March 2024 arXiv papers — page 109

Marco Casadio, Tanvi Dinkar, Ekaterina Komendantskaya, Luca Arnaboldi

Machine Learning (ML) has exhibited substantial success in the field of Natural Language Processing (NLP). For example large language models have empirically proven to be capable of producing text of high complexity and cohesion. However, they are prone to inaccuracies and hallucinations. As these systems are increasingly integrated into real-world applicati

Vittoria Sposini, Sankaran Nampoothiri, Aleksei Chechkin, Enzo Orlandini

Redundancy in biology may be explained by the need to optimize extreme searching processes, where one or few among many particles are requested to reach the target like in human fertilization. We show that non-Gaussian rare fluctuations in Brownian diffusion dominates such searches, introducing drastic corrections to the known Gaussian behavior. Our demonstr

H. Gfrerer

In this paper we present GSSN, a globalized SCD semismooth* Newton method for solving nonsmooth nonconvex optimization problems. The global convergence properties of the method are ensured by the proximal gradient method, whereas locally superlinear convergence is established via the SCD semismooth* Newton method under quite weak assumptions. The Newton dire

Subhadip Manna, Sambhu G Nath, Samrat Roy, Soumik Aon

We studied longitudinal and Hall photothermal voltages under a planar magnetic field scan in epitaxial thin films of the Topological Insulator (TI) Sb2Te3, grown using pulsed laser deposition (PLD). Unlike prior research that utilised polarised light-induced photocurrent to investigate the TI, our study introduces advancements based on unpolarized light-indu

Bruno Maric, Filip Zoric, Frano Petric, Matko Orsag

Programming by demonstration (PbD) is a simple and efficient way to program robots without explicit robot programming. PbD enables unskilled operators to easily demonstrate and guide different robots to execute task. In this paper we present comparison of demonstration methods with comprehensive user study. Each participant had to demonstrate drawing simple

Ruethaichanok Kardkasem, Meagan Carney

The purpose of this paper is to illustrate new techniques for computing multiday extreme precipitation taken from recent theoretical advancements in extreme value theory in the framework of dynamical systems, using historical precipitation data along the eastern coast of Australia as a case study. We explore the numerical pitfalls of applying standard extrem

Vittoria Sposini, Sankaran Nampoothiri, Aleksei Chechkin, Enzo Orlandini

Diffusing diffusivity models, polymers in the grand canonical ensemble and polydisperse, and continuous-time random walks all exhibit stages of non-Gaussian diffusion. Is non-Gaussian targeting more efficient than Gaussian? We address this question, central to, e.g., diffusion-limited reactions and some biological processes, through a general approach that m

Qi Zhang, Wei Zhong, Ming-Ming Du, Shu-Ting Shen

Device-independent (DI) quantum secret sharing (QSS) can relax the security assumptions about the devices' internal workings and provide QSS the highest level of security in theory. The original DI QSS protocol proved its correctness and completeness under a causal independence assumption regarding measurement devices. However, there has been a lack of DI QS

Shunsuke Minusa, Tadayuki Matsumura, Kanako Esaki, Yang Shao

Self-report measures (e.g., Likert scales) are widely used to evaluate subjective health perceptions. Recently, the visual analog scale (VAS), a slider-based scale, has become popular owing to its ability to precisely and easily assess how people feel. These data can be influenced by the response style (RS), a user-dependent systematic tendency that occurs r

Dan Crisan, Oana Lang, Alexander Lobbe

In recent work, the authors have developed a generic methodology for calibrating the noise in fluid dynamics stochastic partial differential equations where the stochasticity was introduced to parametrize subgrid-scale processes. The stochastic parameterization of sub-grid scale processes is required in the estimation of uncertainty in weather and climate pr

Lei Wang, Ee-Peng Lim

Large language models (LLMs) have shown excellent performance on various NLP tasks. To use LLMs as strong sequential recommenders, we explore the in-context learning approach to sequential recommendation. We investigate the effects of instruction format, task consistency, demonstration selection, and number of demonstrations. As increasing the number of demo

The H1 collaboration, V. Andreev, M. Arratia, A. Baghdasaryan

The H1 Collaboration at HERA reports the first measurement of groomed event shape observables in deep inelastic electron-proton scattering (DIS) at $\sqrt{s}=319$ GeV, using data recorded between the years 2003 and 2007 with an integrated luminosity of $351$ pb$^{-1}$. Event shapes provide incisive probes of perturbative and non-perturbative QCD. Grooming te

