April 2024 arXiv papers — page 39

Brent F. West, Peter A. Becker, Georgios Vasilopoulos

We model the spectral formation occurring in the binary X-ray pulsar RX~J0209.6-7427 during the 2019 super-Eddington outburst. Using a theoretical model previously developed by the authors, we are able to produce spectra that closely resemble the phase-averaged X-ray spectra observed using NuSTAR and Insight-HXMT during low and high luminosity states of the

Yousef Emami, Hao Gao, Kai Li, Luis Almeida

Unmanned Aerial Vehicle (UAV) swarms play an effective role in timely data collection from ground sensors in remote and hostile areas. Optimizing the collective behavior of swarms can improve data collection performance. This paper puts forth a new mean field flight resource allocation optimization to minimize age of information (AoI) of sensory data, where

Abdurro'uf, Rebecca L. Larson, Dan Coe, Tiger Yu-Yang Hsiao

We present JWST/NIRSpec high-resolution spectroscopy G395H/F290LP of MACS0647-JD, a gravitationally lensed galaxy merger at $z=10.167$. The new spectroscopy, which is acquired for the two lensed images (JD1 and JD2), detects and resolves emission lines in the rest-frame ultraviolet (UV) and blue optical, including the resolved [OII]3726,3729 doublet, [NeIII]

Tiger Yu-Yang Hsiao, Javier Álvarez-Márquez, Dan Coe, Alejandro Crespo Gómez

JWST spectroscopy has revolutionized our understanding of galaxies in the early universe. Covering wavelengths up to $5.3\,{\rm \mu m}$, NIRSpec can detect rest-frame optical emission lines H$\alpha$ out to $z = 7$ and [O III] to $z = 9.5$. Observing these lines in more distant galaxies requires longer wavelength spectroscopy with MIRI. Here we present MIRI

Teng Ye, Jingnan Zheng, Junhui Jin, Jingyi Qiu

While small businesses are increasingly turning to online crowdfunding platforms for essential funding, over 40% of these campaigns may fail to raise any money, especially those from low socio-economic areas. We utilize the latest advancements in AI technology to identify crucial factors that influence the success of crowdfunding campaigns and to improve the

Kam Cheong Au, Steven Charlton

We show how to convert the generating series of interpolated multiple zeta values, or multiple $t$ values, with repeating blocks of length 1 into hypergeometric series. Then we invoke creative telescoping on their generating functions, in some known cases for illustration, and in some apparently new cases, reducing them to polynomials in Riemann zeta values.

Mojdeh Rahmanian, Seyed Mostafa Fakhrahmad, Seyedeh Zahra Mousavi

Objective: Clinical trials are essential for advancing pharmaceutical interventions, but they face a bottleneck in selecting eligible participants. Although leveraging electronic health records (EHR) for recruitment has gained popularity, the complex nature of unstructured medical texts presents challenges in efficiently identifying participants. Natural Lan

Adrian Wurm, Honglu Sun

In this work, we study a class of hybrid dynamical systems called hybrid gene regulatory networks (HGRNs) which was proposed to model gene regulatory networks. In HGRNs, there exist well-behaved trajectories that reach a fixed point or converge to a limit cycle, as well as chaotic trajectories that behave non-periodic or indeterministic. In our work, we inve

Jakub Adamczyk, Jakub Poziemski, Pawel Siedlecki

The global decline in bee populations poses significant risks to agriculture, biodiversity, and environmental stability. To bridge the gap in existing data, we introduce ApisTox, a comprehensive dataset focusing on the toxicity of pesticides to honey bees (Apis mellifera). This dataset combines and leverages data from existing sources such as ECOTOX and PPDB

Ya-Ting Yang, Tao Zhang, Quanyan Zhu

Privacy-preserving AI algorithms are widely adopted in various domains, but the lack of transparency might pose accountability issues. While auditing algorithms can address this issue, machine-based audit approaches are often costly and time-consuming. Herd audit, on the other hand, offers an alternative solution by harnessing collective intelligence. Nevert

C. David Hinostroza, Leandro Rodrigues de Faria, Gustavo H. Cassemiro, J. Larrea Jiménez

The bismuth-halide Bi$_4$I$_4$ undergoes a structural transition around $T_P\sim 300$K, which separates a high-temperature $\beta$ phase ($T>T_P$) from a low-temperature $\alpha$ phase ($T<T_P$). $\alpha$ and $\beta$ phases are suggested to host electronic band structures with distinct topological classifications. Rapid quenching was reported to stabilize a

Samyak Rawlekar, Shubhang Bhatnagar, Vishnuvardhan Pogunulu Srinivasulu, Narendra Ahuja

