December 2023 arXiv papers — page 95

Qianwen Sun

In this paper, we establish that the mapping torus of a one-ended torsion-free hyperbolic group exhibits a quadratic isoperimetric inequality.

Nobuo Namura

The landscapes of real-world optimization problems can vary strongly depending on the application. In engineering design optimization, objective functions and constraints are often derived from governing equations, resulting in moderate multimodality. However, benchmark problems with such moderate multimodality are typically confined to low-dimensional cases

Kefu Yi, Kai Luo, Xiaolei Luo, Jiangui Huang

Multi-object tracking (MOT) in video sequences remains a challenging task, especially in scenarios with significant camera movements. This is because targets can drift considerably on the image plane, leading to erroneous tracking outcomes. Addressing such challenges typically requires supplementary appearance cues or Camera Motion Compensation (CMC). While

Yan Gao, Haojun Xu, Nannan Wang, Jie Li

The global multi-object tracking (MOT) system can consider interaction, occlusion, and other ``visual blur'' scenarios to ensure effective object tracking in long videos. Among them, graph-based tracking-by-detection paradigms achieve surprising performance. However, their fully-connected nature poses storage space requirements that challenge algorithm handl

David Nordlund, Zheng Chen, Erik G. Larsson

Over-the-Air (OtA) computation is a newly emerged concept for computing functions of data from distributed nodes by taking advantage of the wave superposition property of wireless channels. Despite its advantage in communication efficiency, OtA computation is associated with significant security and privacy concerns that have so far not been thoroughly inves

Frank Sippel, Jürgen Seiler, André Kaup

Exploiting the infrared area of the spectrum for classification problems is getting increasingly popular, because many materials have characteristic absorption bands in this area. However, sensors in the short wave infrared (SWIR) area and even higher wavelengths have a very low spatial resolution in comparison to classical cameras that operate in the visibl

Abiodun Finbarrs Oketunji, James Hanify, Salter Heffron-Smith

This study harnesses the predictive capabilities of Long Short-Term Memory (LSTM) networks to analyse and predict road traffic accidents in Great Britain. It addresses the challenge of traffic accident forecasting, which is paramount for devising effective preventive measures. We utilised an extensive dataset encompassing reported collisions, casualties, and

M. G. Guarcello, E. Flaccomio, J. F. Albacete-Colombo, V. Almendros-Abad

Context. With a mass exceeding several 10^4 solar masses and a rich and dense population of massive stars, supermassive young star clusters represent the most massive star-forming environment that is dominated by the feedback from massive stars and gravitational interactions among stars. Aims. In this paper we present the "Extended Westerlund 1 and 2 Open Cl

Frank Sippel, Nils Genser, Hannah Och, Jürgen Seiler

Since camera modules become more and more affordable, multispectral camera arrays have found their way from special applications to the mass market, e.g., in automotive systems, smartphones, or drones. Due to multiple modalities, the registration of different viewpoints and the required cross-spectral disparity estimation is up to the present extremely chall

Anto Benedetti, Tiphaine Henry, Sara Tucci-Piergiovanni

Blockchain applications are witnessing rapid evolution, necessitating the integration of upgradeable smart contracts. Software patterns have been proposed to summarize upgradeable smart contract best practices. However, research is missing on the comparison of these upgradeable smart contract patterns, especially regarding gas costs related to deployment and

Zi-Yu Khoo, Delong Zhang, Stéphane Bressan

We present several methods for predicting the dynamics of Hamiltonian systems from discrete observations of their vector field. Each method is either informed or uninformed of the Hamiltonian property. We empirically and comparatively evaluate the methods and observe that information that the system is Hamiltonian can be effectively informed, and that differ

M. Mirac Serim, Danjela Serim, Çağatay Kerem Dönmez, Youli Tuo

We aim to explore the properties of the Be/X-ray binary system MXB 0656-072 from a timing analysis perspective through an investigation of the RXTE/PCA and Fermi/GBM data during its 2007-2008 type I outbursts. We applied two new techniques, for the first time, along with the conventional Deeter method to produce higher-resolution power density spectra (PDS)

Christian Saugbjerg Lange, Thomas Hansen, Lars Bojer Madsen

We study the effect of electron-electron correlations on the quantum state of the light emitted from high-harmonic generation (HHG). The quantum state of the emitted light is obtained by using a fully quantum mechanical description of both the optical modes as well as the electronic system. This is different from the usual semiclassical description of HHG, w

