April 2024 arXiv papers — page 59

Julien Quarez, Marc Modat, Sebastien Ourselin, Jonathan Shapey

In surgical skill assessment, the Objective Structured Assessments of Technical Skills (OSATS) and Global Rating Scale (GRS) are well-established tools for evaluating surgeons during training. These metrics, along with performance feedback, help surgeons improve and reach practice standards. Recent research on the open-source JIGSAWS dataset, which includes

Seung-Yeal Ha, Myeongju Kang, Jaeyoung Yoon, Mattia Zanella

We study the measure-valued death state and local sensitivity analysis of the Winfree model and its mean-field counterpart with uncertain high-order couplings. The Winfree model is the first mathematical model for synchronization, and it can cast as the effective approximation of the pulse-coupled model for synchronization, and it exhibits diverse asymptotic

Kieran Gilday, Irena Zubak, Andreas Raabe, Josie Hughes

Robotic assistance has significantly improved the outcomes of open microsurgery and rigid endoscopic surgery, however is yet to make an impact in flexible endoscopic neurosurgery. Some of the most common intracranial procedures for treatment of hydrocephalus and tumors stand to benefit from increased dexterity and reduced invasiveness offered by robotic syst

Lizhi Ma, Tong Zhao, Huachuan Qiu, Zhenzhong Lan

The pervasive use of AI applications is increasingly influencing our everyday decisions. However, the ethical challenges associated with AI transcend conventional ethics and single-discipline approaches. In this paper, we propose aspirational ethical principles specifically tailored for human-bot psycho-counseling during an era when AI-powered mental health

Theo Drane, Samuel Coward, Mertcan Temel, Joe Leslie-Hurd

In many instances of fixed-point multiplication, a full precision result is not required. Instead it is sufficient to return a faithfully rounded result. Faithful rounding permits the machine representable number either immediately above or below the full precision result, if the latter is not exactly representable. Multipliers which take full advantage of t

Laura Weidinger, Joslyn Barnhart, Jenny Brennan, Christina Butterfield

Safety and responsibility evaluations of advanced AI models are a critical but developing field of research and practice. In the development of Google DeepMind's advanced AI models, we innovated on and applied a broad set of approaches to safety evaluation. In this report, we summarise and share elements of our evolving approach as well as lessons learned fo

Koki Shiraishi, Masaya Nakagawa, Takashi Mori, Masahito Ueda

The local Gorini-Kossakowski-Sudarshan-Lindblad (GKSL) quantum master equation is a powerful tool for the study of open quantum many-body systems. However, its microscopic derivation applicable to many-body systems is available only in limited cases of weak internal couplings, and it has yet to be fully understood under what microscopic conditions the local

Xuzheng Yu, Chen Jiang, Xingning Dong, Tian Gan

The user base of short video apps has experienced unprecedented growth in recent years, resulting in a significant demand for video content analysis. In particular, text-video retrieval, which aims to find the top matching videos given text descriptions from a vast video corpus, is an essential function, the primary challenge of which is to bridge the modali

Subhadip Bisal

We present the analytical results for the anomalous chromomagnetic moment of a quark at the one-loop and two-loop levels through model-independent parameterizations, considering the external gluon to be off-shell. Then, we consider different types of two-Higgs doublet models with the momentum transfer of the external gluon, $q^2 = \pm M_Z^2$, and $0$, for th

Mhairi Dunion, Stefano V. Albrecht

The performance of image-based Reinforcement Learning (RL) agents can vary depending on the position of the camera used to capture the images. Training on multiple cameras simultaneously, including a first-person egocentric camera, can leverage information from different camera perspectives to improve the performance of RL. However, hardware constraints may

Jinyue Guo, Anna-Maria Christodoulou, Balint Laczko, Kyrre Glette

Evolutionary Algorithms and Generative Deep Learning have been two of the most powerful tools for sound generation tasks. However, they have limitations: Evolutionary Algorithms require complicated designs, posing challenges in control and achieving realistic sound generation. Generative Deep Learning models often copy from the dataset and lack creativity. I

Lalita Kumari, Sukhdeep Singh, Vaibhav Varish Singh Rathore, Anuj Sharma

The Handwritten Text Recognition problem has been a challenge for researchers for the last few decades, especially in the domain of computer vision, a subdomain of pattern recognition. Variability of texts amongst writers, cursiveness, and different font styles of handwritten texts with degradation of historical text images make it a challenging problem. Rec

