February 2025 arXiv papers — page 17

Junling Chen, Tomonori Totani

Star-forming galaxies (SFGs) are considered to be an important component of the diffuse extragalactic gamma-ray background (EGB) radiation observed in 0.1 -- 820 GeV, but their quantitative contribution has not yet been precisely determined. In this study, we aim to provide the currently most reliable estimate of the contribution of SFGs based on careful cal

Alexander Medvedev, Anton V. Proskurnikov, Zhanybai T. Zhusubaliyev

Pulse-modulated feedback is utilized in drug dosing to mimic sustained over a longer period of time manual discrete dose administration, the latter is in contrast with continuous drug infusion. The intermittent mode of dosing calls for a hybrid (continuous-discrete) modeling of the closed-loop system, where the pharmacokinetics and pharmacodynamics of the dr

Weiyan Shi, Viet Hai Le, Kenny Tsu Wei Choo

Joint attention is a critical component of early speech-language development and a key indicator of effective parent-child interaction. However, research on detecting and analysing joint attention remains limited, particularly for Multimodal Large Language Models (MLLMs). This study evaluates MLLMs' ability to comprehend joint attention by analysing 26 paren

Marian Staggl, Wolfgang Sanz, Paul Pieringer

Numerical simulations are a valuable research and layout tool for fluid flow problems, yet repeated evaluations of parametrized problems, necessary to solve optimization problems, can be very costly. One option to speed up this process is to replace the costly CFD model with a cheaper one. These surrogate models can be either data-driven or they can also rel

Hideo Suganuma, Atsuya Tokutake, Kei Tohme

Motivated by color-magnetic instabilities in QCD, we investigate field-strength correlations in both SU(2) and SU(3) lattice QCD. In the Euclidean Landau gauge, we numerically calculate the perpendicular-type color-magnetic correlation, $C_{\perp}(r) \equiv g^2 \langle H_z^a(s)H_z^a(s + r\hat \perp)) \rangle$ with $\perp \equiv x, y$, and the parallel-type o

Weijie Yue, Zhongwei Si, Bolin Wu, Sixian Wang

We introduce NeRFCom, a novel communication system designed for end-to-end 3D scene transmission. Compared to traditional systems relying on handcrafted NeRF semantic feature decomposition for compression and well-adaptive channel coding for transmission error correction, our NeRFCom employs a nonlinear transform and learned probabilistic models, enabling fl

C. Carmeli, T. Heinosaari, A. Toigo

Simultaneously implementing two arbitrary quantum measurements on the same system is impossible. The consequence of this limitation is that selecting one measurement actively excludes other possibilities. Two incompatible choices can then be forced together only at the cost of adding enough noise to the measurements. An intriguing alternative is to postpone

Yuntao Du, Kailin Jiang, Zhi Gao, Chenrui Shi

Knowledge editing techniques have emerged as essential tools for updating the factual knowledge of large language models (LLMs) and multimodal models (LMMs), allowing them to correct outdated or inaccurate information without retraining from scratch. However, existing benchmarks for multimodal knowledge editing primarily focus on entity-level knowledge repre

Yuqing Wang

This study investigates event-related desynchronization (ERD) phenomena during motor imagery and actual movement. Using sLORETA software, we analyzed the cortical current source density distributions in Mu and Beta frequency bands for 33 subjects during rest, motor imagery, and actual movement conditions. The results were normalized for analysis. Using sLORE

Yuhao Li, Mirana Claire Angel, Salman Khan, Yu Zhu

Trajectory-based motion control has emerged as an intuitive and efficient approach for controllable video generation. However, the existing trajectory-based approaches are usually limited to only generating the motion trajectory of the controlled object and ignoring the dynamic interactions between the controlled object and its surroundings. To address this

Nan An, Long Ma, Guangchao Han, Xin Fan

Deep learning-based low-light image enhancers have made significant progress in recent years, with a trend towards achieving satisfactory visual quality while gradually reducing the number of parameters and improving computational efficiency. In this work, we aim to delving into the limits of image enhancers both from visual quality and computational efficie

Guanting Liu, Yi Wang, Xi Chen, Baoyu Du

Landslides are among the most common natural disasters globally, posing significant threats to human society. Deep learning (DL) has proven to be an effective method for rapidly generating landslide inventories in large-scale disaster areas. However, DL models rely heavily on high-quality labeled landslide data for strong feature extraction capabilities. And

