April 2024 arXiv papers — page 52

G. A. Gontcharov, S. S. Savchenko, A. A. Marchuk, C. J. Bonatto

We fit various colour-magnitude diagrams (CMDs) of the high-latitude Galactic globular clusters NGC\,5024 (M53), NGC\,5053, NGC\,5272 (M3), NGC\,5466, and NGC\,7099 (M30) by isochrones from the Dartmouth Stellar Evolution Database and Bag of Stellar Tracks and Isochrones for $\alpha$-enrichment [$\alpha$/Fe]$=+0.4$. For the CMDs, we use data sets from {\it H

Tatsuya Suzuki, Masanori Nagao, Yuki Maruyama, Satoshi Watauchi

Sb- and Se-substituted CeOBiS2 single crystals have been successfully grown using CsCl/KCl flux. Sb and Se substitution dependence of the superconductivity on CeOBiS2 was investigated and the superconducting phase diagram at above 0.3 K was described. The non-linear boundary between superconductivity and non-superconductivity was revealed.

Jiaming He, Wenbo Jiang, Guanyu Hou, Wenshu Fan

Mainstream backdoor attacks on large language models (LLMs) typically set a fixed trigger in the input instance and specific responses for triggered queries. However, the fixed trigger setting (e.g., unusual words) may be easily detected by human detection, limiting the effectiveness and practicality in real-world scenarios. To enhance the stealthiness of ba

Thomas Guyet, Pierre Pinson, Enoal Gesny

Improving the future of healthcare starts by better understanding the current actual practices in hospital settings. This motivates the objective of discovering typical care pathways from patient data. Revealing typical care pathways can be achieved through clustering. The difficulty in clustering care pathways, represented by sequences of timestamped events

Carynelisa Haspel, Yoav Yair

Transient luminous events (TLEs) is the collective name given to mesospheric electrical breakdown phenomena occurring in conjunction with strong lightning discharges in tropospheric thunderstorms. They include elves, sprites, haloes and jets, and are characterized by short lived optical emissions, mostly of red (665 nm) and blue (337 nm) wavelengths. Sprites

Kiyoon Eum

Let $\Omega$ be a bounded pseudoconvex domain in $\mathbb{C}^n$, and let $\phi$ be a strictly plurisubharmonic function on $\Omega$. For each $k\in\mathbb{N}$, we consider determinantal point process $\Lambda_k$ with kernel $K_{k\phi}$, where $K_{k\phi}$ is the reproducing kernel of infinite dimensional weighted Bergman space $H(k\phi)$ with weight $e^{-k\ph

Feodor F. Dragan, Guillaume Ducoffe

A graph is called $\alpha_i$-metric ($i \in {\cal N}$) if it satisfies the following $\alpha_i$-metric property for every vertices $u, w, v$ and $x$: if a shortest path between $u$ and $w$ and a shortest path between $x$ and $v$ share a terminal edge $vw$, then $d(u,x) \ge d(u,v) + d(v,x) - i$. The latter is a discrete relaxation of the property that in Eucl

Uyen Ngoc Le, Hieu Binh Le, Hiroki Saito

We investigate the ground state and dynamics of a mixture of spin-1 and spin-2 Bose-Einstein condensates of ${}^{87}{\rm{Rb}}$ atoms. For the experimentally measured interaction coefficients, the ground state exhibits phase separation between the spin-1 ferromagnetic state and the spin-2 nematic state. At the interface between them, a partially polarized spi

F. Stier, A. -A. Haghighirad, G. Garbarino, S. Mishra

We present a high-resolution single crystal x-ray diffraction study of kagome-superconductor \cvs, exploring its response to variations in pressure and temperature. We discover that at low temperatures, the structural modulations of the electronic superlattice, commonly associated with charge-density-wave order, undergo a transformation around $p \sim$ 0.7 G

Mário S. Alvim, Sophia Knight, José C. Oliveira

Subjective Logic (SL) is a logic incorporating uncertainty and opinions for agents in dynamic systems. In this work, we investigate the use of subjective logic to model opinions and belief change in social networks. In particular, we work toward the development of a subjective logic belief/opinion update function appropriate for modeling belief change as com

Dongwook Ghim, Masazumi Honda

This note discusses a method for computing the energy spectra of quantum field theory utilizing digital quantum simulation. A quantum algorithm, called coherent imaging spectroscopy, quenches the vacuum with a time-oscillating perturbation and then reads off the excited energy levels from the loss in the vacuum-to-vacuum probability following the quench. As

