April 2024 arXiv papers — page 154

Kevin Du, Vésteinn Snæbjarnarson, Niklas Stoehr, Jennifer C. White

To answer a question, language models often need to integrate prior knowledge learned during pretraining and new information presented in context. We hypothesize that models perform this integration in a predictable way across different questions and contexts: models will rely more on prior knowledge for questions about entities (e.g., persons, places, etc.)

Tomoya Yamada, Nobuyuki Sakai, Yoshiyuki Inoue, Tomonari Michiyama

Unraveling the origins of radio emissions from radio-quiet active galactic nuclei (RQ AGNs) remains a pivotal challenge in astrophysics. One potential source of this radiation is the shock interaction between AGN disk winds and the interstellar medium (ISM). To understand this phenomenon, we construct a spherical, one-zone, and self-similar expansion model o

Tosin Adewumi, Nudrat Habib, Lama Alkhaled, Elisa Barney

In this work, we introduce a new hallucination metric - Simple Hallucination Index (SHI) and provide insight into one important limitation of the parametric knowledge of large language models (LLMs), i.e. false attribution. The task of automatic author attribution for relatively small chunks of text is an important NLP task but can be challenging. We empiric

Bogdan Mielnik, Jesús Fuentes

In studies of quantum squeezing, the emphasis is typically placed more on specific squeezed states and their evolution rather than on the dynamical operations that could simultaneously squeeze a broader range of quantum states, regardless of their initial configuration. We explore new developments in this area, facilitated by gently acting external fields wh

Rafik Aramyan

Hyperplane is a set of non-injectivity of the spherical Radon transform (SRT) in the space of continuous functions in R^d. In this article, for the reconstruction of an unknown function f from C(R^3) (the support can be non-compact), using the spherical Radon transform over spheres centered on a plane, the injectivity of the so-called two data spherical Rado

Duy-Tho Le, Hengcan Shi, Jianfei Cai, Hamid Rezatofighi

Diffusion models have recently gained prominence as powerful deep generative models, demonstrating unmatched performance across various domains. However, their potential in multi-sensor fusion remains largely unexplored. In this work, we introduce DifFUSER, a novel approach that leverages diffusion models for multi-modal fusion in 3D object detection and BEV

Jing Guo, Cheng Wang, Yue Yan, Xingye Yue

In this article we present a refined convergence analysis for a second order accurate in time, fourth order finite difference numerical scheme for the 3-D Cahn-Hilliard equation, with an improved convergence constant. A modified backward differentiation formula temporal discretization is applied, and a Douglas-Dupont artificial regularization is included to

Zaid Khan, Vijay Kumar BG, Samuel Schulter, Yun Fu

Visual program synthesis is a promising approach to exploit the reasoning abilities of large language models for compositional computer vision tasks. Previous work has used few-shot prompting with frozen LLMs to synthesize visual programs. Training an LLM to write better visual programs is an attractive prospect, but it is unclear how to accomplish this. No

Duanyu Feng, Bowen Qin, Chen Huang, Zheng Zhang

Direct Preference Optimization (DPO), which derives reward signals directly from pairwise preference data, has shown its effectiveness on aligning Large Language Models (LLMs) with human preferences. Despite its widespread use across various tasks, DPO has been criticized for its sensitivity to the SFT's effectiveness and its hindrance to the learning capaci

Boriša Kuzeljević, Stepan Milošević

The purpose of this note is to start the systematic analysis of cofinal types of topological groups.

Mingxin Huang, Hongliang Li, Yuliang Liu, Xiang Bai

Modularity plays a crucial role in the development and maintenance of complex systems. While end-to-end text spotting efficiently mitigates the issues of error accumulation and sub-optimal performance seen in traditional two-step methodologies, the two-step methods continue to be favored in many competitions and practical settings due to their superior modul

Abhishek Sahu, Peter H. Aaen, Praveen Damacharla

In this paper, we present a machine learning based architecture for microwave characterization of inkjet printed components on flexible substrates. Our proposed architecture uses several machine learning algorithms and automatically selects the best algorithm to extract the material parameters (ink conductivity and dielectric properties) from on-wafer measur

Yikun Qiao

We consider geometric invariant theory for \emph{graded additive groups}, groups of the form $\mathbb{G}_a^r\rtimes_w\mathbb{G}_m$ such that the $\mathbb{G}_m$-action on $\mathbb{G}_a^r$ is a scalar multiplication with weight $w\in\mathbb{N}_+$. We provide an algorithm of equivariant birational modifications, such that we can apply the geometric invariant th