Tianrui Huang, Pu Cao, Lu Yang, Chun Liu

Diffusion-based image editing is a composite process of preserving the source image content and generating new content or applying modifications. While current editing approaches have made improvements under text guidance, most of them have only focused on preserving the information of the input image, disregarding the importance of editability and alignment

Florian Schoeppl, Remy Dubertrand, Juan-Diego Urbina, Klaus Richter

The apparent randomness of chaotic eigenstates in interacting quantum systems hides subtle correlations dynamically imposed by their finite energy per particle. These correlations are revealed when Berrys approach for chaotic eigenfunctions in single-particle systems is lifted into many-body space. We achieve this by a many-body semiclassics analysis, approp

Tianjun Zhang, Shishir G. Patil, Naman Jain, Sheng Shen

Pretraining Large Language Models (LLMs) on large corpora of textual data is now a standard paradigm. When using these LLMs for many downstream applications, it is common to additionally bake in new knowledge (e.g., time-critical news, or private domain knowledge) into the pretrained model either through RAG-based-prompting, or fine-tuning. However, the opti

Amirreza Panahi, Di Pu, Giovanniantonio Natale, Anne M. Benneker

In this work, a framework for deriving theoretical equations for mean squared displacement (MSD) and fractional Fokker-Planck (FFP) is developed for any arbitrary rheological model. The obtained general results are then specified for different fractional rheological models. To test the novel equations extracted from our framework and bridge the gap between m

A. V. Kimel, Th. Rasing, B. A. Ivanov

Finding methods for the most efficient and fastest detection and control of magnetic domains in antiferromagnets is presently among the main challenges of magnetic research at large. We analyse the problem of optical read-out and control of the antiferromagnetic Neel vector using symmetry analysis and the principles of equilibrium thermodynamics. Following t

Cong Wang

A general-order open-shell coupled-cluster method based on spatial orbitals is formulated. The method is an extension of the partial-spin adaptation (PSA) scheme from Janssen and Schaefer (Theor. Chim. Acta, 79, 1-42, 1991). By increasing the order of excitation operator and spin adaptation, the full configuration interaction (CI) limit is expected to be ach

Changhong Hou, Junchuan Yu, Daqing Ge, Liu Yang

Landslides are one of the most destructive natural disasters in the world, posing a serious threat to human life and safety. The development of foundation models has provided a new research paradigm for large-scale landslide detection. The Segment Anything Model (SAM) has garnered widespread attention in the field of image segmentation. However, our experime

Andrea Cattaneo, Adriano Tomassini

We provide families of compact $(n + 1)$-dimensional complex non K\"ahler manifolds satisfying the $\partial\bar{\partial}$-Lemma, with holomoprhically trivial canonical bundle, carrying a balanced metric and with no $p$-K\"ahler structures. Such a construction extends to the completely solvable case in any dimension Nakamura's construction of low-dimensiona

Amit Singh Vishen, Jacques Prost, Madan Rao

Several enzymes exhibit enhanced diffusion in the presence of a substrate. One explanation of this enhancement arises from fluctuating dimer models, which suggest that enzymes have a higher diffusion constant when interacting with substrates compared to when they are free. Another possible mechanism, suggested in both experimental and theoretical studies, is

Yu Liu, Wenlin Zhang, Shaochu Wang, Fangyu Zuo

Early diagnosis of Alzheimer's Disease (AD) is very important for following medical treatments, and eye movements under special visual stimuli may serve as a potential non-invasive biomarker for detecting cognitive abnormalities of AD patients. In this paper, we propose an Depth-induced saliency comparison network (DISCN) for eye movement analysis, which may

Zixiao Wang, Yunheng Shen, Xufeng Yao, Wenqian Zhao

Existing works focus on fixed-size layout pattern generation, while the more practical free-size pattern generation receives limited attention. In this paper, we propose ChatPattern, a novel Large-Language-Model (LLM) powered framework for flexible pattern customization. ChatPattern utilizes a two-part system featuring an expert LLM agent and a highly contro

Yuanhang Zhang, Zhidi Lin, Yiyong Sun, Feng Yin

Deep state-space models (DSSMs) have gained popularity in recent years due to their potent modeling capacity for dynamic systems. However, existing DSSM works are limited to single-task modeling, which requires retraining with historical task data upon revisiting a forepassed task. To address this limitation, we propose continual learning DSSMs (CLDSSMs), wh

M. Zhao, M. Taani, J. Cole, B. Crudele

Liquid scintillators are typically composed from organic compounds dissolved in organic solvents. However, usage of such material is often restricted due to fire safety and environmental reasons. Because of this, R\&D of water-based liquid scintillators is of extreme relevance; yet, no such scintillators have been made commercially available as yet. Here, we

