November 2024 arXiv papers — page 96

Langrui Zhou, Ziteng Zhou, Xinyu Huang, Huiru Wang

In the last few years, with the rapid development of deep learning technologies, supervised methods based on convolutional neural networks have greatly enhanced the performance of medical image denoising. However, these methods require large quantities of noisy-clean image pairs for training, which greatly limits their practicality. Although some researchers

Zihao Li, Yuan Cao, Cheng Gao, Yihan He

Transformers have achieved great success in recent years. Interestingly, transformers have shown particularly strong in-context learning capability -- even without fine-tuning, they are still able to solve unseen tasks well purely based on task-specific prompts. In this paper, we study the capability of one-layer transformers in learning one of the most clas

Vadim Gorin, Jiaming Xu, Lingfu Zhang

The Airy$_\beta$ line ensemble is a random collection of continuous curves, which should serve as a universal edge scaling limit in problems related to eigenvalues of random matrices and models of 2d statistical mechanics. This line ensemble unifies many existing universal objects including Tracy-Widom distributions, eigenvalues of the Stochastic Airy Operat

Seyed Ali Farokh, Hossein Zeinali

This paper presents our submission to the Iranian division of the Text-Dependent Speaker Verification Challenge (TdSV) 2024. Conventional TdSV approaches typically jointly model speaker and linguistic features, requiring unsegmented inputs during training and incurring high computational costs. Additionally, these methods often fine-tune large-scale pre-trai

Nikita Evseev, Malte Kampschulte, Alexander Menovschikov

We extend the definition of weak and strong convergence to sequences of Sobolev-functions whose underlying domains themselves are converging. In contrast to previous works, we do so without ever assuming any sort of reference configuration. We then develop the respective theory and counterparts to classical compactness theorems from the fixed domain case. Fi

Michael Köhler-Bußmeier

In this paper we study Hornets extended with firing probabilities. Hornets are a Nets-within-Nets formalism, i.e., a Petri net formalism where the tokens are Petri nets again. Each of these net-tokens has its own firing rate, independent from the rates of other net-tokens. Hornets provide algebraic operations to modify net-tokens during the firing. For our s

Xiang Feng, Chang Yu, Zoubin Bi, Yintong Shang

Recent image-to-3D reconstruction models have greatly advanced geometry generation, but they still struggle to faithfully generate realistic appearance. To address this, we introduce ARM, a novel method that reconstructs high-quality 3D meshes and realistic appearance from sparse-view images. The core of ARM lies in decoupling geometry from appearance, proce

Claudia Maria Chanu, Giovanni Rastelli

We study the degeneracy of the energy levels of the Schroedinger equation with Kepler-Coulomb potential and of the Klein-Gordon equation on Riemannian coverings of the Euclidean space and of the Schwarzschild space-time respectively. Degeneracy of energy levels is a consequence of the superintegrability of the system. We see how the degree of degeneracy chan

Hugo Beirão da Veiga, Jiaqi Yang

Recently Qi S. Zhang provides examples of solutions to the Navier-Stokes equations which, under suitable hypothesis, blow up in finite time. He considers axially symmetric solutions in a cylinder $D\,$ under appropriate boundary conditions and under the effect of super critical external forces $f\,.$ The loss of boundedness for the velocity field, as $t\righ

Ahmed Shoyeb Raihan, Austin Harper, Israt Zarin Era, Omar Al-Shebeeb

Ensuring the quality and reliability of Metal Additive Manufacturing (MAM) components is crucial, especially in the Laser Powder Bed Fusion (L-PBF) process, where melt pool defects such as keyhole, balling, and lack of fusion can significantly compromise structural integrity. This study presents SL-RF+ (Sequentially Learned Random Forest with Enhanced Sampli

Teng Xiao, Chao Cui, Huaisheng Zhu, Vasant G. Honavar

Pretraining molecular representations is crucial for drug and material discovery. Recent methods focus on learning representations from geometric structures, effectively capturing 3D position information. Yet, they overlook the rich information in biomedical texts, which detail molecules' properties and substructures. With this in mind, we set up a data coll

Taskin Mehereen, Shorup Chanda, Afrina Ayrin Nitu, Jubaer Tanjil Jami

Although real surfaces exhibit intricate topologies at the nanoscale, rough surface consideration is often overlooked in nanoscale heat transfer studies. Superimposed sinusoidal functions effectively model the complexity of these surfaces. This study investigates the impact of sinusoidal roughness on liquid argon condensation over a functional gradient wetti

