December 2024 arXiv papers — page 203

Benjamin Taysum, Iris van Zelst, John Lee Grenfell, Franz Schreier

Warm rocky exoplanets within the habitable zone of Sun-like stars are favoured targets for current and future missions. Theory indicates these planets could be wet at formation and remain habitable long enough for life to develop. In this work we test the climate-chemistry response, maintenance, and detectability of biosignatures in warm, water-rich atmosphe

Eriko Shigetsugu, Hiroki Sakaji, Itsuki Noda

In this paper, we design indices of economic fluctuation narratives derived from economic surveys. Companies, governments, and investors rely on key metrics like GDP and industrial production indices to predict economic trends. However, they have yet to effectively leverage the wealth of information contained in economic text, such as causal relationships, i

Marc Goerigk, Michael Hartisch, Sebastian Merten

An important factor in the practical implementation of optimization models is the acceptance by the intended users. This is influenced among other factors by the interpretability of the solution process. Decision rules that meet this requirement can be generated using the framework for inherently interpretable optimization models. In practice, there is often

Peng Huang

We present a classically equivalent reformulation of the Standard Model. In this framework, the Higgs doublet is recast as a $2\times2$ matrix and right-handed fermion singlets are organized into novel doublets. This restructuring reveals a latent algebraic geometry that naturally realizes a new local gauge principle: the \textbf{extended Weyl symmetry}. Gen

Michelle Elizabeth, Morgan Veyret, Miguel Couceiro, Ondrej Dusek

Large language models (LLMs) gained immense popularity due to their impressive capabilities in unstructured conversations. Empowering LLMs with advanced prompting strategies such as reasoning and acting (ReAct) (Yao et al., 2022) has shown promise in solving complex tasks traditionally requiring reinforcement learning. In this work, we apply the ReAct strate

Dawei Li, Taotao Fang, Chong Ge, Teng Liu

Over the last several decades, extensive research has been conducted on the baryon cycles within cosmic structures, encompassing a broad mass range from dwarf galaxies to galaxy clusters. However, a notable gap in understanding the cosmic baryon cycle is the poor galaxy groups with halo masses around $10^{13}\ M_{\odot}$ (e.g., McGaugh et al. 2010). Poor gal

Mingyue Guo, Zhenhua Shi

This paper examines a generalization of the Camassa-Holm equation from the perspective of integrability. Using the framework developed by Dubrovin on bi-Hamiltonian deformations and the general theory of quasi-integrability, we demonstrate that a unique bi-Hamiltonian structure is possible for this generalized equation only when it reduces to the original CH

Fan Xiao, Jingwei Wang, Zhongfei Xiong, Yuntian Chen

In this work we propose an efficient and accurate multi-scale optical simulation algorithm by applying a numerical version of slowly varying envelope approximation in FEM. Specifically, we employ the fast iterative method to quickly compute the phase distribution of the electric field within the computational domain and construct a novel multi-scale basis fu

Lin Guifang, Li Yan, Su Jie, Wu Tao

The magnetic field is a significant and universal physical phenomenon in modern astrophysics. Small-scale magnetic fields are very important in the stellar atmosphere. They are ubiquitous, and strongly couple with the acoustic waves. Therefore, their presence affects the properties of acoustic waves in the stellar outer layer. In the present work, under the

Rodrigo De Pool, Juan Souto

We prove that forgetful maps are the only non-constant holomorphic maps $\mathcal{M}_{g,r}\to \mathcal{M}_{g',r'}$ between moduli spaces, as long as $g\ge 4$ and $g'\le 3\cdot 2^{g-3}$.

Bikang Pan, Qun Li, Xiaoying Tang, Wei Huang

The emergence of vision-language foundation models, such as CLIP, has revolutionized image-text representation, enabling a broad range of applications via prompt learning. Despite its promise, real-world datasets often contain noisy labels that can degrade prompt learning performance. In this paper, we demonstrate that using mean absolute error (MAE) loss in

Oriana Presacan, Alexandru Dorobantiu, Vajira Thambawita, Michael A. Riegler

Accurate embryo morphology assessment is essential in assisted reproductive technology for selecting the most viable embryo. Artificial intelligence has the potential to enhance this process. However, the limited availability of embryo data presents challenges for training deep learning models. To address this, we trained two generative models using two data

Liangwei Jiang, Ruida Li, Zhifeng Zhang, Shuo Fang

This paper aims to bring fine-grained expression control while maintaining high-fidelity identity in portrait generation. This is challenging due to the mutual interference between expression and identity: (i) fine expression control signals inevitably introduce appearance-related semantics (e.g., facial contours, and ratio), which impact the identity of the

