March 2024 arXiv papers — page 13

Ryui Kaneko, Masatoshi Imada, Yoshiyuki Kabashima, Tomi Ohtsuki

Reliable numerical computation of quantum dynamics is a fundamental challenge when the long-ranged quantum entanglement plays essential roles as in the cases governed by quantum criticality in strongly correlated systems. Here we apply a method that utilizes reliable short-time data of physical quantities to accurately forecast long-time behavior of the stro

André Yuji Yasutomi, Hiroki Mori, Tetsuya Ogata

A method that enables an industrial robot to accomplish the peg-in-hole task for holes in concrete is proposed. The proposed method involves slightly detaching the peg from the wall, when moving between search positions, to avoid the negative influence of the concrete's high friction coefficient. It uses a deep neural network (DNN), trained via reinforcement

Ian Ball, Teemu Pekkarinen

We study the design of an auction for an income-generating asset such as an intellectual property license. Each bidder has a signal about his future income from acquiring the asset. After the asset is allocated, the winner's income from the asset is realized privately. The principal can audit the winner, at a cost, and then charge a payment contingent on the

Gengchen Zhang, Yulun Zhang, Xin Yuan, Ying Fu

Recently, deep neural networks have achieved excellent performance on low-light raw video enhancement. However, they often come with high computational complexity and large memory costs, which hinder their applications on resource-limited devices. In this paper, we explore the feasibility of applying the extremely compact binary neural network (BNN) to low-l

Zhongzhi Li, Rong Fan, Jingqi Tu, Jinyi Ma

Fault diagnosis plays a crucial role in maintaining the operational integrity of mechanical systems, preventing significant losses due to unexpected failures. As intelligent manufacturing and data-driven approaches evolve, Deep Learning (DL) has emerged as a pivotal technique in fault diagnosis research, recognized for its ability to autonomously extract com

Jianbo Lu, Shining Yang, Yuying Zhang, Liu Yang

This paper investigates the high frequency quasi-periodic oscillations (HFQPOs) phenomenon around the black bounce-type (BBT) spacetime using the resonance models. We calculated the location of the innermost stable circular orbit (ISCO) for different types of celestial bodies, and derived the expression for the epicyclic frequencies of test particles. The re

Sejik Park

Our paper addresses the problem of models struggling to learn diverse features, due to either forgetting previously learned features or failing to learn new ones. To overcome this problem, we introduce Diverse Feature Learning (DFL), a method that combines an important feature preservation algorithm with a new feature learning algorithm. Specifically, for pr

Beichen Shao, Nieqing Cao, Yan Ding, Xingchen Wang

In this work, we present MoMa-Pos, a framework that optimizes base placement for mobile manipulators, focusing on navigation-manipulation tasks in environments with both rigid and articulated objects. Base placement is particularly critical in such environments, where improper positioning can severely hinder task execution if the object's kinematics are not

Rajiv G Pereira, Ananya Janardhanan, Mustansir Barma

We determine the arrangement of spins in the ground state of the XY model with quenched, random fields, on a fully connected graph. Two types of disordered fields are considered, namely randomly oriented magnetic fields, and randomly oriented crystal fields. Orientations are chosen from a uniformly isotropic distribution, but disorder fluctuations in each re

Bingxi Liu, Cheng-Ying Tsai, Yi Jiao, Weihang Liu

Coherent synchrotron radiations (CSR) emitted by a high-brightness electron beam during transport in a bending magnet is a double-edged sword in electron accelerators. While CSR contributes to a stronger radiation field than the incoherent radiation, it simultaneously leads to degradation of the electron beam quality. Specifically, CSR effects manifest in in

Douglas Scott, Ali Frolop

It is well known that the best way to understand astronomical data is through machine learning, where a "black box" is set up, inside which a kind of artificial intelligence learns how to interpret the features in the data. We suggest that perhaps there may be some merit to a new approach in which humans are used instead of machines to understand the data. T

Hao Wu, Fan Xu

Building natural language interfaces typically uses a semantic parser to parse the user's natural language and convert it into structured \textbf{S}emantic \textbf{L}ogic \textbf{F}orms (SLFs). The mainstream approach is to adopt a sequence-to-sequence framework, which requires that natural language commands and SLFs must be represented serially. Since a sin

Artur Santos Nascimento, Valter Guilherme Silva de Souza, Daniel Oliveira Dantas, Beatriz Trinchão Andrade