Yang Yan, Zhong Chen, Cai Xu, Xinglei Shen

Patient-reported outcomes (PROs) directly collected from cancer patients being treated with radiation therapy play a vital role in assisting clinicians in counseling patients regarding likely toxicities. Precise prediction and evaluation of symptoms or health status associated with PROs are fundamental to enhancing decision-making and planning for the requir

Hmrishav Bandyopadhyay, Yi-Zhe Song

Sketch animations offer a powerful medium for visual storytelling, from simple flip-book doodles to professional studio productions. While traditional animation requires teams of skilled artists to draw key frames and in-between frames, existing automation attempts still demand significant artistic effort through precise motion paths or keyframe specificatio

Xingyu Chen, Zihao Feng, Kun Qian, Xinyu Zhang

Radio frequency (RF) propagation modeling poses unique electromagnetic simulation challenges. While recent neural representations have shown success in visible spectrum rendering, the fundamentally different scales and physics of RF signals require novel modeling paradigms. In this paper, we introduce RFScape, a novel framework that bridges the gap between n

Sohil Atul Shah, Vladlen Koltun

Estimating three-dimensional conformations of a molecular graph allows insight into the molecule's biological and chemical functions. Fast generation of valid conformations is thus central to molecular modeling. Recent advances in graph-based deep networks have accelerated conformation generation from hours to seconds. However, current network architectures

Bijo S Anand, Arun Anil, Manoj Changat, Revathy S. Nair

This article discusses $\Delta$-convexity on simple connected graphs. We establish general bounds for the Helly number, Radon number, and rank with respect to $\Delta$-convexity on graphs. Additionally, we give the exact values for the Helly number and Radon number for chordal graphs, as well as the rank for block graphs.

Ziqi Rong, Qiushi Zheng, Zhishu Shen, Xiaolong Li

With the rapid advancement of the Internet of Things (IoT) and Artificial Intelligence (AI), intelligent information services are being increasingly integrated across various sectors, including healthcare, industry, and transportation. Traditional solutions rely on centralized cloud processing, which encounters considerable challenges in fulfilling the Quali

Donggyu Lee, Juhyeon Park, Taesup Moon

While deep learning-based Alzheimer's disease (AD) diagnosis has recently made significant advancements, particularly in predicting the conversion of mild cognitive impairment (MCI) to AD based on MRI images, there remains a critical gap in research regarding the group robustness of the diagnosis. Although numerous studies pointed out that deep learning-base

Prasoon Bajpai, Sarah Masud, Tanmoy Chakraborty

Large Language Models (LLMs) have demonstrated strong performance as knowledge repositories, enabling models to understand user queries and generate accurate and context-aware responses. Extensive evaluation setups have corroborated the positive correlation between the retrieval capability of LLMs and the frequency of entities in their pretraining corpus. We

Qi-Cheng Wu, Yu-Liang Fang, Yan-Hui Zhou, Jun-Long Zhao

Realizing efficient and controlled state transfer is necessary for implementing a wide range of classical and quantum information protocols. Recent studies have demonstrated that both asymmetric and symmetric state transfer can be achieved by encircling an exceptional point (EP) in non-Hermitian (NH) systems. However, the application of this phenomenon has b

Konstantin D. Efimov

The study aimed at detecting cartel collusion involved analyzing decisions of the Russian Federal Antimonopoly Service and data on auctions. As a result, a machine learning model was developed that predicts with 91% accuracy the signs of collusion between bidders based on their history after dividing 40 auctions into test and training samples in a 30/70 rati

Demin Zhou

The crab-waist collision scheme has been the baseline choice for SuperKEKB and future circular $e^+e^-$ colliders. Achieved through properly phased sextupoles, the crab-waist transform is essential in suppressing beam-beam resonances, thereby enabling high luminosity in these colliders. In this paper, we explore potential sources of imperfections that may co

Feng Chen, Fuguang Han, Cong Guan, Lei Yuan

Given the inherent non-stationarity prevalent in real-world applications, continual Reinforcement Learning (RL) aims to equip the agent with the capability to address a series of sequentially presented decision-making tasks. Within this problem setting, a pivotal challenge revolves around \textit{catastrophic forgetting} issue, wherein the agent is prone to

Arghya Sinha, Kunal N. Chaudhury

The effectiveness of denoising-driven regularization for image reconstruction has been widely recognized. Two prominent algorithms in this area are Plug-and-Play ($\texttt{PnP}$) and Regularization-by-Denoising ($\texttt{RED}$). We consider two specific algorithms $\texttt{PnP-FISTA}$ and $\texttt{RED-APG}$, where regularization is performed by replacing the

