April 2024 arXiv papers — page 185

D. V. Khveshchenko

This note addresses the effects of long-ranged and/or retarded interactions on the bosonic collective modes in the so-called 'strange metals'. Recently, there have been conflicting reports on the very existence of such stable collective excitations and their properties. Extending a number of approaches that were previously used in the analyses of the standar

Otilio Rojas, Marisol Monterrubio-Velasco, Juan E. Rodriguez, Scott Callaghan

CyberShake (CS) is a high-performance computing workflow for Probabilistic Seismic Hazard Assessment (PSHA) developed by the Statewide California Earthquake Center. Here, we employ CS to generate a set of 2103 fault ruptures and simulate the corresponding two horizontal velocity components time histories of ground motion (GM) on a 5-km grid of 625 stations i

Xingwei Tan, Yuxiang Zhou, Gabriele Pergola, Yulan He

Event temporal graphs have been shown as convenient and effective representations of complex temporal relations between events in text. Recent studies, which employ pre-trained language models to auto-regressively generate linearised graphs for constructing event temporal graphs, have shown promising results. However, these methods have often led to suboptim

Yoonsoo Kim, Elias R. Most, William Throwe, Saul A. Teukolsky

Relativistic plasmas around compact objects can sometimes be approximated as being force-free. In this limit, the plasma inertia is negligible and the overall dynamics is governed by global electric currents. We present a novel numerical approach for simulating such force-free plasmas, which allows for high accuracy in smooth regions as well as capturing dis

Nguyen Phuc Tran, Oscar Delgado, Brigitte Jaumard, Fadi Bishay

Network operators are facing new challenges when meeting the needs of their customers. The challenges arise due to the rise of new services, such as HD video streaming, IoT, autonomous driving, etc., and the exponential growth of network traffic. In this context, 5G and B5G networks have been evolving to accommodate a wide range of applications and use cases

Ilya D. Shkredov

We study the concept of universal sets from the additive--combinatorial point of view. Among other results we obtain some applications of this type of uniformity to sets avoiding solutions to linear equations, and get an optimal upper bound for the covering number of general sumsets.

Anıl Zenginoğlu

Time functions with asymptotically hyperbolic geometry play an increasingly important role in many areas of relativity, from computing black-hole perturbations to analyzing wave equations. Despite their significance, many of their properties remain underexplored. In this expository article, I discuss hyperbolic time functions by considering the hyperbola as

Supratim Pait, Sumita Sharma, Ashley Frith, Michael Nitsche

Children are the builders of the future and crucial to how the technologies around us develop. They are not voters but are participants in how the public spaces in a city are used. Through a workshop designed around kids of age 9-12, we investigate if novel technologies like artificial intelligence can be integrated in existing ways of play and performance t

Carlos Leandro

The integration of knowledge extracted from diverse models, whether described by domain experts or generated by machine learning algorithms, has historically been challenged by the absence of a suitable framework for specifying and integrating structures, learning processes, data transformations, and data models or rules. In this work, we extend algebraic sp

Mat Langford, Yuxing Liu, George McNamara

Given any non-central interior point $o$ of the unit disc $D$, the diameter $L$ through $o$ is the union of two linear arcs emanating from $o$ which meet $\partial D$ orthogonally, the shorter of them stable and the longer unstable (under these boundary conditions). In each of the two half discs bounded by $L$, we construct a convex eternal solution to curve

Chull Hwan Song, Jooyoung Yoon, Taebaek Hwang, Shunghyun Choi

How important is it for training and evaluation sets to not have class overlap in image retrieval? We revisit Google Landmarks v2 clean, the most popular training set, by identifying and removing class overlap with Revisited Oxford and Paris [34], the most popular evaluation set. By comparing the original and the new RGLDv2-clean on a benchmark of reproduced

Nguyen Phuc Tran, Oscar Delgado, Brigitte Jaumard

The customization of services in Fifth-generation (5G) and Beyond 5G (B5G) networks relies heavily on network slicing, which creates multiple virtual networks on a shared physical infrastructure, tailored to meet specific requirements of distinct applications, using Software Defined Networking (SDN) and Network Function Virtualization (NFV). It is imperative

Maud Lemercier, Terry Lyons, Cristopher Salvi

Signature kernels, inner products of path signatures, underpin several machine learning algorithms for multivariate time series analysis. For bounded variation paths, signature kernels were recently shown to solve a Goursat PDE. However, existing PDE solvers only use increments as input data, leading to first order approximation errors. These approaches beco

