March 2024 arXiv papers — page 157

L. Tenhu, J. Larsson, J. Sollerman, P. Lundqvist

The supernova remnant SNR 0540-69.3, twin of the Crab Nebula, offers an excellent opportunity to study the continuum emission from a young pulsar and pulsar-wind nebula (PWN). We present observations taken with the VLT instruments MUSE and X-shooter in the wavelength range 3000-25,000 \r{A}, which allow us to study spatial variations of the optical spectra,

Liang Yang, Shi-Ting Ni, Yuyang Wang, Ao Yu

The Metaverse is at the vanguard of the impending digital revolution, with the potential to significantly transform industries and lifestyles. However, in 2023, skepticism surfaced within industrial and academic spheres, raising concerns that excitement may outpace actual technological progress. Interoperability, recognized as a major barrier to the Metavers

Leo Egghe

The degree distribution, referred to as the delta-sequence of a network is studied. Using the non-normalized Lorenz curve, we apply a generalized form of the classical majorization partial order. Next, we introduce a new class of small worlds, namely those based on degree centralities of networks. Similar to a previous study, small worlds are defined as sequ

Adrian Jarret, Valérie Costa, Julien Fageot

We consider a linear inverse problem whose solution is expressed as a sum of two components: one smooth and the other sparse. This problem is addressed by minimizing an objective function with a least squares data-fidelity term and a different regularization term applied to each of the components. Sparsity is promoted with an $\ell_1$ norm, while the smooth

Youyi Zhao

We revisit the global existence of solutions with some large perturbations to the incompressible, viscous, and non-resistive MHD system in a three-dimensional periodic domain, where the impressed magnetic field satisfies the Diophantine condition, and the intensity of the impressed magnetic field, denoted by $m$, is large compared to the perturbations. It wa

Giacomo Ascione, Anna Vidotto

In this paper, we consider the time change of the diffusion process on the 2-dimensional unit sphere generated by the Laplace-Beltrami operator, perturbed by means of a longitudinal vector field. First, this is done by addressing the problem of finding strong solutions to suitable time-nonlocal Kolmogorov equations, via a spectral decomposition approach. Nex

Tianhao He, Evangelos Niforatos, Gerd Kortuem

Video-Based Design (VBD) uses video as a primary medium for analyzing user interactions, prototyping, and generating design insights. However, current VBD workflows are constrained by labor-intensive, inconsistent manual annotations that fragment attention and delay insights. Computer Vision (CV)-powered automatic annotation offers opportunities to reduce ma

Ke Zhang

A diffusion interface two-phase magnetohydrodynamic model has been used for matched densities in our previous work [1,2], which may limit the applications of the model. In this work, we derive a thermodynamically consistent diffuse interface model for diffusion interface two-phase magnetohydrodynamic fluids with large density ratios by Onsager's variational

K. Zioutas, A. Zhitnitsky, C. Zamantzas, Y. K. Semertzidis

Anti-quark nuggets (AQNs) have been suggested to solve the dark matter (DM) and the missing antimatter problem in the universe and have been proposed as an explanation of various observations. Their size is in the {\mu}m range and their density is about equal to the nuclear density with an expected flux of about $0.4 / km^2 / year$. For the typical velocity

Soichiro Izumine, Kazunori Nakayama

The gravitational interaction is ubiquitous and the effect of gravitational particle production necessarily contributes to the dark matter abundance. A simple candidate of dark matter is a scalar particle, whose only renormalizable interaction is the Higgs portal coupling. We show that the abundance of Higgs portal dark matter is significantly affected by th

Mingji Yang, Hanzhi Wang, Zhewei Wei, Sibo Wang

Personalized PageRank (PPR) is a traditional measure for node proximity on large graphs. For a pair of nodes $s$ and $t$, the PPR value $\pi_s(t)$ equals the probability that an $\alpha$-discounted random walk from $s$ terminates at $t$ and reflects the importance between $s$ and $t$ in a bidirectional way. As a generalization of Google's celebrated PageRank

Elena Cáceres, Stefan Eccles, Jason Pollack, Sarah Racz

The Eigenstate Thermalization Hypothesis (ETH) has played a key role in recent advances in the high energy and condensed matter communities. It explains how an isolated quantum system in a far-from-equilibrium initial state can evolve to a state that is indistinguishable from thermal equilibrium, with observables relaxing to almost time-independent results t

