March 2024 arXiv papers — page 96

Shinwoo An, Eunjin Oh

In this paper, we consider the Cycle Packing problem on unit disk graphs defined as follows. Given a unit disk graph G with n vertices and an integer k, the goal is to find a set of $k$ vertex-disjoint cycles of G if it exists. Our algorithm runs in time $2^{O(\sqrt k)}n^{O(1)}$. This improves the $2^{O(\sqrt k\log k)}n^{O(1)}$-time algorithm by Fomin et al.

Xiaoqiong Liu, Yunhe Feng, Shu Hu, Xiaohui Yuan

The robustness of unmanned aerial vehicle (UAV) tracking is crucial in many tasks like surveillance and robotics. Despite its importance, little attention is paid to the performance of UAV trackers under common corruptions due to lack of a dedicated platform. Addressing this, we propose UAV-C, a large-scale benchmark for assessing robustness of UAV trackers

Yuan Shi, Bin Xia, Xiaoyu Jin, Xing Wang

Image restoration is a critical task in low-level computer vision, aiming to restore high-quality images from degraded inputs. Various models, such as convolutional neural networks (CNNs), generative adversarial networks (GANs), transformers, and diffusion models (DMs), have been employed to address this problem with significant impact. However, CNNs have li

Aditi Verma

This paper describes the underlying philosophy, design, and implementation of a course on "Nuclear Technology, Policy, and Society" taught in the Department of Nuclear Engineering and Radiological Sciences at the University of Michigan. The course explores some of nuclear technology's most pressing challenges or its 'wicked problems'. Through this course stu

Jiaao He, Jidong Zhai

Cost of serving large language models (LLM) is high, but the expensive and scarce GPUs are poorly efficient when generating tokens sequentially, unless the batch of sequences is enlarged. However, the batch size is limited by some constantly reused intermediate results, namely KV-Cache. They occupy too much memory to fit more sequences into a GPU simultaneou

Xin Tang, Qian Chen, Rong Yu, Xiaohuan Li

To meet the demands for ubiquitous communication and temporary edge computing in 6G networks, aerial mobile edge computing (MEC) networks have been envisioned as a new paradigm. However, dynamic user requests pose challenges for task assignment strategies. Most of the existing research assumes that the strategy is deployed on ground-based stations or UAVs, w

Mayu Koyama, Toshifumi Shimizu

A proposed mechanism for solar chromospheric heating is that magnetohydrodynamic waves propagate upward along magnetic field lines and dissipate their energy in the chromosphere. In particular, compressible magneto-acoustic waves may contribute to the heating. Theoretically, the components below the cutoff frequency cannot propagate into the chromosphere; ho

Jurijs Nazarovs, Zhichun Huang, Xingjian Zhen, Sourav Pal

A deterministic temporal process can be determined by its trajectory, an element in the product space of (a) initial condition $z_0 \in \mathcal{Z}$ and (b) transition function $f: (\mathcal{Z}, \mathcal{T}) \to \mathcal{Z}$ often influenced by the control of the underlying dynamical system. Existing methods often model the transition function as a different

Yang Yang, Mingzhe Chen, Yufei Blankenship, Jemin Lee

Positioning has recently received considerable attention as a key enabler in emerging applications such as extended reality, unmanned aerial vehicles and smart environments. These applications require both data communication and high-precision positioning, and thus they are particularly well-suited to be offered in wireless networks (WNs). The purpose of thi

Cao Peng, Yiwei Li, Xu Chen, Shenghao Dai

Electronic states near surface regions can be distinct from bulk states, which are paramount in understanding various physical phenomena occurring at surfaces and in applications in semiconductors, energy, and catalysis. Here, we report an abnormal surface region band enhancement effect in angle-resolved photoemission spectroscopy on kagome superconductor Rb

Jeongsol Kim, Geon Yeong Park, Jong Chul Ye

Reverse sampling and score-distillation have emerged as main workhorses in recent years for image manipulation using latent diffusion models (LDMs). While reverse diffusion sampling often requires adjustments of LDM architecture or feature engineering, score distillation offers a simple yet powerful model-agnostic approach, but it is often prone to mode-coll

Daniel Gerlinghoff, Benjamin Chen Ming Choong, Rick Siow Mong Goh, Weng-Fai Wong

Recent advancements in neural network quantisation have yielded remarkable outcomes, with three-bit networks reaching state-of-the-art full-precision accuracy in complex tasks. These achievements present valuable opportunities for accelerating neural networks by computing in reduced precision. Implementing it on FPGAs can take advantage of bit-level reconfig

