October 2023 arXiv papers — page 160

Ali Nassiri

To avoid the impact of inherent natural imperfections on experimental outcomes during testing, a recently designed genuine triaxial apparatus has enabled the replication of conditions where the three principal stresses exhibit varying magnitudes. This setup facilitates the examination of fracture behaviors in materials similar to the 4-series under genuine t

Yangze Li, Fan Yu, Yuhao Liang, Pengcheng Guo

Joint modeling of multi-speaker ASR and speaker diarization has recently shown promising results in speaker-attributed automatic speech recognition (SA-ASR).Although being able to obtain state-of-the-art (SOTA) performance, most of the studies are based on an autoregressive (AR) decoder which generates tokens one-by-one and results in a large real-time facto

Sudhir K. Saini, Evangelos Marakis, Kayleigh Start, Gerwin Osnabrugge

We investigate the effect of a mirror-symmetry plane in multiple-scattering media under plane-wave illumination along the symmetry plane. Designed and fabricated samples' optical transport properties are compared quantitatively with three-dimensional modeling. Strong polarization-dependent deviations of the bulk speckle-averaged intensity distribution at the

Jiajun Song, Yiqiao Zhong

Transformers are widely used to extract semantic meanings from input tokens, yet they usually operate as black-box models. In this paper, we present a simple yet informative decomposition of hidden states (or embeddings) of trained transformers into interpretable components. For any layer, embedding vectors of input sequence samples are represented by a tens

Siavash Mirzaei-Ghormish, S. Griffith, Daniel Smalley, Ryan M. Camacho

In this paper, we introduce a horizontally-oriented, photophoretic `boat' trap that is capable of capturing and self-loading large (radius ${\ge}1$ $\mu$m) solid gold particles in air for more than one hour. Once trapped, particles are held stably, even as the trap is modified to scan axially or expanded to a larger size to increase the capture cross-section

Isaac Reid, Krzysztof Choromanski, Eli Berger, Adrian Weller

We propose a novel random walk-based algorithm for unbiased estimation of arbitrary functions of a weighted adjacency matrix, coined universal graph random features (u-GRFs). This includes many of the most popular examples of kernels defined on the nodes of a graph. Our algorithm enjoys subquadratic time complexity with respect to the number of nodes, overco

Emilian-Claudiu Mănescu, Răzvan-Alexandru Smădu, Andrei-Marius Avram, Dumitru-Clementin Cercel

Lip reading or visual speech recognition has gained significant attention in recent years, particularly because of hardware development and innovations in computer vision. While considerable progress has been obtained, most models have only been tested on a few large-scale datasets. This work addresses this shortcoming by analyzing several architectures and

Siva Satya Sri Ganesh Seeram, Shuai Zhang, Mustafa Ozger, Andre Grabs

Aerial base stations (ABSs) have emerged as a promising solution to meet the high traffic demands of future wireless networks. Nevertheless, their practical implementation requires efficient utilization of limited payload and onboard energy. Understanding the power consumption streams, such as mechanical and communication power, and their relationship to the

Hongbo Zhu, Angelo Cangelosi, Procheta Sen, Anirbit Mukherjee

In this work, we instantiate a novel perturbation-based multi-class explanation framework, LIPEx (Locally Interpretable Probabilistic Explanation). We demonstrate that LIPEx not only locally replicates the probability distributions output by the widely used complex classification models but also provides insight into how every feature deemed to be important

S. M. F. Sani, Seyed Abbas Hosseini, Hamid R. Rabiee

Often, recommendation systems employ continuous training, leading to a self-feedback loop bias in which the system becomes biased toward its previous recommendations. Recent studies have attempted to mitigate this bias by collecting small amounts of unbiased data. While these studies have successfully developed less biased models, they ignore the crucial fac

Isaac Reid, Eli Berger, Krzysztof Choromanski, Adrian Weller

We present a novel quasi-Monte Carlo mechanism to improve graph-based sampling, coined repelling random walks. By inducing correlations between the trajectories of an interacting ensemble such that their marginal transition probabilities are unmodified, we are able to explore the graph more efficiently, improving the concentration of statistical estimators w

B. Cooper Boniece, Lajos Horváth, Lorenzo Trapani

We propose a novel family of test statistics to detect the presence of changepoints in a sequence of dependent, possibly multivariate, functional-valued observations. Our approach allows to test for a very general class of changepoints, including the "classical" case of changes in the mean, and even changes in the whole distribution. Our statistics are based