Yazhe Li, Jorg Bornschein, Ting Chen

In this paper, we explore a new generative approach for learning visual representations. Our method, DARL, employs a decoder-only Transformer to predict image patches autoregressively. We find that training with Mean Squared Error (MSE) alone leads to strong representations. To enhance the image generation ability, we replace the MSE loss with the diffusion

Marc Geiller

We study the subleading structure of asymptotically-flat spacetimes and its relationship to the $w_{1+\infty}$ loop algebra of higher spin charges. We do so using both the Bondi-Sachs and the Newman-Penrose formalism, via a dictionary built from a preferred choice of tetrad. This enables us to access properties of the so-called higher Bondi aspects, such as

Sebastian Baader, Claire Burrin, Luca Studer

We show that the family of systoles of hyperbolic surfaces associated with congruence lattices in $\mathrm{SL}_2(\mathbb{Z})$ have asymptotically minimal crossing number.

Martina Benini, Silvia Gallucci, Marta Bonato, Marta Parazzini

This study aims to investigate the variability of exposure levels among road users generated in a realistic urban scenario by Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication technologies operating at 5.9 GHz. The exposure levels were evaluated in terms of whole-body Specific Absorption Rate (wbSAR) [W/kg] in three different human m

Leonardo Peroni, Sergey Gorinsky

Remaining a dominant force in Internet traffic, video streaming captivates end users, service providers, and researchers. This paper takes a pragmatic approach to reviewing recent advances in the field by focusing on the prevalent streaming paradigm that involves delivering long-form two-dimensional videos over the best-effort Internet with client-side adapt

Igor Dolinka, James East, Nik Ruškuc

Let $T_X$ be the full transformation monoid over a finite set $X$, and fix some $a\in T_X$ of rank $r$. The variant $T_X^a$ has underlying set $T_X$, and operation $f\star g=fag$. We study the congruences of the subsemigroup $P=Reg(T_X^a)$ consisting of all regular elements of $T_X^a$, and the lattice $Cong(P)$ of all such congruences. Our main structure the

Xavier Blot, Danilo Lewański, Paolo Rossi, Sergei Shadrin

We propose a new system of conjectural relations in the tautological ring of the moduli space of curves involving stable rooted trees with level structure decorated by Hodge and {\Omega}-classes and prove these conjectures in different cases.

Xin Zhao, Naoki Yoshinaga, Daisuke Oba

Acquiring factual knowledge for language models (LMs) in low-resource languages poses a serious challenge, thus resorting to cross-lingual transfer in multilingual LMs (ML-LMs). In this study, we ask how ML-LMs acquire and represent factual knowledge. Using the multilingual factual knowledge probing dataset, mLAMA, we first conducted a neuron investigation o

Alejandro Mata Ali, Iñigo Perez Delgado, Aitor Moreno Fdez. de Leceta

In this paper we present a study of the applicability and feasibility of quantum-inspired algorithms and techniques in tensor networks for industrial environments and contexts, with a compilation of the available literature and an analysis of the use cases that may be affected by such methods. In addition, we explore the limitations of such techniques in ord

Maximilian Schall, Tamara Czinczoll, Gerard de Melo

Writing commit messages is a tedious daily task for many software developers, and often remains neglected. Automating this task has the potential to save time while ensuring that messages are informative. A high-quality dataset and an objective benchmark are vital preconditions for solid research and evaluation towards this goal. We show that existing datase

Zhenzi Weng, Zhijin Qin, Geoffrey Ye Li

In this paper, we propose a robust semantic communication system for speech transmission, named Ross-S2T, by delivering the essential semantic information. Specifically, we consider the speech-to-text translation (S2TT) as the transmission goal. First, a new deep semantic encoder is developed to convert speech in the source language to textual features assoc

Sotaro Takeshita, Simone Paolo Ponzetto, Kai Eckert

Keywords, that is, content-relevant words in summaries play an important role in efficient information conveyance, making it critical to assess if system-generated summaries contain such informative words during evaluation. However, existing evaluation metrics for extreme summarization models do not pay explicit attention to keywords in summaries, leaving de

Marco De Nadai, Francesco Fabbri, Paul Gigioli, Alice Wang

In the ever-evolving digital audio landscape, Spotify, well-known for its music and talk content, has recently introduced audiobooks to its vast user base. While promising, this move presents significant challenges for personalized recommendations. Unlike music and podcasts, audiobooks, initially available for a fee, cannot be easily skimmed before purchase,

Ayse Bat

This research investigates the separation of Cherenkov and Scintillation light signals within a simulated Water-based Liquid Scintillator (WbLS) detector, utilizing the XGBoost machine learning algorithm. The simulation data were gathered using the Rat-Pac software, which was built on the Geant4 architecture. The use of the WbLS medium has the capability to