Uriel Elinos Calderón, Edgar Guzmán-González, Marco Maceda

We study numerically an $s$-wave holographic superconductor from an anti-de Sitter-Einstein-Born-Infeld black hole with backreaction in the context of the anti-de Sitter/conformal field theory (AdS/CFT) correspondence. By introducing a parameter $G$ to tune the effects of the backreaction, we can study non-perturbatively how the condensation and conductivity

Peiwen Li, Xin Wang, Zeyang Zhang, Yuan Meng

In the field of Artificial Intelligence for Information Technology Operations, causal discovery is pivotal for operation and maintenance of graph construction, facilitating downstream industrial tasks such as root cause analysis. Temporal causal discovery, as an emerging method, aims to identify temporal causal relationships between variables directly from o

Mitsuru Urushibata, Masayuki Ohzeki

We investigate a quantum perceptron implemented on a quantum circuit using a repeat until method. We evaluate this from the perspective of capacity, one of the performance evaluation measures for perceptions. We assess a Gardner volume, defined as a volume of coefficients of the perceptron that can correctly classify given training examples using the replica

Raghav G. Jha

The circuit complexity for Hamiltonian simulation of the sparsified SYK model with $N$ Majorana fermions and $q=4$ (quartic interactions) which retains holographic features (referred to as `minimal holographic sparsified SYK') with $k\ll N^{3}/24$ (where $k$ is the total number of interaction terms times 1/$N$) using second-order Trotter method and Jordan-Wi

Chao Chang, Yuning Yang

Recently, randomized algorithms for low-rank approximation of quaternion matrices have received increasing attention. However, for large-scale problems, existing quaternion orthonormalizations are inefficient, leading to slow rangefinders. To address this, by appropriately leveraging efficient scientific computing libraries in the complex arithmetic, this wo

Hoin Jung, Hyunsoo Cho, Myungje Choi, Joowon Lee

When it comes to a personalized item recommendation system, It is essential to extract users' preferences and purchasing patterns. Assuming that users in the real world form a cluster and there is common favoritism in each cluster, in this work, we introduce Co-Clustering Wrapper (CCW). We compute co-clusters of users and items with co-clustering algorithms

Matthew D. Duez

The gravitational wave signals of black hole-neutron star (BHNS) binary systems have now been detected, and future detections might be accompanied by electromagnetic counterparts. BHNS mergers involve much of the same physics as binary neutron star mergers: strong gravity, nuclear density matter, neutrino radiation, and magnetic turbulence. They also share w

Yuxi Fu, Qizhe Yang, Yangluo Zheng

An $\mathsf{F}_{d}$ upper bound for the reachability problem in vector addition systems with states (VASS) in fixed dimension is given, where $\mathsf{F}_d$ is the $d$-th level of the Grzegorczyk hierarchy of complexity classes. The new algorithm combines the idea of the linear path scheme characterization of the reachability in the $2$-dimension VASSes with

Shounak Sural, Nishad Sahu, Ragunathan Rajkumar

The fusion of multimodal sensor data streams such as camera images and lidar point clouds plays an important role in the operation of autonomous vehicles (AVs). Robust perception across a range of adverse weather and lighting conditions is specifically required for AVs to be deployed widely. While multi-sensor fusion networks have been previously developed f

Clément Christophe, Praveen K Kanithi, Prateek Munjal, Tathagata Raha

This study presents a comprehensive analysis and comparison of two predominant fine-tuning methodologies - full-parameter fine-tuning and parameter-efficient tuning - within the context of medical Large Language Models (LLMs). We developed and refined a series of LLMs, based on the Llama-2 architecture, specifically designed to enhance medical knowledge retr

Shiyuan Sun, Fang Yang, Weidong Mei, Jian Song

Optical intelligent reflecting surface (OIRS) has attracted increasing attention due to its capability of overcoming signal blockages in visible light communication (VLC), an emerging technology for the next-generation advanced transceivers. However, current works on OIRS predominantly assume known channel state information (CSI), which is essential to pract

Ling Yue, Sixue Xing, Jintai Chen, Tianfan Fu

Large Language Models (LLMs) and multi-agent systems have shown impressive capabilities in natural language tasks but face challenges in clinical trial applications, primarily due to limited access to external knowledge. Recognizing the potential of advanced clinical trial tools that aggregate and predict based on the latest medical data, we propose an integ

Wenzhi Wang, Wei Yi

Density-matrix topology, defined through the geometric property of the relevant modular Hamiltonian, can undergo transitions in the corresponding open-system dynamics. While symmetry considerations are crucial to ensure such a dynamic topological transition, we show that a hidden parity-time symmetry can further facilitate it. Considering the Lindbladian dyn