Anuja Tayal, Aman Tyagi

When interacting with Retrieval-Augmented Generation (RAG)-based conversational agents, the users must carefully craft their queries to be understood correctly. Yet, understanding the system's capabilities can be challenging for the users, leading to ambiguous questions that necessitate further clarification. This work aims to bridge the gap by developing a

Guilherme Christmann, Ying-Sheng Luo, Wei-Chao Chen

We propose the expert composer policy, a framework to reliably expand the skill repertoire of quadruped agents. The composer policy links pair of experts via transitions to a sampled target state, allowing experts to be composed sequentially. Each expert specializes in a single skill, such as a locomotion gait or a jumping motion. Instead of a hierarchical o

Tianyi Cui, Chenxingyu Zhao, Wei Zhang, Kaiyuan Zhang

Load balancers are pervasively used inside today's clouds to scalably distribute network requests across data center servers. Given the extensive use of load balancers and their associated operating costs, several efforts have focused on improving their efficiency by implementing Layer-4 load-balancing logic within the kernel or using hardware acceleration.

Danial Khorasanian, Jonathan Patrick, Antoine Sauré

Despite the rapid growth of the home care industry, research on the scheduling and routing of home care visits in the presence of uncertainty is still limited. This paper investigates a dynamic version of this problem in which the number of referrals and their required number of visits are uncertain. We develop a Markov decision process (MDP) model for the s

Jiahui Zhang, Yinhua Xia, Yan Xu

This paper introduces well-balanced path-conservative discontinuous Galerkin (DG) methods for two-layer shallow water equations, ensuring exactness for both still water and moving water equilibrium steady states. The approach involves approximating the equilibrium variables within the DG piecewise polynomial space, while expressing the DG scheme in the form

Wei Duan, Jie Lu, Yu Guang Wang, Junyu Xuan

Graph neural networks (GNNs) are a powerful solution for various structure learning applications due to their strong representation capabilities for graph data. However, traditional GNNs, relying on message-passing mechanisms that gather information exclusively from first-order neighbours (known as positive samples), can lead to issues such as over-smoothing

Yazid Janati, Badr Moufad, Alain Durmus, Eric Moulines

Recent advancements in solving Bayesian inverse problems have spotlighted denoising diffusion models (DDMs) as effective priors. Although these have great potential, DDM priors yield complex posterior distributions that are challenging to sample. Existing approaches to posterior sampling in this context address this problem either by retraining model-specifi

Junyuan Hong, Jinhao Duan, Chenhui Zhang, Zhangheng Li

Compressing high-capability Large Language Models (LLMs) has emerged as a favored strategy for resource-efficient inferences. While state-of-the-art (SoTA) compression methods boast impressive advancements in preserving benign task performance, the potential risks of compression in terms of safety and trustworthiness have been largely neglected. This study c

Kohki Sakamoto

For the simple random walks in percolation clusters on hyperbolic groups, we show that the associated harmonic measures are exact dimensional and their Hausdorff dimensions are equal to the entropy over the speed. Our method is inspired by cluster relations introduced by Gaboriau and applies to a large class of random environments on the groups.

Jun Lei, Yuxi Zhou, Xue Tian, Qinghao Zhao

Atrial Fibrillation (AF) is a common cardiac arrhythmia. Many AF patients experience complications such as stroke and other cardiovascular issues. Early detection of AF is crucial. Existing algorithms can only distinguish ``AF rhythm in AF patients'' from ``sinus rhythm in normal individuals'' . However, AF patients do not always exhibit AF rhythm, posing a

Takato Yoshida, Daichi Okuno, Takahiro Kashiwazaki, Takeshi Umeki

Quantum computing has been pursued with various hardware platforms, and an optical system is one of the most reasonable choices for large-scale computation. In the optical continuous-variable computation scheme, the incorporation of Gaussian gates and a highly non-classical non-Gaussian state enables universal quantum computation. Although basic technologies

Zhao-Chen Zhang, Xiao-Jun Bi, Peng-Fei Yin

Observations have revealed a significant dark matter deficit in the ultra-diffuse galaxy NGC1052-DF2 (DF2). It is widely accepted that the formation of this unique galaxy can be attributed to the tidal stripping of its host galaxy, NGC1052. In this study, we simulate the evolution of a satellite system containing globular clusters (GCs) within an accreting h

Raza Nowrozy, David Jam

The rapid advancement of generative Artificial Intelligence (AI) technologies, particularly Generative Pre-trained Transformer (GPT) models such as ChatGPT, has the potential to significantly impact cybersecurity. In this study, we investigated the impact of GPTs, specifically ChatGPT, on tertiary education in cybersecurity, and provided recommendations for