In computer vision, characteristics refer to image regions with unique properties, such as corners, edges, textures, or areas with high contrast. These regions can be represented through feature points (FPs). FP detection and description are fundamental steps to many computer vision tasks. Most FP detection and description methods use low dynamic range (LDR)

Jonathan Harper, Hiroki Kanda, Tadashi Takayanagi, Kenya Tasuki

We show that strong subadditivity provides a simple derivation of the $g$-theorem for the boundary renormalization group flow in two-dimensional conformal field theories. We work out its holographic interpretation and also give a derivation of the $g$-theorem for the case of an interface in two-dimensional conformal field theories. We also geometrically conf

Chao Zhang, Guoyang Fu, Chunyu Zhang

The galactic black hole is a supermassive black hole located at the center of a galaxy surrounded by a dark matter halo. For the first time, we establish a generic, fully-relativistic formalism to calculate solutions of Einstein's gravity minimally coupled to an anisotropic fluid modeled by the Einstein cluster in axisymmetric, non-vacuum spacetimes, which a

Hugo Marsan, Mathieu Sablik

We define the notion of stochastic stability, already present in the literature in the context of smooth dynamical systems, for invariant measures of cellular automata perturbed by a random noise, and the notion of strongly stochastically stable cellular automaton. We study these notions on basic examples (nilpotent cellular automata, spreading symbols) usin

Chaoqi Zhou, Jingpu Duan, YuPeng Xiao, Qing Li

With the increasing popularity of smart homes, more and more devices need to connect to home networks. Traditional home networks mainly rely on centralized networking, where an excessive number of devices in the centralized topology can increase the pressure on the central router, potentially leading to decreased network performance metrics such as communica

Shulin Liu, Chengcheng Xu, Hao Liu, Tinghao Yu

The recent success of Large Language Models (LLMs) has garnered significant attention in both academia and industry. Prior research on LLMs has primarily focused on enhancing or leveraging their generalization capabilities in zero- and few-shot settings. However, there has been limited investigation into effectively fine-tuning LLMs for a specific natural la

Qianhan Zeng, Jing Zhou, Ying Ji, Hansheng Wang

Computed tomography (CT) has been a powerful diagnostic tool since its emergence in the 1970s. Using CT data, three-dimensional (3D) structures of human internal organs and tissues, such as blood vessels, can be reconstructed using professional software. This 3D reconstruction is crucial for surgical operations and can serve as a vivid medical teaching examp

Hanting Chen, Zhicheng Liu, Xutao Wang, Yuchuan Tian

In an effort to reduce the computational load of Transformers, research on linear attention has gained significant momentum. However, the improvement strategies for attention mechanisms typically necessitate extensive retraining, which is impractical for large language models with a vast array of parameters. In this paper, we present DiJiang, a novel Frequen

Congpei An, Mou Cai

This paper explores the incorporation of Tikhonov regularization into the least squares approximation scheme using trigonometric polynomials on the unit circle. This approach encompasses interpolation and hyperinterpolation as specific cases. With the aid of the de la Vall\'ee-Poussin approximation, we derive a uniform error bound and a concrete $L_2$ error

Jijie He, Wenwu Yang

By leveraging temporal dependency in video sequences, multi-frame human pose estimation algorithms have demonstrated remarkable results in complicated situations, such as occlusion, motion blur, and video defocus. These algorithms are predominantly based on heatmaps, resulting in high computation and storage requirements per frame, which limits their flexibi

Toshihiro Ota

Decision Transformer, a promising approach that applies Transformer architectures to reinforcement learning, relies on causal self-attention to model sequences of states, actions, and rewards. While this method has shown competitive results, this paper investigates the integration of the Mamba framework, known for its advanced capabilities in efficient and e

Bo Wang, Jian Li, Yang Yu, Li Liu

Considering that scene flow estimation has the capability of the spatial domain to focus but lacks the coherence of the temporal domain, this study proposes long-term scene flow estimation (LSFE), a comprehensive task that can simultaneously capture the fine-grained and long-term 3D motion in an online manner. We introduce SceneTracker, the first LSFE networ

Shengfeng Xu, Chang Yan, Zhenxu Sun, Renfang Huang

Physics-Informed Neural Networks (PINNs) serve as a flexible alternative for tackling forward and inverse problems in differential equations, displaying impressive advancements in diverse areas of applied mathematics. Despite integrating both data and underlying physics to enrich the neural network's understanding, concerns regarding the effectiveness and pr

