February 2024 arXiv papers — page 47

Yingying Guo, Weikui Ye

In this paper, we consider the Cauchy problem for the $b$-equation. Firstly, for $s>\frac32,$ if $u_{0}(x)\in H^{s}(\mathbb{R})$ and $m_{0}(x)=u_{0}(x)-u_{0xx}(x)\in L^{1}(\mathbb{R}),$ the global solutions of the $b$-equation is established when $b\geq1$ or $b\leq1.$ It's worth noting that our global result is a new result which doesn't need the condition t

Junwen Yang, Tianyuan Jin, Vincent Y. F. Tan

We introduce a novel extension of the canonical multi-armed bandit problem that incorporates an additional strategic innovation: abstention. In this enhanced framework, the agent is not only tasked with selecting an arm at each time step, but also has the option to abstain from accepting the stochastic instantaneous reward before observing it. When opting fo

Subhajit Acharya, Biman Bagchi

The task of a first principles theoretical calculation of the rate of gas to liquid nucleation has remained largely incomplete despite the existence of reliable results from unbiased simulation studies at large supersaturation. Although the classical nucleation theory, formulated by Becker-Doring-Zeldovich (BDZ) about a century ago, provides an elegant, wide

Zhiwei Liu, Weiran Yao, Jianguo Zhang, Liangwei Yang

The booming success of LLMs initiates rapid development in LLM agents. Though the foundation of an LLM agent is the generative model, it is critical to devise the optimal reasoning strategies and agent architectures. Accordingly, LLM agent research advances from the simple chain-of-thought prompting to more complex ReAct and Reflection reasoning strategy; ag

Yuya Kawamura, Satoshi Takabe

Stein variational gradient descent (SVGD) is a prominent particle-based variational inference method used for sampling a target distribution. SVGD has attracted interest for application in machine-learning techniques such as Bayesian inference. In this paper, we propose novel trainable algorithms that incorporate a deep-learning technique called deep unfoldi

Naoyuki Haba, Keisuke Nagano, Yasuhiro Shimizu, Toshifumi Yamada

We study the gauge coupling unification (GCU) and proton decay in a non-supersymmetric SU(5) grand unified theory (GUT) incorporating a 45 representation Higgs field. Our analysis is based on the assumption that Georgi-Jarlskog-type mass matrices for fermions are responsible for explaining the mass ratio of the strange quark and the muon. We examine the cond

Sylvain L. Walsh, Lei Zhan, Carsten Mehring, Feng Liu

This work presents a numerical study of a diffusion flame in a reacting, two-dimensional, turbulent, viscous, multi-component, compressible mixing layer subject to a large favorable streamwise pressure gradient. The boundary-layer equations are solved coupled with both the $k$-$\omega$ and SST turbulence models. A compressible extension of the flamelet progr

S. H. Lee, H. W. Joo, H. J. Kim, K. W. Kim

Elastic scattering off nuclei in target detectors, involving interactions with dark matter and coherent elastic neutrino nuclear recoil (CE$\nu$NS), results in the deposition of low energy within the nuclei, dissipating rapidly through a combination of heat and ionization. The primary energy loss mechanism for nuclear recoil is heat, leading to consistently

Md Abdullah-Al Kaiser, Gourav Datta, Peter A. Beerel, Akhilesh R. Jaiswal

The desire to empower resource-limited edge devices with computer vision (CV) must overcome the high energy consumption of collecting and processing vast sensory data. To address the challenge, this work proposes an energy-efficient non-von-Neumann in-pixel processing solution for neuromorphic vision sensors employing emerging (X) magnetic domain wall magnet

Hyunjae Kim, Seunghyun Yoon, Trung Bui, Handong Zhao

Contrastive language-image pre-training (CLIP) models have demonstrated considerable success across various vision-language tasks, such as text-to-image retrieval, where the model is required to effectively process natural language input to produce an accurate visual output. However, current models still face limitations in dealing with linguistic variations

Narayan Tondapu

In this paper, we explore the application of Gaussian Processes (GPs) for predicting mean-reverting time series with an underlying structure, using relatively unexplored functional and augmented data structures. While many conventional forecasting methods concentrate on the short-term dynamics of time series data, GPs offer the potential to forecast not just

Wentao Xu, Kazutoshi Sasahara, Jianxun Chu, Bin Wang

Anthropomorphic social bots are engineered to emulate human verbal communication and generate toxic or inflammatory content across social networking services (SNSs). Bot-disseminated misinformation could subtly yet profoundly reshape societal processes by complexly interweaving factors like repeated disinformation exposure, amplified political polarization,

