December 2024 arXiv papers — page 118

Cassie Huang, Li Zhang

Large Language Models have been found to create plans that are neither executable nor verifiable in grounded environments. An emerging line of work demonstrates success in using the LLM as a formalizer to generate a formal representation of the planning domain in some language, such as Planning Domain Definition Language (PDDL). This formal representation ca

Moonyoung Lee, Uksang Yoo, Jean Oh, Jeffrey Ichnowski

In cluttered environments where visual sensors encounter heavy occlusion, such as in agricultural settings, tactile signals can provide crucial spatial information for the robot to locate rigid objects and maneuver around them. We introduce SonicBoom, a holistic hardware and learning pipeline that enables contact localization through an array of contact micr

Ye Zhang, Linyue Chu, Letian Xu, Kangtong Mo

In this paper, we present an advanced strategy for the coordinated control of a multi-agent aerospace system, utilizing Deep Neural Networks (DNNs) within a reinforcement learning framework. Our approach centers on optimizing autonomous task assignment to enhance the system's operational efficiency in object relocation tasks, framed as an aerospace-oriented

Tobias Barker, Hideyuki Miura, Jin Takahashi

We prove the first classification of blow-up rates of the critical norm for solutions of the energy supercritical nonlinear heat equation, without any assumptions such as radial symmetry or sign conditions. Moreover, the blow-up rates we obtain are optimal, for solutions that blow-up with bounded $L^{n(p-1)/2,\infty}(\mathbf{R}^n)$-norm up to the blow-up tim

Chee Ng, Yuen Fung

Large Vision-Language Models (LVLMs) have demonstrated remarkable performance across a wide range of multimodal tasks. However, fine-tuning these models for domain-specific applications remains a computationally intensive challenge. This paper introduces State Space Memory Integration (SSMI), a novel approach for efficient fine-tuning of LVLMs. By integratin

Jianhua Zhang, Yi Gao, Ruyu Liu, Xu Cheng

Knowledge distillation (KD) is a model compression technique that transfers knowledge from a large teacher model to a smaller student model to enhance its performance. Existing methods often assume that the student model is inherently inferior to the teacher model. However, we identify that the fundamental issue affecting student performance is the bias tran

He-bin Zhang, Yuanjiang Tang, Yong-Chun Liu

Photon correlation is at the heart of quantum optics and has important applications in quantum technologies. Here we propose a universally applicable mechanism that can generate the superbunching light with ultrastrong second-order and higher-order correlations hitherto unreachable. This mechanism arises from the combined effect of electron shelving and time

Qingzhao Zhong, Yanxi Hou

$L_p$-quantile has recently been receiving growing attention in risk management since it has desirable properties as a risk measure and is a generalization of two widely applied risk measures, Value-at-Risk and Expectile. The statistical methodology for $L_p$-quantile is not only feasible but also straightforward to implement as it represents a specific form

Artidoro Pagnoni, Ram Pasunuru, Pedro Rodriguez, John Nguyen

We introduce the Byte Latent Transformer (BLT), a new byte-level LLM architecture that, for the first time, matches tokenization-based LLM performance at scale with significant improvements in inference efficiency and robustness. BLT encodes bytes into dynamically sized patches, which serve as the primary units of computation. Patches are segmented based on

Tianwei Chang

Existing studies have shown that the monetization of silver in the Ming Dynasty effectively promoted the prosperity of trade in the Ming Dynasty, while the prices of labor, handicraft products and grain were long suppressed by the deformed economic structure. With the expansion of silver application, the fluctuation of silver supply and demand exacerbated th

Liu Jing, Amirul Rahman

Semantic location prediction from multimodal social media posts is a critical task with applications in personalized services and human mobility analysis. This paper introduces \textit{Contextualized Vision-Language Alignment (CoVLA)}, a discriminative framework designed to address the challenges of contextual ambiguity and modality discrepancy inherent in t

Mingsheng Ying

In this paper, we present a Hoare-style logic for reasoning about quantum programs with classical variables. Our approach offers several improvements over previous work: (1) Enhanced expressivity of the programming language: Our logic applies to quantum programs with classical variables that incorporate quantum arrays and parameterised quantum gates, which h

Lizhi Bai, Chunqi Tian, Jun Yang, Siyu Zhang

3D Gaussian Splatting has emerged as a promising technique for high-quality 3D rendering, leading to increasing interest in integrating 3DGS into realism SLAM systems. However, existing methods face challenges such as Gaussian primitives redundancy, forgetting problem during continuous optimization, and difficulty in initializing primitives in monocular case

