December 2024 arXiv papers — page 5

Jean Bertoin

Consider first a memoryless population model described by the usual branching process with a given mean reproduction matrix on a finite space of types. Motivated by the consequences of atavism in Evolutionary Biology, we are interested in a modification of the dynamics where individuals keep full memory of their forebears and procreation involves the reactiv

Zhaobin Mo, Haotian Xiang, Xuan Di

Spatiotemporal prediction over graphs (STPG) is challenging, because real-world data suffers from the Out-of-Distribution (OOD) generalization problem, where test data follow different distributions from training ones. To address this issue, Invariant Risk Minimization (IRM) has emerged as a promising approach for learning invariant representations across di

Peng Fei Zhang, Chen Xiang Wang, Li Hong Li, Lei Wang

The China Space Station (CSS) is currently in orbit and carries the high-precision optical atomic clock with stability of approximately $2.0 \times 10^{-15} / \sqrt{\tau}$ in its experiment module. We have developed a model to determine the gravity potential (GP) based on the gravity frequency shift equation and have created both one-way and dual-frequency t

Shi-Feng Peng, Guolei Sun, Yong Li, Hongsong Wang

The primary challenge of cross-domain few-shot segmentation (CD-FSS) is the domain disparity between the training and inference phases, which can exist in either the input data or the target classes. Previous models struggle to learn feature representations that generalize to various unknown domains from limited training domain samples. In contrast, the larg

Mohamed Belfkir, Mohamed Amin Loualidi, Salah Nasri

Modular invariance has recently paved new promising directions in flavor model building. Motivated by this development, we present in this work the first implementation of the $S_3$ modular symmetry within the Pati-Salam unification framework, addressing the flavor structure of quarks and leptons. Assigning left- and right-handed matter fields as $S_3$ doubl

Miftahul Hilmi, Nicha Leethochawalit, Michele Trenti, Benjamin Metha

Potential contamination from low/intermediate-redshift galaxies, such as objects with a prominent Balmer break, affects the photometric selection of high-redshift galaxies through identification of a Lyman break. Traditionally, contamination is estimated from spectroscopic follow-up and/or simulations. Here, we introduce a novel approach to estimating contam

Haocheng Guo, Yaqiong Zhang, Lieyang Chen, Arfat Ahmad Khan

The key to ensuring the safe obstacle avoidance function of autonomous driving systems lies in the use of extremely accurate vehicle recognition techniques. However, the variability of the actual road environment and the diverse characteristics of vehicles and pedestrians together constitute a huge obstacle to improving detection accuracy, posing a serious c

Huanting Wang, Patrick Lenihan, Zheng Wang

Supervised machine learning techniques have shown promising results in code analysis and optimization problems. However, a learning-based solution can be brittle because minor changes in hardware or application workloads -- such as facing a new CPU architecture or code pattern -- may jeopardize decision accuracy, ultimately undermining model robustness. We i

Haotian Liu, Zhiqing Wei, Xiyang Wang, Yangyang Niu

Integrated sensing and communication (ISAC) has emerged as a pivotal enabling technology for sixth-generation (6G) mobile communication system. The ISAC research in dense urban areas has been plaguing by severe multipath interference, propelling the thorough research of ISAC multipath interference elimination. However, transforming the multipath component (M

Ashay Athalye, Nishanth Kumar, Tom Silver, Yichao Liang

Our aim is to learn to solve long-horizon decision-making problems in complex robotics domains given low-level skills and a handful of short-horizon demonstrations containing sequences of images. To this end, we focus on learning abstract symbolic world models that facilitate zero-shot generalization to novel goals via planning. A critical component of such

Shi Pi, Misao Sasaki, Volodymyr Takhistov, Jianing Wang

Primordial black holes (PBHs) can form from gravitational collapse of large overdensities in the early Universe, giving rise to rich phenomena in astrophysics and cosmology. We develop a novel, general, and systematic method based on theory of density contrast peaks to calculate the abundance of PBHs for a broad power spectrum of curvature perturbations with

Takeru K. Suzuki, Keiichi Ohnaka, Yuki Yasuda

We investigate the driving mechanism of Alfv\'en wave-driven stellar winds from red giant stars, Arcturus ($\alpha$ Boo; K1.5 III) and Aldebaran ($\alpha$ Tau; K5 III), with nonideal MHD simulations in 1D super-radially open flux tubes. Since the atmosphere is not fully ionized, upward propagating Alfv\'enic waves excited by surface convection are affected b

Takayuki Suzuki

In this study, we theoretically analyzed a control protocol based on ``time-dependent resonance" in nearly adiabatic two-level quantum systems, demonstrating that it exhibits properties equivalent to adiabatic control. This protocol is based on ``time-dependent resonance", where the frequency corresponds to the time-dependent energy gap. Through numerical ca