Judith U. De Mel, Sudipta Gupta, Gerald J. Schneider

Despite intense research, methods for controlling soft matter's spontaneous self-assembly in-to well-defined layers remain a significant challenge. We observed ion-induced structural disconti-nuities of phospholipid vesicles that can be exploited for controlled self-assembly of soft materials. We used dynamic light scattering, zeta-potential measurement, cry

Lucas Falcao

This study reports \textit{CP} asymmetry measurements in charmless three-body decays of \textit{B} mesons, both using the Run II data from proton-proton collisions at a center-of-mass energy of 13 $TeV$, collected by the LHCb detector from 2015 to 2018, with an integrated luminosity of 5.9 $fb^{-1}$. Significant \textit{CP} asymmetries are observed in $B^\pm

Aggelina Chatziagapi, Grigorios G. Chrysos, Dimitris Samaras

In this work, we introduce a method that learns a single dynamic neural radiance field (NeRF) from monocular talking face videos of multiple identities. NeRFs have shown remarkable results in modeling the 4D dynamics and appearance of human faces. However, they require per-identity optimization. Although recent approaches have proposed techniques to reduce t

Qianliang Wu, Haobo Jiang, Lei Luo, Jun Li

Establishing reliable correspondences is essential for registration tasks such as 3D and 2D3D registration. Existing methods commonly leverage geometric or semantic point features to generate potential correspondences. However, these features may face challenges such as large deformation, scale inconsistency, and ambiguous matching problems (e.g., symmetry).

Luke Rowe, Roger Girgis, Anthony Gosselin, Bruno Carrez

Evaluating autonomous vehicle stacks (AVs) in simulation typically involves replaying driving logs from real-world recorded traffic. However, agents replayed from offline data are not reactive and hard to intuitively control. Existing approaches address these challenges by proposing methods that rely on heuristics or generative models of real-world data but

Daeyoung Ham, Ted Westling, Charles R. Doss

We consider the problem of causal inference based on observational data (or the related missing data problem) with a binary or discrete treatment variable. In that context, we study inference for the counterfactual density functions and contrasts thereof, which can provide more nuanced information than counterfactual means and the average treatment effect. W

Yingbai Hu, Fares J. Abu-Dakka, Fei Chen, Xiao Luo

Imitation Learning (IL), also referred to as Learning from Demonstration (LfD), holds significant promise for capturing expert motor skills through efficient imitation, facilitating adept navigation of complex scenarios. A persistent challenge in IL lies in extending generalization from historical demonstrations, enabling the acquisition of new skills withou

Ardyn Nordstrom, Morgan Nordstrom, Matthew D. Webb

This paper details an innovative methodology to integrate image data into traditional econometric models. Motivated by forecasting sales prices for residential real estate, we harness the power of deep learning to add "information" contained in images as covariates. Specifically, images of homes were categorized and encoded using an ensemble of image classif

Mahdi Manavi

The Asynchronous Transfer Mode (ATM) network is crucial due to its ability to efficiently transmit data, provide reliable connections, and support various service classes with specific Quality of Service (QoS) requirements. In this paper, we utilize the OPNET network simulation software to model an ATM network and analyze the impact of QoS classification on

Peng Ding, Jiading Fang, Peng Li, Kangrui Wang

Large language models such as ChatGPT and GPT-4 have recently achieved astonishing performance on a variety of natural language processing tasks. In this paper, we propose MANGO, a benchmark to evaluate their capabilities to perform text-based mapping and navigation. Our benchmark includes 53 mazes taken from a suite of textgames: each maze is paired with a

Zihao Song, Huaxi Chen, Donghui Quan, Di Li

Identifying neutral hydrogen (\hi) galaxies from observational data is a significant challenge in \hi\ galaxy surveys. With the advancement of observational technology, especially with the advent of large-scale telescope projects such as FAST and SKA, the significant increase in data volume presents new challenges for the efficiency and accuracy of data proc

Xi Chen, Yuhao Li, Mihalis Yannakakis

We give an algorithm for finding an $\epsilon$-fixed point of a contraction map $f:[0,1]^k\mapsto[0,1]^k$ under the $\ell_\infty$-norm with query complexity $O (k\log (1/\epsilon ) )$.

