October 2024 arXiv papers — page 59

Tomislav Petkovic, Simone Gasparini, Tomislav Pribanic

Geometric calibration of cameras and projectors is an essential step that must be performed before any imaging system can be used. There are many well-known geometric calibration methods for calibrating systems comprised of multiple cameras, but simultaneous geometric calibration of multiple projectors and cameras has received less attention. This leaves unr

Juliette Marais, Quentin Mayolle, Martin Fasquelle, Vincent Tardif

Context Progresses in GNSS-based solution introduction in rail applications GNSS (Global Navigation Satellite System) is now used in most of our travels and each of our smartphone apps. Most of the usages are not safety-critical. But Europe identified GNSS for more applications and to be integrated in rail in general as part of the toolset to help railway to

Eugene Bilokopytov

We present a general result about generating group topologies by pseudo-norms. Namely, we show that if a topology has a base of sets which are closed in a certain sense, then it can be generated by a collection of pseudo-norms such that the balls in these pseudo-norms are also closed in the same sense. The examples include linear and locally convex topologie

Tetiana Berger-Hrynova

This document reviews international cooperation of the Joint Institute of Nuclear Research (JINR). It shows that of the original 11 founding members of the JINR, only one -- Mongolia -- is still fully active within the organization. Out of 16 existing members four have their participation suspended. The review of the Topical Plan for JINR Research and Intern

Haoran Wang, Cunxi Dai, Siyuan Wang, Ximan Zhang

This paper introduces two field transportation robots. Both robots are equipped with transformable wheel-leg modules, which can smoothly switch between operation modes and can work in various challenging terrains. SWhegPro, with six S-shaped legs, enables transporting loads in challenging uneven outdoor terrains. SWhegPro3, featuring four three-impeller whee

Ali Vosoughi, Akhil Kasturi, Axel Wismueller

In the present research, the effectiveness of large-scale Augmented Granger Causality (lsAGC) as a tool for gauging brain network connectivity was examined to differentiate between marijuana users and typical controls by utilizing resting-state functional Magnetic Resonance Imaging (fMRI). The relationship between marijuana consumption and alterations in bra

Liangdong Wang, Bo-Wen Zhang, Chengwei Wu, Hanyu Zhao

We present CCI3.0-HQ (https://huggingface.co/datasets/BAAI/CCI3-HQ), a high-quality 500GB subset of the Chinese Corpora Internet 3.0 (CCI3.0)(https://huggingface.co/datasets/BAAI/CCI3-Data), developed using a novel two-stage hybrid filtering pipeline that significantly enhances data quality. To evaluate its effectiveness, we trained a 0.5B parameter model fr

Nicolas Portal, Nadjia Kachenoura, Thomas Dietenbeck, Catherine Achard

In the past few years, deep learning algorithms have been widely used for cardiac image segmentation. However, most of these architectures rely on convolutions that hardly model long-range dependencies, limiting their ability to extract contextual information. In order to tackle this issue, this article introduces the Swin Filtering Block network (SFB-net) w

Thomas Stoffregen, Bruno Mantel, Benoît G. Bardy

J. J. Gibson (1966) rejected many classical assumptions about perception but retained 1 that dates back to classical antiquity: the assumption of separate senses. We suggest that Gibson's retention of this assumption compromised his novel concept of perceptual systems. We argue that lawful, 1:1 specification of the animal--environment interaction, which is n

Matthias U. Kruckow, Jeff J. Andrews, Tassos Fragos, Berry Holl

Context. The black holes discovered using Gaia, especially Gaia BH1 and BH2, have low mass companions of solar-like metallicity in wide orbits. For standard isolated binary evolution formation channels including interactions such an extreme mass ratio is unexpected; especially in orbits of hundreds to thousands of days. Aims. Here, we investigate a non-inter

Jongseon Kim, Hyungjoon Kim, HyunGi Kim, Dongjun Lee

Time series forecasting is a critical task that provides key information for decision-making. After traditional statistical and machine learning approaches, various fundamental deep learning architectures such as MLPs, CNNs, RNNs, and GNNs have been developed. However, the structural limitations caused by the inductive biases of each deep learning architectu

A. Benchikha, B. Hamil, B. C. Lütfüoğlu

This study explores the time-dependent Dunkl-Pauli oscillator in two dimensions. We constructed the Dunkl-Pauli Hamiltonian, which incorporates a time-varying magnetic field and a harmonic oscillator characterized by time-dependent mass and frequency, initially in Cartesian coordinates. Subsequently, we reformulated the Hamiltonian in polar coordinates and a