Hsin-Chieh Liao

It is well known that the Eulerian polynomial is the Hilbert series of the cohomology of the permutahedral variety. Stanley obtained a formula showing that the cohomology carries a permutation representation of $\mathfrak{S}_n$. We answer a question of Stembridge on finding an explicit permutation basis of this cohomology. We observe that the Feichtner-Yuzvi

Stefan Tappe

In this paper we provide necessary and sufficient conditions for invariance of finite dimensional submanifolds for rough differential equations (RDEs) with values in a Banach space. Furthermore, we apply our findings to the particular situation of random RDEs driven by $Q$-Wiener processes and random RDEs driven by $Q$-fractional Brownian motion.

Omar Faris, Mohammad I. Awad, Murana A. Awad, Yahya Zweiri

Tactile sensing represents a crucial technique that can enhance the performance of robotic manipulators in various tasks. This work presents a novel bioinspired neuromorphic vision-based tactile sensor that uses an event-based camera to quickly capture and convey information about the interactions between robotic manipulators and their environment. The camer

Bo Xu, Ziao Liu, Mengqi Guo, Jiancheng Li

We propose a novel rolling shutter bundle adjustment method for neural radiance fields (NeRF), which utilizes the unordered rolling shutter (RS) images to obtain the implicit 3D representation. Existing NeRF methods suffer from low-quality images and inaccurate initial camera poses due to the RS effect in the image, whereas, the previous method that incorpor

Mike Nkongolo

Numerous studies confirm Cybersecurity Awareness Games (CAGs) effectively bolster organisational security against cyberattacks. This article introduces a serious CAG, integrating the traditional South African Morabaraba board game into cybersecurity education. Players adopt roles of defenders or attackers, strategically placing tokens to enhance awareness. E

Wei Dong, Qiyao Luo, Giulia Fanti, Elaine Shi

Differentially private mechanisms achieving worst-case optimal error bounds (e.g., the classical Laplace mechanism) are well-studied in the literature. However, when typical data are far from the worst case, \emph{instance-specific} error bounds -- which depend on the largest value in the dataset -- are more meaningful. For example, consider the sum estimati

David Kiessling, Katrin Baumgärtner, Jonathan Frey, Wilm Decré

This paper examines the question of finding feasible points to discrete-time optimal control problems. The optimization problem of finding a feasible trajectory is transcribed to an unconstrained optimal control problem. An efficient algorithm, called FP-DDP, is proposed that solves the resulting problem using Differential Dynamic Programming preserving feas

Feifei Long, Xiangze Xia, Jian Liu, Zixi Liu

The accurate construction of tokamak equilibria, which is critical for the effective control and optimization of plasma configurations, depends on the precise distribution of magnetic fields and magnetic fluxes. Equilibrium fitting codes, such as EFIT relying on traditional equilibrium algorithms, require solving the GS equation by iterations based on the le

Wladimir Zholobenko, Kaiyu Zhang, Andreas Stegmeir, Jan Pfennig

The design of commercially feasible magnetic confinement fusion reactors strongly relies on the reduced turbulent transport in the plasma edge during operation in the high confinement mode (H-mode). We present first global turbulence simulations of the ASDEX Upgrade tokamak edge and scrape-off layer (SOL) in ITER baseline H-mode conditions. Reasonable agreem

George Stamatelis, Angelos-Nikolaos Kanatas, Ioannis Asprogerakas, George C. Alexandropoulos

Active hypothesis testing is a thoroughly studied problem that finds numerous applications in wireless communications and sensor networks. In this paper, we focus on one centralized and one decentralized problem of active hypothesis testing in the presence of an eavesdropper. For the centralized problem including a single legitimate agent, we present a new f

Ansgar Jüngel, Katharina Schuh

Continuous-time Markov chains associated to finite-volume discretization schemes of Fokker-Planck equations are constructed. Sufficient conditions under which quantitative exponential decay in the $\phi$-entropy and Wasserstein distance are established, implying modified logarithmic Sobolev, Poincar\'e, and discrete Beckner inequalities. The results are not

Hang Yin, Zihao Wang, Yangqiu Song

Knowledge graphs contain informative factual knowledge but are considered incomplete. To answer complex queries under incomplete knowledge, learning-based Complex Query Answering (CQA) models are proposed to directly learn from the query-answer samples to avoid the direct traversal of incomplete graph data. Existing works formulate the training of complex qu