Vladislav V. Kravchenko, L. Estefania Murcia-Lozano

Cardinal series representations for solutions of the Sturm-Liouville equation $-y''+q(x)y=\rho^{2}y$, $x\in(0,L)$ with a complex valued potential $q(x)$ are obtained, by using the corresponding transmutation operator. Consequently, partial sums of the series approximate the solutions uniformly with respect to $\rho$ in any strip $\left|\text{Im}\rho\right|<C

P. B. Wiegmann

We present a hydrodynamic framework derived from the action of a perfect fluid, modified by the hydrodynamic analog of Novikov's multivalued functional. This modification introduces spin degrees of freedom into the fluid. The structure closely resembles the Abelian version of the Wess-Zumino functional, commonly applied in field theories with chiral anomalie

Huihui Zheng, Chan Zhao, Liangyun Zhang

This paper is primarily devoted to the study of Hopf heaps and Hopf heap modules. We redefine the structure of Hopf trusses by means of Hopf heaps, establish the connection between Hopf trusses and Hopf braces, and provide a series of examples of Hopf truss structures from the perspective of Hopf heaps. Most importantly, we introduce the conception of Hopf h

Yucheng Jin, Yun Xiong, Juncheng Fang, Xixi Wu

Node classification on graphs is of great importance in many applications. Due to the limited labeling capability and evolution in real-world open scenarios, novel classes can emerge on unlabeled testing nodes. However, little attention has been paid to novel class discovery on graphs. Discovering novel classes is challenging as novel and known class nodes a

Mahdi Manavi

Materialized views can significantly improve database query performance but identifying the optimal set of views to materialize is challenging. Prior work on automating and optimizing materialized view selection has limitations in execution time and total cost. In this paper, we present a novel genetic algorithm based approach to materialized view selection

Inas Al-Kamachy, Reza Hassanpour, Roya Choupani

Diabetic Retinopathy (DR) stands as the leading cause of blindness globally, particularly affecting individuals between the ages of 20 and 70. This paper presents a Computer-Aided Diagnosis (CAD) system designed for the automatic classification of retinal images into five distinct classes: Normal, Mild, Moderate, Severe, and Proliferative Diabetic Retinopath

Mei Qiu, Wei Lin, Lauren Ann Christopher, Stanley Chien

In the US, thousands of Pan, Tilt, and Zoom (PTZ) traffic cameras monitor highway conditions. There is a great interest in using these highway cameras to gather valuable road traffic data to support traffic analysis and decision-making for highway safety and efficient traffic management. However, there are too many cameras for a few human traffic operators t

Junho Kim, Jiwon Jeong, Young Min Kim

We introduce a lightweight and accurate localization method that only utilizes the geometry of 2D-3D lines. Given a pre-captured 3D map, our approach localizes a panorama image, taking advantage of the holistic 360 view. The system mitigates potential privacy breaches or domain discrepancies by avoiding trained or hand-crafted visual descriptors. However, as

Shailaja Mallick, Vishwaraj Doshi, Do Young Eun

When a new product enters a market already dominated by an existing product, will it survive along with this dominant product? Most of the existing works have shown the coexistence of two competing products spreading/being adopted on overlaid graphs with same set of users. However, when it comes to the survival of a weaker product on the same graph, it has b

Jianfeng Cai, Yue Ma, Zhixi Feng, Shuyuan Yang

Polarimetric synthetic aperture radar (PolSAR) image interpretation is widely used in various fields. Recently, deep learning has made significant progress in PolSAR image classification. Supervised learning (SL) requires a large amount of labeled PolSAR data with high quality to achieve better performance, however, manually labeled data is insufficient. Thi

Felipe Morales, Rafael Cisneros, Romeo Ortega, Antonio Sanchez-Squella

In this article, we propose a nonlinear voltage control to ensure power exchange in a multiport interconnected system, which consists of a bidirectional DC-DC converter and generating-storing devices. The converter topology under consideration is two-stage, composed of an interconnection of a buck with a boost converter. The motivation for this work is the e

Shi-Lei Xue, Qiutan Yang, Prisca Liberali, Edouard Hannezo

How pattern and form are generated in a reproducible manner during embryogenesis remains poorly understood. Intestinal organoid morphogenesis involves a number of mechanochemical regulators, including cell-type specific cytoskeletal forces and osmotically-driven lumen volume changes. However, whether and how these forces are coordinated in time and space via

Yue Zheng, Lei Yu, Junmian Chen, Tianyu Xia

The digital realm has witnessed the rise of various search modalities, among which the Image-Based Conversational Search System stands out. This research delves into the design, implementation, and evaluation of this specific system, juxtaposing it against its text-based and mixed counterparts. A diverse participant cohort ensures a broad evaluation spectrum

