March 2024 arXiv papers — page 152

Jiaqi Zhang, Kirankumar Shiragur, Caroline Uhler

Understanding causal relationships between variables is a fundamental problem with broad impact in numerous scientific fields. While extensive research has been dedicated to learning causal graphs from data, its complementary concept of testing causal relationships has remained largely unexplored. While learning involves the task of recovering the Markov equ

Fan Yang, Benjamin Planche, Meng Zheng, Cheng Chen

For decades, three-dimensional C-arm Cone-Beam Computed Tomography (CBCT) imaging system has been a critical component for complex vascular and nonvascular interventional procedures. While it can significantly improve multiplanar soft tissue imaging and provide pre-treatment target lesion roadmapping and guidance, the traditional workflow can be cumbersome a

Yeping Wang, Pragathi Praveena, Michael Gleicher

Teleoperation systems map operator commands from an input device into some coordinate frame in the remote environment. This frame, which we call a control coordinate system, should be carefully chosen as it determines how operators should move to get desired robot motions. While specific choices made by individual systems have been described in prior work, a

R. Torres, D. J. Nott, S. A. Sisson, T. Rodrigues

Artificial neural networks (ANNs) are highly flexible predictive models. However, reliably quantifying uncertainty for their predictions is a continuing challenge. There has been much recent work on "recalibration" of predictive distributions for ANNs, so that forecast probabilities for events of interest are consistent with certain frequency evaluations of

Ming-Hong Luo, Qing-Wen Tang, Xiu-Rong Mo

Spatial template is important to study the nearby supernova remnant (SNR). For SNR G332.5-5.6, we report a gaussian disk with radius of about 1.06 degrees to be a potential good spatial model in the gamma-ray band. Employing this new gaussian disk, its GeV lightcurve shows a significant variability of about 7 sigma. The $\gamma$-ray observations of this SNR

Guillem Casadesus-Vila, Juan Jose Garau-Luis, Nils Pachler, Edward Crawley

Mobile users such as airplanes or ships will constitute an important segment of the future satellite communications market. Operators are now able to leverage digital payloads that allow flexible resource allocation policies that are robust against dynamic user bases. One of the key problems is managing the frequency spectrum efficiently, which has not been

Andi Chen, Hua-Lei Yin, Zeng-Bing Chen, Shengjun Wu

In this paper, we propose two hybrid quantum-inspired neural networks with adaptive residual and dense connections respectively for pattern recognition. We explain the frameworks of the symmetrical circuit models in the quantum-inspired layers in our hybrid models. We also illustrate the potential superiority of our hybrid models to prevent gradient explosio

Vy Le, Jayson M. Nissen, Xiuxiu Tang, Yuxiao Zhang

In physics education research, instructors and researchers often use research-based assessments (RBAs) to assess students' skills and knowledge. In this paper, we support the development of a mechanics cognitive diagnostic to test and implement effective and equitable pedagogies for physics instruction. Adaptive assessments using cognitive diagnostic models

Wentao Liu, Bowen Liang, Weijin Xu, Tong Tian

The rigid registration of aortic Digital Subtraction Angiography (DSA) and Computed Tomography Angiography (CTA) can provide 3D anatomical details of the vasculature for the interventional surgical treatment of conditions such as aortic dissection and aortic aneurysms, holding significant value for clinical research. However, the current methods for 2D/3D im

Hussein Abdallah, Waleed Afandi, Panos Kalnis, Essam Mansour

A Knowledge Graph (KG) is a heterogeneous graph encompassing a diverse range of node and edge types. Heterogeneous Graph Neural Networks (HGNNs) are popular for training machine learning tasks like node classification and link prediction on KGs. However, HGNN methods exhibit excessive complexity influenced by the KG's size, density, and the number of node an

Xinyao Fan, Yueying Wu, Chang Xu, Yuhao Huang

Recently, diffusion probabilistic models have attracted attention in generative time series forecasting due to their remarkable capacity to generate high-fidelity samples. However, the effective utilization of their strong modeling ability in the probabilistic time series forecasting task remains an open question, partially due to the challenge of instabilit

Sara Abdali, Richard Anarfi, CJ Barberan, Jia He

Large Language Models (LLMs) have revolutionized the field of Natural Language Generation (NLG) by demonstrating an impressive ability to generate human-like text. However, their widespread usage introduces challenges that necessitate thoughtful examination, ethical scrutiny, and responsible practices. In this study, we delve into these challenges, explore e

