernova explosions are among the most extreme events in the Universe, making them a promising environment in which to search for the effects of light, weakly coupled new particles. As significant sources of energy, they are known to have an important effect on the dynamics of ordinary matter in their host galaxies but their potential impact on the dark mat

Andrew Eberhardt, Qiuyue Liang, Elisa G. M. Ferreira

The study of ultralight dark matter helps to constrain the lower bound of the mass in minimally coupled dark matter models. The granular structure of ultralight dark matter density fields produces metric perturbations which have been identified as a potentially interesting probe of this model. For dark matter masses $m \gtrsim 10^{-17} \, \mathrm{eV}$, these

Anmol Dwivedi, Ali Tajer, Santiago Paternain, Nurali Virani

Electricity grid's resiliency and climate change strongly impact one another due to an array of technical and policy-related decisions that impact both. This paper introduces a physics-informed machine learning-based framework to enhance grid's resiliency. Specifically, when encountering disruptive events, this paper designs remedial control actions to preve

Seon Gyeom Kim, Juhyeong Park, Yutaek Song, Donggun Lee

An increasing number of web articles engage the reader with the feeling of being immersed in the data space. However, the exact characteristics of spatial immersion in the context of visual storytelling remain vague. For example, what are the common design patterns of data stories with spatial immersion? How do they affect the reader's experience? To gain a

Manav Manav, Nathanael Perraudin, Yunong Lin, Mohamadreza Afrasiabi

We present a data-driven, differentiable neural network model designed to learn the temperature field, its gradient, and the cooling rate, while implicitly representing the melt pool boundary as a level set in laser powder bed fusion. The physics-guided model combines fully connected feed-forward neural networks with Fourier feature encoding of the spatial c

Jennifer Hu

The increasing proportion of the older adult population has made the smart home care industry one of the critical markets for virtual human-like agents. It is crucial to effectively promote a trustworthy human-computer partnership with older adults, enhancing service acceptance and effectiveness. However, few studies have focused on the facial features of th

Cong Zhou, Yuhe Zeng, Zhen Pan

Quasi-periodic eruptions (QPEs) are intense repeating soft X-ray bursts with recurrence times about a few hours to a few weeks from galactic nuclei. More and more analyses show that QPEs are the result of collisions between a stellar mass object (SMO, a stellar mass black hole or a main sequence star) and an accretion disk around a supermassive black hole (S

Feihao Pan, Songnan Sun, Alexander I. Kolesnikov, Matthew B. Stone

Triangular lattice antiferromagnets are prototypes for frustrated magnetism and may potentially realize novel quantum magnetic states such as a quantum spin liquid ground state. A recent work suggests NdTa$_7$O$_{19}$ with rare-earth triangular lattice is a quantum spin liquid candidate and highlights the large family of rare-earth heptatantalates as a frame

D. T. Hoai, P. T. Nhung, P. Darriulat, M. N. Tan

A new analysis of a sample of visual light curves of Mira variables is presented. The curves cover the past four decades and are selected from the AAVSO database as including a very large number of high-density and high-quality observations. The aim of the analysis is to offer a more precise, more quantitative and more systematic picture than available from

Mingsen Du, Meng Chen, Yongjian Li, Cun Ji

Multivariate time series (MTS) classification is widely applied in fields such as industry, healthcare, and finance, aiming to extract key features from complex time series data for accurate decision-making and prediction. However, existing methods for MTS often struggle due to the challenges of effectively modeling high-dimensional data and the lack of labe

Trong-Thuan Nguyen, Pha Nguyen, Jackson Cothren, Alper Yilmaz

Multimodal LLMs have advanced vision-language tasks but still struggle with understanding video scenes. To bridge this gap, Video Scene Graph Generation (VidSGG) has emerged to capture multi-object relationships across video frames. However, prior methods rely on pairwise connections, limiting their ability to handle complex multi-object interactions and rea