Alan Wake, Bei Chen, C. X. Lv, Chao Li

This technical report presents Yi-Lightning, our latest flagship large language model (LLM). It achieves exceptional performance, ranking 6th overall on Chatbot Arena, with particularly strong results (2nd to 4th place) in specialized categories including Chinese, Math, Coding, and Hard Prompts. Yi-Lightning leverages an enhanced Mixture-of-Experts (MoE) arc

Luca Sortino, Marcos H. D. Guimarães, Alejandro Molina-Sánchez, Jiamin Quan

Layered two-dimensional (2D) materials have revolutionized how we approach light-matter interactions, offering unprecedented optical and electronic properties with the potential for vertical heterostructures and manipulation of spin-valley degrees of freedom. The discovery of moir\'e physics in twisted heterostructures has further unlocked new possibilities

Dongsheng Han, Peng Wang, Wanli Ni, Wen Wang

In this paper, we propose a novel multi-functional reconfigurable intelligent surface (MF-RIS) that supports signal reflection, refraction, amplification, and target sensing simultaneously. Our MF-RIS aims to enhance integrated communication and sensing (ISAC) systems, particularly in multi-user and multi-target scenarios. Equipped with reflection and refrac

Francesco Taioli, Edoardo Zorzi, Gianni Franchi, Alberto Castellini

Language-driven instance object navigation assumes that human users initiate the task by providing a detailed description of the target instance to the embodied agent. While this description is crucial for distinguishing the target from visually similar instances in a scene, providing it prior to navigation can be demanding for human. To bridge this gap, we

Hao Yang, Zhenyu Zhang, Yanyan Zhao, Bing Qin

As a fine-grained task, multimodal aspect-based sentiment analysis (MABSA) mainly focuses on identifying aspect-level sentiment information in the text-image pair. However, we observe that it is difficult to recognize the sentiment of aspects in low-quality samples, such as those with low-resolution images that tend to contain noise. And in the real world, t

Joy Dhar, Nayyar Zaidi, Maryam Haghighat, Puneet Goyal

Multimodal fusion learning has shown significant promise in classifying various diseases such as skin cancer and brain tumors. However, existing methods face three key limitations. First, they often lack generalizability to other diagnosis tasks due to their focus on a particular disease. Second, they do not fully leverage multiple health records from divers

Khadija Khatun, Chen Shen, Jun Tanimoto, Interdisciplinary Graduate School of Engineering Sciences

Understanding how cooperation emerges in public goods games is crucial for addressing societal challenges. While optional participation can establish cooperation without identifying cooperators, it relies on specific assumptions -- that individuals abstain and receive a non-negative payoff, or that non-participants cause damage to public goods -- which limit

Gorkem Polat, Ümit Mert Çağlar, Alptekin Temizel

Assessing disease severity with ordinal classes, where each class reflects increasing severity levels, benefits from loss functions designed for this ordinal structure. Traditional categorical loss functions, like Cross-Entropy (CE), often perform suboptimally in these scenarios. To address this, we propose a novel loss function, Class Distance Weighted Cros

Jinouwen Zhang, Rongkun Xue, Yazhe Niu, Yun Chen

Generative models, particularly diffusion models, have achieved remarkable success in density estimation for multimodal data, drawing significant interest from the reinforcement learning (RL) community, especially in policy modeling in continuous action spaces. However, existing works exhibit significant variations in training schemes and RL optimization obj

Lingyun Zhang, Yu Xie, Yanwei Fu, Ping Chen

As large-scale diffusion models continue to advance, they excel at producing high-quality images but often generate unwanted content, such as sexually explicit or violent content. Existing methods for concept removal generally guide the image generation process but can unintentionally modify unrelated regions, leading to inconsistencies with the original mod

Zilyu Ye, Zhiyang Chen, Tiancheng Li, Zemin Huang

Diffusion and flow matching models have achieved remarkable success in text-to-image generation. However, these models typically rely on the predetermined denoising schedules for all prompts. The multi-step reverse diffusion process can be regarded as a kind of chain-of-thought for generating high-quality images step by step. Therefore, diffusion models shou

Aric Cutuli, Upmanu Lall, Michael J. Puma, Émile Esmaili

Understanding and predicting human migration patterns is a central challenge in population dynamics research. Traditional physics-inspired gravity and radiation models represent migration flows as functions of attractiveness using socio-economic features as proxies. They assume that the relationship between features and migration is spatially invariant, rega