Yinlin Zhu, Xunkai Li, Zhengyu Wu, Di Wu

Subgraph federated learning (subgraph-FL) is a new distributed paradigm that facilitates the collaborative training of graph neural networks (GNNs) by multi-client subgraphs. Unfortunately, a significant challenge of subgraph-FL arises from subgraph heterogeneity, which stems from node and topology variation, causing the impaired performance of the global GN

Song-lin Zhao, Xiao-hui Feng, Wei Feng

As local and nonlocal reductions of a discrete second-order Ablowitz-Kaup-Newell-Segur equation, two discrete nonlinear Schr\"odinger type equations are considered. Through the bilinearization reduction method, we construct double Casoratian solutions of the reduced discrete nonlinear Schr\"odinger type equations, including soliton solutions and Jordan-block

Shengjun Fan, Ying Hu, Shanjian Tang

The dynamic concave utility (or the dynamic convex risk measure) of an unbounded endowment is studied and represented as the value process in the unique solution of a backward stochastic differential equation (BSDE) with an unbounded terminal value, with the help of our recent existence and uniqueness results on unbounded solutions of scalar BSDEs whose gene

Shuang Su, Duc-Viet Vu

Let $X$ be a compact K\"ahler manifold of dimension $n$, and let $T$ be a closed positive $(1,1)$-current in a nef cohomology class on $X$. We establish an optimal upper bound for the volume of components of Lelong upper level sets of $T$ in terms of cohomology classes of non-pluripolar self-products of $T$.

Shir Lissak, Yaakov Ophir, Refael Tikochinski, Anat Brunstein Klomek

Background: Recent advancements in Artificial Intelligence (AI) contributed significantly to suicide assessment, however, our theoretical understanding of this complex behavior is still limited. Objective: This study aimed to harness AI methodologies to uncover hidden risk factors that trigger or aggravate suicide behaviors. Method: The primary dataset inclu

Abdelaziz Bounhar, Mireille Sarkiss, Michèle Wigger

In this paper, we characterize the fundamental limits of a communication system with three users (i.e., three transmitters) and a single receiver where communication from two covert users must remain undetectable to an external warden. Our results show a tradeoff between the highest rates that are simultaneously achievable for the three users. They further s

Hai Ci, Pei Yang, Yiren Song, Mike Zheng Shou

We revisit Tree-Ring Watermarking, a recent diffusion model watermarking method that demonstrates great robustness to various attacks. We conduct an in-depth study on it and reveal that the distribution shift unintentionally introduced by the watermarking process, apart from watermark pattern matching, contributes to its exceptional robustness. Our investiga

Adelina Bärligea, Benedikt Poggel, Jeanette Miriam Lorenz

In the pursuit of achieving near-term quantum advantage for combinatorial optimization problems, the Quantum Approximate Optimization Algorithm (QAOA) and the Variational Quantum Eigensolver (VQE) are the primary methods of interest, but their practical effectiveness remains uncertain. Therefore, there is a persistent need to develop and evaluate alternative

Rupert Klein, Maikel Nadolski, Christian Zenker, Michael Oevermann

The ``Shockless Explosion Combustion" (SEC) concept for gas turbine combustors, introduced in 2014, approximates constant volume combustion (CVC) by harnessing acoustic confinement of autoigniting gas packets. The resulting pressure waves simultaneously transmit combustion energy to a turbine plenum and facilitate the combustor's recharging against an averag

Kun Sun, Rong Wang

Data-driven approaches have revolutionized scientific research, with machine learning and statistical analysis being commonly used methodologies. Despite their widespread use, these approaches differ significantly in their techniques, objectives and implementations. Few studies have systematically applied both methods to identical datasets to highlight poten

Faouzi Triki, Kristoffer Linder-Steinlein, Mirza Karamehmedovic

We consider the multi-frequency inverse source problem in the presence of a non-homogeneous medium using passive measurements. Precisely, we derive stability estimates for determining the source from the knowledge of only the imaginary part of the radiated field on the boundary for multiple frequencies. The proof combines a spectral decomposition with a quan

Hilde Weerts, Aislinn Kelly-Lyth, Reuben Binns, Jeremias Adams-Prassl

Emerging scholarship suggests that the EU legal concept of direct discrimination - where a person is given different treatment on grounds of a protected characteristic - may apply to various algorithmic decision-making contexts. This has important implications: unlike indirect discrimination, there is generally no 'objective justification' stage in the direc

William Atherton, Dmitrii V. Pasechnik

We provide a counterexample to a crucial lemma in the ICALP 2008 paper "Simpler Linear-Time Modular Decomposition Via Recursive Factorizing Permutations", invalidating the algorithm described there.