Sandeep Silwal, David P. Woodruff, Qiuyi Zhang

We study beyond worst-case dimensionality reduction for $s$-sparse vectors. Our work is divided into two parts, each focusing on a different facet of beyond worst-case analysis: We first consider average-case guarantees. A folklore upper bound based on the birthday-paradox states: For any collection $X$ of $s$-sparse vectors in $\mathbb{R}^d$, there exists a

Liang Li, Xiaopei Chen, Wen Wu

To enable large model (LM) based edge intelligent service provisioning, on-device fine-tuning with locally personalized data allows for continuous and privacy-preserving LM customization. In this paper, we propose RingAda, a collaborative training framework designed for fine-tuning transformer-based LMs on edge devices. Particularly, RingAda performs paramet

Junguk Lee

In this paper, we concern the model theory of finitely ramified henselian valued fields via higher valued hyperfields. Most of all, we provide a number of Ax-Kochen-Ershov Theorems for finitely ramified henselian valued fields relative to higher valued hyperfields. As corollaries, we deduce a transfer of decidability for full theories and existential theorie

Bastien Baude, Damien Challet, Ioane Muni Toke

Interest rates in decentralized lending protocols are set algorithmically and adjust to supply and demand for liquidity. In this study, we propose an optimal interest rate model that maximizes the expected lender wealth while incorporating penalties for liquidity risk and interest rate stabilization. This objective benefits both sides of the market: it impro

Marina Kontalexi, Alexandros Gelastopoulos, Pantelis P. Analytis

Theoretical work on sequential choice and large-scale experiments in online ranking and voting systems has demonstrated that social influence can have a drastic impact on social and technological systems. Yet, the effect of social influence on online rating systems remains understudied and the few existing contributions suggest that online ratings would self

Yujia Chen, Changsong Li, Yiming Wang, Tianjie Ju

Mental health issues are worsening in today's competitive society, such as depression and anxiety. Traditional healings like counseling and chatbots fail to engage effectively, they often provide generic responses lacking emotional depth. Although large language models (LLMs) have the potential to create more human-like interactions, they still struggle to c

Stefano Viel, Luca Viano, Volkan Cevher

This paper introduces the SOAR framework for imitation learning. SOAR is an algorithmic template that learns a policy from expert demonstrations with a primal dual style algorithm that alternates cost and policy updates. Within the policy updates, the SOAR framework uses an actor critic method with multiple critics to estimate the critic uncertainty and buil

K. É. Gabányi, S. Frey, K. Perger, E. Kun

The dominant fraction of the extragalactic $\gamma$-ray sources are blazars, active galactic nuclei with jets inclined at a small angle to the line of sight. Apart from blazars, a few dozen narrow-line Seyfert 1 galaxies (NLS1) and a number of radio galaxies are associated with $\gamma$-ray sources. The identification of $\gamma$-ray sources requires multiwa

Zohreh Jalilvand, Daniele Notarmuzi, Ubaldo M. Córdova-Figueroa, Emanuela Bianchi

After our attention was brought to recent work by Pelargonio and Zaconne regarding the deviation of the white noise function for systems at very large shear, we have included the work as a reference of interest to our readers and clarified the definition of our Peclet number with respect to the Brownian diffusion of the particle as well as included a brief d

P Sam Sahil, Anupam Jamatia

This paper describes the system submitted by Team A to SemEval 2025 Task 11, ``Bridging the Gap in Text-Based Emotion Detection.'' The task involved identifying the perceived emotion of a speaker from text snippets, with each instance annotated with one of six emotions: joy, sadness, fear, anger, surprise, or disgust. A dataset provided by the task organizer

Jyoti Rani, Arnab Patra, Riddhick Birbonshi

This study investigates the $A$-$q$-numerical range of an operator within the framework of semi-Hilbertian spaces. Several fundamental properties of the $A$-$q$-numerical range are established, including spectral inclusion results and a disk union formula. Bounds for the $A$-$q$-numerical radius are derived, extending and generalizing previously known result

Liang Li, Xingke Yang, Wen Wu, Hao Wang

Large Language Model (LLM) at mobile devices and its potential applications never fail to fascinate. However, on-device LLM fine-tuning poses great challenges due to extremely high memory requirements and slow training speeds. Even with parameter-efficient fine-tuning (PEFT) methods that update only a small subset of parameters, resource-constrained mobile d

Chunyang Cheng, Tianyang Xu, Zhenhua Feng, Xiaojun Wu

Advanced image fusion methods mostly prioritise high-level missions, where task interaction struggles with semantic gaps, requiring complex bridging mechanisms. In contrast, we propose to leverage low-level vision tasks from digital photography fusion, allowing for effective feature interaction through pixel-level supervision. This new paradigm provides stro