Kun Shi, Shibo He, Zhenyu Shi, Anjun Chen

Multi-modal fusion is imperative to the implementation of reliable object detection and tracking in complex environments. Exploiting the synergy of heterogeneous modal information endows perception systems the ability to achieve more comprehensive, robust, and accurate performance. As a nucleus concern in wireless-vision collaboration, radar-camera fusion ha

Boyi Liu, Jingwen Tong, Jun Zhang

The rapid adoption of large language models (LLMs) presents new challenges for existing network architectures due to significant peak traffic and high communication uncertainty. Traditional wireless networks struggle to support efficiently, leading to intolerable response delays, disconnections, and resource wastage. To address these issues, we propose LLM-S

P. Laskos-Patkos, Ch. C. Moustakidis

A recent analysis on the properties of the XTE J1814-338 pulsar yielded a small radius value around ~ 7 km. Notably, this estimation is significantly lower compared to the currently inferred values for the radius of neutron stars (as they are derived from both theoretical calculations and corresponding observations). In this paper, we focus on the constructi

Eli J. Weissler, Mohit Bhat, Zhenxing Liu, Joshua Combes

Nonlinear superconducting circuits can be used as amplifiers, transducers, and qubits. Only a handful of superconducting circuits have been analyzed or built, so many high-performing configurations likely remain undiscovered. We seek to catalog this design space by enumerating all superconducting circuits -- up to five nodes in size -- built of capacitors, i

Shigeki Matsutani

The real and imaginary parts of the focusing modified Korteweg-de Vries (MKdV) equation defined over the complex field $\mathbb{C}$ give rise to the focusing gauged MKdV (FGMKdV) equations. As a generalization of Euler's elastica whose curvature obeys the focusing static MKdV (FSMKdV) equation, we study real plane curves whose curvature obeys the FGMKdV equa

Sadataka Furui, Serge Dos Santos

Localization and classification of scattered nonlinear ultrasonic signatures in 2 dimensional complex damaged media using Time Reversal based Nonlinear Elastic Wave Spectroscopy (TR-NEWS) approach is extended to 3 dimensional complex damaged media. In (2+1)D, i.e. space 2 dimensional time 1 dimensional spacetime, we used quaternion bases for analyses, while

Yuqing Xie, Chao Yu, Hongzhi Zang, Feng Gao

This paper tackles the challenging task of maintaining formation among multiple unmanned aerial vehicles (UAVs) while avoiding both static and dynamic obstacles during directed flight. The complexity of the task arises from its multi-objective nature, the large exploration space, and the sim-to-real gap. To address these challenges, we propose a two-stage re

Martin Mirchev, Andreea Costea, Abhishek Kr Singh, Abhik Roychoudhury

With the advent of AI-based coding engines, it is possible to convert natural language requirements to executable code in standard programming languages. However, AI-generated code can be unreliable, and the natural language requirements driving this code may be ambiguous. In other words, the intent may not be accurately captured in the code generated from A

Tony J. Puthenpurakal

Let $R$ be a regular ring of dimension $d$ containing a field $K$ of characteristic zero. If $E$ is an $R$-module let $Ass^i E = \{ Q \in \ Ass E \mid \ height Q = i \}$. Let $P$ be a prime ideal in $R$ of height $g$. We show that if $R/P$ satisfies Serre's condition $R_i$ then $Ass^{g+i+1}H^{g+1}_P(R)$ is a finite set. As an application of our techniques we

Songbo Yang

With the widespread use of online social media platforms, information diffusion has become a prevalent phenomenon, making Information Diffusion Prediction (IDP) increasingly important for various applications. Despite significant advancements in IDP research, existing methods often overlook issues of noise and sparsity in information diffusion data. User beh

Hengxiang Zhang, Hongfu Gao, Qiang Hu, Guanhua Chen

With the rapid development of Large language models (LLMs), understanding the capabilities of LLMs in identifying unsafe content has become increasingly important. While previous works have introduced several benchmarks to evaluate the safety risk of LLMs, the community still has a limited understanding of current LLMs' capability to recognize illegal and un

Shancong Mou, Raviteja Vemulapalli, Shiyu Li, Yuxuan Liu

Defect segmentation is crucial for quality control in advanced manufacturing, yet data scarcity poses challenges for state-of-the-art supervised deep learning. Synthetic defect data generation is a popular approach for mitigating data challenges. However, many current methods simply generate defects following a fixed set of rules, which may not directly rela