Min Ju, Pengcheng Chu, Xuhao Wu, He Liu

In this study, we extend the MIT bag model by incorporating the vector interaction among quarks and introducing a density-dependent bag pressure.Then we proceed to investigate the thermodynamic properties of strange quark matter (SQM) and pure up-down quark matter (udQM) in quark stars (QSs).Our findings demonstrate that the density dependence of bag pressur

Jieming Zhu, Mengqun Jin, Qijiong Liu, Zexuan Qiu

Embedding-based retrieval serves as a dominant approach to candidate item matching for industrial recommender systems. With the success of generative AI, generative retrieval has recently emerged as a new retrieval paradigm for recommendation, which casts item retrieval as a generation problem. Its model consists of two stages: semantic tokenization and auto

N. K. Karn, Kapil Kumar, Naveen Kumar, Yogesh Kumar

In the present work, we report the synthesis of Pb-Bi alloy with enhanced Tc of up to 9K, which is higher than that of Pb. The alloy is synthesized via a solid-state reaction route in the vacuum-encapsulated quartz tube at 7000C in an automated furnace. The synthesized sample is characterized by X-ray Diffraction(XRD) and Energy dispersive X-ray analysis(EDA

Chris Samarinas, Pracha Promthaw, Atharva Nijasure, Hansi Zeng

This paper explores SynTOD, a new synthetic data generation approach for developing end-to-end Task-Oriented Dialogue (TOD) Systems capable of handling complex tasks such as intent classification, slot filling, conversational question-answering, and retrieval-augmented response generation, without relying on crowdsourcing or real-world data. SynTOD utilizes

Hong Huang, Yuyi Wang, Luyao Li, Jun Lin

Previous studies on music style transfer have mainly focused on one-to-one style conversion, which is relatively limited. When considering the conversion between multiple styles, previous methods required designing multiple modes to disentangle the complex style of the music, resulting in large computational costs and slow audio generation. The existing musi

Supriyo Dutta

This article presents a new quantum PageRank algorithm on graphs using discrete-time open quantum walks. Google's PageRank is a widely used algorithm for ranking the web pages on the World Wide Web in classical computation. From a broader perspective, it is also a fundamental measure for quantifying the importance of vertices in a network. Similarly, the new

Yuchao Liao, Tosiron Adegbija, Roman Lysecky

Embedded systems continue to rapidly proliferate in diverse fields, including medical devices, autonomous vehicles, and more generally, the Internet of Things (IoT). Many embedded systems require application-specific hardware components to meet precise timing requirements within limited resource (area and energy) constraints. High-level synthesis (HLS) is an

Hongyu Chen, Yiqi Gao, Min Zhou, Peng Wang

Recently, integrating visual controls into text-to-image~(T2I) models, such as ControlNet method, has received significant attention for finer control capabilities. While various training-free methods make efforts to enhance prompt following in T2I models, the issue with visual control is still rarely studied, especially in the scenario that visual controls

Hao Tu, Manashita Borah, Scott Moura, Yebin Wang

Lithium-ion batteries have found their way into myriad sectors of industry to drive electrification, decarbonization, and sustainability. A crucial aspect in ensuring their safe and optimal performance is monitoring their energy levels. In this paper, we present the first study on predicting the remaining energy of a battery cell undergoing discharge over wi

Phongpichit Channuie

Using suitable Renormalization Group (RG) based re-summation of quantum corrections to $R^2$ term, a re-summed version of the effective Lagrangian can be obtained \cite{Demmel:2015oqa}. In the context of gravity as an Asymptotically Safe (AS) theory, authors of Refs.\cite{Liu:2018hno,Koshelev:2022olc} proposed a refined Starobinsky model, $L_{\rm AS} = M^{2}

Michael Hellus, Anton Rechenauer, Rolf Waldi

Let $p$ be a prime number, and let $S$ be the numerical semigroup generated by the prime numbers not less than $p$. We compare the orders of magnitude of some invariants of $S$ with each other, e. g., the biggest atom $u$ of $S$ with $p$ itself: By Harald Helfgott (arXiv:1312.7748 [math.NT]), every odd integer $N$ greater than five can be written as the sum

Yuxuan Shi

We analyse the massless particles orbiting a spherically symmetric, asymptotically flat black hole with a radius equal to the photon sphere and a circular geodesic. Asymptotic observers record the orbital period of the null circular geodesic as the lowest among all possible paths around the compact object. We proceed with the analytical study of massless par

Chengpeng Hu, Jialin Liu, Xin Yao

Recently, evolutionary reinforcement learning has obtained much attention in various domains. Maintaining a population of actors, evolutionary reinforcement learning utilises the collected experiences to improve the behaviour policy through efficient exploration. However, the poor scalability of genetic operators limits the efficiency of optimising high-dime