Multi-label Recognition (MLR) involves the identification of multiple objects within an image. To address the additional complexity of this problem, recent works have leveraged information from vision-language models (VLMs) trained on large text-images datasets for the task. These methods learn an independent classifier for each object (class), overlooking c

Cuong Nhat Ha, Shima Asaadi, Sanjeev Kumar Karn, Oladimeji Farri

Vision-language models, while effective in general domains and showing strong performance in diverse multi-modal applications like visual question-answering (VQA), struggle to maintain the same level of effectiveness in more specialized domains, e.g., medical. We propose a medical vision-language model that integrates large vision and language models adapted

Yan Chen, Jasmine Jones

Developing embedded systems is a complex endeavor that frequently requires collaborative teamwork. With the rise of freelance work and the global shift towards remote work, the need for effective remote collaboration has become crucial for many developers and their clients. However, current communication and coordination tools are predominantly tailored for

Joshua Lin, William Detmold, Stefan Meinel

X-space schemes are gauge-invariant, regulator-independent renormalization schemes that are defined by requiring position-space correlation functions of gauge invariant operators to be equal to their noninteracting values at particular kinematic points. These schemes can be used to nonperturbatively renormalize composite operators in Lattice Quantum Chromody

Adnan Machado, Kecheng Xu, Pierre E. Sullivan

This study investigates the aerodynamic effects of low- and high-frequency synthetic jet control strategies on a National Advisory Committee for Aeronautics (NACA) 0025 airfoil. Visualizations and measurements are employed to assess the stability of the flow, focusing on the shear layer and wake dynamics under two forcing frequencies. High-frequency actuatio

Mohamad Fazelnia, Viktoria Koscinski, Spencer Herzog, Mehdi Mirakhorli

We investigate the use of Natural Language Inference (NLI) in automating requirements engineering tasks. In particular, we focus on three tasks: requirements classification, identification of requirements specification defects, and detection of conflicts in stakeholders' requirements. While previous research has demonstrated significant benefit in using NLI

Baoli Hao, Chun Liu, Ulisses Braga-Neto, Lifan Wang

Training physics-informed neural networks (PINNs) on stiff, time-dependent PDEs remains a fundamental challenge due to optimization instabilities and gradient pathologies. Through a series of rigorous ablation studies and Neural Tangent Kernel (NTK) analysis, we identify that the exact enforcement of initial conditions (ICs) is a decisive factor in stabilizi

Harit Vishwakarma, Reid, Chen, Sui Jiet Tay

Auto-labeling is an important family of techniques that produce labeled training sets with minimum manual labeling. A prominent variant, threshold-based auto-labeling (TBAL), works by finding a threshold on a model's confidence scores above which it can accurately label unlabeled data points. However, many models are known to produce overconfident scores, le

Rajni Chahal, Michael D. Toomey, Logan T. Kearney, Ada Sedova

Polyacrylonitrile (PAN) is an important commercial polymer, bearing atactic stereochemistry resulting from nonselective radical polymerization. As such, an accurate, fundamental understanding of governing interactions among PAN molecular units are indispensable to advance the design principles of final products at reduced processability costs. While ab initi

Michael A. Henning, Jerzy Topp

A set $S$ of vertices in a graph $G$ is a total dominating set of $G$ if every vertex is adjacent to a vertex in $S$. The total domination number $\gamma_t(G)$ is the minimum cardinality of a total dominating set of $G$. The total domination subdivision number $\mbox{sd}_{\gamma_t}(G)$ of a graph $G$ is the minimum number of edges that must be subdivided (wh

M. Echeveste, M. L. Novarino, O. G. Benvenuto, M. A. De Vito

We revisit the evolution of low-mass close binary systems under different magnetic braking (MB) prescriptions. We study binaries with a neutron star accretor. During mass transfer episodes, these systems emit X-rays and are known as Low Mass X-ray Binaries (LMXBs). When mass transfer stops, they can be observed as binary pulsars. Additionally, some of these

Zhaozhou An, Matthew G. Walker, Andrew B. Pace

We present results from simultaneous modeling of 2D (projected along the line of sight) position, proper motion and line-of-sight velocity for \textit{Gaia}- and APOGEE-observed stars near the centre of the Sagittarius (Sgr) dwarf spheroidal galaxy. We use a mixture model that allows for independent sub-populations contributed by the Sgr galaxy, its nuclear

Sarala Naidu, Ning Xiong

Anomaly detection in industrial systems is crucial for preventing equipment failures, ensuring risk identification, and maintaining overall system efficiency. Traditional monitoring methods often rely on fixed thresholds and empirical rules, which may not be sensitive enough to detect subtle changes in system health and predict impending failures. To address