Max Taylor-Davies, Christopher G. Lucas

To successfully navigate its environment, an agent must construct and maintain representations of the other agents that it encounters. Such representations are useful for many tasks, but they are not without cost. As a result, agents must make decisions regarding how much information they choose to store about the agents in their environment. Using selective

Harshit Kumar Choudhary, Swati Kumari, I. Vinod Reddy

A star coloring of a graph $G$ is a proper vertex coloring such that no path on four vertices is bicolored. The smallest integer $k$ for which $G$ admits a star coloring with $k$ colors is called the star chromatic number of $G$, denoted as $\chi_s(G)$. In this paper, we study the star coloring of tensor product of two graphs and obtain the following results

I. Deme, L. G. S. Duarte, L. A. C. P. da Mota

Here we present an efficient method for finding and using a nonlocal symmetry admitted by a rational second order ordinary differential equation (rational 2ODE) in order to find a Liouvillian first integral (belonging to a vast class of Liouvillian functions). In a first stage, we construct an algorithm (improving the methodde veloped in [1]) that computes a

Jie Xiao, Fan Xu, Fang Yang

We introduce a notion of motivic cluster characters via virtual Poincar\'{e} polynomials, and prove a motivic version of multiplication formulas obtained by Chen-Xiao-Xu for weighted quantum cluster characters associated to a 2-Calabi-Yau triangulated category $\mathcal{C}$ with a cluster tilting object. Furthermore, a refined form of this formula is also gi

Gianluca Brilli, Paolo Burgio

In modern heterogeneous architectures, the access to data that the application needs is a key factor, in order to make the compute task efficient, in terms of power dissipation and execution time. The new generation SoCs are equipped with large LLCs, in order to make data access as efficient as possible. However, these systems introduce a new level of comple

Yu-Hui Chen, Xiangdong Zhang

Time crystals are many-body states that spontaneously break translation symmetry in time the way that ordinary crystals do in space. While experimental observations have confirmed the existence of discrete or continuous time crystals, these realizations have relied on the utilization of periodic forces or effective modulation through cavity feedback. The ori

Hun Jang, Valentyn Petrichenko, Joonbum Bae, Kevin Haninger

Soft robotic fingers can safely grasp fragile or variable form objects, but their force capacity is limited, especially with less contact area: precision grasps and when objects are smaller or not spherical. Current research is improving force capacity through mechanical design by increasing contact area or stiffness, typically without models which explain s

Chenhao Lin, Fangbin Yi, Hang Wang, Qian Li

Face forgery techniques have emerged as a forefront concern, and numerous detection approaches have been proposed to address this challenge. However, existing methods predominantly concentrate on single-face manipulation detection, leaving the more intricate and realistic realm of multi-face forgeries relatively unexplored. This paper proposes a novel framew

Leonardo Pires

In this paper, we study the Poincar\'e compactification of the limiting planar semiflow of a coupled PDE-ODE system composed by a reaction-diffusion equation with large diffusion coupled with an ODE by a boundary condition in a heating transition region. The nonlinear sources are dissipative polynomials. We guarantee conditions to apply the Invariant Manifol

Chen Zhu-Tian, Yijia Su, Yifang Wang, Qianwen Wang

Recent advances in mobile augmented reality (AR) techniques have shed new light on personal visualization for their advantages of fitting visualization within personal routines, situating visualization in a real-world context, and arousing users' interests. However, enabling non-experts to create data visualization in mobile AR environments is challenging gi

Damianos Tranos, Alexandre Proutiere

We consider the problem of adaptive Model Predictive Control (MPC) for uncertain linear-systems with additive disturbances and with state and input constraints. We present STT-MPC (Self-Tuning Tube-based Model Predictive Control), an online algorithm that combines the certainty-equivalence principle and polytopic tubes. Specifically, at any given step, STT-M

Ekaterina Smirnova, Mehdi Nourazar, Pavel A. Korzhavyi

A combinatorial approach is employed to investigate the atomic and electronic structures of a metal vacancy in titanium carbide. It turns out that the usual relaxed geometry of the vacancy is just a metastable state representing a local energy minimum. Using ab initio calculations and by systematically searching through the configurational space of a Ti mono

Kristian Stølevik Olsen

The non-equilibrium steady states emerging from stochastic resetting to a distribution is studied. We show that for a range of processes, the steady-state moments can be expressed as a linear combination of the moments of the distribution of resetting positions. The coefficients of this series are universal in the sense that they do not depend on the resetti