Takahiko Miyakawa, Eiji Nakano, Hiroyuki Yabu

Dynamical properties of homogeneous Fermi-Fermi mixtures of dipolar and non-dipolar atoms are studied at zero temperature, where dipoles are polarized by an external field. We calculate the density-density correlation functions in a ring-diagram approximation and analyze the pole structure to obtain eigenfrequencies of collective excitations. We first determ

Riku Kishida

In this paper, we focus on a conformally flat Riemannian manifold $(M^n,g)$ of dimension $n$ isometrically immersed into the $(n+1)$-dimensional light-cone $\Lambda^{n+1}$ as a hypersurface. We compute the first and the second variational formulas on the volume of such hypersurfaces. Such a hypersurface $M^n$ is not only immersed in $\Lambda^{n+1}$ but also

Sanat Sharma, David Seunghyun Yoon, Franck Dernoncourt, Dewang Sultania

Question answering (QA) has become an important application in the advanced development of large language models. General pre-trained large language models for question-answering are not trained to properly understand the knowledge or terminology for a specific domain, such as finance, healthcare, education, and customer service for a product. To better cate

Yao Yuan, Wutao Liu, Pan Gao, Qun Dai

Recently, unsupervised salient object detection (USOD) has gained increasing attention due to its annotation-free nature. However, current methods mainly focus on specific tasks such as RGB and RGB-D, neglecting the potential for task migration. In this paper, we propose a unified USOD framework for generic USOD tasks. Firstly, we propose a Progressive Curri

Sachin Garg, Albert S. Berahas, Michał Dereziński

We show that, for finite-sum minimization problems, incorporating partial second-order information of the objective function can dramatically improve the robustness to mini-batch size of variance-reduced stochastic gradient methods, making them more scalable while retaining their benefits over traditional Newton-type approaches. We demonstrate this phenomeno

Xiongxiao Xu, Canyu Chen, Yueqing Liang, Baixiang Huang

Time series forecasting has made significant advances, including with Transformer-based models. The attention mechanism in Transformer effectively captures temporal dependencies by attending to all past inputs simultaneously. However, its quadratic complexity with respect to sequence length limits the scalability for long-range modeling. Recent state space m

Jin Young Kim

We consider the bending of light around a compact astrophysical object with both the electric field and the magnetic field in Einstein-Born-Infeld theory. From the null geodesic of a light ray passing a massive object with electric charge and magnetic dipole, the effective metric was obtained from the light cone condition reflecting the nonlinear electromagn

Bo Lin, Yingjing Xu, Xuanwen Bao, Zhou Zhao

With the continuous advancement of vision language models (VLMs) technology, remarkable research achievements have emerged in the dermatology field, the fourth most prevalent human disease category. However, despite these advancements, VLM still faces explainable problems to user in diagnosis due to the inherent complexity of dermatological conditions, exist

Yuchao Liao, Tosiron Adegbija, Roman Lysecky, Ravi Tandon

High-Level Synthesis (HLS) Design Space Exploration (DSE) is a widely accepted approach for efficiently exploring Pareto-optimal and optimal hardware solutions during the HLS process. Several HLS benchmarks and datasets are available for the research community to evaluate their methodologies. Unfortunately, these resources are limited and may not be sufficie

Ye Yan, Qi Huang, Xinmei Zhu, Hongxia Huang

We have investigated the $qss\bar{q}q$ ($q = u$ or $d$) system to find possible pentaquark explanations for the $\Xi$ resonances. The bound state calculation is carried out within the framework of the quark delocalization color screening model. The scattering processes are also studied to examine the possible resonance states. The current results indicate th

Masamitsu Aoki

We show that the second bounded cohomology of the free product of racks and quandles is infinite-dimensional as a real vector space. This is similar to the case of groups. As a corollary, we show that the second bounded cohomology of the free rack and the free quandle is infinite-dimensional. We also give another proof of this corollary using homogeneous gro

Xiucai Ding, Yun Li, Fan Yang

This paper focuses on investigating Stein's invariant shrinkage estimators for large sample covariance matrices and precision matrices in high-dimensional settings. We consider models that have nearly arbitrary population covariance matrices, including those with potential spikes. By imposing mild technical assumptions, we establish the asymptotic limits of

Qiao Deng, Zhongzhen Huang, Yunqi Wang, Zhichuan Wang

Medical foundation models have the potential to revolutionize healthcare by providing robust and generalized representations of medical data. Medical vision-language pre-training has emerged as a promising approach for learning domain-general representations of medical image and text. Current algorithms that exploit global and local alignment between medical