Emmanuel G. Blanchard, Jeffrey Wiseman

This manuscript introduces the concept of Rapid Virtual Simulations, a new techno-pedagogical activity that fosters expert autonomy for creating virtual educational simulations. It is grounded in a Realistic-Enough Philosophy that consists of pursuing the development of the least complex simulation while still ensuring a Satisficing (or good enough) Learning

Muhammad Faris, Mario Zanon, Paolo Falcone

In this paper, we address a coordination problem for connected and autonomous vehicles (CAVs) in mixed traffic settings with human-driven vehicles (HDVs). The main objective is to have a safe and optimal crossing order for vehicles approaching unsignalized intersections. This problem results in a mixed-integer quadratic programming (MIQP) formulation which i

Wei Huang, Xingyu Zheng, Xudong Ma, Haotong Qin

The LLaMA family, a collection of foundation language models ranging from 7B to 65B parameters, has become one of the most powerful open-source large language models (LLMs) and the popular LLM backbone of multi-modal large language models (MLLMs), widely used in computer vision and natural language understanding tasks. In particular, LLaMA3 models have recen

S. E. Chorfi, L. Maniar, M. Yamamoto

We consider a class of diffusion equations with the Caputo time-fractional derivative $\partial_t^\alpha u=L u$ subject to the homogeneous Dirichlet boundary conditions. Here, we consider a fractional order $0<\alpha < 1$ and a second-order operator $L$ which is elliptic and non-symmetric. In this paper, we show that the logarithmic convexity extends to this

Yuhma Asano, Jun Nishimura, Worapat Piensuk, Naoyuki Yamamori

The type IIB matrix model is a promising nonperturbative formulation of superstring theory, which may elucidate the emergence of (3+1)-dimensional space-time. However, the partition function is divergent due to the Lorentz symmetry, which is represented by a noncompact group. This divergence has been regularized conventionally by introducing some infrared cu

Jiahao Ma, Miaomiao Liu, David Ahmedt-Aristizaba, Chuong Nguyen

In this paper, we address the problem of efficient point searching and sampling for volume neural rendering. Within this realm, two typical approaches are employed: rasterization and ray tracing. The rasterization-based methods enable real-time rendering at the cost of increased memory and lower fidelity. In contrast, the ray-tracing-based methods yield supe

Keheng Wang, Feiyu Duan, Peiguang Li, Sirui Wang

Retrieval-Augmented Generation (RAG) demonstrates great value in alleviating outdated knowledge or hallucination by supplying LLMs with updated and relevant knowledge. However, there are still several difficulties for RAG in understanding complex multi-hop query and retrieving relevant documents, which require LLMs to perform reasoning and retrieve step by s

Wenhao Lan, Yijun Yang, Haihua Shen, Shan Li

The increasing adoption of 3D point cloud data in various applications, such as autonomous vehicles, robotics, and virtual reality, has brought about significant advancements in object recognition and scene understanding. However, this progress is accompanied by new security challenges, particularly in the form of backdoor attacks. These attacks involve inse

O. Bertolami

The unfolding climate crisis is a physical manifestation of the damage that market economy, driven by the high intensity consumption of fossil fuels, has inflicted on the Earth System and on the stability conditions that were established by a complex conjugation of natural factors during the Holoecene. The magnitude of the human activities and its predatory

Yuyang Sheng, Sophia Bano, Matthew J. Clarkson, Mobarakol Islam

Purpose: The recent Segment Anything Model (SAM) has demonstrated impressive performance with point, text or bounding box prompts, in various applications. However, in safety-critical surgical tasks, prompting is not possible due to (i) the lack of per-frame prompts for supervised learning, (ii) it is unrealistic to prompt frame-by-frame in a real-time track

Olli Mansikkamäki, Alexander Tyner, Alexander Bilmes, Ilya Drozdov

Two-level systems (TLS) of unclear physical origin are a major contributor to decoherence in superconducting qubits. The interactions of individual TLS with a qubit can be detected via various spectroscopic methods, most of which have relied on the tunability of the qubit frequency. We propose a novel method that requires only a microwave drive and dispersiv

Xiongzhi Zeng, Huili Zhang, Shizheng Zhang, Pei Liu

Quantum computing is of great potential for chemical system simulations. In this study, we propose an efficient protocol of quantum computer based simulation of chemical systems which enables accurate chemical reaction modeling on noisy intermediate-scale quantum (NISQ) devices. In this protocol, we combine an correlation energy-based active orbital selectio

Hongyun Yu, Zhan Qu, Qihang Yu, Jianchuan Chen

Recent works on audio-driven talking head synthesis using Neural Radiance Fields (NeRF) have achieved impressive results. However, due to inadequate pose and expression control caused by NeRF implicit representation, these methods still have some limitations, such as unsynchronized or unnatural lip movements, and visual jitter and artifacts. In this paper, w