Nicolas Gastellu, Ata Madanchi, Lena Simine

Amorphous graphene or amorphous monolayer carbon (AMC) is a family of carbon films that exhibit a surprising sensitivity of electronic conductance to morphology. We combine deep learning-enhanced simulation techniques with percolation theory to analyze three morphologically distinct mesoscale AMCs. Our approach avoids the pitfalls of applying periodic bounda

Chanoul Seo, Suk-Jin Yoon, Sanjaya Paudel, Sung-Ho An

The majority of satellite galaxies around the Milky Way (MW) show disk-like distributions (the disk of satellites; DoS), which is a small-scale problem of the $\Lambda$CDM cosmology. The conventional definition of the MW-like DoS is a satellite system with a minor-to-major axis ratio ($c$/$a$) lower than the MW's $c$/$a$ value of 0.181. Here we question the

Sebastian Waeber, Amos Yarom

We consider magnetically charged AdS black branes with vanishing entropy at zero temperature. We argue that in the presence of a large enough Chern-Simons coupling the quasi normal modes of the brane will have a diminishing imaginary part. Since our result is agnostic to the matter content of the theory it implies that, generically, the boundary theory will

Donggoo Kang, Dasol Jeong, Hyunmin Lee, Sangwoo Park

The Large Vision Language Model (VLM) has recently addressed remarkable progress in bridging two fundamental modalities. VLM, trained by a sufficiently large dataset, exhibits a comprehensive understanding of both visual and linguistic to perform diverse tasks. To distill this knowledge accurately, in this paper, we introduce a novel approach that explicitly

Stavros Anagnou, Daniel Polani, Christoph Salge

Breaking a norm elicits both material and emotional consequences, yet how this coupling arose evolutionarily remains unclear. We investigate this question in light of emerging work suggesting that normativity's building blocks emerged earlier in evolution than previously considered, arguing that normative processes should inform accounts of how even ancient

Xiao-Fan Zhen, Hui-Juan Zuo, Fei Shi, Shao-Ming Fei

In 2003, DiVincenzo {\it et al}. put forward the question that whether there exists an unextendible product basis (UPB) which is an uncompletable product basis (UCPB) in every bipartition [\href{https://link.springer.com/article/10.1007/s00220-003-0877-6}{DiVincenzo {\it et al}. Commun. Math. Phys. \textbf{238}, 379-410(2003)}]. Recently, Shi {\it et al}. pr

Marcelo M. Miller Bertolami

We present a brief review on the formation and evolution of hydrogen deficient central stars of planetary nebulae. We include a detailed description of the main observable features of both the central stars and their surrounding nebulae and review their main classifications. We also provide a brief description of the possible progenitor systems of hydrogen d

Kazuki Koyama, Jun Ishihara, Takeshi Odagawa, Makito Aoyama

Both tin monosulfide (SnS) and tin disulfide (SnS2) are thermodynamically stable layered materials with potential for spin-valleytronic devices and photodetectors. Notably, monolayer SnS, owing to its low symmetry, exhibits interesting properties such as ferroelectricity, shift-current, and a persistent spin helix state in the monolayer limit. However, creat

Razvan G. Romanescu

In the multiple regression model we prove that the coefficient t-test for a variable of interest is uniformly most powerful unbiased, with the other parameters considered nuisance. The proof is based on the theory of tests with Neyman-structure and does not assume unbiasedness or linearity of the test statistic. We further show that the Gram-Schmidt decompos

Guanghui Chen, Zheng Wang, Hongxin Lin, Pengguang Du

In this paper, we consider real-time beamforming design for dynamic wireless environments with varying channels and different numbers of access points (APs) and users in cell-free systems. Specifically, a sum-rate maximization optimization problem is formulated for the beamforming design in dynamic wireless environments of cell-free systems. To efficiently s

Akira Yasuhara

This is an English translation of the expository article written by the author in Japanese for publication in {\em Sugaku}. The author will explain Milnor invariants from the viewpoint of his research.

