March 2024 arXiv papers — page 94

Hemant K. Mishra

In this paper, we provide an algebraic condition on any $2n \times 2n$ real symmetric positive definite matrix which is necessary and sufficient for the matrix to be diagonalized by an orthosymplectic matrix in the sense of Williamson's theorem.

Xiangyu Ding, Lisa Hui Sun

In 2012, Andrews and Merca obtained a truncated version of Euler's pentagonal number theorem and showed the nonnegativity related to partition functions. Meanwhile, Andrews-Merca and Guo-Zeng independently conjectured that the truncated Jacobi triple product series has nonnegative coefficients, which has been confirmed analytically and also combinatorially.

Eduardo Fernandes Montesuma, Fred Maurice Ngolè Mboula, Antoine Souloumiac

Machine learning systems operate under the assumption that training and test data are sampled from a fixed probability distribution. However, this assumptions is rarely verified in practice, as the conditions upon which data was acquired are likely to change. In this context, the adaptation of the unsupervised domain requires minimal access to the data of th

Junming Wang, Zekai Sun, Xiuxian Guan, Tianxiang Shen

The exceptional mobility and long endurance of air-ground robots are raising interest in their usage to navigate complex environments (e.g., forests and large buildings). However, such environments often contain occluded and unknown regions, and without accurate prediction of unobserved obstacles, the movement of the air-ground robot often suffers a suboptim

Damiano Ricciarelli, Jonas Müller, Guilhem Larrieu, Ioannis Deretzis

Ultraviolet nanosecond laser annealing (UV-NLA) proves to be an important technique, particularly when tightly controlled heating and melting are necessary. In the realm of semiconductor technologies, the significance of nanosecond laser annealing (NLA) grows in tandem with the escalating intricacy of integration schemes in nano-scaled devices. Silicon-germa

A. V. Lakeyev

Necessary and sufficient conditions for the internal stability of formations whose dynamics are obtained is determined by linear differential equations.

Dinesh Chandra Maurya, K. Yesmakhanova, R. Myrzakulov, G. Nugmanova

We investigate some FLRW cosmological models in the context of Metric-Affine $F(R,Q)$ gravity, as proposed in [arXiv:1205.52666]. Here, $R$ and $Q$ are the curvature and nonmetricity scalars using non-special connections, respectively. We get the modified field equations using a flat Friedmann-Lema\^{i}tre-Robertson-Walker (FLRW) metric. We then find a conne

Enzo Scaffi, Antoine Bonneau, Frédéric Le Mouël, Fabien Mieyeville

This research empirically examines embedded development tools viable for on-device TinyML implementation. The research evaluates various development tools with various abstraction levels on resource-constrained IoT devices, from basic hardware manipulation to deployment of minimalistic ML training. The analysis encompasses memory usage, energy consumption, a

Ziqun Chen, Kechao Cai, Jinbei Zhang, Zhigang Yu

In intelligent Internet of Things (IoT) systems, edge servers within a network exchange information with their neighbors and collect data from sensors to complete delivered tasks. In this paper, we propose a multiplayer multi-armed bandit model for intelligent IoT systems to facilitate data collection and incorporate fairness considerations. In our model, we

Diego Corro

In this manuscript we present how to collapse a manifold equipped with a closed flat regular Riemannian foliation with leaves of positive dimension, while keeping the sectional curvature uniformly bounded from above and below. From this deformation, we show that in the case when the manifold is compact and simply connected the foliation is given by torus act

Gabriel Claret, Michael Hinz, Anna Rozanova-Pierrat, Alexander Teplyaev

We use the well-posedness of transmission problems on classes of two-sided Sobolev extension domains to give variational definitions for (boundary) layer potential operators and Neumann-Poincar{\'e} operators. These classes of domains contain Lipschitz domains, and also domains with fractal boundaries. Although our variational formulation does not involve an

Yachen Hong, Wenhan Zhang, Lina Zhao, Haibiao Zheng

In this paper, we combine the multiscale flnite element method to propose an algorithm for solving the non-stationary Stokes-Darcy model, where the permeability coefflcient in the Darcy region exhibits multiscale characteristics. Our algorithm involves two steps: first, conducting the parallel computation of multiscale basis functions in the Darcy region. Se

Siyu Cen, Bangti Jin, Yavar Kian, Eric Soccorsi

In this work we investigate an inverse coefficient problem for the one-dimensional subdiffusion model, which involves a Caputo fractional derivative in time. The inverse problem is to determine two coefficients and multiple parameters (the order, and length of the interval) from one pair of lateral Cauchy data. The lateral Cauchy data are given on disjoint s