Yangjun Wu, Han Wang

Conventional text-to-SQL parsers are not good at synthesizing complex SQL queries that involve multiple tables or columns, due to the challenges inherent in identifying the correct schema items and performing accurate alignment between question and schema items. To address the above issue, we present a schema-aware multi-task learning framework (named MTSQL)

Shashank A Deshpande, Hamsa Balakrishnan

Series-parallel network topologies generally exhibit simplified dynamical behavior and avoid high combinatorial complexity. A comprehensive analysis of how flow complexity emerges with a graph's deviation from series-parallel topology is therefore of fundamental interest. We introduce the notion of a robust $k$-path on a directed acycylic graph, with increas

Wentao Liu, Weijin Xu, Xiaochuan Li, Bowen Liang

Autonomous robots for endovascular interventions hold significant potential to enhance procedural safety and reliability by navigating guidewires with precision, minimizing human error, and reducing surgical time. However, existing methods of guidewire navigation rely on manual demonstration data and have a suboptimal success rate. In this work, we propose a

Ze-quan Wang, Feng Xue, Liang Qiu, Zhe Wang

Magnetic skyrmions, topologically nontrivial whirling spin textures at nanometer scales, have emerged as potential information carriers for spintronic devices. The ability to efficiently create and erase magnetic skyrmions is vital yet challenging for such applications. Based on first-principles studies, we find that switching between intrinsic magnetic skyr

Xiaozhu Zhang, Marc Timme

Networked dynamical systems, i.e., systems of dynamical units coupled via nontrivial interaction topologies, constitute models of broad classes of complex systems, ranging from gene regulatory and metabolic circuits in our cells to pandemics spreading across continents. Most of such systems are driven by irregular and distributed fluctuating input signals fr

Ryan K. Cosner, Preston Culbertson, Aaron D. Ames

When deployed in the real world, safe control methods must be robust to unstructured uncertainties such as modeling error and external disturbances. Typical robust safety methods achieve their guarantees by always assuming that the worst-case disturbance will occur. In contrast, this paper utilizes Freedman's inequality in the context of discrete-time contro

Ting Chen, Feng Li, Yun Tian, Pei Yu

In this paper, we generalize the Poincar\'e-Lyapunov method for systems with linear type centers to study nilpotent centers in switching polynomial systems and use it to investigate the bi-center problem of planar $Z_2$-equivariant cubic switching systems associated with two symmetric nilpotent singular points. With a properly designed perturbation, 6 explic

Xinyi Wang, Qing Zhao, Lang Tong

This paper introduces a generative AI approach to probabilistic forecasting of real-time electricity market signals, including locational marginal prices, interregional price spreads, and demand-supply imbalances. We present WIAE-GPF, a Weak Innovation AutoEncoder-based Generative Probabilistic Forecasting architecture that generates future samples of multiv

Heeseung Bang, Aditya Dave, Andreas A. Malikopoulos

In this letter, we present an approach for learning human driving behavior, without relying on specific model structures or prior distributions, in a mixed-traffic environment where connected and automated vehicles (CAVs) coexist with human-driven vehicles (HDVs). We employ conformal prediction to obtain theoretical safety guarantees and use real-world traff

Shin Nishihara, Toru Ohira

This study examines how patterns on mammal body surfaces change as they transition from juveniles to adults and with seasonal variations. Our previous research suggests that patterns formed in infancy may fade due to the growing domain effects, typically linked to the body's surface expanding as it grows, but this transition is influenced by various factors.

Masato Tajima

We see that convolutional coding/Viterbi decoding has the structure of the Kalman filter (or the linear minimum variance filter). First, we calculate the covariance matrix of the innovation (i.e., the soft-decision input to the main decoder in a Scarce-State-Transition (SST) Viterbi decoder). Then a covariance matrix corresponding to that of the one-step pre

Filippos N. Tzortzoglou, Logan E. Beaver, Andreas A. Malikopoulos

In this letter, we address the problem of improving the feasible domain of the solution of a decentralized control framework for coordinating connected and automated vehicles (CAVs) at signal-free intersections as the traffic volume increases. The framework provides the optimal trajectories of CAVs to cross the intersection safely without stop-and-go driving

Min Cheng, Ruida Zhou, P. R. Kumar, Chao Tian

We study Markov potential games under the infinite horizon average reward criterion. Most previous studies have been for discounted rewards. We prove that both algorithms based on independent policy gradient and independent natural policy gradient converge globally to a Nash equilibrium for the average reward criterion. To set the stage for gradient-based me

Xiying Du, Rose McCarty

Suppose a graph has no large balanced bicliques, but has large minimum degree. Then what can we say about its induced subgraphs? This question motivates the study of degree-boundedness, which is like $\chi$-boundedness but for minimum degree instead of chromatic number. We survey this area with an eye towards open problems.

