May 2024 arXiv papers — page 98

Yudong Luo, Shuai Zha, Toshitaka Kajino

We study the impacts of magnetic field on the neutrino transport inside core-collapse supernovae (CCSNe). Magnetic field quantizes the momentum of electrons and positrons, resulting in the modification of weak-interaction cross sections and the chemical potentials of electrons and positrons. We include these changes in the leakage scheme of neutrino transpor

David Braun

We present an open-source implementation of the Descript Audio Codec (DAC) using Google's JAX ecosystem of Flax, Optax, Orbax, AUX, and CLU. Our codebase enables the reuse of model weights from the original PyTorch DAC, and we confirm that the two implementations produce equivalent token sequences and decoded audio if given the same input. We provide a train

Frederik K. Jørgensen, Mickaël G. Delcey, Erik D. Hedegård

Transition metal ions play crucial roles in the structure and function of numerous proteins, contributing to essential biological processes such as catalysis, electron transfer, and oxygen binding. However, accurately modeling the electronic structure and properties of metalloproteins poses significant challenges due to the complex nature of their electronic

Sachiko Takeuchi, Makoto Takizawa, Yasuhiro Yamaguchi, Atsushi Hosaka

We investigate the exotic hadrons consisting of two light quarks and two heavy antiquarks, $(q\bar Q)$-$(q\bar Q)$. The spin-dependent term between quarks is known to give an attraction to the $ud$ spin-0 component in the isospin-0 $u\bar c d\bar c$ system, $T_{cc}$. However, the said component also gets a repulsion from the partial Pauli-blocking. By the dy

Yangming Chen

Backdoor attacks have severely threatened deep neural network (DNN) models in the past several years. These attacks can occur in almost every stage of the deep learning pipeline. Although the attacked model behaves normally on benign samples, it makes wrong predictions for samples containing triggers. However, most existing attacks use visible patterns (e.g.

Ethan Sequeira, Hussein Saad, Stephen Kelly, Matthew Giamou

Range-based localization is ubiquitous: global navigation satellite systems (GNSS) power mobile phone-based navigation, and autonomous mobile robots can use range measurements from a variety of modalities including sonar, radar, and even WiFi signals. Many of these localization systems rely on fixed anchors or beacons with known positions acting as transmitt

Darong Chen, Liang Jiang, Shuai Chen, Bao Wang

Nuclear fusion is recognized as the energy of the future, and huge efforts and capitals have been put into the research of controlled nuclear fusion in the past decades. The most challenging thing for controlled nuclear fusion is to generate and keep a super high temperature. Here, a sonication system, combining with micro-scale fluid control techniques, was

Muhammad Qasim Elahi, Lai Wei, Murat Kocaoglu, Mahsa Ghasemi

Causal discovery aims to uncover cause-and-effect relationships encoded in causal graphs by leveraging observational, interventional data, or their combination. The majority of existing causal discovery methods are developed assuming infinite interventional data. We focus on data interventional efficiency and formalize causal discovery from the perspective o

Ruihan Zhang, Jun Sun

Adversarial examples pose a security threat to many critical systems built on neural networks. While certified training improves robustness, it also decreases accuracy noticeably. Despite various proposals for addressing this issue, the significant accuracy drop remains. More importantly, it is not clear whether there is a certain fundamental limit on achiev

Natasha Hurley-Walker, L. D. Anderson, M. Luisi, N. M. McClure-Griffiths

The Galactic center lobe (GCL) is a $\sim 1^\circ$ object located north of the Galactic center. In the mid-infrared (MIR), the GCL appears as two 8.0-micron filaments that roughly define an ellipse. There is strong 24-micron and radio continuum emission in the interior of the ellipse. Due to its morphology and location in the sky, previous authors have argue

Manuel Santos Gutiérrez, Mickaël David Chekroun, Ilan Koren

Ubiquitous, yet elusive to a complete understanding: Tiny, warm clouds with faint visual signatures play a critical role in Earth's energy balance. These "twilight clouds", as they are sometimes called, form under weak updraft conditions. Their constituent particles exist in a precarious state, teetering between hazy wisps and activated droplets. This delica

Chayan Mondal, Kanak Saha

The relation between the observed UV continuum slope ($\beta$) and the infrared excess (IRX) is used as a powerful probe to understand the nature of dust attenuation law in high-redshift galaxies. We present a study of 83 UV-selected galaxies between redshift 0.5 and 0.7 from the AstroSat UV Deep Field north (AUDFn) that encloses the GOODS-north field. Using