Chujun Geng, Spyros Blanas, Michael D. Bond, Yang Wang

This paper presents the first dynamic predictive analysis for data store applications under weak isolation levels, called Isopredict. Given an observed serializable execution of a data store application, Isopredict generates and solves SMT constraints to find an unserializable execution that is a feasible execution of the application. Isopredict introduces n

Salahuddin Abdul Rahman, Özkan Karabacak, Rafal Wisniewski

Recently, feedback-based quantum algorithms have been introduced to calculate the ground states of Hamiltonians, inspired by quantum Lyapunov control theory. This paper aims to generalize these algorithms to the problem of calculating an eigenstate of a given Hamiltonian, assuming that the lower energy eigenstates are known. To this aim, we propose a new des

Zhonghan Zhao, Ke Ma, Wenhao Chai, Xuan Wang

With the power of large language models (LLMs), open-ended embodied agents can flexibly understand human instructions, generate interpretable guidance strategies, and output executable actions. Nowadays, Multi-modal Language Models~(MLMs) integrate multi-modal signals into LLMs, further bringing richer perception to entity agents and allowing embodied agents

Manuel Hurtado, Mohammad Jafarian, Taulant Kerci, Simon Tweed

This paper discusses the stability assessment of low-inertia power systems through a real-world large-scale low-inertia system, namely, the All-Island power system (AIPS) of Ireland and Northern Ireland. This system currently accommodates world-record levels of system non-synchronous penetration namely 75% (planning to increase to 80% next year). The paper d

Juan Wen, Yawei Li, Chao Zhang, Weiyan Hou

We propose Diverse Restormer (DART), a novel image restoration method that effectively integrates information from various sources (long sequences, local and global regions, feature dimensions, and positional dimensions) to address restoration challenges. While Transformer models have demonstrated excellent performance in image restoration due to their self-

Yongding Tian, Zaid Al-Ars, Maksim Kitsak, Peter Hofstee

Federated learning and gossip learning are emerging methodologies designed to mitigate data privacy concerns by retaining training data on client devices and exclusively sharing locally-trained machine learning (ML) models with others. The primary distinction between the two lies in their approach to model aggregation: federated learning employs a centralize

Rajat Sarkar, Krishna Sai Sudhir Aripirala, Vishal Sudam Jadhav, Sagar Srinivas Sakhinana

Computational Fluid Dynamics (CFD) serves as a powerful tool for simulating fluid flow across diverse industries. High-resolution CFD simulations offer valuable insights into fluid behavior and flow patterns, aiding in optimizing design features or enhancing system performance. However, as resolution increases, computational data requirements and time increa

Taulant Kerci, Manuel Hurtado, Simon Tweed, Marta Val Escudero

This paper discusses emerging operational challenges associated with the integration of solar photovoltaic (PV) in the All-Island power system (AIPS) of Ireland and Northern Ireland. These include the impact of solar PV on: (i) dispatch down levels; (ii) long-term frequency deviations; (iii) voltage magnitude variations; and (iv) operational demand variation

MIT CompGeom Group, Hugo A. Akitaya, Erik D. Demaine, Adam Hesterberg

We explore an Art Gallery variant where each point of a polygon must be seen by k guards, and guards cannot see through other guards. Surprisingly, even covering convex polygons under this variant is not straightforward. For example, covering every point in a triangle k=4 times (a 4-cover) requires 5 guards, and achieving a 10-cover requires 12 guards. Our m

Adarsh Jamadandi, Celia Rubio-Madrigal, Rebekka Burkholz

Message Passing Graph Neural Networks are known to suffer from two problems that are sometimes believed to be diametrically opposed: over-squashing and over-smoothing. The former results from topological bottlenecks that hamper the information flow from distant nodes and are mitigated by spectral gap maximization, primarily, by means of edge additions. Howev

Yabin Zhang, Wenhui Yu, Erhan Zhang, Xu Chen

ChatGPT has achieved remarkable success in natural language understanding. Considering that recommendation is indeed a conversation between users and the system with items as words, which has similar underlying pattern with ChatGPT, we design a new chat framework in item index level for the recommendation task. Our novelty mainly contains three parts: model,

Jaya Kumar Alageshan, Yashodhan Hatwalne, Rahul Pandit

We introduce a novel model, comprising self-avoiding surfaces and incorporating edges and tubules, that is designed to characterize the structural morphologies and transitions observed within the endoplasmic reticulum (ER). By employing discretized models, we model smooth membranes with triangulated surfaces, and we utilize numerical variational methods to m