David García-Lorite, Raul Merino

In this paper, we present a new method for pricing CMS derivatives. We use Mallaivin's calculus to establish a model-free connection between the price of a CMS derivative and a quadratic payoff. Then, we apply Watanabe's expansions to quadratic payoffs case under local and stochastic local volatility. Our approximations are generic. To evaluate their accurac

Camille Olivia Little, Genevera I. Allen

Ensemble methods, particularly boosting, have established themselves as highly effective and widely embraced machine learning techniques for tabular data. In this paper, we aim to leverage the robust predictive power of traditional boosting methods while enhancing fairness and interpretability. To achieve this, we develop Fair MP-Boost, a stochastic boosting

Lorcan O. Conlon, Jun Suzuki, Ping Koy Lam, Syed M. Assad

The symmetric logarithmic derivative Cram\'er-Rao bound (SLDCRB) provides a fundamental limit to the minimum variance with which a set of unknown parameters can be estimated in an unbiased manner. It is known that the SLDCRB can be saturated provided the optimal measurements for the individual parameters commute with one another. However, when this is not th

Katharine M. Jenike, Lucía Campos-Domínguez, Marilou Boddé, José Cerca

The wide array of currently available genomes display a wonderful diversity in size, composition and structure with many more to come thanks to several global biodiversity genomics initiatives starting in recent years. However, sequencing of genomes, even with all the recent advances, can still be challenging for both technical (e.g. small physical size, con

Ming Xu, Stephen Gould

We propose a novel approach to the action segmentation task for long, untrimmed videos, based on solving an optimal transport problem. By encoding a temporal consistency prior into a Gromov-Wasserstein problem, we are able to decode a temporally consistent segmentation from a noisy affinity/matching cost matrix between video frames and action classes. Unlike

Shourya Bose, Yu Zhang, Kibaek Kim

The advent of smart meters has enabled pervasive collection of energy consumption data for training short-term load forecasting models. In response to privacy concerns, federated learning (FL) has been proposed as a privacy-preserving approach for training, but the quality of trained models degrades as client data becomes heterogeneous. In this paper we prop

Ziv Aqua, Barak Dayan

Linear-optics gates, the enabling tool of photonic quantum information processing, depend on indistinguishable photons, as they harness quantum interference to achieve nonlinear operations. Traditionally, meeting this criterion involves generating pure identical photons, a task that remains a significant challenge in the field. Yet, the required indistinguis

Andreas Karch, Yuya Kusuki, Hirosi Ooguri, Hao-Yu Sun

The effective central charge (denoted by $c_{\text{eff}}$) is a measure of entanglement through a conformal interface, while the transmission coefficient (encoded in the coefficient $c_{LR}$ of the two-point function of the energy-momentum tensor across the interface) is a measure of energy transmission through the interface. It has been pointed out that the

Nicholas Williams, Kara E. Rudolph

Objective: Freely preprocess drug codes recorded in electronic health records and insurance claims to drug classes that may then be used in biomedical research. Materials and Methods: We developed a drug classification pipeline for linking National Drug Codes to the World Health Organization Anatomical Therapeutic Chemical classification. To implement our so

Juho Leinonen, Paul Denny

Understanding how individuals make decisions involving risk is a fundamental aspect of behavioral research. Despite the ubiquity of risk in various aspects of life, limited empirical work has explored student risk-taking behavior in computing education. This study aims to partially replicate prior research on risk-taking behavior in software engineers while

Narayan Khadka, Simon Birrer, Alexie Leauthaud, Holden Nix

The Hubble constant ($H_0$), a crucial parameter in cosmology, quantifies the expansion rate of the universe so its precise measurement is important to understand the fundamental dynamics of our evolving universe. One of the major limitations of measuring $H_0$ using time-delay cosmography is the presence of the mass-sheet degeneracy (MSD) in the lens mass m

Emily Krucker-Velasquez, Martin Z. Bazant, Alfredo Alexander-Katz, James W. Swan

This study uses advanced numerical methods to estimate the mean force potential (PMF) between charged, polarizable colloidal particles in dense electrolytes. We observe that when the Debye screening length, $\lambda_{\mathrm{D}}$, is below the hydrated ion size, the PMF shows discernible oscillations of purely electrostatic origin as opposed to chemical affi