An Ning, Tai-Yue Li, Nan-Yow Chen

In this study, we propose a novel architecture, the Quantum Pointwise Convolution, which incorporates pointwise convolution within a quantum neural network framework. Our approach leverages the strengths of pointwise convolution to efficiently integrate information across feature channels while adjusting channel outputs. By using quantum circuits, we map dat

Xiaoqi Zhao, Youwei Pang, Shijie Chang, Yuan Zhao

As large-scale foundation models trained on billions of image--mask pairs covering a vast diversity of scenes, objects, and contexts, SAM and its upgraded version, SAM~2, have significantly influenced multiple fields within computer vision. Leveraging such unprecedented data diversity, they exhibit strong open-world segmentation capabilities, with SAM~2 furt

Kyung Ik Sim, Byung Cheol Park, Taesoo Kim, Byeong Wook Cho

Research on manipulating materials using light has garnered significant interest, yet examples of controlling electronic polarization in magnetic materials remain scarce. Here, we demonstrate the hysteresis of electronic polarization in the antiferromagnetic semiconductor FePS3 via light. Below the N\'eel temperature, we observe linear dichroism (i.e., optic

Yu Jiang

Filaments play a crucial role in providing the necessary environmental conditions for star formation, actively participating in the process. To facilitate the identification and analysis of filaments, we introduce DPConCFil (Directional and Positional Consistency between Clumps and Filaments), a suite of algorithms comprising one identification method and tw

Anthony Tang, Alexander Mamishev, Igor Novosselov

Dielectric barrier discharge (DBD) plasma actuator arrays have been suggested as active flow control devices due to the robust electrohydrodynamic (EHD) force generation in variable atmospheric conditions. DBD plasma augmentation schemes allow for significant performance improvements. However, the transitions to sliding discharge or counter-flow discharge li

Abdurrahman Javid Shaikh, Abdul Ghani Abro, Mirza Muhammad Ali Baig, Muhammad Adeel Ahmad Siddiqui

Full-vectorial finite difference method with perfectly matched layers boundaries is used to identify the single mode operation region of submicron rib waveguides fabricated using sili-con-on-insulator material system. Achieving high mode power confinement factors is emphasized while maintaining the single mode operation. As opposed to the case of large cross

Canqiang Weng, Can Chen, Jingjun Tan, Tianlu Pan

Given the spatial heterogeneity of land use patterns in most cities, large-scale UAM deployments will likely focus on specific areas, such as intertransfer traffic between suburbs and city centers. However, large-scale UAM operations connecting multiple origin-destination pairs raise concerns about air traffic safety and efficiency due to potential conflict

Wenru Liu, Yongkang Song, Chengzhen Meng, Zhiyu Huang

We address the decision-making capability within an end-to-end planning framework that focuses on motion prediction, decision-making, and trajectory planning. Specifically, we formulate decision-making and trajectory planning as a differentiable nonlinear optimization problem, which ensures compatibility with learning-based modules to establish an end-to-end

Christian Bluethgen, Dave Van Veen, Cyril Zakka, Katherine Link

At the heart of radiological practice is the challenge of integrating complex imaging data with clinical information to produce actionable insights. Nuanced application of language is key for various activities, including managing requests, describing and interpreting imaging findings in the context of clinical data, and concisely documenting and communicati

N. Sukumar, Amit Acharya

Many partial differential equations (PDEs) such as Navier--Stokes equations in fluid mechanics, inelastic deformation in solids, and transient parabolic and hyperbolic equations do not have an exact, primal variational structure. Recently, a variational principle based on the dual (Lagrange multiplier) field was proposed. The essential idea in this approach

M. G. Kozlov, M. Y. Kaygorodov, Yu. A. Demidov, V. A. Yerokhin

We present calculations of the self-energy correction to the $E1$ transition amplitudes in hydrogen-like ions, performed to all orders in the nuclear binding strength parameter. Our results for the $1s$-$2p_{1/2}$ transition for the hydrogen isoelectronic sequence show that the perturbed-orbital part of the self-energy correction provides the dominant contri

Qianlong Li, Chen Huang, Shuai Li, Yuanxin Xiang

Complex Table Question Answering involves providing accurate answers to specific questions based on intricate tables that exhibit complex layouts and flexible header locations. Despite considerable progress having been made in the LLM era, the reasoning processes of existing methods are often implicit, feeding the entire table into prompts, making it difficu

Jin-Da Li, He Gao, Shunke Ai, Wei-Hua Lei

The broadband afterglow of Gamma-ray bursts (GRBs) is usually believed to originate from the synchrotron radiation of electrons accelerated by the external shock of relativistic jets. Therefore, the jet structure should have a significant impact on the GRB afterglow features. The latest observations indicate that the GRB jets may possess intricate structures