Nugzar Shavlakadze, Nana Odishelidze, Francisco Criado-Aldeanueva

A contact problem of the theory of electroelasticity for piecewise-homogeneous plate of piezo-electric material with infinite cut and elastic finite inclusion of variable bending rigidity is considered. By using methods of the theory of analytic function, the problem is reduced to a system of singular integro-differential equation with fixed singularity. Usi

Xin Li, Jingdong Zhang, Qunxi Zhu, Chengli Zhao

Modeling complex systems using standard neural ordinary differential equations (NODEs) often faces some essential challenges, including high computational costs and susceptibility to local optima. To address these challenges, we propose a simulation-free framework, called Fourier NODEs (FNODEs), that effectively trains NODEs by directly matching the target v

Huiying Yang, Zihan Jin, Chenhao Wu, Rujing Xiong

Recently, ray tracing has gained renewed interest with the advent of Reflective Intelligent Surfaces (RIS) technology, a key enabler of 6G wireless communications due to its capability of intelligent manipulation of electromagnetic waves. However, accurately modeling RIS-enabled wireless environments poses significant challenges due to the complex variations

George Kumi Kyeremeh, M. Abdul-Al, R. Qahwaji, R. A. Abd-Alhameed

Finger vein biometrics is an approach to identifying individuals based on the unique patterns of blood vessels in their fingers, and the technology is advanced in image capture and processing techniques, which is leading to more efficient, accurate, and reliable systems. This article focuses on a verification system that compares the matrices of an efficient

Salvatore Miccichè

We are interested in investigating the statistical properties of extreme values for strongly correlated variables. The starting motivation is to understand how the strong-correlation properties of power-law distributed processes affect the possibility of exploring the whole domain of a stochastic process (the real axis in most cases) when performing time-ave

Hung Yean Loke, Tomasz Przebinda

We show that Howe's big quotient is obtained via the tensoring over appropriate algebra.

Daniel Chin, Yuxuan Wang, Gus Xia

Large Language Model (LLM) -in-the-loop applications have been shown to effectively interpret the human user's commands, make plans, and operate external tools/systems accordingly. Still, the operation scope of the LLM agent is limited to passively following the user, requiring the user to frame his/her needs with regard to the underlying tools/systems. We n

Mikhail Konenkov, Artem Lykov, Daria Trinitatova, Dzmitry Tsetserukou

The advent of immersive Virtual Reality applications has transformed various domains, yet their integration with advanced artificial intelligence technologies like Visual Language Models remains underexplored. This study introduces a pioneering approach utilizing VLMs within VR environments to enhance user interaction and task efficiency. Leveraging the Unit

Mohamed Nagy, Naoufel Werghi, Bilal Hassan, Jorge Dias

This paper addresses limitations in 3D tracking-by-detection methods, particularly in identifying legitimate trajectories and reducing state estimation drift in Kalman filters. Existing methods often use threshold-based filtering for detection scores, which can fail for distant and occluded objects, leading to false positives. To tackle this, we propose a no

Yican Sun, Ruyi Ji, Jian Fang, Xuanlin Jiang

Proving equivalence between functional programs is a fundamental problem in program verification, which often amounts to reasoning about algebraic data types (ADTs) and compositions of structural recursions. Modern theorem provers address this problem by applying structural induction, which is insufficient for proving many equivalence theorems. In such cases

Vikrant Yadav, Rajpal, Pardeep, Manish Yadav

In this letter, we report the observational constraints on a Bianchi type I anisotropic extension of $w$CDM model with spatial curvature from observational data including Baryon Acoustic Oscillations (BAO), Cosmic chronometers (CC), Big Bang nucleosynthesis (BBN), Pantheon+ (PP) compilation of SNe Ia and SH0ES Cepheid host distance anchors. The anisotropy is

Shani Goren, Ido Galil, Ran El-Yaniv

Deploying deep neural networks for risk-sensitive tasks necessitates an uncertainty estimation mechanism. This paper introduces hierarchical selective classification, extending selective classification to a hierarchical setting. Our approach leverages the inherent structure of class relationships, enabling models to reduce the specificity of their prediction

Ehsan Sadeghi, Zinan Guo, Alessandro Chiumento, Paul Havinga

This study presents a non-invasive method using thermal imaging to estimate heart and respiration rates in calves, avoiding the stress from wearables. Using Kernelised Correlation Filters (KCF) for movement tracking and advanced signal processing, we targeted one ROI for respiration and four for heart rate based on their thermal correlation. Achieving Mean A