Hojae Han, Seung-won Hwang, Rajhans Samdani, Yuxiong He

Large language models (LLMs) have proven invaluable for code generation, particularly in interactive settings. However, existing code generation benchmarks fail to capture the diverse feedback encountered in multi-turn interactions, limiting our ability to evaluate LLMs in these contexts. To address this gap, we present a set of novel benchmarks that explici

Raphael Rossellini, Jake A. Soloff, Rina Foygel Barber, Zhimei Ren

Forecast probabilities often serve as critical inputs for binary decision making. In such settings, calibration$\unicode{x2014}$ensuring forecasted probabilities match empirical frequencies$\unicode{x2014}$is essential. Although the common notion of Expected Calibration Error (ECE) provides actionable insights for decision making, it is not testable: it cann

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

Using $(2259.3 \pm 11.1)\times10^{6}$ $\psi(2S)$ events acquired with the BESIII detector, the branching fraction of $\psi(2S)\rightarrow\tau^{+}\tau^{-}$ is measured with improved precision to be $\mathcal{B}_{\psi(2S)\rightarrow\tau^{+}\tau^{-}}=(3.240~\pm~0.023~\pm~0.081)\times 10^{-3}$, where the first and second uncertainties are statistical and systema

Youngjoon Lee, Jinu Gong, Sun Choi, Joonhyuk Kang

Federated Learning (FL) is a distributed machine learning paradigm enabling collaborative model training across decentralized clients while preserving data privacy. In this paper, we revisit the stability of the vanilla FedAvg algorithm under diverse conditions. Despite its conceptual simplicity, FedAvg exhibits remarkably stable performance compared to more

Xiaofan Li, Xin Tan, Zhuo Chen, Zhizhong Zhang

With the rise of generative models, there is a growing interest in unifying all tasks within a generative framework. Anomaly detection methods also fall into this scope and utilize diffusion models to generate or reconstruct normal samples when given arbitrary anomaly images. However, our study found that the diffusion model suffers from severe ``faithfulnes

Bumsu Park, Heedong Do, Namyoon Lee

We propose a novel approach for channel state information (CSI) compression in multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems, where the frequency-domain channel matrix is treated as a high-dimensional complex-valued image. Our method leverages transformer-based nonlinear transform coding (NTC), an advanced deep

Benton Li, Nativ Levy, Brit Youngmann, Sainyam Galhotra

Prescriptions, or actionable recommendations, are commonly generated across various fields to influence key outcomes such as improving public health, enhancing economic policies, or increasing business efficiency. While traditional association-based methods may identify correlations, they often fail to reveal the underlying causal factors needed for informed

Chengyuan Huang, Zhenlan Chen, Mengke Ha, Haoyuan Wang

Milli-Kelvin atomic force microscopy (mK-AFM) presents an ongoing experimental challenge due to the intense vibrations in a cryogen-free dilution refrigerator and the low cooling power available at mK temperatures. A viable approach is to make the system exceptionally rigid and thermally insulating to decouple external vibrations and isolate heat dissipation

Xiangyan Qu, Gaopeng Gou, Jiamin Zhuang, Jing Yu

Vision-language models (VLMs) have made significant progress in image classification by training with large-scale paired image-text data. Their performances largely depend on the prompt quality. While recent methods show that visual descriptions generated by large language models (LLMs) enhance the generalization of VLMs, class-specific prompts may be inaccu

Hubert Baty

I will demonstrate the effectiveness of Physics-Informed Neural Networks (PINNs) in solving partial differential equations (PDEs) when training data are scarce or noisy. The training data can be located either at the boundaries or within the domain. Additionally, PINNs can be used as an inverse method to determine unknown coefficients in the equations. This

Reza Abbasi, Ali Nazari, Aminreza Sefid, Mohammadali Banayeeanzade

Contrastive Language-Image Pre-training (CLIP) models excel in zero-shot classification, yet face challenges in complex multi-object scenarios. This study offers a comprehensive analysis of CLIP's limitations in these contexts using a specialized dataset, ComCO, designed to evaluate CLIP's encoders in diverse multi-object scenarios. Our findings reveal signi

Takeshi Mizushima, Yukio Tanaka, Jorge Cayao

In superconductor-semiconductor hybrids, applying a magnetic field closes a trivial bulk gap and causes a topological phase transition (TPT), resulting in the emergence of Majorana zero modes at both ends of the wires. However, trivial Andreev bound states formed at the interface with metallic leads mimic the local Majorana properties, making it difficult to