Yukio Ohsawa, Dingming Xue, Kaira Sekiguchi

Previous models for learning the semantic vectors of items and their groups, such as words, sentences, nodes, and graphs, using distributed representation have been based on the assumption that the basic sense of an item corresponds to one vector composed of dimensions corresponding to hidden contexts in the target real world, from which multiple senses of t

Ben Allanach

Several current measurements of $b \rightarrow s l^+ l^-$ processes are in tension with Standard Model predictions, whereas others (e.g.\ $R_K$ and $R_{K^\ast}$) are in reasonable agreement with them. We examine some recent $Z^\prime$ models that fit the data as a whole appreciably better than does the Standard Model, confronting the models with ATLAS resona

Mareike Thies, Noah Maul, Siyuan Mei, Laura Pfaff

Motion artifacts can compromise the diagnostic value of computed tomography (CT) images. Motion correction approaches require a per-scan estimation of patient-specific motion patterns. In this work, we train a score-based model to act as a probability density estimator for clean head CT images. Given the trained model, we quantify the deviation of a given mo

Phoebe Jing, Yijing Gao, Xianlong Zeng

In the field of fraud detection, the availability of comprehensive and privacy-compliant datasets is crucial for advancing machine learning research and developing effective anti-fraud systems. Traditional datasets often focus on transaction-level information, which, while useful, overlooks the broader context of customer behavior patterns that are essential

Yexiao He, Ziyao Wang, Zheyu Shen, Guoheng Sun

The pre-trained Large Language Models (LLMs) can be adapted for many downstream tasks and tailored to align with human preferences through fine-tuning. Recent studies have discovered that LLMs can achieve desirable performance with only a small amount of high-quality data, suggesting that a large amount of the data in these extensive datasets is redundant or

Runqi Wang, Caoyuan Ma, Guopeng Li, Hanrui Xu

Text to Motion aims to generate human motions from texts. Existing settings rely on limited Action Texts that include action labels, which limits flexibility and practicability in scenarios difficult to describe directly. This paper extends limited Action Texts to arbitrary ones. Scene texts without explicit action labels can enhance the practicality of mode

Victoria Quirós-Cordero, Esteban Rojas-Gatjens, Martín Gómez-Dominguez, Hao Li

Multi-particle correlations of exciton-polaritons and reservoir-excitons in the strong light-matter coupling regime dictate the quantum dynamics of optical microcavities. In this letter, we examine the many-body exciton-polariton dynamics in a Fabry-P\'erot microcavity of a two-dimensional metal-halide semiconductor over timescales involving polariton ($\ll

Yingqing Guo, Hui Yuan, Yukang Yang, Minshuo Chen

Diffusion models have demonstrated empirical successes in various applications and can be adapted to task-specific needs via guidance. This paper studies a form of gradient guidance for adapting a pre-trained diffusion model towards optimizing user-specified objectives. We establish a mathematical framework for guided diffusion to systematically study its op

Peter K. S. Dunsby, Seoktae Koh, Abbas M. Sherif

We study the causal dynamics of an embedded null horizon foliated by marginally outer trapped surfaces (MOTS) for a locally rotationally symmetric background spacetime subjected to linear perturbations. We introduce a simple procedure which characterizes the transition of the causal character of the null horizon. We apply our characterization scheme to non-d

Yao Xu, Shizhu He, Jiabei Chen, Zihao Wang

To address the issues of insufficient knowledge and hallucination in Large Language Models (LLMs), numerous studies have explored integrating LLMs with Knowledge Graphs (KGs). However, these methods are typically evaluated on conventional Knowledge Graph Question Answering (KGQA) with complete KGs, where all factual triples required for each question are ent

Ben Hutchinson

This position paper concerns the use of religious texts in Natural Language Processing (NLP), which is of special interest to the Ethics of NLP. Religious texts are expressions of culturally important values, and machine learned models have a propensity to reproduce cultural values encoded in their training data. Furthermore, translations of religious texts

Utkarsh Gupta, Emmanouil Nikolakakis, Moritz Zaiss, Razvan Marinescu

Reconstructing digital brain phantoms in the form of voxel-based, multi-channeled tissue probability maps for individual subjects is essential for capturing brain anatomical variability, understanding neurological diseases, as well as for testing image processing methods. We demonstrate the first framework that estimates brain tissue probability maps (Grey M