Xiaolei Zhang

Let $R$ be a ring and $S$ a multiplicative subset of $R$. In this note, we study the localization of $S$-injective modules and $u$-$S$-injective modules under $S$-Noetherian rings and $u$-$S$-Noetherian rings, respectively. The $u$-$S$-absolutely pure property is showed to be preserved under localizations over $S$-coherent rings. Besides, we give an example

Hubing Xiao, Wenxin Yang, Yutao Zhang, Shaohua Zhang

The particle acceleration of blazar jets is crucial to high-energy astrophysics, yet the acceleration mechanism division in blazar subclasses and the underlying nature of these mechanisms remain elusive. In this work, we utilized the synchrotron spectral information (synchrotron peak frequency, $\log \nu_{\rm sy}$, and corresponding curvature, $b_{\rm sy}$)

Danpei Zhao, Bo Yuan, Ziqiang Chen, Tian Li

Current remote-sensing interpretation models often focus on a single task such as detection, segmentation, or caption. However, the task-specific designed models are unattainable to achieve the comprehensive multi-level interpretation of images. The field also lacks support for multi-task joint interpretation datasets. In this paper, we propose Panoptic Perc

Jia-Le Yu, Zhe Hou, Irfan Hussain Bhat, Pei-Jia Hu

Impurities exist inevitably in two-dimensional materials as they spontaneously adsorb onto the surface during fabrication, usually exerting detrimental effects on electronic transport. Here, we focus on a special type of impurities that preferentially adsorb onto the hollow regions of graphene nanoribbons (GNRs), and study how they affect the quantum transpo

Patryk Rygiel

Coronary artery disease (CAD) is one of the most common causes of death in the European Union and the USA. The crucial biomarker in its diagnosis is called Fractional Flow Reserve (FFR) and its in-vivo measurement is obtained via an invasive diagnostic technique in the form of coronagraphy. In order to address the invasive drawbacks associated with a procedu

Yanbo Zhang

This paper proposes a novel signal processing technique that doubles the range resolution of FMCW~(Frequency Modulated Continuous Wave) sensing without increasing the required bandwidth. The proposed design overcomes the resolution limit imposed by bandwidth by exploiting the phase consistency observed in the special frequency variation of the beat signal de

Syed M. Arslan, Saif Al Kuwari, Tasawar Abbas

Superdense coding (SDC) is a popular protocol demonstrating the potential of using quantum mechanics to transfer data, where The sender (Alice) can transfer 2 bits of classical information over a single qubit. We present a scheme for quantum superdense coding through Bragg diffracted hyperentangled atoms generated using cavity quantum electrodynamics (QED).

Yijie Li, Wei Zhang, Ye Wu, Li Yin

The study of brain differences across Eastern and Western populations provides vital insights for understanding potential cultural and genetic influences on cognition and mental health. Diffusion MRI (dMRI) tractography is an important tool in assessing white matter (WM) connectivity and brain tissue microstructure across different populations. However, a co

Anupam Garg, Aryaman Raina, Aryan Gupta, Jaskaran Singh

This study examines the use of large language models (LLMs) by undergraduate and graduate students for programming assignments in advanced computing classes. Unlike existing research, which primarily focuses on introductory classes and lacks in-depth analysis of actual student-LLM interactions, our work fills this gap. We conducted a comprehensive analysis i

Xiu-Cai Jiang, Ze Ruan, Yu-Zhong Zhang

The paramagnetic phase diagrams of the half-filled Hubbard model on a twisted bilayer square lattice are investigated using coherent potential approximation. Besides the conventional metallic, band insulating, and Mott insulating phases, we find two site-selective insulating phases where certain sites exhibit band insulating behaviors while the others displa

Yuxiang Yang, Xuewei Feng, Qi Li, Kun Sun

In this paper, we uncover a new side-channel vulnerability in the widely used NAT port preservation strategy and an insufficient reverse path validation strategy of Wi-Fi routers, which allows an off-path attacker to infer if there is one victim client in the same network communicating with another host on the Internet using TCP. After detecting the presence

Magdalena J. Hamel-Bravo, Deanne B. Fisher, Danielle Berg, Bjarki Björgvinsson

We present the results of direct-method metallicity measurements in the disk and outflow of the low-metallicity starburst galaxy NGC 1569. We use Keck Cosmic Web Imager observations to map the galaxy across 54$\arcsec$ (800 pc) along the major axis and 48$\arcsec$ (700 pc) along the minor axis with a spatial resolution of 1$\arcsec$ ($\sim$15 pc). We detect