Mingyu Guo, Menghe Wu, Hong Guo, Xiaomei Kuang

In this letter, we investigate the strong gravitational lensing effects around two classes of rotating regular black holes, which behave as non-singular Minkowski core at the center. Starting from the null geodesic in the equatorial plane of the regular black holes, we analyze the deflection angle for both prograde photons and retrograde photons, which are f

Botao Yu, Guanqun Song, Ting Zhu

Achieving carbon neutrality has become a critical goal in mitigating the environmental impacts of human activities, particularly in the face of global climate challenges. Input/Output (I/O) devices, such as keyboards, mice, displays, and printers, contribute significantly to greenhouse gas emissions through their manufacturing, operation, and disposal proces

Volodymyr Mazorchuk, Xiaoyu Zhu

We determine the combinatorics of transitive module categories over the monoidal category of finite dimensional $\mathfrak{sl}_3$-modules which arise when acting by the latter monoidal category on arbitrary simple $\mathfrak{sl}_3$-modules. This gives us a family of eight graphs which can be viewed as $\mathfrak{sl}_3$-generalizations of the classical infini

Kennett L. Dela Rosa

The zero-dilation index $d(A) $ of a matrix $A$ is the largest integer $k$ for which $\begin{bmatrix}0_k& *\\ * & *\end{bmatrix}$ is unitarily similar to $A$. In this study, the zero-dilation indices of certain block matrices are considered, namely, the block matrix analogues of companion matrices and upper triangular KMS matrices, respectively shown as \[\m

Zijie Li, Henry Li, Yichun Shi, Amir Barati Farimani

Diffusion models have gained tremendous success in text-to-image generation, yet still lag behind with visual understanding tasks, an area dominated by autoregressive vision-language models. We propose a large-scale and fully end-to-end diffusion model for multi-modal understanding and generation that significantly improves on existing diffusion-based multim

Chunyang Liao

Machine learning based partial differential equations (PDEs) solvers have received great attention in recent years. Most progress in this area has been driven by deep neural networks such as physics-informed neural networks (PINNs) and kernel method. In this paper, we introduce a random feature based framework toward efficiently solving PDEs. Random feature

Miran Jeong, Sejong Kim, Tin-Yau Tam

A new class of weighted spectral geometric means has recently been introduced. In this paper, we present its inequalities in terms of the L\"{o}wner order, operator norm, and trace. Moreover, we establish a log-majorization relationship between the new spectral geometric mean, and the R\'{e}nyi relative operator entropy. We also give the quantum divergence o

Motokazu Abe, Okuto Morikawa, Hiroshi Suzuki

We carry out a hybrid Monte Carlo (HMC) simulation of the $SU(2)/\mathbb{Z}_2$ Yang--Mills theory in which the $\mathbb{Z}_N$ 2-form flat gauge field (the 't~Hooft flux) is explicitly treated as one of the dynamical variables. We observe that our HMC algorithm in the $SU(2)/\mathbb{Z}_2$ theory drastically reduces autocorrelation lengths of the topological c

F. S. Al-Kharousi, A. Umar, M. M. Zubairu

Let $[n]$ be a finite chain $\{1, 2, \ldots, n\}$, and let $\mathcal{IC}_{n}$ be the semigroup consisting of all isotone and order-decreasing injective partial transformations on $[n]$. In addition, let $\mathcal{Q}^{\prime}_{n} = \{\alpha \in \mathcal{IC}_{n} : \, 1\not \in \text{Dom } \alpha\}$ be the subsemigroup of $\mathcal{IC}_{n}$, consisting of all t

Seongsu Jeon, Yuchan Lee

For an irreducible polynomial $\chi(x)\in \mathcal{O}_k[x]$ of degree $n$, where $k$ is a number field and $\mathcal{O}_k$ its ring of integers, let $N(X, T)$ denote the number of $n \times n$ integral matrices whose characteristic polynomial is $\chi(x)$, bounded by a positive real number $T$ with respect to a certain norm. In this paper, we provide an asym

Jiexin Yu, Bingwei Long

The operator product expansion (OPE) is applied in conjunction with Pionless effective field theory to study the short-rang structure of nuclei. By matching the OPE with the selected nuclear potentials for nucleon-nucleon scattering states, we obtain the Wilson coefficients. The nucleon momentum distribution in the deuteron is then used to test the OPE again

