November 2024 arXiv papers — page 9

Gourab Kumar Sar, Md Sayeed Anwar, Martin Moriamé, Dibakar Ghosh

Synchronization forms the basis of many coordination phenomena in natural systems, enabling them to function cohesively and support their fundamental operations. However, there are scenarios where synchronization disrupts a system's proper functioning, necessitating mechanisms to control or suppress it. While several methods exist for controlling synchroniza

A. D. Panov, I. I. Astapov, G. M. Beskin, P. A. Bezyazykov

It has been previously demonstrated [Panov et al. Physics of Atomic Nuclei 84(2021)1037] that the TAIGA-HiSCORE Cherenkov array, originally built for cosmic ray physics and ultrahigh-energy gamma-ray astronomy studies using the extensive air shower method, can be used in conventional optical astronomy for wide-field searches for rare nanosecond optical trans

Amirhossein Rezaei, Mahmood Hasani, Alireza Rezaei, S. M. Hassan Halataei

In this study, we present a novel analytical approach to solving large-scale Ising problems by reformulating the discrete Ising Hamiltonian into a continuous framework. This transformation enables us to derive exact solutions for a non-trivial class of fully connected Ising models. To validate our method, we conducted numerical experiments comparing our anal

Dario Bini, Steve Kirkland, Guy Latouche, Beatrice Meini

A centrality measure of the cut-edges of an undirected graph, given in [Altafini et al.~SIMAX 2023] and based on Kemeny's constant, is revisited. A numerically more stable expression is given to compute this measure, and an explicit expression is provided for some classes of graphs, including one-path graphs and trees formed by three or more branches. These

Semen Goreninskii, Igor Akimchenko, Alexander Vorobyev, Mikhail Konoplyannikov

The development of tissue engineering structures (scaffolds) for the reconstruction of bone tissue defects is the relevant task of modern biomedical materials science. Compared to metal-based structures, polymer constructs provide numerous advantages, among them - better processibility and metallosis avoidance. Owing to its high mechanical performance and bi

Anish Kumar, Pawan Kumar Mishra, Riya Singh, Shradha Mishra

A one-dimensional flocking model using active Ising spins is studied, where the system evolves through the reinforcement learning approach \textit{via} defining state, action, and cost function for each spin. The orientation of spin with respect to its neighbouring spins defines its state. The state of spin is updated by altering its spin orientation in acco

Iván Cuntín, Wenyang Qian, Bin Wu

We present our recent studies on thermal field theories using quantum algorithms. We first delve into the representation of quantum fields via qubits on general digital quantum computers alongside the quantum algorithms employed to evaluate thermal properties of generic quantum field theories. Then, we show our numerical results of thermal field theories in

E. Santos

The Pierre Auger Observatory has a public data policy following the FAIR principles (Findable, Accessible, Interoperable, and Reusable). We aim to share the data with the scientific community as part of the multi-messenger effort at different levels and for educational activities to engage the general public. Following the first portal created in 2007, a new

Jasmin Fielder, Michael Gnewuch, Christian Weiß

We consider Poissonian pair correlations (PPC) for uniformly distributed sequences of random numbers with a dependency structure. More specifically, we treat two classes of dependent random variables which have widely been studied in the literature, namely sequences of jittered samples and random walks on the torus. We show that for the former class, the PPC

David Pesquera, Kumara Cordero-Edwards, Marti Checa, Ilia Ivanov

Flat elastic sheets tend to display wrinkles and folds. From pieces of clothing down to two-dimensional crystals, these corrugations appear in response to strain generated by sheet compression or stretching, thermal or mechanical mismatch with other elastic layers, or surface tension. Extensively studied in metals, polymers and, more recently, in van der Waa

Gourab Ghatak, Geethu Joseph, Chen Quan

We develop a framework for communication-control co-design in a wireless networked control system with multiple geographically separated controllers and controlled systems, modeled via a Poisson point process. Each controlled system consists of an actuator, plant, and sensor. Controllers receive state estimates from sensors and design control inputs, which a

Long Meng, Hong-Wei Zhang, Junyong Zhang

We prove a sharp-in-time dispersive estimate of the Dirac equation on spinor bundles over the real hyperbolic space. Compared with the Euclidean counterparts, our result shows that the dispersive estimate differs between short and long times, reflecting the intuitive influence of negative curvature on the dispersion. Moreover, the well-known equivalence betw

Ariane Lambert-Mogiliansky, Irénée Frérot

In this paper, we investigate deliberation procedures that invite citizens with contextual opinions to explore alternative thinking frames. Contextuality is captured in a simple quantum cognitive model. We show how disagreeing citizens endowed with contextual opinions, can reach consensus in a binary collective decision problem with no improvement in their i