Rao Fu, Jingyu Liu, Xilun Chen, Yixin Nie

This paper introduces Scene-LLM, a 3D-visual-language model that enhances embodied agents' abilities in interactive 3D indoor environments by integrating the reasoning strengths of Large Language Models (LLMs). Scene-LLM adopts a hybrid 3D visual feature representation, that incorporates dense spatial information and supports scene state updates. The model e

Ying Yang, Chengchun Shi, Fang Yao, Shouyang Wang

This article studies the benefits of using spatially randomized experimental designs which partition the experimental area into distinct, non-overlapping units with treatments assigned randomly. Such designs offer improved policy evaluation in online experiments by providing more precise policy value estimators and more effective A/B testing algorithms than

Dongjae Shin, Hyeonseok Lim, Inho Won, Changsu Choi

The impressive development of large language models (LLMs) is expanding into the realm of large multimodal models (LMMs), which incorporate multiple types of data beyond text. However, the nature of multimodal models leads to significant expenses in the creation of training data. Furthermore, constructing multilingual data for LMMs presents its own set of ch

Valerie Barr, Carla E. Brodley, Manuel A. Pérez-Quiñones

Concerns about representation in computing within the U.S. have driven numerous activities to broaden participation. Assessment of the impact of these efforts and, indeed, a clear assessment of the actual "problem" being addressed are limited by the nature of the most common data analysis which looks at the representation of each population as a percentage o

Yujia Liu, Chenxi Yang, Dingquan Li, Jianhao Ding

The task of No-Reference Image Quality Assessment (NR-IQA) is to estimate the quality score of an input image without additional information. NR-IQA models play a crucial role in the media industry, aiding in performance evaluation and optimization guidance. However, these models are found to be vulnerable to adversarial attacks, which introduce imperceptibl

Guangyi Liu, Wen Jiang, Boshu Lei, Vivek Pandey

The active view acquisition problem has been extensively studied in the context of robot navigation using NeRF and 3D Gaussian Splatting. To enhance scene reconstruction efficiency and ensure robot safety, we propose the Risk-aware Environment Masking (RaEM) framework. RaEM leverages coherent risk measures to dynamically prioritize safety-critical regions of

Alhassan Mumuni, Fuseini Mumuni

Modern approach to artificial intelligence (AI) aims to design algorithms that learn directly from data. This approach has achieved impressive results and has contributed significantly to the progress of AI, particularly in the sphere of supervised deep learning. It has also simplified the design of machine learning systems as the learning process is highly

Yejia Liu, Shijin Duan, Xiaolin Xu, Shaolei Ren

Brain-Computer interfaces (BCIs) are typically designed to be lightweight and responsive in real-time to provide users timely feedback. Classical feature engineering is computationally efficient but has low accuracy, whereas the recent neural networks (DNNs) improve accuracy but are computationally expensive and incur high latency. As a promising alternative

Eli Ben-Michael, D. James Greiner, Melody Huang, Kosuke Imai

The use of Artificial Intelligence (AI), or more generally data-driven algorithms, has become ubiquitous in today's society. Yet, in many cases and especially when stakes are high, humans still make final decisions. The critical question, therefore, is whether AI helps humans make better decisions compared to a human-alone or AI-alone system. We introduce a

C. S. Kim, Claudio Dib, Sechul Oh

Panic-induced herding in individuals often leads to social disasters, resulting in people being trapped and trampled in crowd stampedes triggered by panic. We introduce a novel approach that offers fresh insights into studying the phenomenon of asymmetrical panic-induced escape. Our approach is based on the concept of Spontaneous Symmetry Breaking (SSB), a f

Soo Teck Lee, Ruibin Zhang

We develop an algebraic approach to the branching of representations of the general linear Lie superalgebra $\mathfrak{gl}_{p|q}({\mathbb C})$, by constructing certain super commutative algebras whose structure encodes the branching rules. Using this approach, we derive the branching rules for restricting any irreducible polynomial representation $V$ of $\ma

Benjamin Grace, Karl Wette, Susan Scott

We present the results of two searches for gravitational waves from the post-merger remnants of the binary neutron star coalescence events GW170817 and GW190425. The searches are fully coherent over 1800~s of data from the 2nd (for GW170817) and 3rd (for GW190425) observing runs of the LIGO and Virgo observatories. The searches compute the matched filter $\m