The H1 collaboration, V. Andreev, M. Arratia, A. Baghdasaryan

The H1 Collaboration reports the first measurement of the 1-jettiness event shape observable $\tau_1^b$ in neutral-current deep-inelastic electron-proton scattering (DIS). The observable $\tau_1^b$ is equivalent to a thrust observable defined in the Breit frame. The data sample was collected at the HERA $ep$ collider in the years 2003-2007 with center-of-mas

Shunichi Hato, Nozomi Ogawa

This paper presents a proof-of-concept study that examines the utilization of generative AI and mobile robotics for autonomous laboratory monitoring in the pharmaceutical R&D laboratory. The study investigates the potential advantages of anomaly detection and automated reporting by multi-modal model and Vision Foundation Model (VFM), which have the potential

Hang Zhang, Wenxiao Zhang, Haoxuan Qu, Jun Liu

Human-centered dynamic scene understanding plays a pivotal role in enhancing the capability of robotic and autonomous systems, in which Video-based Human-Object Interaction (V-HOI) detection is a crucial task in semantic scene understanding, aimed at comprehensively understanding HOI relationships within a video to benefit the behavioral decisions of mobile

Chul-Ung Woo, Jae Dong Noh

We report a motility-induced pinning transition in the active Ising model for a self-propelled particle system with discrete symmetry. This model was known to exhibit a liquid-gas type flocking phase transition, but a recent study reveals that the polar order is metastable due to droplet excitation. Using extensive Monte Carlo simulations, we demonstrate tha

Jinyeob Kim, Daewon Kwak, Hyunwoo Rim, Donghan Kim

Recent research on mobile robot navigation has focused on socially aware navigation in crowded environments. However, existing methods do not adequately account for human robot interactions and demand accurate location information from omnidirectional sensors, rendering them unsuitable for practical applications. In response to this need, this study introduc

Xiaotong Yu, Ruihan Xie, Zhihe Zhao, Chang-Wen Chen

While we enjoy the richness and informativeness of multimodal data, it also introduces interference and redundancy of information. To achieve optimal domain interpretation with limited resources, we propose CSDNet, a lightweight \textbf{C}ross \textbf{S}hallow and \textbf{D}eep Perception \textbf{Net}work designed to integrate two modalities with less cohere

Huiqiang Sun, Xingyi Li, Liao Shen, Xinyi Ye

Recent advancements in dynamic neural radiance field methods have yielded remarkable outcomes. However, these approaches rely on the assumption of sharp input images. When faced with motion blur, existing dynamic NeRF methods often struggle to generate high-quality novel views. In this paper, we propose DyBluRF, a dynamic radiance field approach that synthes

André Großardt

We present an algorithm that uses a single ancilla qubit that can evolve nonlinearly, and show how to use it to efficiently solve generic nonlinear Schr\"odinger equations, including nonlocal Hartree equations and the Navier-Stokes equation for an irrotational, non-viscous flow. We propose a realization of such nonlinear qubits via spin-spin coupling of neut

Wentao Zhang, Shaohang Xu, Peiyuan Cai, Lijun Zhu

Quadruped robots demonstrate robust and agile movements in various terrains; however, their navigation autonomy is still insufficient. One of the challenges is that the motion capabilities of the quadruped robot are anisotropic along different directions, which significantly affects the safety of quadruped robot navigation. This paper proposes a navigation f

Rui Zhong, Yuefeng Xu, Chao Zhang, Jun Yu

This paper introduces a novel metaheuristic algorithm, known as the efficient multiplayer battle game optimizer (EMBGO), specifically designed for addressing complex numerical optimization tasks. The motivation behind this research stems from the need to rectify identified shortcomings in the original MBGO, particularly in search operators during the movemen

Ruida Zhang, Chenyangguang Zhang, Yan Di, Fabian Manhardt

In this paper, we present KP-RED, a unified KeyPoint-driven REtrieval and Deformation framework that takes object scans as input and jointly retrieves and deforms the most geometrically similar CAD models from a pre-processed database to tightly match the target. Unlike existing dense matching based methods that typically struggle with noisy partial scans, w

Nan Gao, Jia Li, Huaibo Huang, Zhi Zeng

Blind face restoration (BFR) is a highly challenging problem due to the uncertainty of degradation patterns. Current methods have low generalization across photorealistic and heterogeneous domains. In this paper, we propose a Diffusion-Information-Diffusion (DID) framework to tackle diffusion manifold hallucination correction (DiffMAC), which achieves high-g

Wenwen Li, Hu Shao, Sizhe Wang, Xiran Zhou

Big earth science data offers the scientific community great opportunities. Many more studies at large-scales, over long-terms and at high resolution can now be conducted using the rich information collected by remote sensing satellites, ground-based sensor networks, and even social media input. However, the hundreds of terabytes of information collected and