Ahmed R. Sadik, Sebastian Brulin, Markus Olhofer

Leveraging Large Language Models (LLM) like GPT4 in the auto generation of code represents a significant advancement, yet it is not without its challenges. The ambiguity inherent in natural language descriptions of software poses substantial obstacles to generating deployable, structured artifacts. This research champions Model Driven Development (MDD) as a

Tom Meyerovitch, Benjamin Weiss

Kac's lemma determines the expected return time to a set of positive measure under iterations of an ergodic probability preserving transformations. We introduce the notion of an \emph{allocation} for a probability preserving action of a countable group. Using this notion, we formulate and prove generalization of Kac's lemma for an action of a general countab

Jiashun Cheng, Zinan Zheng, Yang Liu, Jianheng Tang

Graph Anomaly Detection (GAD) is a challenging and practical research topic where Graph Neural Networks (GNNs) have recently shown promising results. The effectiveness of existing GNNs in GAD has been mainly attributed to the simultaneous learning of node representations and the classifier in an end-to-end manner. Meanwhile, graph pre-training, the two-stage

Jan Vávra, Bettina Grün, Paul Hofmarcher

The world is evolving and so is the vocabulary used to discuss topics in speech. Analysing political speech data from more than 30 years requires the use of flexible topic models to uncover the latent topics and their change in prevalence over time as well as the change in the vocabulary of the topics. We propose the temporal Poisson factorisation (TPF) mode

Omer Nacar, Mohamed Abdelkader, Lahouari Ghouti, Kahled Gabr

This paper tackles the challenge of real-time 3D trajectory prediction for UAVs, which is critical for applications such as aerial surveillance and defense. Existing prediction models that rely primarily on position data struggle with accuracy, especially when UAV movements fall outside the position domain used in training. Our research identifies a gap in u

Emily Archer, Bangshan Sun, Roman Walczak, Martin Booth

Simultaneous space-time focusing (SSTF) is sometimes claimed to reduce the longitudinal extent of the high-intensity region near the focus, in contradiction to the original work on this topic. Here we seek to address this confusion by using numerical and analytical methods to investigate the degree of localization of the spatio-temporal intensity of an SSTF

S. A. N. Nouwens, B. de Jager, M. M. Paulides, W. P. M. H. Heemels

Model Predictive Control (MPC) is a successful control methodology, which is applied to increasingly complex systems. However, real-time feasibility of MPC can be challenging for complex systems, certainly when an (extremely) large number of constraints have to be adhered to. For such scenarios with a large number of state constraints, this paper proposes tw

Boyu Chang, Binbin Zhao, Qiao Zhang, Peiyu Liu

While fuzzing has demonstrated its effectiveness in exposing vulnerabilities within embedded firmware, the discovery of crashing test cases is only the first step in improving the security of these critical systems. The subsequent fault localization process, which aims to precisely identify the root causes of observed crashes, is a crucial yet time-consuming

Mohammad R. Garousi

It is well-established that the dimensional reduction of the classical effective action of string theory at any order of $\alpha'$ on a circle of arbitrary radius remains invariant under the higher-derivative extension of Buscher transformations. In this study, we extend this symmetry to higher-genus levels. By leveraging the validity of Buscher rules for an

Sergio Burdisso, Srikanth Madikeri, Petr Motlicek

Efficiently deriving structured workflows from unannotated dialogs remains an underexplored and formidable challenge in computational linguistics. Automating this process could significantly accelerate the manual design of workflows in new domains and enable the grounding of large language models in domain-specific flowcharts, enhancing transparency and cont

Kentaro Kameoka, Shu Nakamura

We consider complex resonances for discrete and continuous Schr\"odinger operators, and we show that the resonances of discrete models converge to resonances of continuous models in the continuum limit. The potential is supposed to be a sum of an exterior dilation analytic function and an exponentially decaying function, which may have local singularities. T

Zhonghao Jiang, Weifeng Sun, Xiaoyan Gu, Jiaxin Wu

Software vulnerabilities represent one of the most pressing threats to computing systems. Identifying vulnerabilities in source code is crucial for protecting user privacy and reducing economic losses. Traditional static analysis tools rely on experts with knowledge in security to manually build rules for operation, a process that requires substantial time a

Junbao Chen, Jingfeng Xue, Yong Wang, Zhenyan Liu

Data heterogeneity is one of the key challenges in federated learning, and many efforts have been devoted to tackling this problem. However, distributed concept drift with data heterogeneity, where clients may additionally experience different concept drifts, is a largely unexplored area. In this work, we focus on real drift, where the conditional distributi