Zi Haur Pang, Yahui Fu, Divesh Lala, Mikey Elmers

This paper introduces the human-like embodied AI interviewer which integrates android robots equipped with advanced conversational capabilities, including attentive listening, conversational repairs, and user fluency adaptation. Moreover, it can analyze and present results post-interview. We conducted a real-world case study at SIGDIAL 2024 with 42 participa

Yuting Sun, Shixu Liu, Xin-Gao Gong, Ji-Hui Yang

Searching for ambient-pressure conventional superconductors with critical temperatures (TC) higher than 40 K is a key challenge in the field of high-temperature superconductivity, mainly due to lack of efficient and effective models to estimate TC of potential systems. In this work, we propose a simplified model to estimate the dimensionless electron-phonon

Weizhang Huang, Zhuoran Wang

Finite element discretization of Stokes problems can result in singular, inconsistent saddle point linear algebraic systems. This inconsistency can cause many iterative methods to fail to converge. In this work, we consider the lowest-order weak Galerkin finite element method to discretize Stokes flow problems and study a consistency enforcement by modifying

Angel Yanguas-Gil, Matthew T. Dearing, Jeffrey W. Elam, Jessica C. Jones

In this work we introduce an open-ended question benchmark, ALDbench, to evaluate the performance of large language models (LLMs) in materials synthesis, and in particular in the field of atomic layer deposition, a thin film growth technique used in energy applications and microelectronics. Our benchmark comprises questions with a level of difficulty ranging

M. Zhang, K. Walker, A. Sullivan, C. Power

Observations of the intracluster medium (ICM) in the outskirts of galaxy clusters reveal shocks associated with gas accretion from the cosmic web. Previous work based on non-radiative cosmological hydrodynamical simulations have defined the shock radius, $r_\text{shock}$, using the ICM entropy, $K \propto T/{n_\mathrm{e}}^{2/3}$, where $T$ and $n_\text{e}$ a

Jun-Ren Luo, Ti-Jun Xiao

We study the asymptotic behavior of compressible isentropic flow when the initial mass is finite and the friction varies with time, which is modeled by the compressible Euler equation with time-dependent damping. In this paper, we obtain the best $L^1$-convergence rates to date, for any $\gamma\in(1,+\infty)$ and $\nu\in[0,1)$. Here, $\gamma$ is the adiabati

Aritra Lahiri, Sang-Jun Choi, Björn Trauzettel

The current-voltage characteristics of Josephson junctions exhibit a subharmonic gap structure (SGS), denoting jumps at specific voltages. While the prevalent multiple Andreev reflection theory matches the experimentally observed SGS, it is limited to a DC \emph{voltage} bias. For a DC \emph{current} bias, existing theories are restricted to low-transparency

Xiaobo Ma, Hyunsoo Noh, Ryan Hatch, James Tokishi

Urban transportation networks are vital for the efficient movement of people and goods, necessitating effective traffic management and planning. An integral part of traffic management is understanding the turning movement counts (TMCs) at intersections, Accurate TMCs at intersections are crucial for traffic signal control, congestion mitigation, and road saf

Peng Tao, Kazuyuki Aihara, Luonan Chen

Graph neural networks (GNNs) with unsupervised learning can solve large-scale combinatorial optimization problems (COPs) with efficient time complexity, making them versatile for various applications. However, since this method maps the combinatorial optimization problem to the training process of a graph neural network, and the current mainstream backpropag

Abraham Atsiwo

The Efficient Market Hypothesis (EMH) highlights the essence of financial news in stock price movement. Financial news comes in the form of corporate announcements, news titles, and other forms of digital text. The generation of insights from financial news can be done with sentiment analysis. General-purpose language models are too general for sentiment ana

Charles Xu, Qiyang Li, Jianlan Luo, Sergey Levine

Recent advances in robotic foundation models have enabled the development of generalist policies that can adapt to diverse tasks. While these models show impressive flexibility, their performance heavily depends on the quality of their training data. In this work, we propose Reinforcement Learning Distilled Generalists (RLDG), a method that leverages reinfor

Haolin Pan, Han Li, Jixiang Huang, Zheng Liu

The Magneto-Optical Kerr Effect (MOKE) is a fundamental tool in magnetometry, pivotal for advancing research in optics, magnetism, and spintronics as a direct probe of magnetization. Traditional MOKE measurements primarily detect the magnetization components parallel to the Poynting vector, which can only access the magnitude but not the direction of the ort