Kean Chen, Qisheng Wang, Zhicheng Zhang

We study the power of local test for bipartite quantum states. Our central result is that, for properties of bipartite pure states, unitary invariance on one part implies an optimal (over all global testers) local tester acting only on the other part. As an application, we show that - Purified samples offer no advantage in property testing of mixed states. -

Nicolas Baù, Antimo Marrazzo

The topological phases of two-dimensional time-reversal symmetric insulators are classified by a $\mathbb{Z}_{2}$ topological invariant. Usually, the invariant is introduced and calculated by exploiting the way time-reversal symmetry acts in reciprocal space, hence implicitly assuming periodicity and homogeneity. Here, we introduce two space-resolved $\mathb

Zemin Sun, Geng Sun, Long He, Fang Mei

Unmanned aerial vehicles (UAV)-assisted mobile edge computing (MEC) is emerging as a promising paradigm to provide aerial-terrestrial computing services close to mobile devices (MDs). However, meeting the demands of computation-intensive and delay-sensitive tasks for MDs poses several challenges, including the demand-supply contradiction between MDs and MEC

Bappaditya Bhowmik, Sambhunath Sen

In this article, we obtain certain estimates for the Taylor coefficients of $(K,K')$-elliptic harmonic mappings and using these estimates, we prove a Landau-type theorem for these mappings. We also derive Bloch constant for the class of $(K, K')$-elliptic harmonic mappings.

Guillermo A. Mena Marugán, Antonio Vicente-Becerril, Jesús Yébana Carrilero

The possibility that quantum geometry effects may alleviate the apparent tensions existing at large angular scales in the observations of the Cosmic Microwave Background explains the increasing interest in considering primordial perturbations within the framework of Loop Quantum Cosmology. In this framework, a number of approximations have been suggested to

Dario Pierotti, Gianmaria Verzini, Junwei Yu

We study the existence and multiplicity of positive solutions with prescribed $L^2$-norm for the Sobolev critical Schr\"odinger equation on a bounded domain $\Omega\subset\mathbb{R}^N$, $N\ge3$: \[ -\Delta U = \lambda U + U^{2^{*}-1},\qquad U\in H^1_0(\Omega),\qquad \int_\Omega U^2\,dx = \rho^{2}, \] where $2^*=\frac{2N}{N-2}$. First, we consider a general b

Atilla Alpay Nalcaci, Fidan Mehmeti, Wolfgang Kellerer, Florian Schiegg

Navigation and trajectorial estimation of maritime vessels are contingent upon the context of positional accuracy. Even the smallest deviations in the estimation of a given vessel may result in detrimental consequences in terms of economic and ecologic quotients. To ensure an agile and precise environment for maritime vessel positional estimation, preexistin

Madeleine Bignon, Marc Bernacki

This study proposes a new analytical model for grain boundary pinning by second phase particles in two-dimensional polycrystals. This approach not only considers how particles impede grain growth, but also elucidates their role in preventing grain disappearance, thereby leading to stabilised microstructures characterised by heterogeneous grain size distribut

Z. E. Krix, O. A. Tkachenko, V. A. Tkachenko, D. Q. Wang

Quantum magnetic oscillations in crystals are typically understood in terms of Bohr-Sommerfeld quantisation, the frequency of oscillation is given by the area of a closed electron trajectory. However, since the 1970s, oscillations have been observed with frequencies that do not correspond to closed electron trajectories and this effect has remained not fully

Fatma Zahra Abdeldjouad, Menaouer Brahami, Mohammed Sabri

Adverse drug reactions considerably impact patient outcomes and healthcare costs in cancer therapy. Using artificial intelligence to predict adverse drug reactions in real time could revolutionize oncology treatment. This study aims to assess the performance of artificial intelligence models in predicting adverse drug reactions in patients with cancer. This

Noah Porcelli

We prove that (under appropriate orientation assumptions), the action of a Hamiltonian homeomorphism \phi on the cohomology of a relatively exact Lagrangian fixed by \phi is the identity. This extends results of Hu-Lalonde-Leclercq and the author in the setting of Hamiltonian diffeomorphisms. We also prove a similar result regarding the action of \phi on rel

Montacer Ben Cheikh Larbi, Sina Belkhiria

Technical efficiency indices (TEIs) can be estimated using the traditional stochastic frontier analysis approach, which yields relative indices that do not allow self-interpretations. In this paper, we introduce a single-step estimation procedure for TEIs that eliminates the need to identify best practices and avoids imposing restrictive hypotheses on the er

Fernando Fernández-Calatayud, Lucía Coto-Elena, David Alejo, José J. Carpio-Jiménez

