April 2024 arXiv papers — page 175

Taku Okawara, Kenji Koide, Shuji Oishi, Masashi Yokozuka

Tunnels and long corridors are challenging environments for mobile robots because a LiDAR point cloud should degenerate in these environments. To tackle point cloud degeneration, this study presents a tightly-coupled LiDAR-IMU-wheel odometry algorithm with an online calibration for skid-steering robots. We propose a full linear wheel odometry factor, which n

Yisheng He, Weihao Yuan, Siyu Zhu, Zilong Dong

This paper enables high-fidelity, transferable NeRF editing by frequency decomposition. Recent NeRF editing pipelines lift 2D stylization results to 3D scenes while suffering from blurry results, and fail to capture detailed structures caused by the inconsistency between 2D editings. Our critical insight is that low-frequency components of images are more mu

Yozo Tonaki, Yusuke Kaino, Masayuki Uchida

We consider parameter estimation of the reaction term for a second order linear parabolic stochastic partial differential equation in two space dimensions driven by a $Q$-Wiener process under small diffusivity. We first construct an estimator of the reaction parameter based on continuous spatio-temporal data, and then derive an estimator of the reaction para

Vandan Mujadia, Pruthwik Mishra, Arafat Ahsan, Dipti Misra Sharma

With the primary focus on evaluating the effectiveness of large language models for automatic reference-less translation assessment, this work presents our experiments on mimicking human direct assessment to evaluate the quality of translations in English and Indian languages. We constructed a translation evaluation task where we performed zero-shot learning

Hoang Huy Nguyen, Yan Li, Tuo Zhao

In modern decentralized applications, ensuring communication efficiency and privacy for the users are the key challenges. In order to train machine-learning models, the algorithm has to communicate to the data center and sample data for its gradient computation, thus exposing the data and increasing the communication cost. This gives rise to the need for a d

Alberto Argente-Garrido, Cristina Zuheros, M. Victoria Luzón, Francisco Herrera

Trustworthy Artificial Intelligence solutions are essential in today's data-driven applications, prioritizing principles such as robustness, safety, transparency, explainability, and privacy among others. This has led to the emergence of Federated Learning as a solution for privacy and distributed machine learning. While decision trees, as self-explanatory m

Hengyue Li, Yusheng Yang, Pin Lv, Jinglong Qu

This study introduces a systematic approach for analyzing strongly correlated systems by adapting the conventional quantum cluster method to a quantum circuit model. We have developed a more concise formula for calculating the cluster's Green's function, requiring only real-number computations on the quantum circuit instead of complex ones. This approach is

Bufang Yang, Lixing He, Kaiwei Liu, Zhenyu Yan

Individuals with visual impairments, encompassing both partial and total difficulties in visual perception, are referred to as visually impaired (VI) people. An estimated 2.2 billion individuals worldwide are affected by visual impairments. Recent advancements in multi-modal large language models (MLLMs) have showcased their extraordinary capabilities across

Chengwei Qin, Ruirui Chen, Ruochen Zhao, Wenhan Xia

To mitigate forgetting, existing lifelong event detection methods typically maintain a memory module and replay the stored memory data during the learning of a new task. However, the simple combination of memory data and new-task samples can still result in substantial forgetting of previously acquired knowledge, which may occur due to the potential overlap

Avraham Binnenfeld, Sahar Shahaf, Shay Zucker

We present an improvement of the phase distance correlation (PDC) periodogram to account for uncertainties in the time-series data. The PDC periodogram introduced in our previous papers is based on the statistical concept of distance correlation. By viewing each measurement and its accompanying error estimate as a probability distribution, we are able to use

Zhe Xu, Daoyuan Chen, Jiayi Kuang, Zihao Yi

Emotional Support Conversation (ESC) systems are pivotal in providing empathetic interactions, aiding users through negative emotional states by understanding and addressing their unique experiences. In this paper, we tackle two key challenges in ESC: enhancing contextually relevant and empathetic response generation through dynamic demonstration retrieval,

K Prabith, Georgios Theocharis, Rajesh Chaunsali

We investigate a higher-order topological insulator (HOTI) under strong nonlinearity, focusing on the existence and stability of high-amplitude corner states, which can find applications in optics, acoustics, elastodynamics, and other wave-based systems. Our study centers on a breathing Kagome lattice composed of point masses and springs known to exhibit edg

Hannes Böckmann, Jan Gerrit Horstmann, Felix Kurtz, Manuel Buriks

Spatial heterogeneity and phase competition are hallmarks of strongly-correlated materials, promising tunable functionality on the nanoscale. Light-induced switching of a correlated insulator to a metallic state is well established. However, optical excitation generally lacks the specificity to select sub-wavelength domains and control final textures. Here,

