March 2024 arXiv papers — page 168

Ziyue Li, Tian Li, Virginia Smith, Jeff Bilmes

Optimizing the performance of many objectives (instantiated by tasks or clients) jointly with a few Pareto stationary solutions (models) is critical in machine learning. However, previous multi-objective optimization methods often focus on a few number of objectives and cannot scale to many objectives that outnumber the solutions, leading to either subpar pe

Artiom Skripka, Zhuolei Zhang, Xiao Qi, Benedikt Ursprung

Optically bistable materials respond to a single input with two possible optical outputs, contingent upon excitation history. Such materials would be ideal for optical switching and memory, yet limited understanding of intrinsic optical bistability (IOB) prevents development of nanoscale IOB materials suitable for devices. Here, we demonstrate IOB in Nd3+-do

Yu Huang

AI agents are defined as artificial entities to perceive the environment, make decisions and take actions. Inspired by the 6 levels of autonomous driving by Society of Automotive Engineers, the AI agents are also categorized based on utilities and strongness, as the following levels: L0, no AI, with tools taking into account perception plus actions; L1, usin

David W. Tam, Nicola Colonna, Fatima Alarab, Vladimir N. Strocov

We present high-quality angle-resolved photoemission (ARPES) and density functional theory calculations (DFT+U) of SmCoIn$_5$. We find broad agreement with previously published studies of LaCoIn$_5$ and CeCoIn$_5$, confirming that the Sm $4f$ electrons are mostly localized. Nevertheless, our model is consistent with an additional delocalized Sm component, st

Yongle Zhang, Ge Gao

International migrants face difficulties obtaining information for a quality life and well-being in the host country. Prior research indicates that international migrants often seek information from their co-national cohort or contacts from the same country. The downside of this practice, however, is that people can end up clustering in a small-world environ

David Lee, Alberto F. Martín, Kieran Ricardo

Implicit solvers for atmospheric models are often accelerated via the solution of a preconditioned system. For block preconditioners this typically involves the factorisation of the (approximate) Jacobian resulting from linearization of the coupled system into a Helmholtz equation for some function of the pressure. Here we present a preconditioner for the co

Siddharth Nair, Timothy F. Walsh, Greg Pickrell, Fabio Semperlotti

This work presents a physics-driven machine learning framework for the simulation of acoustic scattering problems. The proposed framework relies on a physics-informed neural network (PINN) architecture that leverages prior knowledge based on the physics of the scattering problem as well as a tailored network structure that embodies the concept of the superpo

Oana Cojocaru-Mirédin, Yuan Yu, Jan Köttgen, Tanmoy Ghosh

Atom probe tomography is frequently employed to characterize the elemental distribution in solids with atomic resolution. Here we review and discuss the potential of this technique to locally probe chemical bonds. Two processes characterize the bond rupture in laser-assisted field emission, the probability of molecular ions, i.e. the probability that molecul

Paul L. Schechter, Richard Luhtaru

When a rectangular hyperbola is constructed from the image positions of a quadruply lensed quasar, as proposed by Witt (1996), it passes very close to the the lensing galaxy. The median measured perpendicular offset between the observed light center of the lens and Witt's hyperbola is 0.013" for a sample for 39 systems lensed by a relatively isolated galaxy.

Adam K. Taras, J. Gordon Robertson, Fatme Allouche, Benjamin Courtney-Barrer

High angular resolution imaging is an increasingly important capability in contemporary astrophysics. Of particular relevance to emerging fields such as the characterisation of exoplanetary systems, imaging at the required spatial scales and contrast levels results in forbidding challenges in the correction of atmospheric phase errors, which in turn drives d

J. G. Dai, Dongyan Huo

In this work, we study the stationary distribution of the scaled queue length vector process in multiclass queueing networks operating under static buffer priority service policies. We establish that when subjected to a multi-scale heavy traffic condition, the stationary distribution converges to a product-form limit, with each component in the product form

Pak-Yeung Chan, Ronan J. Conlon, Yi Lai

In $1996$, H.-D. Cao constructed a $U(n)$-invariant steady gradient Kähler-Ricci soliton on $\mathbb{C}^{n}$ and asked whether every steady gradient Kähler-Ricci soliton of positive curvature on $\mathbb{C}^{n}$ is necessarily $U(n)$-invariant (and hence unique up to scaling). Recently, Apostolov-Cifarelli answered this question in the negative for $n=2$. He