Yagna Kaasaragadda, Silvija Kokalj-Filipovic

We present ReFormer, a generative AI (GAI) model that can efficiently generate synthetic radio-frequency (RF) data, or RF fakes, statistically similar to the data it was trained on, or with modified statistics, in order to augment datasets collected in real-world experiments. For applications like this, adaptability and scalability are important issues. This

Jiawei Wu, Masahito Hayashi, Marco Tomamichel

Noisy channels are a foundational resource for constructing cryptographic primitives such as string commitment and oblivious transfer. The noisy channel model has been extended to unfair noisy channels, where adversaries can influence the parameters of a memoryless channel. In this work, we introduce the unstructured noisy channel model as a generalization o

Yichen Gao, Guanqun Song, Ting Zhu

In this paper, we investigate the optimization of global quantum communication through satellite constellations. We address the challenge of quantum key distribution (QKD) across vast distances and the limitations posed by terrestrial fiber-optic networks. Our research focuses on the configuration of satellite constellations to improve QKD between ground sta

Junjie Li

BLAS is a fundamental building block of advanced linear algebra libraries and many modern scientific computing applications. GPUs are known for their strong arithmetic computing capabilities and are highly suited for BLAS operations. However, porting code to GPUs often requires significant effort, especially for large, complex codes or legacy codes, even for

Ulbossyn Ualikhanova, Aziza Altaibayeva, Surajit Chattopadhyay

The holographic principle, which has its roots in string theory and black hole thermodynamics, connects the maximum distance of a quantum field theory to its infrared cutoff, which is correlated with the vacuum energy. The present study explores a reconstruction scheme for the k-essence form of dark energy with the most generalized version of holographic dar

Megan Morrison, Lai-Sang Young

C. elegans locomotion is composed of switches between forward and reversal states punctuated by turns. This locomotory capability is necessary for the nematode to move towards attractive stimuli, escape noxious chemicals, and explore its environment. Although experimentalists have identified a number of premotor neurons as drivers of forward and reverse moti

Yiran Huang, Jian-Feng Yang, Haoda Fu

Modern AI algorithms require labeled data. In real world, majority of data are unlabeled. Labeling the data are costly. this is particularly true for some areas requiring special skills, such as reading radiology images by physicians. To most efficiently use expert's time for the data labeling, one promising approach is human-in-the-loop active learning algo

Sabah Al-Fedaghi

This paper is a sequel to an evolving research project on a diagrammatic methodology called thinging machine (TM). Initially, it was proposed as a base for conceptual modelling (e.g., conceptual UML) in areas such as requirement engineering. Conceptual modelling involves a high-level representation of a real-world system that integrates various components to

Arvind Ayyer, Nishu Kumari

We revisit factorizations of classical characters under various specializations, some old and some new. We first show that all characters of classical families of groups twisted by odd powers of an even primitive root of unity factorize into products of characters of smaller groups. Motivated by conjectures of Wagh and Prasad (Manuscr. Math. 2020), we then o

Helia Hashemi, Jason Eisner, Corby Rosset, Benjamin Van Durme

This paper introduces a framework for the automated evaluation of natural language texts. A manually constructed rubric describes how to assess multiple dimensions of interest. To evaluate a text, a large language model (LLM) is prompted with each rubric question and produces a distribution over potential responses. The LLM predictions often fail to agree we

Jia-Mian Li, Jian-Yi Chen, Bing-Zhao Li

The performance of space-time adaptive processing (STAP) is often degraded by factors such as limited sample size and moving targets. Traditional clutter covariance matrix (CCM) estimation relies on Euclidean metrics, which fail to capture the intrinsic geometric and structural properties of the covariance matrix, thus limiting the utilization of structural

Weijia Xu, Nebojsa Jojic, Sudha Rao, Chris Brockett

With rapid advances in large language models (LLMs), there has been an increasing application of LLMs in creative content ideation and generation. A critical question emerges: can current LLMs provide ideas that are diverse enough to truly bolster collective creativity? We examine two state-of-the-art LLMs, GPT-4 and LLaMA-3, on story generation and discover

Diji Yang, Linda Zeng, Kezhen Chen, Yi Zhang

While inference-time thinking allows Large Language Models (LLMs) to address complex problems, the extended thinking process can be unreliable or inconsistent because of the model's probabilistic nature, especially near its knowledge boundaries. Existing approaches attempt to mitigate this by having the model critique its own reasoning to make corrections. H

Yao Ge, Lingsheng Meng, David González G., Miaowen Wen

Even orthogonal time frequency space (OTFS) has been shown as a promising modulation scheme for high mobility doubly-selective fading channels, its attainability of full diversity order in either time or frequency selective fading channels has not been clarified. By performing pairwise error probability (PEP) analysis, we observe that the original OTFS syste