Gareth A. Tribello, Massimiliano Bonomi, Giovanni Bussi, Carlo Camilloni

In computational physics, chemistry, and biology, the implementation of new techniques in a shared and open source software lowers barriers to entry and promotes rapid scientific progress. However, effectively training new software users presents several challenges. Common methods like direct knowledge transfer and in-person workshops are limited in reach an

Patrick Antweiler

We generalize the higher Riemann-Hilbert correspondence in the presence of scalar curvature for a (possibly non-compact) smooth manifold $M$. We show that the dg-category of curved $\infty$-local systems, the dg-category of graded vector bundles with projectively flat $\mathbb Z$-graded connections and the dg-category of curved representations of the singula

Emilien Valat, Andreas Hauptmann, Ozan Öktem

Reconstructing images using Computed Tomography (CT) in an industrial context leads to specific challenges that differ from those encountered in other areas, such as clinical CT. Indeed, non-destructive testing with industrial CT will often involve scanning multiple similar objects while maintaining high throughput, requiring short scanning times, which is n

Thijs Steel, Julien Langou

We present an efficient algorithm for the application of sequences of planar rotations to a matrix. Applying such sequences efficiently is important in many numerical linear algebra algorithms for eigenvalues. Our algorithm is novel in three main ways. First, we introduce a new kernel that is optimized for register reuse in a novel way. Second, we introduce

Jan Dereziński, Adam Latosiński

We consider N Dirac fermions on a 4-dimensional Euclidean space with a quadratic interaction given by arbitrary external Clifford-valued fields. The divergence of the axial current satisfies on the classical level a relation that is violated after quantization. Using the Pauli-Villars method to regularize the fields, we find the conditions that guarantee the

Szymon Matczak, Dorota Stachowiak, Grzegorz Soboń

In this study, we introduce a new approach to fabricating fiber optic microlenses using a three-electrode arc fusion splicer. Through beam propagation method-based simulations, we verified the performance of our lenses, achieving highly consistent results across both simulations and experiments. We fabricated three distinctive microlens types: ball-type, fib

Antoine C. Petit

Exoplanet detection surveys revealed the existence of numerous multi-planetary systems packed close to their stability limit. In this proceeding, we review the mechanism driving the instability of compact systems, originally published in Petit et al. (2020). Compact systems dynamics are dominated by the interactions between resonances involving triplets of p

Anthony G Cohn, Robert E Blackwell

Qualitative Spatial Reasoning is a well explored area of Knowledge Representation and Reasoning and has multiple applications ranging from Geographical Information Systems to Robotics and Computer Vision. Recently, many claims have been made for the reasoning capabilities of Large Language Models (LLMs). Here, we investigate the extent to which a set of repr

Nir Mualem, Roy Amoyal, Oren Freifeld, Derya Akkaynak

In underwater images, most useful features are occluded by water. The extent of the occlusion depends on imaging geometry and can vary even across a sequence of burst images. As a result, 3D reconstruction methods robust on in-air scenes, like Neural Radiance Field methods (NeRFs) or 3D Gaussian Splatting (3DGS), fail on underwater scenes. While a recent und

Jesús M. Ceresuela, Nacho López

Radial Moore graphs are approximations of Moore graphs that preserve the distance-preserving spanning tree for its central vertices. One way to classify their resemblance with a Moore graph is the status measure. The status of a graph is defined as the sum of the distances of all pairs of ordered vertices and equals twice the Wiener index. In this paper we s

Michail Kalntis, José Suárez-Varela, Jesús Omaña Iglesias, Anup Kiran Bhattacharjee

Cellular networks rely on handovers (HOs) as a fundamental element to enable seamless connectivity for mobile users. A comprehensive analysis of HOs can be achieved through data from Mobile Network Operators (MNOs); however, the vast majority of studies employ data from measurement campaigns within confined areas and with limited end-user devices, thereby pr

Yi-Neng Zhou, Chang Liu

Quantum information scrambling, typically explored in closed quantum systems, describes the spread of initially localized information throughout a system and can be quantified by measures such as the Loschmidt echo (LE) and out-of-time-order correlator (OTOC). In this paper, we explore information scrambling in the presence of dissipation by generalizing the

Zewen Du, Zhenjiang Hu, Guiyu Zhao, Ying Jin

Feature upsampling is an essential operation in constructing deep convolutional neural networks. However, existing upsamplers either lack specific feature guidance or necessitate the utilization of high-resolution feature maps, resulting in a loss of performance and flexibility. In this paper, we find that the local self-attention naturally has the feature g