Anuththara Sarathchandra, Azadeh Aghaeeyan, Pouria Ramazi

Researchers develop models to explain the unknowns. These models typically involve parameters that capture tangible quantities, the estimation of which is desired. Parameter identifiability investigates the recoverability of the unknown parameters given the error-free outputs, inputs, and the developed equations of the model. Different notions of and methods

Hirofumi Kakemoto

Recently colossal Seebeck coefficient ($S$) has found in the several thermoelectric (TE) materials. We present colossal $S$ and large thermal electron motivate force (EMF) reproduced by space charge (SC) model, introducing multi-Debye lengths within grain boundaries (GBs) of TE materials with phonon drag (PD) effect accompanying with electron by electron-pho

Richard Evan Schwartz

We study the $(k+1,k)$ diagonal map for $k=2,3,4,...$. We call this map $\Delta_k$. The map $\Delta_1$ is the pentagram map and $\Delta_k$ is a generalization. $\Delta_k$ does not preserve convexity, but we prove that $\Delta_k$ preserves a subset $B_k$ of certain star-shaped polygons which we call $k$-birds. The action of $\Delta_k$ on $B_k$ seems similar t

Alexander Habib, Dylan Helliwell

Given two points $p$ and $q$ in the plane and a nonnegative number $r$, the Cassini oval is the set of points $x$ that satisfy $d(x, p) d(x, q) = r^2$. In this paper, we study this set using the taxicab metric. We find that these sets have characteristics that are qualitatively similar to their Euclidean counterparts while also reflecting the underlying taxi

Alvise Sommariva

The purpose of this work is to introduce a strategy for determining the nodes and weights of a low-cardinality positive cubature formula nearly exact for polynomials of a given degree over spherical polygons. In the numerical section we report the results about numerical cubature over a spherical polygon $\cal P$ approximating Australia and reconstruction of

W. Wen

The Feynman path integral plays a crucial role in quantum mechanics, offering significant insights into the interaction between classical action and propagators, and linking quantum electrodynamics (QED) with Feynman diagrams. However, the formulations of path integrals in classical quantum mechanics and QED are neither unified nor interconnected, suggesting

Krishnendu Maji, Joydip Sarkar, Supriya Mandal, Sriram H.

The superconducting coplanar waveguide (SCPW) cavity plays an essential role in various areas like superconducting qubits, parametric amplifiers, radiation detectors, and studying magnon-photon and photon-phonon coupling. Despite its wide-ranging applications, the use of SCPW cavities to study various van der Waals 2D materials is relatively unexplored. The

Jingtian Yan, Jiaoyang Li

Multi-Agent Motion Planning (MAMP) is a problem that seeks collision-free dynamically-feasible trajectories for multiple moving agents in a known environment while minimizing their travel time. MAMP is closely related to the well-studied Multi-Agent Path-Finding (MAPF) problem. Recently, MAPF methods have achieved great success in finding collision-free path

Nitsan Soffair, Shie Mannor

DDPG is hindered by the overestimation bias problem, wherein its $Q$-estimates tend to overstate the actual $Q$-values. Traditional solutions to this bias involve ensemble-based methods, which require significant computational resources, or complex log-policy-based approaches, which are difficult to understand and implement. In contrast, we propose a straigh

Ernesto Mordecki, Facundo Oliú

A relationship between two sided discounted singular control problems and Dynkin games is established for real valued L\'evy processes. In addition, the solution of a two-sided ergodic singular control problem is obtained as the limit of the corresponding solution of the discounted one. With these results, we conclude that the optimal controls within the cla

Rodrigo Berté, Thomas Possmayer, Andreas Tittl, Leonardo de S. Menezes

Resonances are usually associated with finite systems - the vibrations of clamped strings in a guitar or the optical modes in a cavity defined by mirrors. In optics, resonances may be induced in infinite continuous media via periodic modulations of their optical properties. Here we demonstrate that periodic modulations of the permittivity of a featureless th

Haochen Yu, Matteo Giantomassi, Giuliana Materzanini, Junjie Wang

Machine-learning interatomic potentials have revolutionized materials modeling at the atomic scale. Thanks to these, it is now indeed possible to perform simulations of \abinitio quality over very large time and length scales. More recently, various universal machine-learning models have been proposed as an out-of-box approach avoiding the need to train and