Yi Gao

We studied the superconducting pairing symmetry based on a newly constructed tight-binding model of La$_3$Ni$_2$O$_7$ under pressure, where the $\gamma$ band sinks below the Fermi level and does not form the Fermi surface. The superconducting pairing symmetry is $s_\pm$-wave and is robust against the variation of the interaction strength. In this model, alth

David Gunawan, David Nott, Robert Kohn

We consider the problem of estimating complex statistical latent variable models using variational Bayes methods. These methods are used when exact posterior inference is either infeasible or computationally expensive, and they approximate the posterior density with a family of tractable distributions. The parameters of the approximating distribution are est

Wenhai Lin, Xinghua Sun, Anshan Yuan, Yayu Gao

The scarcity of the licensed spectrum is forcing emerging Internet of Things (IoT) networks to operate within the unlicensed spectrum. Yet there has been extensive observation indicating that performance deterioration and significant unfairness would arise, when newly deployed Aloha-based networks coexist with incumbent Carrier Sense Multiple Access (CSMA)-b

Muhammad Riaz, Jameel-Un Nabi, Muhammad Majid

Calculating weak decay rates under stellar conditions for studying presupernova evolution of massive stars is a challenging task. Here we show the importance of odd A nuclei for presupernova simulations. In order to calculate the required nuclear matrix elements we apply the pn QRPA model in a deformed basis. Nuclear deformation, thought to play an integral

Ze-Kai Yu, Lei Liu, Tao Zhu

The chiral scalar-tensor theory is an extension of the Chern-Simons modified gravity by introducing couplings between the first and second derivatives of the scalar field and parity-violating spacetime curvatures. A key feature of this theory is its explicit breaking of parity symmetry in the gravitational sector, which is expected to affect the spatial-time

Shaharukh Khan, Ayush Tarun, Ali Faraz, Palash Kamble

In this work, we provide the system description of our submission as part of the English to Lowres Multimodal Translation Task at the Workshop on Asian Translation (WAT2024). We introduce Chitranuvad, a multimodal model that effectively integrates Multilingual LLM and a vision module for Multimodal Translation. Our method uses a ViT image encoder to extract

Jana K. Nickel

Let $B$ be a bidirected multigraph with signing $\sigma$, let $X$ be a set of vertices in $B$, and let $k$ be a non-negative integer. For any pair of vertex sets $S,T\subset V(B)$ satisfying $X\cap S = X\cap T$, we denote by $B_{S,T}$ the multigraph with the same vertex set as $B$ and with edge set consisting of those edges $e$ of $B$ each of whose endvertic

Guanzhou Ke, Shengfeng He, Xiao Li Wang, Bo Wang

Previous successful approaches to missing modality completion rely on carefully designed fusion techniques and extensive pre-training on complete data, which can limit their generalizability in out-of-domain (OOD) scenarios. In this study, we pose a new challenge: can we develop a missing modality completion model that is both resource-efficient and robust t

Zhihui Wang, Shijun Guan, Guansheng Teng, Pengfei Yang

We experimentally realize a new cavity quantum electrodynamics (QED) platform with defect-free single-atom array strongly coupled to an optical cavity. The defect-free single-atom array is obtained by rearranging a probabilistically loaded one-dimensional (1D) optical tweezer array with dimensions of $1 \times 40$. The atom array is enclosed with two cavity

Long Xu, Kaixin Chai, Boyuan An, Jiaxiang Gan

The tractor-trailer robot consists of a drivable tractor and one or more non-drivable trailers connected via hitches. Compared to typical car-like robots, the addition of trailers provides greater transportation capability. However, this also complicates motion planning due to the robot's complex kinematics, high-dimensional state space, and deformable struc

Qinjun Jian, Jing Hu, Lihe Yan, Jinhai Si

By numerically solving the nonlinear Schr\"odinger equation, we theoretically study the nonlinear propagation dynamics and self-healing properties of elliptical Airy beams (EABs) propagating in water under Kerr nonlinearity. Compared to linear propagation, EABs exhibit extended propagation distances and enhanced stability in nonlinear media. Furthermore, par

Yiwei Li, Ji Zhang, Shaoxiong Feng, Peiwen Yuan

Self-consistency improves reasoning by aggregating diverse stochastic samples, yet the dynamics behind its efficacy remain underexplored. We reframe self-consistency as a dynamic distributional alignment problem, revealing that decoding temperature not only governs sampling randomness but also actively shapes the latent answer distribution. Given that high t