Fuli Ku

Let $T$ be a $m$-linear Calder\'{o}n-Zygmund operator of type $\omega$ with $\omega$ being nondecreasing and $\omega \in$ Dini(1) and $[\vec{b},\,T]$ be the commutator generated by $T$ with symbols $\vec{b}=(b_1,\,\ldots,\,b_m)$ belonging to generalized Campanato spaces. We give necessary and sufficient conditions for the boundedness of $[\vec{b},\,T]$ on ge

Zongji Li, Tobias Seidel, Michael Bortz, Raoul Heese

Current quantum computers can only solve optimization problems of a very limited size. For larger problems, decomposition methods are required in which the original problem is broken down into several smaller sub-problems. These are then solved on the quantum computer and their solutions are merged into a final solution for the original problem. Often, these

Tong Wei, Bo-Lin Wang, Min-Ling Zhang

Despite recent advancements in out-of-distribution (OOD) detection, most current studies assume a class-balanced in-distribution training dataset, which is rarely the case in real-world scenarios. This paper addresses the challenging task of long-tailed OOD detection, where the in-distribution data follows a long-tailed class distribution. The main difficult

Jiawei Tan, Qingying Xue

In this paper, the weighted estimates for multilinear pseudo-differential operators were systematically studied in rearrangement invariant Banach and quasi-Banach spaces. These spaces contain the Lebesgue space, the classical Lorentz space and Marcinkiewicz space as typical examples. More precisely, the weighted boundedness and weighted modular estimates, in

Xingrun Xing, Li Du, Xinyuan Wang, Xianlin Zeng

Pretrained foundation models offer substantial benefits for a wide range of downstream tasks, which can be one of the most potential techniques to access artificial general intelligence. However, scaling up foundation transformers for maximal task-agnostic knowledge has brought about computational challenges, especially on resource-limited devices such as mo

Sam Foreman, Xiao-Yong Jin, James C. Osborn

We present a trainable framework for efficiently generating gauge configurations, and discuss ongoing work in this direction. In particular, we consider the problem of sampling configurations from a 4D $SU(3)$ lattice gauge theory, and consider a generalized leapfrog integrator in the molecular dynamics update that can be trained to improve sampling efficien

Peiyi Wang, Lei Li, Zhihong Shao, R. X. Xu

In this paper, we present an innovative process-oriented math process reward model called \textbf{Math-Shepherd}, which assigns a reward score to each step of math problem solutions. The training of Math-Shepherd is achieved using automatically constructed process-wise supervision data, breaking the bottleneck of heavy reliance on manual annotation in existi

Alan Junzhe Zhou, Xiangchong Li, Scott Dodelson, Rachel Mandelbaum

We present $\texttt{Miko}$, a catalog-to-cosmology pipeline for general flat-sky field-level inference, which provides access to cosmological information beyond the two-point statistics. In the context of weak lensing, we identify several new field-level analysis systematics (such as aliasing, Fourier mode-coupling, and density-induced shape noise), quantify

Matteo Zambra, Nicolas Farrugia, Dorian Cazau, Alexandre Gensse

Wind speed at sea surface is a key quantity for a variety of scientific applications and human activities. Due to the non-linearity of the phenomenon, a complete description of such variable is made infeasible on both the small scale and large spatial extents. Methods relying on Data Assimilation techniques, despite being the state-of-the-art for Numerical W

Josh Stein, Maxime Di Folco, Julia A. Schnabel

The Segment Anything Model (SAM) has recently emerged as a significant breakthrough in foundation models, demonstrating remarkable zero-shot performance in object segmentation tasks. While SAM is designed for generalization, it exhibits limitations in handling specific medical imaging tasks that require fine-structure segmentation or precise boundaries. In t

Sandeep Dalal, Sanjay Mukherjee, Kamal Lochan

Let $\Gamma$ be a simple finite graph with vertex set $V(\Gamma)$ and edge set $E(\Gamma)$. Let $\mathcal{R}$ be an equivalence relation on $V(\Gamma)$. The $\mathcal{R}$-super $\Gamma$ graph $\Gamma^{\mathcal{R}}$ is a simple graph with vertex set $V(\Gamma)$ and two distinct vertices are adjacent if either they are in the same $\mathcal{R}$-equivalence cla

Igor Meglinski, Ivan Lopushenko, Anton Sdobnov, Alexander Bykov

Recent advancements in wavefront shaping techniques have facilitated the study of complex structured light's propagation with orbital angular momentum (OAM) within various media. The introduction of a spiral phase modulation to the Laguerre-Gaussian (LG) beam during its paraxial propagation is facilitated by the negative gradient of the medium's refractive i