Haipeng Liu, Yang Wang, Biao Qian, Meng Wang

Denoising diffusion probabilistic models for image inpainting aim to add the noise to the texture of image during the forward process and recover masked regions with unmasked ones of the texture via the reverse denoising process. Despite the meaningful semantics generation, the existing arts suffer from the semantic discrepancy between masked and unmasked re

Abbas Shah Syed, Aron Patrick, Adrian Lauf, Adel Elmaghraby

Recently, there has been a push by countries to diversify their energy mix considering various factors. In this regard, there have been several studies conducted to assess the potential for using sources such as wind and solar to generate supplemental energy to the already present energy generation setup. In this regard, this study explores the potential of

Seyma Yucer, Amir Atapour Abarghouei, Noura Al Moubayed, Toby P. Breckon

Achieving an effective fine-grained appearance variation over 2D facial images, whilst preserving facial identity, is a challenging task due to the high complexity and entanglement of common 2D facial feature encoding spaces. Despite these challenges, such fine-grained control, by way of disentanglement is a crucial enabler for data-driven racial bias mitiga

David Yevick

A general procedure for introducing parametric, learned, nonlinearity into activation functions is found to enhance the accuracy of representative neural networks without requiring significant additional computational resources. Examples are given based on the standard rectified linear unit (ReLU) as well as several other frequently employed activation funct

Wenliang Liu, Guanding Yu, Lele Wang, Renjie Liao

We study the Out-of-Distribution (OOD) generalization in machine learning and propose a general framework that establishes information-theoretic generalization bounds. Our framework interpolates freely between Integral Probability Metric (IPM) and $f$-divergence, which naturally recovers some known results (including Wasserstein- and KL-bounds), as well as y

Alex Gilman

The current precision on the CKM angle $\gamma$ is driven by averages of measurements from multiple final states and multiple experiments. As the next generation of experiments targets a total experimental precision on $\gamma$ less than one degree, systematic uncertainties must be well-controlled and correlated uncertainties between different final states a

Ruining Yang, Yuqi Peng

Autonomous driving has garnered significant attention as a key research area within artificial intelligence. In the context of autonomous driving scenarios, the varying physical locations of objects correspond to different levels of danger. However, conventional evaluation criteria for automatic driving object detection often overlook the crucial aspect of a

Sarath R. Adapa, Xintong Zhang, Tianshi Feng, Ka Man Chung

Determination of heat transfer coefficients for flowing packed particle beds is essential to the design of particle heat exchangers, and other thermal processes. While such dense granular flows fall into the well-known plug-flow regime, the discrete nature of granular materials alters the thermal transport processes in both the near-wall and bulk regions of

Eve Armstrong

I investigate the peculiar situation in which I find myself healthy and strong, with a darling family, stimulating job, top-notch dental plan, and living far from active war and wildfire zones -- yet perpetually ill at ease and prone to sudden-onset exasperation when absolutely nothing has happened. My triggers include dinner parties, chairs, therapists, and

Kevin D. Stubbs, Simon Becker, Lin Lin

Recent experiments have shown that magic angle twisted bilayer graphene (MATBG) can exhibit correlated insulator behavior at half-filling. Seminal theoretical results towards understanding this phase in MATBG has shown that Hartree-Fock ground states (with a positive charge gap) can be exact many-body ground states of an idealized flat band interacting (FBI)

Shengjie Liu, Jing Wu, Jingyuan Bao, Wenyi Wang

This paper describes an investigation of the robustness of large language models (LLMs) for retrieval augmented generation (RAG)-based summarization tasks. While LLMs provide summarization capabilities, their performance in complex, real-world scenarios remains under-explored. Our first contribution is LogicSumm, an innovative evaluation framework incorporat

Orson Mengara

Audio-based machine learning systems frequently use public or third-party data, which might be inaccurate. This exposes deep neural network (DNN) models trained on such data to potential data poisoning attacks. In this type of assault, attackers can train the DNN model using poisoned data, potentially degrading its performance. Another type of data poisoning

Ali Behrouz, Michele Santacatterina, Ramin Zabih

Recent advances in deep learning have mainly relied on Transformers due to their data dependency and ability to learn at scale. The attention module in these architectures, however, exhibits quadratic time and space in input size, limiting their scalability for long-sequence modeling. Despite recent attempts to design efficient and effective architecture bac