Tram Thi Minh Tran, Callum Parker, Yiyuan Wang, Martin Tomitsch

Wearable augmented reality (AR) offers new ways for supporting the interaction between autonomous vehicles (AVs) and pedestrians due to its ability to integrate timely and contextually relevant data into the user's field of view. This article presents novel wearable AR concepts that assist crossing pedestrians in multi-vehicle scenarios where several AVs fre

Alexander Marrapese, Basem Suleiman, Imdad Ullah, Juno Kim

Understanding the conversation abilities of Large Language Models (LLMs) can help lead to its more cautious and appropriate deployment. This is especially important for safety-critical domains like mental health, where someone's life may depend on the exact wording of a response to an urgent question. In this paper, we propose a novel framework for evaluatin

Matthew Gaughan, Kaylea Champion, Sohyeon Hwang

While free/libre and open source software (FLOSS) is critical to global computing infrastructure, the maintenance of widely-adopted FLOSS packages is dependent on volunteer developers who select their own tasks. Risk of failure due to the misalignment of engineering supply and demand -- known as underproduction -- has led to code base decay and subsequent cy

Tram Thi Minh Tran, Callum Parker, Martin Tomitsch

Autonomous vehicles (AVs) may use external interfaces, such as LED light bands, to communicate with pedestrians safely and intuitively. While previous research has demonstrated the effectiveness of these interfaces in simple traffic scenarios involving one pedestrian and one vehicle, their performance in more complex scenarios with multiple road users remain

Warren Morningstar, Alex Bijamov, Chris Duvarney, Luke Friedman

We study the relative effects of data augmentations, pretraining algorithms, and model architectures in Self-Supervised Learning (SSL). While the recent literature in this space leaves the impression that the pretraining algorithm is of critical importance to performance, understanding its effect is complicated by the difficulty in making objective and direc

Tram Thi Minh Tran, Callum Parker, Marius Hoggenmuller, Yiyuan Wang

Policymakers advocate for the use of external Human-Machine Interfaces (eHMIs) to allow autonomous vehicles (AVs) to communicate their intentions or status. Nonetheless, scalability concerns in complex traffic scenarios arise, such as potentially increasing pedestrian cognitive load or conveying contradictory signals. Building upon precursory works, our stud

Sarath Menon, Yury Lysogorskiy, Alexander L. M. Knoll, Niklas Leimeroth

We present a comprehensive and user-friendly framework built upon the pyiron integrated development environment (IDE), enabling researchers to perform the entire Machine Learning Potential (MLP) development cycle consisting of (i) creating systematic DFT databases, (ii) fitting the Density Functional Theory (DFT) data to empirical potentials or MLPs, and (ii

Yixue Zhao, Tianyi Li, Michael Sobolev

The prevalence of social media and its escalating impact on mental health has highlighted the need for effective digital wellbeing strategies. Current digital wellbeing interventions have primarily focused on reducing screen time and social media use, often neglecting the potential benefits of these platforms. This paper introduces a new perspective centered

Marianna Euler, Norbert Euler

We consider here the class of fully-nonlinear symmetry-integrable third-order evolution equations in 1+1 dimensions that were proposed recently in the journal Open Communications in Nonlinear Mathematical Physics, vol. 2, 216--228 (2022). In particular, we report all zero-order and higher-order potentialisations for this class of equations using their integr

Zhuolin Yang, Cathy Yuanchen Li, Arman Bhalla, Ben Y. Zhao

Today's virtual reality (VR) systems provide immersive interactions that seamlessly connect users with online services and one another. However, these immersive interfaces also introduce new vulnerabilities, making it easier for users to fall prey to new attacks. In this work, we introduce the immersive hijacking attack, where a remote attacker takes control

Asad Aali, Dave Van Veen, Yamin Ishraq Arefeen, Jason Hom

Brief hospital course (BHC) summaries are clinical documents that summarize a patient's hospital stay. While large language models (LLMs) depict remarkable capabilities in automating real-world tasks, their capabilities for healthcare applications such as synthesizing BHCs from clinical notes have not been shown. We introduce a novel pre-processed dataset, t

Izabella Łaba, Charlotte Trainor

For $p$ prime, let $\mathcal{H}^n$ be the linear span of characteristic functions of hyperplanes in $(\mathbb{Z}/p^k\mathbb{Z})^n$. We establish new upper bounds on the dimension of $\mathcal{H}^n$ over $\mathbb{Z}/p\mathbb{Z}$, or equivalently, on the rank of point-hyperplane incidence matrices in $(\mathbb{Z}/p^k\mathbb{Z})^n$ over $\mathbb{Z}/p\mathbb{Z}$