Qin Chang, Wei Tao, Zhen-Jun Xiao, Ruilin Zhu

Within the framework of Non-Relativistic Quantum Chromodynamics (NRQCD) factorization, we calculate the next-to-leading order (NLO) perturbative QCD corrections to the form factors for the semileptonic decays of $B_c^*$ into $J/\psi$ via (axial-)vector and (axial-)tensor currents. We obtain the complete analytical results for the form factors up to NLO, and

Nobin Sarwar

With the increasing prevalence of mental health conditions worldwide, AI-powered chatbots and conversational agents have emerged as accessible tools to support mental health. However, deploying Large Language Models (LLMs) in mental healthcare applications raises significant privacy concerns, especially regarding regulations like HIPAA and GDPR. In this work

Reza Abbasi, Ali Nazari, Aminreza Sefid, Mohammadali Banayeeanzade

Contrastive Language-Image Pre-training (CLIP) models have demonstrated remarkable performance in zero-shot classification tasks, yet their efficacy in handling complex multi-object scenarios remains challenging. This study presents a comprehensive analysis of CLIP's performance limitations in multi-object contexts through controlled experiments. We introduc

David Theurel

Building upon a recent analysis of the measurement process in Hamiltonian mechanics, this article investigates the Bayesian epistemology of classical physics -- the landscape of accessible probability distributions over phase space. I prove a thermodynamic limitation on the information that can be obtained about a classical system by means of observations: A

Yu-Peng Chen, Shu Zhang, Long Ji, Shuang-Nan Zhang

Thermonuclear X-ray bursts occur on the surface of an accreting neutron star (NS), and their characteristics and interplay with the surrounding circumstance could be a clue to understand the nature of the NS and accretion process. For this purpose, Insight-HXMT has performed high cadence observations on the bright thermonuclear X-ray burster--4U~1608--52 dur

Shangfei Wu, Xianghan Xu, Fei-Ting Huang, Turan Birol

We employ polarization-resolved Raman spectroscopy to study the lattice dynamics of the polar charge density wave phase of the superconductor Mo$_3$Al$_2$C with structural chirality. We show the phononic signatures of the charge density wave transition at $T^*$ = 155\,K in Mo$_3$Al$_2$C. The detailed temperature dependence of these phonon modes' frequency, h

Shuvayan Banerjee, James Saunderson, Radhendushka Srivastava, Ajit Rajwade

In high-dimensional sparse regression, the \textsc{Lasso} estimator offers excellent theoretical guarantees but is well-known to produce biased estimates. To address this, \cite{Javanmard2014} introduced a method to ``debias" the \textsc{Lasso} estimates for a random sub-Gaussian sensing matrix $\boldsymbol{A}$. Their approach relies on computing an ``approx

Vidhi Lalchand, Anna-Christina Eilers

This work proposes a scalable probabilistic latent variable model based on Gaussian processes (Lawrence, 2004) in the context of multiple observation spaces. We focus on an application in astrophysics where data sets typically contain both observed spectral features and scientific properties of astrophysical objects such as galaxies or exoplanets. In our app

Weiyang Kong, Kaiqi Wu, Sen Zhang, Yubao Liu

Traffic flow forecasting is a critical spatio-temporal data mining task with wide-ranging applications in intelligent route planning and dynamic traffic management. Recent advancements in deep learning, particularly through Graph Neural Networks (GNNs), have significantly enhanced the accuracy of these forecasts by capturing complex spatio-temporal dynamics.

Yugin Tan, Kai Xin Soh, Renwen Zhang, Jungup Lee

In social service, administrative burdens and decision-making challenges often hinder practitioners from performing effective casework. Generative AI (GenAI) offers significant potential to streamline these tasks, yet exacerbates concerns about overreliance, algorithmic bias, and loss of identity within the profession. We explore these issues through a two-s

Erkan Bayram, Mohamed-Ali Belabbas

The convergence of the gossip process has been extensively studied; however, algorithms that generate a set of stochastic matrices, the infinite product of which converges to a rank-one matrix determined by a given weight vector, have been less explored. In this work, we propose an algorithm for constructing (local) stochastic matrices based on a given gossi

Zixuan Weng, Xiaolong Jin, Jinyuan Jia, Xiangyu Zhang

Ensuring AI safety is crucial as large language models become increasingly integrated into real-world applications. A key challenge is jailbreak, where adversarial prompts bypass built-in safeguards to elicit harmful disallowed outputs. Inspired by psychological foot-in-the-door principles, we introduce FITD,a novel multi-turn jailbreak method that leverages