Vikas Arora, Victor S Muthu, Arijit Sinha, Luminita Harnagea

An excitonic insulator (EI) phase is a consequence of collective many-body effects where an optical band gap is formed by the condensation of electron-hole pairs or excitons. We report pressure-dependent optical pump optical probe spectroscopy of EI Ta$_2$NiSe$_5$ in an on-site in situ geometry. The fast relaxation process depicts the transition across P$_{C

Francesco Dimiccoli, Rita Dolesi, Michele Fabi, Valerio Ferroni

Context. The LISA space observatory will explore the sub-Hz spectrum of gravitational wave emission from the Universe. The space environment, where will be immersed in, is responsible for charge accumulation on its free falling test masses (TMs) due to the galactic cosmic rays (GCRs) and solar energetic particles (SEP) impinging on the spacecraft. Primary an

Jacques Distler, Grant Elliot

This paper is a continuation of our investigation into the Coulomb branches of twisted $A_{2n}$ of class-S. In arXiv:2411.17675, we found predictions for the contributions of twisted punctures to the graded dimensions of the Coulomb branch, based on the behaviour under nilpotent Higgsings and S-duality. While surprisingly powerful, these arguments were indir

Jeff Giliberti, David G. Harris

A central approach to algorithmic derandomization is to construct probability distributions with small support that "fool" randomized algorithms, often enabling efficient parallel (NC) implementations. An abstraction of this idea is fooling polynomial-space statistical tests computed via finite automata (Sivakumar 2002); this encompasses a wide range of prop

Yang Yang, Aryan Mohammadi Pasikhani, Prosanta Gope, Biplab Sikdar

Package delivery is a critical aspect of various industries, but it often incurs high financial costs and inefficiencies when relying solely on human resources. The last-mile transport problem, in particular, contributes significantly to the expenditure of human resources in major companies. Robot-based delivery systems have emerged as a potential solution f

Yasuhiro Matsumoto, Taizo Maruyama

This paper proposes an $O(N)$ fast direct solver for two-dimensional elastic wave scattering problems. The proxy surface method is extended to elastodynamics to obtain shared coefficients for low-rank approximations from discretized integral operators. The proposed method is a variant of the Martinsson-Rokhlin-type fast direct solver. Our variant avoids the

Jinnyeong Kim, Seung-Hwan Baek

Integrating RGB and NIR stereo imaging provides complementary spectral information, potentially enhancing robotic 3D vision in challenging lighting conditions. However, existing datasets and imaging systems lack pixel-level alignment between RGB and NIR images, posing challenges for downstream vision tasks. In this paper, we introduce a robotic vision system

Tian Tian, Lucas R Timmerman, Shashikant Kumar, Ben Comer

Density Functional Theory (DFT) is the de facto workhorse for large-scale electronic structure calculations in chemistry and materials science. While plane-wave DFT implementations remain the most widely used, real-space DFT provides advantages in handling complex boundary conditions and scaling to very large systems by allowing for the efficient use of larg

Yang Yang, Xun Yuan, Arwa Alromih, Aryan Mohammadi Pasikhani

Detecting energy theft is vital for effectively managing power grids, as it ensures precise billing and prevents financial losses. Split-learning emerges as a promising decentralized machine learning technique for identifying energy theft while preserving user data confidentiality. Nevertheless, traditional split learning approaches are vulnerable to privacy

Neel Jawale, Byron Boots, Balakumar Sundaralingam, Mohak Bhardwaj

We investigate the problem of teaching a robot manipulator to perform dynamic non-prehensile object transport, also known as the `robot waiter' task, from a limited set of real-world demonstrations. We propose an approach that combines batch reinforcement learning (RL) with model-predictive control (MPC) by pretraining an ensemble of value functions from dem