Francisco J. Vargas, Marco A. Gordon, Andrés A. Peters, Alejandro I. Maass

This paper addresses the string stabilization of vehicular platooning when stochastic phenomena are inherent in inter-vehicle communication. To achieve this, we first provide two definitions to analytically assess the string stability in stochastic scenarios, considering the mean and variance of tracking errors as the platoon size grows. Subsequently, we ana

S. S. Varghese, J. Dowell, K. S. Obenberger, G. B. Taylor

We conducted an all-sky imaging transient search with the Owens Valley Radio Observatory Long Wavelength Array (OVRO-LWA) data collected during the Perseid meteor shower in 2018. The data collection during the meteor shower was motivated to conduct a search for intrinsic radio emission from meteors below 60 MHz known as the meteor radio afterglows (MRAs). Th

Huan-Ke Hsu, I-Chyn Wey, T. Hui Teo

Generative Artificial Intelligence (AI) has become incredibly popular in recent years, and the significance of traditional accelerators in dealing with large-scale parameters is urgent. With the diffusion model's parallel structure, the hardware design challenge has skyrocketed because of the multiple layers operating simultaneously. Convolution Neural Netwo

Lloyd Montgomery, Clara Lüders, Walid Maalej

An issue tracker is a software tool used by organisations to interact with users and manage various aspects of the software development lifecycle. With the rise of agile methodologies, issue trackers have become popular in open and closed-source settings alike. Internal and external stakeholders report, manage, and discuss "issues", which represent different

Kandai Watanabe, Georgios Fainekos, Bardh Hoxha, Morteza Lahijanian

This paper addresses the challenge of planning a sequence of tasks to be performed by multiple robots while minimizing the overall completion time subject to timing and precedence constraints. Our approach uses the Timed Partial Orders (TPO) model to specify these constraints. We translate this problem into a Traveling Salesman Problem (TSP) variant with tim

Aditya Dave, Heeseung Bang, Andreas A. Malikopoulos

Many cyber-physical-human systems (CPHS) involve a human decision-maker who may receive recommendations from an artificial intelligence (AI) platform while holding the ultimate responsibility of making decisions. In such CPHS applications, the human decision-maker may depart from an optimal recommended decision and instead implement a different one for vario

Chien-Yeah Seng

Built on the seminal works by Jackson-Treiman-Wyld and Ebel-Feldman, we derive the most general free neutron differential decay rate where all massive particles (neutron, proton and electron) are polarized. This introduces 33 new correlations in addition to the 18 existing ones, which over-constrain the coupling constants in the low-energy effective field th

Łukasz Kuciński, Witold Drzewakowski, Mateusz Olko, Piotr Kozakowski

Time series methods are of fundamental importance in virtually any field of science that deals with temporally structured data. Recently, there has been a surge of deterministic transformer models with time series-specific architectural biases. In this paper, we go in a different direction by introducing tsGT, a stochastic time series model built on a genera

Dylan Langharst, Francisco Marín Sola, Jacopo Ulivelli

The Rogers-Shephard and Zhang's projection inequalities are two reverse, affine isoperimetric-type inequalities for convex bodies. Following a classical work by Schneider, both inequalities have been extended to the so-called $m$th-order setting. In this work, we establish the $m$th-order analogues for these inequalities in the setting of log-concave functio

Tyler Hanks, Matthew Klawonn, Evan Patterson, Matthew Hale

Optimization decomposition methods are a fundamental tool to develop distributed solution algorithms for large scale optimization problems arising in fields such as machine learning and optimal control. In this paper, we present an algebraic framework for hierarchically composing optimization problems defined on hypergraphs and automatically generating distr

Anna Ivagnes, Niccolò Tonicello, Paola Cinnella, Gianluigi Rozza

In this manuscript, we combine non-intrusive reduced order models (ROMs) with space-dependent aggregation techniques to build a mixed-ROM. The prediction of the mixed formulation is given by a convex linear combination of the predictions of some previously-trained ROMs, where we assign to each model a space-dependent weight. The ROMs taken into account to bu

C. A. Galante, J. Saponara, G. E. Romero, P. Benaglia

Starburst galaxies are undergoing intense episodes of star formation. In these galaxies, gas is ejected into the surrounding environment through winds created by the effect of hot stars and supernova explosions. When interacting with the intergalactic medium, these winds can produce strong shocks capable of accelerating cosmic rays. The radiation from these