Hongbin Zhu, Hua Qian

We investigate fast data aggregation via over-the-air computation (AirComp) over wireless networks. In this scenario, an access point (AP) with multiple antennas aims to recover the arithmetic mean of sensory data from multiple wireless devices. To minimize estimation distortion, we formulate a mean-squared-error (MSE) minimization problem that considers joi

Rui She, Qiyu Kang, Sijie Wang, Wee Peng Tay

Point cloud registration is a fundamental technique in 3-D computer vision with applications in graphics, autonomous driving, and robotics. However, registration tasks under challenging conditions, under which noise or perturbations are prevalent, can be difficult. We propose a robust point cloud registration approach that leverages graph neural partial diff

Mohak Chadha, Alexander Jensen, Jianfeng Gu, Osama Abboud

Federated Learning (FL) is an emerging machine learning paradigm that enables the collaborative training of a shared global model across distributed clients while keeping the data decentralized. Recent works on designing systems for efficient FL have shown that utilizing serverless computing technologies, particularly Function-as-a-Service (FaaS) for FL, can

Tao Sun, Yuanzi Fu, Kaicheng Yang, Jian Wu

This paper presents the winning solution for the 1st SkatingVerse Challenge. We propose a method that involves several steps. To begin, we leverage the DINO framework to extract the Region of Interest (ROI) and perform precise cropping of the raw video footage. Subsequently, we employ three distinct models, namely Unmasked Teacher, UniformerV2, and InfoGCN,

M. N. Mooij, M. Baudena, A. S. von der Heydt, I. Kryven

The Lotka-Volterra system is a set of ordinary differential equations describing growth of interacting ecological species. This model has gained renewed interest in the context of random interaction networks. One of the debated questions is understanding how the number of species in the system, $n$, influences the stability of the model. Robert May demonstra

Aleksandr Sidorenko, Mahdi Rezapour, Achim Wagner, Martin Ruskowski

The Industry 4.0 paradigm manifests the shift towards mass customization and cyber-physical production systems (CPPS) and sets new requirements for industrial automation software in terms of modularity, flexibility, and short development cycles of control programs. Though programmable logical controllers (PLCs) have been evolving into versatile and powerful

Marco Torchiano, Antonio Vetrò, Riccardo Coppola

The Scrum framework has gained widespread adoption in the industry for its emphasis on collaboration and continuous improvement. However, it has not reached a similar relevance in Software Engineering (SE) curricula. This work reports the experience of five editions of a SE course within an MSc. Degree in Computer Engineering. The course primary educational

Takahiro Oba

We show that, given a closed integral symplectic manifold $(\Sigma, \omega)$ of dimension $2n \geq 4$, for every integer $k>\int_{\Sigma}\omega^{n}$, the Boothby-Wang bundle over $(\Sigma, k\omega)$ carries no Stein fillable contact structure. This negatively answers a question raised by Eliashberg. A similar result holds for Boothby-Wang orbibundles. As an

Sophia Sirko-Galouchenko, Alexandre Boulch, Spyros Gidaris, Andrei Bursuc

We introduce a self-supervised pretraining method, called OccFeat, for camera-only Bird's-Eye-View (BEV) segmentation networks. With OccFeat, we pretrain a BEV network via occupancy prediction and feature distillation tasks. Occupancy prediction provides a 3D geometric understanding of the scene to the model. However, the geometry learned is class-agnostic.

Tullio Valent

Two approaches to Lipschitz structures for any set are presented, studied and compared. The first approach is similar to the one proposed in Fraser, Jr. R. B., Axiom systems for Lipschitz structures, Fundamenta Mathematicae, (1970), where Lipschitz structures are defined as families of pseudo-metrics satisfying suitable conditions. The other one, here introd

Yonghao Dang, Jianqin Yin, Liyuan Liu, Pengxiang Ding

Multi-person pose estimation (MPPE) presents a formidable yet crucial challenge in computer vision. Most existing methods predominantly concentrate on isolated interaction either between instances or joints, which is inadequate for scenarios demanding concurrent localization of both instances and joints. This paper introduces a novel CNN-based single-stage m

Alexandre Bittar, Philip N. Garner

Understanding cognitive processes in the brain demands sophisticated models capable of replicating neural dynamics at large scales. We present a physiologically inspired speech recognition architecture, compatible and scalable with deep learning frameworks, and demonstrate that end-to-end gradient descent training leads to the emergence of neural oscillation

M. Dajczer, Th. Vlachos

We identify as topological spheres those complete submanifolds lying with any codimension in hyperbolic space whose Ricci curvature satisfies a lower bound contingent solely upon the length of the mean curvature vector of the immersion.