Irfansha Shaik, Jaco van de Pol

Layout synthesis is mapping a quantum circuit to a quantum processor. SWAP gate insertions are needed for scheduling 2-qubit gates only on connected physical qubits. With the ever-increasing number of qubits in NISQ processors, scalable layout synthesis is of utmost importance. With large optimality gaps observed in heuristic approaches, scalable exact metho

Zhijun Li, Zhengyun You

The rare and forbidden processes within the Standard Model offer an opportunity to explore potential new physics beyond the SM. We summarize the research method and the recent results of rare charm decays at BESIII based on the extensive data samples in the $\tau-c$ energy region, many of which impose stringent constraints on the new physics.

Swann Marx

This paper proposes the construction of a coercive ISS-Lyapunov functional for linear regular infinite-dimensional system. Indeed, as already known, Lyapunov functionals for infinite-dimensional systems might be not coercive. Under the assumption that there exists an exactly observable output, we are able to make coercive a Lyapunov functional which is not c

Shweta, Saddam Hussain, Rajesh Kumar

Population balance models often integrate fundamental kernels, including sum, gelling and Brownian aggregation kernels. These kernels have demonstrated extensive utility across various disciplines such as aerosol physics, chemical engineering, astrophysics, pharmaceutical sciences and mathematical biology for the purpose of elucidating particle dynamics. The

A. D. Brynes, G. Perosa, C. -Y. Tsai, E. Allaria

The microbunching instability has been a long-standing issue for high-brightness free-electron lasers (FELs), and is a significant show-stopper to achieving full longitudinal coherence in the x-ray regime. This paper reports the first experimental demonstration of microbunching instability mitigation through transverse Landau damping, based on linear optics

Sándor Tóth, Stephen Wilson, Alexia Tsoukara, Enric Moreu

Product matching, the task of identifying different representations of the same product for better discoverability, curation, and pricing, is a key capability for online marketplace and e-commerce companies. We present a robust multi-modal product matching system in an industry setting, where large datasets, data distribution shifts and unseen domains pose c

Yijue Ding, Loren Greenman, Daniel Rolles

We present a highly efficient method to directly simulate the photodissociation followed by Coulomb explosion of methyl iodide. In order to achieve statistical reliability, more than 40,000 trajectories are calculated on accurate potential energy surfaces of both the neutral molecule and the doubly charged cation. Non-adiabatic effects during photodissociati

Dimitrios G. Patsatzis, Lucia Russo, Constantinos Siettos

We present a physics-informed neural network (PINN) approach for the discovery of slow invariant manifolds (SIMs), for the most general class of fast/slow dynamical systems of ODEs. In contrast to other machine learning (ML) approaches that construct reduced order black box surrogate models using simple regression, and/or require a priori knowledge of the fa

Andrey V. Savchenko

This article presents our results for the sixth Affective Behavior Analysis in-the-wild (ABAW) competition. To improve the trustworthiness of facial analysis, we study the possibility of using pre-trained deep models that extract reliable emotional features without the need to fine-tune the neural networks for a downstream task. In particular, we introduce s

Yujiao Jiang, Qingmin Liao, Xiaoyu Li, Li Ma

Reconstructing photo-realistic drivable human avatars from multi-view image sequences has been a popular and challenging topic in the field of computer vision and graphics. While existing NeRF-based methods can achieve high-quality novel view rendering of human models, both training and inference processes are time-consuming. Recent approaches have utilized

Thomas Botzung, Pierre Nataf

We provide numerical evidence of the Nagaoka's theorem in the $\mathrm{SU}(N)$ Fermi-Hubbard model on various cluster geometries, such as the square, the honeycomb and the triangular lattices. In particular, by diagonalizing several finite-size clusters, we show that for one hole away from filling $1/N$, the itinerant ferromagnetism arises for $U$ (the posit

Frank O Wagner

A dp-minimal group is virtually nilpotent.