Shangshuai Li, Shuzhi Liu, Da-jun Zhang

A reduction from the self-dual Yang-Mills (SDYM) equation to the unreduced Fokas-Lenells (FL) system is described in this paper. It has been known that the SDYM equation can be formulated from the Cauchy matrix schemes of the matrix Kadomtsev-Petviashvili (KP) hierarchy and the Ablowitz-Kaup-Newell-Segur (AKNS) hierarchy. We show that the reduction can be re

Eduard Keilmann, Slawa Kabanovic, Nicola Schneider, Volker Ossenkopf-Okada

Understanding mass, size, and surface mass density of giant molecular clouds (GMCs) in galaxies is key to insights into star formation processes. We analyze these in M33 using Herschel dust and archival IRAM 30m telescope data, compared to Milky Way CO data. A Dendrogram algorithm on a 2D dust map and a Xco factor map are used for M33 instead of a constant v

Naci Saldi, Gurdal Arslan, Serdar Yuksel

Establishing the existence of exact or near Markov or stationary perfect Nash equilibria in nonzero-sum Markov games over Borel spaces is a challenging problem with limited positive results. Motivated by problems in multi-agent and Bayesian learning, this paper demonstrates the existence of approximate Markov and stationary Nash equilibria for such games und

Soumya Jana, Vivek Sharma, Suman Ghosh

We investigate null trajectories, deflection angles, and gravitational lensing in the spacetime of generalized Ellis-Bronnikov (GEB) wormholes and their embedding in a five-dimensional warped braneworld background (WGEB). The GEB geometry extends the standard Ellis-Bronnikov (EB) wormhole by introducing a steepness parameter $m \geq 2$, which controls the sh

Ting Liu, Liangtao Shi, Richang Hong, Yue Hu

The vision tokens in multimodal large language models usually exhibit significant spatial and temporal redundancy and take up most of the input tokens, which harms their inference efficiency. To solve this problem, some recent works were introduced to drop the unimportant tokens during inference where the importance of each token is decided only by the infor

Kazuki Sato, Futoshi Takahashi

In this paper, we study one-dimensional boundary blow up problems with Kirchhoff type nonlocal terms on an interval. We perform a bifurcation analysis on the problems and obtain the precise number of solutions according to the value of the bifurcation parameter. We also obtain the precise asymptotic formula for solutions for special cases.

Jaehyuk Jang, Suehyun Kim, Kwonsang Lee

In observational studies, the assumption of sufficient overlap (positivity) is fundamental for the identification and estimation of causal effects. Failing to account for this assumption yields inaccurate and potentially infeasible estimators. To address this issue, we introduce a simple yet novel approach, \textit{mixing}, which mitigates overlap violations

Tripti Shukla, Srikrishna Karanam, Balaji Vasan Srinivasan

We consider the problem of conditional text-to-image synthesis with diffusion models. Most recent works need to either finetune specific parts of the base diffusion model or introduce new trainable parameters, leading to deployment inflexibility due to the need for training. To address this gap in the current literature, we propose our method called TINTIN:

Kawamori Eiichirou

We propose a method for reconstructing the fluctuation components of the electron velocity distribution function f(v_perp), and the electron entropy, which is a functional of f(v_perp) expressed as -f(v_perp)lnf(v_perp)dv_perp, using the harmonic spectrum from pure X-mode electron cyclotron emission (ECE) in optically thin plasmas. Here, v_perp represents th

Naoto Yoshida, Kingson Man

When regarding the suffering of others, we often experience personal distress and feel compelled to help. Inspired by living systems, we investigate the emergence of prosocial behavior among autonomous agents that are motivated by homeostatic self-regulation. We perform multi-agent reinforcement learning, treating each agent as a vulnerable homeostat charged

Long Peng, Wenbo Li, Jiaming Guo, Xin Di

Real-world image super-resolution (Real SR) aims to generate high-fidelity, detail-rich high-resolution (HR) images from low-resolution (LR) counterparts. Existing Real SR methods primarily focus on generating details from the LR RGB domain, often leading to a lack of richness or fidelity in fine details. In this paper, we pioneer the use of details hidden i

Mihai-Silviu Lazorec

The order sequence of a finite group $G$ is a non-decreasing finite sequence formed of the element orders of $G$. Several properties of order sequences were studied by P. J. Cameron and H. K. Dey in a recent paper that concludes with a list of open problems. In this paper we solve two of these problems by showing the following facts: 1) if there is a non-sup

Javier Conde, Andrés Munoz-Arcentales, Álvaro Alonso, Joaquín Salvachúa

The rise of AI and the Internet of Things is accelerating the digital transformation of society. Mobility computing presents specific barriers due to its real-time requirements, decentralization, and connectivity through wireless networks. New research on edge computing and tiny machine learning (tinyML) explores the execution of AI models on low-performance