Luca Dall'Ava, Aleksander Horawa

Let $E$ be an elliptic curve over $\mathbb{Q}$ and $\varrho_1, \varrho_2 \colon \mathrm{Gal}(H/\mathbb{Q}) \to \mathrm{GL}_2(L)$ be two odd Artin representations. We use $p$-adic methods to investigate the part of the Mordell-Weil group $E(H) \otimes L$ on which the Galois group acts via $\varrho_1 \otimes \varrho_2$. When the rank of the group is two, Darmo

Shaoyu Cai, Zhenlin Chen, Haichen Gao, Ya Huang

Extensive research has been done in haptic feedback for texture simulation in virtual reality (VR). However, it is challenging to modify the perceived tactile texture of existing physical objects which usually serve as anchors for virtual objects in mixed reality (MR). In this paper, we present ViboPneumo, a finger-worn haptic device that uses vibratory-pneu

Eda Yilmaz, Hacer Yalim Keles

We introduce Adversarial Sparse Teacher (AST), a robust defense method against distillation-based model stealing attacks. Our approach trains a teacher model using adversarial examples to produce sparse logit responses and increase the entropy of the output distribution. Typically, a model generates a peak in its output corresponding to its prediction. By le

Florian Bemmann, Timo Koch, Maximilian Bergmann, Clemens Stachl

While the study of language as typed on smartphones offers valuable insights, existing data collection methods often fall short in providing contextual information and ensuring user privacy. We present a privacy-respectful approach - context-enriched keyboard logging - that allows for the extraction of contextual information on the user's input motive, which

Jing Xu, Yongbo Zhang

The quality of power grid equipment forms the material foundation for the safety of the large power grid. Ensuring the quality of equipment entering the grid is a core task in material management. Currently, the inspection of incoming materials involves the generation of sampling plans, sampling, sealing, sample delivery, and testing. Due to the lack of a co

Sebastian Mies, Benjamin Moore

The arboricity $\Gamma(G)$ of an undirected graph $G =(V,E)$ is the minimal number $k$ such that $E$ can be partitioned into $k$ forests on $V$. Nash-Williams' formula states that $k = \lceil \gamma(G) \rceil$, where $\gamma(G)$ is the maximum of $\frac{|E_{H}|}{|V_{H}|-1}$ over all subgraphs $(V_H , E_H )$ of $G$ with $|V_H | \geq 2$. The Strong Nine Dragon

Zehang Weng, Peng Zhou, Hang Yin, Alexander Kravberg

Interactive perception enables robots to manipulate the environment and objects to bring them into states that benefit the perception process. Deformable objects pose challenges to this due to significant manipulation difficulty and occlusion in vision-based perception. In this work, we address such a problem with a setup involving both an active camera and

Arthur Barbosa, Najib Kacem, Noureddine Bouhaddi

In this paper, the vibration energy localization in coupled nonlinear oscillators is investigated, based on the creation of standing solitons. The main objective is to establish a design methodology for mechanical lattices using the Nonlinear Schr\"odinger Equation (NLSE) as a guide strategy, even in the presence of damping. A three-dimensional diagram is us

Gido M. van de Ven, Nicholas Soures, Dhireesha Kudithipudi

This book chapter delves into the dynamics of continual learning, which is the process of incrementally learning from a non-stationary stream of data. Although continual learning is a natural skill for the human brain, it is very challenging for artificial neural networks. An important reason is that, when learning something new, these networks tend to quick

Soumi Das, Shubhadip Nag, Shreyyash Sharma, Suparna Bhattacharya

Trustworthy AI is crucial to the widespread adoption of AI in high-stakes applications with fairness, robustness, and accuracy being some of the key trustworthiness metrics. In this work, we propose a controllable framework for data-centric trustworthy AI (DCTAI)- VTruST, that allows users to control the trade-offs between the different trustworthiness metri

Kongyang Chen, Yi Lin, Hui Luo, Bing Mi

In contemporary edge computing systems, decentralized edge nodes aggregate unprocessed data and facilitate data analytics to uphold low transmission latency and real-time data processing capabilities. Recently, these edge nodes have evolved to facilitate the implementation of distributed machine learning models, utilizing their computational resources to ena

Zhongqiang Xiong, Ryohei Seto, Masao Doi

Brownian motion and viscoelasticity of semiflexible polymers is a subject that has been studied for many years. Still, rigorous analysis has been hindered due to the difficulty in handling the constraint that polymer chains cannot be stretched along the contour. Here, we show a straightforward method to solve the problem. We consider a stiff polymer that has