Melih Yazgan, Mythra Varun Akkanapragada, J. Marius Zoellner

This survey offers a comprehensive examination of collaborative perception datasets in the context of Vehicle-to-Infrastructure (V2I), Vehicle-to-Vehicle (V2V), and Vehicle-to-Everything (V2X). It highlights the latest developments in large-scale benchmarks that accelerate advancements in perception tasks for autonomous vehicles. The paper systematically ana

Arif Ullah, Yu Huang, Ming Yang, Pavlo O. Dral

Neural networks (NNs) accelerate simulations of quantum dissipative dynamics. Ensuring that these simulations adhere to fundamental physical laws is crucial, but has been largely ignored in the state-of-the-art NN approaches. We show that this may lead to implausible results measured by violation of the trace conservation. To recover the correct physical beh

Sahar Diskin, Anna Geisler

For $t\in \mathbb{N}$ and ever $i\in [t]$, let $H_i$ be a $d_i$-regular connected graph with $1<|V(H_i)|\le M$ for some integer $M\ge 2$. Let $G=\square_{i=1}^tH_i$ be the $t$-dimensional Cartesian product of $H_1,\ldots, H_t$. We prove that if $t\ge 2\ln M$ then $G$ has a (nearly-)perfect matching. We further show that this bound on the dimension is tight u

Ming Kang, Fung Fung Ting, Shier Nee Saw, Raphaël C. -W. Phan

Existing brain tumor segmentation methods usually utilize multiple Magnetic Resonance Imaging (MRI) modalities in brain tumor images for segmentation, which can achieve better segmentation performance. However, in clinical applications, some modalities are often missing due to resource constraints, resulting in significant performance degradation for methods

Peter Schenzel

Let $\xx= x_1,\ldots,x_r$ denote a system of elements of a commutative ring $R$. For an $R$-module $M$ we investigate when $\xx$ is $M$-pro-regular resp. $M$-weakly pro-regular as generalizations of $M$-regular sequences. This is done in terms of \v{C}ech co-homology resp. homology, defined by $H^i(\check{C}_{\xx} \otimes_R \cdot)$ resp. by $H_i({\textrm{R}}

Paweł Golik, Maciej Grzenda, Elżbieta Sienkiewicz

Travel mode choice (TMC) prediction, which can be formulated as a classification task, helps in understanding what makes citizens choose different modes of transport for individual trips. This is also a major step towards fostering sustainable transportation. As behaviour may evolve over time, we also face the question of detecting concept drift in the data.

Jingwen Ye, Xinchao Wang

The training of contemporary deep learning models heavily relies on publicly available data, posing a risk of unauthorized access to online data and raising concerns about data privacy. Current approaches to creating unlearnable data involve incorporating small, specially designed noises, but these methods strictly limit data usability, overlooking its poten

Jiacheng Yu, Abdelali Zaki, Killian Mache, Omar Ibder

Electrostrictive materials exhibit a strain that is proportional to the square of the induced polarization. In linear dielectrics where the permittivity is constant, this electromechanical strain is also proportional to the square of the electric field. However, under increasing amplitudes of the driving field, the electromechanical strain sometimes saturate

Ning Wang, Liu Min

Based on Weizs\"acker-Skyrme (WS4) mass model, the fission barriers of nuclei are systematically studied. Considering the shell corrections, the macroscopic deformation energy and a phenomenological residual correction, the fission barrier heights for nuclei with $Z\geqslant 82$ can be well described, with an rms deviation of 0.481 MeV with respect to 71 emp

Mingming Cao, Honghai Liu, Zengyan Si, Kôzô Yabuta

In this paper we solve a long standing problem about the bilinear $T1$ theorem to characterize the (weighted) compactness of bilinear Calder\'{o}n-Zygmund operators. Let $T$ be a bilinear operator associated with a standard bilinear Calder\'{o}n-Zygmund kernel. We prove that $T$ can be extended to a compact bilinear operator from $L^{p_1}(w_1^{p_1}) \times L

Li Jiapeng, Liu Runze, Li Yabo, Zhou Tong

Multi-hop question answering is a knowledge-intensive complex problem. Large Language Models (LLMs) use their Chain of Thoughts (CoT) capability to reason complex problems step by step, and retrieval-augmentation can effectively alleviate factual errors caused by outdated and unknown knowledge in LLMs. Recent works have introduced retrieval-augmentation in t