Fake Lin, Xi Zhu, Ziwei Zhao, Deqiang Huang

Recent years have witnessed the prosperity of knowledge graph based recommendation system (KGRS), which enriches the representation of users, items, and entities by structural knowledge with striking improvement. Nevertheless, its unaffordable computational cost still limits researchers from exploring more sophisticated models. We observe that the bottleneck

Sejik Park

We observe that incorporating a shared layer in a mixture-of-experts can lead to performance degradation. This leads us to hypothesize that learning shared features poses challenges in deep learning, potentially caused by the same feature being learned as various different features. To address this issue, we track each expert's usage frequency and merge the

Rodrigo Laigner, Yongluan Zhou

Microservice architectures are a popular choice for deploying large-scale data-intensive applications. This architectural style allows microservice practitioners to achieve requirements related to loose coupling, fault contention, workload isolation, higher data availability, scalability, and independent schema evolution. Although the industry has been emplo

Francis Brown, Melody Chan, Søren Galatius, Sam Payne

We describe a bigraded cocommutative Hopf algebra structure on the weight zero compactly supported rational cohomology of the moduli space of principally polarized abelian varieties. By relating the primitives for the coproduct to graph cohomology, we deduce that $\dim H^{2g+k}_c(\mathcal{A}_g)$ grows at least exponentially with $g$ for $k = 0$ and for all b

Heleni Krelman, Ori Nefesh, Kfir Levi, Douglas G. Bopp

Achieving precise and adjustable control over laser frequency is an essential requirement in numerous applications such as precision spectroscopy, quantum control, and sensing. In many such applications it is desired to stabilize a laser with a variable detuning from an atomic line. In this study, we employ an offset-stabilization scheme by utilizing phase c

Xuan Yu, Zhenyong Fu

Visual Place Recognition (VPR) refers to the process of using computer vision to recognize the position of the current query image. Due to the significant changes in appearance caused by season, lighting, and time spans between query images and database images for retrieval, these differences increase the difficulty of place recognition. Previous methods oft

Chun-Yin Huang, Kartik Srinivas, Xin Zhang, Xiaoxiao Li

Conventional Federated Learning (FL) involves collaborative training of a global model while maintaining user data privacy. One of its branches, decentralized FL, is a serverless network that allows clients to own and optimize different local models separately, which results in saving management and communication resources. Despite the promising advancements

Mengyu Li, Yonghao Liu, Fausto Giunchiglia, Ximing Li

Text classification is a crucial and fundamental task in web content mining. Compared with the previous learning paradigm of pre-training and fine-tuning by cross entropy loss, the recently proposed supervised contrastive learning approach has received tremendous attention due to its powerful feature learning capability and robustness. Although several studi

Youmin Xu, Xuanyu Zhang, Jiwen Yu, Chong Mou

This paper introduces Hierarchical Image Steganography, a novel method that enhances the security and capacity of embedding multiple images into a single container using diffusion models. HIS assigns varying levels of robustness to images based on their importance, ensuring enhanced protection against manipulation. It adaptively exploits the robustness of th

Wataru Hongo, Shuji Ando, Jun Tsuchida, Takashi Sozu

When estimating causal effects from observational data with numerous covariates, employing penalized covariate selection can improve the estimation efficiency. Outcome-oriented covariate selection, which involves selecting covariates related to the outcome, can enhance efficiency, even for propensity score (PS) methods. For outcome-oriented covariate selecti

Jingwen Han

We show that any bounded smooth helically symmetric solution $(\Bu, \Bh)$ in $\mathbb{R}^3$ must be constant vectors. This is an extension of previous result \cite[Theorem 1.1]{HWXAHE} from Navier-Stokes system to MHD system. The proof relies on establishing a Saint-Venant type estimate to characterize the growth of Dirichlet integral of nontrivial solutions

Farshad Rostami Ghadi, Masoud Kaveh, Kai-Kit Wong, Riku Jantti

This paper studies the performance of a wireless powered communication network (WPCN) under the non-orthogonal multiple access (NOMA) scheme, where users take advantage of an emerging fluid antenna system (FAS). More precisely, we consider a scenario where a transmitter is powered by a remote power beacon (PB) to send information to the planar NOMA FAS-equip

Quentin Meeus, Marie-Francine Moens, Hugo Van hamme

While extensively explored in text-based tasks, Named Entity Recognition (NER) remains largely neglected in spoken language understanding. Existing resources are limited to a single, English-only dataset. This paper addresses this gap by introducing MSNER, a freely available, multilingual speech corpus annotated with named entities. It provides annotations t