Michael Ruderman

Nonlinear extension of the integral part of a standard proportional-integral-derivative (PID) feedback control is proposed for perturbed second-order systems. The approach is model-free and requires solely the Lipschitz boundedness of the unknown matched perturbations. For constant disturbances, the global asymptotic stability is shown based on the circle cr

Amal Jayawardena, Andrew Hung, Greg Qiao, Elnaz Hajizadeh

Multidrug resistance (MDR) to conventional antibiotics is one of the most urgent global health threats, necessitating the development of effective and biocompatible antimicrobial agents that are less inclined to provoke resistance. Structurally Nanoengineered Antimicrobial Peptide Polymers (SNAPPs) are a novel and promising class of such alternatives. These

Hesham Gaballa, Chaouki Habchi, Jean-Charles de Hemptinne, Gerard Mouokue

The reduction of greenhouse gases (GHG) emitted into the earth's atmosphere, such as carbon dioxide, has obviously become a priority. Replacing fossil fuels with cleaner renewable fuels (such as ammonia) in internal combustion engines for heavy-duty vehicles is one promising solution to reduce GHG emissions. This paper aims to study the cavitation formation

Till Hofmann, Hector Geffner

General policies represent reactive strategies for solving large families of planning problems like the infinite collection of solvable instances from a given domain. Methods for learning such policies from a collection of small training instances have been developed successfully for classical domains. In this work, we extend the formulations and the resulti

Sang Hu, Zihan Zhou

For optimal stopping problems with time-inconsistent preference, we measure the inherent level of time-inconsistency by taking the time needed to turn the naive strategies into the sophisticated ones. In particular, when in a repeated experiment the naive agent can observe her actual sequence of actions which are inconsistent with what she has planned at the

Lei Bill Wang, Zhenbang Jiao, Om Prakash Bedant, Haoran Wang

This paper presents a three-step empirical framework for optimizing classroom assignments under endogenous peer effects, using data from the China Education Panel Survey (CEPS). We design \textit{PeerNN}, a neural network that mimics endogenous network formation as a discrete choice model, generating a friendship-intensity matrix ($\Omega$) that captures stu

Ignacio Magaña Hernandez, Virginia D'Emilio, Soichiro Morisaki, Mohit Bhardwaj

Recent work by Moroianu et al. (2022) has suggested that the binary neutron star (BNS) merger GW190425 might have a potential fast radio burst (FRB) counterpart association, FRB 20190425A, at the 2.8$\sigma$ level of confidence with a likely host galaxy association, namely UGC10667. The authors argue that the observations are consistent with a long-lived hyp

Chengkun Zhang, Yasutomo Ota, Satoshi Iwamoto

We designed slow-light waveguides with a wide mode area based on slab-type valley photonic crystal (VPhC) heterostructures which are composed of a graphene-like PhC sandwiched by two topologically distinct VPhCs. The group velocity of the topological guided mode hosted in a VPhC heterostructure can be slowed down by shifting the VPhC lattice toward the graph

Chen Cui, Kai Jiang, Shi Shu

In this paper, we propose a deep learning-enhanced multigrid solver for high-frequency and heterogeneous Helmholtz equations. By applying spectral analysis, we categorize the iteration error into characteristic and non-characteristic components. We eliminate the non-characteristic components by a multigrid wave cycle, which employs carefully selected smoothe

Dmitry Zinoviev

This paper introduces the practicalities and benefits of using SimPy, a discrete event simulation (DES) module written in Python, for modeling and simulating complex systems. Through a step-by-step exploration of the classical Dining Philosophers Problem, we demonstrate how SimPy enables the efficient construction of discrete event models, emphasizing system

Xinpeng Wang, Ying-li Zhang, Misao Sasaki

We investigate a model of $R^2$-gravity with a non-minimally coupled scalar field that gives rise to two-stage inflation with a break, that is, with an intermediate stage where inflation momentarily halts. We find that the power spectrum of the primordial curvature perturbation is significantly enhanced at the break scale, which can account for the primordia

Ye Yuan, Kexin Tang, Jianhao Shen, Ming Zhang

We present a new challenge to examine whether large language models understand social norms. In contrast to existing datasets, our dataset requires a fundamental understanding of social norms to solve. Our dataset features the largest set of social norm skills, consisting of 402 skills and 12,383 questions covering a wide set of social norms ranging from opi

Zhongtao Miao, Qiyu Wu, Kaiyan Zhao, Zilong Wu

The field of cross-lingual sentence embeddings has recently experienced significant advancements, but research concerning low-resource languages has lagged due to the scarcity of parallel corpora. This paper shows that cross-lingual word representation in low-resource languages is notably under-aligned with that in high-resource languages in current models.