Kieran Calvert, Kyo Nishiyama, Pavle Pandžić

We study various kinds of Grassmannians or Lagrangian Grassmannians over $\mathbb{R}$, $\mathbb{C}$ or $\mathbb{H}$, all of which can be expressed as $\mathbb{G}/\mathbb{P}$ where $\mathbb{G}$ is a classical group and $\mathbb{P}$ is a parabolic subgroup of $\mathbb{G}$ with abelian unipotent radical. The same Grassmannians can also be realized as (classical

Mateo Casariego

The thesis establishes a link between theoretical foundations and practical applications in the emerging field of propagating quantum microwaves. Although the concrete focus of the main results lies in specific quantum communication and sensing protocols, the thesis also gives a self-contained introduction to quantum parameter estimation and Gaussian quantum

Elton F. de S. Soares, Carlos Alberto V. Campos

Image-based depth estimation has gained significant attention in recent research on computer vision for autonomous vehicles in intelligent transportation systems. This focus stems from its cost-effectiveness and wide range of potential applications. Unlike binocular depth estimation methods that require two fixed cameras, monocular depth estimation methods o

Luoming Zhang, Wen Fei, Weijia Wu, Yefei He

Large Language Models (LLMs) pose significant hardware challenges related to memory requirements and computational ability. There are two mainstream quantization schemes for LLMs: coarse-grained ($\textit{e.g.,}$ channel-wise) quantization and fine-grained ($\textit{e.g.,}$ group-wise) quantization. Fine-grained quantization has smaller quantization loss, co

Xuhui Jiang, Chengjin Xu, Yinghan Shen, Xun Sun

Knowledge graphs (KGs) are structured representations of diversified knowledge. They are widely used in various intelligent applications. In this article, we provide a comprehensive survey on the evolution of various types of knowledge graphs (i.e., static KGs, dynamic KGs, temporal KGs, and event KGs) and techniques for knowledge extraction and reasoning. F

Ayan Banerjee, Arka Bandyopadhyay, Ronika Sarkar, Awadhesh Narayan

In recent years, non-Hermitian phases in classical and quantum systems have garnered significant attention. In particular, their intriguing band geometry offers a platform for exploring unique topological states and unconventional quantum dynamics. However, their topological characterization becomes particularly interesting and challenging in complex multiba

Juliann Zhou

Sarcasm, as defined by Merriam-Webster, is the use of words by someone who means the opposite of what he is trying to say. In the field of sentimental analysis of Natural Language Processing, the ability to correctly identify sarcasm is necessary for understanding people's true opinions. Because the use of sarcasm is often context-based, previous research ha

Vincent Fromion, Philippe Robert, Jana Zaherddine

Motivated by a general principle governing regulation mechanisms in biological cells, we investigate a general interaction scheme between different populations of particles and specific particles, referred to as agents. Assuming that each particle follows a random path in the medium, when a particle and an agent meet, they may bind and form a pair which has

Mozes Jacobs, Bingni W. Brunton, Steven L. Brunton, J. Nathan Kutz

The discovery of governing differential equations from data is an open frontier in machine learning. The sparse identification of nonlinear dynamics (SINDy) \citep{brunton_discovering_2016} framework enables data-driven discovery of interpretable models in the form of sparse, deterministic governing laws. Recent works have sought to adapt this approach to th

Yichen Zhang, Yiming Bian, Zhengyu Li, Song Yu

Quantum key distribution provides secure keys with information-theoretic security ensured by the principle of quantum mechanics. The continuous-variable version of quantum key distribution using coherent states offers the advantages of its compatibility with telecom industry, e.g., using commercial laser and homodyne detector, is now going through a booming

Ferdinand Kossmann, Ziniu Wu, Eugenie Lai, Nesime Tatbul

Social media, self-driving cars, and traffic cameras produce video streams at large scales and cheap cost. However, storing and querying video at such scales is prohibitively expensive. We propose to treat large-scale video analytics as a data warehousing problem: Video is a format that is easy to produce but needs to be transformed into an application-speci

Denis Mbey Akola, Gianni Franchi

This paper presents a new approach for training two-stage object detection ensemble models, more specifically, Faster R-CNN models to estimate uncertainty. We propose training one Region Proposal Network(RPN) and multiple Fast R-CNN prediction heads is all you need to build a robust deep ensemble network for estimating uncertainty in object detection. We pre