Yihao Xue, Eric Gan, Jiayi Ni, Siddharth Joshi

An effective technique for obtaining high-quality representations is adding a projection head on top of the encoder during training, then discarding it and using the pre-projection representations. Despite its proven practical effectiveness, the reason behind the success of this technique is poorly understood. The pre-projection representations are not direc

Crystal L. Martin, Zixuan Peng, Yuan Li

We present integral field spectroscopy toward and around J1044+0353, a rapidly growing, low-metallicity galaxy which produces extreme [O III] line emission. A new map of the O32 flux ratio reveals a density-bounded ionization cone emerging from the starburst. The interaction of the hydrogen ionizing radiation, produced by the very young starburst, with a cav

Dalia Osman, Xinli Du, Timothy Minton, Yohan Noh

Three-dimensional shape sensing in soft and continuum robotics is a crucial aspect for stable actuation and control in fields such as Minimally Invasive surgery, as the estimation of complex curvatures while using continuum robotic tools is required to manipulate through fragile paths. This challenge has been addressed using a range of different sensing tech

Wenderson R. F. Silva, Larissa C. P. Monteiro, Renato L. Senra, Eduardo N. D. de Araujo

The study proposes a new efficient wireless biosensor based on magnetoelastic waves for the detection of antibodies in human plasma, aiming at the serological diagnosis of COVID-19. The biosensor was functionalized with the N antigen - nucleocapsid phosphoprotein of the SARS-CoV-2 virus. Validation analyses, by sodium dodecyl-sulfate polyacrylamide gel elect

Piotr Lechowicz, Aleksandra Knapińska, Adam Włodarczyk, Krzysztof Walkowiak

Traffic Weaver is a Python package developed to generate a semi-synthetic signal (time series) with finer granularity, based on averaged time series, in a manner that, upon averaging, closely matches the original signal provided. The key components utilized to recreate the signal encompass oversampling with a given strategy, stretching to match the integral

Jon Hakkila, Geoffrey N. Pendleton, Robert D. Preece, Timothy W. Giblin

We propose that gamma-ray burst pulses are produced when highly-relativistic jets sweep across an observer's line-of-sight. We hypothesize that axisymmetric jet profiles, coupled with special relativistic effects, produce the time-reversed properties of gamma-ray burst pulses. Curvature resulting from rapid jet expansion is responsible for much of the observ

Haoyu Dong, Tram Thi Minh Tran, Pavlo Bazilinskyy, Marius Hoggenmüller

As the field of automated vehicles (AVs) advances, it has become increasingly critical to develop human-machine interfaces (HMI) for both internal and external communication. Critical dialogue is emerging around the potential necessity for a holistic approach to HMI designs, which promotes the integration of both in-vehicle user and external road user perspe

Jihun Han, Yoonsang Lee

A wide range of applications in science and engineering involve a PDE model in a domain with perforations, such as perforated metals or air filters. Solving such perforated domain problems suffers from computational challenges related to resolving the scale imposed by the geometries of perforations. We propose a neural network-based mesh-free approach for pe

Xian Wang, Luyao Shen, Lik-Hang Lee

The rising interest of generalist robots seek to create robots with versatility to handle multiple tasks in a variety of environments, and human will interact with such robots through immersive interfaces. In the context of human-robot interaction (HRI), this survey provides an exhaustive review of the applications of extended reality (XR) technologies in th

Giulio Turrisi, Valerio Modugno, Lorenzo Amatucci, Dimitrios Kanoulas

Quadrupedal robots excel in mobility, navigating complex terrains with agility. However, their complex control systems present challenges that are still far from being fully addressed. In this paper, we introduce the use of Sample-Based Stochastic control strategies for quadrupedal robots, as an alternative to traditional optimal control laws. We show that S

Byungkyun Kang, Zachary Brown, Myoung-Hwan Kim, Hyunsoo Kim

We discovered that abnormal Mott physics can emerge even in weakly correlated 4f fermions through their interplay with topological singularity. Employing ab initio many-body perturbation theory combined with dynamical mean field theory, we show that 4f electrons near the topological singular point experience strong effective Coulomb repulsion, as the hybridi

Manuel Mosquera, Juan Sebastian Pinzon, Manuel Rios, Yesid Fonseca

As the field of AI continues to evolve, a significant dimension of this progression is the development of Large Language Models and their potential to enhance multi-agent artificial intelligence systems. This paper explores the cooperative capabilities of Large Language Model-augmented Autonomous Agents (LAAs) using the well-known Meltin Pot environments alo