Jintian Zhu

In this paper, we are able to prove an analogy of the Calabi-Yau theorem for complete Riemannian manifolds with nonnegative scalar curvature which are aspherical at infinity. The key tool is an existence result for arbitrarily large bounded regions with weakly mean-concave boundary in Riemannian manifolds with sublinear volume growth. As an application, we u

Valery Alexeev, Hülya Argüz, Pierrick Bousseau

We prove that every irreducible component of the coarse Koll\'ar-Shepherd-Barron and Alexeev (KSBA) moduli space of stable log Calabi--Yau surfaces admits a finite cover by a projective toric variety. This verifies a conjecture of Hacking-Keel-Yu. The proof combines tools from log smooth deformation theory, the minimal model program, punctured log Gromov-Wit

Junlin Xie, Zhihong Chen, Ruifei Zhang, Xiang Wan

Large language models (LLMs) have achieved superior performance in powering text-based AI agents, endowing them with decision-making and reasoning abilities akin to humans. Concurrently, there is an emerging research trend focused on extending these LLM-powered AI agents into the multimodal domain. This extension enables AI agents to interpret and respond to

Himanshu Sharma, Lukáš Novák, Michael D. Shields

We present a novel physics-constrained polynomial chaos expansion as a surrogate modeling method capable of performing both scientific machine learning (SciML) and uncertainty quantification (UQ) tasks. The proposed method possesses a unique capability: it seamlessly integrates SciML into UQ and vice versa, which allows it to quantify the uncertainties in Sc

V. I. Gokul, Arun Bahuleyan, S. P. Dinesh, V. R. Thakar

The photoassociation (PA) of rubidium dimer (Rb2) in a dark magneto-optic trap (MOT) is studied using atom-cavity collective strong coupling. This allows non-destructive detection of the molecule formation process as well as rapid and repeated interrogation of the atom-molecule system. The vacuum Rabi splitting (VRS) measurements from the bright MOT are care

Guangming Sheng, Junwei Su, Chao Huang, Chuan Wu

Memory-based Temporal Graph Neural Networks (MTGNNs) are a class of temporal graph neural networks that utilize a node memory module to capture and retain long-term temporal dependencies, leading to superior performance compared to memory-less counterparts. However, the iterative reading and updating process of the memory module in MTGNNs to obtain up-to-dat

Kai-Bao Chen, Jian-Ping Ma, Xuan-Bo Tong

We study one-loop contributions to semi-inclusive deep inelastic scattering in the target fragmentation region for a polarized lepton beam and nucleon target. Complete one-loop results at leading twist are derived, with a particular focus on the gluonic channel. It shows that four structure functions are generated uniquely by the gluon fracture functions sta

Kaiping Cui, Xia Feng, Liangmin Wang, Haiqin Wu

Secure aggregation enables federated learning (FL) to perform collaborative training of clients from local gradient updates without exposing raw data. However, existing secure aggregation schemes inevitably perform an expensive fresh setup per round because each client needs to establish fresh input-independent secrets over different rounds. The latest resea

Md Arif Shaikh, Sajad A. Bhat, Shasvath J. Kapadia

The Inspiral Merger Ringdown Consistency Test (IMRCT) is one among a battery of tests of general relativity (GR) employed by the LIGO-Virgo-KAGRA (LVK) collaboration. It is used to search for deviations from GR in detected gravitational waves (GWs) from compact binary coalescences (CBCs) in a model-agnostic way. The test compares source parameter estimates e

Xinwen Cheng, Zhehao Huang, Wenxin Zhou, Zhengbao He

Machine unlearning (MU) aims to remove the influence of specific training samples from a well-trained model, a task of growing importance due to the ``right to be forgotten.'' The unlearned model should approach the retrained model, where forgetting data do not contribute to the training process. Therefore, unlearning should withdraw their contribution from

Franklin Mingzhe Li, Michael Xieyang Liu, Shaun K. Kane, Patrick Carrington

Individuals with vision impairments employ a variety of strategies for object identification, such as pans or soy sauce, in the culinary process. In addition, they often rely on contextual details about objects, such as location, orientation, and current status, to autonomously execute cooking activities. To understand how people with vision impairments coll

Miaofen Chen, Jilong Tong

We consider the Harder-Narasimhan formalism on the category of normed isocrystals and show that the Harder-Narasimhan filtration is compatible with tensor products which generalizes a result of Cornut. As an application of this result, we are able to define a (weak) Harder-Narasimhan stratification on the $B_{\mathrm{dR}}^+$-affine Grassmannian for arbitrary