Yikai Wang, Mengdi Xu, Guanya Shi, Ding Zhao

Recent advancements in optimal control and reinforcement learning have enabled quadrupedal robots to perform various agile locomotion tasks over diverse terrains. During these agile motions, ensuring the stability and resiliency of the robot is a primary concern to prevent catastrophic falls and mitigate potential damages. Previous methods primarily focus on

Shengyu Yan, Xiaofeng Wang, Xing Gao, Jujia Zhang

We present the discovery and studies of the helium-rich, fast-evolving supernova (SN) 2021agco at a distance of $\sim$ 40 Mpc. Its early-time flux is found to rise from half peak to the peak of $-16.06\pm0.42$ mag in the $r$ band within $2.4^{+1.5}_{-1.0}$ days, and the post-peak light curves also decline at a much faster pace relative to normal stripped-env

Chen Zhu-Tian, Wai Tong, Qianwen Wang, Benjamin Bach

This paper presents an authoring environment for augmenting static visualizations with virtual content in augmented reality. Augmenting static visualizations can leverage the best of both physical and digital worlds, but its creation currently involves different tools and devices, without any means to explicitly design and debug both static and virtual conte

Denis Mbey Akola

This paper presents a study of two tracking algorithms (SORT~\cite{7533003} and Tracktor++~\cite{2019}) that were ranked first positions on the MOT Challenge leaderboard (The MOTChallenge web page: https://motchallenge.net ). The purpose of this study is to discover the techniques used and to provide useful insights about these algorithms in the tracking pip

Won Ik Cho, Eunjung Cho, Kyunghyun Cho

Academic writing process has benefited from various technological developments over the years including search engines, automatic translators, and editing tools that review grammar and spelling mistakes. They have enabled human writers to become more efficient in writing academic papers, for example by helping with finding relevant literature more effectivel

Y. Fujishiro, C. Terakura, A. Miyake, N. Kanazawa

A plethora of correlated and exotic metallic states have been identified on the border of itinerant magnetism, where the long-range spin texture is melted by tuning the magnetic transition temperature (T$_C$) towards zero, referred to as the quantum phase transition (QPT). So far, the study of QPT in itinerant magnets has mainly focused on low-T$_C$ material

Filippo Rozzi, Loris Roveda, Kevin Haninger

Planning over discontinuous dynamics is needed for robotics tasks like contact-rich manipulation, which presents challenges in the numerical stability and speed of planning methods when either neural network or analytical models are used. On the one hand, sampling-based planners require higher sample complexity in high-dimensional problems and cannot describ

Shuyang Liu, Zixuan Chen, Ge Shi, Ji Wang

Efforts to decode deep neural networks (DNNs) often involve mapping their predictions back to the input features. Among these methods, Integrated Gradients (IG) has emerged as a significant technique. The selection of appropriate baselines in IG is crucial for crafting meaningful and unbiased explanations of model predictions in diverse settings. The standar

Haihua Deng, Hexiang Huang, Guobiao Weng, Qing Xiang

Let $\Gamma$ be a simple connected graph on $n$ vertices, and let $C$ be a code of length $n$ whose coordinates are indexed by the vertices of $\Gamma$. We say that $C$ is a \textit{storage code} on $\Gamma$ if for any codeword $c \in C$, one can recover the information on each coordinate of $c$ by accessing its neighbors in $\Gamma$. The main problem here i

Sravani Yanamandra, P V Srinidhi, Samyadeb Bhattacharya, Indranil Chakrabarty

Absolute separable (AS) quantum states are those states from which it is impossible to create entanglement, even under global unitary operations. It is known from the resource theory of non-absolute separability that the set of absolute separable states forms a convex and compact set, and global unitaries are free operations. We show that the action of a qua

Danrui Li

Pedestrian studies in retail areas are critical for comfort and convenience in transportation facility designs. But existing literature lacks detailed empirical observations that focus on pedestrian speed variations and their mechanisms in front of stores. This paper bridges this gap by analyzing 1193 pedestrian trajectories in front of a convenience store l

Lehan Wang, Jingzhou Sun, Yuxuan Sun, Sheng Zhou

We consider a status information updating system where a fusion center collects the status information from a large number of sources and each of them has its own age of information (AoI) constraints. A novel grouping-based scheduler is proposed to solve this complex large-scale problem by dividing the sources into different scheduling groups. The problem is

Min Yi, Wei Tang, Yiqi Zhu, Chenguang Liang

Additive manufacturing (AM) technology is undergoing rapid development and emerging as an advanced technique that can fabricate complex near-net shaped and light-weight metallic parts with acceptable strength and fatigue performance. A number of studies have indicated that the strength or other mechanical properties of AM metals are comparable or even superi