Beichen Zhang, Xiaoxing Wang, Xiaohan Qin, Junchi Yan

Supernet is a core component in many recent Neural Architecture Search (NAS) methods. It not only helps embody the search space but also provides a (relative) estimation of the final performance of candidate architectures. Thus, it is critical that the top architectures ranked by a supernet should be consistent with those ranked by true performance, which is

Amogh A. Thatte, Sourabh Dalvi, Vincent Carag, Jiazi Zhang

This study addresses the challenges in optimizing long-duration energy storage (LDES) dispatch within future power systems featuring high integration of variable renewable energy (VRE). The research focuses on conducting a comparative analysis between traditional and extended horizon methods for the optimization of LDES dispatch, using open-source and commer

Tram Thi Minh Tran, Callum Parker, Martin Tomitsch

An increasing number of studies employ virtual reality (VR) to evaluate interactions between autonomous vehicles (AVs) and pedestrians. VR simulators are valued for their cost-effectiveness, flexibility in developing various traffic scenarios, safe conduct of user studies, and acceptable ecological validity. Reviewing the literature between 2010 and 2020, we

Tram Thi Minh Tran, Callum Parker, Marius Hoggenmüller, Luke Hespanhol

Augmented reality (AR) has the potential to fundamentally change how people engage with increasingly interactive urban environments. However, many challenges exist in designing and evaluating these new urban AR experiences, such as technical constraints and safety concerns associated with outdoor AR. We contribute to this domain by assessing the use of virtu

Minh Tran, Winston Bounsavy, Khoa Vo, Anh Nguyen

Amodal Instance Segmentation (AIS) presents a challenging task as it involves predicting both visible and occluded parts of objects within images. Existing AIS methods rely on a bidirectional approach, encompassing both the transition from amodal features to visible features (amodal-to-visible) and from visible features to amodal features (visible-to-amodal)

Hongxiao Wang, Yang Yang, Zhuo Zhao, Pengfei Gu

For predicting cancer survival outcomes, standard approaches in clinical research are often based on two main modalities: pathology images for observing cell morphology features, and genomic (e.g., bulk RNA-seq) for quantifying gene expressions. However, existing pathology-genomic multi-modal algorithms face significant challenges: (1) Valuable biological in

Samawel Jaballi, Azer Mahjoubi, Manar Joundy Hazar, Salah Zrigui

In this study, the authors present a novel methodology adept at decoding multilingual topic dynamics and identifying communication trends during crises. We focus on dialogues within Tunisian social networks during the Coronavirus Pandemic and other notable themes like sports and politics. We start by aggregating a varied multilingual corpus of comments relev

Marco Sutti, Mei-Heng Yueh

We propose a new Riemannian gradient descent method for computing spherical area-preserving mappings of topological spheres using a Riemannian retraction-based framework with theoretically guaranteed convergence. The objective function is based on the stretch energy functional, and the minimization is constrained on a power manifold of unit spheres embedded

Koji Hashimoto, Yuji Hirono, Jun Maeda, Jojiro Totsuka-Yoshinaka

It has been proposed that random wide neural networks near Gaussian process are quantum field theories around Gaussian fixed points. In this paper, we provide a novel map with which a wide class of quantum mechanical systems can be cast into the form of a neural network with a statistical summation over network parameters. Our simple idea is to use the unive

Rodrique G. M. Badr, Lukas Hauer, Doris Vollmer, Friederike Schmid

We study the equilibrium properties and the wetting behavior of a simple liquid on a polymer brush, with and without presence of lubricant by multibody Dissipative Particle Dynamics simulations. The lubricant is modelled as a polymeric liquid consisting of short chains that are chemically identical to the brush polymers. We investigate the behavior of the br

Aditi Tomar, Lok Pati Tripathi, Amiya K. Pani

Stability and optimal convergence analysis of a non-uniform implicit-explicit L1 finite element method (IMEX-L1-FEM) is studied for a class of time-fractional linear partial differential/integro-differential equations with non-self-adjoint elliptic part having (space-time) variable coefficients. The proposed scheme is based on a combination of an IMEX-L1 met

Zhijian He, Hejin Wang, Xiaoqun Wang

Importance Sampling (IS), an effective variance reduction strategy in Monte Carlo (MC) simulation, is frequently utilized for Bayesian inference and other statistical challenges. Quasi-Monte Carlo (QMC) replaces the random samples in MC with low discrepancy points and has the potential to substantially enhance error rates. In this paper, we integrate IS with