Hemal Mahmud, Hasan Mahmud, Mohammad Rifat Ahmmad Rashid

Sentiment analysis (SA) is a process of identifying the emotional tone or polarity within a given text and aims to uncover the user's complex emotions and inner feelings. While sentiment analysis has been extensively studied for languages like English, research in Bengali, remains limited, particularly for fine-grained sentiment categorization. This work aim

Yicheng Lin, Siyu Liang

The Rubiks Cube, with its vast state space and sparse reward structure, presents a significant challenge for reinforcement learning (RL) due to the difficulty of reaching rewarded states. Previous research addressed this by propagating cost-to-go estimates from the solved state and incorporating search techniques. These approaches differ from human strategie

Salman Ghori, Ania Adil, Eric Feron

Intersections pose critical challenges in traffic management, where maintaining operational constraints and ensuring safety are essential for efficient flow. This paper investigates the effect of intervention timing in management strategies on maintaining operational constraints at intersections while ensuring safe separation distance, avoiding collisions, a

Junyong Kang, Donghyun Son, Hwanjun Song, Buru Chang

In-context learning refers to the emerging ability of large language models (LLMs) to perform a target task without additional training, utilizing demonstrations of the task. Recent studies aim to enhance in-context learning performance by selecting more useful demonstrations. However, they overlook the presence of inevitable noisy labels in task demonstrati

Giannis Petousakis, Angelo Cangelosi, Rustam Stolkin, Manolis Chiou

This paper presents a novel method to quantify Trust in HRI. It proposes an HRI framework for estimating the Robot Trust towards the Human in the context of a narrow and specified task. The framework produces a real-time estimation of an AI agent's Artificial Trust towards a Human partner interacting with a mobile teleoperation robot. The approach for the fr

Soham Poddar, Biswajit Paul, Moumita Basu, Saptarshi Ghosh

A lot of claims are made in social media posts, which may contain misinformation or fake news. Hence, it is crucial to identify claims as a first step towards claim verification. Given the huge number of social media posts, the task of identifying claims needs to be automated. This competition deals with the task of 'Claim Span Identification' in which, give

Alexander D. Popov

We have recently shown that the space of initial data (covariant phase space) of the relativistic oscillator in Minkowski space $\mathbb{R}^{3,1}$ is a homogeneous K\"ahler-Einstein manifold $Z_6$=AdS$_7$/U(1)=U(3,1)/U(3)$\times$U(1). It was also shown that the energy eigenstates of the quantum relativistic oscillator form a direct sum of two weighted Bergma

Fan Yang, You Lu, Bihuan Chen, Peng Qin

With the rapid development of autonomous vehicles, there is an increasing demand for scenario-based testing to simulate diverse driving scenarios. However, as the base of any driving scenarios, road scenarios (e.g., road topology and geometry) have received little attention by the literature. Despite several advances, they either generate basic road componen

Alan Said

The rise of Large Language Models (LLMs), such as LLaMA and ChatGPT, has opened new opportunities for enhancing recommender systems through improved explainability. This paper provides a systematic literature review focused on leveraging LLMs to generate explanations for recommendations -- a critical aspect for fostering transparency and user trust. We condu

Anton Faustmann, Patrick Liebisch, Benjamin Bennemann, Pujitha Perla

Semiconductor nanowires can be utilized to create quantum dot qubits. The formation of quantum dots is typically achieved by means of bottom gates created by a lift-off process. As an alternative, we fabricated flat buried bottom gate structures by filling etched trenches in a Si substrate with sputtered TiN, followed by mechanical polishing. This method ach

Mingyu Xu, Wei Cheng, Bingning Wang, Weipeng Chen

The current large language models are mainly based on decode-only structure transformers, which have great in-context learning (ICL) capabilities. It is generally believed that the important foundation of its ICL capability is the induction heads mechanism, which requires at least two layers attention. In order to more efficiently implement the ability of th

Andrea Bracco, Marco Padovani, Daniele Galli, Stefania Pezzuto

Cosmic rays (CRs) are fundamental to the chemistry and physics of star-forming regions, influencing molecular gas ionization, mediating interactions with interstellar magnetic fields, and regulating star formation from the diffuse interstellar medium to the creation of stellar cores. The electronic GeV component of CRs is expected to produce non-thermal sync