Nic Brody, Eduardo Reyes

We show that an isometric action of a torsion-free uniform lattice $\Gamma$ on hyperbolic space $\mathbb{H}^n$ can be metrically approximated by geometric actions of $\Gamma$ on $\mathrm{CAT}(0)$ cube complexes, provided that either $n$ is at most three, or the lattice is arithmetic of simplest type. This solves a conjecture of Futer and Wise. Our main tool

Sho Hasui, Daisuke Kishimoto, Yichen Tong, Mitsunobu Tsutaya

We prove that the loop space of a quasitoric manifold is homotopy commutative if and only if the underlying polytope is a product of $3$-simplices $(\Delta^3)^n$ and the characteristic matrix is equivalent to a matrix of certain type. Quasitoric manifolds over $(\Delta^3)^n$ include generalized Bott manifolds, and we also construct an infinite family of homo

Paul Gavrikov, Janis Keuper

The robust generalization of models to rare, in-distribution (ID) samples drawn from the long tail of the training distribution and to out-of-training-distribution (OOD) samples is one of the major challenges of current deep learning methods. For image classification, this manifests in the existence of adversarial attacks, the performance drops on distorted

Miguel Angel Lopez

In this paper we attack the Erdos-Straus conjecture by means of the structure of its solutions, extending and improving the results of a previous paper. Using previous results and supported by the works of Elsholtz and Tao and Monks and Velingker we define a system of congruences for which there are always solutions to the Erdos-Straus conjecture and which w

Valeriy A. Slipko, Yuriy V. Pershin

This study investigates strategies for minimizing Joule losses in resistive random access memory (ReRAM) cells, which are also referred to as memristive devices. Typically, the structure of ReRAM cells involves a nanoscale layer of resistance-switching material sandwiched between two metal electrodes. The basic question that we ask is what is the optimal dri

Diederik Beckers, Jeff D. Eldredge

This study explores the application of deep reinforcement learning (RL) to design an airfoil pitch controller capable of minimizing lift variations in randomly disturbed flows. The controller, treated as an agent in a partially observable Markov decision process, receives non-Markovian observations from the environment, simulating practical constraints where

Evan Miller

In this paper, we study permutation symmetric solutions of the incompressible Euler equation. We show that the dynamics of these solutions can be reduced to an evolution equation on a single vorticity component $\omega_1$, and we characterize the relevant constraint space for this vorticity component under permutation symmetry. We also give single vorticity

Gerardo L. Maldonado, Edgardo Roldán-Pensado

Gr\"unbaum's equipartition problem asked if for any measure $\mu$ on $\mathbb{R}^d$ there are always $d$ hyperplanes which divide $\mathbb{R}^d$ into $2^d$ $\mu$-equal parts. This problem is known to have a positive answer for $d\le 3$ and a negative one for $d\ge 5$. A variant of this question is to require the hyperplanes to be mutually orthogonal. This va

Michael Katz, Junkyu Lee, Jungkoo Kang, Shirin Sohrabi

The ability to generate multiple plans is central to using planning in real-life applications. Top-quality planners generate sets of such top-cost plans, allowing flexibility in determining equivalent ones. In terms of the order between actions in a plan, the literature only considers two extremes -- either all orders are important, making each plan unique,

Vincent Yariv, Matthieu Ravet

"Everyone knows the moon is made of cheese..." This line, famously uttered by Wallace to his canine sidekick Gromit in the 80s classic, may be one of the most cruelly underappreciated movie quotations of our time. Indeed, while most scientists today would simply reject Wallace's claim as preposterous, we aim to revisit his theory on the composition of our na

Md. Faiyaz Abdullah Sayeedi, Fahim Hafiz, Md Ashiqur Rahman

In this paper, we present an integrated approach to real-time mosquito detection using our multiclass dataset (MosquitoFusion) containing 1204 diverse images and leverage cutting-edge technologies, specifically computer vision, to automate the identification of Mosquitoes, Swarms, and Breeding Sites. The pre-trained YOLOv8 model, trained on this dataset, ach

Sarah Libanore, Ely D. Kovetz

Decaying dark matter (DDM) can be tested via different astrophysical and cosmological probes. In particular, particles in the $\sim$ 9.5 - 30 eV mass range that decay into monochromatic photons, would contribute to the extragalactic background light (EBL) in the ultraviolet (UV) bandwidth. In this work, we show that an intriguing improvement to the constrain