Soumya Chakraborty, Gordon K. L. Wong, Ferdi Oda, Vanessa Wachter

We explore a magnetooptomechanical system consisting of a single magnetic microparticle optically levitated within the core of a helically twisted single-ring hollow-core photonic crystal fibre. We use newly-developed magnetic particles that have a core of antiferromagnetic yttrium-ortho-ferrite (YFeO3) and a shell of ferrimagnetic YIG (Y3Fe5O12) approximate

Shuang Zhou, Alfonso Aragón-Salamanca, Michael Merrifield

We analyse a sample of massive disk galaxies selected from the SDSS-IV/MaNGA survey to investigate how the evolution of these galaxies depends on their stellar and halo masses. We applied a semi-analytic spectral fitting approach to the data from different regions in the galaxies to derive several of their key physical properties. From the best-fit model res

Chris Bispels, Muhammet Boran, Steven J. Miller, Eliel Sosis

Around the year 2007, one of the authors, Tsai, accidentally discovered a property of the number $198$ he saw on the license plate of a car. Namely, if we take $198$ and its reversal $891$, which have prime factorizations $198 = 2\cdot 3^2\cdot 11$ and $891 = 3^4\cdot 11$ respectively, and sum the numbers appearing in each factorization getting $2+3+2+11 = 1

Jose L. Salmeron, Irina Arévalo

Federated learning is an emerging machine learning approach that allows the construction of a model between several participants who hold their own private data. This method is secure and privacy-preserving, suitable for training a machine learning model using sensitive data from different sources, such as hospitals. In this paper, the authors propose two in

Sarala Naidu, Ning Xiong

Anomaly detection plays a crucial role in industrial settings, particularly in maintaining the reliability and optimal performance of cooling systems. Traditional anomaly detection methods often face challenges in handling diverse data characteristics and variations in noise levels, resulting in limited effectiveness. And yet traditional anomaly detection of

A. Roman-Lopes

O-type stars are known to significantly contribute to both the dynamics and evolution of galaxies. Massive and luminous, they probably control and regulate the galaxies star formation rates. For this work I performed a redetermination of the spectral types and effective temperatures of the Galactic O-type stars MSP182, MSP183, MSP199, VPHAS-01338, and VPHAS-

Aditya Chichani, Juzer Golwala, Tejas Gundecha, Kiran Gawande

Considering the premise that the number of products offered grow in an exponential fashion and the amount of data that a user can assimilate before making a decision is relatively small, recommender systems help in categorizing content according to user preferences. Collaborative filtering is a widely used method for computing recommendations due to its good

Xingjian Bai, Christian Coester, Romain Cosson

Introduced by Papadimitriou and Yannakakis in 1989, layered graph traversal is a central problem in online algorithms and mobile computing that has been studied for several decades, and which now is essentially resolved in its original formulation. In this paper, we demonstrate that what appears to be an innocuous modification of the problem actually leads t

Kara Smith, Daniel Proga, Randall Dannen, Sergei Dyda

In disk wind models for active galactic nuclei (AGN) outflows, high-energy radiation poses a significant problem wherein the gas can become overionized, effectively disabling what is often inferred to be the largest force acting on the gas: the radiation force due to spectral line opacity. Calculations of this radiation force depend on the magnitude of ioniz

Grace Guo, Lifu Deng, Animesh Tandon, Alex Endert

The recent prevalence of publicly accessible, large medical imaging datasets has led to a proliferation of artificial intelligence (AI) models for cardiovascular image classification and analysis. At the same time, the potentially significant impacts of these models have motivated the development of a range of explainable AI (XAI) methods that aim to explain

Lulwah Al-Essa, Mohamed Majdoub

We investigate the lifespan of solutions to a specific variant of the semilinear wave equation, which incorporates weighted nonlinearity $$ u_{tt}-u_{xx} =|x|^\alpha |u|^p, \quad\mbox{for}\;\;\; (t,x)\in (0,\infty)\times\mathbb{R}, $$ where $p>1$, $\alpha\in\mathbb{R}$. We explore the behavior of solutions for small initial data, considering the influence of

Jiawei Hu, Siu-Cheong Lau, Ju Tan

In this paper, we construct the ADHM quiver representations and the corresponding sheaves as the mirror objects of formal deformations of the framed immersed Lagrangian sphere decorated with flat bundles. More generally, we construct Nakajima quiver varieties as localized mirrors of framed nodal unions of Lagrangian spheres in dimension two. This produces a