Dmitrii Karp, Alexey Kuznetsov

By replacing the Euler gamma function by the Barnes double gamma function in the definition of the Meijer $G$-function, we introduce a new family of special functions, which we call $K$-functions. This is a very general class of functions, which includes as special cases Meijer $G$-functions (thus also all hypergeometric functions ${}_p F_q$) as well as seve

Filip Bár

Integration is the final key step when turning an infinitesimal argument into a result applicable to quantities of finite size. Conceptually, it is about combining infinitesimal contributions to a finite whole. We make a first step towards a geometric theory of integration in the context of Synthetic Differential Geometry (SDG) by analysing the differential

Federico Belliardo, Fabio Zoratti, Vittorio Giovannetti

An important practical problem in the field of quantum metrology and sensors is to find the optimal sequences of controls for the quantum probe that realize optimal adaptive estimation. In Belliardo et al., arXiv:2312.16985 (2023), we solved this problem in general, by introducing a procedure capable of optimizing a wide range of tasks in quantum metrology a

Xin Qin, Ioannis Lestas, Bolun Xu

Economic withholding in electricity markets refers to generators bidding higher than their true marginal fuel cost, and is a typical approach to exercising market power. However, existing market designs require storage to design bids strategically based on their own future price predictions, motivating storage to conduct economic withholding without assuming

Arun G. Chandrasekhar, Paul Goldsmith-Pinkham, Tyler H. McCormick, Samuel Thau

Network diffusion models are used to study disease transmission, information spread, technology adoption, and other socio-economic processes. We show that estimates of these diffusions are highly non-robust to mismeasurement. First, even when the network is measured perfectly, small and local mismeasurement in the initial seed generates a large shift in the

Junde Wu, Jiayuan Zhu, Min Xu, Yueming Jin

Some visual recognition tasks are more challenging then the general ones as they require professional categories of images. The previous efforts, like fine-grained vision classification, primarily introduced models tailored to specific tasks, like identifying bird species or car brands with limited scalability and generalizability. This paper aims to design

Shentong Mo, Jing Shi, Yapeng Tian

In recent times, the focus on text-to-audio (TTA) generation has intensified, as researchers strive to synthesize audio from textual descriptions. However, most existing methods, though leveraging latent diffusion models to learn the correlation between audio and text embeddings, fall short when it comes to maintaining a seamless synchronization between the

Mona Ashtari-Majlan, David Masip

Glaucoma, a leading cause of irreversible blindness, necessitates early detection for accurate and timely intervention to prevent irreversible vision loss. In this study, we present a novel deep learning framework that leverages the diagnostic value of 3D Optical Coherence Tomography (OCT) imaging for automated glaucoma detection. In this framework, we integ

Lennart Wachowiak, Andrew Coles, Oya Celiktutan, Gerard Canal

Large language models (LLMs) are increasingly used in robotics, especially for high-level action planning. Meanwhile, many robotics applications involve human supervisors or collaborators. Hence, it is crucial for LLMs to generate socially acceptable actions that align with people's preferences and values. In this work, we test whether LLMs capture people's

Lorenzo Lupo, Paul Bose, Mahyar Habibi, Dirk Hovy

Social scientists increasingly use demographically stratified social media data to study the attitudes, beliefs, and behavior of the general public. To facilitate such analyses, we construct, validate, and release publicly the representative DADIT dataset of 30M tweets of 20k Italian Twitter users, along with their bios and profile pictures. We enrich the us

Marcella Marconi, Giulia De Somma, Roberto Molinaro, Anupam Bhardwaj

A new fine grid of nonlinear convective pulsation models for the so-called "bump Cepheids" is presented to investigate the Hertzprung progression (HP) phenomenon shown by their light and radial pulsation velocity curves. The period corresponding to the center of the HP is investigated as a function of various model assumptions, such as the efficiency of supe

Avi Kenny, Charles J. Wolock

This article describes SimEngine, an open-source R package for structuring, maintaining, running, and debugging statistical simulations on both local and cluster-based computing environments. Several R packages exist for structuring simulations, but SimEngine is the only package specifically designed for running simulations in parallel via job schedulers on

Bruce Brewer, Gerth Stølting Brodal, Haitao Wang

We consider the planar dynamic convex hull problem. In the literature, solutions exist supporting the insertion and deletion of points in poly-logarithmic time and various queries on the convex hull of the current set of points in logarithmic time. If arbitrary insertion and deletion of points are allowed, constant time updates and fast queries are known to