Shu Zhao, Xiaohan Zou, Tan Yu, Huijuan Xu

Pre-trained large multi-modal models (LMMs) exploit fine-tuning to adapt diverse user applications. Nevertheless, fine-tuning may face challenges due to deactivated sensors (e.g., cameras turned off for privacy or technical issues), yielding modality-incomplete data and leading to inconsistency in training data and the data for inference. Additionally, conti

Leon Escobar-Diaz, Chris Stevens

In this study, we employ eth-operators and spin-weighted spherical harmonics to express the ADM mass of a static space-time based on the mean values of its components over a a radius-$r$ sphere. While initially derived for standard spherical coordinates, we showcase its adaptability by demonstrating its usefulness in expressing a quasilocal mass; specificall

Baolu Li, Jinlong Li, Xinyu Liu, Runsheng Xu

Current LiDAR-based Vehicle-to-Everything (V2X) multi-agent perception systems have shown the significant success on 3D object detection. While these models perform well in the trained clean weather, they struggle in unseen adverse weather conditions with the domain gap. In this paper, we propose a Domain Generalization based approach, named \textit{V2X-DGW}

Theresa Huber, Simon Schaefer, Stefan Leutenegger

The assumption of a static environment is common in many geometric computer vision tasks like SLAM but limits their applicability in highly dynamic scenes. Since these tasks rely on identifying point correspondences between input images within the static part of the environment, we propose a graph neural network-based sparse feature matching network designed

KV Aditya Srivatsa, Ekaterina Kochmar

This paper investigates the question of what makes math word problems (MWPs) in English challenging for large language models (LLMs). We conduct an in-depth analysis of the key linguistic and mathematical characteristics of MWPs. In addition, we train feature-based classifiers to better understand the impact of each feature on the overall difficulty of MWPs

Ruoxuan Yang, Xinyue Zhang, Anais Fernandez-Laaksonen, Xin Ding

Recently, LLM-powered driver agents have demonstrated considerable potential in the field of autonomous driving, showcasing human-like reasoning and decision-making abilities.However, current research on aligning driver agent behaviors with human driving styles remains limited, partly due to the scarcity of high-quality natural language data from human drivi

Peng Jiang, Gaurav Pandey, Srikanth Saripalli

This paper presents a novel system designed for 3D mapping and visual relocalization using 3D Gaussian Splatting. Our proposed method uses LiDAR and camera data to create accurate and visually plausible representations of the environment. By leveraging LiDAR data to initiate the training of the 3D Gaussian Splatting map, our system constructs maps that are b

Anique Tahir, Lu Cheng, Huan Liu

The scaling of Large Language Models (LLMs) for retrieval-based tasks, particularly in Retrieval Augmented Generation (RAG), faces significant memory constraints, especially when fine-tuning extensive prompt sequences. Current open-source libraries support full-model inference and fine-tuning across multiple GPUs but fall short of accommodating the efficient

Gaoming Chen, Fadil Santosa, Aseel Titi

We consider the problem of determining a small elliptical conductivity anomaly in a unit disc from boundary measurements. The conductivity of the anomaly is assumed to be a small perturbation from the constant background. A measurement of voltage across two point-electrodes on the boundary through which a constant current is passed. We further assume the lim

Sai Tarun Sathyan, Thomas B. Kinsman

It is desirable to create 3D object models and 3D maps from 2D input images for applications such as navigation, virtual tourism, and urban planning. The traditional methods of creating 3D maps, (such as photogrammetry), require a large number of images and odometry. Additionally, traditional methods have difficulty with reflective surfaces and specular refl

Theodor Stoecker, Nico Hambauer, Patrick Zschech, Mathias Kraus

Feature selection is a critical component in predictive analytics that significantly affects the prediction accuracy and interpretability of models. Intrinsic methods for feature selection are built directly into model learning, providing a fast and attractive option for large amounts of data. Machine learning algorithms, such as penalized regression models

Elias Zea, Marco Laudato, Joakim Andén

Sparse representation systems that encode signal architecture have had a profound impact on sampling and compression paradigms. Remarkable examples are multi-scale directional systems, which, similar to our vision system, encode the underlying architecture of natural images with sparse features. Inspired by this philosophy, we introduce a representation syst

Dorota Filipczuk, Enrico H. Gerding, George Konstantinidis

Through legislation and technical advances users gain more control over how their data is processed, and they expect online services to respect their privacy choices and preferences. However, data may be processed for many different purposes by several layers of algorithms that create complex data workflows. To date, there is no existing approach to automati

Ádám Sagmeister

A convex body $R$ in the hyperbolic plane is reduced if any convex body $K\subset R$ has a smaller minimal width than $R$. We examine the area of a family of hyperbolic reduced $n$-gons, and prove that, within this family, regular $n$-gons have maximal area.