Dong Jun Choi, Alexander Molev, Uhi Rinn Suh

We construct a new family of affine $W$-algebras $W^k(\lambda,\mu)$ parameterized by partitions $\lambda$ and $\mu$ associated with the centralizers of nilpotent elements in $\mathfrak{gl}_N$. The new family unifies a few known classes of $W$-algebras. In particular, for the column-partition $\lambda$ we recover the affine $W$-algebras $W^k(\mathfrak{gl}_N,f

Gi-Ren Liu, Yuan-Chung Sheu, Hau-Tieng Wu

While ridges in the scalogram, determined by the squared modulus of analytic wavelet transform (AWT), is a widely accepted concept and utilized in nonstationary time series analysis, their behavior in noisy environments remains underexplored. Our object is to provide a theoretical foundation for scalogram ridges by defining ridges as a potentially set-valued

Ben Malin, Tatiana Kalganova, Nikoloas Boulgouris

This review examines the means with which faithfulness has been evaluated across open-ended summarization, question-answering and machine translation tasks. We find that the use of LLMs as a faithfulness evaluator is commonly the metric that is most highly correlated with human judgement. The means with which other studies have mitigated hallucinations is di

Yu-Bin Zhang, Li-Ye Xiao, Xian-Hui Zhong

Inspired by the latest experimental progress, we systematically study the OZI-allowed two-body strong decay properties of $1P$-, $1D$-, $2S$- and $2P$-wave $\Lambda_c$ baryons within the $j $-$j$ coupling scheme in the framework of the quark pair creation model. The calculations indicate that: (i) Taking the observed states $\Lambda_c(2595)^+$ and $\Lambda_c

Gaofeng Chen, Yaoduo Zhang, Li Huang, Pengfei Wang

Supervised deep-learning (SDL) techniques with paired training datasets have been widely studied for X-ray computed tomography (CT) image reconstruction. However, due to the difficulties of obtaining paired training datasets in clinical routine, the SDL methods are still away from common uses in clinical practices. In recent years, self-supervised deep-learn

Mehran Ebrahimi, Adrian Butscher, Hyunmin Cheong

Heretofore, the Serret-Frenet frame has been the ubiquitous choice for analyzing the elastic deformations of beam elements. It is well known that this frame is undefined at the inflection points and straight segments of the beam where its curvature is zero, leading to singularities and errors in their numerical analysis. On the other hand, there exists a les

Pablo G. Barrientos, Raul R. Chavez

We study the occurrence of historical behavior for almost every point in the setting of skew products with one-dimensional fiber dynamics. Under suitable ergodic conditions, we establish that a weak form of the arcsine law leads to the non-convergence of Birkhoff averages along almost every orbit. As an application, we show that this phenomenon occurs for on

Rajat Talak, Charis Georgiou, Jingnan Shi, Luca Carlone

Robust training of machine learning models in the presence of outliers has garnered attention across various domains. The use of robust losses is a popular approach and is known to mitigate the impact of outliers. We bring to light two literatures that have diverged in their ways of designing robust losses: one using M-estimation, which is popular in robotic

Syed Atif Ali, Salwa Din

Wireless Sensor Networks (WSNs) continue to experience rapid developments and integration into modern-day applications. Overall, WSNs collect and process relevant data through sensors or nodes and communicate with different networks for superior information management. Nevertheless, a primary concern relative to WSNs is security. Considering the high constra

Kai Du, Lei Li, Yongle Xie, Yang Yu

In this paper, we propose and implement a structure-preserving stochastic particle method for the Landau equation. The method is based on a particle system for the Landau equation, where pairwise grazing collisions are modeled as diffusion processes. By exploiting the unique structure of the particle system and a spherical Brownian motion sampling, the metho

Oraib Dawaghreh, Sharaf K. Magableh, Xuesong Wang, Mohammad Adnan Magableh

As traditional large hydropower has been extensively exploited, micro-hydro systems have caught research increasing interest. New engineering challenges arise in developing micro-hydro systems in areas with significant elevation but prohibitive horizontal distances between primary reservoirs. This study addresses these challenges by proposing a cascade-pumpe

Syed Atif Ali, Salwa Din

The introduction sets the stage for exploring collaborative approaches to bolstering smart vehicle cybersecurity through AI-driven threat detection. As the automotive industry increasingly adopts connected and automated vehicles (CAVs), the need for robust cybersecurity measures becomes paramount. With the emergence of new vulnerabilities and security requir

Diksha Goel

Phishing is an online identity theft technique where attackers steal users personal information, leading to financial losses for individuals and organizations. With the increasing adoption of smartphones, which provide functionalities similar to desktop computers, attackers are targeting mobile users. Smishing, a phishing attack carried out through Short Mes