Jingyi Zhang, Peng Zhang, Jingjing Wang, Di Xie

Previous face forgery detection methods mainly focus on appearance features, which may be easily attacked by sophisticated manipulation. Considering the majority of current face manipulation methods generate fake faces based on a single frame, which do not take frame consistency and coordination into consideration, artifacts on frame sequences are more effec

Xiaoying Zhang, Jean-Francois Ton, Wei Shen, Hongning Wang

We introduce Adversarial Policy Optimization (AdvPO), a novel solution to the pervasive issue of reward over-optimization in Reinforcement Learning from Human Feedback (RLHF) for Large Language Models (LLMs). Over-optimization occurs when a reward model serves as an imperfect proxy for human preference, and RL-driven policy optimization erroneously exploits

Jie Shao, Ke Zhu, Hanxiao Zhang, Jianxin Wu

This paper proposes a new pipeline for long-tail (LT) recognition. Instead of re-weighting or re-sampling, we utilize the long-tailed dataset itself to generate a balanced proxy that can be optimized through cross-entropy (CE). Specifically, a randomly initialized diffusion model, trained exclusively on the long-tailed dataset, is employed to synthesize new

Eiichi Bannai, Hirotake Kurihara, Da Zhao, Yan Zhu

The study of $P$-polynomial association schemes (distance-regular graphs) and $Q$-polynomial association schemes, and in particular $P$- and $Q$-polynomial association schemes, has been a central theme not only in the theory of association schemes but also in the whole study of algebraic combinatorics in general. Leonard's theorem (1982) says that the spheri

Hai Huang, Yan Xia, Shengpeng Ji, Shulei Wang

To enhance the interpretability of multimodal unified representations, many studies have focused on discrete unified representations. These efforts typically start with contrastive learning and gradually extend to the disentanglement of modal information, achieving solid multimodal discrete unified representations. However, existing research often overlooks

Roland Meyer, Jakob Tepe, Sebastian Wolff

We present realizability and realization logic, two program logics that jointly address the problem of finding solutions in semantics-guided synthesis. What is new is that we proceed eagerly and not only analyze a single candidate program but a whole set. Realizability logic computes information about the set of candidate programs in a forward fashion. Reali

Jinfeng Song

The quantum duality principal (QDP) by Drinfeld predicts a connection between the quantized universial enveloping algebras and the quantized coordinate algebras, where the underlying classical objects are related by the duality in Poisson geometry. The current paper gives an explicit formulization of the QDP for quantum symmetric pairs. Let $\mathfrak{g}$ be

Chloé Vernière, Hugo Defienne

Photon-pair correlations in spontaneous parametric down conversion are ubiquitous in quantum photonics. The ability to engineer their properties for optimising a specific task is essential, but often challenging in practice. We demonstrate the shaping of spatial correlations between entangled photons in the form of arbitrary amplitude and phase objects. By d

Pallavi Kalikotay, Snigdha Ghosh, Nilanjan Chaudhuri, Pradip Roy

We evaluate the electrical conductivity and shear viscosity of a interacting pion gas in a thermo-magnetic medium using the kinetic theory. The collision term of the relativistic Boltzmann transport equation in presence of background magnetic field is solved using the relaxation time approximation. The medium modified relaxation time is obtained from the cor

Dario Piga, Matteo Rufolo, Gabriele Maroni, Manas Mejari

This paper addresses the challenge of overfitting in the learning of dynamical systems by introducing a novel approach for the generation of synthetic data, aimed at enhancing model generalization and robustness in scenarios characterized by data scarcity. Central to the proposed methodology is the concept of knowledge transfer from systems within the same c

Oskar Kefer, Pavel V. Kolesnichenko, Lukas Ahrens, Jan Freudenberg

The electronic interaction between an optically active singlet state ($S_1S_0$) and a dark state of singlet multiplicity, known as correlated triplet pair ($^1[TT]$), plays a crucial role in the effective transformation from $S_1S_0$ to $^1[TT]$ during intramolecular singlet fission (iSF). This process is understood through mechanisms such as direct exchange

Yuka Fujiki, Shogo Mizutaka

We examined the structure of projections of random bipartite networks characterized by the degree distribution of individual and group nodes through the generating function method. We decomposed a projection into two subgraphs, the giant component, and finite components and analyzed their degree correlation. The projections never exhibit a negative degree co