Ramraj Chandradevan, Kaustubh D. Dhole, Eugene Agichtein

State-of-the-art neural rankers pre-trained on large task-specific training data such as MS-MARCO, have been shown to exhibit strong performance on various ranking tasks without domain adaptation, also called zero-shot. However, zero-shot neural ranking may be sub-optimal, as it does not take advantage of the target domain information. Unfortunately, acquiri

Eliana Francot, Alessandro Montinaro

In this paper, we provide a complete classification of the $2$-$(v,3,\lambda )$ designs with $v\equiv 1,3\pmod{6}$ and $% v \equiv 6 \pmod{\lambda}$ admitting a flag-transitive automorphism group non-isomorphic to a subgroup of $A\Gamma L_{1}(v)$.

Richard E. Spinney, Richard G. Morris

We derive a stochastic partial differential equation that describes the fluctuating behaviour of reaction-diffusion systems of N particles, undergoing Markovian, unary reactions. This generalises the work of Dean [J. Phys. A: Math. and Gen., 29 (24), L613, (1996)] through the inclusion of random Poisson fields. Our approach is based on weak interactions, whi

Hyeonho Noh, Harim Lee, Hyun Jong Yang

This letter tackles a joint user scheduling, frequency resource allocation (USRA), multi-input-multi-output mode selection (MIMO MS) between single-user MIMO and multi-user (MU) MIMO, and MU-MIMO user selection problem, integrating uplink orthogonal frequency division multiple access (OFDMA) in IEEE 802.11ax. Specifically, we focus on \textit{unsaturated tra

Ej Bouâzzaoui Choubabi, Bouchaib Lemaalem, Mohamed Raggui, Abdelhadi Belouad

This paper aims to theoretically analyze the behavior of Dirac fermions in tilted Dirac cone material, particularly those that have diffused a barrier potential.Our results show that the degree of tilt in the y-direction can lead to different collimations of the Dirac fermion beams relative to the Fermi and confinement surfaces. To study the transmission pro

Fatemeh Abbasi, Juho Rousu

In this mini-review, we explore the new prediction methods for drug combination synergy relying on high-throughput combinatorial screens. The fast progress of the field is witnessed in the more than thirty original machine learning methods published since 2021, a clear majority of them based on deep learning techniques. We aim to put these papers under a uni

Byung-Hak Hwang, Jihyeug Jang, Jang Soo Kim, Minho Song

This paper is the sequel of the paper under the same title with part 1, where we introduced refined canonical stable Grothendieck polynomials and their duals with two families of infinite parameters. In this paper we give combinatorial interpretations for these polynomials using generalizations of set-valued tableaux and reverse plane partitions, respectivel

Zachary Cooper-Baldock, Brenda Vara Almirall, Kiao Inthavong

Computational Fluid Dynamics (CFD) is the simulation of fluid flow undertaken with the use of computational hardware. The underlying equations are computationally challenging to solve and necessitate high performance computing (HPC) to resolve in a practical timeframe when a reasonable level of fidelity is required. The simulations are memory intensive, havi

I. Dror, O. Hadar

Autonomous vehicles are a promising solution to traffic congestion, air pollution, accidents, and wasted time and resources. However, remote driver intervention may be necessary for extreme situations to ensure safe roadside parking or complete remote takeover. In such cases, high-quality real-time video streaming is crucial for practical remote driving. In

Sanjib Sabhapandit, Satya N. Majumdar

We study a system of $N$ noninteracting particles on a line in the presence of a harmonic trap $U(x)=\mu \bigl[x-z(t)\bigr]^2/2$, where the trap center $z(t)$ undergoes a bounded stochastic modulation. We show that this stochastic modulation drives the system into a nonequilibrium stationary state, where the joint distribution of the positions of the particl

Georgios Antoniou, Caio F. B. Macedo, Andrea Maselli, Thomas P. Sotiriou

Gravitational wave observations can test the validity of General Relativity (GR) in the strong field regime. Certain classes of scalar-tensor theories indeed predict that compact objects can exhibit significant deviations from their GR counterparts. Here we explore the quasinormal modes of axial perturbations in spherically symmetric black holes in three suc