Cédric Gernigon, Silviu-Ioan Filip, Olivier Sentieys, Clément Coggiola

Large-scale deep neural networks (DNNs) have achieved remarkable success in many application scenarios. However, high computational complexity and energy costs of modern DNNs make their deployment on edge devices challenging. Model quantization is a common approach to deal with deployment constraints, but searching for optimized bit-widths can be challenging

Zhongda Chu, Fei Teng

Security and stability challenges in future power systems with high penetration Inverter-Based Resources (IBR) have been anticipated as one of the main barriers to decarbonization. Grid-following IBRs may become unstable under small disturbances in weak grids, while during transient processes, system stability and protection may be jeopardized due to the lac

Tyler Lutz, Andreas M. Menzel, Abdallah Daddi-Moussa-Ider

Functionalized thin elastic films and membranes frequently feature internal sites of net forces or stresses. These are, for instance, active sites of actuation, or rigid inclusions in a strained membrane that induce counterstress upon externally imposed deformations. We theoretically analyze the geometry of isotropic, flat, thin, linearly elastic films or me

San-Dong Guo, Liguo Zhang, Yiwen Zhang, Guangzhao Wang

Superior to ferromagnetic (FM) materials, antiferromagnetic (AFM) materials do not have any net magnetic moment and are robust to external magnetic perturbation with ultra-high dynamic speed. To achieve spontaneous valley polarization and anomalous valley Hall effect (AVHE) in AFM materials is of great significance for potential applications in spintronics a

Rôlin Gabriel Rasoanaivo, Morteza Yazdani, Pascale Zaraté, Amirhossein Fateh

Each decision-making tool should be tested and validated in real case studies to be practical and fit to global problems. The application of multi-criteria decision-making methods (MCDM) is currently a trend to rank alternatives. In the literature, there are several multi-criteria decision-making methods according to their classification. During our experime

Sami Ben Ali, Silviu-Ioan Filip, Olivier Sentieys

Training Deep Neural Networks (DNNs) can be computationally demanding, particularly when dealing with large models. Recent work has aimed to mitigate this computational challenge by introducing 8-bit floating-point (FP8) formats for multiplication. However, accumulations are still done in either half (16-bit) or single (32-bit) precision arithmetic. In this

Pablo Valgañón, Andrés Felipe Useche, Felipe Montes, Alex Arenas

We introduce a surveillance strategy specifically designed for urban areas to enhance preparedness and response to disease outbreaks by leveraging the unique characteristics of human behavior within urban contexts. By integrating data on individual residences and travel patterns, we construct a Mixing matrix that facilitates the identification of critical pa

Xiaoyu Fu, Yuan Gao

In this paper, we obtain a Carleman estimate for the higher order partial differential operator. In the process of establishing this estimate, we developed a new method, which is called the back-propagation method (the BPM, for short). This method can also be used to build up Carleman estimates for some other partial differential operators, and might provide

Weili Zeng, Yichao Yan, Qi Zhu, Zhuo Chen

Text-to-image (T2I) customization aims to create images that embody specific visual concepts delineated in textual descriptions. However, existing works still face a main challenge, concept overfitting. To tackle this challenge, we first analyze overfitting, categorizing it into concept-agnostic overfitting, which undermines non-customized concept knowledge,

Jingwen Ye, Ruonan Yu, Songhua Liu, Xinchao Wang

The proliferation of large-scale AI models trained on extensive datasets has revolutionized machine learning. With these models taking on increasingly central roles in various applications, the need to understand their behavior and enhance interpretability has become paramount. To investigate the impact of changes in training data on a pre-trained model, a c

Victoria Gould, Ambroise Grau, Marianne Johnson, Mark Kambites

We consider the translational hull $\Omega(I)$ of an arbitrary subsemigroup $I$ of an endomorphism monoid $\mathrm{End}(A)$ where $A$ is a universal algebra. We give conditions for every bi-translation of $I$ to be realised by transformations, or by endomorphisms, of $A$. We demonstrate that certain of these conditions are also sufficient to provide natural

Jonas Ney, Christoph Füllner, Vincent Lauinger, Laurent Schmalen

To satisfy the growing throughput demand of data-intensive applications, the performance of optical communication systems increased dramatically in recent years. With higher throughput, more advanced equalizers are crucial, to compensate for impairments caused by inter-symbol interference (ISI). The latest research shows that artificial neural network (ANN)-

A. Skarlatos, A. Martínez-De-Guerenu, R. Miorelli, A. Lasaosa

In this work, two different parametric hysteresis models, the Jiles-Atherton model and the Mel'gui relation, have been combined to form a more general hysteresis operator, suitable for the description of families of experimental B(H) curves obtained for low carbon (LC) steel specimens after isothermal annealing at different temperatures and times. As it has