Massimo Franchi, Iliyan Georgiev, Paolo Paruolo

This paper proposes a novel approach for semiparametric inference on the number $s$ of common trends and their loading matrix $\psi$ in $I(1)/I(0)$ systems. It combines functional approximation of limits of random walks and canonical correlations analysis, performed between the $p$ observed time series of length $T$ and the first $K$ discretized elements of

Ziming Wang, Yun Gao, Apostolos I. Rikos, Ning Pang

This paper investigates event-triggered consensus tracking in nonlinear semi-strict-feedback multi-agent systems involving one leader and multiple followers. We first employ radial basis function neural networks and backstepping techniques to approximate the unknown nonlinear dynamics, facilitating the design of dual observers to measure the unknown states a

Yuji Ando, Ryota Fujii, Hiroshi Kunitomo, Jojiro Totsuka-Yoshinaka

Extending a recent development in the bosonic string field theory, we construct a map from the Witten-type gauge-invariant superstring field theory based on an $A_{\infty}$ structure to a light-cone-gauge superstring field theory via two intermediate theories, which we call the Kaku-type and Kugo-Zwiebach-type superstring field theories. We find that a naive

Armen Asratian

Bonamy et al. (2023) proved that an optimal edge coloring of a simple triangle--free graph $G$ can be reached from any given proper edge coloring of $G$ through a series of Kempe changes. We show that a small modification of their proof gives a possibility to obtain a similar result for a larger class of simple graphs consisting of all triangle-free and all

Songqiying Yang, Ania Adil, Eric Feron

With advancements in technology, commercial aircraft formation flying is becoming increasingly feasible as an efficient and environmentally friendly flight method. However, gaps remain in practical implementation, particularly in collision avoidance for aircraft formations. Existing avoidance algorithms mainly focus on single aircraft or UAV swarms, lacking

Haoyang Nie, Zhixiong Li, Xiansi Wang, Zhenyu Wang

Domain-wall skyrmions (DWSKs) are topological spin textures confined within domain walls that have recently attracted significant attention due to their potential applications in racetrack memory technologies. In this study, we theoretically investigated the motion of DWSKs driven by spin-polarized currents in ferromagnetic strips. Our findings reveal that t

Sander J. W. Vonk, P. Tim Prins, Tong Wang, Jan Matthys

Indium phosphide is the leading material for commercial applications of colloidal quantum dots. To date, however, the community has failed to achieve successful operation under strong excitation conditions, contrasting sharply with other materials. Here, we report how the unusual photophysics of state-of-the-art InP-based quantum dots make them unattractive

William Pham, Alexander Jarema, Donggyu Rim, Zhibin Chen

Background: Enlargement of perivascular spaces (PVS) is common in neurodegenerative disorders including cerebral small vessel disease, Alzheimer's disease, and Parkinson's disease. PVS enlargement may indicate impaired clearance pathways and there is a need for reliable PVS detection methods which are currently lacking. Aim: To optimise a widely used deep le

Georg Niess, Roman Kern

As large language models (LLMs) reach human-like fluency, reliably distinguishing AI-generated text from human authorship becomes increasingly difficult. While watermarks already exist for LLMs, they often lack flexibility and struggle with attacks such as paraphrasing. To address these issues, we propose a multi-feature method for generating watermarks that

Marcin Bownik, Jordy Timo van Velthoven

Given a relatively compact set $\Omega \subseteq \mathbb{R}$ of Lebesgue measure $|\Omega|$ and $\varepsilon > 0$, we show the existence of a set $\Lambda \subseteq \mathbb{R}$ of uniform density $D (\Lambda) \leq (1+\varepsilon) |\Omega|$ such that the exponential system $\{ \exp(2\pi i \lambda \cdot) \mathbf{1}_{\Omega}: \lambda \in \Lambda \}$ is a frame

Ying Huang, Tishuo Wang, Haochuan Yin, Min Jiang

Continuous time crystal (CTC) and quasi-crystal (CTQC) are two novel phases of matter characterized by the spontaneous breaking of continuous time-translation symmetry. To date, realizations of CTCs with periodic oscillations have been reported in only a few physical platforms, and their complex properties still require further exploration. Additionally, CTQ

Francesca Arrigo, Daniele Bertaccini, Alessandro Filippo

We develop efficient and effective strategies for the update of Katz centralities after node and edge removal in simple graphs. We provide explicit formulas for the ``loss of walks" a network suffers when nodes/edges are removed, and use these to inform our algorithms. The theory builds on the newly introduced concept of $\cF$-avoiding first-passage walks. F