Giacomo Aldegheri, Alina Rogalska, Ahmed Youssef, Eugenia Iofinova

In this work, we propose a method to 'hack' generative models, pushing their outputs away from the original training distribution towards a new objective. We inject a small-scale trainable module between the intermediate layers of the model and train it for a low number of iterations, keeping the rest of the network frozen. The resulting output images displa

Liangchen Luo, Zi Lin, Yinxiao Liu, Lei Shu

Critical thinking is essential for rational decision-making and problem-solving. This skill hinges on the ability to provide precise and reasoned critiques and is a hallmark of human intelligence. In the era of large language models (LLMs), this study explores the ability of LLMs to deliver accurate critiques across various tasks. We are interested in this t

Taishi Kurahashi, Albert Visser

We study the effective versions of several notions related to incompleteness, undecidability and inseparability along the lines of Pour-El's insights. Firstly, we strengthen Pour-El's theorem on the equivalence between effective essential incompleteness and effective inseparability. Secondly, we compare the notions obtained by restricting that of effective e

Jonathan F. Schonfeld

The Mott problem is a simpler version of the quantum measurement problem that asks: Is there a microscopic physical mechanism - based (explicitly or implicitly) only on Schroedinger's equation - that explains why a single alpha particle emitted in a spherically symmetric s-wave nuclear decay produces a manifestly non-spherically-symmetric single track in a c

Lehan Wang, Jingzhou Sun, Yuxuan Sun, Sheng Zhou

We consider collaborative perception (CP) systems where a fusion center monitors various regions by multiple sources. The center has different age of information (AoI) constraints for different regions. Multi-view sensing data for a region generated by sources can be fused by the center for a reliable representation of the region. To ensure accurate percepti

Hunter Chase, James Freitag, Lev Reyzin

In this paper we give several applications of Littlestone dimension. The first is to the model of \cite{angluin2017power}, where we extend their results for learning by equivalence queries with random counterexamples. Second, we extend that model to infinite concept classes with an additional source of randomness. Third, we give improved results on the relat

Franco Caspe, Andrew McPherson, Mark Sandler

Tone Transfer is a novel deep-learning technique for interfacing a sound source with a synthesizer, transforming the timbre of audio excerpts while keeping their musical form content. Due to its good audio quality results and continuous controllability, it has been recently applied in several audio processing tools. Nevertheless, it still presents several sh

Zhong Shen, Shuai Dong, Xiaoyan Yao

Multi-functional manipulation of magnetic topological textures such as skyrmions and bimerons in energy-efficient ways is of great importance for spintronic applications, but still being a big challenge. Here, by first-principles calculations and atomistic simulations, the creation and annihilation of skyrmions/bimerons, as key operations for the reading and

Valentin Touzeau, Jan Reineke

While instruction cache analysis is essentially a solved problem, data cache analysis is more challenging. In contrast to instruction fetches, the data accesses generated by a memory instruction may vary with the program's inputs and across dynamic occurrences of the same instruction in loops. We observe that the plain control-flow graph (CFG) abstraction em

Zhenkuan Wang

Semantic segmentation is a critical tool in computer vision, applied in various domains like autonomous driving and medical imaging. This study focuses on aircraft contrail detection in global satellite images to improve contrail models and mitigate their impact on climate change.An innovative data preprocessing technique for NOAA GOES-16 satellite images is

Manting Peng, Zheng Sun, Kailiang Wu

Controlling spurious oscillations is crucial for designing reliable numerical schemes for hyperbolic conservation laws. This paper proposes a novel, robust, and efficient oscillation-eliminating discontinuous Galerkin (OEDG) method on general meshes, motivated by the damping technique in [Lu, Liu, and Shu, SIAM J. Numer. Anal., 59:1299-1324, 2021]. The OEDG

Maxim Dvornikov

We study the formation of a superfluid condensate of neutrinos inside a neutrino cluster. The attractive interaction between neutrinos is mediated by a scalar boson which is lighter than a neutrino. We consider the appearance of neutrino bound states consisting of particles with oppositely directed spins. The gap equation for such a system is derived. Based

Shintaro Ueki, Fujio Toriumi, Toshiharu Sugawara

Numerous forms of consumer-generated media (CGM), such as social networking services (SNS), are widely used. Their success relies on users' voluntary participation, often driven by psychological rewards like recognition and connection from reactions by other users. Furthermore, a few CGM platforms offer monetary rewards to users, serving as incentives for sh