Garry Goldstein

In this work we study One Axis Twisting (OAT) spin squeezing for metrology in the presence of decoherence. We study Linbladian evolution in the presence of both T_1 and T_2 (longitudinal and transverse relaxation processes). We show that spin squeezing can be an effective way to improve metrological accuracy even in the presence of decoherence for OAT squeez

Yuxin Yao, Siyu Ren, Junhui Hou, Zhi Deng

This paper explores the problem of reconstructing temporally consistent surfaces from a 3D point cloud sequence without correspondence. To address this challenging task, we propose DynoSurf, an unsupervised learning framework integrating a template surface representation with a learnable deformation field. Specifically, we design a coarse-to-fine strategy fo

Ekaterina Trofimova, Emil Sataev, Andrey E. Ustyuzhanin

In the ever-evolving landscape of machine learning, seamless translation of natural language descriptions into executable code remains a formidable challenge. This paper introduces Linguacodus, an innovative framework designed to tackle this challenge by deploying a dynamic pipeline that iteratively transforms natural language task descriptions into code thr

Maciej Tadej

This paper explores a non-linear, non-local model describing the evolution of a single species. We investigate scenarios where the spatial domain is either an arbitrary bounded and open subset of the $n$-dimensional Euclidean space or a periodic environment modeled by $n$-dimensional torus. The analysis includes the study of spectrum of the linear, bounded o

Eugenio Tufino, Pasquale Onorato, Stefano Oss

This study presents a case study of active learning within the Investigative Science Learning Environment (ISLE), using the iOLab digital devices. We designed a pilot lab format to enhance student engagement and understanding through direct experimentation, taking advantage of the multifunctional capabilities of the iOLab devices. This paper evaluates the pe

Seungbeom Woo, Geonwoo Baek, Taehoon Kim, Jaemin Na

Multi-target domain adaptation (MTDA) for semantic segmentation poses a significant challenge, as it involves multiple target domains with varying distributions. The goal of MTDA is to minimize the domain discrepancies among a single source and multi-target domains, aiming to train a single model that excels across all target domains. Previous MTDA approache

A. Reyes-Torrecilla, J. R. Pelaez, P. C. Magalhães

LHCb has observed giant CP violation in localized regions of the Dalitz plots of B to three charmless light mesons. This has been interpreted as an enhancement due to strong two-body final state interactions. In this talk, we show how such interactions, described with dispersive analyses of data, can be implemented beyond the leading order expansion in the t

Soumya Mukherjee, Anjan Kumar NM, Subhadip Manna, Sambhu G Nath

MnBi$_{2}$Te$_{4}$, the first topological insulator with inherent magnetic ordering, has attracted significant attention recently for providing a platform to realize several exotic quantum phenomena at relatively higher temperatures. In this work, we have carried out an exhaustive investigation of MnBi$_{2}$Te$_{4}$ and Sb doped MnBi$_{2}$Te$_{4}$ thin films

Lakshminarayan Sharma, Carlos Rodriguez-Fernandez, Humeyra Caglayan

We demonstrated the potential of the fractional dimensional approach to understand exciton parameters in the exemplary atomically thin semiconductor material, a monolayer of WS$_2$. This approach has proved to be successful in finding the exciton binding energy and quasiparticle bandgap for the WS$_2$ monolayer in varying dielectric environments. A tuning of

SooHwan Eom, Eunseop Yoon, Hee Suk Yoon, Chanwoo Kim

In Automatic Speech Recognition (ASR) systems, a recurring obstacle is the generation of narrowly focused output distributions. This phenomenon emerges as a side effect of Connectionist Temporal Classification (CTC), a robust sequence learning tool that utilizes dynamic programming for sequence mapping. While earlier efforts have tried to combine the CTC los

Quentin Herau, Moussab Bennehar, Arthur Moreau, Nathan Piasco

Reliable multimodal sensor fusion algorithms require accurate spatiotemporal calibration. Recently, targetless calibration techniques based on implicit neural representations have proven to provide precise and robust results. Nevertheless, such methods are inherently slow to train given the high computational overhead caused by the large number of sampled po

Chih-Chung Hsu, Chia-Ming Lee

Instance segmentation, a cornerstone task in computer vision, has wide-ranging applications in diverse industries. The advent of deep learning and artificial intelligence has underscored the criticality of training effective models, particularly in data-scarce scenarios - a concern that resonates in both academic and industrial circles. A significant impedim

Lingyun Xu, Bowen Wang, Ziyang Cheng

This paper investigates the issues of the hybrid beamforming design for the orthogonal frequency division multiplexing dual-function radar-communication (DFRC) system in multiple task scenarios involving the radar scanning and detection task and the target tracking task. To meet different task requirements of the DFRC system, we introduce two novel radar bea

Haque Ishfaq, Thanh Nguyen-Tang, Songtao Feng, Raman Arora

We study offline multitask representation learning in reinforcement learning (RL), where a learner is provided with an offline dataset from different tasks that share a common representation and is asked to learn the shared representation. We theoretically investigate offline multitask low-rank RL, and propose a new algorithm called MORL for offline multitas