Shin'ya Yamaguchi, Sekitoshi Kanai, Kazuki Adachi, Daiki Chijiwa

While fine-tuning is a de facto standard method for training deep neural networks, it still suffers from overfitting when using small target datasets. Previous methods improve fine-tuning performance by maintaining knowledge of the source datasets or introducing regularization terms such as contrastive loss. However, these methods require auxiliary source in

Ana Carpio, Gema Duro

Free boundaries of biofilms advancing on surfaces evolve according to conservation laws coupled with systems of partial differential equations for velocities, pressures and chemicals affecting cell behavior. Thin film approximations lead to complicated quasi-stationary systems coupling stationary transport equations and compressible Stokes systems with conve

Xuhong Li, Xuesong Cai, Erik Leitinger, Fredrik Tufvesson

In this paper, we present a multipath-based simultaneous localization and mapping (SLAM) algorithm that continuously adapts mulitiple map feature (MF) models describing specularly reflected multipath components (MPCs) from flat surfaces and point-scattered MPCs, respectively. We develop a Bayesian model for sequential detection and estimation of interacting

Qianjiang Hu, Zhimin Zhang, Wei Hu

Autonomous driving demands high-quality LiDAR data, yet the cost of physical LiDAR sensors presents a significant scaling-up challenge. While recent efforts have explored deep generative models to address this issue, they often consume substantial computational resources with slow generation speeds while suffering from a lack of realism. To address these lim

Jiawei Chen, Luyu Liu, Yibing Lv, Debdas Ghosh

This paper investigates constrained nonsmooth multiobjective fractional programming problem (NMFP) in real Banach spaces. It derives a quotient calculus rule for computing the first- and second-order Clarke derivatives of fractional functions involving locally Lipschitz functions. A novel second-order Abadie-type regularity condition is presented, defined wi

Tanjila Mawla, Maanak Gupta, Ravi Sandhu

The evolving smart and interconnected systems are designed to operate with minimal human intervention. Devices within these smart systems often engage in prolonged operations based on sensor data and contextual factors. Recently, an Activity-Centric Access Control (ACAC) model has been introduced to regulate these prolonged operations, referred to as activit

Masakazu Yoshimura, Junji Otsuka, Takeshi Ohashi

Full DNN-based image signal processors (ISPs) have been actively studied and have achieved superior image quality compared to conventional ISPs. In contrast to this trend, we propose a lightweight ISP that consists of simple conventional ISP functions but achieves high image quality by increasing expressiveness. Specifically, instead of tuning the parameters

Bruno Dular, Jean-Marc Schlenker

Convex co-compact 3-dimensional hyperbolic manifolds are uniquely determined by the pleating measured lamination on the boundary of their convex core.

Frank Nielsen

The Fisher-Rao distance between two probability distributions of a statistical model is defined as the Riemannian geodesic distance induced by the Fisher information metric. In order to calculate the Fisher-Rao distance in closed-form, we need (1) to elicit a formula for the Fisher-Rao geodesics, and (2) to integrate the Fisher length element along those geo

Amey Hengle, Aswini Kumar, Sahajpreet Singh, Anil Bandhakavi

Counterspeech, defined as a response to mitigate online hate speech, is increasingly used as a non-censorial solution. Addressing hate speech effectively involves dispelling the stereotypes, prejudices, and biases often subtly implied in brief, single-sentence statements or abuses. These implicit expressions challenge language models, especially in seq2seq t

Junzhuo Chen, Zonghan Lu, Shitong Kang

In the wake of the global spread of monkeypox, accurate disease recognition has become crucial. This study introduces an improved SE-InceptionV3 model, embedding the SENet module and incorporating L2 regularization into the InceptionV3 framework to enhance monkeypox disease detection. Utilizing the Kaggle monkeypox dataset, which includes images of monkeypox

Denis Schwachhofer, Peter Domanski, Steffen Becker, Stefan Wagner

System-Level Test (SLT) has been a part of the test flow for integrated circuits for over a decade and still gains importance. However, no systematic approaches exist for test program generation, especially targeting non-functional properties of the Device under Test (DUT). Currently, test engineers manually compose test suites from off-the-shelf software, a

Guiyu Zhao, Zewen Du, Zhentao Guo, Hongbin Ma

Addressing the challenges posed by the substantial gap in point cloud data collected from diverse sensors, achieving robust cross-source point cloud registration becomes a formidable task. In response, we present a novel framework for point cloud registration with broad applicability, suitable for both homologous and cross-source registration scenarios. To t