Hongjie Wang, Chih-Yao Ma, Yen-Cheng Liu, Ji Hou

Text-to-video generation enhances content creation but is highly computationally intensive: The computational cost of Diffusion Transformers (DiTs) scales quadratically in the number of pixels. This makes minute-length video generation extremely expensive, limiting most existing models to generating videos of only 10-20 seconds length. We propose a Linear-co

Yan Jiang, Jiang-Tao Li, Qing-Hua Tan, Li Ji

Molecular gas, as the fuel for star formation, and its relationship with atomic gas are crucial for understanding how galaxies regulate their star forming (SF) activities. We conducted IRAM 30m observations of 23 nearby spiral galaxies from the CHANG-ES project to investigatet the distribution of molecular gas and the Kennicutt-Schmidt law. Combining these r

L. Meng, X. Jiang, J. Huang, W. Li

A brain-computer interface (BCI) establishes a direct communication pathway between the brain and an external device. Electroencephalogram (EEG) is the most popular input signal in BCIs, due to its convenience and low cost. Most research on EEG-based BCIs focuses on the accurate decoding of EEG signals; however, EEG signals also contain rich private informat

Youran Sun, Yihua Liu, Yi-Shuai Niu

Difference-of-Convex Algorithm (DCA) is a well-known nonconvex optimization algorithm for minimizing a nonconvex function that can be expressed as the difference of two convex ones. Many famous existing optimization algorithms, such as SGD and proximal point methods, can be viewed as special DCAs with specific DC decompositions, making it a powerful framewor

Dominic Keehan, Arkadii Slinko

Inspecting known maximal Condorcet domains on 4 variables classified by Tobias Dittrich we find that 9 out of 18 of them are created using a certain composition of smaller domains. In this paper we describe this composition. We give sufficient conditions for the composition of two Condorcet domain to be a maximal Condorcet domain.

Luca Marchetti, Hassan Mehmood, Viqar Husain

We present a Group Field Theory (GFT) quantization of the Husain-Kucha\v{r} (HK) model formulated as a non-interacting GFT. We demonstrate that the path-integral formulation of this HK-GFT provides a complete spinfoam model and a unique Fock representation that describes the quantum three-geometries of the HK model. These results provide a link to the canoni

Xiaobin Sun, Jian Wang, Yingchao Xie

The purpose of this paper is to establish asymptotic behaviors of time-inhomogeneous multi-scale stochastic differential equations (SDEs). To achieve them, we analyze the evolution system of measures for time-inhomogeneous Markov semigroups, and investigate regular properties of nonautonomous Poisson equations. The strong and the weak averaging principle for

Guangliang Pan, David K. Y. Yau, Bo Zhou, Qihui Wu

Spectrum prediction is considered to be a promising technology that enhances spectrum efficiency by assisting dynamic spectrum access (DSA) in cognitive radio networks (CRN). Nonetheless, the highly nonlinear nature of spectrum data across time, frequency, and space domains, coupled with the intricate spectrum usage patterns, poses challenges for accurate sp

Keiko I. Nagao

We investigate the detection of boosted dark matter in a two component dark matter model with the hidden gauge $U(1)_D$ symmetry. The model introduces heavy and light fermionic dark matter components, where the heavy dark matter annihilation in the Galactic center boosts the light dark matter. Directional direct detection experiments, such as NEWSdm, are sui

Jing Sun, Qiangqiang Yuan, Huanfeng Shen, Jie Li

The objective of image super-resolution is to reconstruct a high-resolution (HR) image with the prior knowledge from one or several low-resolution (LR) images. However, in the real world, due to the limited complementary information, the performance of both single-frame and multi-frame super-resolution reconstruction degrades rapidly as the magnification inc

Jun Lu, Sanjib Basu

Enhancing the external validity of trial results is essential for their applicability to real-world populations. However, violations of the positivity assumption can limit both the generalizability and transportability of findings. To address positivity violations in estimating the average treatment effect for a target population, we propose a framework that

Hanzhong Guo, Shen Nie, Chao Du, Tianyu Pang

Personalized generative diffusion models, capable of synthesizing highly realistic images based on a few reference portraits, may pose substantial social, ethical, and legal risks via identity replication. Existing defense mechanisms rely on computationally intensive adversarial perturbations tailored to individual images, rendering them impractical for real