Mincheol Chang, Siyeong Lee, Jinkyu Kim, Namil Kim

Typical LiDAR-based 3D object detection models are trained in a supervised manner with real-world data collection, which is often imbalanced over classes (or long-tailed). To deal with it, augmenting minority-class examples by sampling ground truth (GT) LiDAR points from a database and pasting them into a scene of interest is often used, but challenges still

Chih-Chung Hsu, Chia-Ming Lee, Ming-Shyen Wu

Instance segmentation is a fundamental task in computer vision with broad applications across various industries. In recent years, with the proliferation of deep learning and artificial intelligence applications, how to train effective models with limited data has become a pressing issue for both academia and industry. In the Visual Inductive Priors challeng

Hadrien Cambazard, Nicolas Catusse, A. Chomez, A. -M. Lagrange

Direct imaging of exoplanets requires to separate the background noise from the exoplanet signals. Statistical methods have been recently proposed to avoid subtracting any signal of interest as opposed to initial self-subtracting methods based on Angular Differential Imaging (ADI). However, unless conservative thresholds are chosen to claim for a detection,

Yuhe Liu, Mengxue Kang, Zengchang Qin, Xiangxiang Chu

Large text-to-image models have achieved astonishing performance in synthesizing diverse and high-quality images guided by texts. With detail-oriented conditioning control, even finer-grained spatial control can be achieved. However, some generated images still appear unreasonable, even with plentiful object features and a harmonious style. In this paper, we

Seyed Reza Nabavi, Zhiyuan Wang, Gade Pandu Rangaiah

This chapter assesses the sensitivity of multi-criteria decision-making (MCDM) methods to modifications within the decision or objective matrix (DOM) in the context of chemical engineering optimization applications. Employing eight common or recent MCDM methods and three weighting methods, this study evaluates the impact of three specific DOM alterations: li

Zhenghao Zhang, Zuozhuo Dai, Long Qin, Weizhi Wang

Large-scale text-to-video models have shown remarkable abilities, but their direct application in video editing remains challenging due to limited available datasets. Current video editing methods commonly require per-video fine-tuning of diffusion models or specific inversion optimization to ensure high-fidelity edits. In this paper, we introduce EffiVED, a

Michele Antonazzi, Matteo Luperto, N. Alberto Borghese, Nicola Basilico

We introduce a novel approach for scalable domain adaptation in cloud robotics scenarios where robots rely on third-party AI inference services powered by large pre-trained deep neural networks. Our method is based on a downstream proposal-refinement stage running locally on the robots, exploiting a new lightweight DNN architecture, R2SNet. This architecture

Garry Goldstein

In this work we study STM signal in the presence of Coulomb assisted hopping. We perform an extended two site Hubbard model analysis between the atom on the tip and the atom in the sample nearest to each other. We show that in the presence of Coulomb assisted hopping the STM signal depends on several spectral functions thereby complicating its interpretation

Siyuan Zhang, Nachuan Xiao, Xin Liu

In this paper, we focus on the decentralized stochastic subgradient-based methods in minimizing nonsmooth nonconvex functions without Clarke regularity, especially in the decentralized training of nonsmooth neural networks. We propose a general framework that unifies various decentralized subgradient-based methods, such as decentralized stochastic subgradien

Jean-François Coeurjolly, Thibault Espinasse, Anne-Laure Fougères, Mathieu Ribatet

Cloud-to-ground lightning strikes observed in a specific geographical domain over time can be naturally modeled by a spatio-temporal point process. Our focus lies in the parametric estimation of its intensity function, incorporating both spatial factors (such as altitude) and spatio-temporal covariates (such as field temperature, precipitation, etc.). The ev

Murat Isik, Sols Miziev, Wiktoria Pawlak, Newton Howard

This paper introduces a groundbreaking digital neuromorphic architecture that innovatively integrates Brain Code Unit (BCU) and Fundamental Code Unit (FCU) using mixedsignal design methodologies. Leveraging open-source datasets and the latest advances in materials science, our research focuses on enhancing the computational efficiency, accuracy, and adaptabi

Pekka Korhonen, Francis K. C. Hui, Jenni Niku, Sara Taskinen

1. Joint species distribution models (JSDMs) have gained considerable traction among ecologists over the past decade, due to their capacity to answer a wide range of questions at both the species- and the community-level. The family of generalized linear latent variable models in particular has proven popular for building JSDMs, being able to handle many res