Kensuke Aoki

For a finite extension $K/\mathbb{Q}_p$ and a split reductive group $G$ over $\mathcal{O}_K$, let $\overline{\rho} \colon \mathrm{Gal}_K \to G(\overline{\mathbb{F}}_p)$ be a continuous quasi-semisimple mod $p$ $G$-valued representation of the absolute Galois group $\mathrm{Gal}_K$. Let $\overline{\rho}^{\mathrm{ab}}$ be the abelianization of $\overline{\rho}

Mehran Ebrahimi, Hyunmin Cheong, Pradeep Kumar Jayaraman, Farhad Javid

In optimizing real-world structures, due to fabrication or budgetary restraints, the design variables may be restricted to a set of standard engineering choices. Such variables, commonly called categorical variables, are discrete and unordered in essence, precluding the utilization of gradient-based optimizers for the problems containing them. In this paper,

Raymond Bernard, Shaina Raza, Subhabrata Das, Rahul Murugan

Despite the remarkable coherence of Large Language Models (LLMs), existing evaluation methods often suffer from fluency bias and rely heavily on multiple-choice formats, making it difficult to assess factual accuracy and complex reasoning effectively. LLMs thus frequently generate factually inaccurate responses, especially in complex reasoning tasks, highlig

Xiaoqing Wang, Hongli Fan, Zhengguo Tan, Serge Vasylechko

Purpose: To develop a rapid, high-resolution and distortion-free technique for simultaneous water-fat separation, $R_{2}^{*}$ and $B_{0}$ mapping of the fetal brain at 3T. Methods: A 2D multi-echo radial FLASH sequence with blip gradients is adapted for data acquisition during maternal free breathing. A calibrationless model-based reconstruction with sparsit

Sripan Mondal, Akash Bairagi, A. K. Srivastava

We study the asymmetric interaction of wave-like velocity perturbation with a coronal current sheet (CS) in the presence of resistivity, thermal conduction (TC) and radiative cooling (RC). We analyze the dynamics and energetics of CS in four cases, namely, (i) no energy loss, (ii) TC only, (iii) RC only and, (iv) TC+RC. Before fragmentation, thinning and elo

Zongbiao Li, Xiezhao Li, Yinghao Cui, Yijun Chen

The number of parameters in large-scale language models based on transformers is gradually increasing, and the scale of computing clusters is also growing. The technology of quickly mobilizing large amounts of computing resources for parallel computing is becoming increasingly important. In this paper, we propose an automatic parallel algorithm that automati

Grigoris Paouris, Peter Pivovarov, Kateryna Tatarko

The Petty projection inequality is a fundamental affine isoperimetric principle for convex sets. It has shaped several directions of research in convex geometry which forged new connections between projection bodies, centroid bodies, and mixed volume inequalities. We establish several different empirical forms of the Petty projection inequality by re-examini

Jiasheng Zhang, Deqiang Ouyang, Shuang Liang, Jie Shao

Predicting missing facts for temporal knowledge graphs (TKGs) is a fundamental task, called temporal knowledge graph completion (TKGC). One key challenge in this task is the imbalance in data distribution, where facts are unevenly spread across entities and timestamps. This imbalance can lead to poor completion performance or long-tail entities and timestamp

Chen Yang, Chao Chen, Runyu Ma, Ying Liang

A recently discovered family of Kagome lattice materials, $\emph{A}\mathrm{V}_{3}\mathrm{Sb}_{5}$($\emph{A}$= $\mathrm{K,Rb,Cs}$), has attracted great interest, especially in the debate over its dominant superconducting pairing symmetry. To explore this issue, we study the superconducting pairing behavior within the Kagome-Hubbard model through the constrain

Stavros Garoufalidis, Tao Yu

We conjecture (and prove for once-punctured torus bundles) that the Bonahon--Wong--Yang invariants of pseudo-Anosov homeomorphisms of a punctured surface at roots of unity coincide with the 1-loop invariant of their mapping torus at roots of unity. This explains the topological invariance of the BWY invariants and how their volume conjecture, to all orders,

Fariba Fateh

This paper presents the development of "Control-Sync," a novel firmware for universal inverters in microgrids, designed to enhance grid stability and flexibility. As hybrid PV-battery systems become increasingly prevalent, there is a critical need for inverters capable of efficiently transitioning between grid-forming (GFM) and grid-following (GFL) modes. Ou

Ramla Ijaz, Kevin Boos, Lin Zhong

This paper reports our experience of providing lightweight correctness guarantees to an open-source Rust OS, Theseus. First, we report new developments in intralingual design that leverage Rust's type system to enforce additional invariants at compile time, trusting the Rust compiler. Second, we develop a hybrid approach that combines formal verification, ty