B. Baibussinov, M. Bettini, F. Fabris, R. Gan

A novel liquid argon purity monitor based on a 207 Bi radioactive source, emitting monochromatic internal-conversion electrons, is presented. This new monitor allows for a very precise and fast measurement of the electronegative impurities concentration in liquid argon. It can be operated continuously in liquid argon TPC experiments without interfering with

Yu Peng, Zewen Long, Fangming Dong, Congyi Li

The advent of large language models (LLMs) has spurred the development of numerous jailbreak techniques aimed at circumventing their security defenses against malicious attacks. An effective jailbreak approach is to identify a domain where safety generalization fails, a phenomenon known as mismatched generalization. In this paper, we introduce two novel jail

Sudarshan Regmi

Out-of-distribution (OOD) detection is critical to ensure the safe deployment of deep learning models in critical applications. Deep learning models can often misidentify OOD samples as in-distribution (ID) samples. This vulnerability worsens in the presence of spurious correlation in the training set. Likewise, in fine-grained classification settings, detec

Tanmoy Ghosh, Peter Sollich, Saroj Kumar Nandi

How activity affects the glassy dynamics is crucial for several biological processes. Furthermore, active glasses offer fascinating phenomenologies, extend the scope of equilibrium glasses, and can provide novel insights into the original problem. We introduce a family of novel approaches to investigating the relaxation dynamics of active glasses via an acti

Zhikang Fan, Weiran Shen

Consider a trade market with one seller and multiple buyers. The seller aims to sell an indivisible item and maximize their revenue. This paper focuses on a simple and popular mechanism--the fixed-price mechanism. Unlike the standard setting, we assume there is information asymmetry between buyers and the seller. Specifically, we allow the seller to design i

Sahar Pakdel, Thomas Olsen, Kristian S. Thygesen

We conduct a systematic investigation of the role of Hubbard U corrections in electronic structure calculations of two-dimensional (2D) materials containing 3d transition metals. Specifically, we use density functional theory (DFT) with the PBE and PBE+U approximations to calculate the crystal structure, band gaps, and magnetic parameters of 638 monolayers.

Yijian Gao, Dominic Marshall, Xiaodan Xing, Junzhi Ning

Radiology reporting generative AI holds significant potential to alleviate clinical workloads and streamline medical care. However, achieving high clinical accuracy is challenging, as radiological images often feature subtle lesions and intricate structures. Existing systems often fall short, largely due to their reliance on fixed size, patch-level image fea

Zixian Su, Jingwei Guo, Xi Yang, Qiufeng Wang

Deploying deep models in real-world scenarios remains challenging due to significant performance drops under distribution shifts between training and deployment environments. Test-Time Adaptation (TTA) has recently emerged as a promising solution, enabling on-the-fly model adaptation. However, its effectiveness deteriorates in the presence of mixed distribut

Jeonghyeok Do, Jaehyup Lee, Munchurl Kim

Synthetic Aperture Radar (SAR) imagery provides robust environmental and temporal coverage (e.g., during clouds, seasons, day-night cycles), yet its noise and unique structural patterns pose interpretation challenges, especially for non-experts. SAR-to-EO (Electro-Optical) image translation (SET) has emerged to make SAR images more perceptually interpretable

Yao Xu, Shizhu He, Jiabei Chen, Zeng Xiangrong

Structured data, such as tables, graphs, and databases, play a critical role in plentiful NLP tasks such as question answering and dialogue system. Recently, inspired by Vision-Language Models, Graph Neutral Networks (GNNs) have been introduced as an additional modality into the input of Large Language Models (LLMs) to improve their performance on Structured

Kian Anvari Hamedani, Narges Razizadeh, Shahabedin Nabavi, Mohsen Ebrahimi Moghaddam

Cardiovascular magnetic resonance (CMR) imaging is the gold standard for diagnosing several heart diseases due to its non-invasive nature and proper contrast. MR imaging is time-consuming because of signal acquisition and image formation issues. Prolonging the imaging process can result in the appearance of artefacts in the final image, which can affect the

Bo Su, Chaoxin Huang, Jianzhou Zhao, Mengwu Huo

We report an optical spectroscopy study of the charge-dynamics anisotropy in the La3Ni2O7 single crystals with the electric field of the incident light parallel to the crystalline c-axis and ab-plane respectively. The evolution of the low-energy part of its c-axis optical conductivity spectra ({\sigma}1c({\omega})) from a Drude component to a finite-energy p

Chris Sherlock

The general applicability and ease of use of the pseudo-marginal Metropolis--Hastings (PMMH) algorithm, and particle Metropolis--Hastings in particular, makes it a popular method for inference on discretely observed Markovian stochastic processes. The performance of these algorithms and, in the case of particle Metropolis--Hastings, the trade off between imp