Liren He, Zhengkai Jiang, Jinlong Peng, Liang Liu

In the field of multi-class anomaly detection, reconstruction-based methods derived from single-class anomaly detection face the well-known challenge of "learning shortcuts", wherein the model fails to learn the patterns of normal samples as it should, opting instead for shortcuts such as identity mapping or artificial noise elimination. Consequently, the mo

Vivek Mehta, Utpal Roy

There are schemes for realizing different types of kernels by quantum states of light. It is particularly interesting to realize the Gaussian kernel due to its wider applicability. A multimode coherent state can generate the Gaussian kernel with a constant value of hyperparameter. This constant hyperparameter has limited the application of the Gaussian kerne

E. Vitagliano, L. Improta, L. Pizzino, N. D'Agostino

Subsurface pore pressure studies are crucial for understanding the geomechanical behaviours of the geological formations and for preventing the failure conditions of the rocks. Although the interplay between pore pressure changes and rock deformation is nowadays widely treated in the literature, the magnitude and the distribution of the fluid pressure regime

Wendi Li, Wei Wei, Kaihe Xu, Wenfeng Xie

To meet the requirements of real-world applications, it is essential to control generations of large language models (LLMs). Prior research has tried to introduce reinforcement learning (RL) into controllable text generation while most existing methods suffer from overfitting issues (finetuning-based methods) or semantic collapse (post-processing methods). H

Dongyu Han, Kun Liu, Yeming Lin, Yuanqing Xia

This paper considers a distributed stochastic non-convex optimization problem, where the nodes in a network cooperatively minimize a sum of $L$-smooth local cost functions with sparse gradients. By adaptively adjusting the stepsizes according to the historical (possibly sparse) gradients, a distributed adaptive gradient algorithm is proposed, in which a grad

Natalia De La Calzada, Théo Alves Da Costa, Annabelle Blangero, Nicolas Chesneau

This research paper investigates public views on climate change and biodiversity loss by analyzing questions asked to the ClimateQ&A platform. ClimateQ&A is a conversational agent that uses LLMs to respond to queries based on over 14,000 pages of scientific literature from the IPCC and IPBES reports. Launched online in March 2023, the tool has gathered over

Qianyu Zhang, Bolun Zheng, Xinying Chen, Quan Chen

Video compression artifacts arise due to the quantization operation in the frequency domain. The goal of video quality enhancement is to reduce compression artifacts and reconstruct a visually-pleasant result. In this work, we propose a hierarchical frequency-based upsampling and refining neural network (HFUR) for compressed video quality enhancement. HFUR c

S. Kitano

An odd coloring of a graph $G$ is a proper vertex coloring $\varphi$ with the property that for each non-isolated vertex $v\in V(G)$, there exists a color $c$ such that the cardinality of $\varphi^{-1}(c)\cap N(v)$ is odd. The concept of odd colorings is introduced by Petru\v{s}evski and \v{S}krekovski. In this paper, we investigate upper bounds of the odd c

Sheikh Noman Shiddique, Syeda Samiha Nushin, Bipanko Kumar Mondal, Ahnaf Tahmid Abir

Zinc telluride (ZnTe) thin films have special semiconducting characteristics that make them very promising for a broad range of optoelectronic applications. In this work, a novel approach for synthesizing ZnTe thin films by spin coating technique is followed using a unique solution process with ZnTe directly dissolving in thiol-amine co-solvents. Thin films

Regina Finsterhoelzl, Wolf-Rüdiger Hannes, Guido Burkard

Motivated by the recent experimental progress in exploring the use of a nitrogen-vacancy (NV) center in diamond as a quantum computing platform, we propose schemes for fast and high-fidelity entangling gates on this platform. Using both analytical and numerical calculations, we demonstrate that synchronization effects between resonant and off-resonant transi

Chris Fields, James F. Glazebrook, Antonino Marciano

Topological quantum field theories (TQFTs) provide a general, minimal-assumption language for describing quantum-state preparation and measurement. They therefore provide a general language in which to express multi-agent communication protocols, e.g. local operations, classical communication (LOCC) protocols. In the accompanying Part I, we construct LOCC pr

Shu Wang, Muzhi Han, Ziyuan Jiao, Zeyu Zhang

Conventional Task and Motion Planning (TAMP) approaches rely on manually crafted interfaces connecting symbolic task planning with continuous motion generation. These domain-specific and labor-intensive modules are limited in addressing emerging tasks in real-world settings. Here, we present LLM^3, a novel Large Language Model (LLM)-based TAMP framework feat