Priyesh Kakka, Sheel Nidhan, Rishikesh Ranade, Jay Pathak

In this study, we introduce a domain-decomposition-based distributed training and inference approach for message-passing neural networks (MPNN). Our objective is to address the challenge of scaling edge-based graph neural networks as the number of nodes increases. Through our distributed training approach, coupled with Nystr\"om-approximation sampling techni

Xin Zheng, Qiming Zhu, Hongyu Lin, Yaojie Lu

Executing computer programs described in natural language has long been a pursuit of computer science. With the advent of enhanced natural language understanding capabilities exhibited by large language models (LLMs), the path toward this goal has been illuminated. In this paper, we seek to examine the capacity of present-day LLMs to comprehend and execute a

Zejun Zhang, Li Zhang, Xin Yuan, Anlan Zhang

Following OpenAI's introduction of GPTs, a surge in GPT apps has led to the launch of dedicated LLM app stores. Nevertheless, given its debut, there is a lack of sufficient understanding of this new ecosystem. To fill this gap, this paper presents a first comprehensive longitudinal (5-month) study of the evolution, landscape, and vulnerability of the emergin

Nozomi Nakatsuyama, Masatomo Takahashi

We investigate vertices for plane curves with singular points. As plane curves with singular points, we consider Legendre curves (respectively, Legendre immersions) in the unit tangent bundle over the Euclidean plane and frontals (respectively, fronts) in the Euclidean plane. We define a vertex using evolutes of frontals. After that we define a vertex of a f

Juho Lee, Hyeonwoo Yeo, Ryong-Gyu Lee, Yong-Hoon Kim

While first-principles calculations of electrode-molecule binding play an indispensable role in obtaining atomic-level understanding in surface science and electrochemistry, a significant challenge remains because the adsorption energy is well-defined only in equilibrium. Herein, a theory to calculate the electric enthalpy for electrochemical interfaces is f

Haoming Li, Yusen Huo, Shuai Dou, Zhenzhe Zheng

In online advertising, advertisers participate in ad auctions to acquire ad opportunities, often by utilizing auto-bidding tools provided by demand-side platforms (DSPs). The current auto-bidding algorithms typically employ reinforcement learning (RL). However, due to safety concerns, most RL-based auto-bidding policies are trained in simulation, leading to

Christian Wolf, Samuel Lai, Christopher A. Onken, Neelesh Amrutha

Around a million quasars have been catalogued in the Universe by probing deeper and using new methods for discovery. However, the hardest ones to find seem to be the rarest and brightest specimen. In this work, we study the properties of the most luminous of all quasars found so far. It has been overlooked until recently, which demonstrates that modern all-s

Toufique Ahmed, Christian Bird, Premkumar Devanbu, Saikat Chakraborty

Large Language models (LLMs) are finding wide use in software engineering practice. These models are extremely data-hungry, and are largely trained on open-source (OSS) code distributed with permissive licenses. In terms of actual use however, a great deal of software development still occurs in the for-profit/proprietary sphere, where the code under develop

Charles Jose, Luke Chamandy, Anvar Shukurov, Kandaswamy Subramanian

We explore the redshift evolution of the radio luminosity function (RLF) of star-forming galaxies using GALFORM, a semi-analytic model of galaxy formation and a dynamo model of the magnetic field evolving in a galaxy. Assuming energy equipartition between the magnetic field and cosmic rays, we derive the synchrotron luminosity of each sample galaxy. In a mod

M. S. Akshaya, Thiem Hoang

We have mapped the magnetic field ($B$-field) for a region of about 30 pc around the centre of our Galaxy, which encompasses the circumnuclear disk (CND), the minispiral, and the 20 km s$^{-1}$ and 50 km s$^{-1}$ molecular clouds, using thermal dust polarization observations obtained from SOFIA/HAWC+ and JCMT/SCUPOL. We decompose the spectra of $^{12}$CO ($J

Shanshan Xiao, Pengzhan Jin, Yifa Tang

In this work, we propose a method to learn the solution operators of PDEs defined on varying domains via MIONet, and theoretically justify this method. We first extend the approximation theory of MIONet to further deal with metric spaces, establishing that MIONet can approximate mappings with multiple inputs in metric spaces. Subsequently, we construct a set

Sungjin Park, Edward Choi

Transformer-based models have significantly improved performance across a range of multimodal understanding tasks, such as visual question answering and action recognition. However, multimodal Transformers significantly suffer from a quadratic complexity of the multi-head attention with the input sequence length, especially as the number of modalities increa

Shuyang Gong, Zhangsong Li

Motivated by the problem of matching two correlated random geometric graphs, we study the problem of matching two Gaussian geometric models correlated through a latent node permutation. Specifically, given an unknown permutation $\pi^*$ on $\{1,\ldots,n\}$ and given $n$ i.i.d. pairs of correlated Gaussian vectors $\{X_{\pi^*(i)},Y_i\}$ in $\mathbb{R}^d$ with

Tariq Syed

We give a conceptual explanation for the somewhat mysterious origin of Suslin matrices. This enables us to generalize the construction of Suslin matrices and to give more conceptual proofs of some well-known results.