Andrei Caldararu, Junwu Tu

We introduce enumerative invariants $F_{g,n}$ $(g\geq0$, $n \geq 1)$ associated to a cyclic $A_\infty$ algebra and a splitting of its non-commutative Hodge filtration. These invariants are defined by explicitly computable Feynman sums, and encode the same information as Costello's partition function of the corresponding field theory. Our invariants are stabl

Théo Durandard, Bruno Strulovici

We establish the existence, uniqueness, and $W^{1,2,p}$-regularity of solutions to fully-nonlinear, parabolic obstacle problems when the obstacle is the pointwise supremum of functions in $W^{1,2,p}$ and the nonlinear operator is required only to be measurable in the state and time variables. In particular, the results hold for all convex obstacles. Applied

Nicolina Chrysaphi, Milan Maksimovic, Eduard P. Kontar, Antonio Vecchio

Radio photons interact with anisotropic density fluctuations in the heliosphere, which can alter their trajectory and influence properties deduced from observations. This is particularly evident in solar radio observations, where anisotropic scattering leads to highly-directional radio emissions. Consequently, observers at varying locations will measure diff

Abderrahim Zagane

This work introduces a new class of $F$-structures satisfying $\alpha F^{K+1} +\beta F^{K} + F=0$, where $K$ is a positive integer, $K\geq3$, and $\alpha, \beta$ are real or complex numbers. We investigate the Cauchy-Riemann structure and its link with the $F$-structure. We also address the integrability of this structure, including partial and complete inte

Stephane Bonhomme, Angela Denis

A growing number of applications involve settings where, in order to infer heterogeneous effects, a researcher compares various units. Examples of research designs include children moving between different neighborhoods, workers moving between firms, patients migrating from one city to another, and banks offering loans to different firms. We present a unifie

Yue-Hui Yao, Yong Tang

Ultralight particles are theoretically well-motivated dark matter candidates. In the vicinity of the solar system, these ultralight particles can be described as a superposition of plane waves, resulting in a stochastic field with sizable amplitude fluctuations on scales determined by the velocity dispersion of dark matter. In this work, we systematically in

Carlos A. M. André, Inês Legatheaux Martins

The rook monoid, also known as the symmetric inverse monoid, is the archetypal structure when it comes to extend the principle of symmetry. In this paper, we establish a Schur-Weyl duality between this monoid and an extension of the classical Schur algebra, which we name the extended Schur algebra. We also explain how this relates to Solomon's Schur-Weyl dua

Mohsen Aliabadi, Peter Taylor

A matching from a finite subset $A\subset\mathbb{Z}^n$ to another subset $B\subset\mathbb{Z}^n$ is a bijection $f : A \rightarrow B$ with the property that $a+f(a)$ never lies in $A$. A matching is called acyclic if it is uniquely determined by its multiplicity function. Alon et al. established the acyclic matching property for $\mathbb{Z}^n$, which was late

Heitor Rapela Medeiros, Masih Aminbeidokhti, Fidel Guerrero Pena, David Latortue

A common practice in deep learning involves training large neural networks on massive datasets to achieve high accuracy across various domains and tasks. While this approach works well in many application areas, it often fails drastically when processing data from a new modality with a significant distribution shift from the data used to pre-train the model.

Shizhe Chen, Ricardo Garcia, Ivan Laptev, Cordelia Schmid

Learning generalizable visual representations from Internet data has yielded promising results for robotics. Yet, prevailing approaches focus on pre-training 2D representations, being sub-optimal to deal with occlusions and accurately localize objects in complex 3D scenes. Meanwhile, 3D representation learning has been limited to single-object understanding.

Miaoran Zhang, Mingyang Wang, Jesujoba O. Alabi, Dietrich Klakow

This paper presents our system developed for the SemEval-2024 Task 1: Semantic Textual Relatedness for African and Asian Languages. The shared task aims at measuring the semantic textual relatedness between pairs of sentences, with a focus on a range of under-represented languages. In this work, we propose using machine translation for data augmentation to a

Denise Yewell, R. Alexander Bentley, Benjamin D. Horne

In this study, we examine the perceived influence of others, across both strong and weak social ties, on COVID-19 vaccination decisions in the United States. We add context to social influence by measuring related concepts, such as perceived agreement of others and perceived danger of COVID-19 to others. We find that vaccinated populations perceived more inf

Mara Solen, Nigar Sultana, Laura Lukes, Tamara Munzner

While previous work has found success in deploying visualizations as museum exhibits, it has not investigated whether museum context impacts visitor behaviour with these exhibits. We present an interactive Deep-time Literacy Visualization Exhibit (DeLVE) to help museum visitors understand deep time (lengths of extremely long geological processes) by improvin