Rishub Tamirisa, Chulin Xie, Wenxuan Bao, Andy Zhou

Standard federated learning approaches suffer when client data distributions have sufficient heterogeneity. Recent methods addressed the client data heterogeneity issue via personalized federated learning (PFL) - a class of FL algorithms aiming to personalize learned global knowledge to better suit the clients' local data distributions. Existing PFL methods

Youjin Kim, Jonggyu Jang, Hyun Jong Yang

Despite the extensive research on massive MIMO systems for 5G telecommunications and beyond, the reality is that many deployed base stations are equipped with a limited number of antennas rather than supporting massive MIMO configurations. Furthermore, while the cell-less network concept, which eliminates cell boundaries, is under investigation, practical de

Haofeng Yuan, Rongping Zhu, Wanlu Yang, Shiji Song

The traveling purchaser problem (TPP) is an important combinatorial optimization problem with broad applications. Due to the coupling between routing and purchasing, existing works on TPPs commonly address route construction and purchase planning simultaneously, which, however, leads to exact methods with high computational cost and heuristics with sophistic

Tian Huang, Chun Yu, Weinan Shi, Zijian Peng

UI task automation enables efficient task execution by simulating human interactions with graphical user interfaces (GUIs), without modifying the existing application code. However, its broader adoption is constrained by the need for expertise in both scripting languages and workflow design. To address this challenge, we present Prompt2Task, a system designe

Pouya Sadeghi, Amirhossein Abaskohi, Yadollah Yaghoobzadeh

Inspired by human cognition, Jiang et al.(2023c) create a benchmark for assessing LLMs' lateral thinking-thinking outside the box. Building upon this benchmark, we investigate how different prompting methods enhance LLMs' performance on this task to reveal their inherent power for outside-the-box thinking ability. Through participating in SemEval-2024, task

Chunyan Li, Jing Zhong, Songmei Qin, Dengkai Jiang

NGC 752 is a famous Galactic open cluster of intermediate age. In recent works, a very long and asymmetric tail was newly revealed. A blue straggler star (BSS) at the periphery of the tidal tail of the cluster has been identified subsequently. We aim to perform a detailed analysis of the newly detected BSS based on the available comprehensive spectroscopic a

Zheng Gong, Boyang Li, Sylvia Herbert

Real-time navigation in a priori unknown environment remains a challenging task, especially when an unexpected (unmodeled) disturbance occurs. In this paper, we propose the framework Safe Returning Fast and Safe Tracking (SR-F) that merges concepts from 1) Robust Control Lyapunov-Value Functions (R-CLVF), and 2) the Fast and Safe Tracking (FaSTrack) framewor

Hongwei Zhu, Shitao Li, Minjia Shi, Shu-Tao Xia

The size of the Hamming distance spectrum of a code has received great attention in recent research. The main objective of this paper is to extend these significant theories to the $b$-symbol distance spectrum. We examine this question for various types of codes, including unrestricted codes, additive codes, linear codes, and cyclic codes, successively. For

Maria Spichkova

This paper presents an overview of mobile application projects conducted at the RMIT University as a part of the Learning and Teaching activities within Bachelor and Master programs, in collaboration with industrial partners. We discuss the lessons learned over eight years of teaching the corresponding courses and compare the results of our student project t

Gaspard Duchêne, Jean-Baptiste LeBouquin, François Ménard, Nicolás Cuello

High-order multiple (triple and beyond) systems are relatively common. Their interaction with circumstellar and circumbinary material can have a large impact on the formation and evolution of planetary systems and depends on their orbital properties. GG\,Tau and UX\,Tau are two pre-main sequence high-order multiple systems in which the tightest pair has a pr

Taylor D. Swaim, Emalee Hough, Zachary Yap, Jamey D. Jacob

Heliotropes are passive solar hot air balloons that are capable of achieving nearly level flight within the lower stratosphere for several hours. These inexpensive flight platforms enable stratospheric sensing with high-cadence enabled by the low cost to manufacture, but their performance has not yet been assessed systematically. During July to September of

Hanwei Wang, Yang Zhao

Enantioselective interactions of chiral molecules include distinct absorptions to opposite-handed circularly polarized light, known as chiral absorption. Traditionally, chiral absorption has been primarily attributed to electric dipole and magnetic dipole interaction with molecular chirality. However, this approach falls short for large molecules that suppor

Hao Zhang, Fuhui Zhou, Qihui Wu, Naofal Al-Dhahir

Wireless signal recognition (WSR) is crucial in modern and future wireless communication networks since it aims to identify properties of the received signal. Although many deep learning-based WSR models have been developed, they still rely on a large amount of labeled training data. Thus, they cannot tackle the few-sample problem in the practically and dyna