Steve Desch

Recently Loeb et al. (2024, "Recovery and Classification of Spherules from the Pacific Ocean Site of the CNEOS 2014 January 8 (IM1) Bolide", Res. Notes. Amer. Astron. Soc. 8, 39) reported the magnetic collection of millimeter-sized spherules from the seafloor near Papua New Guinea. About 22% had Mg/Si < 1/3 and were identified as a new "differentiated" varie

Zijie Fang, Yifeng Wang, Ye Zhang, Zhi Wang

Recently, pathological diagnosis has achieved superior performance by combining deep learning models with the multiple instance learning (MIL) framework using whole slide images (WSIs). However, the giga-pixeled nature of WSIs poses a great challenge for efficient MIL. Existing studies either do not consider global dependencies among instances, or use approx

Kongyang Chen, Yikai Li, Wenjun Lan, Bing Mi

Low Earth Orbit (LEO) satellite communication is a crucial component of future 6G communication networks, contributing to the development of an integrated satellite-terrestrial network. In the forthcoming satellite-to-ground network, the idle computational resources of LEO satellites can serve as edge servers, delivering intelligent task computation services

Zichao Dong, Bowen Pang, Xufeng Huang, Hang Ji

Multi-modality fusion is proven an effective method for 3d perception for autonomous driving. However, most current multi-modality fusion pipelines for LiDAR semantic segmentation have complicated fusion mechanisms. Point painting is a quite straight forward method which directly bind LiDAR points with visual information. Unfortunately, previous point painti

Zuguang Li, Wen Wu, Shaohua Wu, Wei Wang

Split learning (SL) is a promising approach for training artificial intelligence (AI) models, in which devices collaborate with a server to train an AI model in a distributed manner, based on a same fixed split point. However, due to the device heterogeneity and variation of channel conditions, this way is not optimal in training delay and energy consumption

Carlos Mejuto-Zaera

Strong/static electronic correlation mediates the emergence of remarkable phases of matter, and underlies the exceptional reactivity properties in transition metal-based catalysts. Modeling strongly correlated molecules and solids calls for multi-reference Ans\"atze, which explicitly capture the competition of energy scales characteristic of such systems. Wi

Biwei Yan, Kun Li, Minghui Xu, Yueyan Dong

Large language models (LLMs) are complex artificial intelligence systems capable of understanding, generating and translating human language. They learn language patterns by analyzing large amounts of text data, allowing them to perform writing, conversation, summarizing and other language tasks. When LLMs process and generate large amounts of data, there is

Jiayan Cao, Xueyu Zhu, Cheng Qian

Lane detection plays a critical role in the field of autonomous driving. Prevailing methods generally adopt basic concepts (anchors, key points, etc.) from object detection and segmentation tasks, while these approaches require manual adjustments for curved objects, involve exhaustive searches on predefined anchors, require complex post-processing steps, and

Francesco Palandra, Andrea Sanchietti, Daniele Baieri, Emanuele Rodolà

We present GSEdit, a pipeline for text-guided 3D object editing based on Gaussian Splatting models. Our method enables the editing of the style and appearance of 3D objects without altering their main details, all in a matter of minutes on consumer hardware. We tackle the problem by leveraging Gaussian splatting to represent 3D scenes, and we optimize the mo

Kentaro Tamura, Yohichi Suzuki, Rudy Raymond, Hiroshi C. Watanabe

QRAO (Quantum Random Access Optimization) is a relaxation algorithm that reduces the number of qubits required to solve a problem by encoding multiple variables per qubit using QRAC (Quantum Random Access Code). Reducing the number of qubits is a common way of dealing with the impact of noise on a quantum algorithm. Our interest lies in the impact of noise o

Markus Huff, Elanur Ulakçı

Large language models (LLMs), such as ChatGPT, have shown remarkable abilities in natural language processing, opening new avenues in psychological research. This study explores whether LLMs can predict human memory performance in tasks involving garden-path sentences and contextual information. In the first part, we used ChatGPT to rate the relatedness and

M. Ślęzak, T. Wagner, V. K. Bharadwaj, O. Gomonay

Magnetic vortices are topological spin structures frequently found in ferromagnets, yet novel to antiferromagnets. By combining experiment and theory, we demonstrate that in a nanostructured antiferromagnetic-ferromagnetic NiO(111)-Fe(110) bilayer, a magnetic vortex is naturally stabilized by magnetostatic interactions in the ferromagnet and is imprinted ont