Yaoling Yang, Victor Montenegro, Abolfazl Bayat

Measuring the temperature of a quantum system is an essential task in almost all aspects of quantum technologies. Theoretically, an optimal strategy for thermometry requires measuring energy which demands full accessibility over the entire system as well as complex entangled measurement basis. In this paper, we take a different approach and show that single

Xinyu Zhou, Songhao Piao, Wenzheng Chi, Liguo Chen

Crowd navigation has received significant research attention in recent years, especially DRL-based methods. While single-robot crowd scenarios have dominated research, they offer limited applicability to real-world complexities. The heterogeneity of interaction among multiple agent categories, like in decentralized multi-robot pedestrian scenarios, are frequ

Tingbing Yan, Wenzheng Zeng, Yang Xiao, Xingyu Tong

Most existing one-shot skeleton-based action recognition focuses on raw low-level information (e.g., joint location), and may suffer from local information loss and low generalization ability. To alleviate these, we propose to leverage text description generated from large language models (LLM) that contain high-level human knowledge, to guide feature learni

Saeed Nasehi, Farhana Choudhury, Egemen Tanin

Due to the limited driving range, inadequate charging facilities, and time-consuming recharging, the process of finding an optimal charging route for electric vehicles (EVs) differs from that of other vehicle types. The time and location of EV charging during a trip impact not only the individual EV's travel time but also the travel time of other EVs, due to

Weihang Su, Yichen Tang, Qingyao Ai, Zhijing Wu

Dynamic retrieval augmented generation (RAG) paradigm actively decides when and what to retrieve during the text generation process of Large Language Models (LLMs). There are two key elements of this paradigm: identifying the optimal moment to activate the retrieval module (deciding when to retrieve) and crafting the appropriate query once retrieval is trigg

Anthony Conway, Irving Dai, Maggie Miller

We study locally flat disks in $(\mathbb{C} P^2)^\circ:=(\mathbb{C} P^2)\setminus \mathring{B^4}$ with boundary a fixed knot $K$ and whose complement has fundamental group $\mathbb{Z}$. We show that up to topological isotopy rel. boundary, such disks necessarily arise by performing a positive crossing change on $K$ to an Alexander polynomial one knot and cap

Huilin Xu, Tao Chen, Feng Xu

The ability to model the underlying dynamics of visual scenes and reason about the future is central to human intelligence. Many attempts have been made to empower intelligent systems with such physical understanding and prediction abilities. However, most existing methods focus on pixel-to-pixel prediction, which suffers from heavy computational costs while

G. Bougas, N. L. Harshman, P. Schmelcher

We present a generalization of the two-body contact interaction for non-relativistic particles trapped in one dimension. The particles interact only when they are a distance c apart. The competition of the interaction length scale with the oscillator length leads to three regimes identified from the energy spectra. When c is less than the oscillator length,

Alex Terrasson, Nicolas P. Mauranyapin, Catxere A. Casacio, Joel Q. Grim

Stimulated Raman scattering (SRS) microscopy is a powerful label-free imaging technique that probes the vibrational response of chemicals with high specificity and sensitivity. High-power, quantum-enhanced SRS microscopes have been recently demonstrated and applied to polymers and biological samples. Quantum correlations, in the form of squeezed light, enabl

Chong Wang, Yi Yu, Lanqing Guo, Bihan Wen

Shadow removal is a task aimed at erasing regional shadows present in images and reinstating visually pleasing natural scenes with consistent illumination. While recent deep learning techniques have demonstrated impressive performance in image shadow removal, their robustness against adversarial attacks remains largely unexplored. Furthermore, many existing

Alhassan Mumuni, Fuseini Mumuni, Nana Kobina Gerrar

The standard approach to tackling computer vision problems is to train deep convolutional neural network (CNN) models using large-scale image datasets which are representative of the target task. However, in many scenarios, it is often challenging to obtain sufficient image data for the target task. Data augmentation is a way to mitigate this challenge. A co

Evgeny Feigin, with an appendix in collaboration with Wojciech Samotij

We study the closure of the graph of the birational map from a projective space to a Grassmannian. We provide explicit description of the graph closure and compute the fibers of the natural projection to the Grassmannian. We construct embeddings of the graph closure to the projectivizations of certain cyclic representations of a degenerate special linear Lie

Xinli Yue, Ningping Mou, Qian Wang, Lingchen Zhao

Deep neural networks are vulnerable to adversarial attacks, often leading to erroneous outputs. Adversarial training has been recognized as one of the most effective methods to counter such attacks. However, existing adversarial training techniques have predominantly been tested on balanced datasets, whereas real-world data often exhibit a long-tailed distri