Minh Khoa Le, Kien Do, Truyen Tran

In this study, we address causal inference when only observational data and a valid causal ordering from the causal graph are available. We introduce a set of flow models that can recover component-wise, invertible transformation of exogenous variables. Our flow-based methods offer flexible model design while maintaining causal consistency regardless of the

Yujin Choi, Jinseong Park, Junyoung Byun, Jaewook Lee

Programmatically generated synthetic data has been used in differential private training for classification to enhance performance without privacy leakage. However, as the synthetic data is generated from a random process, the distribution of real data and the synthetic data are distinguishable and difficult to transfer. Therefore, the model trained with the

Jing Sun, Huanfeng Shen, Qiangqiang Yuan, Liangpei Zhang

Image super-resolution (SR) is an effective way to enhance the spatial resolution and detail information of remote sensing images, to obtain a superior visual quality. As SR is severely ill-conditioned, effective image priors are necessary to regularize the solution space and generate the corresponding high-resolution (HR) image. In this paper, we propose a

Jianheng Ling, Pratik Worah, Yawen Wang, Yunchuan Kong

Scheduling virtual machines (VMs) on hosts in cloud data centers dictates efficiency and is an NP-hard problem with incomplete information. Prior work improved VM scheduling with predicted VM lifetimes. Our work further improves lifetime-aware scheduling using repredictions with lifetime distributions versus one-shot prediction. Our approach repredicts and a

Wentao Yu, Hengtao He, Shenghui Song, Jun Zhang

This study explored the transformative potential of artificial intelligence (AI) in addressing the challenges posed by terahertz ultra-massive multiple-input multiple-output (UM-MIMO) systems. It begins by outlining the characteristics of terahertz UM-MIMO systems and identifies three primary challenges for transceiver design: computational complexity, model

Tao Li, Chenhui Cui, Zhengdong Huang, Rubing Huang

As one of the most popular software applications, a web application is a program accessible through the web that dynamically generates content based on user interactions or contextual data; examples include online shopping platforms, social networking sites, and financial services. Web applications operate in diverse environments and leverage web technologie

Ila Kumar, Craig Ferguson, Jiayi Wu, Rosalind W Picard

Approximately 400,000 youth in the US are living in foster care due to experiences with abuse or neglect at home. For multiple reasons, these youth often don't receive adequate social support from those around them. Despite technology's potential, very little work has explored how these tools can provide more support to foster-involved youth. To begin to fil

Ullas Chandran S. V., Sandi Klavžar, Neethu P. K., James Tuite

The general position problem in graph theory asks for the number of vertices in a largest set $S$ of vertices of a graph $G$ such that no shortest path of $G$ contains more than two vertices of $S$. The analogous monophonic position problem is obtained from the general position problem by replacing ``shortest path'' by ``induced path.'' In this paper the mon

Rong Du, David O. Cook, Soumyadeep Bhattacharjee, Shrinivas R. Kulkarni

We investigate the efficacy of a systematic planetary nebula (PN) search in the Census of the Local Universe (CLU) narrowband (H$\alpha$) survey that covers a considerably larger sky region of above declination $-20^\circ$ than most previous surveys. Using PNe observed by the Isaac Newton Telescope Photometric H$\alpha$ Survey (IPHAS) as validation, we are a

Miguel Costa, Manuel Marques, Carlos Lima Azevedo, Felix Wilhelm Siebert

Cycling is critical for cities to transition to more sustainable transport modes. Yet, safety concerns remain a critical deterrent for individuals to cycle. If individuals perceive an environment as unsafe for cycling, it is likely that they will prefer other means of transportation. Yet, capturing and understanding how individuals perceive cycling risk is c

Ila Kumar, Jocelyn Shen, Craig Ferguson, Rosalind W Picard

This paper explores the design, development and evaluation of a digital platform that aims to assist young people who have experienced trauma in understanding and expressing their emotions and fostering social connections. Integrating principles from expressive arts and narrative-based therapies, we collaborate with lived experts to iteratively design a nove

Justin C. Goodrich, Ryan Mahon, Joseph Hanrahan, Dennis Bollweg

Quantum imaging encompasses a broad range of methods that exploit the quantum properties of light to capture information about an object. One such approach involves using a two-photon quantum state, where only one photon interacts with the object being imaged while its entangled partner carries spatial or temporal information. To implement this technique, it

Rutuja Gurav, Isaac Kelly, Pooyan Goodarzi, Anamaria Effler

Gravitational-wave observatories like LIGO are large-scale, terrestrial instruments housed in infrastructure that spans a multi-kilometer geographic area and which must be actively controlled to maintain operational stability for long observation periods. Despite exquisite seismic isolation, they remain susceptible to seismic noise and other terrestrial dist