Dali Sun, Jing Min, Xiangchuan Yan, Lu Wang

SO(2,1) dynamical symmetry makes a remarkable prediction that the breathing oscillation of a scale invariant quantum gas in an isotropic harmonic trap is isentropic and can persist indefinitely. In 2D, this symmetry is broken due to quantum anomaly in the strongly interacting range, and consequently the lifetime of the breathing mode becomes finite. The pers

Lewen Yang, Xuanyu Zhou, Juao Fan, Xinyi Xie

Over the past few decades, Artificial Intelligence(AI) has progressed from the initial machine learning stage to the deep learning stage, and now to the stage of foundational models. Foundational models have the characteristics of pre-training, transfer learning, and self-supervised learning, and pre-trained models can be fine-tuned and applied to various do

J. Ng, J. Yoo, L. -J. Chen, N. Bessho

Collisionless plasma systems are often studied using fully kinetic simulations, where protons and electrons are treated as particles. Due to their computational expense, it is necessary to reduce the ion-to-electron mass ratio $m_i/m_e$ or the ratio between plasma and cyclotron frequencies in simulations of large systems. In this work we show that when elect

Ruida Hu, Chao Peng, Jingyi Ren, Bo Jiang

Automatically resolving software issues is crucial for software development in practice, impacting the software quality and user experience. The process of resolving real-world issues encompasses tasks such as question-answering (QA), fault localization, and code editing. Existing benchmarks such as HumanEval fall short in their ability to assess LLMs' profi

Hao Ding, Zhongpai Gao, Benjamin Planche, Tianyu Luan

Surgical phase recognition (SPR) is crucial for applications in workflow optimization, performance evaluation, and real-time intervention guidance. However, current deep learning models often struggle with fragmented predictions, failing to capture the sequential nature of surgical workflows. We propose the Neural Finite-State Machine (NFSM), a novel approac

Peiyang Chen, Kai Xiang Lee, Tim Colin Meiler, Yijie Shen

Topological quasiparticles such as skyrmions and merons have recently attracted enormous attentions in the form of diverse optical degrees of freedom. However, these structures have not been explored in the fundamental momentum vectors of optical fields yet. Here, we reveal the universality of forming skyrmion and meron topological textures from the Poynting

Jack Naylor, Viorela Ila, Donald G. Dansereau

Neural Radiance Fields (NeRFs) provide a high fidelity, continuous scene representation that can realistically represent complex behaviour of light. Despite works like Ref-NeRF improving geometry through physics-inspired models, the ability for a NeRF to overcome shape-radiance ambiguity and converge to a representation consistent with real geometry remains

Dominique Maldague, Changkeun Oh

This paper proves sharp small cap decoupling estimates for the moment curve $\mathcal{M}^n=\{(t,t^2,\ldots,t^n):0\leq t\leq 1\}$ in the remaining small cap parameter ranges for $\mathbb{R}^2$ and $\mathbb{R}^3$.

Xinchen Wang, Pengfei Gao, Xiangxin Meng, Chao Peng

In software maintenance, bug reproduction is essential for effective fault localization and repair. Manually writing reproduction scripts is a time-consuming task with high requirements for developers. Hence, automation of bug reproduction has increasingly attracted attention from researchers and practitioners. However, the existing studies on bug reproducti

Zan Ahmad, Shiyi Chen, Minglang Yin, Avisha Kumar

A computed approximation of the solution operator to a system of partial differential equations (PDEs) is needed in various areas of science and engineering. Neural operators have been shown to be quite effective at predicting these solution generators after training on high-fidelity ground truth data (e.g. numerical simulations). However, in order to genera

Kangan Qian, Zhikun Ma, Yangfan He, Ziang Luo

Ensuring safe, comfortable, and efficient navigation is a critical goal for autonomous driving systems. While end-to-end models trained on large-scale datasets excel in common driving scenarios, they often struggle with rare, long-tail events. Recent progress in large language models (LLMs) has introduced enhanced reasoning capabilities, but their computatio