Di Fan, Ayan Biswas, James Paul Ahrens

Wildfires present intricate challenges for prediction, necessitating the use of sophisticated machine learning techniques for effective modeling\cite{jain2020review}. In our research, we conducted a thorough assessment of various machine learning algorithms for both classification and regression tasks relevant to predicting wildfires. We found that for class

Sourav Biswas, Sergio Casas, Quinlan Sykora, Ben Agro

A self-driving vehicle must understand its environment to determine the appropriate action. Traditional autonomy systems rely on object detection to find the agents in the scene. However, object detection assumes a discrete set of objects and loses information about uncertainty, so any errors compound when predicting the future behavior of those agents. Alte

Mara Solen, Matt Oddo, Tamara Munzner

Designing multiscale visualizations, particularly when the ratio between the largest scale and the smallest item is large, can be challenging, and designers have developed many approaches to overcome this challenge. We present a design space for visualization with multiple scales. The design space includes three dimensions, with eight total subdimensions. We

Zhe Zhou, Qianchuan Ye, Benjamin Delaware, Suresh Jagannathan

Functional programs typically interact with stateful libraries that hide state behind typed abstractions. One particularly important class of applications are data structure implementations that rely on such libraries to provide a level of efficiency and scalability that may be otherwise difficult to achieve. However, because the specifications of the method

Robert Dougherty-Bliss, Charles Kenney, Doron Zeilberger

Generalizing an argument of Matiyasevich, we illustrate a method to generate infinitely many diophantine equations whose solutions can be completely described by linear recurrences. In particular, we provide an integer-coefficient polynomial $p(x, y, z)$ whose only integer roots are consecutive triples of Tribonacci numbers.

Dominic W. Pesce, Lindy Blackburn, Ryan Chaves, Sheperd S. Doeleman

Very long baseline interferometry (VLBI) provides the highest-resolution images in astronomy. The sharpest resolution is nominally achieved at the highest frequencies, but as the observing frequency increases so too does the atmospheric contribution to the system noise, degrading the sensitivity of the array and hampering detection. In this paper, we explore

Syeda Sabrina Akter, Antonios Anastasopoulos

Media framing is the study of strategically selecting and presenting specific aspects of political issues to shape public opinion. Despite its relevance to almost all societies around the world, research has been limited due to the lack of available datasets and other resources. This study explores the possibility of dataset creation through crowdsourcing, u

Yohei Zushi, Cody D. Schimming, Kazumasa A. Takeuchi

Topological defects are a universal concept across many disciplines, such as crystallography, liquid-crystalline physics, low-temperature physics, cosmology, and even biology. In nematic liquid crystals, topological defects called disclinations have been widely studied. For their three-dimensional (3D) dynamics, however, only recently have theoretical approa

Prabhav Gaur, Chengkuan Gao, Karl Johnson, Shimon Rubin

Physical Reservoir Computing (PRC) is a recently developed variant of Neuromorphic Computing, where a pertinent physical system effectively projects information encoded in the input signal into a higher-dimensional space. While various physical hardware has demonstrated promising results for Reservoir Computing (RC), systems allowing tunability of their dyna

Louis Davis, Boris Baeumer, Ting Wang

Most point process models for earthquakes currently in the literature assume the magnitude distribution is i.i.d. potentially hindering the ability of the model to describe the main features of data sets containing multiple earthquake mainshock aftershock sequences in succession. This study presents a novel multidimensional fractional Hawkes process model de

Ashot Avanesov, Alexander Shurinov, Ivan Dyakonov, Stanislav Straupe

We adopt a method of the quantum gate teleportation for converting circuit-based quantum computation primitives into fusion networks. By using the presented scheme for the CNOT gate we construct translation of the circuit for the foliated surface code into a fault tolerant fusion network. Finally, we construct two new fusion based quantum computation models

Chuyi Shang, Amos You, Sanjay Subramanian, Trevor Darrell

Recently, image-based Large Multimodal Models (LMMs) have made significant progress in video question-answering (VideoQA) using a frame-wise approach by leveraging large-scale pretraining in a zero-shot manner. Nevertheless, these models need to be capable of finding relevant information, extracting it, and answering the question simultaneously. Currently, e