Xueyan Chen, Whan-Hyuk Choi, Hongwei Liu

DNA codes have many applications, such as in data storage, DNA computing, etc. Good DNA codes have large sizes and satisfy some certain constraints. In this paper, we present a new construction method for reversible DNA codes. We show that the DNA codes obtained using our construction method can satisfy some desired constraints and the lower bounds of the si

Weiran Chen, Xin Li, Jiaqi Su, Guiqian Zhu

As a cross-modal task, visual storytelling aims to generate a story for an ordered image sequence automatically. Different from the image captioning task, visual storytelling requires not only modeling the relationships between objects in the image but also mining the connections between adjacent images. Recent approaches primarily utilize either end-to-end

Jiazuo Yu, Yunzhi Zhuge, Lu Zhang, Ping Hu

Continual learning can empower vision-language models to continuously acquire new knowledge, without the need for access to the entire historical dataset. However, mitigating the performance degradation in large-scale models is non-trivial due to (i) parameter shifts throughout lifelong learning and (ii) significant computational burdens associated with full

Tim Bode, Frank K. Wilhelm

The correspondence between long-range interacting quantum spin glasses and combinatorial optimization problems underpins the physical motivation for adiabatic quantum computing. On one hand, in disordered (quantum) spin systems, the focus is on exact methods such as the replica trick that allow the calculation of system quantities in the limit of infinite sy

I. Gheorghe, S. Goriely, N. Wagner, T. Aumann

Photoneutron reactions on $^{208}$Pb in the Giant Dipole Resonance energy region have been investigated at the $\gamma$-ray beam line of the NewSUBARU facility in Japan. The measurements made use of quasi-monochromatic laser Compton backscattering $\gamma$-ray beams in a broad energy range, from the neutron threshold up to 38 MeV, and of a flat-efficiency mo

Adrian Göß, Alexander Martin, Sebastian Pokutta, Kartikey Sharma

In this paper, we consider a finite-dimensional optimization problem minimizing a continuous objective on a compact domain subject to a multi-dimensional constraint function. For the latter, we assume the availability of a global Lipschitz constant. In recent literature, methods based on non-convex outer approximation are proposed for tackling one-dimensiona

Frédéric Chyzak, Thomas Dreyfus, Philippe Dumas, Marc Mezzarobba

We develop and compare two algorithms for computing first-order right-hand factors in the ring of linear Mahler operators$\ell_r M^r + \dots + \ell_1 M + \ell_0$where $\ell_0, \dots, \ell_r$ are polynomials in~$x$ and $Mx = x^b M$ for some integer $b \geq 2$. In other words, we give algorithms for finding all formal infinite product solutions of linear funct

Junyi Fan, Yuxuan Han, Jialin Zeng, Jian-Feng Cai

Efficiently learning equilibria with large state and action spaces in general-sum Markov games while overcoming the curse of multi-agency is a challenging problem. Recent works have attempted to solve this problem by employing independent linear function classes to approximate the marginal $Q$-value for each agent. However, existing sample complexity bounds

Yosuke Imamura

We discuss giant graviton expansions for the Schur index of ${\cal N}=4$ $U(N)$ SYM with the insertion of Wilson lines of the fundamental and the anti-fundamental representations. We first propose a double-sum giant graviton expansion and numerically confirm that it correctly reproduces the line-operator index. We also find that it reduces to a simple-sum ex

Fei Ni, Rongpeng Li, Zhifeng Zhao, Honggang Zhang

Semantic communication (SemCom) aims to achieve high fidelity information delivery under low communication consumption by only guaranteeing semantic accuracy. Nevertheless, semantic communication still suffers from unexpected channel volatility and thus developing a re-transmission mechanism (e.g., hybrid automatic repeat request [HARQ]) is indispensable. In

Ming Xu, Zilong Xie

Most Vision-and-Language Navigation (VLN) algorithms are prone to making inaccurate decisions due to their lack of visual common sense and limited reasoning capabilities. To address this issue, we propose a Hierarchical Spatial Proximity Reasoning (HSPR) method. First, we introduce a scene understanding auxiliary task to help the agent build a knowledge base

João Luís Rosa, Joaquín Pelle, Daniela Pérez

In this work, we analyze the observational properties of static, spherically symmetric boson stars with fourth and sixth-order self-interactions, using the Julia-based general-relativistic radiative transfer code Skylight. We assume the boson stars are surrounded by an optically thick, geometrically thin accretion disk. We use the Novikov-Thorne model to com