Saak Gabriyelyan

Let $p\in[1,\infty]$. Being motivated by weakly $p$-convergent and weak$^\ast$ $p$-convergent operators between Banach spaces introduced by Fourie and Zeekoei, we introduce and study the classes of weakly $p$-convergent and weak$^\ast$ $p$-convergent operators between arbitrary locally convex spaces. Relationships between these classes of operators are given

Omer Madmon, Idan Pipano, Itamar Reinman, Moshe Tennenholtz

Publishers who publish their content on the web act strategically, in a behavior that can be modeled within the online learning framework. Regret, a central concept in machine learning, serves as a canonical measure for assessing the performance of learning agents within this framework. We prove that any proportional content ranking function with a concave a

Bingyang Hu, Son N. T. Tu, Jianlu Zhang

In this paper, we demonstrate a polynomial convergence rate for homogenization of Hamilton-Jacobi equations with quasi-periodic potentials. We establish a connection between the convergence rate of homogenization and the regularity of the effective Hamiltonian, by using a new quantitative ergodic estimate for bounded quasi-periodic functions with Diophantine

Pavan Uttarkar, Ryan M. Shannon, Marcus E. Lower, Pravir Kumar

Fast Radio Bursts (FRBs) are short-timescale transients of extragalactic origin. The number of detected FRBs has grown dramatically since their serendipitous discovery from archival data. Some FRBs have also been seen to repeat. The polarimetric properties of repeating FRBs show diverse behaviour and, at times, extreme polarimetric morphology, suggesting a c

Hasan Ferit Eniser, Hanliang Zhang, Cristina David, Meng Wang

Large language models (LLMs) show promise in code translation - the task of translating code written in one programming language to another language - due to their ability to write code in most programming languages. However, LLM's effectiveness on translating real-world code remains largely unstudied. In this work, we perform the first substantial study on

Ali Karkehabadi, Mitra Bakhshi, Seyed Behnam Razavian

Effective data routing is vital in the Internet of Things (IoT) paradigm, especially in underwater mobile sensor networks where inefficiency can lead to significant resource consumption. This article presents an innovative method designed to enhance network performance and reduce resource usage, while also accurately determining component weights in these ne

Mingzhou Xu

Using a pointwise version of Fej\'{e}r's theorem about Fourier series, we obtain two formulae related to the series representations of positive integral powers of $\pi$. We also check the correctness of our formulae by the applications of the R software.

Jan Oberst, Johann Bonneau

The possibilities of robot control have multiplied across various domains through the application of deep reinforcement learning. To overcome safety and sampling efficiency issues, deep reinforcement learning models can be trained in a simulation environment, allowing for faster iteration cycles. This can be enhanced further by parallelizing the training pro

Vishnu S Nair, Sneha Sree, Jayaraj Joseph, Mohanasankar Sivaprakasam

This paper addresses the critical need for online action representation, which is essential for various applications like rehabilitation, surveillance, etc. The task can be defined as representation of actions as soon as they happen in a streaming video without access to video frames in the future. Most of the existing methods use predefined window sizes for

Geni Gupur

We study a stochastic scheduling on an unreliable machine with general up-times and general set-up times which is described by a group of partial differential equations with Dirac-delta functions in the boundary and initial conditions. In special case that the random processing rate of job $i,$ the random up-time rate of job $i$ and the random repair rate of

Marijan Markovic

This paper is motivated by the classical theorem due to Hardy and Littlewood which concerns analytic mappings on the unit disk and relates the growth of the derivative with the H\"{o}lder continuity. We obtain a version of this result in a very general setting -- for regularly oscillating mappings on a metric space equipped with a weight, which is a continuo

Dimple Rani, Subarata Jana, Manish Kumar Niranjan, Prasanjit Samal

Our investigation explores the intricate domain of thermoelectric phenomena within silver (Ag)-infused chalcopyrites, focusing on compositions such as AgXTe$_2$ (where X=Ga, In) and the complex quaternary system Ag$_2$ZnSn/GeY$_2$ (with Y=S, Se). Using a sophisticated combination of methodologies, we integrate a non-empirical screened dielectric-dependent hy

Richard De la cruz, Wladimir Neves

In this paper, we study the Riemann solutions for two systems: the nonsymmetric Keyfitz-Kranzer system and the pressureless system, both of which have a time-dependent Coulomb-like friction term. Our analysis identified two types of Riemann solutions: contact discontinuities and delta-shock solutions. We obtain generalized Rankine-Hugoniot conditions, which