The ARS 548 RDI Radar is a premium model of the fifth generation of 77 GHz long range radar sensors with new RF antenna arrays, which offer digital beam forming. This radar measures independently the distance, speed and angle of objects without any reflectors in one measurement cycle based on Pulse Compression with New Frequency Modulation. Unfortunately, to

Jiyou Li, Sicheng Zhao

Biases in integer partitions have been studied recently. For three disjoint subsets $R,S,I$ of positive integers, let $p_{RSI}(n)$ be the number of partitions of $n$ with parts from $R\cup S\cup I$ and $p_{R>S,I}(n)$ be the number of such partitions with more parts from $R$ than that from $S$. In this paper, in the case that $R,S,I$ are finite we obtain a co

Derek Whitley

Neuroevolution is a powerful method of applying an evolutionary algorithm to refine the performance of artificial neural networks through natural selection; however, the fitness evaluation of these networks can be time-consuming and computationally expensive, particularly for continuous time recurrent neural networks (CTRNNs) that necessitate the simulation

Dong Liang, Zhengyan Xu, Ling Li, Mingqiang Wei

In this paper, we propose a physics-inspired contrastive learning paradigm for low-light enhancement, called PIE. PIE primarily addresses three issues: (i) To resolve the problem of existing learning-based methods often training a LLE model with strict pixel-correspondence image pairs, we eliminate the need for pixel-correspondence paired training data and i

Yuxuan Peng

This paper investigates the influence of matter fields on the geometry of black hole horizons within higher-order gravity theories. Focusing on five-dimensional Einstein-Gauss-Bonnet gravity at a critical coupling constant ($\alpha = -3/(4\Lambda)$), we demonstrate that while vacuum solutions permit horizons with arbitrary geometry, the introduction of a sca

Yongqi Yang, Zhihao Qian, Ye Zhu, Olga Russakovsky

The boom of Generative AI brings opportunities entangled with risks and concerns. Existing literature emphasizes the generalization capability of deepfake detection on unseen generators, significantly promoting the detector's ability to identify more universal artifacts. This work seeks a step toward a universal deepfake detection system with better generali

Valentino Magnani, Daniele Tiberio

We introduce infinite dimensional Hilbertian H-type groups equipped with weak, graded, left invariant Riemannian metrics. For these Lie groups, we show that the vanishing of the geodesic distance and the local unboundedness of the sectional curvature coexist. The result validates a deep phenomenon conjectured in an influential 2005 paper by Michor and Mumfor

Felix Ritort

I review basic concepts in the nonequilibrium physics of small systems, emphasizing single molecule experiments and how they contribute to expanding our current understanding of energy and information.

Supanat Kamtue, Shiping Liu, Florentin Münch, Norbert Peyerimhoff

In this paper we consider global $\theta$-curvatures of finite Markov chains with associated means $\theta$ in the spirit of the entropic curvature (based on the logarithmic mean) by Erbar-Maas and Mielke. As in the case of Bakry-\'Emery curvature, we also allow for a finite dimension parameter by making use of an adapted $\Gamma$ calculus for $\theta$-curva

Hatef Otroshi Shahreza, Christophe Ecabert, Anjith George, Alexander Unnervik

Large-scale face recognition datasets are collected by crawling the Internet and without individuals' consent, raising legal, ethical, and privacy concerns. With the recent advances in generative models, recently several works proposed generating synthetic face recognition datasets to mitigate concerns in web-crawled face recognition datasets. This paper pre

Ruyi Li, Yaxin Zhu, Min Liu, Yihang Zeng

Telepresence robots can be used to support users to navigate an environment remotely and share the visiting experience with their social partners. Although such systems allow users to see and hear the remote environment and communicate with their partners via live video feed, this does not provide enough awareness of the environment and their remote partner'

Florentina Tatrin Kurniati, Daniel HF Manongga, Eko Sediyono, Sri Yulianto Joko Prasetyo

In the era of modern technology, object detection using the Gray Level Co-occurrence Matrix (GLCM) extraction method plays a crucial role in object recognition processes. It finds applications in real-time scenarios such as security surveillance and autonomous vehicle navigation, among others. Computational efficiency becomes a critical factor in achieving r

Zhi-Qing Zhang, Zi-Yu Zhang, Ming-Xuan Xie, Ming-Yang Li

In this paper, we investigate the quasi-two-body decays $B_c \to K_0^{*}(1430,1950) D_{(s)} \to K \pi D_{(s)}$ within the perterbative QCD (PQCD) framework. The S-wave two-meson distribution amplitudes (DAs) are introduced to describe the final state interactions of the $K\pi$ pair, which involve the time-like form factors and the Gegenbauer polynomials. In