Bogdan Chornomaz, Shay Moran, Tom Waknine

Many practical prediction algorithms represent inputs in Euclidean space and replace the discrete 0/1 classification loss with a real-valued surrogate loss, effectively reducing classification tasks to stochastic optimization. In this paper, we investigate the expressivity of such reductions in terms of key resources, including dimension and the role of rand

J. M. P. Carmelo, P. D. Sacramento

The use of hydrodynamic transport theory seems to indicate that the charge diffusion constant D of the one-dimensional (1D) half-filled Hubbard model, whose Drude weight vanishes, diverges for temperature T>0, which would imply anomalous superdiffusive charge transport. Here the leading term of that constant is derived for low finite temperatures much smalle

Antony Valentini

We show how pilot-wave theory points to new physics, beyond quantum mechanics, in three distinct ways. First, generalised cosmological initial conditions, departing from the Born rule, can lead to observable anomalies in the cosmic microwave background and in relic cosmological particles. Second, a breakdown of the Born rule in the deep quantum-gravity regim

Shitong Shao, Zikai Zhou, Tian Ye, Lichen Bai

Text-to-image diffusion models (DMs) develop at an unprecedented pace, supported by thorough theoretical exploration and empirical analysis. Unfortunately, the discrepancy between DMs and autoregressive models (ARMs) complicates the path toward achieving the goal of unified vision and language generation. Recently, the masked generative Transformer (MGT) ser

Yu Wu, Linxuan Ji, Shuming Zeng, Yimin Ding

The low-frequency phonon branches of metal halide perovskites typically exhibit the characteristic of hardening with the increase of the cation mass, which leads to anomalous thermal transport phenomenon. However, the underlying physical mechanism is not yet understood. Here, we theoretically compare the thermal transport properties of $A_2$SnI$_6$ ($A$=K, R

Zhangchi Qiu, Linhao Luo, Shirui Pan, Alan Wee-Chung Liew

Conversational Recommender Systems (CRSs) aim to provide personalized recommendations by capturing user preferences through interactive dialogues. Explainability in CRSs is crucial as it enables users to understand the reasoning behind recommendations, increasing system transparency and trustworthiness. However, current CRSs often leverage knowledge graphs (

Tanu Sharma, Rupesh Kumar Tiwari, Sourav Dey, Lorenzo A. Mariano

This study presents the first-ever investigation of spin-phonon coupling mechanisms in fullerene-based single-molecule magnets (SMMs) using ab initio CASSCF combined with DFT calculations. While lanthanide-based SMMs, particularly those with DyIII ions, are known for their impressive blocking temperatures and relaxation barriers, endohedral metallofullerene

Kaiji Motoyama, Yoshimichi Teratani, Kazuhiko Tsutsumi, Kohei Wake

We study the three-body Fermi liquid effects in the SU($N$) Anderson impurity model in the strong interaction limit where the occupation number $N_d^{}$ of the impurity levels varies over the range of $0<N_d^{}<1$. The three-body correlation of impurity electrons contributes to the next-to-leading order terms of transport coefficients at low energies when th

Wei Zhuo, Zemin Liu, Bryan Hooi, Bingsheng He

Label imbalance and homophily-heterophily mixture are the fundamental problems encountered when applying Graph Neural Networks (GNNs) to Graph Fraud Detection (GFD) tasks. Existing GNN-based GFD models are designed to augment graph structure to accommodate the inductive bias of GNNs towards homophily, by excluding heterophilic neighbors during message passin

Ruijun Zhang, Mingkun Zhang, Guoliang Zhang, Yujian Chen

A minority carrier lifetime of 25.46 $\mu$s in a P-type 4H-SiC epilayer has been attained through sequential thermal oxidation and hydrogen annealing. Thermal oxidation can enhance the minority carrier lifetime in the 4H-SiC epilayer by reducing carbon vacancies. However, this process also generates carbon clusters with limited diffusivity and contributes to

Laura Casabella, Michael Joswig, Rafael Mohr

We study certain generic systems of real polynomial equations associated with triangulations of convex polytopes and investigate their number of real solutions. Our main focus is set on pairs of plane algebraic curves which form a so-called Wronski system. The computational tasks arising in the analysis of such Wronski pairs lead us to the frontiers of curre

Gang He, Kepeng Xu, Li Xu, Siqi Wang

The rise of HDR-WCG display devices has highlighted the need to convert SDRTV to HDRTV, as most video sources are still in SDR. Existing methods primarily focus on designing neural networks to learn a single-style mapping from SDRTV to HDRTV. However, the limited information in SDRTV and the diversity of styles in real-world conversions render this process a