Viktória Klász, René Marczinzik, Judith Marquardt

We give a combinatorial classification of Nakayama algebras of small homological dimension using the Krattenthaler bijection between Dyck paths and 132-avoiding permutations.

Abhi Kamboj, Minh Do

Despite living in a multi-sensory world, most AI models are limited to textual and visual understanding of human motion and behavior. In fact, full situational awareness of human motion could best be understood through a combination of sensors. In this survey we investigate how knowledge can be transferred and utilized amongst modalities for Human Activity/A

Priyanuj Bordoloi, Jefferson Dixon, Zachary N. Mauri, Christopher J. Ciccarino

We introduce the spin dissymmetry factor, a measure of the spin-selectivity in the optical transition rate of quantum particles. This spin dissymmetry factor is valid locally, including at material interfaces and within optical cavities. We design and numerically demonstrate a metasurface optical cavity with three-fold rotational symmetry that maximizes spin

Anton Korinek, Donghyun Suh

We analyze how output and wages behave under different scenarios for technological progress that may culminate in Artificial General Intelligence (AGI), defined as the ability of AI systems to perform all tasks that humans can perform. We assume that human work can be decomposed into atomistic tasks that differ in their complexity. Advances in technology mak

Christopher Cabezas, Julien Leroy

An important question in dynamical systems is the classification problem, i.e., the ability to distinguish between two isomorphic systems. In this work, we study the topological factors between a family of multidimensional substitutive subshifts generated by morphisms with uniform support. We prove that it is decidable to check whether two minimal aperiodic

Zhi Liu, Housen Li

For robust and efficient detection of change points, we introduce a novel methodology MUSCLE (Multiscale qUantile Segmentation Controlling Local Error) that partitions serial data into multiple segments, each sharing a common quantile. It leverages multiple tests for quantile changes over different scales and locations, and variational estimation. Unlike the

A. Giachero, M. Vissers, J. Wheeler, L. Howe

A Kinetic Inductance Traveling Wave amplifier (KIT) utilizes the nonlinear kinetic inductance of superconducting films, particularly Niobium Titanium Nitride (NbTiN), for parametric amplification. These amplifiers achieve remarkable performance in terms of gain, bandwidth, compression power, and frequently approach the quantum limit for noise. However, most

Yunrui Li, Hao Xu, Ambrish Kumar, Duosheng Wang

Nuclear Magnetic Resonance (NMR) spectroscopy is essential for revealing molecular structure, electronic environment, and dynamics. Accurate NMR shift prediction allows researchers to validate structures by comparing predicted and observed shifts. While Machine Learning (ML) has improved one-dimensional (1D) NMR shift prediction, predicting 2D NMR remains ch

CMS Collaboration

The J/$\psi$ $\to$ $\mu^+\mu^-\mu^+\mu^-$ decay has been observed with a statistical significance in excess of five standard deviations. The analysis is based on an event sample of proton-proton collisions at a center-of-mass energy of 13 TeV, collected by the CMS experiment in 2018 and corresponding to an integrated luminosity of 33.6 fb$^{-1}$. Normalizing

Antonio M. Sudoso

Biclustering, also called co-clustering, block clustering, or two-way clustering, involves the simultaneous clustering of both the rows and columns of a data matrix into distinct groups, such that the rows and columns within a group display similar patterns. As a model problem for biclustering, we consider the $k$-densest-disjoint biclique problem, whose goa

V. O. Manturov, I. M. Nikonov

The motivation for this work is to construct a map from classical knots to virtual ones. What we get in the paper is a series of maps from knots in the full torus (thickened torus) to flat-virtual knots. We give definition of flat-virtual knots and presents Alexander-like polynomial and (picture-valued) Kauffman bracket for them.

Yiran Wang, Yimin Zhong

The limited angle Radon transform is notoriously difficult to invert due to its ill-posedness. In this work, we give a mathematical explanation that data-driven approaches can stably reconstruct more information compared to traditional methods like filtered backprojection. In addition, we use experiments based on the U-Net neural network to validate our theo

Jaume Alonso, Yuri B. Suris, Kangning Wei

Discrete Painlev\'e equations constitute a famous class of integrable non-autonomous second order difference equations. A classification scheme proposed by Sakai interprets a discrete Painlev\'e equation as a birational map between generalized Halphen surfaces (surfaces obtained from $\mathbb P^1\times\mathbb P^1$ by blowing up at eight points). We propose a