Yuto Watanabe, Sotaro Fushimi, Kazunori Sakurama

This study addresses a distributed state feedback controller design problem for continuous-time linear time-invariant systems by means of linear matrix inequalities (LMIs). As structural constraints on a control gain result in non-convexity in general, the block-diagonal relaxation of Lyapunov functions has been prevalent despite its conservatism. In this wo

Zi-Hao Qiu, Siqi Guo, Mao Xu, Tuo Zhao

The temperature parameter plays a profound role during training and/or inference with large foundation models (LFMs) such as large language models (LLMs) and CLIP models. Particularly, it adjusts the logits in the softmax function in LLMs, which is crucial for next token generation, and it scales the similarities in the contrastive loss for training CLIP mod

Kenichiro Umezu

For bifurcation analysis, we study the positive solution set for a semilinear elliptic equation of the logistic type, equipped with a sublinear boundary condition modeling coastal fishery harvesting. This work is a continuation of the author's previous studies, where certain results were obtained in a non resonant case, including the existence, uniqueness, m

Kerim Demirel, Bayram Tekin

A particular extension of Einstein's General Relativity up to and including quartic terms in the curvature tensor is minimal in the sense that it has a unique maximally symmetric vacuum and only a massless spin-2 excitation in its spectrum around the vacuum. We study the inflation phase of the universe in this minimal quartic extension of Einstein's gravity

Hiya Bhatt, Shrikara Arun, Adyansh Kakran, Karthik Vaidhyanathan

In today's dynamic technological landscape, sustainability has emerged as a pivotal concern, especially with respect to architecting Machine Learning enabled Systems (MLS). Many ML models fail in transitioning to production, primarily hindered by uncertainties due to data variations, evolving requirements, and model instabilities. Machine Learning Operations

Jiagang Zhang, Ting Zhang

The combination of metallicity and ferroelectricity breaks traditional boundaries, paving new avenues for innovative electronic materials and devices. This breakthrough is particularly notable, as metallicity and ferroelectricity have traditionally been considered mutually exclusive physical properties. In this work, starting with non-polar metallic single l

Jiayang Li, Jiale Li

The outstanding performance capabilities of large language model have driven the evolution of current AI system interaction patterns. This has led to considerable discussion within the Human-AI Interaction (HAII) community. Numerous studies explore this interaction from technical, design, and empirical perspectives. However, the majority of current literatur

Denis M. Krichevsky, Daria O. Ignatyeva, Vladimir I. Belotelov

Nowadays, dielectric nanophotonics enables almost lossless resonant interaction between light and matter at the nanoscale. We show both theoretically and by electromagnetic simulations, that the peculiar nature of Mie-resonance induced effective magnetic fields contrasts sharply with the optomagnetism of smooth bulk materials. Mie resonances produce strongly

Zhuchao Ji, Junyi Xie

Let $f$ be a rational map of degree $d\geq 2$. The moduli space $\mathcal{M}_f$, introduced by McMullen and Sullivan, is a complex analytic space consisting all quasiconformal conjugacy classes of $f$. For $f$ that is not flexible Latt\`es, we show that there is a normal affine variety $X_f$ of dimension $2d-2$ and a holomorphic injection $i:\mathcal{M}_f\to

Felicia Lo, Shin-Ming Cheng, Rafael Kaliski

Malware intrusion is problematic for Internet of Things (IoT) and Artificial Intelligence of Things (AIoT) devices as they often reside in an ecosystem of connected devices, such as a smart home. If any devices are infected, the whole ecosystem can be compromised. Although various Machine Learning (ML) models are deployed to detect malware and network intrus

Jieke Shi, Zhou Yang, David Lo

Large Language Models (LLMs) have recently shown remarkable capabilities in various software engineering tasks, spurring the rapid growth of the Large Language Models for Software Engineering (LLM4SE) area. However, limited attention has been paid to developing efficient LLM4SE techniques that demand minimal computational cost, time, and memory resources, as

Tao Wu, Runyu He, Gangshan Wu, Limin Wang

Video-based visual relation detection tasks, such as video scene graph generation, play important roles in fine-grained video understanding. However, current video visual relation detection datasets have two main limitations that hinder the progress of research in this area. First, they do not explore complex human-human interactions in multi-person scenario

Hai-Dang Huynh-Lam, Ngoc-Phuong Ho-Thi, Minh-Triet Tran, Trung-Nghia Le

Video summarization is a crucial research area that aims to efficiently browse and retrieve relevant information from the vast amount of video content available today. With the exponential growth of multimedia data, the ability to extract meaningful representations from videos has become essential. Video summarization techniques automatically generate concis

Michiya Mori, Shiho Oi

We study surjective maps between the sets of all self-adjoint elements of unital $C^*$-algebras which satisfy the multiplicatively spectrum-preserving property. We show that such maps are characterized by Jordan isomorphisms and central symmetries. This is an answer to a problem posed by Moln\'ar.