Min Long, Hongyu Lu, Han-Qing Wu, Zi Yang Meng

Employing the state-of-the-art time-dependent variational principle (TDVP) algorithm, we compute the spectra of charge-neutral excitations in the $\nu=1/2$ (bosonic) \updated{ and $1/3$ (fermionic) fractional Chern insulator (FCI)} on the Haldane honeycomb lattice model. The magnetoroton visualized from the dynamic density structure factor acquires a minimum

Xiaofang Liu, Changjing Zeng, Jieci Wang

A comprehensive study integrating the microscopic structure of spacetime and the principle of quantum superposition is capable of offering a fundamental bottom-up approach for understanding the quantum aspect of gravity. In this paper, we present a framework for the superposition causal diamond spacetime and analyze the behavior of quantum entanglement influ

Geonho Hwang, Yesom Park, Yueun Lee, Jooyoung Hahn

This paper proposes a theoretical framework for analyzing Modified Incomplete LU (MILU) preconditioners. Considering a generalized MILU preconditioner on a weighted undirected graph with self-loops, we extend its applicability beyond matrices derived by Poisson equation solvers on uniform grids with compact stencils. A major contribution is, a novel measure,

Yichi Zhang, Zhuo Chen, Lingbing Guo, Yajing Xu

Large language models (LLMs) have demonstrated exceptional performance in text generation within current NLP research. However, the lack of factual accuracy is still a dark cloud hanging over the LLM skyscraper. Structural knowledge prompting (SKP) is a prominent paradigm to integrate external knowledge into LLMs by incorporating structural representations,

Edwin Arkel Rios, Jansen Christopher Yuanda, Vincent Leon Ghanz, Cheng-Wei Yu

Ultra-fine-grained image recognition (UFGIR) is a challenging task that involves classifying images within a macro-category. While traditional FGIR deals with classifying different species, UFGIR goes beyond by classifying sub-categories within a species such as cultivars of a plant. In recent times the usage of Vision Transformer-based backbones has allowed

Sophia Nguyen, Beihao Zhou, Yi Ding, Sihang Liu

In this work, we study LLMs from a carbon emission perspective, addressing both operational and embodied emissions, and paving the way for sustainable LLM serving. We characterize the performance and energy of LLaMA with 1B, 3B, and 7B parameters using two Nvidia GPU types, a latest-generation RTX6000 Ada and an older-generation T4. We analytically model ope

Hany Ragab, Sidney Givigi, Aboelmagd Noureldin

Modern on-road navigation systems heavily depend on integrating speed measurements with inertial navigation systems (INS) and global navigation satellite systems (GNSS). Telemetry-based applications typically source speed data from the On-Board Diagnostic II (OBD-II) system. However, the method of deriving speed, as well as the types of sensors used to measu

Giuliano Lorenzoni, Ivens Portugal, Paulo Alencar, Donald Cowan

This study evaluates fine-tuning strategies for text classification using the DistilBERT model, specifically the distilbert-base-uncased-finetuned-sst-2-english variant. Through structured experiments, we examine the influence of hyperparameters such as learning rate, batch size, and epochs on accuracy, F1-score, and loss. Polynomial regression analyses capt

Becca Spejcher, Noel D. Richardson, Herbert Pablo, Marina Beltran

Luminous Blue Variables (LBVs) are enigmatic, evolved, massive stars. Their variability has been observed to be episodic with large eruptions, along with variations on time-scales of days to decades. We have extracted light curves of 37 LBVs from the first four years of the TESS mission. These light curves provide two years of photometric time-series for sta

Bing Zhang

Recently, a short-duration GRB with supernova association (GRB 200826A) and two long-duration GRBs with kilonova associations (GRB 211211A and GRB 230307A) have been detected, which demolished the hope for a tidy connection between GRB duration and their progenitor systems. Here I summarize various physical factors that can shape the duration of a GRB and pr

Jiajia Lu, Shuyong Luan, Shenghui Guo, Libing Duan

A knowledge gap exists for flows and transport phenomena at the Angstrom scale when the Poisson Nernst Planck equation based on the concept of electrical double layer (EDL) fails. We discovered that streaming conductance becomes pressure dependent in Angstrom channels using latent track membranes. The streaming current emerges only when the applied pressure

Jianfei Xu, Thanet Markchom, Huizhi Liang

The complexity of stacked imaging and the massive number of radiographs make writing radiology reports complex and inefficient. Even highly experienced radiologists struggle to maintain accuracy and consistency in interpreting radiographs under prolonged high-intensity work. To address these issues, this work proposes the CRRG-CLIP Model (Chest Radiology Rep