Shijing Si, Yuwei Wu, Le Tang, Yugui Zhang

Email continues to be a pivotal and extensively utilized communication medium within professional and commercial domains. Nonetheless, the prevalence of spam emails poses a significant challenge for users, disrupting their daily routines and diminishing productivity. Consequently, accurately identifying and filtering spam based on content has become crucial

Yoshiaki Suzuki

We study the eigenvalues and eigenfunctions of the Folland-Stein operator $\mathscr{L}_\alpha$ on some examples of 3-dimensional Heisenberg Bieberbach manifolds, that is, compact quotients $\Gamma\backslash\mathbb{H}$ of the Heisenberg group $\mathbb{H}$ by a discrete torsion-free subgroup $\Gamma$ of $\mathbb{H}\rtimes U(1)$.

Sorb Yesudhas, Valery I. Levitas, Feng Lin, K. K. Pandey

High-pressure synchrotron X-ray diffraction (XRD) studies have been conducted on three types of Si particles (micron, 100 nm, and 30 nm). The pressure for initiation of Si-I->Si-II phase transformation (PT) essentially increases with a reduction in particle size. For 30 nm Si particles, Si-I directly transforms to Si-XI by skipping the intermediate Si-II pha

Zehua Si, Zhixue He, Chen Shen, Jun Tanimoto

Evolutionary game theory, encompassing discrete, continuous, and mixed strategies, is pivotal for understanding cooperation dynamics. Discrete strategies involve deterministic actions with a fixed probability of one, whereas continuous strategies employ intermediate probabilities to convey the extent of cooperation and emphasize expected payoffs. Mixed strat

Zheng-Yang Zhou, Clemens Gneiting, J. Q. You, Franco Nori

Frustration, that is, the impossibility of satisfying the energetic preferences between all spin pairs simultaneously, underlies the complexity of many fundamental properties in spin systems, including the computational difficulty in determining their ground states. Coherent Ising machines (CIMs) have been proposed as a promising analog computational approac

Yifei Li, Xiang Yue, Zeyi Liao, Huan Sun

Modern generative search engines enhance the reliability of large language model (LLM) responses by providing cited evidence. However, evaluating the answer's attribution, i.e., whether every claim within the generated responses is fully supported by its cited evidence, remains an open problem. This verification, traditionally dependent on costly human evalu

Sheng-Qi Zhang, Xuan-Heng Zhang, Cong-Feng Qiao

We investigate the hyperon semileptonic decays within the framework of QCD sum rules. The flavor $ SU(3) $ symmetry breaking effects are analyzed via the relevant form factors and corresponding branching fractions. Employing the $ z $-series parameterization to capture the $ q^2 $ dependence of form factors, we calculate the hyperon semileptonic decay rates

Rhuaidi Antonio Burke

Dimension 4 is the first dimension in which exotic smooth manifold pairs appear -- manifolds which are topologically the same but for which there is no smooth deformation of one into the other. Whilst smooth and triangulated 4-manifolds do coincide, comparatively little work has been done towards gaining an understanding of smooth 4-manifolds from the discre

Youpeng Su, Siqi Xu, Yilei Ma, Ping Yin

Mendelian randomization uses genetic variants as instrumental variables to make causal inferences about the effects of modifiable risk factors on diseases from observational data. One of the major challenges in Mendelian randomization is that many genetic variants are only modestly or even weakly associated with the risk factor of interest, a setting known a

Dan Zhang, Huajie Gong, Guoyang Fu, Jian-Pin Wu

This paper explores the properties of the quasinormal modes (QNMs) of a regular black hole(BH) characterized by a Minkowski core and sub-Planckian curvature. When focusing on a special case, this regular BH exhibits identical large-scale behavior with the Hayward BH and some loop quantum gravity corrected (LQG-corrected) BH. A notable characteristic of the Q

E. O. Sevost'yanov, V. A. Targonskii, N. S. Ilkevych

We study quasilinear Beltrami equations, the complex coefficients of which depend on the unknown function. In terms of the so-called tangential dilatation, we have found conditions under which these equations have homeomorphic $ACL$-solutions. Sepa\-ra\-tely, we have found some conditions that ensure the existence of the corresponding con\-ti\-nuous $ACL$-so