Jin-Da Li, He Gao, Shunke Ai, Wei-Hua Lei

As the most energetic explosion in the universe, gamma-ray bursts (GRBs) are usually believed to be generated by relativistic jets. Some mechanisms (e.g. internal non-uniform magnetic dissipation processes or the precession of the central engine) may generate asymmetric jet structures, which is characterized by multiple fluctuations in the light curve of aft

Chaviva Sirote-Katz, Yotam M. Y. Feldman, Guy Cohen, Tamás Kálmán

Combinatorial mechanical metamaterials are made of anisotropic, flexible blocks, such that multiple metamaterials may be constructed using a single block type, and the system's response depends on the frustration (or its absence) due to the mutual orientations of the blocks within the lattice. Specifically, any minimal loop of blocks that may not simultaneou

Swayambhoo Jain, Ravi Raju, Bo Li, Zoltan Csaki

Large Language Models (LLMs) have achieved remarkable advancements, but their monolithic nature presents challenges in terms of scalability, cost, and customization. This paper introduces the Composition of Experts (CoE), a modular compound AI system leveraging multiple expert LLMs. CoE leverages a router to dynamically select the most appropriate expert for

Huijie Qiao

This work focuses on multivalued stochastic differential equations with jumps. First, by employing the weak convergence approach, we establish the Freidlin-Wentzell uniform large deviation principle and the Dembo-Zeitouni uniform large deviation principle for these equations. Subsequently, based on these results, we derive both upper and lower bounds for the

Zeyuan Li, Qingdao Huang

With the rapid advancement of neural networks, methods for option pricing have evolved significantly. This study employs the Black-Scholes-Merton (B-S-M) model, incorporating an additional variable to improve the accuracy of predictions compared to the traditional Black-Scholes (B-S) model. Furthermore, Convolutional Kolmogorov-Arnold Networks (Conv-KANs) an

Ruichen Wang, Junliang Zhang, Qingsong Xie, Chen Chen

Recently, diffusion models have exhibited superior performance in the area of image inpainting. Inpainting methods based on diffusion models can usually generate realistic, high-quality image content for masked areas. However, due to the limitations of diffusion models, existing methods typically encounter problems in terms of semantic consistency between im

Steve Prabu, Steven J Tingay, Arash Bahramian, James C. A. Miller-Jones

The Centaurus A radio galaxy, due to its proximity, presents itself as one of the few systems that allow the study of relativistic jet outflows at sub-parsec distances from the central supermassive black holes, with high signal to noise. We present the results from the first multi-epoch spectropolarimetric observations of Centaurus A at milliarcsecond resolu

Alexei Kaltchenko

This paper investigates the uncertainty of Generative Pre-trained Transformer (GPT) models in extracting mathematical equations from images of varying resolutions and converting them into LaTeX code. We employ concepts of entropy and mutual information to examine the recognition process and assess the model's uncertainty in this Optical Character Recognition

Onurcan Kaya, Luca Gabatel, Sebastiano Bellani, Fabrizio Barberis

The corrosion of metallic surfaces poses significant challenges across industries such as petroleum, energy, and biomedical sectors, leading to structural degradation, safety risks, and substantial maintenance costs. Traditional organic and metallic coatings provide some protection, but their limited durability and susceptibility to harsh environmental condi

Chi Tian, Ran Ding, Xiao-Xiao Kou

We introduce a new method designed for Bayesian inference of the angular power spectrum of the Gravitational Wave Background (GWB) anisotropy. This scheme works with time-series data and can optionally incorporate the cross-correlations between the GWB anisotropy and other cosmological tracers, enhancing the significance of Bayesian inference. We employ the

Hamzah A. A. M. Qaid, Bo Zhang, Dan Li, See-Kiong Ng

Large language models (LLMs) are effective at capturing complex, valuable conceptual representations from textual data for a wide range of real-world applications. However, in fields like Intelligent Fault Diagnosis (IFD), incorporating additional sensor data-such as vibration signals, temperature readings, and operational metrics-is essential but it is chal

Zhenzhong Cao, Chenyang Zhao, Qianyi Zhang, Jinzheng Guang

High-quality reconstruction is crucial for dense SLAM. Recent popular approaches utilize 3D Gaussian Splatting (3D GS) techniques for RGB, depth, and semantic reconstruction of scenes. However, these methods often overlook issues of detail and consistency in different parts of the scene. To address this, we propose RGBDS-SLAM, a RGB-D semantic dense SLAM sys