Moyan Li, Lexin Li, Jian Kang

A central question in multimodal neuroimaging analysis is to understand the association between two imaging modalities and to identify brain regions where such an association is statistically significant. In this article, we propose a Bayesian nonparametric spatially varying correlation model to make inference of such regions. We build our model based on the

Jiahao Zhang, Anoop Cherian, Cristian Rodriguez, Weijian Deng

Assembling furniture amounts to solving the discrete-continuous optimization task of selecting the furniture parts to assemble and estimating their connecting poses in a physically realistic manner. The problem is hampered by its combinatorially large yet sparse solution space thus making learning to assemble a challenging task for current machine learning m

Feiran You, Hongyang Du, Kaibin Huang, Abbas Jamalipour

Large Language Models (LLMs) have demonstrated remarkable capabilities in various tasks, leading to their increasing deployment in wireless networks for a wide variety of user services. However, the growing longer prompt setting highlights the crucial issue of computational resource demands and huge communication load. To address this challenge, we propose J

Haochen Chai, Meimei Su, Yang Lyu, Zhunga Liu

Fixed-wing Unmanned Aerial Vehicles (UAVs) are one of the most commonly used platforms for the burgeoning Low-altitude Economy (LAE) and Urban Air Mobility (UAM), due to their long endurance and high-speed capabilities. Classical obstacle avoidance systems, which rely on prior maps or sophisticated sensors, face limitations in unknown low-altitude environmen

Mingsen Du, Yanxuan Wei, Xiangwei Zheng, Cun Ji

Recently, time series classification has attracted the attention of a large number of researchers, and hundreds of methods have been proposed. However, these methods often ignore the spatial correlations among dimensions and the local correlations among features. To address this issue, the causal and local correlations based network (CaLoNet) is proposed in

R. B. Dastagir, J. T. Jami, S. Chanda, F. Hafiz

Rapid diagnostic tests are crucial for timely disease detection and management, yet accurate interpretation of test results remains challenging. In this study, we propose a novel approach to enhance the accuracy and reliability of rapid diagnostic test result interpretation by integrating artificial intelligence (AI) algorithms, including convolutional neura

Oya Kawashima, Satoshi Kasahara, Yoshifumi Saito, Masafumi Hirahara

In some types of mass spectrometers, such as Time of Flight mass spectrometers (TOF-MSs), it is necessary to control pulsed beams of ions. This can be easily accomplished by applying a pulsed voltage to the pusher electrode while the ionizer is continuously flowing ions. This method is preferred for its simplicity, although the ion utilization efficiency is

Weiwen Yuan, Jinke Ren, Chongjie Wang, Ruichen Zhang

Semantic communication has emerged as a promising technology for enhancing communication efficiency. However, most existing research emphasizes single-task reconstruction, neglecting model adaptability and generalization across multi-task systems. In this paper, we propose a novel generative semantic communication system that supports both image reconstructi

Shosuke Kiami

Lossless Convexification (LCvx) is a convexification technique that transforms a class of nonconvex optimal control problems$\unicode{x2013}$where the nonconvexity arises from a lower bound on the control norm$\unicode{x2013}$into equivalent convex problems, with the goal being to apply fast polynomial-time solvers. However, to solve these infinite-dimension

Song-Jiang Lai, Tsun-Hin Cheung, Ka-Chun Fung, Kai-wen Xue

In the research area of image super-resolution, Swin-transformer-based models are favored for their global spatial modeling and shifting window attention mechanism. However, existing methods often limit self-attention to non overlapping windows to cut costs and ignore the useful information that exists across channels. To address this issue, this paper intro

Songjiang Lai, Tsun-Hin Cheung, Jiayi Zhao, Kaiwen Xue

Rolling bearings play a crucial role in industrial machinery, directly influencing equipment performance, durability, and safety. However, harsh operating conditions, such as high speeds and temperatures, often lead to bearing malfunctions, resulting in downtime, economic losses, and safety hazards. This paper proposes the Residual Attention Single-Head Visi