Manpreet Kaur, Raj Singh, Sandeep Kumar

As the demand for internet of things (IoT) and device-to-device (D2D) applications in next generation communication systems increases, we are confronted with a challenge of spectrum scarcity. One promising solution to this problem is cognitive radio network (CRN), where the key element is the spectrum - a valuable and sharable natural resource that should no

Chudamani Poudyal, Gokarna R. Aryal, Keshav Pokhrel

Statistical modeling of claim severity distributions is essential in insurance and risk management, where achieving a balance between robustness and efficiency in parameter estimation is critical against model contaminations. Two \( L \)-estimators, the method of trimmed moments (MTM) and the method of winsorized moments (MWM), are commonly used in the liter

Quang-Anh N. D., Minh-Duc Pham, Thai Kim Dinh

In the evolving landscape of customer service within the digital economy, traditional methods of service quality assessment have shown significant limitations, this research proposes a novel deep-learning approach to service quality assessment, focusing on the Vietnamese service sector. By leveraging a multi-modal pipeline that transcends traditional evaluat

Xunnong Xu, Mengying Cao

Diffusion transformers enable flexible generative modeling for video. However, it is still technically challenging and computationally expensive to generate high-resolution videos with rich semantics and complex motion. Similar to languages, video data are also auto-regressive by nature, so it is counter-intuitive to use attention mechanism with bi-direction

Changqun Li, Chaofan Ding, Kexin Luan, Xinhan Di

Fine-tuning pre-trained large language models in a parameter-efficient manner is widely studied for its effectiveness and efficiency. LoRA is one of the most widely used methods, which assumes that the optimization process is essentially low dimensional. Although LoRA has demonstrated commendable performance, there remains a significant performance gap betwe

Jie Gao, Jing Hu, Lihang Liu, Yang Xue

The accurate prediction of antigen-antibody structures is essential for advancing immunology and therapeutic development, as it helps elucidate molecular interactions that underlie immune responses. Despite recent progress with deep learning models like AlphaFold and RoseTTAFold, accurately modeling antigen-antibody complexes remains a challenge due to their

Tao Wang, Zhicong Tu, Yixing Ma, Yiheng Li

We present an experimental investigation into the generation of self-sustained and fast square oscillations from the TE mode of semiconductor VCSELs with delayed orthogonal polarization feedback. We find that the low frequency switching originates from the rotation of the TE and TM modes facilitated by a long time delay, but the fast oscillations are anchore

Nicole L. Thomas, Imogen H. Whittam, Catherine L. Hale, Leah K. Morabito

We present a qualitative comparison between the host and black hole properties of radio galaxies in the MeerKAT GigaHertz Tiered Extragalactic Exploration~(MIGHTEE) survey with the radio galaxy population in the SIMBA suite of cosmological hydrodynamical simulations. The MIGHTEE data includes a $\sim$1deg$^{2}$ pointing of the COSMOS field observed at 1.28GH

Ping Zhang, Yiru Wang

Digital transformation significantly impacts firm investment, financing, and value enhancement. A systematic investigation from the corporate finance perspective has not yet been formed. This paper combines bibliometric and content analysis methods to systematically review the evolutionary trend, status quo, hotspots and overall structure of research in digi

Jun Zheng, Jing Wang, Fuwei Zhao, Xujie Zhang

Video try-on stands as a promising area for its tremendous real-world potential. Previous research on video try-on has primarily focused on transferring product clothing images to videos with simple human poses, while performing poorly with complex movements. To better preserve clothing details, those approaches are armed with an additional garment encoder,

Vivekananda Bal, Moo Sun Hong, Jacqueline M. Wolfrum, Paul W. Barone

Crystallization of proteins, specifically proteins of medical relevance, is performed for various reasons such as to understand the protein structure and to design therapies. Obtaining kinetic constants in rate laws for nucleation and growth of advanced biotherapeutics such as capsids, an assembly of macromolecules, is challenging and essential to the design

Md Al Amin, Hemanth Tummala, Rushabh Shah, Indrajit Ray

Digital healthcare systems have revolutionized medical services, facilitating provider collaboration, enhancing diagnosis, and optimizing and improving treatments. They deliver superior quality, faster, reliable, and cost-effective services. Researchers are addressing pressing health challenges by integrating information technology, computing resources, and