Abdullah Irfan, Mingxing Yao, Andrew Lingenfelter, Xi Cao

Establishing limits of entanglement in open quantum systems is a problem of fundamental interest, with strong implications for applications in quantum information science. Here, we study limits of entanglement stabilization between remote qubits. We theoretically investigate the loss resilience of driven-dissipative entanglement between remote qubits coupled

Trang Quynh Nguyen, Elizabeth A. Stuart, Daniel O. Scharfstein, Elizabeth L. Ogburn

An important strategy for identifying principal causal effects, which are often used in settings with noncompliance, is to invoke the principal ignorability (PI) assumption. As PI is untestable, it is important to gauge how sensitive effect estimates are to its violation. We focus on this task for the common one-sided noncompliance setting where there are tw

Gautam Kishore Shahi, Oliver Hummel

Research organisations and their research outputs have been growing considerably in the past decades. This large body of knowledge attracts various stakeholders, e.g., for knowledge sharing, technology transfer, or potential collaborations. However, due to the large amount of complex knowledge created, traditional methods of manually curating catalogues are

Zhikai Li, Steve Vott, Bhaskar Krishnamachar

With the growing concern of AI safety, there is a need to trust the computations done by machine learning (ML) models. Blockchain technology, known for recording data and running computations transparently and in a tamper-proof manner, can offer this trust. One significant challenge in deploying ML Classifiers on-chain is that while ML models are typically w

Opher Lieber, Barak Lenz, Hofit Bata, Gal Cohen

We present Jamba, a new base large language model based on a novel hybrid Transformer-Mamba mixture-of-experts (MoE) architecture. Specifically, Jamba interleaves blocks of Transformer and Mamba layers, enjoying the benefits of both model families. MoE is added in some of these layers to increase model capacity while keeping active parameter usage manageable

Han Song, Cong Liu, Huafeng Dai

Multi-camera SLAM systems offer a plethora of advantages, primarily stemming from their capacity to amalgamate information from a broader field of view, thereby resulting in heightened robustness and improved localization accuracy. In this research, we present a significant extension and refinement of the state-of-the-art stereo SLAM system, known as ORB-SLA

Colin Keil, Aniket Gupta, Pushyami Kaveti, Hanumant Singh

Visual SLAM with thermal imagery, and other low contrast visually degraded environments such as underwater, or in areas dominated by snow and ice, remain a difficult problem for many state of the art (SOTA) algorithms. In addition to challenging front-end data association, thermal imagery presents an additional difficulty for long term relocalization and map

Michael Lambert, Evan Patterson

Category theory offers a mathematical foundation for knowledge representation and database systems. Popular existing approaches model a database instance as a functor into the category of sets and functions, or as a 2-functor into the 2-category of sets, relations, and implications. The functional and relational models are unified by double functors into the

Moein Heidari, Reza Azad, Sina Ghorbani Kolahi, René Arimond

Intrigued by the inherent ability of the human visual system to identify salient regions in complex scenes, attention mechanisms have been seamlessly integrated into various Computer Vision (CV) tasks. Building upon this paradigm, Vision Transformer (ViT) networks exploit attention mechanisms for improved efficiency. This review navigates the landscape of re

Jiapu Wang, Zheng Cui, Boyue Wang, Shirui Pan

Temporal Knowledge Graphs (TKGs) incorporate a temporal dimension, allowing for a precise capture of the evolution of knowledge and reflecting the dynamic nature of the real world. Typically, TKGs contain complex geometric structures, with various geometric structures interwoven. However, existing Temporal Knowledge Graph Completion (TKGC) methods either mod

Pooria Ashrafian, Milad Yazdani, Moein Heidari, Dena Shahriari

High-quality, large-scale data is essential for robust deep learning models in medical applications, particularly ultrasound image analysis. Diffusion models facilitate high-fidelity medical image generation, reducing the costs associated with acquiring and annotating new images. This paper utilizes recent vision-language models to produce diverse and realis

Kevin Doherty, Alan Papalia, Yewei Huang, David Rosen

Simultaneous localization and mapping (SLAM) is a critical capability in autonomous navigation, but memory and computational limits make long-term application of common SLAM techniques impractical; a robot must be able to determine what information should be retained and what can safely be forgotten. In graph-based SLAM, the number of edges (measurements) in