Mukul Bhutani, Kevin Robinson, Vinodkumar Prabhakaran, Shachi Dave

While generative multilingual models are rapidly being deployed, their safety and fairness evaluations are largely limited to resources collected in English. This is especially problematic for evaluations targeting inherently socio-cultural phenomena such as stereotyping, where it is important to build multi-lingual resources that reflect the stereotypes pre

Esteban Calzetta, Alejandra Kandus

We compute the propagation speeds for a conformal real relativistic fluid. We begin from a kinetic equation in the relaxation time approximation, where the relaxation time is an arbitrary function of the particle energy in the Landau frame. We propose a parameterization of the one particle distribution function designed to contain a second order Chapman-Ensk

Booy Vitas Faassen, Jorge Serrano, Paul D. Rosero-Montalvo

Solar energy is rapidly becoming a robust renewable energy source to conventional finite resources such as fossil fuels. It is harvested using interconnected photovoltaic panels, typically built with crystalline silicon cells, i.e. semiconducting materials that convert effectively the solar radiation into electricity. However, crystalline silicon is fragile

Robert Reed, Hanspeter Schaub, Morteza Lahijanian

Autonomous spacecraft control via Shielded Deep Reinforcement Learning (SDRL) has become a rapidly growing research area. However, the construction of shields and the definition of tasking remains informal, resulting in policies with no guarantees on safety and ambiguous goals for the RL agent. In this paper, we first explore the use of formal languages, nam

Jonathan Will, Dominik Scheinert, Jan Bode, Cedric Kring

Performance modeling for large-scale data analytics workloads can improve the efficiency of cluster resource allocations and job scheduling. However, the performance of these workloads is influenced by numerous factors, such as job inputs and the assigned cluster resources. As a result, performance models require significant amounts of training data. This da

Mingtao Xia, Tom Chou

We formulate a general, high-dimensional kinetic theory describing the internal state (such as gene expression or protein levels) of cells in a stochastically evolving population. The resolution of our kinetic theory also allows one to track subpopulations associated with each generation. Both intrinsic noise of the cell's internal attribute and randomness i

Lixu Wang, Xinyu Du, Qi Zhu

Cross-domain retrieval (CDR), as a crucial tool for numerous technologies, is finding increasingly broad applications. However, existing efforts face several major issues, with the most critical being the need for accurate supervision, which often demands costly resources and efforts. Cutting-edge studies focus on achieving unsupervised CDR but typically ass

Jimmy-Xuan Shen, Lars F. Voss, Joel Basile Varley

Point defects have a strong influence on the physical properties of materials, often dominating the electronic and optical behavior in semiconductors and insulators. The simulation and analysis of point defects is therefore crucial for understanding the growth and operation of materials especially for optoelectronics applications. In this work, we present a

E. F. Schlafly, D. Schlegel, S. BenZvi, A. Raichoor

Highly multiplexed, fiber-fed spectroscopy is enabling surveys of millions of stars and galaxies. The performance of these surveys depends on accurately positioning fibers in the focal plane to capture target light. We describe a technique to measure the positioning accuracy of fibers by dithering fibers slightly around their ideal locations. This approach a

Jun Wang, Lixing Zhu, Abhir Bhalerao, Yulan He

Radiology report generation (RRG) methods often lack sufficient medical knowledge to produce clinically accurate reports. The scene graph contains rich information to describe the objects in an image. We explore enriching the medical knowledge for RRG via a scene graph, which has not been done in the current RRG literature. To this end, we propose the Scene

Yu Liu, Aitor Hernandez Herranz

Container orchestration platform is the foundation of modern cloud infrastructure. In recent years, container orchestration platform has been evolving to cross the boundary of device, edge, and cloud. More and more IoT applications such as robotics and XR have been deployed across the device-cloud continuum through the container orchestration platform, e.g.,

Mohammad Ramezaninia, Mohammad Zoofaghari

The microwave imaging system(MIS) stands out among prominent imaging tools for capturing images of concealed obstacles. Leveraging its capability to penetrate through heterogeneous environments MIS has been widely used for subsurface imaging. Monitoring subsurface drip irrigation(SDI) as an efficient procedure in agricultural irrigation is essential to maint

Emilie Pietersoone, Jean Michel Létang, Simon Rit, Max Langer

X-ray phase-contrast imaging enhances soft tissue visualization by leveraging the phase shift of X-rays passing through materials. It permits to minimize radiation exposure due to high contrast, as well as high resolution imaging limited by the wavelength of the X-rays. Phase retrieval extracts the phase shift computationally, but simulated images fail to re