Rishabh Upadhyay, Bayan Karimi, Diego Subero, Christoforus Dimas Satrya

Thermodynamics in quantum circuits aims to find improved functionalities of thermal machines, highlight fundamental phenomena peculiar to quantum nature in thermodynamics, and point out limitations in quantum information processing due to coupling of the system to its environment. An important aspect to achieve some of these goals is the regime of strong cou

Kepeng Xu, Zijia Ma, Li Xu, Gang He

Recent advances in neural camera imaging pipelines have demonstrated notable progress. Nevertheless, the real-world imaging pipeline still faces challenges including the lack of joint optimization in system components, computational redundancies, and optical distortions such as lens shading.In light of this, we propose an end-to-end camera imaging pipeline (

Feng Xiong, Xinguo Yu, Hon Wai Leong

This paper explores the transformative role of artificial intelligence (AI) in enhancing scientific research, particularly in the fields of brain science and social sciences. We analyze the fundamental aspects of human research and argue that it is high time for researchers to transition to human-AI joint research. Building upon this foundation, we propose t

Peizhe Xia, Long Peng, Xin Di, Renjing Pei

Arbitrary scale super-resolution (ASSR) aims to super-resolve low-resolution images to high-resolution images at any scale using a single model, addressing the limitations of traditional super-resolution methods that are restricted to fixed-scale factors (e.g., $\times2$, $\times4$). The advent of Implicit Neural Representations (INR) has brought forth a ple

Moucheng Xu, Yukun Zhou, Tobias Goodwin-Allcock, Kimia Firoozabadi

We introduce and demonstrate a new paradigm for quantitative parameter mapping in MRI. Parameter mapping techniques, such as diffusion MRI and quantitative MRI, have the potential to robustly and repeatably measure biologically-relevant tissue maps that strongly relate to underlying microstructure. Quantitative maps are calculated by fitting a model to multi

Haobin Zhou, Bowen Liu, Taoming Guo, Hanbin Ma

The demand for high-quality neurostimulation, driven by the development of brain-computer interfaces, has outpaced the capabilities of passive microelectrode-arrays, which are limited by channel-count and biocompatibility. This work proposes a back-end-of-line (BEOL) process for 1024-channel stimulator with bioelectrodes and waterproof encapsulation to stimu

Wenxiang Xu, Tian Qiu, Linyun Zhou, Zunlei Feng

Deep neural networks (DNNs) have numerous applications across various domains. Several optimization techniques, such as ResNet and SENet, have been proposed to improve model accuracy. These techniques improve the model performance by adjusting or calibrating feature responses according to a uniform standard. However, they lack the discriminative calibration

Chuan Zhang, Xixi Zheng, Xiaolong Tao, Chenfei Hu

With the rapid advancement of artificial intelligence, generative artificial intelligence (GAI) has taken a leading role in transforming data processing methods. However, the high computational demands of GAI present challenges for devices with limited resources. As we move towards the sixth generation of mobile networks (6G), the higher data rates and impro

Guoling Liang, Chunhai Li, Feng Zhao, Chuan Zhang

Parked-assisted vehicular edge computing (PVEC) fully leverages communication and computing resources of parking vehicles, thereby significantly alleviating the pressure on edge servers. However, resource sharing and trading for vehicular task offloading in the PVEC environment usually occur between untrustworthy entities, which compromises the security of d

Sanjeev Kumar Pandey, Neetish Patel

This study investigates remote synchronization in arbitrary network clusters of coupled nonlinear oscillators, a phenomenon inspired by neural synchronization in the brain. Employing a multi-faceted approach encompassing analytical, numerical, and experimental methodologies, we leverage the Master Stability Function (MSF) to analyze network stability. We pro

Aryan Eftekhari, Doris Folini, Aleksandra Friedl, Felix Kübler

We introduce a framework for developing efficient and interpretable climate emulators (CEs) for economic models of climate change. The paper makes two main contributions. First, we propose a general framework for constructing carbon-cycle emulators (CCEs) for macroeconomic models. The framework is implemented as a generalized linear multi-reservoir (box) mod

Ji Lin

The aim of this note is to clarify the relationship between Green's formula and the associativity of multiplication for derived Hall algebra in the sense of To\"{e}n (Duke Math J 135(3):587-615, 2006), Xiao and Xu (Duke Math J 143(2):357-373, 2008) and Xu and Chen (Algebr Represent Theory 16(3):673-687, 2013). Let $\mathcal{A}$ be a finitary hereditary abeli