Hui Zhi, Xiaojun Jiang, Jianfeng Wang

The development of low earth orbit (LEO) mega-constellation fundamentally threatens ground-based optical astronomical observations. To study the photometric properties of the LEO mega-constellations, we used the Xinglong 50 cm telescope to conduct a large-sample, high-precision, and multicolor target-tracking photometry of two typical LEO Mega-constellations

Ceyao Zhang, Renjie Li, Cheng Zhang, Zhaoyu Zhang

Photonic Crystal Surface Emitting Lasers (PCSEL)'s inverse design demands expert knowledge in physics, materials science, and quantum mechanics which is prohibitively labor-intensive. Advanced AI technologies, especially reinforcement learning (RL), have emerged as a powerful tool to augment and accelerate this inverse design process. By modeling the inverse

Jasmin Jörg

We consider systems of simple closed curves on surfaces and their total number of intersection points, their so-called crossing number. For a fixed number of curves, we aim to minimise the crossing number. We determine the minimal crossing number of up to 12 curves on a surface of genus 2 and prove that minimising systems are unique up to homeomorphisms of t

Giuseppe Carleo, Bela Bauer, Matthias Troyer

The theoretical analysis of the Adiabatic Quantum Computation protocol presents several challenges resulting from the difficulty of simulating, with classical resources, the unitary dynamics of a large quantum device. We present here a variational approach to substantially alleviate this problem in many situations of interest. Our approach is based on the ti

Yuelin Zhang, Kim Yan, Chun Ping Lam, Chengyu Fang

Flexible endoscope motion tracking and analysis in mechanical simulators have proven useful for endoscopy training. Common motion tracking methods based on electromagnetic tracker are however limited by their high cost and material susceptibility. In this work, the motion-guided dual-camera vision tracker is proposed to provide robust and accurate tracking o

A. Vacchi, A. Adamczak, D. Bakalov, G. Baldazzi

The article gives the motivations for the measurement of the hyperfine splitting (hfs) in the ground state of muonic hydrogen to explore the properties of the proton at low momentum transfer. It summarizes these proposed measurement methods and finally describes the FAMU experiment in more detail.

Daniel Doehring, Michael Schlottke-Lakemper, Gregor J. Gassner, Manuel Torrilhon

In this paper, we apply the Paired-Explicit Runge-Kutta (P-ERK) schemes by Vermeire et. al. (2019, 2022) to dynamically partitioned systems arising from adaptive mesh refinement. The P-ERK schemes enable multirate time-integration with no changes in the spatial discretization methodology, making them readily implementable in existing codes that employ a meth

Emily L. Hunt, Sabine Reffert

The census of open clusters has exploded in size thanks to data from the Gaia satellite. However, it is likely that many of these reported clusters are not gravitationally bound, making the open cluster census impractical for many scientific applications. We test different physically motivated methods for distinguishing between bound and unbound clusters, us

Tomasz Brzeziński, Krzysztof Radziszewski

Lie brackets or Lie affgebra structures on several classes of affine spaces of matrices are studied. These include general normalised affine matrices, special normalised affine matrices, anti-symmetric and anti-hermitian normalised affine matrices and special anti-hermitian normalised affine matrices. It is shown that, when retracted to the underlying vector

Qiuhui Chen, Huping Ye, Yi Hong

Understanding 3D medical image volumes is a critical task in the medical domain. However, existing 3D convolution and transformer-based methods have limited semantic understanding of an image volume and also need a large set of volumes for training. Recent advances in multi-modal large language models (MLLMs) provide a new and promising way to understand ima

Laurent Loosveldt, Ciprian A. Tudor

We define an asymptotically normal wavelet-based strongly consistent estimator for the Hurst parameter of any Hermite processes. This estimator is obtained by considering a modified wavelet variation in which coefficients are wisely chosen to be, up to negligeable remainders, independent. We use Stein-Malliavin calculus to prove that this wavelet variation s

Yisol Choi, Sangkyung Kwak, Kyungmin Lee, Hyungwon Choi

This paper considers image-based virtual try-on, which renders an image of a person wearing a curated garment, given a pair of images depicting the person and the garment, respectively. Previous works adapt existing exemplar-based inpainting diffusion models for virtual try-on to improve the naturalness of the generated visuals compared to other methods (e.g

Fabiana Camattari, Sabrina Guastavino, Francesco Marchetti, Michele Piana

The purpose of this study is to introduce a new approach to feature ranking for classification tasks, called in what follows greedy feature selection. In statistical learning, feature selection is usually realized by means of methods that are independent of the classifier applied to perform the prediction using that reduced number of features. Instead, greed