Laura Baldelli, Umberto Guarnotta

We prove optimal decay estimates for positive solutions to elliptic p-Laplacian problems in the entire Euclidean space, when a critical nonlinearity with a decaying source term is considered. Also gradient decay estimates are furnished. Our results extend previous theorems in the literature, in which a purely critical reaction is treated. The technique is ba

Xinran Li, Aritoki Suzuki, Maurice Garcia-Sciveres

Kinetic inductance devices (KIDs) are superconducting resonators with high kinetic inductance sensitive to external energy perturbations. KIDs made with superconductors having $T_c$ far below one Kelvin are of particular interest for sensing minuscule signals, such as light dark matter detection and millimeter wave telescopes for astronomy and cosmology. In

Andrew K. Yi, Byeong Rok Ko

The sensitivity of axion dark matter searches depends on the signal window that results from the velocity dispersion of axion dark matter. Since the ratio of signal windows is about 6500 between the standard halo and the big flow axion dark matter, each axion dark matter search usually uses a separate data acquisition (DAQ) channel with a different frequency

Xin-yang Zhao, Jian Jin, Yang-yang Li, Yazhou Yao

The Coarse-to-Fine Few-Shot (C2FS) task is designed to train models using only coarse labels, then leverages a limited number of subclass samples to achieve fine-grained recognition capabilities. This task presents two main challenges: coarse-grained supervised pre-training suppresses the extraction of critical fine-grained features for subcategory discrimin

Niklas Cichutek, Peter Kopietz, Andreas Rückriegel

We show that magnons in two-dimensional altermagnets can spontaneously decay at zero temperature. The decay rate is determined by quantum fluctuations and scattering processes involving the decay of a single magnon into three. These processes are kinematically allowed due to the convexity of the altermagnetic magnon dispersion. For small wavevectors $k$ the

Nazim I. Mahmudov

A system of inhomogeneous second-order difference equations with linear parts given by noncommutative matrix coefficients are considered. Closed form of its solution is derived by means of newly defined delayed matrix sine/cosine using the Z-transform and determining function.

R. Shehzadi, J. -U. Nabi, F. Farooq

The isotopes of manganese in the mass range A equal to 53 to 63 are abundant in the core material of high mass stars and are believed to be of prime importance in the progression of the pre collapse phases. During these late evolutionary phases, nuclear processes associated with weak interactions, including decay and electron capture (EC) on these isotopes,

Jeong-Wan Lee, Sung-Jun Yang

The active element pattern method is widely employed in beam pattern synthesis of array antenna to account for mutual coupling between antenna elements. Calculating the active element patterns for large number of array requires full-wave analyses of total array structure, which is time consuming. To obtain accurate active element patterns efficiently, this l

Shulai Zhang, Ningxin Zheng, Haibin Lin, Ziheng Jiang

Mixture-of-experts (MoE) has been extensively employed to scale large language models to trillion-plus parameters while maintaining a fixed computational cost. The development of large MoE models in the distributed scenario encounters the problem of large communication overhead. The inter-device communication of a MoE layer can occupy 47% time of the entire

Qihao Lian, Di Wang

Rust has become a popular system programming language that strikes a balance between memory safety and performance. Rust's type system ensures the safety of low-level memory controls; however, a well-typed Rust program is not guaranteed to enjoy high performance. This article studies static analysis for resource consumption of Rust programs, aiming at unders

Boni Paul, Sudhindu Bikash Mandal, Kenji Sugisaki, B. P. Das

The Quantum Phase Difference Estimation (QPDE) algorithm, as an extension of the Quantum Phase Estimation (QPE), is a quantum algorithm designed to compute the differences of two eigenvalues of a unitary operator by exploiting the quantum superposition of two eigenstates. Unlike QPE, QPDE is free of controlled-unitary operations, and is suitable for calculat

Guilong Li, Zibin Zhao, Rui Zhang, Zhaopin Chen

Stability of elongated (``slender") quantum droplets (QDs) with embedded unitary and multiple vorticity is a problem that was not solved previously. In this work, we propose a solution which relies upon the use of the spatial modulation of the inter-species scattering length in the binary Bose-Einstein condensates, in the form of a two-dimensional axisymmetr

Bogdan Oancea

The quest for accurate economic forecasting has traditionally been dominated by econometric models, which most of the times rely on the assumptions of linear relationships and stationarity in of the data. However, the complex and often nonlinear nature of global economies necessitates the exploration of alternative approaches. Machine learning methods offer