Giovanni Colavizza, Lauren Cadwallader, Marcel LaFlamme, Grégory Dozot

Calls to make scientific research more open have gained traction with a range of societal stakeholders. Open Science practices include but are not limited to the early sharing of results via preprints and openly sharing outputs such as data and code to make research more reproducible and extensible. Existing evidence shows that adopting Open Science practice

B. Sbarufatti, F. Coti Zelati, A. Marino, S. Mereghetti

Recent observations have led to the discovery of numerous optically selected binaries containing an undetected component with mass consistent with a compact object (neutron star or white dwarf). Using the the Neil Gehrels Swift Observatory we have carried out X-ray and UV observations of a small sample of these binaries. Four systems are wide (with orbital p

Nathan Cornelius, Lewis Dominguez, David Mehrle, Lakshay Modi

We prove that the image of the total power operation for Burnside rings $A(G) \to A(G\wr\Sigma_n)$ lies inside a relatively small, combinatorial subring $\mathring A(G,n) \subseteq A(G \wr \Sigma_n)$. As $n$ varies, the subrings $\mathring A(G,n)$ assemble into a commutative graded ring $\mathring A(G)$ with a universal property: $\mathring A(G)$ carries the

Minwu Kim, Sidahmed Benabderrahmane, Talal Rahwan

This research presents a predictive model to identify potential targets of activist investment funds--entities that acquire significant corporate stakes to influence strategic and operational decisions, ultimately enhancing shareholder value. Predicting such targets is crucial for companies aiming to mitigate intervention risks, activist funds seeking optima

Fin Amin, Jung-Eun Kim

When neural networks are confronted with unfamiliar data that deviate from their training set, this signifies a domain shift. While these networks output predictions on their inputs, they typically fail to account for their level of familiarity with these novel observations. Prevailing works navigate test-time adaptation with the goal of curtailing model ent

Jameson O'Reilly, George Toh, Isabella Goetting, Sagnik Saha

We entangle two co-trapped atomic barium ion qubits by collecting single visible photons from each ion through in-vacuo 0.8 NA objectives, interfering them through an integrated fiber-beamsplitter and detecting them in coincidence. This projects the qubits into an entangled Bell state with an observed fidelity lower bound of F > 94%. We also introduce an ytt

Bonnie E. Shook-Sa, Paul N. Zivich, Chanhwa Lee, Keyi Xue

Doubly robust estimators have gained popularity in the field of causal inference due to their ability to provide consistent point estimates when either an outcome or exposure model is correctly specified. However, for nonrandomized exposures the influence function based variance estimator frequently used with doubly robust estimators of the average causal ef

Anushka Sharma, Antos Cheeramban Varghese, Anamitra Pal

Recent studies indicate that the noise characteristics of phasor measurement units (PMUs) can be more accurately described by non-Gaussian distributions. Consequently, estimation techniques based on Gaussian noise assumptions may produce poor results with PMU data. This paper considers the PMU based line parameter estimation (LPE) problem, and investigates t

Jiaqing Yuan, Lin Pan, Chung-Wei Hang, Jiang Guo

Large language models (LLMs) have shown remarkable performance on a variety of NLP tasks, and are being rapidly adopted in a wide range of use cases. It is therefore of vital importance to holistically evaluate the factuality of their generated outputs, as hallucinations remain a challenging issue. In this work, we focus on assessing LLMs' ability to recall

Pian Yu, Shufang Zhu, Giuseppe De Giacomo, Marta Kwiatkowska

Consider an agent acting to achieve its temporal goal, but with a "trembling hand". In this case, the agent may mistakenly instruct, with a certain (typically small) probability, actions that are not intended due to faults or imprecision in its action selection mechanism, thereby leading to possible goal failure. We study the trembling-hand problem in the co

He Jiang, Yulun Zhang, Rishi Veerapaneni, Jiaoyang Li

Multi-Agent Path Finding (MAPF) is the problem of moving multiple agents from starts to goals without collisions. Lifelong MAPF (LMAPF) extends MAPF by continuously assigning new goals to agents. We present our winning approach to the 2023 League of Robot Runners LMAPF competition, which leads us to several interesting research challenges and future directio

Hanamichi Kawamura

In this paper, we consider a discrete version of iterated integrals by the naive (equally divided) Riemann sum. In particular, basic three formulas for usual iterated integrals are discritized. Moreover, we proved cyclic sum formulas for discrete iterated integrals. They imply the cyclic sum formula for multiple polylogarithms.