Chuanxiang Yang, Yuanfeng Zhou, Guangshun Wei, Long Ma

As commonly used implicit geometry representations, the signed distance function (SDF) is limited to modeling watertight shapes, while the unsigned distance function (UDF) is capable of representing various surfaces. However, its inherent theoretical shortcoming, i.e., the non-differentiability at the zero level set, would result in sub-optimal reconstructio

Jiakang Bao, Eugene Choi, Yang-Hui He, Rak-Kyeong Seong

Futaki invariants of the classical moduli space of 4d N=1 supersymmetric gauge theories determine whether they have a conformal fixed point in the IR. We systematically compute the Futaki invariants for a large family of 4d N=1 supersymmetric gauge theories coming from D3-branes probing Calabi-Yau 3-fold singularities whose bases are Gorenstein Fano surfaces

Kexuan Xin, Qingyun Wang, Junyu Chen, Pengfei Yu

In the rapidly evolving field of metabolic engineering, the quest for efficient and precise gene target identification for metabolite production enhancement presents significant challenges. Traditional approaches, whether knowledge-based or model-based, are notably time-consuming and labor-intensive, due to the vast scale of research literature and the appro

Jiaxin Zhao, Antonio Fieramosca, Kevin Dini, Qiuyu Shang

Recent advancements in transition metal dichalcogenides (TMDs) have unveiled exceptional optical and electronic characteristics, opened up new opportunities, and provided a unique platform for exploring light-matter interactions under the strong coupling regime. The exploitation of exciton-polaritons, with their peculiar hybrid light-matter properties, for t

Trond A. Abrahamsen, Petr Hájek, Vegard Lima, Stanimir Troyanski

We introduce and study a strict monotonicity property of the norm in solid Banach lattices of real functions that prevents such spaces from having the local diameter two property. Then we show that any strictly convex 1-symmetric norm on $c_0(\Gamma)$ possesses this property. In the opposite direction, we show that any Banach space which is strictly convex r

Boxuan Zhang, Jianing Zhu, Zengmao Wang, Tongliang Liu

Out-of-distribution (OOD) detection aims to identify OOD inputs from unknown classes, which is important for the reliable deployment of machine learning models in the open world. Various scoring functions are proposed to distinguish it from in-distribution (ID) data. However, existing methods generally focus on excavating the discriminative information from

Camilla T. Nyhagen, Alice Schimek, Claudia Cicone, Davide Decataldo

[Abridged] The [OIII]$_{88\mu m}$/[CII]$_{158\mu m}$ and [OIII]$_{88\mu m}$/[NII]$_{122\mu m}$ luminosity ratios have shown to be promising tracers of the ionisation state and gas-phase metallicity of the ISM. Observations of galaxies at redshift $z > 6$ show peculiarly higher [OIII]$_{88\mu m}$/[CII]$_{158\mu m}$ luminosity ratios compared to local sources.

Nhat-Minh Le-Phan, Phuoc Doan Nguyen, Hyo-Sung Ahn, Minh Hoang Trinh

This paper presents novel algorithms for the Fermat-Weber Location Problem, guiding an autonomous agent to the point that minimizes the weighted sum of Euclidean distances to some beacons using only bearing measurements. The existing results address only the simple scenario where the beacons are stationary and the agent is modeled by a single integrator. In

Chung-En Sun, Xiaodong Liu, Weiwei Yang, Tsui-Wei Weng

Recent research has shown that Large Language Models (LLMs) are vulnerable to automated jailbreak attacks, where adversarial suffixes crafted by algorithms appended to harmful queries bypass safety alignment and trigger unintended responses. Current methods for generating these suffixes are computationally expensive and have low Attack Success Rates (ASR), e

Lin Zhang

The presence of symmetries can lead to nontrivial dynamics of operator entanglement in open quantum many-body systems, which characterizes the cost of an matrix product density operator (MPDO) representation of the density matrix in the tensor-network methods and provides a measure for the corresponding classical simulability. One example is the $\mathrm{U}(

Anargyros Katsabekis, Apostolos Thoma

The toric ideal $I_A$ is splittable if it has a toric splitting; namely, if there exist toric ideals $I_{A_1}, I_{A_2}$ such that $I_A=I_{A_1}+I_{A_2}$ and $I_{A_i}\not =I_{A}$ for all $1 \leq i \leq 2$. We provide a necessary and sufficient condition for a toric ideal to be splittable in terms of $A$, and we apply it to prove or disprove that certain classe

Koushik Mandal, M. V. Satyanarayana

We investigate the entanglement dynamics of two atoms interacting with a single-mode cavity field within the Tavis-Cummings model in the presence of noise. The atoms are initially prepared in either pure Bell states or mixed Werner states, allowing a direct comparison of pure- and mixed-state entanglement. The cavity field is described by generalized single-

Wang Chen, Liping Tang, Xinmin Yang

In this paper, we deal with multiobjective composite optimization problems, where each objective function is a combination of smooth and possibly non-smooth functions. We first propose a parameter-dependent conditional gradient method to solve this problem. The step size in this method requires prior knowledge of the parameters related to the H{\"o}lder cont

Hung Manh Nguyen, Trong Hoa Pham

This paper presents a mathematical model for determining the movement of the bearing in an internal gear pump. The paper also performs simulation calculations to find the movement trajectory of the shaft with the given input data.