R. K. Zamanov, K. A. Stoyanov, V. Marchev, M. Minev

We present high resolution (0.06 A/px) spectroscopic observations of the recurrent nova T Coronae Borealis obtained during the last 1.5 years (September 2022 -- January 2024), with the 2.0m RCC telecope of the Rozhen National Astronomical Observatory, Bulgaria. Double-peaked emission is visible in the H-alpha line after the end of the superactive state. We s

Yuchen Wang, Yin-Shan Lin, Ruixin Huang, Jinyin Wang

This paper begins with a theoretical exploration of the rise of large language models (LLMs) in Human-Computer Interaction (HCI), their impact on user experience (HX), and related challenges. It then discusses the benefits of Human-Centered Design (HCD) principles and the possibility of their application within LLMs, subsequently deriving six specific HCD gu

Francesc Wilhelmi, Szymon Szott, Katarzyna Kosek-Szott, Boris Bellalta

Artificial intelligence (AI) and machine learning (ML) are nowadays mature technologies considered essential for driving the evolution of future communications systems. Simultaneously, Wi-Fi technology has constantly evolved over the past three decades and incorporated new features generation after generation, thus gaining in complexity. As such, researchers

Tobias Dornheim, Panagiotis Tolias, Jan Vorberger, Zhandos Moldabekov

We investigate the impact of electronic correlations and quantum delocalization onto the static structure factor and static density response function of the strongly coupled electron liquid. In contrast to a classical system, the density response of the electron liquid vanishes on small length scales due to quantum delocalization effects, which we rigorously

Zijian Du, Luozhijie Jin, Le Shu, Yan Cen

The combination of deep learning algorithm and materials science has made significant progress in predicting novel materials and understanding various behaviours of materials. Here, we introduced a new model called as the Crystal Transformer Graph Neural Network (CTGNN), which combines the advantages of Transformer model and graph neural networks to address

George Martvel, Greta Abele, Annika Bremhorst, Chiara Canori

Affective computing for animals is a rapidly expanding research area that is going deeper than automated movement tracking to address animal internal states, like pain and emotions. Facial expressions can serve to communicate information about these states in mammals. However, unlike human-related studies, there is a significant shortage of datasets that wou

Fanfan Wang, Heqing Ma, Jianfei Yu, Rui Xia

The ability to understand emotions is an essential component of human-like artificial intelligence, as emotions greatly influence human cognition, decision making, and social interactions. In addition to emotion recognition in conversations, the task of identifying the potential causes behind an individual's emotional state in conversations, is of great impo

Conor O'Sullivan, Seamus Coveney, Xavier Monteys, Soumyabrata Dev

We interpret a deep-learning semantic segmentation model used to classify coastline satellite images into land and water. This is to build trust in the model and gain new insight into the process of coastal water body extraction. Specifically, we seek to understand which spectral bands are important for predicting segmentation masks. This is done using a per

Mats Gustafsson

This paper presents an optimal synthesis of material distributions in obstacles for maximal extinction, scattering, or absorption. The material synthesis is based on an explicit construction utilizing the current distribution derived from physical bounds excited from the far-field. The bounds are expressed in radiation modes for materials restricted by their

Conor O'Sullivan, Seamus Coveney, Xavier Monteys, Soumyabrata Dev

We analyse the effectiveness of RMSE, PSNR, SSIM and FOM for evaluating edge detection algorithms used for automated coastline detection. Typically, the accuracy of detected coastlines is assessed visually. This can be impractical on a large scale leading to the need for objective evaluation metrics. Hence, we conduct an experiment to find reliable metrics.

Jacob Quibell

Cybercrime is estimated to cost the global economy almost \$10 trillion annually and with businesses and governments reporting an ever-increasing number of successful cyber-attacks there is a growing demand to rethink the strategy towards cyber security. The traditional, perimeter security approach to cyber defence has so far proved inadequate to combat the

Fan Zhang, Xian-Sheng Hua, Chong Chen, Xiao Luo

Image-text matching has been a long-standing problem, which seeks to connect vision and language through semantic understanding. Due to the capability to manage large-scale raw data, unsupervised hashing-based approaches have gained prominence recently. They typically construct a semantic similarity structure using the natural distance, which subsequently pr