Zhenhua Dong, Jieming Zhu, Weiwen Liu, Ruiming Tang

Huawei's vision and mission is to build a fully connected intelligent world. Since 2013, Huawei Noah's Ark Lab has helped many products build recommender systems and search engines for getting the right information to the right users. Every day, our recommender systems serve hundreds of millions of mobile phone users and recommend different kinds of content

Minmin Wang

This study compares electric field and temperature distributions between non-invasive and invasive tumor treatment fields (TTFields). We employ four-layer spherical head models, representing the scalp, skull, cerebrospinal fluid, and brain, for simulation analysis. Non-invasive TTFields utilize scalp transducers, while invasive methods involve electrode impl

Ayush Sharma, Yash Mehan, Pradyumna Dasu, Sourav Garg

In this paper we present a novel framework for unsupervised topological clustering resulting in improved loop. In this paper we present a novel framework for unsupervised topological clustering resulting in improved loop detection and closure for SLAM. A navigating mobile robot clusters its traversal into visually similar topologies where each cluster (topol

Yongrui Chen, Shenyu Zhang, Guilin Qi, Xinnan Guo

Continual table semantic parsing aims to train a parser on a sequence of tasks, where each task requires the parser to translate natural language into SQL based on task-specific tables but only offers limited training examples. Conventional methods tend to suffer from overfitting with limited supervision, as well as catastrophic forgetting due to parameter u

Ajinkya Khoche, Laura Pereira Sánchez, Nazre Batool, Sina Sharif Mansouri

3D object detection at long range is crucial for ensuring the safety and efficiency of self driving vehicles, allowing them to accurately perceive and react to objects, obstacles, and potential hazards from a distance. But most current state of the art LiDAR based methods are range limited due to sparsity at long range, which generates a form of domain gap b

Shih-Cheng Huang, Pin-Zu Li, Yu-Chi Hsu, Kuang-Ming Chen

Recently, the development of open-source large language models (LLMs) has advanced rapidly. Nevertheless, due to data constraints, the capabilities of most open-source LLMs are primarily focused on English. To address this issue, we introduce the concept of $\textit{chat vector}$ to equip pre-trained language models with instruction following and human value

V. V. Prosentsov

In this work we study the wave scattering by small dispersionless particles with pulsating refractive index. The scattered fields and their resonance frequencies are calculated by using scalar approximation and exponentially time-dependent permittivity. In addition, the scattering by single sphere with the pulsating permittivity is calculated numerically for

Silvan Vollmer, David Schoch, Ulrik Brandes

We compare conversion rates of association football (soccer) penalties during regulation or extra time with those during shootouts. Our data consists of roughly 50,000 penalties from the eleven~most recent seasons in European men's football competitions. About one third of the penalties are from more than 1,500 penalty shootouts. We find that shootout conver

Jiayu Chen, Zelai Xu, Yunfei Li, Chao Yu

Learning Nash equilibrium (NE) in complex zero-sum games with multi-agent reinforcement learning (MARL) can be extremely computationally expensive. Curriculum learning is an effective way to accelerate learning, but an under-explored dimension for generating a curriculum is the difficulty-to-learn of the subgames -- games induced by starting from a specific

Bolun Dai, Prashanth Krishnamurthy, Farshad Khorrami

Using control barrier functions (CBFs) as safety filters provides a computationally inexpensive yet effective method for constructing controllers in safety-critical applications. However, using CBFs requires the construction of a valid CBF, which is well known to be a challenging task, and accurate system dynamics, which are often unavailable. This paper pre

Zahra Rezaei Khavas, Edwin Meriaux, Amin Majdi, Paul Robinette

Developing videos for trust testing is very time-consuming, expensive and potentially dangerous. For trust tests, it requires a person to be flying the drone while another might be filming. The drones can be very expensive and if something goes wrong the costs might be very high. In previous work, we have looked at how collisions and basic communication loss

Neng Wang, Hongyang Yang, Christina Dan Wang

In the swiftly expanding domain of Natural Language Processing (NLP), the potential of GPT-based models for the financial sector is increasingly evident. However, the integration of these models with financial datasets presents challenges, notably in determining their adeptness and relevance. This paper introduces a distinctive approach anchored in the Instr

Souren Adhikary, Sudipta Dutta

Two-dimensional (2D) noncentrosymmetric systems offer potential opportunities for exploiting the valley degrees of freedom for advanced information processing, owing to non-zero Berry curvature. However, such valley polarization in 2D materials is crucially governed by the intervalley excitonic scattering in momentum space due to reduced electronic degrees o