Wei Chen, Yi Zhou

In the realm of class-incremental learning (CIL), alleviating the catastrophic forgetting problem is a pivotal challenge. This paper discovers a counter-intuitive observation: by incorporating domain shift into CIL tasks, the forgetting rate is significantly reduced. Our comprehensive studies demonstrate that incorporating domain shift leads to a clearer sep

Daeyoung Jeong, Tongseok Lim, Euncheol Shin

In economic settings such as learning, social behavior, and financial contagion, agents interact through interdependent networks. This paper examines how a decision maker (DM) can design an optimal intervention strategy under network uncertainty, modeled as a zero-sum game against an adversarial ``Nature'' that reconfigures the network within an uncertainty

Nicholas Christoffersen, Kyle Luh, Hoi H. Nguyen, Jingheng Wang

We show that, with very high probability, the random graph Laplacian has simple spectrum. Our method provides a quantitatively effective estimate of the spectral gaps. Along the way, we establish results on affine no-gaps delocalization, no-structure delocalization, overcrowding and small entries of the eigenvectors for the Laplacian model. These findings ar

Fabian Retkowski, Alexander Waibel

Large language models (LLMs) struggle with precise length control, particularly in zero-shot settings. We conduct a comprehensive study evaluating LLMs' length control capabilities across multiple measures and propose practical methods to improve controllability. Our experiments with LLaMA 3 reveal stark differences in length adherence across measures and hi

Layne Hall

A pseudo-Anosov flow is said to have perfect fits if there are stable and unstable leaves that are asymptotic in the universal cover. We give an algorithm to decide, given a box decomposition of a pseudo-Anosov flow, if the flow has perfect fits. As a corollary, we obtain an algorithm to decide whether two flows without perfect fits are orbit equivalent.

Hui Liu, Hui Li, Sizhong Zou, Kaifan Ji

The in-flight calibration and performance of the Solar Disk Imager (SDI), which is a pivotal instrument of the Lyman-alpha Solar Telescope (LST) onboard the Advanced Space-based Solar Observatory (ASO-S) mission, suggested a much lower spatial resolution than expected. In this paper, we developed the SDI point-spread function (PSF) and Image Bivariate Optimi

Wenjun Shi

Using the Standard Map model, this study explores the quantum Loschmidt Echo (LE) decay laws for mixed-type phase spaces, including edge of chaos and chaotic sea regimes. A universal decay law is proposed and numerically verified, characterizing the intricate dynamics of LE decay processes with a semi-classical statistical approach. Key findings include thre

Wangyu Wu, Siqi Song, Xianglin Qiu, Xiaowei Huang

Cross-Domain Sequential Recommendation (CDSR) aims to predict future user interactions based on historical interactions across multiple domains. The key challenge in CDSR is effectively capturing cross-domain user preferences by fully leveraging both intra-sequence and inter-sequence item interactions. In this paper, we propose a novel method, Image Fusion f

Shane Babcock, Carter Benson, Giacomo De Colle, Sydney Cohen

Infectious diseases remain a critical global health challenge, and the integration of standardized ontologies plays a vital role in managing related data. The Infectious Disease Ontology (IDO) and its extensions, such as the Coronavirus Infectious Disease Ontology (CIDO), are essential for organizing and disseminating information related to infectious diseas

Si-Wei Han, Zhichun Ouyang, Zhiyao Hu, Jun Feng

We revisit the problem of the thermalization process in an entropic formulation for the Unruh-DeWitt (UDW) detector outside a Schwarzschild black hole. We derive the late-time dynamics of the detector in the context of open quantum system, and capture the path distinguishability and thermodynamic irreversibility of detector thermalization process by using qu

Di Wu, Jing-Zhi Zhou, Yu-Ting Kuang, Zhi-Chao Li

Observational constraints on small-scale primordial gravitational waves are considerably weaker than those on large scales. We focus on scenarios with significant primordial gravitational waves and curvature perturbations on small scales, studying the energy density spectrum of the second-order TSIGW. By leveraging current data from CMB, BAO, and PTA, combin

Xuanzhao Gao, Xiaofeng Li, Jinguo Liu

Constraint satisfaction problems (CSPs) are a class of problems that are ubiquitous in science and engineering. It features a collection of constraints specified over subsets of variables. A CSP can be solved either directly or by reducing it to other problems. This paper introduces the Julia ecosystem for solving and analyzing CSPs, focusing on the programm