Adrian Mirza, Nawaf Alampara, Sreekanth Kunchapu, Martiño Ríos-García

Large language models (LLMs) have gained widespread interest due to their ability to process human language and perform tasks on which they have not been explicitly trained. However, we possess only a limited systematic understanding of the chemical capabilities of LLMs, which would be required to improve models and mitigate potential harm. Here, we introduc

Purnachandra Mandadapu

As medium-scale quantum computers progress, the application of quantum algorithms across diverse fields like simulating physical systems, chemistry, optimization, and cryptography becomes more prevalent. However, these quantum computers, known as Noisy Intermediate Scale Quantum (NISQ), are susceptible to noise, prompting the search for applications that can

Parker Riley, Daniel Deutsch, George Foster, Viresh Ratnakar

Reliable human evaluation is critical to the development of successful natural language generation models, but achieving it is notoriously difficult. Stability is a crucial requirement when ranking systems by quality: consistent ranking of systems across repeated evaluations is not just desirable, but essential. Without it, there is no reliable foundation fo

Erik D. Huckvale, Hunter N. B. Moseley

Over the lifetime of a computing task, determining the maximum usage of random-access memory (RAM) on both the motherboard and on a graphical processing unit (GPU), as well as the utilization percentage of the central processing unit (CPU) and GPU, can be extremely useful for troubleshooting points of failure as well as optimizing memory and processing unit

Dhruv Kulshreshtha, Aristotelis Panagiotopoulos

We show that the homeomorphisms of the Sierpi\'nski carpet are not classifiable, up to conjugacy, using isomorphism types of countable structures as invariants.

Andrew Chael

We present a new approach for stably evolving general relativistic magnetohydrodynamic (GRMHD) simulations in regions where the magnetization $\sigma=b^2/\rho c^2$ becomes large. GRMHD codes typically struggle to evolve plasma above $\sigma\approx100$ in simulations of black hole accretion. To ensure stability, GRMHD codes will inject mass density artificial

Jesse Milzman

In this paper, we define a new measure of the redundancy of information from a fault tolerance perspective. The partial information decomposition (PID) emerged last decade as a framework for decomposing the multi-source mutual information $I(T;X_1, ..., X_n)$ into atoms of redundant, synergistic, and unique information. It built upon the notion of redundancy

Dhananjay Saikumar, Blesson Varghese

Modern deep neural networks (DNNs) consist of millions of parameters, necessitating high-performance computing during training and inference. Pruning is one solution that significantly reduces the space and time complexities of DNNs. Traditional pruning methods that are applied post-training focus on streamlining inference, but there are recent efforts to le

Yizeng Li, Dewei Wang, Joshua M. Tebbs

Group testing, a method that screens subjects in pooled samples rather than individually, has been employed as a cost-effective strategy for chlamydia screening among Iowa residents. In efforts to deepen our understanding of chlamydia epidemiology in Iowa, several group testing regression models have been proposed. Different than previous approaches, we expa

Rohan Biswas, Kasturi Ganguly, Arijit Das, Diganta Saha

In today's digital world, cyberbullying is a serious problem that can harm the mental and physical health of people who use social media. This paper explains just how serious cyberbullying is and how it really affects indi-viduals exposed to it. It also stresses how important it is to find better ways to detect cyberbullying so that online spaces can be safe

Sohag Kumar Mondal, Md. Simul Hasan Talukder, Mohammad Aljaidi, Rejwan Bin Sulaiman

Blood cell classification and counting are vital for the diagnosis of various blood-related diseases, such as anemia, leukemia, and thrombocytopenia. The manual process of blood cell classification and counting is time-consuming, prone to errors, and labor-intensive. Therefore, we have proposed a DL based automated system for blood cell classification and co

Sarupa Debnath, Bernard T. Agyeman, Soumya R. Sahoo, Xunyuan Yin

Accurate soil moisture information is crucial for developing precise irrigation control strategies to enhance water use efficiency. Soil moisture estimation based on limited soil moisture sensors is crucial for obtaining comprehensive soil moisture information when dealing with large-scale agricultural fields. The major challenge in soil moisture estimation

Esteban Villa-Turek, Rod Abhari, Erik C. Nisbet, Yu Xu

This study maps the spread of two cases of COVID-19 conspiracy theories and misinformation in Spanish and French in Latin American and French-speaking communities on Facebook, and thus contributes to understanding the dynamics, reach and consequences of emerging transnational misinformation networks. The findings show that co-sharing behavior of public Faceb