Sam K. Miller

Let $G$ be a finite group and $k$ a field of prime characteristic $p$. We give a complete classification of endotrivial complexes, i.e. determine the Picard group $\mathcal{E}_k(G)$ of the tensor-triangulated category $K^b({}_{kG}\mathbf{triv})$, the bounded homotopy category of $p$-permutation modules, which Balmer and Gallauer recently considered. For $p$-

Nik Bear Brown

The Cognitive Type Project is focused on developing computational tools to enable the design of typefaces with varying cognitive properties. This initiative aims to empower typographers to craft fonts that enhance click-through rates for online ads, improve reading levels in children's books, enable dyslexics to create personalized type, or provide insights

Suhan Cui, Prasenjit Mitra

In the realm of big data and digital healthcare, Electronic Health Records (EHR) have become a rich source of information with the potential to improve patient care and medical research. In recent years, machine learning models have proliferated for analyzing EHR data to predict patients future health conditions. Among them, some studies advocate for multi-t

Abhishek Anand, Negar Mokhberian, Prathyusha Naresh Kumar, Anweasha Saha

Researchers have raised awareness about the harms of aggregating labels especially in subjective tasks that naturally contain disagreements among human annotators. In this work we show that models that are only provided aggregated labels show low confidence on high-disagreement data instances. While previous studies consider such instances as mislabeled, we

Venkat Venkatasubramanian, Jessica Shi, Leo Goldman, Arun Sankar E. M.

Contrary to the widely believed hypothesis that larger, denser cities promote socioeconomic mixing, a recent study (Nilforoshan et al. 2023) reports the opposite behavior, i.e. more segregation. Here, we present a game-theoretic model that predicts such a density-dependent segregation outcome in both one- and two-class systems. The model provides key insight

Ziyi Yin, Mathias Louboutin, Olav Møyner, Felix J. Herrmann

Time-lapse seismic monitoring necessitates integrated workflows that combine seismic and reservoir modeling to enhance reservoir property estimation. We present a feasibility study of an end-to-end inversion framework that directly inverts for permeability from prestack time-lapse seismic data. To assess the method's robustness, we design experiments focusin

Benjamin Eysenbach, Vivek Myers, Ruslan Salakhutdinov, Sergey Levine

Given time series data, how can we answer questions like "what will happen in the future?" and "how did we get here?" These sorts of probabilistic inference questions are challenging when observations are high-dimensional. In this paper, we show how these questions can have compact, closed form solutions in terms of learned representations. The key idea is t

Aaron Mishkin, Ahmed Khaled, Yuanhao Wang, Aaron Defazio

We develop new sub-optimality bounds for gradient descent (GD) that depend on the conditioning of the objective along the path of optimization rather than on global, worst-case constants. Key to our proofs is directional smoothness, a measure of gradient variation that we use to develop upper-bounds on the objective. Minimizing these upper-bounds requires so

Abdelrahman Abdallah, Daniel Eberharter, Zoe Pfister, Adam Jatowt

This paper presents a comprehensive survey of research works on the topic of form understanding in the context of scanned documents. We delve into recent advancements and breakthroughs in the field, highlighting the significance of language models and transformers in solving this challenging task. Our research methodology involves an in-depth analysis of pop

N. J. Fine, L. Gillman, J. Lambek

From the original PREFACE: The rings of quotients recently introduced by Johnson and Utumi are applied to the ring $C(X)$ of all continuous real-valued functions on a completely regular space $X$. Let $Q(X)$ denote the maximal ring of quotients of $C(X)$; then $Q(X)$ may be realized as the ring of all continuous functions on the dense open sets of $X$ (modul

Vladimir A. Aksyuk, Maria E. Simon, Flavio Pardo, Susanne Arney

As optical telecommunication networks become more complex, there is an emerging need for systems capable of very complex switching and manipulation of large numbers of optical signals. MEMS enable these systems by combining excellent capabilities and optical properties of macroscopic optomechanics with dense integration of multiple actuators on a single chip