Konark Jain, Nick Firoozye, Jonathan Kochems, Philip Treleaven

Hawkes Process has been used to model Limit Order Book (LOB) dynamics in several ways in the literature however the focus has been limited to capturing the inter-event times while the order size is usually assumed to be constant. We propose a novel methodology of using Compound Hawkes Process for the LOB where each event has an order size sampled from a cali

Haoran Liao, Qinyi Du, Shaohua Hu, Hao He

Large language models (LLMs) face challenges in solving complex mathematical problems that require comprehensive capacities to parse the statements, associate domain knowledge, perform compound logical reasoning, and integrate the intermediate rationales. Tackling all these problems once could be arduous for LLMs, thus leading to confusion in generation. In

Sandra Cerrai, Arnaud Debussche

We consider systems of damped wave equations with a state-dependent damping coefficient and perturbed by a Gaussian multiplicative noise. Initially, we investigate their well-posedness, under quite general conditions on the friction. Subsequently, we study the validity of the so-called Smoluchowski-Kramers diffusion approximation. We show that, under more st

Shitong Sun, Fanghua Ye, Shaogang Gong

Composed image retrieval attempts to retrieve an image of interest from gallery images through a composed query of a reference image and its corresponding modified text. It has recently attracted attention due to the collaboration of information-rich images and concise language to precisely express the requirements of target images. Most current composed ima

Dan Israel, Ilarion V. Melnikov, Ruben Minasian, Yann Proto

We investigate the interrelation between topology and Narain T-duality of heterotic flux vacua. We present evidence that all 5 and 4-dimensional Minkowski space heterotic flux backgrounds with 8 supercharges have a locus in the moduli space with a T-dual description in terms of a compactification on the product of a K3 surface with a circle or a torus. A tes

Matei Ioan Stan, Oliver Rhodes

Spiking neural networks (SNNs) take inspiration from the brain to enable energy-efficient computations. Since the advent of Transformers, SNNs have struggled to compete with artificial networks on modern sequential tasks, as they inherit limitations from recurrent neural networks (RNNs), with the added challenge of training with non-differentiable binary spi

Nikolaos Chalmoukis, Leonardo Colzani, Bianca Gariboldi, Alessandro Monguzzi

Given a probability space $(X,\mu)$, a square integrable function $f$ on such space and a (unilateral or bilateral) shift operator $T$, we prove under suitable assumptions that the ergodic means $N^{-1}\sum_{n=0}^{N-1} T^nf$ converge pointwise almost everywhere to zero with a speed of convergence which, up to a small logarithmic transgression, is essentially

Amr Ali Abdulkader Al-Maktry

Let $q>2$, and let $a$ and $b$ be two elements of the finite field $\mathbb{F}_q$ with $a\ne 0$. Carlitz represented the transposition $(0a)$ by a polynomial of degree $(q-2)^3$. In this note, we represent the transposition $(ab)$ by a polynomial of degree $q-2$. Also, we use this polynomial to construct polynomials that represent permutations of finite loca

Jian Xu, Feng Mei, Yan-Qing Zhu

The need for fast and robust quantum state transfer is an essential element in scalable quantum information processing, leading to widespread interest in shortcuts to adiabaticity for speeding up adiabatic quantum protocols. However, shortcuts to adiabaticity for systems with more than a few levels is occasionally challenging to compute in theory and frequen

Chen Peng, Long Zhang

Quenched randomness strongly affects properties of magnetic materials. Two-dimensional (2D) random singlet (RS) states emerge in random $J$-$Q$ model by destroying valence bond solid order with spatial randomness. We examine the 2D RS state in magnetic field with quantum Monte Carlo simulations. The magnetization and susceptibilities show power-law scaling w

Xiaohu Zheng, Ogasawara Takuma, Huaxue Zhou, Chongli Yang

Creating heterostructures with graphene/graphite is a practical method for charge-doping $\alpha$-RuCl$_3$, but not sufficient to cause the insulator-to-metal transition. In this study, detailed scanning tunneling microscopy/spectroscopy measurements on $\alpha$-RuCl$_3$ with various lattice deformations reveal that both in-plane and out-of-plane lattice dis

Jiaqi Tang, Hao Lu, Xiaogang Xu, Ruizheng Wu

Artificial Intelligence (AI)-driven defect inspection is pivotal in industrial manufacturing. Yet, many methods, tailored to specific pipelines, grapple with diverse product portfolios and evolving processes. Addressing this, we present the Incremental Unified Framework (IUF), which can reduce the feature conflict problem when continuously integrating new ob