Behrad Samari, Gian Paolo Incremona, Antonella Ferrara, Abolfazl Lavaei

Large-scale interconnected networks, composed of multiple low-dimensional subsystems, serve as a crucial framework for modeling a wide range of real-world applications. Despite offering computational scalability, the inherent interdependence among subsystems poses significant challenges to the effective control of such networks. This complexity is further ex

Jiacheng Ye, Zhenyu Wu, Jiahui Gao, Zhiyong Wu

In the post-AlphaGo era, there has been a renewed interest in search techniques such as Monte Carlo Tree Search (MCTS), particularly in their application to Large Language Models (LLMs). This renewed attention is driven by the recognition that current next-token prediction models often lack the ability for long-term planning. Is it possible to instill search

Ronan J. Conlon, Max Hallgren, Zilu Ma

We show that any non-collapsed finite time singularity of the Ricci flow on a compact K\"ahler surface is of Type I. Combined with a previous result of the first author, Cifarelli, and Deruelle, it follows that any such singularity is modeled on the shrinking Ricci soliton of Feldman-Ilmanen-Knopf on the total space of the line bundle $\mathcal{O}_{\mathbb{P

Xiao Lin, Yuge Huang, Jianqing Xu, Yuxi Mi

Face recognition (FR) stands as one of the most crucial applications in computer vision. The accuracy of FR models has significantly improved in recent years due to the availability of large-scale human face datasets. However, directly using these datasets can inevitably lead to privacy and legal problems. Generating synthetic data to train FR models is a fe

Brandon Johns, Zhuomin Zhou, Elahe Abdi

Combining deep learning with classical physics facilitates the efficient creation of accurate dynamical models. In a recent class of neural network, Lagrangian mechanics is hard-coded into the architecture, and training the network learns the given system. However, the current architectures do not facilitate the modelling of dynamical systems that are driven

Bogdan Oancea

In this paper we present the results of an experiment aimed to use machine learning methods to obtain models that can be used for the automatic classification of products. In order to apply automatic classification methods, we transformed the product names from a text representation to numeric vectors, a process called word embedding. We used several embeddi

Dongbo Shi, Shen Cao, Lubin Fan, Bojian Wu

We present TrackGS, a novel method to integrate global feature tracks with 3D Gaussian Splatting (3DGS) for COLMAP-free novel view synthesis. While 3DGS delivers impressive rendering quality, its reliance on accurate precomputed camera parameters remains a significant limitation. Existing COLMAP-free approaches depend on local constraints that fail in comple

Zezeng Li, Xiaoyu Du, Na Lei, Liming Chen

Adversarial attacks exploit the vulnerability of deep models against adversarial samples. Existing point cloud attackers are tailored to specific models, iteratively optimizing perturbations based on gradients in either a white-box or black-box setting. Despite their promising attack performance, they often struggle to produce transferable adversarial sample

Keiju Kato

The interior polynomial was originally defined for hypergraphs and later shown to coincide with the Ehrhart polynomial of the root polytope of an associated bipartite graph. In previous work, we derived an alternating cycle recursion formula for the interior polynomial. Here, we introduce a new, more transparent recursion formula based on the structure of no

Song Li, Yu Xia

This study addresses the blind deconvolution problem with modulated inputs, focusing on a measurement model where an unknown blurring kernel $\boldsymbol{h}$ is convolved with multiple random modulations $\{\boldsymbol{d}_l\}_{l=1}^{L}$(coded masks) of a signal $\boldsymbol{x}$, subject to $\ell_2$-bounded noise. We introduce a more generalized framework for

Taichiro Endo

This study proposes an "AI Development Support" approach that, unlike conventional AI Alignment-which aims to forcefully inject human values-supports the ethical and moral development of AI itself. As demonstrated by the Orthogonality Thesis, the level of intelligence and the moral quality of a goal are independent; merely expanding knowledge does not enhanc

Lianping Yang, Peng Jiao, Jinshan Pan, Hegui Zhu

In the process of performing image super-resolution processing, the processing of complex localized information can have a significant impact on the quality of the image generated. Fractal features can capture the rich details of both micro and macro texture structures in an image. Therefore, we propose a diffusion model-based super-resolution method incorpo

Adam Bretherton, Joshua J. Bon, David J. Warne, Kerrie Mengersen

Updating $\textit{a priori}$ information given some observed data is the core tenet of Bayesian inference. Bayesian transfer learning extends this idea by incorporating information from a related dataset to improve the inference on the observed target dataset which may have been collected under slightly different settings. The use of related information can