Omar Faruque, Sahara Ali, Xue Zheng, Jianwu Wang

The growing availability and importance of time series data across various domains, including environmental science, epidemiology, and economics, has led to an increasing need for time-series causal discovery methods that can identify the intricate relationships in the non-stationary, non-linear, and often noisy real world data. However, the majority of curr

Ming-Jian Ding, Jiang Zeng

Recently Cheng et al. (Adv. in Appl. Math. 143 (2023) 102451) generalized the inversion number to partial permutations, which are also known as Laguerre digraphs, and asked for a suitable analogue of MacMahon's major index. We provide such a major index, namely, the corresponding maj and inv statistics are equidistributed, and exhibit a Haglund-Remmel-Wilson

JungEun Kim, Hangyul Yoon, Geondo Park, Kyungsu Kim

4D medical images, which represent 3D images with temporal information, are crucial in clinical practice for capturing dynamic changes and monitoring long-term disease progression. However, acquiring 4D medical images poses challenges due to factors such as radiation exposure and imaging duration, necessitating a balance between achieving high temporal resol

Abimael Bengochea, Jaime Burgos-García, Ernesto Pérez-Chavela

We study orbits near collision in a non-autonomous restricted planar four-body problem. This restricted problem consists of a massless particle moving under the gravitational influence due to three bodies following the figure-eight choreography. We use regularized coordinates in order to deal numerically with motions near collision, and reversing symmetries

Vincent Fan, Yujie Qian, Alex Wang, Amber Wang

Information extraction from chemistry literature is vital for constructing up-to-date reaction databases for data-driven chemistry. Complete extraction requires combining information across text, tables, and figures, whereas prior work has mainly investigated extracting reactions from single modalities. In this paper, we present OpenChemIE to address this co

Pengda Wang, Zilin Xiao, Hanjie Chen, Frederick L. Oswald

Although large language models (LLMs) have demonstrated remarkable proficiency in modeling text and generating human-like text, they may exhibit biases acquired from training data in doing so. Specifically, LLMs may be susceptible to a common cognitive trap in human decision-making called the representativeness heuristic. This is a concept in psychology that

Julio C Ruivo, Thomas Meltzer, Alex G Harvey, Jakub Benda

We present R-matrix calculations of photoionization of molecular monomers and dimers, focusing on ammonia (NH3) and formic acid (HCOOH), utilizing configuration-interaction models including the Occupation-Restricted Multiple Active Space (ORMAS) approach. We show that ORMAS is a highly efficient choice for calculating photoionization observables, yielding re

Ninad Hogade, Sudeep Pasricha

Data centers are increasingly using more energy due to the rise in Artificial Intelligence (AI) workloads, which negatively impacts the environment and raises operational costs. Reducing operating expenses and carbon emissions while maintaining performance in data centers is a challenging problem. This work introduces a unique approach combining Game Theory

Or Graur

The Milky Way's name and role in ancient Egyptian culture remain unclear. One suggestion is that the Milky Way may have been a celestial depiction of the sky goddess Nut. In this work, I test this association using an interdisciplinary approach. In the first part of this paper, I combine astronomical simulations of the ancient Egyptian night sky with primary

Sarah Safi, Marzieh Farhang, Olga Mena, Eleonora Di Valentino

We explore the possibility of redshift-dependent deviations in the contribution of relativistic degrees of freedom to the radiation budget of the cosmos, conventionally parameterized by the effective number of neutrinos $N_{\rm eff}$, from the predictions of the standard model. We expand the deviations $\Delta N_{\rm eff}(z)$ in terms of top-hat functions an

Elias R. Most, Andrei M. Beloborodov, Bart Ripperda

We perform the first magnetohydrodynamic simulation tracking the magnetosphere of a collapsing magnetar. The collapse is expected for massive rotating magnetars formed in merger events, and may occur many hours after the merger. Our simulation suggests a novel mechanism for a gamma-ray burst (GRB) which is uncollimated and forms a delayed high-energy counter

Alberto Alcalá-Alvarez, Pablo Padilla-Longoria

In this article, a framework for defining and analysing a family of graphs or networks from symbolic music information is discussed. Such graphs concern different types of elements, such as pitches, chords and rhythms, and the relations among them, and are built from quantitative or categorical data contained in digital music scores. They are helpful in visu

Sohang Kundu, Timothy C. Berkelbach

The application of an external, oriented electric field has emerged as an attractive technique for manipulating chemical reactions. Because most applications occur in solution, a theory of electric field catalysis requires treatment of the solvent, whose interaction with both the external field and the reacting species modifies the reaction energetics and th