Song-Jiang Lai, Tsun-Hin Cheung, Ka-Chun Fung, Tian-Shan Liu

With the rapid advancements in deep learning, computer vision tasks have seen significant improvements, making two-stream neural networks a popular focus for video based action recognition. Traditional models using RGB and optical flow streams achieve strong performance but at a high computational cost. To address this, we introduce a representation flow alg

Cody R. Longwell, Conor K. Trygstad, Nestor O. Perez-Arancibia

We present a new evolution of the Very Little Eel-Inspired roBot, the VLEIBot++, a 900-mg swimmer driven by two 10-mg bare high-work density (HWD) actuators, whose functionality is based on the use of shape-memory alloy (SMA) wires. An actuator of this type consumes an average power of about 40 mW during in-air operation. We integrated onboard power and comp

Xiu Yuan

Imitation Learning presents a promising approach for learning generalizable and complex robotic skills. The recently proposed Diffusion Policy generates robot action sequences through a conditional denoising diffusion process, achieving state-of-the-art performance compared to other imitation learning methods. This paper summarizes five key components of Dif

Shuyang Hao, Bryan Hooi, Jun Liu, Kai-Wei Chang

Despite inheriting security measures from underlying language models, Vision-Language Models (VLMs) may still be vulnerable to safety alignment issues. Through empirical analysis, we uncover two critical findings: scenario-matched images can significantly amplify harmful outputs, and contrary to common assumptions in gradient-based attacks, minimal loss valu

Amin Ibrahim, Azam Asilian Bidgoli, Shahryar Rahnamayan, Kalyanmoy Deb

As the interest in multi- and many-objective optimization algorithms grows, the performance comparison of these algorithms becomes increasingly important. A large number of performance indicators for multi-objective optimization algorithms have been introduced, each of which evaluates these algorithms based on a certain aspect. Therefore, assessing the quali

Catherine Drysdale, Samuel Johnson

Trophic coherence and non-normality are both ways of describing the overall directionality of directed graphs, or networks. Trophic coherence can be regarded as a measure of how neatly a graph can be divided into distinct layers, whereas non-normality is a measure of how unlike a matrix is with its transpose. We explore the relationship between trophic coher

Jia Qi Yip, Chin Yuen Kwok, Bin Ma, Eng Siong Chng

Neural audio codecs have revolutionized audio processing by enabling speech tasks to be performed on highly compressed representations. Recent work has shown that speech separation can be achieved within these compressed domains, offering faster training and reduced inference costs. However, current approaches still rely on waveform-based loss functions, nec

Ryoichiro Agata

In earthquake source inversions aimed at understanding diverse fault activities on earthquake faults using seismic observation data, uncertainties in velocity structure models are typically not considered. As a result, biases and underestimations of uncertainty can occur in source inversion. This article provides an overview of the author's efforts to addres

Jie Han, Seonghyuk Im, Jaehoon Kim, Donglei Yang

Ramsey--Tur\'an theory considers Tur\'an type questions in Ramsey-context, asking for the existence of a small subgraph in a graph $G$ where the complement $\overline{G}$ lacks an appropriate subgraph $F$, such as a clique of linear size. Similarly, one can consider Dirac-type questions in Ramsey context, asking for the existence of a spanning subgraph $H$ i

Taeheon Kim, Sangyun Chung, Youngjoon Yu, Yong Man Ro

Multispectral pedestrian detection is a crucial component in various critical applications. However, a significant challenge arises due to the misalignment between these modalities, particularly under real-world conditions where data often appear heavily misaligned. Conventional methods developed on well-aligned or minimally misaligned datasets fail to addre

Hoon-Gyu Chung, Seokjun Choi, Seung-Hwan Baek

Inverse rendering seeks to reconstruct both geometry and spatially varying BRDFs (SVBRDFs) from captured images. To address the inherent ill-posedness of inverse rendering, basis BRDF representations are commonly used, modeling SVBRDFs as spatially varying blends of a set of basis BRDFs. However, existing methods often yield basis BRDFs that lack intuitive s