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

Using $(27.12\pm0.14) \times 10^{8}$ $\psi(2S)$ events collected by the BESIII detector operating at BEPCII, we search for the decay $\eta_c(2S)\to p\bar{p}$ via the process $\psi(2S)\to \gamma\eta_c(2S)$, and only find a signal with a significance of $1.7\,\sigma$. The upper limit of the product branching fraction at the 90% confidence level is determined t

Mark Stocks

Current methods for data access control, especially regarding AI and AI automation, face unique challenges in ensuring appropriate data access. We introduce Integer-Based Access Control (IBAC), addressing the limitations of Role-Based Access Control (RBAC) and Attribute-Based Access Control (ABAC). IBAC's mathematical foundations enable its application to re

Dmitry Solovyev

This paper initiates the study of invariants of links associated to infinite-dimensional representations of $U_q(\mathfrak{sl}_2)$ using graphical representation for quantum $6j$-symbols, the shadow world. We obtain formulae for $q3j$-symbols and $q6j$-symbols for Verma modules and study their properties. This hints at the structure of the possible target ca

Lachlan Mares, Stefan Podgorski, Ian Reid

This work presents the experiments and solution outline for our teams winning submission in the Learn To Race Autonomous Racing Virtual Challenge 2022 hosted by AIcrowd. The objective of the Learn-to-Race competition is to push the boundary of autonomous technology, with a focus on achieving the safety benefits of autonomous driving. In the description the c

Hai Siong Tan, Kuancheng Wang, Rafe Mcbeth

In this work, we examine the effectiveness of an uncertainty quantification framework known as Evidential Deep Learning applied in the context of biomedical image segmentation. This class of models involves assigning Dirichlet distributions as priors for segmentation labels, and enables a few distinct definitions of model uncertainties. Using the cardiac and

Yifan Yang, Qiao Jin, Qingqing Zhu, Zhizheng Wang

Large Language Models (LLMs) have gained significant attention in the medical domain for their human-level capabilities, leading to increased efforts to explore their potential in various healthcare applications. However, despite such a promising future, there are multiple challenges and obstacles that remain for their real-world uses in practical settings.

Duanyutian Zhou, Nomura Katsuya, Shintaro Yamasaki

Electromagnetic interference (EMI) filters are used to reduce electromagnetic noise. It is well known that the performance of an EMI filter in reducing electromagnetic noise largely depends on its conductor layout. Therefore, if a conductor layout optimization method with a high degree of freedom is realized, a drastic performance improvement is expected. Al

Lidija Stanovnik, Miha Moškon, Miha Mraz

This paper concerns non-overlapping codes, block codes motivated by synchronisation and DNA-based storage applications. Most existing constructions of these codes do not account for the restrictions posed by the physical properties of communication channels. If undesired sequences are not avoided, the system using the encoding may start behaving incorrectly.

Aman Sagar, Preeti Mehta, Monika Shrivastva, Suchi Kumari

This paper presents a deep learning framework for the multi-class classification of gastrointestinal abnormalities in Video Capsule Endoscopy (VCE) frames. The aim is to automate the identification of ten GI abnormality classes, including angioectasia, bleeding, and ulcers, thereby reducing the diagnostic burden on gastroenterologists. Utilizing an ensemble

Bingyu Yang, Qingyao Tian, Huai Liao, Xinyan Huang

Continuous and accurate segmentation of airways in chest CT images is essential for preoperative planning and real-time bronchoscopy navigation. Despite advances in deep learning for medical image segmentation, maintaining airway continuity remains a challenge, particularly due to intra-class imbalance between large and small branches and blurred CT scan det

Akitoshi Shiotari, Jun Nishida, Adnan Hammud, Fabian Schulz

Scattering-type scanning near-field optical microscopy (s-SNOM) allows for the observation of the optical response of material surfaces with a resolution far below the diffraction limit. Based on amplitude-modulation atomic force microscopy (AFM) with typical tapping amplitudes of tens of nanometers, a spatial resolution of 10-100 nm is routinely achieved in