Cheng Kang, Daniel Novak, Katerina Urbanova, Yuqing Cheng

Large language models (LLMs) have demonstrated impressive generalization capabilities on specific tasks with human-written instruction data. However, the limited quantity, diversity, and professional expertise of such instruction data raise concerns about the performance of LLMs in psychotherapy tasks when provided with domain-specific instructions. To addre

Nicolas Perrin-Gilbert

This paper presents AFU, an off-policy deep RL algorithm addressing in a new way the challenging "max-Q problem" in Q-learning for continuous action spaces, with a solution based on regression and conditional gradient scaling. AFU has an actor but its critic updates are entirely independent from it. As a consequence, the actor can be chosen freely. In the in

Wanru Zhao, Vidit Khazanchi, Haodi Xing, Xuanli He

Large language model (LLM) services have recently begun offering a plugin ecosystem to interact with third-party API services. This innovation enhances the capabilities of LLMs, but it also introduces risks, as these plugins developed by various third parties cannot be easily trusted. This paper proposes a new attacking framework to examine security and safe

Yu Gao, Juan Camilo Vega, Paul Chow

FPGAs are rarely mentioned when discussing the implementation of large machine learning applications, such as Large Language Models (LLMs), in the data center. There has been much evidence showing that single FPGAs can be competitive with GPUs in performance for some computations, especially for low latency, and often much more efficient when power is consid

Kenneth H. Karlsen, Peter H. C. Pang

Convergence of stochastic integrals driven by Wiener processes $W_n$, with $W_n \to W$ almost surely in $C_t$, is crucial in analyzing SPDEs. Our focus is on the convergence of the form $\int_0^T V_n\, \mathrm{d} W_n \to \int_0^T V\, \mathrm{d} W$, where $\{V_n\}$ is bounded in $L^p(\Omega \times [0,T];X)$ for a Banach space $X$ and some finite $p > 2$. This

Archisman Ghosh, Debarshi Kundu, Avimita Chatterjee, Swaroop Ghosh

Quantum Generative Adversarial Networks (qGANs) are at the forefront of image-generating quantum machine learning models. To accommodate the growing demand for Noisy Intermediate-Scale Quantum (NISQ) devices to train and infer quantum machine learning models, the number of third-party vendors offering quantum hardware as a service is expected to rise. This e

Kuan-I Chung, Daniel Moyer

We introduce an assessment procedure for interactive segmentation models. Based on concepts from Bayesian Experimental Design, the procedure measures a model's understanding of point prompts and their correspondence with the desired segmentation mask. We show that Oracle Dice index measurements are insensitive or even misleading in measuring this property. W

Maximilian Wendlinger, Kilian Tscharke, Pascal Debus

Quantum machine learning (QML) continues to be an area of tremendous interest from research and industry. While QML models have been shown to be vulnerable to adversarial attacks much in the same manner as classical machine learning models, it is still largely unknown how to compare adversarial attacks on quantum versus classical models. In this paper, we sh

Guilherme Zeus Dantas e Moura, Ramanuja Charyulu Telekicherla Kandalam, Dora Woodruff

Laurent phenomenon algebras, first introduced by Lam and Pylyavskyy, are a generalization of cluster algebras that still possess many salient features of cluster algebras. Graph Laurent phenomenon algebras, defined by Lam and Pylyavskyy, are a subclass of Laurent phenomenon algebras whose structure is given by the data of a directed graph. In this paper, we

Minhao Zhu, Yifei Sun, Lizhao You, Zhaorui Wang

This paper revisits the identity detection problem under the current grant-free protocol in massive machine-type communications (mMTC) by asking the following question: for stable identity detection performance, is it enough to permit active devices to transmit preambles without any handshaking with the base station (BS)? Specifically, in the current grant-f

Eduard I. Vorobyov, Aleksandr M. Skliarevskii, Manuel Guedel, Tamara Molyarova

Aims. A new mechanism of dust accumulation and planetesimal formation in a gravitationally unstable disk with suppressed magnetorotational instability is studied and compared with the classical dead zone in a layered disk model. Methods. We use numerical hydrodynamics simulations in the thin-disk limit FEOSAD code to model the formation and long-term evoluti

Jan Gorzny, Martin Derka

Rollups are a popular blockchain paradigm where one blockchain network is anchored to a different blockchain network, typically though smart contracts and data commitments. The rollup executes transactions on its own network and periodically publishes them along with the state root of the rollup network. The state root is determined to be final by a protocol

Anton Nykänen, Leander Thiessen, Elsi-Mari Borrelli, Vijay Krishna

Quantum chemistry simulations offer a cost-effective way for computational design of BODIPY photosensitizers with potential use in photodynamic therapy (PDT). However, accurate predictions of photophysical properties, such as excitation energies, pose a challenge for the popular time-dependent density functional theory (TDDFT) and equation-of-motion coupled