Jingxuan He, Lechao Cheng, Chaowei Fang, Zunlei Feng

Compared to conventional semantic segmentation with pixel-level supervision, Weakly Supervised Semantic Segmentation (WSSS) with image-level labels poses the challenge that it always focuses on the most discriminative regions, resulting in a disparity between fully supervised conditions. A typical manifestation is the diminished precision on the object bound

Maxime Di Folco, Cosmin I. Bercea, Julia A. Schnabel

Interpretability is essential in medical imaging to ensure that clinicians can comprehend and trust artificial intelligence models. In this paper, we propose a novel interpretable approach that combines attribute regularization of the latent space within the framework of an adversarially trained variational autoencoder. Comparative experiments on a cardiac M

Wenyi Hong, Weihan Wang, Qingsong Lv, Jiazheng Xu

People are spending an enormous amount of time on digital devices through graphical user interfaces (GUIs), e.g., computer or smartphone screens. Large language models (LLMs) such as ChatGPT can assist people in tasks like writing emails, but struggle to understand and interact with GUIs, thus limiting their potential to increase automation levels. In this p

Martin R. Bridson, Hamish Short

Every countable group $G$ can be embedded in a finitely generated group $G^*$ that is hopfian and complete, i.e. $G^*$ has trivial centre and every epimorphism $G^*\to G^*$ is an inner automorphism. Every finite subgroup of $G^*$ is conjugate to a finite subgroup of $G$. If $G$ has a finite presentation (respectively, a finite classifying space), then so doe

Mingyang Chen, Bo Huang, Junda Lu, Bing Li

Dataset distillation is the technique of synthesizing smaller condensed datasets from large original datasets while retaining necessary information to persist the effect. In this paper, we approach the dataset distillation problem from a novel perspective: we regard minimizing the prediction discrepancy on the real data distribution between models, which are

Nicolás Bitar

We study the seeded domino problem, the recurring domino problem and the $k$-SAT problem on finitely generated groups. These problems are generalization of their original versions on $\mathbb{Z}^2$ that were shown to be undecidable using the domino problem. We show that the seeded and recurring domino problems on a group are invariant under changes in the ge

Fabao Zhang, Xinwen Shu, Lei Yang, Luming Sun

AT2018cqh is a unique tidal disruption event (TDE) candidate discovered in a dwarf galaxy. Both the light curve fitting and galaxy scaling relationships suggest a central black hole mass in the range of 5.9<logM_BH/M_sun<6.4. A delayed X-ray brightening was found around 590 days after the optical discovery, but shows unusual long-time rising to peak over at

Nikolay Bobev, Marina David, Junho Hong, Valentin Reys

We study the logarithmic corrections to various CFT partition functions in the context of the AdS$_4$/CFT$_3$ correspondence for theories arising on the worldvolume of M2-branes. We utilize four-dimensional gauged supergravity and heat kernel methods and present general expressions for the logarithmic corrections to the gravitational on-shell action and blac

Mattes Ohlenbusch, Christian Rollwage, Simon Doclo

Hearables with integrated microphones may offer communication benefits in noisy working environments, e.g. by transmitting the recorded own voice of the user. Systems aiming at reconstructing the clean and full-bandwidth own voice from noisy microphone recordings are often based on supervised learning. Recording a sufficient amount of noise required for trai

Diego Martínez, Federico Vigolo

We provide a construction of Roe (C*-)algebras of general coarse spaces in terms of coarse geometric modules. This extends the classical theory of Roe algebras of metric spaces and gives a unified framework to deal with either uniform or non-uniform Roe algebras, algebras of operators of controlled propagation, and algebras of quasi-local operators; both in

Zichao Wei, Yewei Qin

Linguistic landscape is an important field in sociolinguistic research. Eye tracking technology is a common technology in psychological research. There are few cases of using eye movement to study linguistic landscape. This paper uses eye tracking technology to study the actual fixation of the linguistic landscape and finds that in the two dimensions of fixa

Chen Wang, Joanne Mason, Andrew D. Gilbert

Zonal flows are mean flows in the east-west direction, which are ubiquitous on planets, and can be formed through 'zonostrophic instability': within turbulence or random waves, a weak large-scale zonal flow can grow exponentially to become prominent. In this paper, we study the statistical behaviour of the zonostrophic instability and the effect of magnetic

Ron M. Adin, Pál Hegedüs, Yuval Roichman

We prove that, for any integer $k$, the $k$-th root enumerator in the classical Weyl group of type $D$ is a proper character. The proof uses higher Lie characters of type $B$.