Jorge Askur Vazquez Fernandez, Jae Joong Lee, Santiago Andrés Serrano Vacca, Alejandra Magana

The paper introduces Hands-Free VR, a voice-based natural-language interface for VR. The user gives a command using their voice, the speech audio data is converted to text using a speech-to-text deep learning model that is fine-tuned for robustness to word phonetic similarity and to spoken English accents, and the text is mapped to an executable VR command u

Zhisheng Lin, Han Fu, Chenghao Liu, Zhuo Li

Parameter-efficient fine-tuning (PEFT) has emerged as an effective method for adapting pre-trained language models to various tasks efficiently. Recently, there has been a growing interest in transferring knowledge from one or multiple tasks to the downstream target task to achieve performance improvements. However, current approaches typically either train

Jiayi Sun, Aarya Patil, Youhai Li, Jin L. C. Guo

Scientific open-source software (OSS) has greatly benefited research communities through its transparent and collaborative nature. Given its critical role in scientific research, ensuring the sustainability of such software has become vital. Earlier studies have proposed sustainability strategies for conventional scientific software and open-source communiti

Haodong Zhao, Ruifang He, Mengnan Xiao, Jing Xu

Multi-level implicit discourse relation recognition (MIDRR) aims at identifying hierarchical discourse relations among arguments. Previous methods achieve the promotion through fine-tuning PLMs. However, due to the data scarcity and the task gap, the pre-trained feature space cannot be accurately tuned to the task-specific space, which even aggravates the co

Innyoung Kim, Jonghyun Chae, Donghyun You

An automatic mesh generation method for optimal computational fluid dynamics (CFD) analysis of a blade passage is developed using deep reinforcement learning (DRL). Unlike conventional automation techniques, which require repetitive tuning of meshing parameters for each new geometry and flow condition, the method developed herein trains a mesh generator to d

Zhijie Liu, Yutian Tang, Meiyun Li, Xin Jin

XML configurations are integral to the Android development framework, particularly in the realm of UI display. However, these configurations can introduce compatibility issues (bugs), resulting in divergent visual outcomes and system crashes across various Android API versions (levels). In this study, we systematically investigate LLM-based approaches for de

Difan Yi, Qian Liu, Hongbang Liu, Fei Xie

This study investigates the charging-up effect on the Topmetal-II- chip in Gas Micro-Pixel Detectors(GMPD). It is found that this effect differs from the charging-up typically observed in gas detector multiplier devices and increases the relative gain of the detector. The research indicates that this effect originates from the accumulation of charges on the

Peng Lin, Martin Neil, Norman Fenton

Hybrid Bayesian networks (HBN) contain complex conditional probabilistic distributions (CPD) specified as partitioned expressions over discrete and continuous variables. The size of these CPDs grows exponentially with the number of parent nodes when using discrete inference, resulting in significant inefficiency. Normally, an effective way to reduce the CPD

Mahyar Radak, Saeed Mirzanejad

In recent times, the Archimedean spiral structure has been considered as a promising design element in construction for specific purposes and opening up new possibilities in various applications. Its distinctive geometry exhibits a continuous growth pattern with a constant separation between its successive turns. One notable application of the Archimedean sp

Yuta Takahashi

Rathjen proved that Aczel's constructive set theory $\mathbf{CZF}$ extended with inaccessible sets of all transfinite orders can be interpreted in Martin-L\"{o}f type theory $\mathbf{MLTT}$ extended with Setzer's Mahlo universe and another universe above it. In this paper we show that this interpretation can be carried out bottom-up without the universe abov

Duy Nguyen, Bao Nguyen, Viet Anh Nguyen

Algorithmic recourse recommends a cost-efficient action to a subject to reverse an unfavorable machine learning classification decision. Most existing methods in the literature generate recourse under the assumption of complete knowledge about the cost function. In real-world practice, subjects could have distinct preferences, leading to incomplete informati

Andrew Ireland, David Johnston, Rachel Knott

We use daily longitudinal data and a within-worker identification approach to examine the impacts of heat on labor force dynamics in Australia. High temperatures during 2001-2019 significantly reduced work attendance and hours worked, which were not compensated for in subsequent days and weeks. The largest reductions occurred in cooler regions and recent yea

Wei Liu, Qingzhi Zhong

The current Poisson factor models often assume that the factors are unknown, which overlooks the explanatory potential of certain observable covariates. This study focuses on high dimensional settings, where the number of the count response variables and/or covariates can diverge as the sample size increases. A covariate-augmented overdispersed Poisson facto