Yongqi Zhao, Wenbo Xiao, Tomislav Mihalj, Jia Hu

The advent of Large Language Models (LLM) provides new insights to validate Automated Driving Systems (ADS). In the herein-introduced work, a novel approach to extracting scenarios from naturalistic driving datasets is presented. A framework called Chat2Scenario is proposed leveraging the advanced Natural Language Processing (NLP) capabilities of LLM to unde

Santanu Tripathy, K Rajesh Nayak

In this article, we investigate the impact of cosmological parameters on black holes using an exact solution to Einstein's equations that satisfies the Whittaker equation of state. We examine a spherically symmetric black hole in the background of a static Einstein Universe with a perfect fluid source with the cosmological constant. This solution is characte

Andreas Stavrou

We endow the cohomology of configuration spaces of a manifold with a product arising from superposing configurations. We prove that, under the scanning isomorphism, this product corresponds to the cup-product of the section space of the standard scanning bundle of the manifold.

Barry Bradlyn, Peter Abbamonte

Sum rules for linear response functions give powerful and experimentally-relevant relations between frequency moments of response functions and ground state properties. In particular, renewed interest has been drawn to optical conductivity and density-density sum rules and their connection to quantum geometry in topological materials. At the same time, recen

Calvin Beck, Irene Yoon, Hanxi Chen, Yannick Zakowski

This paper provides a novel approach to reconciling complex low-level memory model features, such as pointer--integer casts, with desired refinements that are needed to justify the correctness of program transformations. The idea is to use a "two-phased" memory model, one with and unbounded memory and corresponding unbounded integer type, and one with a fini

Kirill A. Rivkin

Interferometers are some of the most important optical devices, yet their spin wave based analogues so far received limited attention. In this work we demonstrate how one can design Mach-Zehnder Interferometer (MZI) operating on spin waves, and use to construct a spin wave version of a well known1 MZI based AI computing device. Modeling, performed both micro

Muralikrishnan Gopalakrishnan Meena, Demetri Liousas, Andrew D. Simin, Aditya Kashi

We develop time-series machine learning (ML) methods for closure modeling of the Unsteady Reynolds Averaged Navier Stokes (URANS) equations applied to stably stratified turbulence (SST). SST is strongly affected by fine balances between forces and becomes more anisotropic in time for decaying cases. Moreover, there is a limited understanding of the physical

Matteo Capucci, Owen Lynch, David I. Spivak

Organizing physics has been a long-standing preoccupation of applied category theory, going back at least to Lawvere. We contribute to this research thread by noticing that Hamiltonian mechanics and gradient descent depend crucially on a consistent choice of transformation -- which we call a reaction structure -- from the cotangent bundle to the tangent bund

Melih Yazgan, Thomas Graf, Min Liu, Tobias Fleck

This survey analyzes intermediate fusion methods in collaborative perception for autonomous driving, categorized by real-world challenges. We examine various methods, detailing their features and the evaluation metrics they employ. The focus is on addressing challenges like transmission efficiency, localization errors, communication disruptions, and heteroge

Yan Zhang, Teng Xue, Amirreza Razmjoo, Sylvain Calinon

Learning from Demonstration (LfD) stands as an efficient framework for imparting human-like skills to robots. Nevertheless, designing an LfD framework capable of seamlessly imitating, generalizing, and reacting to disturbances for long-horizon manipulation tasks in dynamic environments remains a challenge. To tackle this challenge, we present Logic Dynamic M

Fabrizio Carpi, Soheil Rostami, Joonyoung Cho, Siddharth Garg

High peak-to-average power ratio (PAPR) is one of the main factors limiting cell coverage for cellular systems, especially in the uplink direction. Discrete Fourier transform spread orthogonal frequency-domain multiplexing (DFT-s-OFDM) with spectrally-extended frequency-domain spectrum shaping (FDSS) is one of the efficient techniques deployed to lower the P

Filipa Lino, Carlos Santiago, Manuel Marques

In the field of 3D Human Pose Estimation (HPE), accurately estimating human pose, especially in scenarios with occlusions, is a significant challenge. This work identifies and addresses a gap in the current state of the art in 3D HPE concerning the scarcity of data and strategies for handling occlusions. We introduce our novel BlendMimic3D dataset, designed

Titus D. Morris, Ananth Kaushik, Martin Roetteler, Phillip C. Lotshaw

Combinatorial optimization is a promising application for near-term quantum computers, however, identifying performant algorithms suited to noisy quantum hardware remains as an important goal to potentially realizing quantum computational advantages. To address this we present a variational quantum algorithm for solving combinatorial optimization problems wi

Sathwik Chadaga, Xinyu Wu, Eytan Modiano

We consider the problem of predicting power failure cascades due to branch failures. We propose a flow-free model based on graph neural networks that predicts grid states at every generation of a cascade process given an initial contingency and power injection values. We train the proposed model using a cascade sequence data pool generated from simulations.