Robert A. Wilson

I investigate the structure of $E_8$ under the action of the subalgebra/subgroup $A_1+G_2+C_3$, as a potential route to unification of the fundamental forces of nature into a single algebraic structure. The particular real form $E_{8(-24)}$ supports a decomposition into compact $G_2$ plus split $A_1+C_3$, which allows a restriction from $G_2$ to $SU(3)$ for

Tanja Šarčević, Alicja Karlowicz, Rudolf Mayer, Ricardo Baeza-Yates

Large Generative AI (GAI) models have the unparalleled ability to generate text, images, audio, and other forms of media that are increasingly indistinguishable from human-generated content. As these models often train on publicly available data, including copyrighted materials, art and other creative works, they inadvertently risk violating copyright and mi

Lauric Reynes, Louise Fouqueau, D. Aurelle, Stéphane Mauger

The impact of climate change on populations will be contingent upon their contemporary adaptive evolution. In this study, we investigated the contemporary evolution of four populations of the cold-water kelp Laminaria digitata by analysing their spatial and temporal genomic variation using ddRAD-sequencing. These populations were sampled from the center to t

Xiangqi Qiang, Chengjun Hou

We characterise the groupoid $C^*$-algebras associated to the transformation groupoids of injective actions of discrete countable Ore semi-groups on compact topological spaces in terms of the reduced crossed product from the dual actions, and characterise the continuous orbit equivalence for injective actions by means of the transformation groupoids, as well

Kobiljon Abdurasulov, Fatanah Deraman, Azamat Saydaliyev, Siti Hasana Sapar

This article will discussing on $\frac{1}{2}$-derivations of quasi-filiform Lie algebras of maximum length. The non-trivial transposed Poisson algebras with the quasi-filiform Lie algebras of maximum length are constructed by using $\frac{1}{2}$-derivations of Lie algebras. We have established commutative associative multiplication to construct a transposed

Sergio Burdisso, Ernesto Reyes-Ramírez, Esaú Villatoro-Tello, Fernando Sánchez-Vega

Automatic depression detection from conversational data has gained significant interest in recent years. The DAIC-WOZ dataset, interviews conducted by a human-controlled virtual agent, has been widely used for this task. Recent studies have reported enhanced performance when incorporating interviewer's prompts into the model. In this work, we hypothesize tha

Tiansu Hu, Yuzhao Song, Linjing Zhang, Xiaoya Zhou

This paper explores the utilization of blockchain token voting technology in student course selection systems. The current course selection systems face various issues, which can be mitigated through the implementation of blockchain technology. The advantages of blockchain technology, including consensus mechanisms and smart contracts, are discussed in detai

Kanglei Zhou, Junlin Li, Ruizhi Cai, Liyuan Wang

Action Quality Assessment (AQA) is pivotal for quantifying actions across domains like sports and medical care. Existing methods often rely on pre-trained backbones from large-scale action recognition datasets to boost performance on smaller AQA datasets. However, this common strategy yields suboptimal results due to the inherent struggle of these backbones

Supraja Sridhara, Andrin Bertschi, Benedict Schlüter, Shweta Shinde

User programs recover from hardware exceptions and respond to signals by executing custom handlers that they register specifically for such events. We present SIGY attack, which abuses this programming model on Intel SGX to break the confidentiality and integrity guarantees of enclaves. SIGY uses the untrusted OS to deliver fake hardware events and injects f

H. Reinstädtler, C. Schulz, B. Uçar

Given an undirected graph G, the edge orientation problem asks for assigning a direction to each edge to convert G into a directed graph. The aim is to minimize the maximum out degree of a vertex in the resulting directed graph. This problem, which is solvable in polynomial time, arises in many applications. An ongoing challenge in edge orientation algorithm

Fabrice Mayran de Chamisso, Loïc Cotten, Valentine Dhers, Thomas Lompech

With the advent of multispectral imagery and AI, there have been numerous works on automatic plant segmentation for purposes such as counting, picking, health monitoring, localized pesticide delivery, etc. In this paper, we tackle the related problem of automatic and selective plant-clearing in a sustainable forestry context, where an autonomous machine has

Theodoros Theodoulidis, Anastassios Skarlatos

The transient response from a transmitter-receiver coil system inside a multi-layer cylindrical conductive configuration is obtained. The particular set-up applies to well logging as well as to eddy current tube testing. In this work, a number of improvements are presented to existing models for an efficient calculation of the induced voltage. These include:

Fabien Caubet, Joyce Ghantous, Charles Pierre

In this work is considered a spectral problem, involving a second order term on the domain boundary: the Laplace-Beltrami operator. A variational formulation is presented, leading to a finite element discretization. For the Laplace-Beltrami operator to make sense on the boundary, the domain is smooth: consequently the computational domain (classically a poly

Yingxuan Li, Ryota Hinami, Kiyoharu Aizawa, Yusuke Matsui

Recognizing characters and predicting speakers of dialogue are critical for comic processing tasks, such as voice generation or translation. However, because characters vary by comic title, supervised learning approaches like training character classifiers which require specific annotations for each comic title are infeasible. This motivates us to propose a

Junyu Gao, Da Zhang, Qiyu Wang, Zhiyuan Zhao

Crowd localization targets on predicting each instance precise location within an image. Current advanced methods propose the pixel-wise binary classification to tackle the congested prediction, in which the pixel-level thresholds binarize the prediction confidence of being the pedestrian head. Since the crowd scenes suffer from extremely varying contents, c

Haonan Jia, Meng Wang, Biyi Li, Yirui Liu

Last Level Cache (LLC) is a precious and critical resource that impacts the performance of applications running on top of CPUs. In this paper, we reveal the significant impact of LLC on the performance of the 5G user plane function (UPF) when running a cloudified 5G core on general-purposed servers. With extensive measurements showing that the throughput can

David Campos, Bin Yang, Tung Kieu, Miao Zhang

We are witnessing an increasing availability of streaming data that may contain valuable information on the underlying processes. It is thus attractive to be able to deploy machine learning models on edge devices near sensors such that decisions can be made instantaneously, rather than first having to transmit incoming data to servers. To enable deployment o

Feihu Liu, Guoce Xin, Chen Zhang

Let $A$ be a nonempty set of positive integers. The restricted partition function $p_A(n)$ denotes the number of partitions of $n$ with parts in $A$. When the elements in $A$ are pairwise relatively prime positive integers, Ehrhart, Sert\"oz-\"Ozl\"uk, and Brown-Chou-Shiue derived three reduction formulas for $p_A(n)$ for $A$ with three parameters. We extend

Arup Biswas, Stephy Jose, Arnab Pal, Kabir Ramola

We investigate the problem of effusion of particles initially confined in a finite one-dimensional box of size $L$. We study both passive as well active scenarios, involving non-interacting diffusive particles and run-and-tumble particles, respectively. We derive analytic results for the fluctuations in the number of particles exiting the boundaries of the f

Laura Andrianopoli, Riccardo D'Auria

In this contribution, we present the geometric approach to supergravity. In the first part, we discuss in some detail the peculiarities of the approach and apply the formalism to the case of pure supergravity in four space-time dimensions. In the second part, we extend the discussion to theories in higher dimensions, which include antisymmetric tensors of de

Daichi Hiraki, Siddhartha Chib, Yasuhiro Omori

In this paper we consider the simulation-based Bayesian analysis of stochastic volatility in mean (SVM) models. Extending the highly efficient Markov chain Monte Carlo mixture sampler for the SV model proposed in Kim et al. (1998) and Omori et al. (2007), we develop an accurate approximation of the non-central chi-squared distribution as a mixture of thirty

Xiaoxia Cheng, Zeqi Tan, Wei Xue, Weiming Lu

Improving the reasoning capabilities of large language models (LLMs) has attracted considerable interest. Recent approaches primarily focus on improving the reasoning process to yield a more precise final answer. However, in scenarios involving contextually aware reasoning, these methods neglect the importance of first identifying logical relationships from

Jie He, Yuanqing Ma, Youde Wang

In this paper, we employ the Nash-Moser iteration technique to study local and global properties of positive solutions to the equation $$\Delta_pv+bv^q+cv^r =0$$ on complete Riemannian manifolds with Ricci curvature bounded from below, where $b, c\in\mathbb R$, $p>1$, and $q\leq r$ are some real constants. Assuming certain conditions on $b,\, c,\, p,\, q$ an

Chengrui Wang, Pengfei Liu, Min Zhou, Ming Zeng

Although diffusion models can generate high-quality human images, their applications are limited by the instability in generating hands with correct structures. In this paper, we introduce RHanDS, a conditional diffusion-based framework designed to refine malformed hands by utilizing decoupled structure and style guidance. The hand mesh reconstructed from th

Qiwen Deng, Yangcen Liu, Wen Li, Guoqing Wang

Given a source portrait, the automatic human body reshaping task aims at editing it to an aesthetic body shape. As the technology has been widely used in media, several methods have been proposed mainly focusing on generating optical flow to warp the body shape. However, those previous works only consider the local transformation of different body parts (arm