Alma Maria Sebastian, Emma Ryan-Weber, Rebecca L. Davies, George D. Becker

Intervening metal absorbers in quasar spectra at $z > 6$ can be used as probes to study the chemical enrichment of the Universe during the Epoch of Reionization (EoR). This work presents the comoving line densities ($dn/dX$) of low ionisation absorbers, namely, Mg II (2796\r{A}), C II (1334\r{A}) and O I (1302\r{A}) across $2 <z < 6$ using the E-XQR-30 metal

Baoquan Zhang, Huaibin Wang, Luo Chuyao, Xutao Li

Vector-Quantized Image Modeling (VQIM) is a fundamental research problem in image synthesis, which aims to represent an image with a discrete token sequence. Existing studies effectively address this problem by learning a discrete codebook from scratch and in a code-independent manner to quantize continuous representations into discrete tokens. However, lear

Jianyu Hu, Juan-Pablo Ortega, Daiying Yin

A structure-preserving kernel ridge regression method is presented that allows the recovery of nonlinear Hamiltonian functions out of datasets made of noisy observations of Hamiltonian vector fields. The method proposes a closed-form solution that yields excellent numerical performances that surpass other techniques proposed in the literature in this setup.

Han Lu, Yichen Xie, Xiaokang Yang, Junchi Yan

The pretraining-finetuning paradigm has gained widespread adoption in vision tasks and other fields, yet it faces the significant challenge of high sample annotation costs. To mitigate this, the concept of active finetuning has emerged, aiming to select the most appropriate samples for model finetuning within a limited budget. Traditional active learning met

Wanfang Su, Lixing Chen, Yang Bai, Xi Lin

Multi-agent perception (MAP) allows autonomous systems to understand complex environments by interpreting data from multiple sources. This paper investigates intermediate collaboration for MAP with a specific focus on exploring "good" properties of collaborative view (i.e., post-collaboration feature) and its underlying relationship to individual views (i.e.

Shuai Hu, Feng Gao, Xiaowei Zhou, Junyu Dong

Hyperspectral image (HSI) denoising is critical for the effective analysis and interpretation of hyperspectral data. However, simultaneously modeling global and local features is rarely explored to enhance HSI denoising. In this letter, we propose a hybrid convolution and attention network (HCANet), which leverages both the strengths of convolution neural ne

Ziyu Shan, Yujie Zhang, Qi Yang, Haichen Yang

No-reference point cloud quality assessment (NR-PCQA) aims to automatically evaluate the perceptual quality of distorted point clouds without available reference, which have achieved tremendous improvements due to the utilization of deep neural networks. However, learning-based NR-PCQA methods suffer from the scarcity of labeled data and usually perform subo

Binbin Li, Yuqing Li, Siyu Jia, Bingnan Ma

Conversational Aspect-Based Sentiment Analysis (DiaASQ) aims to detect quadruples \{target, aspect, opinion, sentiment polarity\} from given dialogues. In DiaASQ, elements constituting these quadruples are not necessarily confined to individual sentences but may span across multiple utterances within a dialogue. This necessitates a dual focus on both the syn

Chong Wang, Lanqing Guo, Yufei Wang, Hao Cheng

Deep unfolding networks (DUN) have emerged as a popular iterative framework for accelerated magnetic resonance imaging (MRI) reconstruction. However, conventional DUN aims to reconstruct all the missing information within the entire null space in each iteration. Thus it could be challenging when dealing with highly ill-posed degradation, usually leading to u

Mohammad Pedramfar, Yididiya Y. Nadew, Christopher J. Quinn, Vaneet Aggarwal

This paper introduces unified projection-free Frank-Wolfe type algorithms for adversarial continuous DR-submodular optimization, spanning scenarios such as full information and (semi-)bandit feedback, monotone and non-monotone functions, different constraints, and types of stochastic queries. For every problem considered in the non-monotone setting, the prop

Said Bensliman, Ameur Yagoub, Kamel Toumache

In this paper, we study products of asymmetric Toeplitz matrices, we give necessary and sufficient conditions for the product of two asymmetric Toeplitz matrices compatible sizes is asymmetric Toeplitz matrix. We also give some results related to the isometry.