Rashadul Hasan Badhon, Atalie Carina Thompson, Jennifer I. Lim, Theodore Leng

Purpose: This study explores the feasibility of using generative machine learning (ML) to translate Optical Coherence Tomography (OCT) images into Optical Coherence Tomography Angiography (OCTA) images, potentially bypassing the need for specialized OCTA hardware. Methods: The method involved implementing a generative adversarial network framework that inclu

Meshach Ndlovu

This paper presents a compartmental model for the transmission dynamics of Anaplasmosis in resource limited farmers cattle subjected to a biological control method. The study seeks to evaluate the stability and control of cattle herds dynamics relative to finite agitation. Anaplasmosis disease pose a major threat in eradicating cattle population growth in re

Jonatan Nunez, David Huergo, Diego Lodares, Suyash Shrestha

We present the implementation and validation of an immersed boundary volume penalization method in the computational fluid dynamics solver CODA (from ONERA, DLR, and Airbus). Our goal is to model and simulate turbulent fluid flows in complex 3D aerodynamic configurations through the numerical solution of the Reynolds--averaged Navier--Stokes equations using

Vipul Reddy. P, Vishnu Vardhan Reddy. B, Sukriti

Sign language recognition (SLR) technology has enormous promise to improve communication and accessibility for the difficulty of hearing. This paper presents a novel approach for identifying gestures in TSL using the YOLOv5 object identification framework. The main goal is to create an accurate and successful method for identifying TSL gestures so that the d

Vicente Balmaseda, Ying Xu, Yixin Cao, Nate Veldt

Cluster deletion is an NP-hard graph clustering objective with applications in computational biology and social network analysis, where the goal is to delete a minimum number of edges to partition a graph into cliques. We first provide a tighter analysis of two previous approximation algorithms, improving their approximation guarantees from 4 to 3. Moreover,

Darren Edge, Ha Trinh, Newman Cheng, Joshua Bradley

The use of retrieval-augmented generation (RAG) to retrieve relevant information from an external knowledge source enables large language models (LLMs) to answer questions over private and/or previously unseen document collections. However, RAG fails on global questions directed at an entire text corpus, such as "What are the main themes in the dataset?", si

Edward Farhi, Stephen P. Jordan

We demonstrate that a high fidelity approximation to $| \Psi_b \rangle$, the quantum superposition over all bit strings within Hamming distance $b$ of the codewords of a dimension-$k$ linear code over $\mathbb{Z}_2^n$, can be efficiently constructed by a quantum circuit for large values of $n$, $b$ and $k$ which we characterize. We do numerical experiments a

Brian R Williams

In four-dimensional conformal field theory, the numbers a and c are defined as coefficients of particular terms in the operator product expansion (OPE) of the energy-momentum tensor. With supersymmetry there are relations between these coefficients and mixed R-symmetry and gravitational anomalies. In this paper we prove a relationship between these coefficie

Elena Albu, Shan Gao, Pieter Stijnen, Frank Rademakers

Prognostic outcomes related to hospital admissions typically do not suffer from censoring, and can be modeled either categorically or as time-to-event. Competing events are common but often ignored. We compared the performance of random forest (RF) models to predict the risk of central line-associated bloodstream infections (CLABSI) using different outcome o

Schuyler R. Borges, Gabrielle G. Jones, Tyler D. Robinson

Modeling the detection of life has never been more opportune. With next generation space telescopes, like the currently developing Habitable Worlds Observatory (HWO) concept, we will begin to characterize rocky exoplanets potentially similar to Earth. However, currently, few realistic planetary spectra containing surface biosignatures have been paired with d

Ken Kikuchi

We classify connected \'etale algebras $A$'s in multiplicity-free modular fusion categories $\mathcal B$'s with $\text{rank}(\mathcal B)\le9$. We also identify categories $\mathcal B_A$'s of right $A$-modules. The results have physical applications in constraining renormalization group flows. As demonstration, we study massive renormalization group flows fro

Ankolika De

WhatsApp has become a pivotal communication tool in India, transcending cultural boundaries and deeply integrating into the nation's digital landscape. Meta's introduction of WhatsApp for Business aligns seamlessly with the platform's popularity, offering businesses a crucial tool. However, the monetization plans pose challenges, particularly for smaller bus