Masayuki Kawarada, Tatsuya Ishigaki, Hiroya Takamura

Large language models (LLMs) have been applied to a wide range of data-to-text generation tasks, including tables, graphs, and time-series numerical data-to-text settings. While research on generating prompts for structured data such as tables and graphs is gaining momentum, in-depth investigations into prompting for time-series numerical data are lacking. T

Deng Luo, Zainab Alsuwaykit, Dawar Khan, Ondřej Strnad

We introduce DiffFit, a differentiable algorithm for fitting protein atomistic structures into an experimental reconstructed Cryo-Electron Microscopy (cryo-EM) volume map. In structural biology, this process is necessary to semi-automatically composite large mesoscale models of complex protein assemblies and complete cellular structures that are based on mea

Shruthi Ravikumar, Margaret Hamilton, Charles Thevathayan, Maria Spichkova

Many students in introductory programming courses fare poorly in the code writing tasks of the final summative assessment. Such tasks are designed to assess whether novices have developed the analytical skills to translate from the given problem domain to coding. In the past researchers have used instruments such as code-explain and found that the extent of

Siqing Fu, Lizhou Wu, Tiejun Li, Chunyuan Zhang

Biologically-inspired computing models have made significant progress in recent years, but the conventional von Neumann architecture is inefficient for the large-scale matrix operations and massive parallelism required by these models. This paper presents Spin-NeuroMem, a low-power circuit design of Hopfield network for the function of associative memory. Sp

Jie Zhu, Jirong Zha, Ding Li, Leye Wang

Self-supervised learning shows promise in harnessing extensive unlabeled data, but it also confronts significant privacy concerns, especially in vision. In this paper, we aim to perform membership inference on visual self-supervised models in a more realistic setting: self-supervised training method and details are unknown for an adversary when attacking as

Tomoyoshi Kimura, Jinyang Li, Tianshi Wang, Denizhan Kara

This paper demonstrates the potential of vibration-based Foundation Models (FMs), pre-trained with unlabeled sensing data, to improve the robustness of run-time inference in (a class of) IoT applications. A case study is presented featuring a vehicle classification application using acoustic and seismic sensing. The work is motivated by the success of founda

Xiaolin Gong, Zehan Zheng, Heyuan Du

Image dehazing has been a popular topic of research for a long time. Previous deep learning-based image dehazing methods have failed to achieve satisfactory dehazing effects on both synthetic datasets and real-world datasets, exhibiting poor generalization. Moreover, single-stage networks often result in many regions with artifacts and color distortion in ou

Kuniyuki Miwa, Souichi Sakamoto, Ken Funo, Akihito Ishizaki

Lattice softness has a significant impact on charge carrier dynamics in condensed matter systems, contributing to the emergence of various properties and functions. Examples include the remarkable carrier lifetimes and defect tolerances of hybrid organic-inorganic perovskites. Recent studies suggest the contribution of quartic anharmonicity of the lattice vi

Ahmed S. Alahmed, Guido Cavraro, Andrey Bernstein, Lang Tong

The proliferation of behind-the-meter (BTM) distributed energy resources (DER) within the electrical distribution network presents significant supply and demand flexibilities, but also introduces operational challenges such as voltage spikes and reverse power flows. In response, this paper proposes a network-aware dynamic pricing framework tailored for energ

Xianping Ma, Xiaokang Zhang, Man-On Pun

Semantic segmentation of remote sensing images is a fundamental task in geoscience research. However, there are some significant shortcomings for the widely used convolutional neural networks (CNNs) and Transformers. The former is limited by its insufficient long-range modeling capabilities, while the latter is hampered by its computational complexity. Recen

Ashima Suvarna, Harshita Khandelwal, Nanyun Peng

Phonology, the study of speech's structure and pronunciation rules, is a critical yet often overlooked component in Large Language Model (LLM) research. LLMs are widely used in various downstream applications that leverage phonology such as educational tools and poetry generation. Moreover, LLMs can potentially learn imperfect associations between orthograph

Masaaki Kimura, Yasutaka Taniguchi

We introduce a new framework for the low-energy nuclear structure calculations, which describes the single-particle wave function as a superposition of localized Gaussians. It is a hybrid of the Hartree-Fock and antisymmetrized molecular dynamics models. In the numerical calculations of oxygen, calcium isotopes and 100Sn, the framework shows its potential by