Bryan Olmos, Daniel Gerl, Aman Kumar, Djones Lettnin

The design of Systems on Chips (SoCs) is becoming more and more complex due to technological advancements. Missed bugs can cause drastic failures in safety-critical environments leading to the endangerment of lives. To overcome these drastic failures, formal property verification (FPV) has been applied in the industry. However, there exist multiple hardware

Alexei Sherman

Using the strong coupling diagram technique, we calculate the zero-temperature density of states $\rho$ of electrons on a square lattice immersed in a perpendicular uniform magnetic field. The electrons are described by Hubbard Hamiltonian. For moderate doping, Landau subbands are observed for small Hubbard repulsions $U$ only. For larger $U$, the subbands a

Masakazu Yamamoto

Through asymptotic expansion, the large-time behavior of incompressible Navier--Stokes flow in $n$-dimensional whole space is depicted. Especially, from their parabolic scalings, large-time behaviors of any terms on the expansion are clarified. The parabolic scalings also guarantee the uniqueness of the expansion. In the preceding work, the expansion with th

Chris Yuhao Liu, Liang Zeng, Jiacai Liu, Rui Yan

In this report, we introduce a collection of methods to enhance reward modeling for LLMs, focusing specifically on data-centric techniques. We propose effective data selection and filtering strategies for curating high-quality open-source preference datasets, culminating in the Skywork-Reward data collection, which contains only 80K preference pairs -- signi

Athira Unni, Thirupathi Sivarani, Jayesh Goyal, Yogesh C. Joshi

Here, we present the low-resolution transmission spectroscopy of three giant planets using the Himalayan Faint Object Spectrograph Camera (HFOSC) on the 2m Himalayan Chandra Telescope (HCT) in Hanle, India. It is the first application of transmission spectroscopy with HCT. This study presents results from a single transit, each for three planets: HAT-P-1b, K

Shengjie Fang, Kumpei Imamura, Yuta Mizukami, Ryuichi Namba

Kitaev quantum spin liquids (KSLs) in layered honeycomb magnets are known to host Majorana quasiparticles, whose excitations depend strongly on the direction of the applied magnetic field. In the high-field phase of $\alpha$-RuCl$_3$, specific heat measurements have revealed characteristic field-angle dependence of low-energy excitations consistent with the

Yining Wang, Jinman Zhao, Yuri Lawryshyn

In the trading process, financial signals often imply the time to buy and sell assets to generate excess returns compared to a benchmark (e.g., an index). Alpha is the portion of an asset's return that is not explained by exposure to this benchmark, and the alpha research process is a popular technique aiming at developing strategies to generate alphas and g

Zezhong Wang, Xingshan Zeng, Weiwen Liu, Liangyou Li

Supervised fine-tuning (SFT) is a common method to enhance the tool calling capabilities of Large Language Models (LLMs), with the training data often being synthesized. The current data synthesis process generally involves sampling a set of tools, formulating a requirement based on these tools, and generating the call statements. However, tools sampled rand

Tomohiro Karube

We study the noncommutative minimal model program for blowups of surfaces. The program, as defined by Halpern-Leistner, is designed to construct a quasiconvergent path in the space of Bridgeland stability conditions. In this paper, we construct a family of quasi-convergent paths in the case of blowups of surfaces. These paths provide different semiorthogonal

Jedidiah Harwood, Debashis Paul, Jie Peng

Graphs are an intuitive way to represent relationships between variables in fields such as finance and neuroscience. However, these graphs often need to be inferred from data. In this paper, we propose a novel framework to infer a latent graph by treating the observed multidimensional data as graph-referenced stationary signals. Specifically, we introduce th

ChaeHun Park, Hojun Cho, Jaegul Choo

This paper explores integrating Automatic Speech Recognition (ASR) into natural language query systems to improve weather forecasting efficiency for Korean meteorologists. We address challenges in developing ASR systems for the Korean weather domain, specifically specialized vocabulary and Korean linguistic intricacies. To tackle these issues, we constructed

Denis Bouyssou, Thierry Marchant, Marc Pirlot

ELECTRE TRI-nB is a method designed to sort alternatives evaluated on several attributes into ordered categories. It is an extension of ELECTRE TRI-B, using several limiting profiles, instead of just one, to delimit each category. ELECTRE TRI-nB comes in two flavours: pseudo-conjunctive and pseudo-disjunctive. In a previous paper we have characterized the or

Takuma Byakuno

We construct an isometric embedding of a bounded set in a Euclidean space into the Gromov-Hausdorff space. In particular, we can embed a bounded and connected Riemannian manifold into the Gromov-Hausdorff space by a bilipschitz map.