Rong Dai, Yonggang Zhang, Ang Li, Tongliang Liu

One-shot Federated Learning (OFL) has become a promising learning paradigm, enabling the training of a global server model via a single communication round. In OFL, the server model is aggregated by distilling knowledge from all client models (the ensemble), which are also responsible for synthesizing samples for distillation. In this regard, advanced works

H. M. Tedila, R. Yuen, N. Wang, D. Li

We report two phenomena detected in PSR J0344$-$0901 from two observations conducted at frequency centered at 1.25 GHz using the Five-hundred-meter Aperture Spherical radio Telescope (FAST). The first phenomenon manifests as shifting in the pulse emission to later longitudinal phases and then gradually returns to its original location. The event lasts for ab

Zachary Horvitz, Jingru Chen, Rahul Aditya, Harshvardhan Srivastava

Humor is a fundamental facet of human cognition and interaction. Yet, despite recent advances in natural language processing, humor detection remains a challenging task that is complicated by the scarcity of datasets that pair humorous texts with similar non-humorous counterparts. In our work, we investigate whether large language models (LLMs), can generate

Weiyue Xin, Maria M. Santore

Suspensions of colloidal microplates in contoured 2D elastic fluids sheets are dominated by the bending mechanics and shear rigidity of the plates and the contrasting in-plane shear flow of the 2D fluid. Using the phase separated phospholipid membranes of individual giant unilamellar vesicles as models of contoured 2D suspensions, where solid domains act as

Long Li, Shu-Qing Zhong, Di Xiao, Zi-Gao Dai

AT2018cow is the most extensively observed and widely studied fast blue optical transient to date; its unique observational properties challenge all existing standard models. In this paper, we model the luminosity evolution of the optical, soft X-ray, and hard X-ray emission, as well as the X-ray spectrum of AT2018cow with a magnetar-centered engine model. W

Ethan Payne, Kyle Kremer, Michael Zevin

Gravitational-wave observations provide the unique opportunity of studying black hole formation channels and histories -- but only if we can identify their origin. One such formation mechanism is the dynamical synthesis of black hole binaries in dense stellar systems. Given the expected isotropic distribution of component spins of binary black hole in gas-fr

Martin Bridgeman, Kenneth Bromberg

In a seminal paper, Epstein introduced the theory of what are now called Epstein surfaces, which construct surfaces in $\mathbb{H}^3$ associated to a conformal metric on a domain in $\hat{\mathbb{C}}$. More recently, these surfaces have been used by Krasnov-Schlenker to define the W-volume and renormalized volume associated with a convex co-compact hyperboli

Kaito Shimizu, Kai Inoue, Kazutaka Katsumata, Ayumu Naruki

The realization of single-photon generation is important for implementing various quantum information technologies. The use of rare-earth ions in an optical fiber is a promising single photon generation method due to its ability to operate at room temperature as well as the low cost involved. Neodymium ions are especially interesting because the ions are one

Shalosh B. Ekhad, Doron Zeilberger

In their study of water waves, Massimiliano Berti, Livia Corsi, Alberto Maspero, and Paulo Ventura, came up with two intriguing conjectured identities involving certain weighted sums over the Boolean lattice. They were able to prove the first one, while the second is still open. In this methodological note, we will describe how to generate many terms of thes

Cheng Bian, Xiaoyu Li, Qi Bi, Guangpu Zhu

Arterial blood pressure (ABP) holds substantial promise for proactive cardiovascular health management. Notwithstanding its potential, the invasive nature of ABP measurements confines their utility primarily to clinical environments, limiting their applicability for continuous monitoring beyond medical facilities. The conversion of photoplethysmography (PPG)

Yang Deng, Yong Zhao, Moxin Li, See-Kiong Ng

Despite the remarkable abilities of Large Language Models (LLMs) to answer questions, they often display a considerable level of overconfidence even when the question does not have a definitive answer. To avoid providing hallucinated answers to these unknown questions, existing studies typically investigate approaches to refusing to answer these questions. I

Qingcai Jiang, Shaojie Tan, Junshi Chen, Hong An

The performance gap between memory and processor has grown rapidly. Consequently, the energy and wall-clock time costs associated with moving data between the CPU and main memory predominate the overall computational cost. The Processing-in-Memory (PIM) paradigm emerges as a promising architecture that mitigates the need for extensive data movements by strat

Jiawei Zheng, Hanghai Hong, Feiyan Liu, Xiaoli Wang

Large language models (LLMs) have shown great potential in domain-specific machine translation (MT). However, one major issue is that LLMs pre-trained on general domain corpus might not generalize well to specific domains due to the lack of domain-specific knowledge. To address this issue, this paper focuses on enhancing the domain-specific MT capability of