Elaf A. Saeed, Ammar D. Jasim, Munther A. Abdul Malik

Cuneiform writing, an old art style, allows us to see into the past. Aside from Egyptian hieroglyphs, the cuneiform script is one of the oldest writing systems. Many historians place Hebrew's origins in antiquity. For example, we used the same approach to decipher the cuneiform languages; after learning how to decipher one old language, we would visit an arc

Niklas Müller, Gerrit Vosse, Ferdinand Evers, Sascha Schäfer

In a recently developed methodology termed photon induced near-field electron microscopy (PINEM), the inelastic scattering of electrons off illuminated nanostructures provides direct experimental access to the structure of optical near-field modes and their population. Whereas the inelastic scattering probability can be quantitatively linked to the near fiel

Conor O'Sullivan, Seamus Coveney, Xavier Monteys, Soumyabrata Dev

We analyse the effectiveness of edge detection algorithms for the purpose of automatically extracting coastlines from satellite images. Four algorithms - Canny, Sobel, Scharr and Prewitt are compared visually and using metrics. With an average SSIM of 0.8, Canny detected edges that were closest to the reference edges. However, the algorithm had difficulty di

Hongning Ruan, Yulin Shao, Qianqian Yang, Liang Zhao

Point clouds have become increasingly vital across various applications thanks to their ability to realistically depict 3D objects and scenes. Nevertheless, effectively compressing unstructured, high-precision point cloud data remains a significant challenge. In this paper, we present a pioneering point cloud compression framework capable of handling both ge

Jignesh Patel, Yannis Spyridis, Vasileios Argyriou

Aerodynamic design optimisation plays a crucial role in improving the performance and efficiency of automotive vehicles. This paper presents a novel approach for aerodynamic optimisation in car design using deep reinforcement learning (DRL). Traditional optimisation methods often face challenges in handling the complexity of the design space and capturing no

Jun Wang, Benedetta Tondi, Mauro Barni

Synthetic image attribution addresses the problem of tracing back the origin of images produced by generative models. Extensive efforts have been made to explore unique representations of generative models and use them to attribute a synthetic image to the model that produced it. Most of the methods classify the models or the architectures among those in a c

Chu Wing So, Edwin Lok Hei Yuen, Edgar Heung Fat Leung, Jason Chun Shing Pun

Here we present a proof of concept for the application of the Variance of Laplacian (VL) method in quantifying the sharpness of optical solar images. We conducted a comprehensive study using over 65,000 individual solar images acquired on more than 160 days. Each image underwent processing using a VL image processing algorithm, which assigns a 'score' based

S. A. Hosseini, A. Bhadauria, I. V. Karlin

The double distribution function approach is an efficient route towards extension of kinetic solvers to compressible flows. With a number of realizations available, an overview and comparative study in the context of high speed compressible flows is presented. We discuss the different variants of the energy partition, analyses of hydrodynamic limits and a nu

Bin Chen, Yehui Hou, Junyi Li, Ye Shen

Magnetic reconnection within a highly magnetized plasma has been seen as a viable mechanism to extract the energy from a rotating black hole, as it can generate negative energy plasmoids in the ergoregion. For a typical accreting black hole, the ergoregion is filled with bulk plasma plunging from the innermost-stable-circular orbit (ISCO). In this study, we

Aditya Kumar Singh, Dhruv Srivastava, Makarand Tapaswi

We introduce multimodal story summarization by leveraging TV episode recaps - short video sequences interweaving key story moments from previous episodes to bring viewers up to speed. We propose PlotSnap, a dataset featuring two crime thriller TV shows with rich recaps and long episodes of 40 minutes. Story summarization labels are unlocked by matching recap

Zhenyu Sun, Yueqi Su, Aomiao Zhi, Zhicheng Gao

Multiferroic materials, which simultaneously exhibit ferroelectricity and magnetism, have attracted substantial attention due to their fascinating physical properties and potential technological applications. With the trends towards device miniaturization, there is an increasing demand for the persistence of multiferroicity in single-layer materials at eleva

Aleko Khukhunaishvili

A measurement of the effective leptonic weak mixing angle $\sin^2\theta_\mathrm{eff}^\ell$ is presented using the forward-backward asymmetry in Drell-Yan dilepton events produced in pp collisions at $\sqrt{s}=13$ TeV. The data sample corresponds to 137 fb$^{-1}$ of integrated luminosity and consists of dimuon and dielectron events, including the forward elec

Haran Raajesh, Naveen Reddy Desanur, Zeeshan Khan, Makarand Tapaswi

Characters are an important aspect of any storyline and identifying and including them in descriptions is necessary for story understanding. While previous work has largely ignored identity and generated captions with someone (anonymized names), recent work formulates id-aware captioning as a fill-in-the-blanks (FITB) task, where, given a caption with blanks