Mariam Moustafa, Arto Niemi, Philip Ginzboorg, Jan-Erik Ekberg

Allowing a compromised device to receive privacy-sensitive sensor readings, or to operate a safety-critical actuator, carries significant risk. Usually, such risks are mitigated by validating the device's security state with remote attestation, but current remote attestation protocols are not suitable when the beneficiary of attestation, the relying party, i

Louis Esperet, Ugo Giocanti

We study geometric and topological properties of infinite graphs that are quasi-isometric to a planar graph of bounded degree. We prove that every locally finite quasi-transitive graph excluding a minor is quasi-isometric to a planar graph of bounded degree. We use the result to give a simple proof of the result that finitely generated minor-excluded groups

Xijie Huang, Li Lyna Zhang, Kwang-Ting Cheng, Fan Yang

Large Language Models (LLMs) have shown impressive capabilities, yet they still struggle with math reasoning. In this work, we propose CoT-Influx, a novel approach that pushes the boundary of few-shot Chain-of-Thoughts (CoT) learning to improve LLM mathematical reasoning. Motivated by the observation that adding more concise CoT examples in the prompt can im

Avelina Asada Hadji-Kyriacou, Ognjen Arandjelovic

This paper introduces a novel Parameter-Efficient Fine-Tuning (PEFT) framework for multi-modal, multi-task transfer learning with pre-trained language models. PEFT techniques such as LoRA, BitFit and IA3 have demonstrated comparable performance to full fine-tuning of pre-trained models for specific downstream tasks, all while demanding significantly fewer tr

Haoxiang Luo, Qianqian Zhang, Gang Sun, Hongfang Yu

The wireless blockchain network (WBN) concept, born from the blockchain deployed in wireless networks, has appealed to many network scenarios. Blockchain consensus mechanisms (CMs) are key to enabling nodes in a wireless network to achieve consistency without any trusted entity. However, consensus reliability will be seriously affected by the instability of

Hugo Paquet, Philip Saville

Premonoidal categories and Freyd categories provide an encompassing framework for the semantics of call-by-value programming languages. Premonoidal categories are a weakening of monoidal categories in which the interchange law for the tensor product may not hold, modelling the fact that effectful programs cannot generally be re-ordered. A Freyd category is a

Bart Jacobs, Dario Stein

A basic experiment in probability theory is drawing without replacement from an urn filled with multiple balls of different colours. Clearly, it is physically impossible to overdraw, that is, to draw more balls from the urn than it contains. This paper demonstrates that overdrawing does make sense mathematically, once we allow signed distributions with negat

Yifan Zhu, Lijia Yu, Xiao-Shan Gao

Privacy preserving has become increasingly critical with the emergence of social media. Unlearnable examples have been proposed to avoid leaking personal information on the Internet by degrading generalization abilities of deep learning models. However, our study reveals that unlearnable examples are easily detectable. We provide theoretical results on linea

Stefano Gogioso, Vincent Wang-Maścianica, Muhammad Hamza Waseem, Carlo Maria Scandolo

Constructor theory is a meta-theoretic approach that seeks to characterise concrete theories of physics in terms of the (im)possibility to implement certain abstract "tasks" by means of physical processes. Process theory, on the other hand, pursues analogous characterisation goals in terms of the compositional structure of said processes, concretely presente

Lawrence Dunn, Val Tannen, Steve Zdancewic

Formally verifying the properties of formal systems using a proof assistant requires justifying numerous minor lemmas about capture-avoiding substitution. Despite work on category-theoretic accounts of syntax and variable binding, raw, first-order representations of syntax, the kind considered by many practitioners and compiler frontends, have received relat

Sung-Soo Byun, Peter J. Forrester

The moments of the real eigenvalues of real Ginibre matrices are investigated from the viewpoint of explicit formulas, differential and difference equations, and large $N$ expansions. These topics are inter-related. For example, a third order differential equation can be derived for the density of the real eigenvalues, and this can be used to deduce a second

Vincent Tao Hu, Wenzhe Yin, Pingchuan Ma, Yunlu Chen

Human motion synthesis is a fundamental task in computer animation. Recent methods based on diffusion models or GPT structure demonstrate commendable performance but exhibit drawbacks in terms of slow sampling speeds and error accumulation. In this paper, we propose \emph{Motion Flow Matching}, a novel generative model designed for human motion generation fe

Sabri Mustafa Kahya, Muhammet Sami Yavuz, Eckehard Steinbach

We propose HAROOD as a short-range FMCW radar-based human activity classifier and out-of-distribution (OOD) detector. It aims to classify human sitting, standing, and walking activities and to detect any other moving or stationary object as OOD. We introduce a two-stage network. The first stage is trained with a novel loss function that includes intermediate