Theodor Nguyen, Guangzhi Sun, Xianrui Zheng, Chao Zhang

We propose DiffSpEx, a generative target speaker extraction method based on score-based generative modelling through stochastic differential equations. DiffSpEx deploys a continuous-time stochastic diffusion process in the complex short-time Fourier transform domain, starting from the target speaker source and converging to a Gaussian distribution centred on

Shinya Ae

Using the Bethe ansatz method and the TBA equations for the higher spin integrable XXZ chain, the regular zero frequency contribution to the spin current correlation (spin dc conductivity) is analyzed for the spin-1/2 XXZ chain with an anisotropy $0 \le \Delta <1$. In the high temperature limit, we write down the dressed scattering kernels by one quasi-parti

Jie Hou, Zhiying Ma, Shihui Ying, Ying Li

Neural network solvers represent an innovative and promising approach for tackling time-fractional partial differential equations by utilizing deep learning techniques. L1 interpolation approximation serves as the standard method for addressing time-fractional derivatives within neural network solvers. However, we have discovered that neural network solvers

Zhiying Ma, Jie Hou, Wenhao Zhu, Yaxin Peng

In this paper, an innovative Physical Model-driven Neural Network (PMNN) method is proposed to solve time-fractional differential equations. It establishes a temporal iteration scheme based on physical model-driven neural networks which effectively combines deep neural networks (DNNs) with interpolation approximation of fractional derivatives. Specifically,

Wei Zhang, Tiecheng Sun, Sen Wang, Qing Cheng

In this letter, we present a neural field-based real-time monocular mapping framework for accurate and dense Simultaneous Localization and Mapping (SLAM). Recent neural mapping frameworks show promising results, but rely on RGB-D or pose inputs, or cannot run in real-time. To address these limitations, our approach integrates dense-SLAM with neural implicit

Benjamin Avanzi, Xingyun Tan, Greg Taylor, Bernard Wong

Understanding the emergence of data breaches is crucial for cyber insurance. However, analyses of data breach frequency trends in the current literature lead to contradictory conclusions. We put forward that those discrepancies may be (at least partially) due to inconsistent data collection standards, as well as reporting patterns, over time and space. We se

Rajkamal Nailwal

In this paper, we present a complete solution to the Cauchy dual subnormality problem for torally expansive toral $3$-isometric weighted $2$-shifts. This solution is obtained by solving a couple of Hausdorff moment problems arising from $2$-variable polynomials of lower bi-degree.

Haruki Seto, Xueqiao Xu, Benjamin D. Dudson, Masatoshi Yagi

We report a two-stage crash process in edge localized mode (ELM) driven by resistive drift-ballooning modes (RDBMs) numerically simulated in a full annular torus domain. In the early nonlinear phase, the first crash is triggered by linearly unstable RDBMs and m/n = 2/1 magnetic islands are nonlinearly excited via nonlinear couplings of RDBMs. Simultaneously,

Abbas Mousavi, Andrian Uihlein, Lukas Pflug, Eddie Wadbro

This paper focuses on the topology optimization of a broadband acoustic transition section that connects two cylindrical waveguides with different radii. The primary objective is to design a transition section such that it maximizes the transmission of a planar acoustic wave while ensuring the planarity of the transmitted wave. Helmholtz equation is used to

Yuchen Yang, Houqiang Li, Yanfeng Wang, Yu Wang

In recent years, large-scale language models (LLMs) have gained attention for their impressive text generation capabilities. However, these models often face the challenge of "hallucination," which undermines their reliability. In this study, we introduce an uncertainty-aware in-context learning framework to empower the model to enhance or reject its output

Alessandro Saviolo, Pratyaksh Rao, Vivek Radhakrishnan, Jiuhong Xiao

Visual control enables quadrotors to adaptively navigate using real-time sensory data, bridging perception with action. Yet, challenges persist, including generalization across scenarios, maintaining reliability, and ensuring real-time responsiveness. This paper introduces a perception framework grounded in foundation models for universal object detection an

Zhenglin Huang, Xiaoan Bao, Na Zhang, Qingqi Zhang

Data augmentation has been proven effective for training high-accuracy convolutional neural network classifiers by preventing overfitting. However, building deep neural networks in real-world scenarios requires not only high accuracy on clean data but also robustness when data distributions shift. While prior methods have proposed that there is a trade-off b