Ziyi Pan, Yuancheng Jiang, Wenbo Lv, Sisi Li

In MRI-guided laser interstitial thermotherapy (MRgLITT), a signal void sometimes appears at the heating center of the measured temperature map. In neurosurgical MRgLITT treatments, cerebrospinal fluid pulsation (CSF), which may lead to temperature artifacts, also needs to be carefully managed. We find that signal loss in MR magnitude images can be one disti

Seyul Lee, Jayden King, Young Choon Lee, Hyuck Han

Modern vehicles equip dashcams that primarily collect visual evidence for traffic accidents. However, most of the video data collected by dashcams that is not related to traffic accidents is discarded without any use. In this paper, we present a use case for dashcam videos that aims to improve driving safety. By analyzing the real-time videos captured by das

Kaustubh Ponkshe, Raghav Singhal, Eduard Gorbunov, Alexey Tumanov

Low-rank adapters have become standard for efficiently fine-tuning large language models, but they often fall short of achieving the performance of full fine-tuning. We propose a method, LoRA Silver Bullet or LoRA-SB, that approximates full fine-tuning within low-rank subspaces using a carefully designed initialization strategy. We theoretically demonstrate

Parjanya Prashant, Ignavier Ng, Kun Zhang, Biwei Huang

Discovering causal structures with latent variables from observational data is a fundamental challenge in causal discovery. Existing methods often rely on constraint-based, iterative discrete searches, limiting their scalability to large numbers of variables. Moreover, these methods frequently assume linearity or invertibility, restricting their applicabilit

E. A. O'Brien, Mima Stanojkovski

We introduce geometric invariants for $p$-groups of class $2$ and exponent $p$. We report on their effectiveness in distinguishing among 5-generator $p$-groups of this type.

Chiara Antico, Stefano Giordano, Cansu Koyuturk, Dimitri Ognibene

Natural language processing skills of Large Language Models (LLMs) are unprecedented, having wide diffusion and application in different tasks. This pilot study focuses on specializing ChatGPT behavior through a Retrieval-Augmented Generation (RAG) system using the OpenAI custom GPTs feature. The purpose of our chatbot, called Unimib Assistant, is to provide

Xiaosi Gu, Tomoyuki Obuchi

Semi-supervised learning (SSL) is a machine learning methodology that leverages unlabeled data in conjunction with a limited amount of labeled data. Although SSL has been applied in various applications and its effectiveness has been empirically demonstrated, it is still not fully understood when and why SSL performs well. Some existing theoretical studies h

Gianmario Voria, Francesco Casillo, Carmine Gravino, Gemma Catolino

Stakeholders' conversations in requirements elicitation meetings hold valuable insights into system and client needs. However, manually extracting requirements is time-consuming, labor-intensive, and prone to errors and biases. While current state-of-the-art methods assist in summarizing stakeholder conversations and classifying requirements based on their n

Wenbo Zhang, Lu Zhang, Ping Hu, Liqian Ma

Injecting semantics into 3D Gaussian Splatting (3DGS) has recently garnered significant attention. While current approaches typically distill 3D semantic features from 2D foundational models (e.g., CLIP and SAM) to facilitate novel view segmentation and semantic understanding, their heavy reliance on 2D supervision can undermine cross-view semantic consisten

Ziyue Wang, Shu Lin

Motivated by the key role of shear induced polarization in understanding the local spin polarization puzzle of $\Lambda$ hyperons in heavy ion collisions, we perform a complete analysis of spin polarization of massive fermion in a quantum electrodynamic plasma with shear flow. Apart from the well-known spin-shear coupling in free theory, we include two more

Md. Touhidul Islam, Md. Abtahi M. Chowdhury, Sumaiya Salekin, Aye T. Maung

In contrast to non-medical image denoising, where enhancing image clarity is the primary goal, medical image denoising warrants preservation of crucial features without introduction of new artifacts. However, many denoising methods that improve the clarity of the image, inadvertently alter critical information of the denoised images, potentially compromising

Chaojun Ni, Guosheng Zhao, Xiaofeng Wang, Zheng Zhu

Closed-loop simulation is crucial for end-to-end autonomous driving. Existing sensor simulation methods (e.g., NeRF and 3DGS) reconstruct driving scenes based on conditions that closely mirror training data distributions. However, these methods struggle with rendering novel trajectories, such as lane changes. Recent works have demonstrated that integrating w

Dihong Gong, Pu Lu, Zelong Wang, Meng Zhou

Large Language Models (LLMs) offer a promising basis for creating agents that can tackle complex tasks through iterative environmental interaction. Existing methods either require these agents to mimic expert-provided trajectories or rely on definitive environmental feedback for reinforcement learning which limits their application to specific scenarios like