Palak, Tella Rajashekhar Reddy, Bhaskar Kataria, Rohan Gandhi

The widespread adoption of language models (LMs) has caused a huge surge in demand for GPUs. Training large LMs requires tens of thousands of GPUs and housing them in the same datacenter (DC) is a challenge due to many constraints including availability of peak power. We focus on training such models across multiple DCs connected via the Wide-Area-Network (W

Ahmed Aberqi, Zoubida Echchaffani, Touria Karite

This manuscript is concerned with the approximate controllability of fractional nonlinear differential equations with nonlocal conditions of order $1<q<2$ in Banach spaces. As far as we know, few articles have investigated this issue. The idea is to see under which sufficient conditions the proposed control problem is approximately controllable. The discussi

Weiming Xu, Peng Zhang

As core thermal power generation equipment, steam turbines incur significant expenses and adverse effects on operation when facing interruptions like downtime, maintenance, and damage. Accurate anomaly detection is the prerequisite for ensuring the safe and stable operation of steam turbines. However, challenges in steam turbine anomaly detection, including

JooHyoung Cha, Munyoung Lee, Jinse Kwon, Jubin Lee

The increasing complexity of deep learning models necessitates specialized hardware and software optimizations, particularly for deep learning accelerators. Existing autotuning methods often suffer from prolonged tuning times due to profiling invalid configurations, which can cause runtime errors. We introduce ML$^2$Tuner, a multi-level machine learning tuni

Hanlong Fang, Xian Wu

In this paper, we develop a simple uniform picture incorporating the Kausz compactifications and the spaces of complete collineations by blowing up Grassmannians $G(p,n)$ according to a torus action $\mathbb G_m$. We show that each space of complete collineations is isomorphic to any maximal-dimensional connected component of the $\mathbb G_m$-fixed point sc

Tomas Fullana, Yash Kulkarni, Mathis Fricke, Stéphane Popinet

In this work, we revisit the Generalized Navier Boundary condition (GNBC) introduced by Qian et al.\ in the sharp interface Volume-of-Fluid context. We replace the singular uncompensated Young stress by a smooth function with a characteristic width $\varepsilon > 0$ that is understood as a physical parameter of the model. Therefore, we call the model the ``C

Tiantian Feng, Anfeng Xu, Rimita Lahiri, Helen Tager-Flusberg

Large Language Models (LLMs) have shown significant potential in understanding human communication and interaction. However, their performance in the domain of child-inclusive interactions, including in clinical settings, remains less explored. In this work, we evaluate generic LLMs' ability to analyze child-adult dyadic interactions in a clinically relevant

L. Padovani, G. Manduca, D. Paniccia, G. Graziani

Scombrid fishes and tuna are efficient swimmers capable of maximizing performance to escape predators and save energy during long journeys. A key aspect in achieving these goals is the flexibility of the tail, which the fish optimizes during swimming. Though, the robotic counterparts, although highly efficient, have partially investigated the importance of f

Shu Xu, Liqun Cao

We present a finite element semi-discrete error analysis for the Doyle-Fuller-Newman model, which is the most popular model for lithium-ion batteries. Central to our approach is a novel projection operator designed for the pseudo-($N$+1)-dimensional equation, offering a powerful tool for multiscale equation analysis. Our results bridge a gap in the analysis

Cheng Jin, Youde Wang, Fanqi Zeng

In this paper, we consider the nonlinear elliptic equation $$\Delta_fv^\tau+\lambda v=0$$ on a complete smooth metric measure space with $m$-Bakry-\'{E}mery Ricci curvature bounded from below, where $\tau>0$ and $\lambda$ are constant. We obtain some new local gradient estimates for positive solutions to the equation using the Nash-Moser iteration technique.

Hao Liu, Chen Zhang, Qi-Yi Wu, Yahui Jin

We report the observation of a light-induced subpicosecond phase transition in the antiferromagnetic Dirac semimetal EuAgAs, achieved through ultrafast optical excitation. Using ultrafast optical spectroscopy, we probe the nonequilibrium carrier dynamics, discovering distinct fluence-dependent responses in the antiferromagnetic and paramagnetic states, and r

Maria Monzon, Thomas Iff, Ender Konukoglu, Catherine R. Jutzeler

This study introduces a diffusion-based framework for robust and accurate segmenton of vertebrae, intervertebral discs (IVDs), and spinal canal from Magnetic Resonance Imaging~(MRI) scans of patients with low back pain (LBP), regardless of whether the scans are T1w or T2-weighted. The results showed that SpineSegDiff achieved comparable outperformed non-diff