Silvia Ramis Guarinos, Cristina Manresa Yee, Jose Maria Buades Rubio, Francesc Xavier Gaya-Morey

Facial expression recognition plays an important role in human behaviour, communication, and interaction. Recent neural networks have demonstrated to perform well at its automatic recognition, with different explainability techniques available to make them more transparent. In this work, we propose a facial expression recognition study for people with intell

Haowen Luo, Yunze Liu, Li Yi

The credibility and practicality of a reconstructed hand-object interaction sequence depend largely on its physical plausibility. However, due to high occlusions during hand-object interaction, physical plausibility remains a challenging criterion for purely vision-based tracking methods. To address this issue and enhance the results of existing hand tracker

Arup Majumdar, P. Sam Johnson

In this paper, we present some interesting results to characterize the Moore-Penrose inverses of unbounded closable operators and the Cartesian product of closed operators in Hilbert spaces.

Ankush Sharma, Alka Upadhyay

We carried out the systematic analysis of the $s$-wave triply heavy pentaquarks with possible configurations like $q\Bar{q}QQQ$ and $qqQQ\Bar{Q}$, ($q = u, d, s$ and $Q = c, b$ quarks). Special unitary representations are utilized to study the classification scheme for triply heavy configurations like $q\Bar{q}QQQ$ and $qqQQ\Bar{Q}$. We classified the $q\Bar

Igor Morawski, Kai He, Shusil Dangi, Winston H. Hsu

Currently, low-light conditions present a significant challenge for machine cognition. In this paper, rather than optimizing models by assuming that human and machine cognition are correlated, we use zero-reference low-light enhancement to improve the performance of downstream task models. We propose to improve the zero-reference low-light enhancement method

Chien-Ming Chi

We present Collaborative Trees, a novel tree model designed for regression prediction, along with its bagging version, which aims to analyze complex statistical associations between features and uncover potential patterns inherent in the data. We decompose the mean decrease in impurity from the proposed tree model to analyze the additive and interaction effe

Zhiyu Xu, Qingliang Chen

Driven by large data trained segmentation models, such as SAM , research in one-shot segmentation has experienced significant advancements. Recent contributions like PerSAM and MATCHER , presented at ICLR 2024, utilize a similar approach by leveraging SAM with one or a few reference images to generate high quality segmentation masks for target images. Specif

Itai Boneh, Shay Golan, Arseny Shur

A $\lambda$-cover of a string $S$ is a set of strings $\{C_i\}_1^\lambda$ such that every index in $S$ is contained in an occurrence of at least one string $C_i$. The existence of a $1$-cover defines a well-known class of quasi-periodic strings. Quasi-periodicity can be decided in linear time, and all $1$-covers of a string can be reported in linear time plu

Xiupeng Xie, Zhun Lu

We present a model calculation of the T-odd transverse momentum dependent (TMD) gluon fragmentation functions for a spin-1/2 hadron. Our model is based on the postulation that a time-like off-shell gluon can fragment into a hadron and a single spectator particle, which is considered to be on-shell. We consider the effect of the gluon exchange to calculate al

Jihwan Kim, Junoh Kang, Jinyoung Choi, Bohyung Han

We propose a novel inference technique based on a pretrained diffusion model for text-conditional video generation. Our approach, called FIFO-Diffusion, is conceptually capable of generating infinitely long videos without additional training. This is achieved by iteratively performing diagonal denoising, which simultaneously processes a series of consecutive

Zecheng Shen, Jiarui Liu, Hao-Xin Wang, Yao Wang

Identifying the minimal model for cuprates is crucial for explaining the high-$T_c$ pairing mechanism. Recent photoemission experiments have suggested a significant near-neighbor attractive interaction $V$ in cuprate chains, favoring pairing instability. To determine its strength, we systematically investigate the dynamical spin structure factors $S(q,\omega

Kento Uchida, Kenta Nishihara, Shinichi Shirakawa

The covariance matrix adaptation evolution strategy (CMA-ES) is a powerful optimization method for continuous black-box optimization problems. Several noise-handling methods have been proposed to bring out the optimization performance of the CMA-ES on noisy objective functions. The adaptations of the population size and the learning rate are two major approa