Ahmadreza Eslaminia, Adrian Jackson, Beitong Tian, Avi Stern

Fused Deposition Modeling (FDM) is a widely used additive manufacturing (AM) technique valued for its flexibility and cost-efficiency, with applications in a variety of industries including healthcare and aerospace. Recent developments have made affordable FDM machines accessible and encouraged adoption among diverse users. However, the design, planning, and

Yingxu He, Zhuohan Liu, Shuo Sun, Bin Wang

We introduce MERaLiON-AudioLLM (Multimodal Empathetic Reasoning and Learning in One Network), the first speech-text model tailored for Singapore's multilingual and multicultural landscape. Developed under the National Large Language Models Funding Initiative, Singapore, MERaLiON-AudioLLM integrates advanced speech and text processing to address the diverse l

Xiaofeng Zhang, Fanshuo Zeng, Yihao Quan, Zheng Hui

Multimodal large language models have experienced rapid growth, and numerous different models have emerged. The interpretability of LVLMs remains an under-explored area. Especially when faced with more complex tasks such as chain-of-thought reasoning, its internal mechanisms still resemble a black box that is difficult to decipher. By studying the interactio

Edward A. Turner, Francisco Crespo, Josep Sardanyés, Nolbert Morales

Quasispecies theory provides the conceptual and theoretical bases for describing the dynamics of biological information of replicators subject to large mutation rates. This theory, initially conceived within the framework of prebiotic evolution, is also being used to investigate the evolutionary dynamics of RNA viruses and heterogeneous cancer cells populati

Nimesh Khandelwal, Amritanshu Manu, Shakti S. Gupta, Mangal Kothari

This paper presents a distributed inverse dynamics controller (DIDC) for quadruped robots that addresses the limitations of existing reactive controllers: simplified dynamical models, the inability to handle exact friction cone constraints, and the high computational requirements of whole-body controllers. Current methods either ignore friction constraints e

Qian Chen

As analogues of compact objects, solitons have attracted significant attention. We reveal that cylindrical Q-strings exhibit a dynamical instability to perturbations with wavelengths exceeding a threshold $\lambda>\lambda_{c}$. This instability can destroy the invariance in the cylindrical direction, as a generation mechanism for Q-balls, similar to the form

Jianhong Chen, Ying Ma, Xubo Yue

Traditionally, learning the structure of a Dynamic Bayesian Network has been centralized, requiring all data to be pooled in one location. However, in real-world scenarios, data are often distributed across multiple entities (e.g., companies, devices) that seek to collaboratively learn a Dynamic Bayesian Network while preserving data privacy and security. Mo

Steven Phillips, Naotsugu Tsuchiya

Conscious experience permeates our daily lives, yet general consensus on a theory of consciousness remains elusive. In the face of such difficulty, an alternative strategy is to address a more general (meta-level) version of the problem for insights into the original problem at hand. Category theory was developed for this purpose, i.e. as an axiomatic (meta-

Liqian You, Jianlong Zhou, Zhiwei Li, Fang Chen

With the rise of Internet of Things (IoT) technologies in smart homes and the integration of artificial intelligence (AI), ethical concerns have become increasingly significant. This paper explores the ethical implications of AI-driven detection technologies in smart homes using the User Requirements Notation (URN) framework. In this paper, we thoroughly con

Kai Yao, Zhaorui Tan, Tiandi Ye, Lichun Li

Offsite-tuning is a privacy-preserving method for tuning large language models (LLMs) by sharing a lossy compressed emulator from the LLM owners with data owners for downstream task tuning. This approach protects the privacy of both the model and data owners. However, current offsite tuning methods often suffer from adaptation degradation, high computational

Ivan Grytsenko, Sander van Haagen, Oleksiy Rybalko, Asher Jennings

We developed a tunnel diode oscillator and characterized its performance, highlighting its potential applications in the quantum state readout of electrons in semiconductors and electrons on liquid helium. This cryogenic microwave source demonstrates significant scalability potential for large-scale qubit readout systems due to its compact design and low pow

Branton DeMoss, Silvia Sapora, Jakob Foerster, Nick Hawes

We demonstrate the existence of a complexity phase transition in neural networks by studying the grokking phenomenon, where networks suddenly transition from memorization to generalization long after overfitting their training data. To characterize this phase transition, we introduce a theoretical framework for measuring complexity based on rate-distortion t