Partha Bagchi, Biswanath Layek, Dheeraj Saini, Anjishnu Sarkar

It is believed that the core of a neutron star can be host to various novel phases of matter, from nucleon superfluid phase to exotic high baryon density quantum chromodynamics (QCD) phases. Different observational signals for such phase transitions have been discussed in the literature. Here, we point out a unique phenomenon associated with phase transition

Attila Szatmári, Qusay Idrees Sarhan, Gergő Balogh, Péter Attila Soha

Spectrum-Based Fault Localization (SBFL) is a technique to be used during debugging, the premise of which is that, based on the test case outcomes and code coverage, faulty code elements can be automatically detected. SBFL is popular among researchers because it is lightweight and easy to implement, and there is a lot of potential in it when it comes to rese

Jisu Han, Jaemin Na, Wonjun Hwang

Continual learning aims to refine model parameters for new tasks while retaining knowledge from previous tasks. Recently, prompt-based learning has emerged to leverage pre-trained models to be prompted to learn subsequent tasks without the reliance on the rehearsal buffer. Although this approach has demonstrated outstanding results, existing methods depend o

Chih-Chung Hsu, Chia-Ming Lee, Chun-Hung Sun, Kuang-Ming Wu

Automatic optical inspection (AOI) plays a pivotal role in the manufacturing process, predominantly leveraging high-resolution imaging instruments for scanning purposes. It detects anomalies by analyzing image textures or patterns, making it an essential tool in industrial manufacturing and quality control. Despite its importance, the deployment of models fo

Jianzhi Liu, Junchen Zhu, Lianli Gao, Heng Tao Shen

The open-domain video generation models are constrained by the scale of the training video datasets, and some less common actions still cannot be generated. Some researchers explore video editing methods and achieve action generation by editing the spatial information of the same action video. However, this method mechanically generates identical actions wit

Sebastian Khan

Parameterised models that predict the gravitational-wave (GW) signal from merging black holes are used to extract source properties from GW observations. The majority of research in this area has focused on developing methods capable of producing highly accurate, point-estimate, predictions for the GW signal. A key element missing from every model used in th

Yifan Ran, Stefan Vlaski, Wei Dai

Despite the numerous uses of semidefinite programming (SDP) and its universal solvability via interior point methods (IPMs), it is rarely applied to practical large-scale problems. This mainly owes to the computational cost of IPMs that increases in a bad exponential way with the data size. While first-order algorithms such as ADMM can alleviate this issue,

Paul Novello, Joseba Dalmau, Léo Andeol

Research on Out-Of-Distribution (OOD) detection focuses mainly on building scores that efficiently distinguish OOD data from In Distribution (ID) data. On the other hand, Conformal Prediction (CP) uses non-conformity scores to construct prediction sets with probabilistic coverage guarantees. In this work, we propose to use CP to better assess the efficiency

Yezhuo Zhang, Zinan Zhou, Xuanpeng Li

In the domain of Specific Emitter Identification (SEI), it is recognized that transmitters can be distinguished through the impairments of their radio frequency front-end, commonly referred to as Radio Frequency Fingerprint (RFF) features. However, modulation schemes can be deliberately coupled into signal-level data to confound RFF information, often result

Hongbo Zhao, Bolin Ni, Haochen Wang, Junsong Fan

For privacy and security concerns, the need to erase unwanted information from pre-trained vision models is becoming evident nowadays. In real-world scenarios, erasure requests originate at any time from both users and model owners. These requests usually form a sequence. Therefore, under such a setting, selective information is expected to be continuously r

Hantao Zhou, Runze Hu, Xiu Li

Storing intermediate frame segmentations as memory for long-range context modeling, spatial-temporal memory-based methods have recently showcased impressive results in semi-supervised video object segmentation (SVOS). However, these methods face two key limitations: 1) relying on non-local pixel-level matching to read memory, resulting in noisy retrieved fea

Zechen Lan, Yasunobu Arikawa, Alessio Morace, Yuki Abe

Recent progress of laser science provides laser-driven neutron source (LDNS), which has remarkable features such as the short pulse width. One of the key techniques to be developed for more efficient use of the LDNS is neutron collimation tubes to increase the number of neutrons arriving at a detector in the time-of-flight method. However, when a tube with a

Jack C. M. Hughes, Fedor V. Kusmartsev

The unique Nature of the Lorentz group in four dimensions is the root cause of the many remarkable properties of the Einstein spacetimes, in particular their operational structure on the 2-forms. We show how this operational structure can be used for two ends. First, it allows for a simple generalization of the Birkhoff theorem to Schwarzschild (A)de-Sitter