Giuseppe Lancia, Paolo Vidoni

We describe an exact algorithm for finding the best 2-OPT move which, experimentally, was observed to be much faster than the standard quadratic approach. To analyze its average-case complexity, we introduce a family of heuristic procedures and discuss their complexity when applied to a random tour in graphs whose edge costs are either uniform random numbers

Yanyu Jia, Guo Yu, Tiancheng Song, Fang Yuan

Two-dimensional (2D) transition metal dichalcogenides (TMDs) is a versatile class of quantum materials of interest to various fields including, e.g., nanoelectronics, optical devices, and topological and correlated quantum matter. Tailoring the electronic properties of TMDs is essential to their applications in many directions. Here, we report that a highly

Shivani Kapania, Ruiyi Wang, Toby Jia-Jun Li, Tianshi Li

Large language models are increasingly applied in real-world scenarios, including research and education. These models, however, come with well-known ethical issues, which may manifest in unexpected ways in human-computer interaction research due to the extensive engagement with human subjects. This paper reports on research practices related to LLM use, dra

Ioannis Alamanos, George P. Moustris, Costas S. Tzafestas

This paper proposes an efficient hybrid localization framework for the autonomous navigation of an unmanned ground vehicle in uneven or rough terrain, as well as techniques for detailed processing of 3D point cloud data. The framework is an extended version of FAST-LIO2 algorithm aiming at robust localization in known point cloud maps using Lidar and inertia

Nestor Coria, Jamie Haddock, Jaime Pacheco

Large-scale systems of linear equations arise in machine learning, medical imaging, sensor networks, and in many areas of data science. When the scale of the systems are extreme, it is common for a fraction of the data or measurements to be corrupted. The Quantile Randomized Kaczmarz (QRK) method is known to converge on large-scale systems of linear equation

Anwesha Maharana, W. Douglas Cramer, Evangelia Samara, Camilla Scolini

EUropean Heliospheric FORecasting Information Asset (EUHFORIA) is a physics-based data-driven solar wind and CME propagation model designed for space weather forecasting and event analysis investigations. Although EUHFORIA can predict the solar wind plasma and magnetic field properties at Earth, it is not equipped to quantify the geoeffectiveness of the sola

Qi-Jun Hong, Zi-Kui Liu

We build a comprehensive methodology for the fast computation of entropy across both solid and liquid phases. The proposed method utilizes a single trajectory of molecular dynamics (MD) to facilitate the calculation of entropy, which is composed of three components. The electronic entropy is determined through the temporal average acquired from density funct

Dimitris Bertsimas, Vassilis Digalakis, Yu Ma, Phevos Paschalidis

We consider the problem of retraining machine learning (ML) models when new batches of data become available. Existing approaches greedily optimize for predictive power independently at each batch, without considering the stability of the model's structure or analytical insights across retraining iterations. We propose a model-agnostic framework for finding

Craig Hogan

Cosmic structure on the largest scales preserves the pattern laid down by quantum fluctuations of gravity in the early universe on scales comparable to inflationary horizons. It is proposed here that fluctuations create physical correlations only within finite regions enclosed by causal diamonds, like entanglement in other quantum systems. Conformal geometry

Luisa I. Martínez-Merino, Diego Ponce, Justo Puerto

This paper compares different exact approaches to solve the Discrete Ordered Median Problem (DOMP). In recent years, DOMP has been formulated using set packing constraints giving rise to one of its most promising formulations. The use of this family of constraints, known as strong order constraints (SOC), has been validated in the literature by its theoretic

Richa Priyadarshani, Ki-Hong Park, Yalcin Ata, Mohamed-Slim Alouini

As the evolution of wireless communication progresses towards 6G networks, extreme bandwidth communication (EBC) emerges as a key enabler to meet the ambitious key performance indicator set for this next-generation technology. 6G aims for peak data rates of 1 Tb/s, peak spectral efficiency of 60 b/s/Hz, maximum bandwidth of 100 GHz, and mobility support up t

Huy Pham, Hoang Ta, Hoa T. Vu

In this work, we present data stream algorithms to compute optimal splits for decision tree learning. In particular, given a data stream of observations \(x_i\) and their corresponding labels \(y_i\), without the i.i.d. assumption, the objective is to identify the optimal split \(j\) that partitions the data into two sets, minimizing the mean squared error (