Zhecheng Li, Yiwei Wang, Bryan Hooi, Yujun Cai

Question answering represents a core capability of large language models (LLMs). However, when individuals encounter unfamiliar knowledge in texts, they often formulate questions that the text itself cannot answer due to insufficient understanding of the underlying information. Recent studies reveal that while LLMs can detect unanswerable questions, they str

Andreas Madsen

As machine learning becomes more widespread and is used in more critical applications, it's important to provide explanations for these models, to prevent unintended behavior. Unfortunately, many current interpretability methods struggle with faithfulness. Therefore, this Ph.D. thesis investigates the question "How to provide and ensure faithful explanations

Yueqian Wang, Xiaojun Meng, Yuxuan Wang, Jianxin Liang

Recent researches on video large language models (VideoLLM) predominantly focus on model architectures and training datasets, leaving the interaction format between the user and the model under-explored. In existing works, users often interact with VideoLLMs by using the entire video and a query as input, after which the model generates a response. This inte

Jingjia Huang, Chenhao Qi, Octavia A. Dobre, Geoffrey Ye Li

For high-speed train (HST) millimeter wave (mmWave) communications, the use of narrow beams with small beam coverage needs frequent beam switching, while wider beams with small beam gain leads to weaker mmWave signal strength. In this paper, we consider beam switching based beam design, which is formulated as an optimization problem aiming to minimize the nu

Xiaoxuan Li, Yao Liu, Ruoyu Wang, Lina Yao

As the significance of understanding the cause-and-effect relationships among variables increases in the development of modern systems and algorithms, learning causality from observational data has become a preferred and efficient approach over conducting randomized control trials. However, purely observational data could be insufficient to reconstruct the t

Cheng-Fu Yang, Da Yin, Wenbo Hu, Heng Ji

Humans recognize objects after observing only a few examples, a remarkable capability enabled by their inherent language understanding of the real-world environment. Developing verbalized and interpretable representation can significantly improve model generalization in low-data settings. In this work, we propose Verbalized Representation Learning (VRL), a n

David Li-Bland, Eckhard Meinrenken

It is a remarkable fact that the integrability of a Poisson manifold to a symplectic groupoid depends only on the integrability of its cotangent Lie algebroid $A$: The source-simply connected Lie groupoid $G\rightrightarrows M$ integrating $A$ automatically acquires a multiplicative symplectic 2-form. More generally, a similar result holds for the integratio

Yaying Chen, Siamak Layeghy, Liam Daly Manocchio, Marius Portmann

This paper presents a high-performance, scalable network monitoring and intrusion detection system (IDS) implemented in P4. The proposed solution is designed for high-performance environments such as cloud data centers, where ultra-low latency, high bandwidth, and resilient infrastructure are essential. Existing state-of-the-art (SoA) solutions, which rely o

Wei Ci, Chenhao Qi, Xiaohu You

We investigate hybrid beamforming design for covert millimeter wave multiple-input multiple-output systems with finite-resolution digital-to-analog converters (DACs), which impose practical hardware constraints not yet considered by the existing works and have negative impact on the covertness. Based on the additive quantization noise model, we derive the de

Yunjing Shan, Junling Zhou

Let $V$ be an $n$-dimensional vector space over the finite field $\mathbb{F}_{q}$ and let $\left[V\atop k\right]_q$ denote the family of all $k$-dimensional subspaces of $V$. A family $\mathcal{F}\subseteq \left[V\atop k\right]_q$ is called intersecting if for all $F$, $F'\in\mathcal{F}$, we have ${\rm dim}$$(F\cap F')\geq 1$. Let $\delta_{d}(\mathcal{F})$ d

Huiyang Hu, Peijin Wang, Hanbo Bi, Boyuan Tong

Remote sensing foundation models largely break away from the traditional paradigm of designing task-specific models, offering greater scalability across multiple tasks. However, they face challenges such as low computational efficiency and limited interpretability, especially when dealing with large-scale remote sensing images. To overcome these, we draw ins