Junyi Xie

Let $X$ be a smooth irreducible projective variety over a field $\mathbf{k}$ of dimension $d.$ Let $\tau: \mathbb{Q}_l\to \mathbb{C}$ be any field embedding. Let $f: X\to X$ be a surjective endomorphism. We show that for every $i=0,\dots,2d$, the spectral radius of $f^*$ on the numerical group $N^i(X)\otimes \mathbb{R}$ and on the $l$-adic cohomology group $

Ling Huang, Yucheng Xing, Qika Lin, Su Ruan

Multimodal survival analysis aims to combine heterogeneous data sources (e.g., clinical, imaging, text, genomics) to improve the prediction quality of survival outcomes. However, this task is particularly challenging due to high heterogeneity and noise across data sources, which vary in structure, distribution, and context. Additionally, the ground truth is

Kathrin Grunthal, Rowina S. Nathan, Eric Thrane, David J. Champion

In an accompanying publication, the MeerKAT Pulsar Timing Array (MPTA) collaboration reports tentative evidence for the presence of a stochastic gravitational-wave background, following observations of similar signals from the European and Indian Pulsar Timing Arrays, NANOGrav, the Parkes Pulsar Timing Array and the Chinese Pulsar Timing Array. If such a gra

Qiyu Zhuang, Xinyue Shi, Shuang Liu, Wei Lu

The widespread adoption of database middleware for supporting distributed transaction processing is prevalent in numerous applications, with heterogeneous data sources deployed across national and international boundaries. However, transaction processing performance significantly drops due to the high network latency between the middleware and data sources a

Yuma Toji, Jun Takahashi, Vwani Roychowdhury, Hideyuki Miyahara

Several power-law critical properties involving different statistics in natural languages -- reminiscent of scaling properties of physical systems at or near phase transitions -- have been documented for decades. The recent rise of large language models has added further evidence and excitement by providing intriguing similarities with notions in physics suc

Xiaoyang Shen, Chonghao Wang, Xiaodong Hu, Ruiping Guo

The discovery of fractional Chern insulators (FCIs) unlocks exciting opportunities to explore emergent physical excitations arising from topological and geometric effects in novel phases of quantum matter. Here we investigate the intraband neutral excitations, namely magnetorotons, in moir\'e FCIs within twisted $\rm{MoTe}_2$ by applying the Girvin, MacDonal

G. Y. Karapetyan, M. V. Ochkurov, V. E. Kaydashev

We theoretically propose a new approach to in-situ monitor the altered states of pixels in VO2 based active THz amplitude metagrating by using surface acoustic waves (SAWs) generated in LiNbO3 substrate. A single broadband RF response of the SAW device consists of N narrowband frequency channels which code the feedback information on the current pixels state

Antoine Prouff

The smoothing effect states that solutions to the Schr{\"o}dinger equation in the Euclidean space have, for almost-every time, a local-in-space improved regularity (gain of half a derivative in Sobolev spaces). In this note, we show that, for the Schr{\"o}dinger equation with a sub-quadratic confining potential, the smoothing effect is equivalent to an escap

Zhewen Pan, Yifan Zhang

Existing identification and estimation methods for semiparametric sample selection models rely heavily on exclusion restrictions. However, it is difficult in practice to find a credible excluded variable that has a correlation with selection but no correlation with the outcome. In this paper, we establish a new identification result for a semiparametric samp

Songjie Xie, Hengtao He, Shenghui Song, Jun Zhang

In response to the practical demands of the ``right to be forgotten" and the removal of undesired data, machine unlearning emerges as an essential technique to remove the learned knowledge of a fraction of data points from trained models. However, existing methods suffer from limitations such as insufficient methodological support, high computational complex

Zhi-zhong Xing

A brief and personal overview of some theoretical aspects of lepton flavor physics is presented, with a focus on the canonical seesaw mechanism and Majorana nature of massive neutrinos.

Dingshi Li, Ran Li, Tianhao Zeng

We study the long-time behavior of non-autonomous stochastic FitzHugh-Nagumo systems on thin domains. As the $(n+ 1)$-dimensional thin domains collapses onto an n-dimensional domain, an n-dimensional limiting FitzHugh-Nagumo system is derived. This n-dimensional limiting system encodes the defining geometry of the $(n+1)$-dimensional system. To justify this

Aashrita Mangu, Benjamin Westbrook, Shawn Beckman, Lance Corbett

The Simons Observatory (SO) is a cosmic microwave background (CMB) experiment located in the Atacama Desert in Chile that will make precise temperature and polarization measurements over six spectral bands ranging from 27 to 285 GHz. Three small aperture telescopes (SATs) and one large aperture telescope (LAT) will house $\sim$60,000 detectors and cover angu