Fulu Li

In this paper, we give an in-depth analysis on the mathematical problem formulations and the probabilistic optimization explorations for some of the key components in Transformer model [33] in the field of generative AI. We explore and discuss some potential further enhancement for current state of the art methods for some key underlying technologies of gene

Jingyao Wang, Zeqin Zeng, Jinghua Guo, Zhisheng Duan

This study delves into the intricate challenges encountered by multi-agent systems (MASs) operating within environments that are subject to deception attacks and Markovian randomly switching topologies, particularly in the context of event-triggered secure consensus control. To address these complexities, a novel observer-based distributed event-triggered co

Wenrui Hao, Rui Peng Li, Yuanzhe Xi, Tianshi Xu

Neural networks (NNs) have been widely used to solve partial differential equations (PDEs) in the applications of physics, biology, and engineering. One effective approach for solving PDEs with a fixed differential operator is learning Green's functions. However, Green's functions are notoriously difficult to learn due to their poor regularity, which typical

Raigo Nagashima, Titouan Mouilleron, Naoto Tsuji

Lifshitz invariant is a symmetry invariant composed of multiple order parameters that contain a single spatial derivative in a Ginzburg-Landau (GL) free energy, which may induce a nonuniform configuration of the order parameters. In multiband superconductors, we find phase transitions from a uniform superconducting state to qualitatively distinct two pair-de

Zhe Gao, Roulin Wang, Xueqin Wang, Heping Zhang

The exploration of associations between random objects with complex geometric structures has catalyzed the development of various novel statistical tests encompassing distance-based and kernel-based statistics. These methods have various strengths and limitations. One problem is that their test statistics tend to converge to asymptotic null distributions inv

Seoyeon Kim, Huiseo Kim, Chanjun Park, Jinyoung Yeo

Recent large language models (LLMs) demonstrate multilingual abilities, yet they are English-centric due to dominance of English in training corpora. The limited resource for low-resource languages remains a crucial challenge. Code-switching (CS), a phenomenon where multilingual speakers alternate between languages in a discourse, can convey subtle cultural

Yang Yang, Han Lin Shang, James Raymer

Fertility differentials by urban-rural residence and nativity of women in Australia significantly impact population composition at sub-national levels. We aim to provide consistent fertility forecasts for Australian women characterized by age, region, and birthplace. Age-specific fertility rates at the national and sub-national levels obtained from census da

Mengqian Zhang, Sen Yang, Fan Zhang

Automated Market Makers (AMMs) are essential to decentralized finance, offering continuous liquidity and enabling intermediary-free trading on blockchains. However, participants in AMMs are vulnerable to Maximal Extractable Value (MEV) exploitation. Users face threats such as front-running, back-running, and sandwich attacks, while liquidity providers (LPs)

Hongxin Peng, Yongjian Liao, Weijun Li, Chuanyu Fu

Multi-View Stereo plays a pivotal role in civil engineering by facilitating 3D modeling, precise engineering surveying, quantitative analysis, as well as monitoring and maintenance. It serves as a valuable tool, offering high-precision and real-time spatial information crucial for various engineering projects. However, Multi-View Stereo algorithms encounter

Anirban Ghosh, Andy Martin

In the low momentum regime, the Su-Schrieffer-Heeger (SSH) model's key characteristics are encapsulated by a Dirac-type Hamiltonian in continuum space, i.e., the localized states emerge at the boundaries. Building on this, we have developed an effective Hamiltonian to model ultra cold interacting Bosons on an SSH like lattice through variational minimization

Bingzheng Chen, Zongxia Liang, Shunzhi Pang

This paper analyzes the equilibrium of insurance market in a dynamic setting, focusing on the interaction between insurers' underwriting and investment strategies. Three possible equilibrium outcomes are identified: a positive insurance market, a zero insurance market, and market failure. Our findings reveal why insurers may rationally accept underwriting lo

Chunhao Cai, Cong Zhang

Fractional Brownian motions(fBMs) are not semimartingales so the classical theory of It\^o integral can't apply to fBMs. Wick integration as one of the applications of Malliavin calculus to stochastic analysis is a fine definition for fBMs. We consider the fractional forward backward stochastic differential equations(fFBSDEs) driven by a fBM that have the Hu