Tan Van Vu

The precision and response of trajectory observables offer valuable insights into the behavior of nonequilibrium systems. For classical systems, trade-offs between these characteristics and thermodynamic costs, such as entropy production and dynamical activity, have been established through uncertainty relations. Quantum systems, however, present unique chal

Xiangjie Yan, Shaqi Luo, Yongpeng Jiang, Mingrui Yu

The ultrasound scanning robot operates in environments where frequent human-robot interactions occur. Most existing control methods for ultrasound scanning address only one specific interaction situation or implement hard switches between controllers for different situations, which compromises both safety and efficiency. In this paper, we propose a unified i

Niki Martinel, Mariano Serrao, Christian Micheloni

We introduce a novel state-space model (SSM)-based framework for skeleton-based human action recognition, with an anatomically-guided architecture that improves state-of-the-art performance in both clinical diagnostics and general action recognition tasks. Our approach decomposes skeletal motion analysis into spatial, temporal, and spatio-temporal streams, u

Ali BenAmor, Kazuhiro Kuwae

We give a substitute to Feller property for semigroups of time-changed processes; under some conditions this leads to establish sufficient (new) conditions for the semigroups to be Feller. Moreover, given a standard process and a sequence of measures converging vaguely to a final measure, under some assumptions, we establish convergence of the sequence of th

Luo Yu, Liu Yucheng, Shen Haihao

The AIPC concept is gaining popularity, and more and more hybrid CPUs will be running AI models on client devices. However, the current AI inference framework overlooks the imbalanced hardware capability of hybrid CPUs, leading to low inference performance. To address this issue, we have introduced a dynamic parallel method for hybrid CPUs, which significant

Jong-Hyun Baek, Kang-Sin Choi

We study the no-global-symmetry conjecture in quantum gravity by modeling black holes as non-isometric codes that encode the interior states with global charges into the fundamental states. The fluctuation in the inner products of the charged states can be larger compared to the case without charges. The non-isometric map causes states with different charges

Gian Maria Dall'Ara

Given a compact manifold $M$ equipped with smooth vector fields $X_1,\ldots, X_r$, we consider the generalized Dirichlet energy \[\mathbf{E}(f)= \sum_{j=1}^r\int_M |X_jf|^2\, dm,\] where $dm$ is a volume form, and ask if the set \[ \mathcal{B}=\{f\in L^2(M)\colon\,\mathbf{E}(f)+\lVert f\rVert_{L^2(M)}^2\leq 1 \} \] is precompact in $L^2(M)$. We find a geomet

Yuta Ojima, Hiroki Sakaji, Tadashi Nakamura, Hiroaki Sakata

This paper presents a knowledge management system for automobile failure analysis using retrieval-augmented generation (RAG) with large language models (LLMs) and knowledge graphs (KGs). In the automotive industry, there is a growing demand for knowledge transfer of failure analysis from experienced engineers to young engineers. However, failure events are p

Shuo Li, Zhanlong Wu, Yanhong Wang, Jun Luo

We performed single-crystal magnetic susceptibility and $^1$H NMR measurements on a quasi-1D, spin-1 antiferromagnet NiC$_2$O$_4$$\cdot$2NH$_3$, with temperature down to 100 mK and with field up to 26 T. With field applied along the chain direction (crystalline $b$ direction), a spin gap is determined at low fields. Our susceptibility and spin-lattice relaxa

Rongkun Xue, Jinouwen Zhang, Yazhe Niu, Dazhong Shen

Recent generative models based on score matching and flow matching have significantly advanced generation tasks, but their potential in discriminative tasks remains underexplored. Previous approaches, such as generative classifiers, have not fully leveraged the capabilities of these models for discriminative tasks due to their intricate designs. We propose P

Yutong Chen, Daisuke Sumiyoshi, Riki Sakai, Takahiro Yamamoto

In response to the substantial energy consumption in buildings, the Japanese government initiated the BI-Tech (Behavioral Insights X Technology) project in 2019, aimed at promoting voluntary energy-saving behaviors through the utilization of AI and IoT technologies. Our study aimed at small and medium-sized office buildings introduces a cost-effective IoT-ba

Daisuke Yoshida, Kaho Yoshimura

We apply Iyer-Wald's covariant phase space formalism to asymptotically de Sitter spacetimes and establish the thermodynamic first law, expressed in terms of the Abbott-Deser mass. Similar to Iyer-Wald's first law for asymptotically flat black holes, our first law applies to general asymptotically de Sitter perturbations around a Reissner-Nordstr\"{o}m-de Sit