Wenxin Su, Song Tang, Xiaofeng Liu, Xiaojing Yi

Domain shift (the difference between source and target domains) poses a significant challenge in clinical applications, e.g., Diabetic Retinopathy (DR) grading. Despite considering certain clinical requirements, like source data privacy, conventional transfer methods are predominantly model-centered and often struggle to prevent model-targeted attacks. In th

Yi Liao, Yongsheng Gao, Weichuan Zhang

In this paper, we present a Neuron Abandoning Attention Flow (NAFlow) method to address the open problem of visually explaining the attention evolution dynamics inside CNNs when making their classification decisions. A novel cascading neuron abandoning back-propagation algorithm is designed to trace neurons in all layers of a CNN that involve in making its p

M. V. Yashina, A. G. Tatashev

A multi-channel queueing system is considered. The arriving requests differ in their type. Requests of each type arrive according to a Poisson process. The number of channels required for service with the rate equal to 1 depends of the request type. If a request is serviced with the rate equal to 1, then, by definition, the length of the request equals to th

Rashmita Jena, S. K. Biswal, Padmalaya Dash, R. N. Panda

We include the $\Delta$-isobars in the equation of state (EOS) of neutron star (NS) and study its effects with various parameter sets of the RMF model. We compare our results with the NS's constraints from the mass-radius measurement of PSR J0348+0432, PSR J1614-2230, PSR J0030+0451, PSR J0740+6620, PSR J0952-0607, and tidal deformability of GW170817. We cal

Aashrita Mangu, Lance Corbett, Sanah Bhimani, Fred Carl

The Simons Observatory (SO) is a cosmic microwave background (CMB) survey experiment located in the Atacama Desert in Chile at an elevation of 5200 meters, nominally consisting of an array of three 0.42-meter small aperture telescopes (SATs) and one 6-meter large aperture telescope (LAT). SO will make accurate measurements of the CMB temperature and polariza

Gongfan Fang, Kunjun Li, Xinyin Ma, Xinchao Wang

Diffusion Transformers have demonstrated remarkable capabilities in image generation but often come with excessive parameterization, resulting in considerable inference overhead in real-world applications. In this work, we present TinyFusion, a depth pruning method designed to remove redundant layers from diffusion transformers via end-to-end learning. The c

Yuto Nishiwaki, Toru Utsunomiya, Shu Kurokawa, Takashi Ichii

The platinum/iridium (Pt/Ir) alloy tip for scanning probe microscopy (SPM) was fabricated by amplitude-modulated alternating-current (AC) electropolishing. The clean tips with a radius of curvature less than 100 nm were reproducibly obtained by applying the sinusoidal voltage in the frequency ($f_0$) of $900\ \mathrm{Hz} \leq f_0 \leq 1500\ \mathrm{Hz}$ with

Chenyang Zhu, Kai Li, Yue Ma, Longxiang Tang

Recent advances in Customized Concept Swapping (CCS) enable a text-to-image model to swap a concept in the source image with a customized target concept. However, the existing methods still face the challenges of inconsistency and inefficiency. They struggle to maintain consistency in both the foreground and background during concept swapping, especially whe

Xinzhe Shen, Jiale Luo, Hao Wang, Mingyi Liu

In the rapidly evolving digital business landscape, organizations increasingly need to collaborate across boundaries to achieve complex business objectives, requiring both efficient process coordination and flexible decision-making capabilities. Traditional collaboration approaches face significant challenges in transparency, trust, and decision flexibility,

Bei Liu, Yanmin Qian

Recent speaker verification (SV) systems have shown a trend toward adopting deeper speaker embedding extractors. Although deeper and larger neural networks can significantly improve performance, their substantial memory requirements hinder training on consumer GPUs. In this paper, we explore a memory-efficient training strategy for deep speaker embedding lea

Zhaodong Shi, Bing Liu, Rui-zhi Yang

Understanding how cosmic rays (CRs) propagate within the giant molecular clouds (GMCs) is critical for studying the dynamics and chemical processes inside the clouds. The flux of low-energy CRs inside the dense cores of GMCs strongly affects the heating and ionization of the gases and further influences the star-forming process. We analytically calculated th

Arnav Kharbanda, Advait Chandorkar

Ensemble learning has proven effective in improving predictive performance and estimating uncertainty in neural networks. However, conventional ensemble methods often suffer from redundant parameter usage and computational inefficiencies due to entirely independent network training. To address these challenges, we propose the Divergent Ensemble Network (DEN)