Tian Bai, Ying Jin

Model selection/optimization in conformal inference is challenging, since it may break the exchangeability between labeled and unlabeled data. We study this problem in the context of conformal selection, which uses conformal p-values to select ``interesting'' instances with large unobserved labels from a pool of unlabeled data, while controlling the FDR in f

Marco Vieira

LLMs are transforming software engineering by accelerating development, reducing complexity, and cutting costs. When fully integrated into the software lifecycle they will drive design, development and deployment while facilitating early bug detection, continuous improvement, and rapid resolution of critical issues. However, trustworthy LLM-driven software e

Yao Chen, Jiabao Wang, Peichao Wang, Rui Zhang

Low-resolution fine-grained image classification has recently made significant progress, largely thanks to the super-resolution techniques and knowledge distillation methods. However, these approaches lead to an exponential increase in the number of parameters and computational complexity of models. In order to solve this problem, in this letter, we propose

Kobe Marshall-Stevens, Mayu Takada, Yoshihiro Tonegawa, Myles Workman

We study the gradient flow of the Allen-Cahn equation with fixed boundary contact angle in Euclidean domains for initial data with bounded energy. Under general assumptions, we establish both interior and boundary convergence properties for the solutions and associated energy measures. Under various boundary non-concentration assumptions, we show that, for a

Ching-Yi Wang

Multi-modal large language models (MLLMs), such as GPT-4o, excel at integrating text and visual data but face systematic challenges when interpreting ambiguous or incomplete visual stimuli. This study leverages statistical modeling to analyze the factors driving these errors, using a dataset of geometric stimuli characterized by features like 3D, rotation, a

Nikita Klemyatin

We generalize the inverse Monge-Ampere flow, which was introduced in \cite{CHT17}, and provide conditions that guarantee the convergence of the flow without a priori assumption that $X$ has a K\"ahler-Einstein metric. We also show that if the underlying manifold does not admit K\"ahler-Einstein metric, then the flow develops Nadel multiplier ideal sheaves. I

Man Ho Chan, Chak Man Lee

Investigating the signals of dark matter annihilation is one of the most popular ways to understand the nature of dark matter. In particular, many recent studies are focussing on using radio data to examine the possible signals of dark matter revealed in galaxies and galaxy clusters. In this article, we investigate on the spectral data of the central radio h

Roberto Rossi

Multimodal GPTs represent a watershed in the interplay between Software Engineering and Generative Artificial Intelligence. GPT-4 accepts image and text inputs, rather than simply natural language. We investigate relevant use cases stemming from these enhanced capabilities of GPT-4. To the best of our knowledge, no other work has investigated similar use cas

Huimin Chang, Panyue Zhou

Let $\mathcal C$ be a $(d+2)$-angulated category. In this paper, we define the notions of cotorsion pairs and weak cotorsion pairs in $\mathcal C$, which are generalizations of the classical cotorsion pairs in triangulated categories. As an application, we give a geometric characterization of weak cotorsion pairs in $(d+2)$-angulated cluster categories of ty

Dieudonné Zirhumanana Balike, Luigi Frunzo, Maria Rosaria Mattei, Fabiana Russo

This work presents the existence and uniqueness of solution to a free boundary value problem related to biofilm growth. The problem consists of a system of nonlinear hyperbolic partial differential equations governing the microbial species growth, and a system of parabolic partial differential equations describing the substrate dynamics. The free boundary ev

Lily Goli, Sara Sabour, Mark Matthews, Marcus Brubaker

There has been extensive progress in the reconstruction and generation of 4D scenes from monocular casually-captured video. While these tasks rely heavily on known camera poses, the problem of finding such poses using structure-from-motion (SfM) often depends on robustly separating static from dynamic parts of a video. The lack of a robust solution to this p