Tyler Kharazi, Torin F. Stetina, Liwen Ko, Guang Hao Low

We develop and analyze a fault-tolerant quantum algorithm for computing $n$-th order response properties necessary for analysis of non-linear spectroscopies of molecular and condensed phase systems. We use a semi-classical description in which the electronic degrees of freedom are treated quantum mechanically and the light is treated as a classical field. Th

Mahmudul Islam Masum, Arif Sarwat, Hugo Riggs, Alicia Boymelgreen

This paper presents a comparative study of object detection using YOLOv5 and YOLOv8 for three distinct classes: artemia, cyst, and excrement. In this comparative study, we analyze the performance of these models in terms of accuracy, precision, recall, etc. where YOLOv5 often performed better in detecting Artemia and cysts with excellent precision and accura

Sindhu Kishore, Hangfeng He

Unraveling the intricate details of events in natural language necessitates a subtle understanding of temporal dynamics. Despite the adeptness of Large Language Models (LLMs) in discerning patterns and relationships from data, their inherent comprehension of temporal dynamics remains a formidable challenge. This research meticulously explores these intrinsic

Sayok Chakravarty, Nicholas Spanier

We analyze a random greedy process to construct $q$-uniform linear hypergraphs using the differential equation method. We show for $q=o(\sqrt{\log n})$, that this process yields a hypergraph with $\frac{n(n-1)}{q(q-1)}(1-o(1))$ edges. We also give some bounds for maximal linear hypergraphs.

Yuzhen Ding, Jason M. Holmes, Hongying Feng, Baoxin Li

In radiotherapy, 2D orthogonally projected kV images are used for patient alignment when 3D-on-board imaging(OBI) unavailable. But tumor visibility is constrained due to the projection of patient's anatomy onto a 2D plane, potentially leading to substantial setup errors. In treatment room with 3D-OBI such as cone beam CT(CBCT), the field of view(FOV) of CBCT

Katalin Varga, Tibor Szendrei

Tracking the build-up of financial vulnerabilities is a key component of financial stability policy. Due to the complexity of the financial system, this task is daunting, and there have been several proposals on how to manage this goal. One way to do this is by the creation of indices that act as a signal for the policy maker. While factor modelling in finan

Gengyu Zhang, Hao Tang, Yan Yan

Route planning for navigation under partial observability plays a crucial role in modern robotics and autonomous driving. Existing route planning approaches can be categorized into two main classes: traditional autoregressive and diffusion-based methods. The former often fails due to its myopic nature, while the latter either assumes full observability or st

Brendon Rhoades, Vasu Tewari, Andy Wilson

We associate a quotient of superspace to any hyperplane arrangement by considering the differential closure of an ideal generated by powers of certain homogeneous linear forms. This quotient is a superspace analogue of the external zonotopal algebra, and it further contains the central zonotopal algebra in the appropriate grading. We show that an evaluation

Pengcheng Zhang

We prove two results on converse theorems for Hilbert modular forms over totally real fields of degree $r>1$. The first result recovers a Hilbert modular form (of some level) from an $L$-series satisfying functional equations twisted by all the unramified Hecke characters. The second result assumes both the 'unramified' functional equations and an Euler prod

Jiacheng Xie, Hua-Chieh Shao, Yunxiang Li, You Zhang

Cone-beam computed tomography (CBCT) is widely used in image-guided radiotherapy. Reconstructing CBCTs from limited-angle acquisitions (LA-CBCT) is highly desired for improved imaging efficiency, dose reduction, and better mechanical clearance. LA-CBCT reconstruction, however, suffers from severe under-sampling artifacts, making it a highly ill-posed inverse

Dessislava H. Kochloukova, Stefano Vidussi

This survey describes some recent work, by the authors and others, on the existence of algebraic fibrations of group extensions, as well as the finiteness properties of their algebraic fibers, in the realm of both abstract and pro-$p$ groups. We also discuss some applications of these results to (higher) coherence.

Martim Afonso, Praphulla M. S. Bhawsar, Monjoy Saha, Jonas S. Almeida

Whole Slide Images (WSI), obtained by high-resolution digital scanning of microscope slides at multiple scales, are the cornerstone of modern Digital Pathology. However, they represent a particular challenge to AI-based/AI-mediated analysis because pathology labeling is typically done at slide-level, instead of tile-level. It is not just that medical diagnos