Chenchu Xu, Meng Li, Xue Wu

The accurate segmentation of Coronary Computed Tomography Angiography (CCTA) images holds substantial clinical value for the early detection and treatment of Coronary Heart Disease (CHD). The Transformer, utilizing a self-attention mechanism, has demonstrated commendable performance in the realm of medical image processing. However, challenges persist in cor

Yang Li, Shixin Zhu, Edgar Martínez-Moro

The class of $\ell$-maximum distance separable ($\ell$-MDS) codes {is a} generalization of maximum distance separable (MDS) codes {that} has attracted a lot of attention due to its applications in several areas such as secret sharing schemes, index coding problems, informed source coding problems, and combinatorial $t$-designs. In this paper, for $\ell=1$, w

Hao Wang, Yuan-Chuan Zou, Qing-Wen Wu, Yu Liu

Many articles have partially studied the configuration of eccentric orbital binary black hole (BBH) mergers. However, there is a scarcity of systematic and comprehensive research on the effect of eccentricity on BBH dynamics. Thanks to the rich and numerous numerical relativistic simulations of eccentric orbital BBH mergers from RIT catalog, this paper aims

Dongha Lee

We renormalize the Chern-Simons invariant for convex-cocompact hyperbolic 3-manifolds by finding the asymptotics along an equidistance foliation. We prove that the metric Chern-Simons invariant has an exponentially divergent term given by the integral of the torsion 2-form with respect to a Weitzenb\"ock connection. This produces the asymptotics of hyperboli

Chigak Itoi, Hisamitsu Mukaida, Hal Tasaki

We establish relations between different characterizations of order in spin glass models. We first prove that the broadening of the replica overlap distribution indicated by a nonzero standard deviation of the replica overlap $R^{1,2}$ implies the non-differentiability of the two-replica free energy with respect to the replica coupling parameter $\lambda$. I

Xianhua Zhang, Lu lin, Qihua Wang

This paper develops an updatable inverse probability weighting (UIPW) estimation for the generalized linear models with response missing at random in streaming data sets. A two-step online updating algorithm is provided for the proposed method. In the first step we construct an updatable estimator for the parameter in propensity function and hence obtain an

Filippo Ambrosio, Lewis Topley

The Slodowy slice is a flat Poisson deformation of its nilpotent part, and it was demonstrated by Lehn-Namikawa-Sorger that there is an interesting infinite family of nilpotent orbits in symplectic Lie algebras for which the slice is not the universal Poisson deformation of its nilpotent part. This family corresponds to slices to nilpotent orbits in symplect

Ressi Bonti Muhammad, Sergey Alyaev, Reidar Brumer Bratvold

Trajectory adjustment decisions throughout the drilling process, called geosteering, affect subsequent choices and information gathering, thus resulting in a coupled sequential decision problem. Previous works on applying decision optimization methods in geosteering rely on greedy optimization or approximate dynamic programming (ADP). Either decision optimiz

Yuhan Ma, Weiran Zhao, Xiaolin Zhang, Ze Gao

In order to make the non-heritage culture of Yicheng Flower Drum more relevant to the trend of the digital era and promote its dissemination and inheritance, the design and application of gesture recognition and virtual reality technologies guided by embodied cognition theory in the process of non-heritage culture dissemination is studied. At the same time,

Dan Coman, George Marinescu, Viêt-Anh Nguyên

Let $X$ be a compact normal complex space of dimension $n$ and $L$ be a holomorphic line bundle on $X$. Suppose that $\Sigma=(\Sigma_1,\ldots,\Sigma_\ell)$ is an $\ell$-tuple of distinct irreducible proper analytic subsets of $X$, $\tau=(\tau_1,\ldots,\tau_\ell)$ is an $\ell$-tuple of positive real numbers, and let $H^0_0(X,L^p)$ be the space of holomorphic

Zhishan Zhou, Zhi Lv, Shihao Zhou, Minqiang Zou

This report introduce our work on Egocentric 3D Hand Pose Estimation workshop. Using AssemblyHands, this challenge focuses on egocentric 3D hand pose estimation from a single-view image. In the competition, we adopt ViT based backbones and a simple regressor for 3D keypoints prediction, which provides strong model baselines. We noticed that Hand-objects occl

Zhiyong Wang, Jize Xie, Tong Yu, Shuai Li

In real-world online web systems, multiple users usually arrive sequentially into the system. For applications like click fraud and fake reviews, some users can maliciously perform corrupted (disrupted) behaviors to trick the system. Therefore, it is crucial to design efficient online learning algorithms to robustly learn from potentially corrupted user beha