Tadahiro Taniguchi, Ryo Ueda, Tomoaki Nakamura, Masahiro Suzuki

Large Language Models (LLMs) have demonstrated a remarkable ability to capture extensive world knowledge, yet how this is achieved without direct sensorimotor experience remains a fundamental puzzle. This study proposes a novel theoretical solution by introducing the Collective World Model hypothesis. We argue that an LLM does not learn a world model from sc

Jun Murakami

In this paper, the volume conjecture for double twist knots are proved. The main tool is the complexified tetrahedron and the associated $\mathrm{SL}(2, \mathbb{C})$ representation of the fundamental group. A complexified tetrahedron is a version of a truncated or a doubly truncated tetrahedron whose edge lengths and the dihedral angles are complexified. The

Hikari Tomita, Nobuhiro Nakamura, Shoichi Ishida, Toshio Kamiya

Recently, many studies have increasingly explored the use of large language models (LLMs) to generate research ideas and scientific hypotheses. However, real-world research and development often require solving complex, interdisciplinary challenges where solutions may not be readily found through existing knowledge related to the problem. Therefore, it is de

Michael Gubanov, Anna Pyayt, Aleksandra Karolak

Here, we describe one of the first Web-scale hybrid Knowledge Graph (KG)-Large Language Model (LLM), populated with the latest peer-reviewed medical knowledge on colorectal Cancer. It is currently being evaluated to assist with both medical research and clinical information retrieval tasks at Moffitt Cancer Center, which is one of the top Cancer centers in t

Vitor H. Fernandes, Jörg Koppitz, Tiwadee Musunthia

In this paper, we consider the monoids of all endomorphisms, of all weak endomorphisms, of all strong endomorphisms and of all strong weak endomorphisms of a star graph with a finite number of vertices. Our main objective is to exhibit a presentation for each of them.

X. Zou, H. F. Chau

Axelrod model is an opinion dynamics model such that each agent on a square lattice has a finite number of possible nominal opinions on a finite number of issues that are usually called features in the field. Moreover, its dynamics between two agents is assimilative in the sense that the number of agreeing features between them never decreases upon interacti

Zixuan Yin, Han Sun, Ningzhong Liu, Huiyu Zhou

Lidars and cameras play essential roles in autonomous driving, offering complementary information for 3D detection. The state-of-the-art fusion methods integrate them at the feature level, but they mostly rely on the learned soft association between point clouds and images, which lacks interpretability and neglects the hard association between them. In this

Max T. M. Ng, Hani S. Mahmassani

This paper investigates the potential of autonomous minibuses which take on-demand directional routes for pick-up and drop-off in a grid network of wider area with low density, followed by fixed routes in areas with demand. Mathematical formulation for generalized costs demonstrates its benefits, with indicators proposed to select existing bus routes for con

Max T. M. Ng, Joseph Schofer, Hani S. Mahmassani

This paper analyzes and compares patterns of U.S. domestic rail freight volumes during, and after the disruptions caused by the 2007-2009 Great Recession and the COVID-19 pandemic in 2020. Trends in rail and intermodal shipment data are examined in conjunction with economic indicators, focusing on the extent of drop and recovery of freight volumes of various

Betim Sherifi, Khaled Slhoub, Fitzroy Nembhard

Having a high quality software is essential in software engineering, which requires robust validation and verification processes during testing activities. Manual testing, while effective, can be time consuming and costly, leading to an increased demand for automated methods. Recent advancements in Large Language Models (LLMs) have significantly influenced s

Seungyeob Jwa, Jeehun Kim, Sunghee Kim, Sunkyu Lee

We report recent progress on $\varepsilon_K$ evaluated directly from the standard model (SM) with lattice QCD inputs such as $\hat{B}_K$, exclusive $|V_{cb}|$, $|V_{us}|$, $|V_{ud}|$, $\xi_0$, $\xi_2$, $\xi_\text{LD}$, $f_K$, and $m_c$. We find that the standard model with exclusive $|V_{cb}|$ and lattice QCD inputs describes only $2/3 \cong 65\%$ of the exp

Jinyuan Chen

In this work, we propose an error-free, information-theoretically secure, asynchronous multi-valued validated Byzantine agreement (MVBA) protocol, called OciorMVBA. This protocol achieves MVBA consensus on a message $\boldsymbol{w}$ with expected $O(n |\boldsymbol{w}|\log n + n^2 \log q)$ communication bits, expected $O(n^2)$ messages, expected $O(\log n)$ r

Ran Li

We study the decoherence effect of quantum superposition in de Sitter (dS) spacetime due to the presence of the cosmological horizon. Using the algebraic approach of quantum field theory on curved spacetime, we derive the precise expression for the expected number of entangling particles in the scalar field case. This expression establishes the relation betw