Benny Ren, Jeffrey Morris, Ian Barnett

Bayesian Cox semiparametric regression is an important problem in many clinical settings. Bayesian procedures provide finite-sample inference and naturally incorporate prior information if MCMC algorithms and posteriors are well behaved. Survival analysis should also be able to incorporate multilevel modeling such as case weights, frailties and smoothing spl

Antoine Louis, Vageesh Saxena, Gijs van Dijck, Gerasimos Spanakis

State-of-the-art neural retrievers predominantly focus on high-resource languages like English, which impedes their adoption in retrieval scenarios involving other languages. Current approaches circumvent the lack of high-quality labeled data in non-English languages by leveraging multilingual pretrained language models capable of cross-lingual transfer. How

Yang Deng, Xuan Zhang, Wenxuan Zhang, Yifei Yuan

Web agents powered by Large Language Models (LLMs) have demonstrated remarkable abilities in planning and executing multi-step interactions within complex web-based environments, fulfilling a wide range of web navigation tasks. Despite these advancements, the potential for LLM-powered agents to effectively engage with sequential user instructions in real-wor

John D. Shimanek, Zi-Kui Liu, Allison M. Beese

The crystal plasticity finite element method (CPFEM) is a widely used technique for resolving macroscopic stress and strain onto the physically relevant length scales of grains and slip systems in ductile crystalline materials like structural metals. Here, the calibration of a CPFEM model for single crystal applications was found to depend critically on load

Clement Neo, Shay B. Cohen, Fazl Barez

Understanding the inner workings of large language models (LLMs) is crucial for advancing their theoretical foundations and real-world applications. While the attention mechanism and multi-layer perceptrons (MLPs) have been studied independently, their interactions remain largely unexplored. This study investigates how attention heads and next-token neurons

Jiang Zhang, Ruyi Tao, Keng Hou Leong, Mingzhe Yang

The theory of causal emergence (CE) with effective information (EI) posits that complex systems can exhibit CE, where macro-dynamics show stronger causal effects than micro-dynamics. A key challenge of this theory is its dependence on coarse-graining method. In this paper, we introduce a fresh concept of approximate dynamical reversibility and establish a no

Fengyi Li, Ayoub Belhadji, Youssef Marzouk

We study the problem of selecting $k$ experiments from a larger candidate pool, where the goal is to maximize mutual information (MI) between the selected subset and the underlying parameters. Finding the exact solution is to this combinatorial optimization problem is computationally costly, not only due to the complexity of the combinatorial search but also

Zhuang Chen, Jincenzi Wu, Jinfeng Zhou, Bosi Wen

Theory of Mind (ToM) is the cognitive capability to perceive and ascribe mental states to oneself and others. Recent research has sparked a debate over whether large language models (LLMs) exhibit a form of ToM. However, existing ToM evaluations are hindered by challenges such as constrained scope, subjective judgment, and unintended contamination, yielding

Z. Y. Liu, H. R. Qiu, G. Y. Fu, Y. Xiao

A machine learning based surrogate model for fishbone linear instability in tokamaks is constructed. Hybrid simulations with the kinetic-magnetohydrodynamic (MHD) code M3D-K is used to generate the database of fishbone linear instability, through scanning the four key parameters which are thought to determine the fishbone physics. The four key parameters inc

Shao-Jun Zhang

Recently, Comisso and Asenjo proposed a novel mechanism for harnessing energy from black holes through magnetic reconnection. Our study focuses on exploring the utilization of this mechanism on Konoplya-Rezzolla-Zhidenko (KRZ) parametrized black holes to assess the impact of deformation parameters on energy extraction. Among the various parameters, $\{\delta

James Tocknell

Specutils is an Astropy affiliated package which provides a consistent interface to astronomical spectra (primarily 1D). As Specutils can be adapted to parse spectra in many different formats, Specutils plays a key role at Data Central, allowing us to handle the diverse formats provided to us by survey teams. In this poster, I will cover what Specutils is, h

Xuhui Jiang, Yinghan Shen, Zhichao Shi, Chengjin Xu

Entity Alignment (EA) is vital for integrating diverse knowledge graph (KG) data, playing a crucial role in data-driven AI applications. Traditional EA methods primarily rely on comparing entity embeddings, but their effectiveness is constrained by the limited input KG data and the capabilities of the representation learning techniques. Against this backdrop