Fangming Zhao, Nikolaos Pappas, Meng Zhang, Howard H. Yang

We study the age of information (AoI) in a random access network consisting of multiple source-destination pairs, where each source node is empowered by energy harvesting capability. Every source node transmits a sequence of data packets to its destination using only the harvested energy. Each data packet is encoded with finite-length codewords, characterizi

Jiaxing Zhang, Luosong Guo, Kun Zhu, Houming Qiu

3D semantic maps have played an increasingly important role in high-precision robot localization and scene understanding. However, real-time construction of semantic maps requires mobile edge devices with extremely high computing power, which are expensive and limit the widespread application of semantic mapping. In order to address this limitation, inspired

Bang-Xian Han, Deng-Yu Liu, Zhuo-Nan Zhu

We study the Wasserstein barycenter problem in the setting of non-compact, non-smooth extended metric measure spaces. We introduce a couple of new concepts and obtain the existence, uniqueness, absolute continuity of the Wasserstein barycenter, and prove Jensen's inequality in an abstract framework. This generalized several results on Euclidean space, Rieman

Kurukulasooriya Fernando ana Gianluca Demartini

Recent advancements of generative LLMs (Large Language Models) have exhibited human-like language capabilities but have shown a lack of domain-specific understanding. Therefore, the research community has started the development of domain-specific LLMs for many domains. In this work we focus on discussing how to build mining domain-specific LLMs, as the glob

Jin Xi Chen, Jingge Feng

This paper is devoted to the study of two classes of operators related to disjointly weakly compact sets, which we call $DW$-DP operators and $DW$-limited operators, respectively. They carry disjointly weakly compact subsets of a Banach lattice onto Dunford-Pettis sets and limited sets, respectively. We show that $DW$-DP (resp. $DW$-limited) operators are pr

Gokberk Yaylali, Dionysios S. Kalogerias

Modern wireless communication systems necessitate the development of cost-effective resource allocation strategies, while ensuring maximal system performance. While commonly realizable via efficient waterfilling schemes, ergodic-optimal policies often exhibit instantaneous resource constraint fluctuations as a result of fading variability, violating prescrib

Jia Guo, Longxu Dou, Guangtao Zeng, Stanley Kok

In this paper, we introduce SailCompass, a reproducible and robust evaluation benchmark for assessing Large Language Models (LLMs) on Southeast Asian Languages (SEA). SailCompass encompasses three main SEA languages, eight primary tasks including 14 datasets covering three task types (generation, multiple-choice questions, and classification). To improve the

Kabir Belgikar, Vitaly Bergelson, Gabriel Black, David Kruzel

We apply the methods of ergodic theory to both simplify and significantly extend some classical results due to Stewart, Tijdeman, and Ruzsa. One of the notable features of our approach is the utilization of pointwise ergodic theory.

Timothy Buttsworth, Liam Hodgkinson

We prove existence of a non-round Einstein metric $g$ on $S^{12}$ that is invariant under the usual cohomogeneity one action of $\mathsf{O}(3)\times\mathsf{O}(10)$ on $S^{12}\subset \mathbb{R}^{13}= \mathbb{R}^3\oplus \mathbb{R}^{10}$. The proof involves using several rigorous numerical analysis techniques to produce a Riemannian metric $\hat{g}$ which appro

Bin-Han Lu, Peng Wang, Qing-Song Li, Yu-Chun Wu

Optimizing the frequency configuration of qubits and quantum gates in superconducting quantum chips presents a complex NP-complete optimization challenge. This process is critical for enabling practical control while minimizing decoherence and suppressing significant crosstalk. In this paper, we propose a neural network-based frequency configuration approach

Aayush Kumar Tyagi, Vaibhav Mishra, Ashok Tiwari, Lalita Mehra

Celiac disease is an autoimmune disorder triggered by the consumption of gluten. It causes damage to the villi, the finger-like projections in the small intestine that are responsible for nutrient absorption. Additionally, the crypts, which form the base of the villi, are also affected, impairing the regenerative process. The deterioration in villi length, c

Colby Fronk, Linda Petzold

Stiff ordinary differential equations (ODEs) are common in many science and engineering fields, but standard neural ODE approaches struggle to accurately learn these stiff systems, posing a significant barrier to widespread adoption of neural ODEs. In our earlier work, we addressed this challenge by utilizing single-step implicit methods for solving stiff ne

Sara Sadat Parhizgar, Zahra Ardeshiri

This study presented a novel method to prepare hexagonal iron flakes with hexagonal close-packed structures (hcp) on the copper oxide sublayers. Also, it demonstrated that the size and structure of iron grains are functions of sublayer features. The sublayer was prepared by sputtering copper on the glass substrate and annealing it. As the annealing temperatu