Xuanyu Yi, Zike Wu, Qingshan Xu, Pan Zhou

Score distillation sampling~(SDS) has been widely adopted to overcome the absence of unseen views in reconstructing 3D objects from a \textbf{single} image. It leverages pre-trained 2D diffusion models as teacher to guide the reconstruction of student 3D models. Despite their remarkable success, SDS-based methods often encounter geometric artifacts and textu

Jingyi Pan, Zipeng Wang, Lin Wang

3D semantic occupancy prediction is a pivotal task in the field of autonomous driving. Recent approaches have made great advances in 3D semantic occupancy predictions on a single modality. However, multi-modal semantic occupancy prediction approaches have encountered difficulties in dealing with the modality heterogeneity, modality misalignment, and insuffic

Nik Stopar

It is well known that every bivariate copula induces a positive measure on the Borel $\sigma$-algebra on $[0,1]^2$, but there exist bivariate quasi-copulas that do not induce a signed measure on the same $\sigma$-algebra. In this paper we show that a signed measure induced by a bivariate quasi-copula can always be expressed as an infinite combination of meas

Guoming Li, Jian Yang, Shangsong Liang, Dongsheng Luo

Spectral Graph Neural Networks (GNNs) have achieved tremendous success in graph learning. As an essential part of spectral GNNs, spectral graph convolution extracts crucial frequency information in graph data, leading to superior performance of spectral GNNs in downstream tasks. However, in this paper, we show that existing spectral GNNs remain critical draw

Dian Echevarría Pérez, Onel L. Alcaraz López, Hirley Alves

This paper introduces a sophisticated and adaptable framework combining extreme value theory with radio maps to spatially model extreme channel conditions accurately. Utilising existing signal-to-noise ratio (SNR) measurements and leveraging Gaussian processes, our approach predicts the tail of the SNR distribution, which entails estimating the parameters of

Zhiyuan Yu, Zheng Qin, Lintao Zheng, Kai Xu

Multi-instance point cloud registration estimates the poses of multiple instances of a model point cloud in a scene point cloud. Extracting accurate point correspondence is to the center of the problem. Existing approaches usually treat the scene point cloud as a whole, overlooking the separation of instances. Therefore, point features could be easily pollut

Haibo Jin, Haoxuan Che, Hao Chen

Self-training is a simple yet effective method for semi-supervised learning, during which pseudo-label selection plays an important role for handling confirmation bias. Despite its popularity, applying self-training to landmark detection faces three problems: 1) The selected confident pseudo-labels often contain data bias, which may hurt model performance; 2

Nathan Brunetti, Christine D. Wilson, Hao He, Jiayi Sun

We present observations of the central 9 kpc of the Antennae merger (NGC 4038/9) at 55 pc resolution in the CO 2-1 line obtained with the Atacama Large Millimeter/submillimeter Array (ALMA). We use a pixel-based analysis to compare the gas properties in the Antennae to those in 70 nearby spiral galaxies from the PHANGS-ALMA survey, as well as the merger and

Hao Shi, Guofeng Zhang, Ming Zhang

Quantum algorithms offer significant speed-ups over their classical counterparts in various applications. In this paper, we develop quantum algorithms for the Kalman filter widely used in classical control engineering using the block encoding method. The entire calculation process is achieved by performing matrix operations on Hamiltonians based on the block

LvMing Xie, QingWen Wang, ZhuoHeng He

In the field of robotics research, a crucial applied problem is the hand-eye calibration issue, which involves solving the matrix equation $AX = YB$. However, this matrix equation is merely a specific case of the more general dual quaternion matrix equation $AX-YB=C$, which also holds significant applications in system and control theory. Therefore, we in th

Bernhard Haeupler, Richard Hladík, John Iacono, Vaclav Rozhon

We consider the problem of sorting $n$ items, given the outcomes of $m$ pre-existing comparisons. We present a simple and natural deterministic algorithm that runs in $O(m + \log T)$ time and does $O(\log T)$ comparisons, where $T$ is the number of total orders consistent with the pre-existing comparisons. Our running time and comparison bounds are best poss