Yuhang Li, Xin Dong, Chen Chen, Jingtao Li

Synthetic image data generation represents a promising avenue for training deep learning models, particularly in the realm of transfer learning, where obtaining real images within a specific domain can be prohibitively expensive due to privacy and intellectual property considerations. This work delves into the generation and utilization of synthetic images d

Marcin Kolakowski

In this paper localization using UWB positioning system and an inertial unit containing a single accelerometer is considered. The main part of the paper describes a novel algorithm for person localization. The algorithm is based on modified Extended Kalman Filter and utilizes TDOA (Time Difference of Arrival) results obtained from UWB system and results of a

Michael Tanksalvala, Christina L. Porter, Yuka Esashi, Bin Wang

Next-generation nano and quantum devices have increasingly complex 3D structure. As the dimensions of these devices shrink to the nanoscale, their performance is often governed by interface quality or precise chemical or dopant composition. Here we present the first phase-sensitive extreme ultraviolet imaging reflectometer. It combines the excellent phase st

Jonas Richter

The development of powerful numerical techniques has drastically improved our understanding of quantum matter out of equilibrium. Inspired by recent progress in the area of noisy intermediate-scale quantum devices, this paper highlights hybrid Schr\"odinger-Feynman techniques as an innovative approach to efficiently simulate certain aspects of many-body quan

Siyuan Li

Unmanned aerial vehicle (UAV) techniques have developed rapidly within the past few decades. Using UAVs provides benefits in numerous applications such as site surveying, communication systems, parcel delivery, target tracking, etc. The high manoeuvrability of the drone and its ability to replace a certain amount of labour cost are the reasons why it can be

Silpa Vadakkeeveetil Sreelatha, Adarsh Kappiyath, Abhra Chaudhuri, Anjan Dutta

Neural networks trained on biased datasets tend to inadvertently learn spurious correlations, hindering generalization. We formally prove that (1) samples that exhibit spurious correlations lie on a lower rank manifold relative to the ones that do not; and (2) the depth of a network acts as an implicit regularizer on the rank of the attribute subspace that i

Giulio Turrisi, Lucas Schulze, Vivian S. Medeiros, Claudio Semini

In this paper, we introduce the concept of using passive arm structures with intrinsic impedance for robot-robot and human-robot collaborative carrying with quadruped robots. The concept is meant for a leader-follower task and takes a minimalist approach that focuses on exploiting the robots' payload capabilities and reducing energy consumption, without comp

Michael J. Abere, Hyein Choi, Levi Van Bastian, Luis Jauregui

The additive manufacture of compositionally graded Al/Cu parts by laser engineered net shaping (LENS) is demonstrated. The use of a blue light build laser enabled deposition on a Cu substrate. The thermal gradient and rapid solidification inherent to selective laser melting enabled mass transport of Cu up to 4 mm away from a Cu substrate through a pure Al de

Larry Goldstein, Todd Kemp

Sampling bias is a foundational concept in statistics; associated bias transforms, such as size bias, have come to play important roles in probability theory of late. The first author and G. Reinert introduced zero bias, a transform whose unique fixed point is the normal distribution; it has become a standard tool in Stein's method and Gaussian approximation

Panagiotis Papadimitratos, Zygmunt J. Haas

The secure operation of the routing protocol is one of the major challenges to be met for the proliferation of the Mobile Ad hoc Networking (MANET) paradigm. Nevertheless, security enhancements have been proposed mostly for reactive MANET protocols. The proposed here Secure Link State Routing Protocol (SLSP) provides secure proactive topology discovery, whic

William Cooperman, Gautam Iyer, Seungjae Son

In many situations, the combined effect of advection and diffusion greatly increases the rate of convergence to equilibrium -- a phenomenon known as enhanced dissipation. Here we study the situation where the advecting velocity field generates a random dynamical system satisfying certain Harris conditions. If $\kappa$ denotes the strength of the diffusion, t

Xiaomin Ouyang, Mani Srivastava

Most studies on machine learning in sensing systems focus on low-level perception tasks that process raw sensory data within a short time window. However, many practical applications, such as human routine modeling and occupancy tracking, require high-level reasoning abilities to comprehend concepts and make inferences based on long-term sensor traces. Exist

Valeria de Paiva, Alexandre Rademaker

This short paper describes the first steps in a project to construct a knowledge graph for Brazilian history based on the Brazilian Dictionary of Historical Biographies (DHBB) and Wikipedia/Wikidata. We contend that large repositories of Brazilian-named entities (people, places, organizations, and political events and movements) would be beneficial for extra