Winston Hurst, Spilios Evmorfos, Athina Petropulu, Yasamin Mostofi

Uncrewed Vehicles (UVs) functioning as autonomous agents are anticipated to play a crucial role in the 6th Generation of wireless networks. Their seamless integration, cost-effectiveness, and the additional controllability through motion planning make them an attractive deployment option for a wide range of applications, both as assets in the network (e.g.,

Yasuhiro Miyazawa, Dahun Lee, Seonghyun Kim, Chia-Yung Chang

A long-standing challenge in impact mitigation is the development of versatile and omnifarious protective structures capable of encompassing a wide spectrum of scenarios, for example, ranging from low-speed pedestrian impacts to high-speed vehicle collisions. However, most existing impact mitigation strategies rely on fixed geometries or pre-tuned material p

Katie Seaborn, Jacqueline Urakami, Peter Pennefather, Norihisa P. Miyake

Voice is a natural mode of expression offered by modern computer-based systems. Qualitative perspectives on voice-based user experiences (voice UX) offer rich descriptions of complex interactions that numbers alone cannot fully represent. We conducted a systematic review of the literature on qualitative approaches to voice UX, capturing the nature of this bo

Daniel Yang, Davin Tjia, Jacob Berg, Dima Damen

Teaching robots novel skills with demonstrations via human-in-the-loop data collection techniques like kinesthetic teaching or teleoperation puts a heavy burden on human supervisors. In contrast to this paradigm, it is often significantly easier to provide raw, action-free visual data of tasks being performed. Moreover, this data can even be mined from video

Yuxin Wang, Zhiyue Zheng, Qiuxin Zhang, Yonglang Lai

We propose a novel pulsed optical field method that alternately switches the pump beam in conventional saturation absorption to time-division multiplex the same probe beam into both probe and reference beams, followed by digital differential processing to achieve deterministic zero-background Doppler-free spectroscopy. This method effectively mitigates Doppl

Zhenyu Du, Guoding Liu, Xingjian Zhang, Xiongfeng Ma

Quantum key distribution promises information-theoretically secure communication, with data post-processing playing a vital role in extracting secure keys from raw data. While hardware advancements have significantly improved practical implementations, optimizing post-processing techniques offers a cost-effective avenue to enhance performance. Advantage dist

Giuseppina Simone

In the realm of healthcare, label-free sensing is a vital component for various applications. Micro-photonic technology presents a promising avenue to pursue pivotal goals. With the use of this technology, healthcare professionals and researchers can harness the power of label-free sensing to develop effective diagnostic and therapeutic tools. The integratio

MohammadHossein Bateni, Laxman Dhulipala, Kishen N Gowda, D Ellis Hershkowitz

Average linkage Hierarchical Agglomerative Clustering (HAC) is an extensively studied and applied method for hierarchical clustering. Recent applications to massive datasets have driven significant interest in near-linear-time and efficient parallel algorithms for average linkage HAC. We provide hardness results that rule out such algorithms. On the sequenti

Winston Hurst, Anurag Pallaprolu, Yasamin Mostofi

This paper addresses the challenge of incentivizing energy-constrained, non-cooperative user equipment (UE) to serve as cooperative relays. We consider a source UE with a non-line-of-sight channel to an access point (AP), where direct communication may be infeasible or may necessitate a substantial transmit power. Other UEs in the vicinity are viewed as rela

Jay Lee, Dai-Yan Ji, Yuan-Ming Hsu

This paper presents a topological analytics approach within the 5-level Cyber-Physical Systems (CPS) architecture for the Stream-of-Quality assessment in smart manufacturing. The proposed methodology not only enables real-time quality monitoring and predictive analytics but also discovers the hidden relationships between quality features and process paramete

Wenwen Liu, Oubo You, Bumki Min, Shuang Zhang

Non-Hermitian physics has received great attention recently. In particular, band structures in non-Hermitian systems can be engineered to exhibit various topological effects. Among them, one of the most intriguing phenomena is the non-Hermitian skin effect (NHSE). Here, we investigate NHSE in systems featuring directed chains or directed graphs, where the ar

Ethan Mollick, Lilach Mollick

This paper explores how instructors can leverage generative AI to create personalized learning experiences for students that transform teaching and learning. We present a range of AI-based exercises that enable novel forms of practice and application including simulations, mentoring, coaching, and co-creation. For each type of exercise, we provide prompts th

D. M. Gokhfeld, S. Yu. Shalomov, D. B. Sultimov, M. I. Petrov

The levitation of a cylindrical permanent magnet over a high-temperature superconductor cooled by liquid nitrogen can be accompanied by spontaneous oscillations and rotation. The reason for spontaneous rotation of the magnet is magnetization inhomogeneity induced by the temperature gradient. An experiment was carried out on the levitation of Nd-Fe-B magnets