Donglin Di, Weinan Zhang, Yue Zhang, Fanglin Wang

Making use of off-the-shelf resources of resource-rich languages to transfer knowledge for low-resource languages raises much attention recently. The requirements of enabling the model to reach the reliable performance lack well guided, such as the scale of required annotated data or the effective framework. To investigate the first question, we empirically

Zhilong Fang, Jingjing Zong

Wavefield reconstruction inversion (WRI) has been considered a potential solution to the issue of local minima inherent in conventional full waveform inversion (FWI) methods. However, most current WRI research has been confined to 2D problems due to the computational challenges posed by solving augmented systems for optimal data-fitting wavefields. This cons

ZhuangEn Fu, Hong-Fei Huang, Piumi Samarawickrama, Kenji Watanabe

Two-dimensional (2D) van der Waals (vdW) magnets with layer-dependent magnetic states and/or diverse magnetic interactions and anisotropies have attracted extensive research interest. Despite the advances, a notable challenge persists in effectively manipulating the tunneling anisotropic magnetoresistance (TAMR) of 2D vdW magnet-based magnetic tunnel junctio

Nathan Chen, Louis Esser

Rationality specializes in families of surfaces, even with mild singularities. In this paper, we study the analogous question for the degree of irrationality. We prove a specialization result when the degree of irrationality on the generic fiber arises from the quotient by a group action.

Iman Mohammed Attia

A new 2 parameter unit Weibull distribution is defined on the unit interval (0,1). The methodology of deducing its PDF, some of its properties and related functions are discussed. The paper is supplied by many figures illustrating the new distribution and how this can make it illegible to fit a wide range of skewed data. The new distribution holds a name (At

Robert J. Decker, A. Demirkaya, T. J. Alexander, G. A. Tsolias

In the present work we explore the path from a harmonic to a biharmonic PDE of Klein-Gordon type from a continuation/bifurcation perspective. More specifically, we make use of the Riesz fractional derivative as a tool that allows us to interpolate between these two limits. We illustrate, in particular, how the coherent kink structures existing in these model

Anjali N. Albert, Patrick Flaherty, Aaron Schein

We consider the problem of developing interpretable and computationally efficient matrix decomposition methods for matrices whose entries have bounded support. Such matrices are found in large-scale DNA methylation studies and many other settings. Our approach decomposes the data matrix into a Tucker representation wherein the number of columns in the consti

Shrenik Zinage, Ilias Bilionis, Peter Meckl

The stringent regulatory requirements on nitrogen oxides (NOx) emissions from diesel compression ignition engines require accurate and reliable models for real time monitoring and diagnostics. Although traditional methods such as physical sensors and virtual engine control module (ECM) sensors provide essential data, they are only used for estimation. Ubiqui

Joshua Fagin, James Hung-Hsu Chan, Henry Best, Matthew O'Dowd

Quasars are bright active galactic nuclei powered by the accretion of matter around supermassive black holes at the center of galaxies. Their stochastic brightness variability depends on the physical properties of the accretion disk and black hole. The upcoming Rubin Observatory Legacy Survey of Space and Time (LSST) is expected to observe tens of millions o

Emily Casey

Carleson's $\varepsilon^2$-conjecture states that for Jordan domains in $\mathbb{R}^2$, points on the boundary where tangents exist can be characterized in terms of the behavior of the $\varepsilon$-function. This conjecture, which was fully resolved by Jaye, Tolsa, and Villa in 2021, established that qualitative control on the rate of decay of the Carleson

Lei Zhang, Jan Kloppenburg, Chia-Yi Lin, Luka Mitrovic

Understanding the dehydrogenation of transition metal oxide surfaces under electrochemical potential is critical to the control of important chemical processes such as the oxygen evolution reaction (OER). Using first principles computations, we model the thermodynamic dehydrogenation process on RuO$_2$(110) and compare the results to experimental cyclic volt

Nicholas H. Patino, Luca Lomazzi, Luca De Beni, Massimo Ruzzene

We investigate the dynamic properties of elastic lattices defined by tessellations of a hyperbolic strip domain. These strip lattices are generated by a conformal map of tessellations of the hyperbolic disk. Their vibrational modes are organized into three distinct classes: boundary-localized, interior-localized, and global. This mode classification is gover

MicroBooNE collaboration, P. Abratenko, O. Alterkait, D. Andrade Aldana

Large neutrino liquid argon time projection chamber (LArTPC) experiments can broaden their physics reach by reconstructing and interpreting MeV-scale energy depositions, or blips, present in their data. We demonstrate new calorimetric and particle discrimination capabilities at the MeV energy scale using reconstructed blips in data from the MicroBooNE LArTPC