Stefano Fregonese, Mattia Bacca

In this paper we investigate the mechanical problem of piercing a soft solid body with a needle. This phenomenon is controlled by the critical condition of needle insertion. Needle insertion involves physical and geometrical nonlinearities and a complex failure mechanism. To overcome the complexity of the problem, we describe needle insertion as a sharp tran

Patrick Sutanto, Joan Santoso, Esther Irawati Setiawan, Aji Prasetya Wibawa

Multiple Choice Question Answering (MCQA) is an important problem with numerous real-world applications, such as medicine, law, and education. The high cost of building MCQA datasets makes few-shot learning pivotal in this domain. While Large Language Models (LLMs) can enable few-shot learning, their direct application in real-world scenarios is often hinder

Chen-Ming Chang, Jun-Jie Wei, Ke-Lai Meng, Song-Bo Zhang

The gravitational time delays of macro-lenses can be used to constrain the rest mass of the photon with high accuracy. Assuming a point-mass $+$ external shear lens model, we prove that an upper limit of the photon mass can be derived directly from two observables--the time delay $\Delta t$ and the leading-to-trailing flux ratio $R$ of strongly lensed fast r

Ming Gu, Zhuonan Zheng, Sheng Zhou, Meihan Liu

Graph Neural Networks (GNNs) have achieved great success but are often considered to be challenged by varying levels of homophily in graphs. Recent \textit{empirical} studies have surprisingly shown that homophilic GNNs can perform well across datasets of different homophily levels with proper hyperparameter tuning, but the underlying theory and effective ar

Dalei Yu, Xinyu Zhang, Hua Liang

Studying unified model averaging estimation for situations with complicated data structures, we propose a novel model averaging method based on cross-validation (MACV). MACV unifies a large class of new and existing model averaging estimators and covers a very general class of loss functions. Furthermore, to reduce the computational burden caused by the conv

Duncan Taylor, Melissa A. Humphries

A common task in forensic biology is to interpret and evaluate short tandem repeat DNA profiles. The first step in these interpretations is to assign a number of contributors to the profiles, a task that is most often performed manually by a scientist using their knowledge of DNA profile behaviour. Studies using constructed DNA profiles have shown that as DN

Rita Majumdar, Monojit Chatterjee, Rahul Marathe

We study the behavior of a single spin in the presence of a time-varying magnetic field utilizing Glauber dynamics. We engineer the system to function as an engine by changing the magnetic field according to specific protocols. Subsequently, we analyze the engine's performance using various protocols and stochastic thermodynamics to compute average values of

Dhawal Buaria, Alain Pumir

The universality of small scales, a cornerstone of turbulence, has been nominally confirmed for low-order mean-field statistics, such as the energy spectrum. However, small scales exhibit strong intermittency, exemplified by formation of extreme events which deviate anomalously from a mean-field description. Here, we investigate the universality of small sca

Lyudmila Grigoryeva, Hannah Lim Jing Ting, Juan-Pablo Ortega

Next-generation reservoir computing (NG-RC) has attracted much attention due to its excellent performance in spatio-temporal forecasting of complex systems and its ease of implementation. This paper shows that NG-RC can be encoded as a kernel ridge regression that makes training efficient and feasible even when the space of chosen polynomial features is very

Qibo Chen, Weizhong Jin, Jianyue Ge, Mengdi Liu

Recent research on universal object detection aims to introduce language in a SoTA closed-set detector and then generalize the open-set concepts by constructing large-scale (text-region) datasets for training. However, these methods face two main challenges: (i) how to efficiently use the prior information in the prompts to genericise objects and (ii) how to

William Diamond, Carl Ross, Herbert Moore

The production of 225Ac using either a proton or electron accelerator requires a target of 226Ra. Radium metal is difficult to work with and so a radium salt, such as radium carbonate, is preferred as a target material. Normally available as a powder, the average density of the powder is low and the thermal conductivity is poor, thus limiting the beam power

Yanbing Bai, Jinhua Su, Bin Qiao, Xiaoran Ma

Timely and accurate economic data is crucial for effective policymaking. Current challenges in data timeliness and spatial resolution can be addressed with advancements in multimodal sensing and distributed computing. We introduce Senseconomic, a scalable system for tracking economic dynamics via multimodal imagery and deep learning. Built on the Transformer

Keegan Boyle, Wenzhao Chen

We study symmetric crossing change operations for strongly invertible knots. Our main theorem is that the most natural notion of equivariant unknotting number is not additive under connected sum, in contrast with the longstanding conjecture that unknotting number is additive.