Zachary Dana, Ahmed Ammar Naseer, Botros Toro, Sumanth Swaminathan

Chronic kidney disease (CKD) is a significant public health challenge, often progressing to end-stage renal disease (ESRD) if not detected and managed early. Early intervention, warranted by silent disease progression, can significantly reduce associated morbidity, mortality, and financial burden. In this study, we propose a novel approach to modeling CKD pr

Shuyang Hou, Haoyue Jiao, Zhangxiao Shen, Jianyuan Liang

With the rapid growth of interdisciplinary demands for geospatial modeling and the rise of large language models (LLMs), geospatial code generation technology has seen significant advancements. However, existing LLMs often face challenges in the geospatial code generation process due to incomplete or unclear user requirements and insufficient knowledge of sp

Xitong Ling, Yuanyuan Lei, Jiawen Li, Junru Cheng

Advances in optical microscopy scanning have significantly contributed to computational pathology (CPath) by converting traditional histopathological slides into whole slide images (WSIs). This development enables comprehensive digital reviews by pathologists and accelerates AI-driven diagnostic support for WSI analysis. Recent advances in foundational patho

Shunyang Li, Li Wan, Nan Gui, Xingtuan Yang

This paper presents a novel strategy for constructing body source terms in the high-order lattice Boltzmann method (LBM), designed to efficiently introduce various physical phenomena by modifying the non-equilibrium distribution function. The source term, expressed as a Hermite polynomial, provides a flexible framework for simulating complex fluid flows. Thr

Shi Hu, Shihao Li, Meiqing Hu, Zhoutao Lei

We systematically investigate Landau-Zener-St\"uckelberg-Majorana (LZSM) interference under chiral-mirror-like symmetry and propose its application to non-adiabatic topological transport of edge states. Protected by this symmetry, complete destructive interference emerges and can be characterized through occupation probability. This symmetry-protected LZSM i

Ruxi Shi

In this paper, we prove that for a topological dynamical system with positive mean topological dimension and marker property, it has factors of arbitrary small mean topological dimension and zero relative mean topological dimension which separate points.

Yujin Guo, Yong Luo, Juncheng Wei

We study the following one-dimensional cubic nonlinear Schr\"{o}dinger system: \[ u_i''+2\Big(\sum_{k=1}^Nu_k^2\Big)u_i=-\mu_iu_i \ \,\ \mbox{in}\, \ \mathbb{R} , \ \ i=1, 2, \cdots, N, \] where $\mu_1\leq\mu_2\leq\cdots\leq\mu_N<0$ and $N\ge 2$. In this paper, we mainly focus on the case $N=3$ and prove the following results: (i). The solutions of the syste

Hiroshi Itoyama, Reiji Yoshioka

Continuing with our previous series of work, we present a case study of the critical phenomena around Argyres-Douglas singularity of ${\cal N} =2$ susy made at $(A_1, A_{4k-1} ), k =1, 2$ realized by one-unitary matrix model. We determine the phase diagram, which is recast into LEEA of $\mathcal{N}=2$, 4d gauge theory by the 0d-4d connection. There are three

Chin-Wei Huang, Mu-Yi Shen, Kuan-Chang Shih, Shih-Chih Lin

Chest X-rays (CXRs) often display various diseases with disparate class frequencies, leading to a long-tailed, multi-label data distribution. In response to this challenge, we explore the Pruned MIMIC-CXR-LT dataset, a curated collection derived from the MIMIC-CXR dataset, specifically designed to represent a long-tailed and multi-label data scenario. We int

Jeonghyeok Do, Munchurl Kim

In zero-shot skeleton-based action recognition (ZSAR), aligning skeleton features with the text features of action labels is essential for accurately predicting unseen actions. ZSAR faces a fundamental challenge in bridging the modality gap between the two-kind features, which severely limits generalization to unseen actions. Previous methods focus on direct

Lucas Lamata

Machine Learning algorithms are extensively used in an increasing number of systems, applications, technologies, and products, both in industry and in society as a whole. They enable computing devices to learn from previous experience and therefore improve their performance in a certain context or environment. In this way, many useful possibilities have been

Anders Sandberg, Len Fisher

A popular poster from Myanmar lists food pairings that should be avoided, sometimes at all costs. Coconut and honey taken together, for example, are believed to cause nausea, while pork and curdled milk will induce diarrhea. Worst of all, according to the poster, many seemingly innocuous combinations that include jelly and coffee, beef and star fruit, or pig

Jinkai Zheng, Xinchen Liu, Boyue Zhang, Chenggang Yan

Existing studies for gait recognition primarily utilized sequences of either binary silhouette or human parsing to encode the shapes and dynamics of persons during walking. Silhouettes exhibit accurate segmentation quality and robustness to environmental variations, but their low information entropy may result in sub-optimal performance. In contrast, human p