Yingnan Yang, Qingling Zhu, Jianyong Chen

Multivariate time series (MTS) forecasting has been extensively applied across diverse domains, such as weather prediction and energy consumption. However, current studies still rely on the vanilla point-wise self-attention mechanism to capture cross-variable dependencies, which is inadequate in extracting the intricate cross-correlation implied between vari

Dionisio F. Yáñez

In this work, we present some new integration formulas for any order of accuracy as an application of the B-spline relations obtained in [1]. The resulting rules are defined as a perturbation of the trapezoidal integration method. We prove the order of approximation and extend the results to several dimensions. Finally, some numerical experiments are perform

Hu Gao, Bowen Ma, Ying Zhang, Jingfan Yang

Image restoration is a challenging ill-posed problem which estimates latent sharp image from its degraded counterpart. Although the existing methods have achieved promising performance by designing novelty architecture of module, they ignore the fact that different regions in a corrupted image undergo varying degrees of degradation. In this paper, we propose

Suorong Yang, Peijia Li, Xin Xiong, Furao Shen

Data augmentation (DA) is widely employed to improve the generalization performance of deep models. However, most existing DA methods employ augmentation operations with fixed or random magnitudes throughout the training process. While this fosters data diversity, it can also inevitably introduce uncontrolled variability in augmented data, which could potent

Aftab Hussain, Md Rafiqul Islam Rabin, Mohammad Amin Alipour

Large language models (LLMs) have revolutionized software development practices, yet concerns about their safety have arisen, particularly regarding hidden backdoors, aka trojans. Backdoor attacks involve the insertion of triggers into training data, allowing attackers to manipulate the behavior of the model maliciously. In this paper, we focus on analyzing

Zhongxiang Sun, Kepu Zhang, Haoyu Wang, Xiao Zhang

In-context learning has been extensively validated in large language models. However, the mechanism and selection strategy for in-context example selection, which is a crucial ingredient in this approach, lacks systematic and in-depth research. In this paper, we propose a data compression approach to the selection of in-context examples. We introduce a two-s

Pengxiang Lan, Enneng Yang, Yuting Liu, Guibing Guo

Prompt tuning is a promising method to fine-tune a pre-trained language model without retraining its large-scale parameters. Instead, it attaches a soft prompt to the input text, whereby downstream tasks can be well adapted by merely learning the embeddings of prompt tokens. Nevertheless, existing methods still suffer from two challenges: (i) they are hard t

K. Chatterjee, G. Kapetanakis, H. Sharma, S. K. Tiwari

Given positive integers $q,n,m$ and $a\in\mathbb{F}_{q}$, where $q$ is an odd prime power and $n\geq 5$, we investigate the existence of a primitive normal pair $(\epsilon,f(\epsilon))$ in $\mathbb{F}_{q^{n}}$ over $\mathbb{F}_{q}$ such that $\mathrm{STr}_{q^n/q}(\epsilon)=a$, where $f(x)=\frac{f_{1}(x)}{f_{2}(x)}\in\mathbb{F}_{q^n}(x)$ is a rational functio

Xingran Xu, Lingyu Tian, Zhiyuan An, Qihua Xiong

The critical skin effect, an intriguing phenomenon in non-Hermitian systems, displays sensitivity to system size and manifests distinct dynamical behaviors. In this work, we propose a novel scheme to achieve the critical non-Hermitian skin effect of exciton polaritons in an elongated microcavity system. We show that by utilising longitudinal-transverse spin

Gengchen Wei, Xinle Pang, Tianning Zhang, Yu Sun

With over 200 million published academic documents and millions of new documents being written each year, academic researchers face the challenge of searching for information within this vast corpus. However, existing retrieval systems struggle to understand the semantics and domain knowledge present in academic papers. In this work, we demonstrate that by u

Ramcharan Meena, Rajendra S. Dhaka

We investigate the dielectric and impedance spectroscopic investigation of Pr-doped NASICON type Na$_{3+x}$Zr$_{2-x}$Pr$_{x}$Si$_2$PO$_{\rm 12}$ ($x=$ 0.05--0.2) samples as a function of temperature and frequency. The Rietveld refinement of x-ray diffraction patterns confirms the monoclinic phase having C2/c space groups for all the samples. The scanning ele

Hui Zheng, Hai-Teng Wang, Wei-Bang Jiang, Zhong-Tao Chen

Invasive brain-computer interfaces with Electrocorticography (ECoG) have shown promise for high-performance speech decoding in medical applications, but less damaging methods like intracranial stereo-electroencephalography (sEEG) remain underexplored. With rapid advances in representation learning, leveraging abundant recordings to enhance speech decoding is