Wenfang Sun, Yingjun Du, Gaowen Liu, Yefeng Zheng

We tackle the problem of quantifying the number of objects by a generative text-to-image model. Rather than retraining such a model for each new image domain of interest, which leads to high computational costs and limited scalability, we are the first to consider this problem from a domain-agnostic perspective. We propose QUOTA, an optimization framework fo

Fuqiang Zhao, Yijing Guo, Siyuan Yang, Xi Chen

High-quality 3D urban reconstruction is essential for applications in urban planning, navigation, and AR/VR. However, capturing detailed ground-level data across cities is both labor-intensive and raises significant privacy concerns related to sensitive information, such as vehicle plates, faces, and other personal identifiers. To address these challenges, w

Gaojie Zhang, Hao Wu, Li Yang, Wen Jin

Two-dimensional (2D) van der Waals (vdW) magnets with spin-phonon coupling are crucial for next-generation spintronics. Among them, Fe3GaTe2 has attracted widespread attention due to above-room-temperature intrinsic ferromagnetism and large perpendicular magnetic anisotropy. However, the lattice vibrations and the interplay between ferromagnetism and lattice

Taesung Kwon, Jong Chul Ye

In this paper, we propose a novel framework for solving high-definition video inverse problems using latent image diffusion models. Building on recent advancements in spatio-temporal optimization for video inverse problems using image diffusion models, our approach leverages latent-space diffusion models to achieve enhanced video quality and resolution. To a

Anja Madleine Markl, Daniel Nieder, Diana Isabel Sandoval-Bojorquez, Anna Taubenberger

Progress in our knowledge of tumor mechanisms and complexity led to the understanding of the physical parameters of cancer cells and their microenvironment, including the mechanical, thermal, and electrical properties, solid stress, and liquid pressure, as critical regulators of tumor progression and potential prognostic traits associated with clinical outco

Nestor Arsenov, Sandor Frey, András Kovács, Lyuba Slavcheva-Mihova

Quasars are objects of high interest in extragalactic astrophysics, cosmology, and astrometry. One of their useful qualities is their potential radio loudness. However, the fraction of radio-loud vs. radio-quiet quasars is subject to ongoing investigations, where the statistical power is limited by the low number of known quasars with radio counterparts. In

Yule Liu, Zhen Sun, Xinlei He, Xinyi Huang

Recent advancements in fine-tuning proprietary language models enable customized applications across various domains but also introduce two major challenges: high resource demands and security risks. Regarding resource demands, recent work proposes novel partial compression, such as BitDelta, to quantize the delta weights between the fine-tuned model and bas

Gennaro Auricchio, Paolo Giudici, Giuseppe Toscani

The coefficient of variation, which measures the variability of a distribution from its mean, is not uniquely defined in the multidimensional case, and so is the multidimensional Gini index, which measures the inequality of a distribution in terms of the mean differences among its observations. In this paper, we connect these two notions of sparsity, and pro

Xianfeng Tan, Yuhan Li, Wenxiang Shang, Yubo Wu

Standard clothing asset generation involves restoring forward-facing flat-lay garment images displayed on a clear background by extracting clothing information from diverse real-world contexts, which presents significant challenges due to highly standardized structure sampling distributions and clothing semantic absence in complex scenarios. Existing models

Jungbin Cho, Junwan Kim, Jisoo Kim, Minseo Kim

Human motion is inherently continuous and dynamic, posing significant challenges for generative models. While discrete generation methods are widely used, they suffer from limited expressiveness and frame-wise noise artifacts. In contrast, continuous approaches produce smoother, more natural motion but often struggle to adhere to conditioning signals due to

Yang Lv, Jinlong Lei, Peng Yi

In this paper, we explore how to optimize task allocation for robot swarms in dynamic environments, emphasizing the necessity of formulating robust, flexible, and scalable strategies for robot cooperation. We introduce a novel framework using a decentralized partially observable Markov decision process (Dec_POMDP), specifically designed for distributed robot

Tianqi Li, Ruobing Zheng, Bonan Li, Zicheng Zhang

Despite significant progress in talking head synthesis since the introduction of Neural Radiance Fields (NeRF), visual artifacts and high training costs persist as major obstacles to large-scale commercial adoption. We propose that identifying and establishing fine-grained and generalizable correspondences between driving signals and generated results can si

Manon Lizzana, Fabien Malbet, Pierre Kern, Fabrice Pancher

High precision differential Astrometry is the branch of astronomy that evaluates the relative position, distance and motion of celestial objects with respect to the stars present in the field of view. A mission called Theia has been submitted in 2022 for ESA's M7 call for missions, using a diffraction-limited telescope about 1m in diameter and with a field o