Eric Slyman, Stefan Lee, Scott Cohen, Kushal Kafle

Recent dataset deduplication techniques have demonstrated that content-aware dataset pruning can dramatically reduce the cost of training Vision-Language Pretrained (VLP) models without significant performance losses compared to training on the original dataset. These results have been based on pruning commonly used image-caption datasets collected from the

Nestan Tsiskaridze, Clark Barrett, Cesare Tinelli

Optimization Modulo Theories (OMT) has emerged as an important extension of the highly successful Satisfiability Modulo Theories (SMT) paradigm. The OMT problem requires solving an SMT problem with the restriction that the solution must be optimal with respect to a given objective function. We introduce a generalization of the OMT problem where, in particula

I. Y. Vasko, F. S. Mozer, T. Bowen, J. Verniero

Parker Solar Probe measurements have recently shown that coherent fast magnetosonic and Alfv\'{e}n ion-cyclotron waves are abundant in the solar wind and can be accompanied by higher-frequency electrostatic fluctuations. In this letter we reveal the nonlinear process capable of channelling the energy of low-frequency electromagnetic to higher-frequency elect

Simone Soderi, Mariella Särestöniemi, Syifaul Fuada, Matti Hämäläinen

This paper explores novel strategies to strengthen the security of Hybrid Wireless Body Area Networks (HyWBANs), essential in smart healthcare and Internet of Things (IoT) applications. Recognizing the vulnerability of HyWBAN to sophisticated cyber-attacks, we propose an innovative combination of semantic communications and jamming receivers. This dual-layer

Santiago Zamora, Lisan M. M. Durão, Flavio Noronha, Tommaso Macrì

The phenomenon of Anderson localization, occurring in a disordered medium, significantly influences the dynamics of quantum particles. A fascinating manifestation of this is the "quantum boomerang effect" (QBE), observed when a quantum particle, propelled with a finite initial velocity, reverses its average trajectory, eventually halting at its starting poin

Zhaoxing Li, Jujie Yang, Jindi Wang, Lei Shi

The field of Knowledge Tracing aims to understand how students learn and master knowledge over time by analyzing their historical behaviour data. To achieve this goal, many researchers have proposed Knowledge Tracing models that use data from Intelligent Tutoring Systems to predict students' subsequent actions. However, with the development of Intelligent Tu

Daniel Reti, Norman Becker, Tillmann Angeli, Anasuya Chattopadhyay

With the increasing prevalence of security incidents, the adoption of deception-based defense strategies has become pivotal in cyber security. This work addresses the challenge of scalability in designing honeytokens, a key component of such defense mechanisms. The manual creation of honeytokens is a tedious task. Although automated generators exists, they o

Smone Soderi

Wireless communications among wearable and implantable devices implement the information exchange around the human body. Wireless body area network (WBAN) technology enables non-invasive applications in our daily lives. Wireless connected devices improve the quality of many services, and they make procedures easier. On the other hand, they open up large atta

Alphaeus Dmonte, Marcos Zampieri, Kevin Lybarger, Massimiliano Albanese

Politics is one of the most prevalent topics discussed on social media platforms, particularly during major election cycles, where users engage in conversations about candidates and electoral processes. Malicious actors may use this opportunity to disseminate misinformation to undermine trust in the electoral process. The emergence of Large Language Models (

Zekai Chen, Weeden Daniel, Po-yu Chen, Francois Buet-Golfouse

The advent of personalized content generation by LLMs presents a novel challenge: how to efficiently adapt text to meet individual preferences without the unsustainable demand of creating a unique model for each user. This study introduces an innovative online method that employs neural bandit algorithms to dynamically optimize soft instruction embeddings ba

David Barranco, Şengül Kuru, Javier Negro

Electric and magnetic waveguides are considered in planar Dirac materials like graphene as well as their classical version for relativistic particles of zero mass and electric charge. In order to solve the Dirac-Weyl equation analytically, we have assumed the displacement symmetry of the system along a direction. In these conditions we have examined the rest

Diwas Paudel, Luke Wolf, Tapas K. Das

The need for larger-scale fast-charging electric vehicle (EV) hubs is on the rise due to the growth in EV adoption. Another area of power infrastructure growth is the proliferation of independently operated stand-alone battery storage systems (BSS), which is fueled by improvements and cost reductions in battery technology. Many possible uses of the stand-alo

Badri Narayana Patro, Vijay Srinivas Agneeswaran

Sequence modeling is a crucial area across various domains, including Natural Language Processing (NLP), speech recognition, time series forecasting, music generation, and bioinformatics. Recurrent Neural Networks (RNNs) and Long Short Term Memory Networks (LSTMs) have historically dominated sequence modeling tasks like Machine Translation, Named Entity Reco