Yihua Ma, Shuqiang Xia, Chen bai, Yuxin Wang

As a promising 6G technology, integrated sensing and communication (ISAC) gains growing interest. ISAC provides integration gain via sharing spectrum, hardware, and software. However, concerns exist regarding its sensing performance when compared to the dedicated radar. To address this issue, the advantages of widely deployed networks should be utilized. Thi

Dongjun Jang, Jean Seo, Sungjoo Byun, Taekyoung Kim

This paper explores the challenges posed by aspect-based sentiment classification (ABSC) within pretrained language models (PLMs), with a particular focus on contextualization and hallucination issues. In order to tackle these challenges, we introduce CARBD-Ko (a Contextually Annotated Review Benchmark Dataset for Aspect-Based Sentiment Classification in Kor

Guillermo Chacon Acosta, Hector Hernandez Hernandez, Jose Ruvalcaba Rascon

We describe a quantum particle constrained on a catenoid, employing an effective description of quantum mechanics based on expected values of observables and quantum dispersions. We obtain semiclassical trajectories for particles, displaying general features of the quantum behaviour; most interestingly, particles present tunneling through the throat of the c

Purbayan Kar, Vishal Chudasama, Naoyuki Onoe, Pankaj Wasnik

Deep learning methods have led to significant improvements in the performance on the facial landmark detection (FLD) task. However, detecting landmarks in challenging settings, such as head pose changes, exaggerated expressions, or uneven illumination, continue to remain a challenge due to high variability and insufficient samples. This inadequacy can be att

Zhuohao Yu, Chang Gao, Wenjin Yao, Yidong Wang

Automatic evaluation methods for large language models (LLMs) are hindered by data contamination, leading to inflated assessments of their effectiveness. Existing strategies, which aim to detect contaminated texts, focus on quantifying contamination status instead of accurately gauging model performance. In this paper, we introduce KIEval, a Knowledge-ground

Aolong Guo, Runqi Kang, Man Jiao, Xing Rong

We propose a new method to search for exotic spin-spin interactions between electrons and nucleons using a diamond-based vector magnetometer. The vector magnetometer can be constructed from ensembles of nitrogen-vacancy centers along different axes in a diamond. The $^{14}\mathrm{N} $ nuclear spins of nitrogen-vacancy centers in the same diamond can be polar

Hongmin Liu

We report an experiment of observation of classical double-slit interference fringes of two-photon interference. In the experiment, a commercial continuous-wave multi-mode F-P laser diode without either mode-locked or frequency-locked is used as the light source, the density of photons is far more than the single-photon level, and the path difference of the

Li Ou

In this paper, we investigate the value distribution properties for Gauss maps of space-like stationary surfaces in four-dimensional Lorentz-Minkowski space $\mathbb{R}^{3,1}$, focusing on aspects such as the number of totally ramified points and unicity properties. We not only obtain general conclusions similar to situations in four-dimensional Euclidean sp

Stalyn Paucar, Christian Mejía-Escobar y Víctor Collaguazo

The identification and characterization of various rock types is one of the fundamental activities for geology and related areas such as mining, petroleum, environment, industry and construction. Traditionally, a human specialist is responsible for analyzing and explaining details about the type, composition, texture, shape and other properties using rock sa

Xiaomeng Xu, Huy Ha, Shuran Song

We present Dynamics-Guided Diffusion Model (DGDM), a data-driven framework for generating task-specific manipulator designs without task-specific training. Given object shapes and task specifications, DGDM generates sensor-less manipulator designs that can blindly manipulate objects towards desired motions and poses using an open-loop parallel motion. This f

Abhimanyu Nag, Samrat Gupta, Sudipan Sinha, Arka Datta

Decentralized Finance (DeFi) governance models have become increasingly complex due to the involvement of numerous independent agents, each with their own incentives and strategies. To effectively analyze these systems, we propose using Multi Agent Influence Diagrams (MAIDs) as a powerful tool for modeling and studying the strategic interactions within DeFi

Subhaya Bose, Haiqin Wang, Xinpeng Xu, Arvind Gopinath

The directed migration of cells toward stiffer substrate regions or durotaxis is relevant to tissue development and tumor progression. Here, we introduce a phenomenological model for single cell durotaxis that incorporates both elastic deformation-mediated cell-substrate interactions and the stochasticity of cell migration. Our model is motivated by a key ob

Ting Lin

How quarks and gluons conspire to provide the total spin of proton is a long-standing puzzle in quantum chromodynamics (QCD). The unique capability of RHIC, that can provide longitudinally polarized $p+p$ collisions at both $\sqrt{s} = 200$ GeV and $\sqrt{s} = 510$ GeV, opened new territory to constrain the helicity structure of the proton with unprecedented