Qiyao Wang, Shiwen Ni, Huaren Liu, Shule Lu

As the capabilities of Large Language Models (LLMs) continue to advance, the field of patent processing has garnered increased attention within the natural language processing community. However, the majority of research has been concentrated on classification tasks, such as patent categorization and examination, or on short text generation tasks like patent

Zhijin Lyu, Yutong Jin, Sneha Das

Determining the robustness of deep learning models is an established and ongoing challenge within automated decision-making systems. With the advent and success of techniques that enable advanced deep learning (DL), these models are being used in widespread applications, including high-stake ones like healthcare, education, border-control. Therefore, it is c

Nobel Dhar, Bobin Deng, Md Romyull Islam, Kazi Fahim Ahmad Nasif

Deploying local AI models, such as Large Language Models (LLMs), to edge devices can substantially enhance devices' independent capabilities, alleviate the server's burden, and lower the response time. Owing to these tremendous potentials, many big tech companies have released several lightweight Small Language Models (SLMs) to bridge this gap. However, we s

Yuhan Tian, Abolfazl Safikhani

Sequential (online) change-point detection involves continuously monitoring time-series data and triggering an alarm when shifts in the data distribution are detected. We propose an algorithm for real-time identification of alterations in the transition matrices of high-dimensional vector autoregressive models. The algorithm estimates transition matrices and

Nicolas Fraiman, Michael Nisenzon

Spectral clustering is a widely used method for community detection in networks. We focus on a semi-supervised community detection scenario in the Partially Labeled Stochastic Block Model (PL-SBM) with two balanced communities, where a fixed portion of labels is known. Our approach leverages random walks in which the revealed nodes in each community act as a

Tsung-Hung Yao, Suprateek Kundu

There is a rich literature on clustering functional data with applications to time-series modeling, trajectory data, and even spatio-temporal applications. However, existing methods routinely perform global clustering that enforces identical atom values within the same cluster. Such grouping may be inadequate for high-dimensional functions, where the cluster

Shunsuke Kobayashi, Katsumi Takemasa, Ryuichi Tarumi

This study presents a comprehensive mathematical model for Volterra defects and explores their relations using differential geometry on Riemann--Cartan manifolds. Following the standard Volterra process, we derived the Cartan moving frame, a geometric representation of plastic fields, and the associated Riemannian metric using exterior algebra. Although the

Damiano Greco, Tadahiro Oh, Liying Tao, Leonardo Tolomeo

We study log-correlated Gibbs measures on the $d$-dimensional torus with weakly interacting focusing quartic potentials whose coupling constants tend to $0$ as we remove regularization. In particular, we exhibit a phase transition for this model by identifying a critical threshold, separating the weakly and strongly coupling regimes; in the weakly coupling r

Sonal Kumar, Prem Seetharaman, Justin Salamon, Dinesh Manocha

The field of text-to-audio generation has seen significant advancements, and yet the ability to finely control the acoustic characteristics of generated audio remains under-explored. In this paper, we introduce a novel yet simple approach to generate sound effects with control over key acoustic parameters such as loudness, pitch, reverb, fade, brightness, no

Tianji Cong, Fatemeh Nargesian, Junjie Xing, H. V. Jagadish

Modern data stores increasingly rely on metadata for enabling diverse activities such as data cataloging and search. However, metadata curation remains a labor-intensive task, and the broader challenge of metadata maintenance -- ensuring its consistency, usefulness, and freshness -- has been largely overlooked. In this work, we tackle the problem of resolvin

Yufeng Wei, Junzhe Cao, Kun Xu

Reactive flows for rarefied gas mixtures involve a multi-scale transport characterized by particle collisions and free streaming, and non-equilibrium physics containing multi-species interactions, and chemical non-equilibrium. These flows are pivotal in aerospace engineering and semiconductor manufacturing, impacting spacecraft control and thermal protection

Yutong Jin, Peter B. Gilbert, Aaron Hudson

In randomized trials and observational studies, it is often necessary to evaluate the extent to which an intervention affects a time-to-event outcome, which is only partially observed due to right censoring. For instance, in infectious disease studies, it is frequently of interest to characterize the relationship between risk of acquisition of infection with

Haozhe Yang, Zhendong Chi, Garen Avedissian, Eoin Dolan

Spintronic devices require materials that facilitate effective spin transport, generation, and detection. In this regard, graphene emerges as an ideal candidate for long-distance spin transport owing to its minimal spin-orbit coupling, which, however, limits its capacity for effective spin manipulation. This problem can be overcome by putting spin-orbit coup