Muhammad Abdullah, George Ilhwan Park

Stabilization schemes in wall-bounded flows often invoke fluid transpiration through porous boundaries. While these have been extensively validated for external flows, their efficacy in channels, particularly from the standpoint of non-modal perturbations, is yet to be demonstrated. Here, we show that crossflow strengths previously considered ``ideal'' for o

Peiwen Yang, Weisong Wen, Shiyu Bai, Li-Ta Hsu

The execution of flight missions by unmanned aerial vehicles (UAV) primarily relies on navigation. In particular, the navigation pipeline has traditionally been divided into positioning and control, operating in a sequential loop. However, the existing navigation pipeline, where the positioning and control are decoupled, struggles to adapt to ubiquitous unce

Amir Saeidi, Shivanshu Verma, Md Nayem Uddin, Chitta Baral

This study evaluates Direct Preference Optimization (DPO) and its variants for aligning Large Language Models (LLMs) with human preferences, testing three configurations: (1) with Supervised Fine Tuning (SFT), (2) without SFT, and (3) without SFT but using an instruction tuned model. We further investigate how training set size influences model performance.

Aya Bamba, Yukikatsu Terada, Kazumi Kashiyama, Shota Kisaka

Strongly magnetized, rapidly rotating massive white dwarfs (WDs) emerge as potential outcomes of double degenerate mergers. These WDs can act as sources of non-thermal emission and cosmic rays, gethering attention as WD pulsars. In this context, we studied the X-ray emissions from ZTF J190132.9+145808.7 (hereafter ZTF J1901+14), a notable massive isolated WD

Chenxing Hong, Yan Jin, Zhiqi Kang, Yizhou Chen

Existing continual learning literature relies heavily on a strong assumption that tasks arrive with a balanced data stream, which is often unrealistic in real-world applications. In this work, we explore task-imbalanced continual learning (TICL) scenarios where the distribution of task data is non-uniform across the whole learning process. We find that imbal

Lingzhi Wang, Xiangmin Shen, Weijian Li, Zhenyuan Li

As cyber attacks grow increasingly sophisticated and stealthy, it becomes more imperative and challenging to detect intrusion from normal behaviors. Through fine-grained causality analysis, provenance-based intrusion detection systems (PIDS) demonstrated a promising capacity to distinguish benign and malicious behaviors, attracting widespread attention from

Ruitong Liu, Yanbin Wang, Haitao Xu, Jianguo Sun

Code Language Models (codeLMs) and Graph Neural Networks (GNNs) are widely used in code vulnerability detection. However, GNNs often rely on aggregating information from adjacent nodes, limiting structural information propagation across layers. While codeLMs can supplement GNNs with semantic information, existing integration methods underexplore their collab

Wirin Sonsrettee, Piyanate Chuychai, Achara Seripienlert, Paisan Tooprakai

Modeling of time profiles of solar energetic particle (SEP) observations often considers transport along a large-scale magnetic field with a fixed path length from the source to the observer. Here we point out that variability in the turbulent field line path length can affect the fits to SEP data and the inferred mean free path and injection profile. To exp

Alexander J Healey, Priya Singh, Islay O Robertson, Christopher Gavin

Boron vacancy centre ($V_{\rm B}^-$) ensembles in hexagonal boron nitride (hBN) have attracted recent interest for their potential as two-dimensional solid-state quantum sensors. Irradiation is necessary for $V_{\rm B}^-$ creation, however, to date only limited attention has been given to optimising the defect production process, especially in the case of bu

Siyin Wang, Chao-Han Huck Yang, Ji Wu, Chao Zhang

Large language models (LLMs) can adapt to new tasks through in-context learning (ICL) based on a few examples presented in dialogue history without any model parameter update. Despite such convenience, the performance of ICL heavily depends on the quality of the in-context examples presented, which makes the in-context example selection approach a critical c

Hang Hua, Jing Shi, Kushal Kafle, Simon Jenni

Recent progress in large-scale pre-training has led to the development of advanced vision-language models (VLMs) with remarkable proficiency in comprehending and generating multimodal content. Despite the impressive ability to perform complex reasoning for VLMs, current models often struggle to effectively and precisely capture the compositional information

Devojyoti Kansabanik, Surajit Mondal, Divya Oberoi

The geo-effectiveness of coronal mass ejections (CMEs) is determined primarily by their magnetic fields. Modeling of Gyrosynchrotron (GS) emission is a promising remote sensing technique to measure the CME magnetic field at coronal heights. However, faint GS emission from CME flux ropes is hard to detect in the presence of bright solar emission from the sola