Tao Tang, Hong Liu, Yingxuan You, Ti Wang

Human Mesh Reconstruction (HMR) from monocular video plays an important role in human-robot interaction and collaboration. However, existing video-based human mesh reconstruction methods face a trade-off between accurate reconstruction and smooth motion. These methods design networks based on either RNNs or attention mechanisms to extract local temporal corr

Kai Zhang, Y. X. Niu, Yang Meng, Hong-Wu Zhao

The intensive study of non-collinear magnets promotes an urgent demand for the quantitative characterization of the non-collinear magnetic structures, which host numerous exotic phenomena. Here we systematically study the non-collinear magnetic structure of an artificial ferrimagnetic multilayer. The AMR measurements reveal two distinct twisted states whose

Yutaro Kaijima, Yudai Yamamoto

We study moderate toric resolutions introduced by Ch\'avez-Mart\'inez, Duarte and Yasuda, which appears in the relation between F-blowups and essential divisors. In particular, we address the problems, when it exists, and if it is the case, what properties it has in conjunction with the birational geometry and Hilbert basis resolutions, mainly in dimension t

Takaaki Fujita, Florentin Smarandache

Hypergraphs generalize classical graphs by allowing a single edge to connect multiple vertices, providing a natural language for modeling higher-order interactions. Superhypergraphs extend this paradigm further by accommodating nested, set-valued entities and relations, enabling the representation of hierarchical, multi-level structures beyond the expressive

Zijian Chen, Tingzhu Chen, Wenjun Zhang, Guangtao Zhai

We introduce OBI-Bench, a holistic benchmark crafted to systematically evaluate large multi-modal models (LMMs) on whole-process oracle bone inscriptions (OBI) processing tasks demanding expert-level domain knowledge and deliberate cognition. OBI-Bench includes 5,523 meticulously collected diverse-sourced images, covering five key domain problems: recognitio

Daiheng Zhang, Chengyue Gong, Qiang Liu

Deep generative models have achieved tremendous success in structure-based drug design in recent years, especially for generating 3D ligand molecules that bind to specific protein pocket. Notably, diffusion models have transformed ligand generation by providing exceptional quality and creativity. However, traditional diffusion models are restricted by their

Chen-Yu Liu, Samuel Yen-Chi Chen, Kuan-Cheng Chen, Wei-Jia Huang

In this study, the Quantum-Train Quantum Fast Weight Programmer (QT-QFWP) framework is proposed, which facilitates the efficient and scalable programming of variational quantum circuits (VQCs) by leveraging quantum-driven parameter updates for the classical slow programmer that controls the fast programmer VQC model. This approach offers a significant advant

Yi Kuang, Jiang Li, Songsong Li, Chaoping Xing

Coded Distributed Matrix Multiplication (CDMM) is a distributed matrix multiplication (DMM) for large-scale matrices through a coding scheme such that any $R$ worker node among all $N$ worker nodes can recover the final product, where $N$ corresponds to the length of the code and $R\leq N$ is called the recovery threshold. The state-of-art CDMM schemes, such

Yifan Xu, Xue Jiang, Dongrui Wu

Emotion recognition is a critical component of affective computing. Training accurate machine learning models for emotion recognition typically requires a large amount of labeled data. Due to the subtleness and complexity of emotions, multiple evaluators are usually needed for each affective sample to obtain its ground-truth label, which is expensive. To sav

Ed Bennett, Deog Ki Hong, Ho Hsiao, Jong-Wan Lee

We report the results of an extensive numerical study of the $Sp(4)$ lattice gauge theory with three (Dirac) flavors of fermion in the two-index antisymmetric representation. In the presence of (degenerate) fermion masses, the theory has an enhanced global $SU(6)$ symmetry, broken explicitly and spontaneously to its $SO(6)$ subgroup. This symmetry breaking p

Shufan Li, Konstantinos Kallidromitis, Akash Gokul, Zichun Liao

We introduce OmniFlow, a novel generative model designed for any-to-any generation tasks such as text-to-image, text-to-audio, and audio-to-image synthesis. OmniFlow advances the rectified flow (RF) framework used in text-to-image models to handle the joint distribution of multiple modalities. It outperforms previous any-to-any models on a wide range of task

Hanyao Guo, Yunhai Han, Harish Ravichandar

When learning stable linear dynamical systems from data, three important properties are desirable: i) predictive accuracy, ii) verifiable stability, and iii) computational efficiency. Unconstrained minimization of prediction errors leads to high accuracy and efficiency but cannot guarantee stability. Existing methods to enforce stability often preserve accur