Thierry Boy de la Tour

What does it mean for an algebraic rewrite rule to subsume another rule (that may then be called a subrule)? We view subsumptions as rule morphisms such that the simultaneous application of a rule and a subrule (i.e. the application of a subsumption morphism) yields the same result as a single application of the subsuming rule. Simultaneous applications of c

Michał Dereziński, Jiaming Yang

We give a stochastic optimization algorithm that solves a dense $n\times n$ real-valued linear system $Ax=b$, returning $\tilde x$ such that $\|A\tilde x-b\|\leq \epsilon\|b\|$ in time: $$\tilde O((n^2+nk^{\omega-1})\log1/\epsilon),$$ where $k$ is the number of singular values of $A$ larger than $O(1)$ times its smallest positive singular value, $\omega < 2.

Shijie Li, Farhad G. Zanjani, Haitam Ben Yahia, Yuki M. Asano

Novel View Synthesis (NVS), which tries to produce a realistic image at the target view given source view images and their corresponding poses, is a fundamental problem in 3D Vision. As this task is heavily under-constrained, some recent work, like Zero123, tries to solve this problem with generative modeling, specifically using pre-trained diffusion models.

Hauke Maathuis, Roeland De Breuker, Saullo G. P. Castro

Design optimisation potentially leads to lightweight aircraft structures with lower environmental impact. Due to the high number of design variables and constraints, these problems are ordinarily solved using gradient-based optimisation methods, leading to a local solution in the design space while the global space is neglected. Bayesian Optimisation is a pr

Heiko Hoppe, Tobias Enders, Quentin Cappart, Maximilian Schiffer

We study vehicle dispatching in autonomous mobility on demand (AMoD) systems, where a central operator assigns vehicles to customer requests or rejects these with the aim of maximizing its total profit. Recent approaches use multi-agent deep reinforcement learning (MADRL) to realize scalable yet performant algorithms, but train agents based on local rewards,

Hayat Laassiri, Ahmed Daassou, Rachid Benbrik

This research paper explores the interplay between quintessence and a cloud of strings, focusing on their influence on critical points, behaviors, and fractional-order phase transitions in AdS black holes. We analyze the thermodynamic properties of AdS black holes surrounded by quintessence and a cloud of strings, with particular attention to the effects of

Weiyuan Gong, Shuo Zhou, Tongyang Li

Digital quantum simulation has broad applications in approximating unitary evolution of Hamiltonians. In practice, many simulation tasks for quantum systems focus on quantum states in the low-energy subspace instead of the entire Hilbert space. In this paper, we systematically investigate the complexity of digital quantum simulation based on product formulas

Paulina L. A. Goedicke, Jamie Vicary

Classical block designs are important combinatorial structures with a wide range of applications in Computer Science and Statistics. Here we give a new abstract description of block designs based on the arrow category construction. We show that models of this structure in the category of matrices and natural numbers recover the traditional classical combinat

Hao Shao, Quansheng Zeng, Qibin Hou, Jufeng Yang

Efficiently capturing multi-scale information and building long-range dependencies among pixels are essential for medical image segmentation because of the various sizes and shapes of the lesion regions or organs. In this paper, we present Multi-scale Cross-axis Attention (MCA) to solve the above challenging issues based on the efficient axial attention. Ins

Zhiyue Liu, Jinyuan Liu, Fanrong Ma

Although image captioning models have made significant advancements in recent years, the majority of them heavily depend on high-quality datasets containing paired images and texts which are costly to acquire. Previous works leverage the CLIP's cross-modal association ability for image captioning, relying solely on textual information under unsupervised sett

Chen Feng, Duolikun Danier, Haoran Wang, Fan Zhang

Deep learning-based video quality assessment (deep VQA) has demonstrated significant potential in surpassing conventional metrics, with promising improvements in terms of correlation with human perception. However, the practical deployment of such deep VQA models is often limited due to their high computational complexity and large memory requirements. To ad

Xueying Wang, Juyong Zhang

Recently, the reconstruction of high-fidelity 3D head models from static portrait image has made great progress. However, most methods require multi-view or multi-illumination information, which therefore put forward high requirements for data acquisition. In this paper, we study the reconstruction of high-fidelity 3D head models from arbitrary monocular vid

Kai Niu, Zijian Liang, Chao Dong, Jincheng Dai

In-band full-duplex (IBFD) is a theoretically effective solution to increase the overall throughput for the future wireless communications system by enabling transmission and reception over the same time-frequency resources. However, reliable source reconstruction remains a great challenge in the practical IBFD systems due to the non-ideal elimination of the