Patrick Doherty, Andrzej Szalas

The technique of forgetting in knowledge representation has been shown to be a powerful and useful knowledge engineering tool with widespread application. Yet, very little research has been done on how different policies of forgetting, or use of different forgetting operators, affects the inferential strength of the original theory. The goal of this paper is

Yueqi Shen, Matthew A. Psioda, Luiz M. Carvalho, Joseph G. Ibrahim

The power prior is a popular class of informative priors for incorporating information from historical data. It involves raising the likelihood for the historical data to a power, which acts as a discounting parameter. When the discounting parameter is modeled as random, the normalized power prior (NPP) is recommended. When there are multiple historical data

Emilio Villa-Cueva, A. Pastor López-Monroy, Fernando Sánchez-Vega, Thamar Solorio

Zero-Shot Cross-lingual Transfer (ZS-XLT) utilizes a model trained in a source language to make predictions in another language, often with a performance loss. To alleviate this, additional improvements can be achieved through subsequent adaptation using examples in the target language. In this paper, we exploit In-Context Tuning (ICT) for One-Shot Cross-lin

Cory Cherven

Blockchain and its distributed ledger technology have far-reaching implications for consumers across the world. Cryptocurrencies like XRP work to solve key issues in the remittance industry, targeting corridors like Mexico where foreign remittance fuels economies. Blockchain's libertarian principles have the potential to change lives in the third world, repl

Sahil J. Sindhi, Ignas Budvytis

Different fields in applied machine learning such as computer vision, speech or natural language processing have been building domain-specialised solutions. Currently, we are witnessing an opposing trend towards developing more generalist architectures, driven by Large Language Models and multi-modal foundational models. These architectures are designed to t

Haifeng Tang

An intriguing feature of type II$_1$ von Neumann algebra is that the entropy of the mixed states is negative. Although the type classification of von Neumann algebra and its consequence in holography have been extensively explored recently, there has not been an explicit calculation of entropy in some physically interesting models with type II$_1$ algebra. I

Daisuke Kikuta, Hiroki Ikeuchi, Kengo Tajiri, Yuta Toyama

As a telecom provider, our company has a critical mission to maintain telecom services even during power outages. To accomplish the mission, it is essential to maintain the power of the telecom base stations. Here we consider a solution where electric vehicles (EVs) directly supply power to base stations by traveling to their locations. The goal is to find E

Sambit Mallick, Snigdha Paul, Anindya Sen

Breast cancer classification stands as a pivotal pillar in ensuring timely diagnosis and effective treatment. This study with histopathological images underscores the profound significance of harnessing the synergistic capabilities of colour space ensembling and quantum-classical stacking to elevate the precision of breast cancer classification. By delving i

H. Hinduja, N. Mandal

The COVID-19 pandemic has had a long-term impact on industries worldwide, with the hospitality and food industry facing significant challenges, leading to the permanent closure of many restaurants and the loss of jobs. In this study, we developed an innovative analytical framework using Hamiltonian Monte Carlo for predictive modeling with Bayesian regression

Jingyang Zhang, Jingwei Sun, Eric Yeats, Yang Ouyang

The problem of pre-training data detection for large language models (LLMs) has received growing attention due to its implications in critical issues like copyright violation and test data contamination. Despite improved performance, existing methods (including the state-of-the-art, Min-K%) are mostly developed upon simple heuristics and lack solid, reasonab

Druv Pai, Ziyang Wu, Sam Buchanan, Yaodong Yu

Modern learning frameworks often train deep neural networks with massive amounts of unlabeled data to learn representations by solving simple pretext tasks, then use the representations as foundations for downstream tasks. These networks are empirically designed; as such, they are usually not interpretable, their representations are not structured, and their

Jiaang Duan, Shiyou Qian, Dingyu Yang, Hanwen Hu

With its elastic power and a pay-as-you-go cost model, the deployment of deep learning inference services (DLISs) on serverless platforms is emerging as a prevalent trend. However, the varying resource requirements of different layers in DL models hinder resource utilization and increase costs, when DLISs are deployed as a single function on serverless platf

Paiheng Xu, Jing Liu, Nathan Jones, Julie Cohen

Assessing instruction quality is a fundamental component of any improvement efforts in the education system. However, traditional manual assessments are expensive, subjective, and heavily dependent on observers' expertise and idiosyncratic factors, preventing teachers from getting timely and frequent feedback. Different from prior research that mostly focuse

Bakar Chargeishvili

We present a software that automatically generates a multiplet color basis for general $2 \to n$ processes in quantum chromodynamics (QCD). The construction process is guided by the decomposition of the corresponding $\mathrm{SU}(N_c)$ representation into a direct sum of irreducible representations. The projectors of these irreducible multiplet states are th