August Bjerg

For the Dirichlet realization of $-d^2/dx^2-\lambda^2V$ on a bounded interval, with $V$ a positive $C^2$ potential bounded away from $0$ and $\lambda>0$ a large parameter, we prove an asymptotic law for the values $\lambda_n$ of $\lambda$ at the $n^{\text{th}}$ appearance of a new negative eigenvalue. This approximation is correct up to an error of order $1/

Muhammad A. Shah, David Solans Noguero, Mikko A. Heikkila, Bhiksha Raj

As Automatic Speech Recognition (ASR) models become ever more pervasive, it is important to ensure that they make reliable predictions under corruptions present in the physical and digital world. We propose Speech Robust Bench (SRB), a comprehensive benchmark for evaluating the robustness of ASR models to diverse corruptions. SRB is composed of 114 input per

Hoang Viet Do, Yong Hun Kim, Joo Han Lee, Min Ho Lee

In this paper, we propose a radar odometry structure that directly utilizes radar velocity measurements for dead reckoning while maintaining its ability to update estimations within the Kalman filter framework. Specifically, we employ the Doppler velocity obtained by a 4D Frequency Modulated Continuous Wave (FMCW) radar in conjunction with gyroscope data to

Xiwei Hu, Rui Wang, Yixiao Fang, Bin Fu

Diffusion models have demonstrated remarkable performance in the domain of text-to-image generation. However, most widely used models still employ CLIP as their text encoder, which constrains their ability to comprehend dense prompts, encompassing multiple objects, detailed attributes, complex relationships, long-text alignment, etc. In this paper, we introd

Jongyeong Lee, Junya Honda, Shinji Ito, Min-hwan Oh

This paper studies the optimality of the Follow-the-Perturbed-Leader (FTPL) policy in both adversarial and stochastic $K$-armed bandits. Despite the widespread use of the Follow-the-Regularized-Leader (FTRL) framework with various choices of regularization, the FTPL framework, which relies on random perturbations, has not received much attention, despite its

Kai Xiong, Rui Wang, Supeng Leng, Wenyang Che

Urban Air Mobility (UAM) expands vehicles from the ground to the near-ground space, envisioned as a revolution for transportation systems. Comprehensive scene perception is the foundation for autonomous aerial driving. However, UAM encounters the intelligent perception challenge: high perception learning requirements conflict with the limited sensors and com

Jun Xu, Mengshu Sun, Zhiqiang Zhang, Jun Zhou

Recent advancements in large language models have shown impressive performance in general chat. However, their domain-specific capabilities, particularly in information extraction, have certain limitations. Extracting structured information from natural language that deviates from known schemas or instructions has proven challenging for previous prompt-based

Fachrina Dewi Puspitasari, Chaoning Zhang, Joseph Cho, Adnan Haider

The evolution of video generation from text, from animating MNIST to simulating the world with Sora, has progressed at a breakneck speed. Here, we systematically discuss how far text-to-video generation technology supports essential requirements in world modeling. We curate 250+ studies on text-based video synthesis and world modeling. We then observe that r

Wangtao Sun, Shizhu He, Jun Zhao, Kang Liu

With good explanatory power and controllability, rule-based methods play an important role in many tasks such as knowledge reasoning and decision support. However, existing studies primarily focused on learning chain-like rules, which limit their semantic expressions and accurate prediction abilities. As a result, chain-like rules usually fire on the incorre

Shahriar Hojjati Emmami, Ali Pilehvar Meibody, Lobat Tayebi, Mohammadamin Tavakoli

The deliberate manipulation of ammonium persulfate, methylenebisacrylamide, dimethyleacrylamide, and polyethylene oxide concentrations resulted in the development of a hydrogel with an exceptional stretchability, capable of extending up to 260 times its original length. This study aims to elucidate the molecular architecture underlying this unique phenomenon

Tomohiko Oka, Shingo Ogio, Mitsuru Abe, Kenji Hamaguchi

Although the MeV gamma-ray band is a promising energy-band window in astrophysics, the current situation of MeV gamma-ray astronomy significantly lags behind those of the other energy bands in angular resolution and sensitivity. An electron-tracking Compton camera (ETCC), a next-generation MeV detector, is expected to revolutionize the situation. An ETCC tra

Chang-Yan Wang, Yan He

We investigate the Uhlmann phase, a generalization of the celebrated Berry phase, for Bose-Einstein condensates (BECs) at finite temperature. The Uhlmann phase characterizes topological properties of mixed states, in contrast to the Berry phase which is defined for pure states at zero temperature. Using the $SU(1,1)$ symmetry of the Bogoliubov Hamiltonian, w