Jinhao Liang, Kaidi Yang, Chaopeng Tan, Jinxiang Wang

High-speed cruising scenarios with mixed traffic greatly challenge the road safety of autonomous vehicles (AVs). Unlike existing works that only look at fundamental modules in isolation, this work enhances AV safety in mixed-traffic high-speed cruising scenarios by proposing an integrated framework that synthesizes three fundamental modules, i.e., behavioral

Xiao Wang, Siyan Liu, Aristeidis Tsaris, Jong-Youl Choi

Earth system predictability is challenged by the complexity of environmental dynamics and the multitude of variables involved. Current AI foundation models, although advanced by leveraging large and heterogeneous data, are often constrained by their size and data integration, limiting their effectiveness in addressing the full range of Earth system predictio

G. Diguet, B. Ducharne, S. El Hog, F. Kato

We numerically study the stability and morphology of geometrically confined skyrmions in nanodots using Finsler geometry (FG) modeling technique. The FG model dynamically implements anisotropies in ferromagnetic interaction, Dzyaloshinskii-Moriya interaction, and magneto-elastic coupling in response to mechanical stresses. Without the stresses, there exists

Xin Tan, Taichuan Li, Ruohe Chen, Fang Liu

The challenges associated with using pre-trained models (PTMs) have not been specifically investigated, which hampers their effective utilization. To address this knowledge gap, we collected and analyzed a dataset of 5,896 PTM-related questions on Stack Overflow. We first analyze the popularity and difficulty trends of PTM-related questions. We find that PTM

Tong Zhang, Wenxue Cui, Shaohui Liu, Feng Jiang

Convolutional Neural Network (CNN) and Transformer have attracted much attention recently for video post-processing (VPP). However, the interaction between CNN and Transformer in existing VPP methods is not fully explored, leading to inefficient communication between the local and global extracted features. In this paper, we explore the interaction between C

Ngo Phuoc Nguyen Ngoc, Huynh Anh Thi, Nguyen Van Vinh

In 1998, O'Loan et al. introduced a simplified bus route model to illustrate bus dynamics. However, fluctuations in passenger numbers make it challenging to achieve an exact solution for the model's stationary state, as these fluctuations can impact bus behavior. In this study, we present an exactly solvable model for a dual bus route system that builds upon

Kyler Siegel

The ellipsoidal superpotential of the complex projective plane can be interpreted as a count of rigid rational plane curves of a given degree with one prescribed cusp singularity. In this note we present a closed formula for these counts as a sum over trees with certain explicit weights. This is a step towards understanding the combinatorial underpinnings of

Shiyuan Sun, Fang Yang, Weidong Mei, Jian Song

Optical intelligent reflecting surface (OIRS) has been considered a promising technology for visible light communication (VLC) by constructing visual line-of-sight propagation paths to address the signal blockage issue. However, the existing works on OIRSs are mostly based on perfect channel state information (CSI), whose acquisition appears to be challengin

Qingrong He, Kejun Lin, Shizhe Chen, Anwen Hu

This work addresses the 3D situated reasoning task which aims to answer questions given egocentric observations in a 3D environment. The task remains challenging as it requires comprehensive 3D perception and complex reasoning skills. End-to-end models trained on supervised data for 3D situated reasoning suffer from data scarcity and generalization ability.

Clifford Broni-Bediako, Junshi Xia, Naoto Yokoya

Unsupervised domain adaptation (UDA) is a challenging open problem in land cover mapping. Previous studies show encouraging progress in addressing cross-domain distribution shifts on remote sensing benchmarks for land cover mapping. The existing works are mainly built on large neural network architectures, which makes them resource-hungry systems, limiting t

Tatsu-Hiko Miura

We consider the Ginzburg-Landau heat flow without magnetic effect in a curved thin domain under the Naumann boundary condition. When the curved thin domain shrinks to a given closed hypersurface as the thickness of the thin domain tends to zero, we show that the weighted average of a weak solution to the thin-domain problem converges weakly on the limit surf

Mu Wang

The problem of Domain Adaptive in the field of Object Detection involves the transfer of object detection models from labeled source domains to unannotated target domains. Recent advancements in this field aim to address domain discrepancies by aligning pixel-pairs across domains within a non-Euclidean graphical space, thereby minimizing semantic distributio

Dusa McDuff, Kyler Siegel

We show that the infinite staircases which arise in the ellipsoid embedding functions of rigid del Pezzo surfaces (with their monotone symplectic forms) can be entirely explained in terms of rational sesquicuspidal symplectic curves. Moreover, we show that these curves can all be realized algebraically, giving various new families of algebraic curves with on