Aditya Paul, Michael W. Levin, S. Travis Waller, David Rey

In this study, we develop an innovative data-driven optimization approach to solve the drone delivery service planning problem with online demand. Drone-based logistics are expected to improve operations by enhancing flexibility and reducing congestion effects induced by last-mile deliveries. With rising digitalization and urbanization, however, logistics se

Kazuki Oi, Pascal Naidon, Shimpei Endo

We study three-body systems in a mass-imbalanced two-component cold-atom mixture, and we investigate the three-body parameter of their Efimov states for both bosonic and fermionic systems, with a major focus on the Er-Er-Li Efimov states. For a system interacting solely via van der Waals interactions, the van der Waals universality of the three-body paramete

Elena R. Henderson, Jessie M. Henderson, Hiva Shahoei, William V. Oxford

Physically unclonable functions (PUFs) are designed to act as device 'fingerprints.' Given an input challenge, the PUF circuit should produce an unpredictable response for use in situations such as root-of-trust applications and other hardware-level cybersecurity applications. PUFs are typically subcircuits present within integrated circuits (ICs), and while

Yan Zhang, Ming Jia, Meng Li, JianYu Wang

Operators experience complicated physiological and psychological states when exposed to extreme heat stress, which can impair cognitive function and decrease performance significantly, ultimately leading to severe secondary disasters. Therefore, there is an urgent need for a feasible technique to identify their abnormal states to enhance the reliability of h

Shuxian Fan, Adam Visokay, Kentaro Hoffman, Stephen Salerno

In settings where most deaths occur outside the healthcare system, verbal autopsies (VAs) are a common tool to monitor trends in causes of death (COD). VAs are interviews with a surviving caregiver or relative that are used to predict the decedent's COD. Turning VAs into actionable insights for researchers and policymakers requires two steps (i) predicting l

Takuya Nomoto, Susumu Minami, Yuki Yanagi, Michi-To Suzuki

We develop a high-throughput computational scheme based on cluster multipole theory to identify new functional antiferromagnets. This approach is applied to 228 magnetic compounds listed in the AtomWork-Adv database, known for their elevated N\'eel temperatures. We conduct systematic investigations of both stable and metastable magnetic configurations of the

Jianchuan Liu, Haiyan Lin, Xun Li

This work introduces a method for generating generalized structures of amorphous polymers using simulated polymerization and molecular dynamics equilibration, with a particular focus on amorphous polymers. The techniques and algorithms used in this method are described in the main text, and example input scripts are provided for the GMXPolymer code, which is

Javier Roulet, Jonathan Mushkin, Digvijay Wadekar, Tejaswi Venumadhav

We introduce an algorithm to marginalize the likelihood for a gravitational wave signal from a quasi-circular binary merger over its extrinsic parameters, accounting for the effects of higher harmonics and spin-induced precession. The algorithm takes as input the matched-filtering time series of individual waveform harmonics against the data in all operation

Rachel Merrill, Alia Hamdan, Ash Bista, Scott Franklin

The ability to emotionally or intellectually understand another person's thoughts and feelings-empathy-can foster critical connections that facilitate learning and collaboration. We present a case study of physics faculty that examines their experiences empathizing with students, both in and outside of the classroom. We expand on frameworks for understanding

Changqing Ye, Shubin Fu, Eric T. Chung

Predicting effective thermal conductivity by solving a Partial Differential Equation (PDE) defined on a high-resolution Representative Volume Element (RVE) is a computationally intensive task. In this paper, we tackle the task by proposing an efficient and implementation-friendly computational method that can fully leverage the computing power offered by har

Xin Chen, Kai Wang

It is widely known that spoofing is a major threat that adversely impacts the reliability and accuracy of GNSS applications. In this study, a crowdsourcing double differential pseudorange spatial (D2SP) random set is constructed and the distribution of the set is derived.Based on the variance of the D2SP set, a tri-level hypothesis detection algorithm is des

Takeshi Kojima, Itsuki Okimura, Yusuke Iwasawa, Hitomi Yanaka

Current decoder-based pre-trained language models (PLMs) successfully demonstrate multilingual capabilities. However, it is unclear how these models handle multilingualism. We analyze the neuron-level internal behavior of multilingual decoder-based PLMs, Specifically examining the existence of neurons that fire ``uniquely for each language'' within decoder-o