Soumya Roy, Durgesh Tripathi, Sreejith Padinhatteeri, A. N. Ramaprakash

Solar flares are regularly observed in extreme ultraviolet (EUV), soft X-rays (SXR), and hard X-rays (HXR). However, those in near and mid-UV are sparse. The Solar Ultraviolet Imaging Telescope (SUIT) onboard the Aditya-L1, launched on 2nd September, 2023 provides regular observations in the 200-400 nm wavelength range through eleven filters. Here, we report

Chuanliu Fan, Ziqiang Cao, Zicheng Ma, Nan Yu

Goal-oriented de novo molecule design, namely generating molecules with specific property or substructure constraints, is a crucial yet challenging task in drug discovery. Existing methods, such as Bayesian optimization and reinforcement learning, often require training multiple property predictors and struggle to incorporate substructure constraints. Inspir

Shivshankar Nila, Ishapathik Das, N. Balakrishna

In many random phenomena, such as life-testing experiments and environmental data (like rainfall data), there are often positive values and an excess of zeros, which create modeling challenges. In life testing, immediate failures result in zero lifetimes, often due to defects or poor quality, especially in electronics and clinical trials. These failures, cal

Sk. Safique Ahmad, Pinki Khatun

This paper presents a unified framework for investigating the partial condition number (CN) of the solution of double saddle point problems (DSPPs) and provides closed-form expressions for it. This unified framework encompasses the well-known partial normwise CN (NCN), partial mixed CN (MCN) and partial componentwise CN (CCN) as special cases. Furthermore, w

Haris Aziz, Yuhang Guo, Zhaohong Sun

This paper studies cooperative games where coalitions are formed online and the value generated by the grand coalition must be irrevocably distributed among the players at each timestep. We investigate the fundamental issue of strategic pariticipation incentives and address these concerns by formalizing natural participation incentive axioms. Our analysis re

Weiqi Wang, Chenhan Zhang, Zhiyi Tian, Shushu Liu

Machine unlearning allows data owners to erase the impact of their specified data from trained models. Unfortunately, recent studies have shown that adversaries can recover the erased data, posing serious threats to user privacy. An effective unlearning method removes the information of the specified data from the trained model, resulting in different output

Maximilian Böther, Xiaozhe Yao, Tolga Kerimoglu, Dan Graur

State-of-the-art large language and vision models are trained over trillions of tokens that are aggregated from a large variety of sources. As training data collections grow, manually managing the samples becomes time-consuming, tedious, and prone to errors. Yet recent research shows that the data mixture and the order in which samples are visited during tra

Yu Feng, Bin Xu

In this paper, we construct a stable parabolic Higgs bundle of rank two, which corresponds to the uniformization associated with a conformal hyperbolic metric on a compact Riemann surface $\overline{X}$ with prescribed singularities. This provides an alternative proof of the classical existence theorem for singular hyperbolic metrics, originally established

Sina Akbari, Negar Kiyavash, AmirEmad Ghassami

The triple difference causal inference framework is an extension of the well-known difference-in-differences framework. It relaxes the parallel trends assumption of the difference-in-differences framework through leveraging data from an auxiliary domain. Despite being commonly applied in empirical research, the triple difference framework has received relati

Ziqian Lin, Shubham Kumar Bharti, Kangwook Lee

Recent research has investigated the underlying mechanisms of in-context learning (ICL) both theoretically and empirically, often using data generated from simple function classes. However, the existing work often focuses on the sequence consisting solely of labeled examples, while in practice, labeled examples are typically accompanied by an instruction, pr

Zhu-yao Jin, Jun Jing

Error correction is generally demanded in large-scale quantum information processing and quantum computation. We provide here a universal and realtime control strategy to dynamically correct the arbitrary type of errors in the system Hamiltonian. It yields multiple error-resilient paths for the interested system which are activated by the von Neumann equatio

Weiqi Wang, Zhiyi Tian, Chenhan Zhang, Shui Yu

Deep learning (DL) enabled semantic communications leverage DL to train encoders and decoders (codecs) to extract and recover semantic information. However, most semantic training datasets contain personal private information. Such concerns call for enormous requirements for specified data erasure from semantic codecs when previous users hope to move their d

Isa Inuwa-Dutse

With over 500 languages in Nigeria, three languages -- Hausa, Yor\`ub\'a and Igbo -- spoken by over 175 million people, account for about 60% of the spoken languages. However, these languages are categorised as low-resource due to insufficient resources to support tasks in computational linguistics. Several research efforts and initiatives have been presente