Jiarui Xu, Karim Said, Lizhong Zheng, Lingjia Liu

Orthogonal time frequency space (OTFS) is a promising modulation scheme for wireless communication in high-mobility scenarios. Recently, a reservoir computing (RC) based approach has been introduced for online subframe-based symbol detection in the OTFS system, where only the limited over-the-air (OTA) pilot symbols are utilized for training. However, the pr

Narges Rashvand, Kenneth Witham, Gabriel Maldonado, Vinit Katariya

Automatic modulation recognition (AMR) is vital for accurately identifying modulation types within incoming signals, a critical task for optimizing operations within edge devices in IoT ecosystems. This paper presents an innovative approach that leverages Transformer networks, initially designed for natural language processing, to address the challenges of e

Sanket Shah, Arun Suggala, Milind Tambe, Aparna Taneja

The declining participation of beneficiaries over time is a key concern in public health programs. A popular strategy for improving retention is to have health workers `intervene' on beneficiaries at risk of dropping out. However, the availability and time of these health workers are limited resources. As a result, there has been a line of research on optimi

Swapnaja Achintalwar, Ioana Baldini, Djallel Bouneffouf, Joan Byamugisha

The alignment of large language models is usually done by model providers to add or control behaviors that are common or universally understood across use cases and contexts. In contrast, in this article, we present an approach and architecture that empowers application developers to tune a model to their particular values, social norms, laws and other regul

Chen-Kai Qiao, Ping Su

The dark matter halo has non-negligible effects on the gravitational lensing of supermassive black hole in the galaxy center. Our work presents a study on the time-delay of light in gravitational lensing of black holes enclosed by dark matter halos. To provide a precise description on the distribution of dark matter in galaxies, we choose several famous phen

Jennifer Onyeama

Credit card fraud is a major cause of national concern in the Nigerian financial sector, affecting hundreds of transactions per second and impacting international ecommerce negatively. Despite the rapid spread and adoption of online marketing, millions of Nigerians are prevented from transacting in several countries with local credit cards due to bans and po

Alycia N. Carey, Karuna Bhaila, Kennedy Edemacu, Xintao Wu

In-context learning (ICL) enables large language models (LLMs) to adapt to new tasks by conditioning on demonstrations of question-answer pairs and it has been shown to have comparable performance to costly model retraining and fine-tuning. Recently, ICL has been extended to allow tabular data to be used as demonstration examples by serializing individual re

Qingqing Zhu, Benjamin Hou, Tejas S. Mathai, Pritam Mukherjee

Automatically interpreting CT scans can ease the workload of radiologists. However, this is challenging mainly due to the scarcity of adequate datasets and reference standards for evaluation. This study aims to bridge this gap by introducing a novel evaluation framework, named ``GPTRadScore''. This framework assesses the capabilities of multi-modal LLMs, suc

Tianyu Zhang, Jing Lei, Kathryn Roeder

We study high-dimensional two-sample mean comparison and address the curse of dimensionality through data-adaptive projections. Leveraging the low-dimensional and localized signal structures commonly seen in single-cell genomics data, our first proposed method identifies a sparse, informative low-dimensional subspace and then performs statistical inference r

Jean-Paul Allouche, Yining Hu, Claude Morin

Ellipsephic or Kempner-like harmonic series are series of inverses of integers whose expansion in base $B$, for some $B \geq 2$, contains no occurrence of some fixed digit or some fixed block of digits. A prototypical example was proposed by Kempner in 1914, namely the sum inverses of integers whose expansion in base $10$ contains no occurrence of a nonzero

Deepak Bal, Louis DeBiasio, Ella Oren-Dahan

The balanced double star on $2n+2$ vertices, denoted $S_{n,n}$, is the tree obtained by joining the centers of two disjoint stars each having $n$ leaves. Let $R_r(G)$ be the smallest integer $N$ such that in every $r$-coloring of the edges of $K_N$ there is a monochromatic copy of $G$, and let $R_r^{\mathrm{bip}}(G)$ be the smallest integer $N$ such that in

Wenqi Jiang, Shuai Zhang, Boran Han, Jie Wang

Retrieval-augmented generation (RAG) can enhance the generation quality of large language models (LLMs) by incorporating external token databases. However, retrievals from large databases can constitute a substantial portion of the overall generation time, particularly when retrievals are periodically performed to align the retrieved content with the latest