Martina Nibbi, Christian B. Mendl

Quantum signal processing combined with quantum eigenvalue transformation has recently emerged as a unifying framework for several quantum algorithms. In its standard form, it consists of two separate routines: block encoding, which encodes a Hamiltonian in a larger unitary, and signal processing, which achieves an almost arbitrary polynomial transformation

Heng-Tong Ding, Jin-Biao Gu, Arpith Kumar, Sheng-Tai Li

The correlation between net baryon number and electric charge, $\chi_{11}^{\rm BQ}$, can serve as a magnetometer of QCD. This is demonstrated by lattice QCD computations using the highly improved staggered quarks with physical pion mass of $M_\pi=135~$MeV on $N_\tau=8$ and 12 lattices. We find that $\chi_{11}^{\rm BQ}$ along the transition line starts to inc

Chen Feng, Duolikun Danier, Fan Zhang, Alex Mackin

Professionally generated content (PGC) streamed online can contain visual artefacts that degrade the quality of user experience. These artefacts arise from different stages of the streaming pipeline, including acquisition, post-production, compression, and transmission. To better guide streaming experience enhancement, it is important to detect specific arte

Konstantin Gaul, Robert Berger

Detection of parity (P) and time-reversal (T) symmetry-odd electric dipole moments (EDMs) within currently achievable resolution would evidence physics beyond the Standard Model of particle physics. Via the CPT-theorem, which includes charge conjugation (C), such low-energy searches complement high-energy physics experiments that probe CP-violation up to the

Sohan Kumar Jha, Kimet Jusufi

We investigate the shadow images, the relation between Quasinormal Modes (QNMs) and the shadow radius, and the superradiance effect observed in the context of a rotating charged black hole under T-duality. Our investigation places particular emphasis on two key parameters: the electric charge denoted as $Q$ and the quantum deformed parameter represented by t

Bobbi Aditya, Mahdin Rohmatillah, Liang-Hsuan Tai, Jen-Tzung Chien

The prevalence of the powerful multilingual models, such as Whisper, has significantly advanced the researches on speech recognition. However, these models often struggle with handling the code-switching setting, which is essential in multilingual speech recognition. Recent studies have attempted to address this setting by separating the modules for differen

Christian Bertram, Heike Faßbender

A class of (block) rational Krylov subspace based projection method for solving large-scale continuous-time algebraic Riccati equation (CARE) $0 = \mathcal{R}(X) := A^HX + XA + C^HC - XBB^HX$ with a large, sparse $A$ and $B$ and $C$ of full low rank is proposed. The CARE is projected onto a block rational Krylov subspace $\mathcal{K}_j$ spanned by blocks of

Horst Foidl, Tanja Rindler-Daller

(abridged)Cosmological observational programs often compare their data not only with $\Lambda$CDM, but also with extensions applying dynamical models of dark energy (DDE), with a time-dependent equation of state (EoS) parameter $w$. We found a degeneracy in the customary computational procedure for the expansion history, once DDE models are applied. This deg

Lauri Kurki, Niko Oinonen, Adam S. Foster

Scanning tunnelling microscopy (STM) with a functionalized tip apex reveals the geometric and electronic structure of a sample within the same experiment. However, the complex nature of the signal makes images difficult to interpret and has so far limited most research to planar samples with a known chemical composition. Here, we present automated structure

Xinyi Liu, Qian Zhao, Jie Liang, Hui Zeng

Guided image restoration (GIR), such as guided depth map super-resolution and pan-sharpening, aims to enhance a target image using guidance information from another image of the same scene. Currently, joint image filtering-inspired deep learning-based methods represent the state-of-the-art for GIR tasks. Those methods either deal with GIR in an end-to-end wa

Runwei Guan, Haocheng Zhao, Shanliang Yao, Ka Lok Man

Urban water-surface robust perception serves as the foundation for intelligent monitoring of aquatic environments and the autonomous navigation and operation of unmanned vessels, especially in the context of waterway safety. It is worth noting that current multi-sensor fusion and multi-task learning models consume substantial power and heavily rely on high-p

Fan Yu, Haoxu Wang, Ziyang Ma, Shiliang Zhang

Recently audio-visual speech recognition (AVSR), which better leverages video modality as additional information to extend automatic speech recognition (ASR), has shown promising results in complex acoustic environments. However, there is still substantial space to improve as complex computation of visual modules and ineffective fusion of audio-visual modali