Abdelaziz Bensadok, Muhammad Zeeshan Babar

This research will present a hybrid approach to accelerate convergence in a second order optimization. An online finite difference approximation of the diagonal Hessian matrix will be introduced, along with fuzzy inferencing of several hyperparameters. Competitive results have been achieved

Yudong Li, Song-Chuan Zhao

We investigate a subfreezing droplet impact scenario in a low-humidity environment, where the target is a cold granular monolayer. When the undercooling degree of targets passes a threshold, such a granular layer effectively postpones the bulk freezing time of the droplet in comparison with the impact on the bare substrate underneath. In this case, the retra

Muxi Chen, Yi Liu, Jian Yi, Changran Xu

In this paper, we present an empirical study introducing a nuanced evaluation framework for text-to-image (T2I) generative models, applied to human image synthesis. Our framework categorizes evaluations into two distinct groups: first, focusing on image qualities such as aesthetics and realism, and second, examining text conditions through concept coverage a

Pengwei Yin, Guanzhong Zeng, Jingjing Wang, Di Xie

Gaze estimation methods often experience significant performance degradation when evaluated across different domains, due to the domain gap between the testing and training data. Existing methods try to address this issue using various domain generalization approaches, but with little success because of the limited diversity of gaze datasets, such as appeara

Elisabeth J. Schiessler, Roland C. Aydin, Christian J. Cyron

We present ECToNAS, a cost-efficient evolutionary cross-topology neural architecture search algorithm that does not require any pre-trained meta controllers. Our framework is able to select suitable network architectures for different tasks and hyperparameter settings, independently performing cross-topology optimisation where required. It is a hybrid approa

Tianyu Xiong, Xiaohan Yu

In the era of information overload, the value of recommender systems has been profoundly recognized in academia and industry alike. Multi-interest sequential recommendation, in particular, is a subfield that has been receiving increasing attention in recent years. By generating multiple-user representations, multi-interest learning models demonstrate superio

Wendi Zheng, Jiayan Teng, Zhuoyi Yang, Weihan Wang

Recent advancements in text-to-image generative systems have been largely driven by diffusion models. However, single-stage text-to-image diffusion models still face challenges, in terms of computational efficiency and the refinement of image details. To tackle the issue, we propose CogView3, an innovative cascaded framework that enhances the performance of

Dhanya Roy, Sandi Klavžar, Aparna Lakshmanan

The general position problem in graphs is to find the maximum number of vertices that can be selected such that no three vertices lie on a common shortest path. The mutual-visibility problem in graphs is to find the maximum number of vertices that can be selected such that every pair of vertices in the collection has a shortest path between them with no vert

Sagar Prakash Barad, Sajag Kumar, Subhankar Mishra

Machine learning techniques are utilized to estimate the electronic band gap energy and forecast the band gap category of materials based on experimentally quantifiable properties. The determination of band gap energy is critical for discerning various material properties, such as its metallic nature, and potential applications in electronic and optoelectron

Xiaoming Bi, Jan Harms

The response of the Moon to gravitational waves (GWs) is used by some of the proposed lunar GW detectors like the Lunar Gravitational-wave Antenna (LGWA) to turn the Moon into an antenna for GWs. The deep connection between the lunar internal structure, its geophysical environment and the study of the Universe is intriguing, but given our limited understandi

Hang Du, Xuejun Yan, Jingjing Wang, Di Xie

Recently, arbitrary-scale point cloud upsampling mechanism became increasingly popular due to its efficiency and convenience for practical applications. To achieve this, most previous approaches formulate it as a problem of surface approximation and employ point-based networks to learn surface representations. However, learning surfaces from sparse point clo

Liangxin Qian, Chang Liu, Jun Zhao

The convergence of blockchain, Metaverse, and non-fungible tokens (NFTs) brings transformative digital opportunities alongside challenges like privacy and resource management. Addressing these, we focus on optimizing user connectivity and resource allocation in an NFT-centric and blockchain-enabled Metaverse in this paper. Through user work-offloading, we op

Kuan Li, Weijie Lin, Ruixin Guo, Shu Guo

Medium- and high-entropy alloy (MEA and HEA) superconductors have attracted considerable interest since their discovery. This paper reports the superconducting properties of ternary tungsten-containing MEA V4Ti2W for the first time. V4Ti2W is a type II superconductor with a body-centered cubic (BCC) structure. Experimental results of resistivity, magnetizati