Ziyu Shan, Yujie Zhang, Qi Yang, Haichen Yang

No-reference point cloud quality assessment (NR-PCQA) aims to automatically predict the perceptual quality of point clouds without reference, which has achieved remarkable performance due to the utilization of deep learning-based models. However, these data-driven models suffer from the scarcity of labeled data and perform unsatisfactorily in cross-dataset e

Changguang Dong, Yi Shi

For $\lambda>1$, we consider the locally free ${\mathbb Z}\ltimes_\lambda{\mathbb R}$ actions on ${\mathbb T}^2$. We show that, if the action is $C^r$ with $r\geq2$, then it is $C^{r-\epsilon}$-conjugate to an affine action generated by a hyperbolic automorphism and a linear translation flow along expanding eigen-direction of the automorphism. In contrast, t

Di Wu, Wasi Uddin Ahmad, Dejiao Zhang, Murali Krishna Ramanathan

Recent advances in retrieval-augmented generation (RAG) have initiated a new era in repository-level code completion. However, the invariable use of retrieval in existing methods exposes issues in both efficiency and robustness, with a large proportion of the retrieved contexts proving unhelpful or harmful to code language models (code LMs). In this paper, w

Anirban Mukherjee, Monjoy Narayan Choudhury, Dinesh Babu Jayagopi

Face de-identification in videos is a challenging task in the domain of computer vision, primarily used in privacy-preserving applications. Despite the considerable progress achieved through generative vision models, there remain multiple challenges in the latest approaches. They lack a comprehensive discussion and evaluation of aspects such as realism, temp

Abdelrahman Sadallah, Daria Kotova, Ekaterina Kochmar

Cryptic crosswords are puzzles that rely not only on general knowledge but also on the solver's ability to manipulate language on different levels and deal with various types of wordplay. Previous research suggests that solving such puzzles is a challenge even for modern NLP models. However, the abilities of large language models (LLMs) have not yet been tes

Nick Choksi, Eugene Chiang

Many dozens of circumstellar discs show signatures of sculpting by planets. To help find these protoplanets by direct imaging, we compute their broadband spectral energy distributions, which overlap with the JWST (James Webb Space Telescope) and ALMA (Atacama Large Millimeter Array) passbands. We consider how circumplanetary spherical envelopes and circumpla

Yongquan He, Wenyuan Zhang, Xuancheng Huang, Peng Zhang

Instruction tuning for large language models (LLMs) can drive them to produce results consistent with human goals in specific downstream tasks. However, the process of continual instruction tuning (CIT) for LLMs may bring about the catastrophic forgetting (CF) problem, where previously learned abilities are degraded. Recent methods try to alleviate the CF pr

Wei Zhu, Xu-Rong Chen, Yu-Chen Tang

We use a newly recognized gluon distribution in the nucleon, which was predicted by a QCD evolution equation to consistently explain several intriguing phenomena associated with gamma-ray bursts. They are the GeV-TeV spectra of GRB 221009A, the remarkably symmetrical explosion cloud in kilonova AT2017gfo, and the absence of a very high-energy gamma-ray signa

Bejamin A. Huerfano, Fernando Jimenez

Digital image processing (DIP) is of great importance in validating and guaranteeing parameters that ensure the quality of mass-produced products. Therefore, this article focused on developing an industrial automation method for a zone of a production line model using the DIP. The neo-cascade methodology employed allowed for defining each of the stages in an

Wu Liang, X. -G. Ma

Since the advent of the Segment Anything Model(SAM) approximately one year ago, it has engendered significant academic interest and has spawned a large number of investigations and publications from various perspectives. However, the deployment of SAM in practical assembly line scenarios has yet to materialize due to its large image encoder, which weighs in

Daehee Park, Jaeseok Jeong, Sung-Hoon Yoon, Jaewoo Jeong

Trajectory prediction is a challenging problem that requires considering interactions among multiple actors and the surrounding environment. While data-driven approaches have been used to address this complex problem, they suffer from unreliable predictions under distribution shifts during test time. Accordingly, several online learning methods have been pro

Ruoyan Ma, Shengan Zheng, Guifeng Wang, Jin Pu

Regular path queries (RPQs) in graph databases are bottlenecked by the memory wall. Emerging processing-in-memory (PIM) technologies offer a promising solution to dispatch and execute path matching tasks in parallel within PIM modules. We present Moctopus, a PIM-based data management system for graph databases that supports efficient batch RPQs and graph upd

Tian-Xing Xu, Wenbo Hu, Yu-Kun Lai, Ying Shan

3D Gaussian splatting, emerging as a groundbreaking approach, has drawn increasing attention for its capabilities of high-fidelity reconstruction and real-time rendering. However, it couples the appearance and geometry of the scene within the Gaussian attributes, which hinders the flexibility of editing operations, such as texture swapping. To address this i