Yong Hu, Jing Liu, Yisheng Tian

We extend some parts of the representation theory for integral quadratic forms over the ring of integers of a number field to the case over the coordinate ring $k[C]$ of an affine curve $C$ over a general base field $k$. By using the genus theory, we link the strong approximation property of certain spin groups to the Hasse principle for representations of i

Tatsuki Odake, Hlér Kristjánsson, Philip Taranto, Mio Murao

Manipulating Hamiltonians governing physical systems has found a broad range of applications, from quantum chemistry to semiconductor design. In this work, we provide a new way of manipulating Hamiltonians, by transforming their eigenvalues while keeping their eigenstates unchanged. We develop a universal algorithm that deterministically implements any desir

Zhenya Zang, Uwe Dolinsky, Pietro Ghiglio, Stefano Cherubin

Multi-Level Intermediate Representation (MLIR) is gaining increasing attention in reconfigurable hardware communities due to its capability to represent various abstract levels for software compilers. This project aims to be the first to provide an end-to-end framework that leverages open-source, cross-platform compilation technology to generate MLIR from SY

Ivan Donadello, Jonghyeon Ko, Fabrizio Maria Maggi, Jan Mendling

Predictive Process Monitoring (PPM) aims at leveraging historic process execution data to predict how ongoing executions will continue up to their completion. In recent years, PPM techniques for the prediction of the next activities have matured significantly, mainly thanks to the use of Neural Networks (NNs) as a predictor. While their performance is diffic

Chaoya Jiang, Wei ye, Haiyang Xu, Qinghao Ye

Self-supervised Multi-modal Contrastive Learning (SMCL) remarkably advances modern Vision-Language Pre-training (VLP) models by aligning visual and linguistic modalities. Due to noises in web-harvested text-image pairs, however, scaling up training data volume in SMCL presents considerable obstacles in terms of computational cost and data inefficiency. To im

S. Marini, C. Medori, M. Nacinovich

We study the correspondence between equivalence classes of pairs consisting of real semisimple Lie algebras and their Cartan subalgebras and involutions of the corresponding root system. This can be graphically described by introducing \emph{$S\!${-} and $\Sigma$-diagrams}, generalizing those of Satake and Vogan.

Guanju Xiao, Zijian Zhou, Longjiang Qu

Let $p>3$ be a prime and $E$ be a supersingular elliptic curve defined over $\mathbb{F}_{p^2}$. Let $c$ be a prime with $c < 3p/16$ and $G$ be a subgroup of $E[c]$ of order $c$. The pair $(E,G)$ is called a supersingular elliptic curve with level-$c$ structure, and the endomorphism ring $\text{End}(E,G)$ is isomorphic to an Eichler order with level $c$. We c

Keywoong Bae, Suan Lee, Wookey Lee

In our contemporary academic inquiry, we present "Diffusion-C," a foundational methodology to analyze the generative restrictions of Diffusion Models, particularly those akin to GANs, DDPM, and DDIM. By employing input visual data that has been subjected to a myriad of corruption modalities and intensities, we elucidate the performance characteristics of tho

Davide Masoero, Evgeny Mukhin, Andrea Raimondo

We consider the Schr\"odinger operators which are constructed from the $\lambda$-opers corresponding to solutions of the $\widehat{\mathfrak{sl}}_2$ Gaudin Bethe Ansatz equations. We define and study the connection coefficients called the $Q$-functions. We conjecture that the $Q$-functions obtained from the $\lambda$-opers coincide with the $Q$-functions of

Raffi Budakian, Amit Finkler, Alexander Eichler, Martino Poggio

The field of nanoscale magnetic resonance imaging (NanoMRI) was started 30 years ago. It was motivated by the desire to image single molecules and molecular assemblies, such as proteins and virus particles, with near-atomic spatial resolution and on a length scale of 100 nm. Over the years, the NanoMRI field has also expanded to include the goal of useful hi

Alban Pothérat, Kélig Aujogue, François Debray

Tangent Cylinders (TCs) have shaped our understanding of planetary dynamos and liquid cores. The Taylor-Proudman Constraint (TPC) due to planetary rotation creates these imaginary surfaces separating polar and equatorial regions but cannot explain the flows meandering through them. Here we establish and verify experimentally that magnetic fields aligned with

Qibo Chen, Weizhong Jin, Shuchang Li, Mengdi Liu

Text prompts are crucial for generalizing pre-trained open-set object detection models to new categories. However, current methods for text prompts are limited as they require manual feedback when generalizing to new categories, which restricts their ability to model complex scenes, often leading to incorrect detection results. To address this limitation, we