Fabio Ancona, Mohamed Bentaibi, Francesco Rossi

We consider the Follow-the-Leader (FtL) model and study which properties of the initial positioning of the vehicles ensure its convergence to the classical Lighthill-Whitham-Richards (LWR) model for traffic flow. Robustness properties of both FtL and LWR models with respect to the initial discretization schemes are investigated. Some numerical simulations ar

Cecile Monthus

The large deviations at various levels that are explicit for Markov jump processes satisfying detailed-balance are revisited in terms of the supersymmetric quantum Hamiltonian $H$ that can be obtained from the Markov generator via a similarity transformation. We first focus on the large deviations at level 2 for the empirical density ${\hat p}(C) $ of the co

Seok-Jun Chang, Max Gronke

The Mg II resonance doublet at 2796 {\AA} and 2803 {\AA} is an increasingly important tool to study cold, $T \sim 10^{4}\,$K, gas -- an observational driven development requiring theoretical support. We develop a new Monte Carlo radiative transfer code to systematically study the joined Mg II and Ly$\alpha$ escape through homogeneous and `clumpy' multiphase

Benjamin A. Burton, Thiago de Paiva, Alexander He, Connie On Yu Hui

The operation of crushing a normal surface has proven to be a powerful tool in computational $3$-manifold topology, with applications both to triangulation complexity and to algorithms. The main difficulty with crushing is that it can drastically change the topology of a triangulation, so applications to date have been limited to relatively simple surfaces:

Jingke Zhao, Zan Wang, Yongwei Wang, Lanjun Wang

Backdoor attacks have been shown to impose severe threats to real security-critical scenarios. Although previous works can achieve high attack success rates, they either require access to victim models which may significantly reduce their threats in practice, or perform visually noticeable in stealthiness. Besides, there is still room to improve the attack s

Massinissa Merouani, Afif Boudaoud, Iheb Nassim Aouadj, Nassim Tchoulak

While polyhedral compilers have shown success in implementing advanced code transformations, they still face challenges in selecting the ones that lead to the most profitable speedups. This has motivated the use of machine learning based cost models to guide the search for polyhedral optimizations. State-of-the-art polyhedral compilers have demonstrated a vi

Ghadi Alyahya, Abeer Aldayel

Examining the factors that the counterspeech uses are at the core of understanding the optimal methods for confronting hate speech online. Various studies have assessed the emotional base factors used in counter speech, such as emotional empathy, offensiveness, and hostility. To better understand the counterspeech used in conversations, this study distills p

Arpan Das, Pier Marzocca, Giuliano Coppotelli, Oleg Levinski

Flutter flight test involves the evaluation of the airframes aeroelastic stability by applying artificial excitation on the aircraft lifting surfaces. The subsequent responses are captured and analyzed to extract the frequencies and damping characteristics of the system. However, noise contamination, turbulence, non-optimal excitation of modes, and sensor ma

Yuto Tanimoto, Kenji Fukumizu

While many multi-armed bandit algorithms assume that rewards for all arms are constant across rounds, this assumption does not hold in many real-world scenarios. This paper considers the setting of recovering bandits (Pike-Burke & Grunewalder, 2019), where the reward depends on the number of rounds elapsed since the last time an arm was pulled. We propose a

Yuqi Guo, Lin Li, Zhongxiang Zheng, Hanrui Yun

Since the first theoretically feasible full homomorphic encryption (FHE) scheme was proposed in 2009, great progress has been achieved. These improvements have made FHE schemes come off the paper and become quite useful in solving some practical problems. In this paper, we propose a set of novel Federated Learning Schemes by utilizing the latest homomorphic

Chaozhi Huang, Chengyang Xu, Fengfeng Zhu, Shaofeng Duan

High-resolution time- and angle-resolved photoemission measurements were conducted on the topological insulator ZrTe5. With strong femtosecond photoexcitation, a possible ultrafast phase transition from a weak to a strong topological insulating phase was experimentally realized by recovering the energy gap inversion in a time scale that was shorter than 0.15

Haibao Wang, Jun Kai Ho, Fan L. Cheng, Shuntaro C. Aoki

Inter-individual variability in fine-grained functional brain organization poses challenges for scalable data analysis and modeling. Functional alignment techniques can help mitigate these individual differences but typically require paired brain data with the same stimuli between individuals, which is often unavailable. We present a neural code conversion m