Alexander Markin, Vadim Pryadilshchikov, Artem Komarichev, Ruslan Rakhimov

Transient objects in video sequences can significantly degrade the quality of 3D scene reconstructions. To address this challenge, we propose T-3DGS, a novel framework that robustly filters out transient distractors during 3D reconstruction using Gaussian Splatting. Our framework consists of two steps. First, we employ an unsupervised classification network

Yuan Lu

Standard conformal prediction methods guarantee marginal coverage but often produce inefficient intervals that fail to adapt to local heteroscedasticity, while recent localized approaches often struggle to maintain validity across distinct subpopulations with varying noise profiles. To address these challenges, we introduce Localized Conformal Multi-Quantile

Sria Biswas, Balasubramanyam Appina, Priyanka Kokil, Sumohana S Channappayya

We present two major contributions in this work: 1) we create a full HD resolution stereoscopic (S3D) video dataset comprised of 12 reference and 360 distorted videos. The test stimuli are produced by simulating the five levels of fog and haze ambiances on the pristine left and right video sequences. We perform subjective analysis on the created video datase

Alex Fink, Kris Shaw, David E Speyer

The third author introduced the $g$-polynomial $g_M(t)$ of a matroid, a covaluative matroid statistic which is unchanged under series and parallel extension. The $g$-polynomial of a rank $r$ matroid $M$ has the form $g_1 t + g_2 t^2 + \cdots + g_r t^r$. The coefficient $g_1$ is Crapo's classical $\beta$-invariant. In this paper, we study the coefficient $g_r

Emma Lagracie, Lisl Weynans, Yves Coudière

Electrocardiographic imaging non-invasively reconstructs activation maps of the heart from temporal body surface potential maps by post-processing solutions of an inverse problem. Typically, activation times are detected through the maximal deflection of the temporal or spatial derivative of recovered extracellular or transmembrane potentials. However, this

Roméo Troubat

We provide a generalization of global hyperbolicity in pseudo-Riemannian spaces of signature (p, q) for p ___ q ___ 2. We then prove the compactness of causal diamonds in globally hyperbolic spaces and deduce the existence of solutions to a Plateau problem for this class of spaces. Finally, caracterize GH-regular representations in SO(p,q+1) as holonomies of

K. Perger, J. Fogasy, S. Frey, K. É. Gabányi

To investigate the radio properties of the recently found high-redshift population, we collected a sample of $919$ little red dots (LRDs) from the literature. By cross-matching their coordinates with the radio catalogues based on the first- and second-epoch observations of the Very Large Array Sky Survey (VLASS) and the Faint Images of the Radio Sky at Twent

Takasugu Shigenobu, Naoyuki Kamiyama

An integer linear system (ILS) is a linear system with integer constraints. The solution graph of an ILS is defined as an undirected graph defined on the set of feasible solutions to the ILS. A pair of feasible solutions is connected by an edge in the solution graph if the Hamming distance between them is 1. We consider a property of the coefficient matrix o

Yuxiang Zhang, Shangxi Wu, Yuqi Yang, Jiangming Shu

The technical report introduces O1-CODER, an attempt to replicate OpenAI's o1 model with a focus on coding tasks. It integrates reinforcement learning (RL) and Monte Carlo Tree Search (MCTS) to enhance the model's System-2 thinking capabilities. The framework includes training a Test Case Generator (TCG) for standardized code testing, using MCTS to generate

Yoshia Abe, Shuhei Matsuo, Ryoma Kondo, Ryohei Hisano

Large language models (LLMs) have demonstrated promising performance in various financial applications, though their potential in complex investment strategies remains underexplored. To address this gap, we investigate how LLMs can predict price movements in stock and bond portfolios using economic indicators, enabling portfolio adjustments akin to those emp

Siddhartha Bhattacharya, Aarham Wasit, Mason Earles, Nitin Nitin

Rapid detection of foodborne bacteria is critical for food safety and quality, yet traditional culture-based methods require extended incubation and specialized sample preparation. This study addresses these challenges by i) enhancing the generalizability of AI-enabled microscopy for bacterial classification using adversarial domain adaptation and ii) compar

Yiwen Yuan, Zecheng Zhang, Xinwei He, Akihiro Nitta

Recommendation systems predominantly utilize two-tower architectures, which evaluate user-item rankings through the inner product of their respective embeddings. However, one key limitation of two-tower models is that they learn a pair-agnostic representation of users and items. In contrast, pair-wise representations either scale poorly due to their quadrati