Mintong Kang, Nezihe Merve Gürel, Linyi Li, Bo Li

Conformal prediction has shown spurring performance in constructing statistically rigorous prediction sets for arbitrary black-box machine learning models, assuming the data is exchangeable. However, even small adversarial perturbations during the inference can violate the exchangeability assumption, challenge the coverage guarantees, and result in a subsequ

Shiang Fang, Mario Geiger, Joseph G. Checkelsky, Tess Smidt

We present an equivariant neural network for predicting vibrational and phonon modes of molecules and periodic crystals, respectively. These predictions are made by evaluating the second derivative Hessian matrices of the learned energy model that is trained with the energy and force data. Using this method, we are able to efficiently predict phonon dispersi

Kung Yin Hong, Lifeng Han, Riza Batista-Navarro, Goran Nenadic

Neural Machine Translation (NMT) for low-resource languages is still a challenging task in front of NLP researchers. In this work, we deploy a standard data augmentation methodology by back-translation to a new language translation direction Cantonese-to-English. We present the models we fine-tuned using the limited amount of real data and the synthetic data

Muhammad Aneeq uz Zaman, Alec Koppel, Mathieu Laurière, Tamer Başar

We address in this paper Reinforcement Learning (RL) among agents that are grouped into teams such that there is cooperation within each team but general-sum (non-zero sum) competition across different teams. To develop an RL method that provably achieves a Nash equilibrium, we focus on a linear-quadratic structure. Moreover, to tackle the non-stationarity i

Mohsen Shirali, Zahra Ahmadi, Jose-Luis Bayo-Monton, Zoe Valero-Ramon

This study exploits information fusion in IoT systems and uses a clustering method to identify similarities in behaviours and key characteristics within each cluster. This approach facilitates early detection of behaviour changes and provides a more in-depth understanding of behaviour routines for continuous health monitoring.

Shantanu Kodgirwar, Lars Loetgering, Chang Liu, Aleena Joseph

The limited dynamic range of the detector can impede coherent diffractive imaging (CDI) schemes from achieving diffraction-limited resolution. To overcome this limitation, a straightforward approach is to utilize high dynamic range (HDR) imaging through multi-exposure image fusion (MEF). This method involves capturing measurements at different exposure times

Mengchu Li, Ye Tian, Yang Feng, Yi Yu

Federated learning has emerged as a powerful framework for analysing distributed data, yet two challenges remain pivotal: heterogeneity across sites and privacy of local data. In this paper, we address both challenges within a federated transfer learning framework, aiming to enhance learning on a target data set by leveraging information from multiple hetero

Lionel Petit, Tom Fournier, Géraldine Ballon, Cédric Robert

Graphene Hall effect magnetic field sensors hold great promise for the development of ultra-sensitive magnetometers. Their performance is frequently analysed using the two-channel model where electron and hole conductivities are simply added. Unfortunately, this model is unable to capture all the features of the sensor, particularly the bias current dependen

R. Ahrling, R. Mitdank, A. Popp, J. Rehm

The anisotropic thermal conductivity and the phonon mean free path (mfp) in monoclinic $\beta$-Ga$_2$O$_3$ single crystals and homoepitaxial films of several $\mu$m were determined using the 3$\omega$-method in the temperature range from 10K-300 K. The measured effective thermal conductivity of both, single crystal and homoepitaxial films are in the order of

Tushar Kataria, Beatrice Knudsen, Shireen Y. Elhabian

Hematoxylin and Eosin (H&E) staining is widely regarded as the standard in pathology for diagnosing diseases and tracking tumor recurrence. While H&E staining shows tissue structures, it lacks the ability to reveal specific proteins that are associated with disease severity and treatment response. Immunohistochemical (IHC) stains use antibodies to highlight

Bruno Ronchi, Analia Zwick, Gonzalo A. Alvarez

Efficient quantum sensing technologies rely on precise control of quantum sensors, particularly two-level systems or qubits, to optimize estimation processes. We here exploit the Quantum Zeno Effect (QZE) as a tool for maximizing information obtainable by quantum sensors, with a specific focus on the level avoided crossing (LAC) phenomenon in qubit systems.

Fares Bougourzi, Feryal Windal Moula, Halim Benhabiles, Fadi Dornaika

Since the emergence of Covid-19 in late 2019, medical image analysis using artificial intelligence (AI) has emerged as a crucial research area, particularly with the utility of CT-scan imaging for disease diagnosis. This paper contributes to the 4th COV19D competition, focusing on Covid-19 Detection and Covid-19 Domain Adaptation Challenges. Our approach cen