Heng Du, Qingyuan Jiang, Yucheng Liu

In this paper, we apply the theory of Bridgeland stability conditions, which was motivated by ideas from string theory, to study the derived category of coherent sheaves on Fargues--Fontaine curves. This leads us to consider the quasi-coherent sheaves $\mathcal{O}(θ^{\pm})$ via the convergents of an irrational number $θ$. We define the continuum envelope $\m

Samah Khawaled, Moti Freiman

The ability to reconstruct high-quality images from undersampled MRI data is vital in improving MRI temporal resolution and reducing acquisition times. Deep learning methods have been proposed for this task, but the lack of verified methods to quantify the uncertainty in the reconstructed images hampered clinical applicability. We introduce "NPB-REC", a non-

A. Martina Neuman, Dominik Dold, Philipp Christian Petersen

We study the learning problem associated with spiking neural networks. Specifically, we focus on spiking neural networks composed of simple spiking neurons having only positive synaptic weights, equipped with an affine encoder and decoder; we refer to these as affine spiking neural networks. These neural networks are shown to depend continuously on their par

P. P. Divakaran

The unitary representations of the Poincare group of a discrete space-time are constructed, following the Wigner method in continuum relativity. They can be interpreted as elementary particles with one significant new feature: the momentum space being the 4-torus is identified as the Brillouin zone of space-time where all physical phenomena occur. Consequent

Xubin Wang, Yunhe Wang, Zhiqing Ma, Ka-Chun Wong

Accurate screening of cancer types is crucial for effective cancer detection and precise treatment selection. However, the association between gene expression profiles and tumors is often limited to a small number of biomarker genes. While computational methods using nature-inspired algorithms have shown promise in selecting predictive genes, existing techni

Samah Khawaled, Simon K. Warfield, Moti Freiman

Functional Magnetic Resonance Imaging (fMRI) is vital in neuroscience, enabling investigations into brain disorders, treatment monitoring, and brain function mapping. However, head motion during fMRI scans, occurring between shots of slice acquisition, can result in distortion, biased analyses, and increased costs due to the need for scan repetitions. Theref

Weize Quan, Yunfei Feng, Ming Zhou, Yunzhen Zhao

Multimodal Sentiment Analysis (MSA) endeavors to understand human sentiment by leveraging language, visual, and acoustic modalities. Despite the remarkable performance exhibited by previous MSA approaches, the presence of inherent multimodal heterogeneities poses a challenge, with the contribution of different modalities varying considerably. Past research p

Gwanghyun Kim, Hayeon Kim, Hoigi Seo, Dong Un Kang

Generating higher-resolution human-centric scenes with details and controls remains a challenge for existing text-to-image diffusion models. This challenge stems from limited training image size, text encoder capacity (limited tokens), and the inherent difficulty of generating complex scenes involving multiple humans. While current methods attempted to addre

Jason P Davis, Jian Bai Li

How are new technologies like generative AI quickly adopted and used by executive and managerial leaders to create value in organizations? A survey of INSEAD's global alumni base revealed several intriguing insights into perceptions and engagements with generative AI across a broad spectrum of demographics, industries, and geographies. Notably, there's a pre

Aristeides D. Papadopoulos, Yihan Ma, Qi Luo, George C. Alexandropoulos

Sub-terahertz (subTHz) antennas will play an important role in the next generations of wireless communication systems. However, when comes to the subTHz frequency spectrum, the antenna fabrication tolerance needs to be accurately considered during the design stage. The classic approach to studying the average performance of an antenna design considering fabr

Genghan Zhang, Olivia Hsu, Fredrik Kjolstad

Recent years have seen considerable work on compiling sparse tensor algebra expressions. This paper addresses a shortcoming in that work, namely how to generate efficient code (in time and space) that scatters values into a sparse result tensor. We address this shortcoming through a compiler design that generates code that uses sparse intermediate tensors (s

Biplav Srivastava, Vishal Pallagani

Foundation Models (FMs) have revolutionized many areas of computing, including Automated Planning and Scheduling (APS). For example, a recent study found them useful for planning problems: plan generation, language translation, model construction, multi-agent planning, interactive planning, heuristics optimization, tool integration, and brain-inspired planni

Dilki Wijekoon, Amine Mezghani, George C. Alexandropoulos, Ekram Hossain

Mutual Coupling (MC) is an unavoidable feature in Reconfigurable Intelligent Surfaces (RISs) with sub-wavelength inter-element spacing. Its inherent presence naturally leads to non-local RIS structures, which can be efficiently described via non-diagonal phase shift matrices. In this paper, we focus on optimizing MC in RIS-assisted multi-user MIMO wireless c