Ruoxi Zhang, Yi-Fan Zhao, Ling-Jie Zhou, Deyi Zhuo

Interlayer exchange coupling (IEC) between two magnetic layers sandwiched by a nonmagnetic spacer layer plays a critical role in shaping the magnetic properties of such heterostructures. The quantum anomalous Hall (QAH) effect has been realized in a structure composed of two magnetically doped topological insulator (TI) layers separated by an undoped TI laye

Dongsu Lee, Chanin Eom, Minhae Kwon

Offline reinforcement learning has emerged as a promising technology by enhancing its practicality through the use of pre-collected large datasets. Despite its practical benefits, most algorithm development research in offline reinforcement learning still relies on game tasks with synthetic datasets. To address such limitations, this paper provides autonomou

Takuya Nomoto, Ryotaro Arita

In recent years, the skyrmion lattice phase with a short lattice constant has attracted attention due to its high skyrmion density, making it a promising option for achieving high-density storage memory and for observing novel phenomena like the quantized topological Hall effect. Unlike conventional non-centrosymmetric systems where the Dzyaloshinsky-Moriya

Ting-Ting Wang, Sining Dong, Chong Li, Wen-Cheng Yue

Materials with in-plane electrical anisotropy have great potential for designing artificial synaptic devices. However, natural materials with strong intrinsic in-plane electrical anisotropy are rare. We introduce a simple strategy to produce extremely large electrical anisotropy via grating gating of a semiconductor two-dimensional electron gas (2DEG) of AlG

Shanyu Han, Jian Lei, Yang Liu

The consumer store is ubiquitous and plays an important role in our everyday lives. It is an open question why stores usually have such short life cycles (typically around 3 years in China). This paper proposes a theoretical framework based on an equilibrium in style supply of stores and style demand of consumers to characterize store cash flow (revenue), le

Xiongpeng Ren, Jin Cao, Hui Li, Yinghui Zhang

AIoT devices have attracted significant attention within the 3GPP organization. These devices, distinguished from conventional IoT devices, do not rely on additional batteries or have extremely small battery capacities, offering features such as low cost, easy deployment, and maintenance-free operation. Authentication and secure transmission are fundamental

Woo Kyoung Han, Sunghoon Im, Jaedeok Kim, Kyong Hwan Jin

We propose a practical approach to JPEG image decoding, utilizing a local implicit neural representation with continuous cosine formulation. The JPEG algorithm significantly quantizes discrete cosine transform (DCT) spectra to achieve a high compression rate, inevitably resulting in quality degradation while encoding an image. We have designed a continuous c

Shwai He, Ang Li, Tianlong Chen

Vision-Language Models (VLMs) integrate information from multiple modalities and have shown remarkable success across various tasks. However, deploying large-scale VLMs in resource-constrained scenarios is challenging. Pruning followed by finetuning offers a potential solution but remains underexplored for VLMs. This study addresses two key questions: how to

Jeremiah D. Paul, Richard M. Plotkin, W. N. Brandt, Christopher H. Ellis

The supermassive black holes ($M_{\rm BH} \sim 10^{6}$$-$$10^{10}~M_\odot$) that power luminous active galactic nuclei (AGNs), i.e., quasars, generally show a correlation between thermal disk emission in the ultraviolet (UV) and coronal emission in hard X-rays. In contrast, some "massive" black holes (mBHs; $M_{\rm BH} \sim 10^{5}$$-$$10^{6}~M_\odot$) in low

Parth Patwa, Simone Filice, Zhiyu Chen, Giuseppe Castellucci

Large Language Models (LLMs) operating in 0-shot or few-shot settings achieve competitive results in Text Classification tasks. In-Context Learning (ICL) typically achieves better accuracy than the 0-shot setting, but it pays in terms of efficiency, due to the longer input prompt. In this paper, we propose a strategy to make LLMs as efficient as 0-shot text

Xianlong Zeng, Yijing Gao, Fanghao Song, Ang Liu

This study introduces a simple yet effective method for identifying similar data points across non-free text domains, such as tabular and image data, using Large Language Models (LLMs). Our two-step approach involves data point summarization and hidden state extraction. Initially, data is condensed via summarization using an LLM, reducing complexity and high

Lisa A. Upton, Ignacio Ugarte-Urra, Harry P. Warren, David H. Hathaway

Observations the Sun's photospheric magnetic field are often confined to the Sun-Earth line. Surface flux transport (SFT) models, such as the Advective Flux Transport (AFT) model, simulate the evolution of the photospheric magnetic field to produce magnetic maps over the entire surface of the Sun. While these models are able to evolve active regions that tra