Ugo Giuseppe Aglietti, Giancarlo Ferrera

We consider the Energy-Energy Correlation (EEC) function in high-energy electron-positron annihilation to hadrons. In the back-to-back (two-jet) region, we perform the all-order resummation of the logarithmically-enhanced contributions in QCD perturbation theory up to next-to-next-to-next-to-leading logarithmic (N$^3$LL) accuracy. Away from the back-to-back

Clare E Parnell

For 3D magnetic reconnection to occur there must exist a volume within which the electric field component parallel to the magnetic field is non-zero. In numerical experiments, locations of non-zero parallel electric field indicate sites of 3D magnetic reconnection. If these experiments contain all types of topological feature (null points, separatrix surface

Anshika Chugh, Soumen De Karmakar, Rajaraman Ganesh

We study the dependence of alignment and confinement on the aggregate morphology of self-aligning soft disks in a planer box geometry confined along y direction. We show that the wall accumulation of aggregates becomes non-uniform upon increase in alignment strength and decrease in box width. The height of these structures is found to be a non-monotonic func

Abhinav Gupta, Radim Bartos

With the introduction of QUIC, a modern transport-layer network protocol, HTTP/3 leverages its benefits to enhance web content delivery. This paper proposes a mechanism based on the recently standardized Extensible Prioritization Scheme (EPS) for weighted incremental web content delivery. The mechanism augments the sequential scheduling to provide incrementa

Jianfeng He, Hang Su, Jason Cai, Igor Shalyminov

Semi-supervised dialogue summarization (SSDS) leverages model-generated summaries to reduce reliance on human-labeled data and improve the performance of summarization models. While addressing label noise, previous works on semi-supervised learning primarily focus on natural language understanding tasks, assuming each sample has a unique label. However, thes

Chaeeun Han, Jose Paolo Talusan, Dan Freudberg, Ayan Mukhopadhyay

Public transportation systems often suffer from unexpected fluctuations in demand and disruptions, such as mechanical failures and medical emergencies. These fluctuations and disruptions lead to delays and overcrowding, which are detrimental to the passengers' experience and to the overall performance of the transit service. To proactively mitigate such even

Elia Cereda, Manuele Rusci, Alessandro Giusti, Daniele Palossi

Sub-\SI{50}{\gram} nano-drones are gaining momentum in both academia and industry. Their most compelling applications rely on onboard deep learning models for perception despite severe hardware constraints (\ie sub-\SI{100}{\milli\watt} processor). When deployed in unknown environments not represented in the training data, these models often underperform due

Olukorede Fakorede, Modeste Atsague, Jin Tian

Adversarial Training (AT) effectively improves the robustness of Deep Neural Networks (DNNs) to adversarial attacks. Generally, AT involves training DNN models with adversarial examples obtained within a pre-defined, fixed perturbation bound. Notably, individual natural examples from which these adversarial examples are crafted exhibit varying degrees of int

Md Mehedi Hasan Bhuiyan

Link function is a key tool in the binomial regression model defined as non-linear model under GLM approach. It transforms the nonlinear regression to linear model with converting the interval (-\infty,\infty) to the probability [0,1]. The binomial model with link functions (logit, probit, cloglog and cauchy) are applied on the proportional of child malnutri

Timotheus Riedel

The idea that the dynamical properties of quantum systems are invariably relative to other systems has recently regained currency. Using Relational Quantum Mechanics (RQM) for a case study, this paper calls attention to a question that has been underappreciated in the debate about quantum relativism: the question of whether relativity iterates. Are there abs

Martin Riddell, Ansong Ni, Arman Cohan

While large language models have achieved remarkable performance on various code generation benchmarks, there have been growing concerns regarding potential contamination of these benchmarks as they may be leaked into pretraining and finetuning data. While recent work has investigated contamination in natural language generation and understanding tasks, ther

Davide Germani, Farhad Niliani, Antonio D. Polosa

We describe pentaquarks as baryo-charmonia with a color octet $c\bar{c}$ core bonded to a color octet three-quark system. Fermi statistics of the light quark cloud allows to describe two pentaquark triplets: a lower one, well supported by experiment, and a higher one with strangeness. For the time being, the lowest line of the strange triplet has been experi

Rajat Kumar Jenamani, Daniel Stabile, Ziang Liu, Abrar Anwar

Robot-assisted feeding can greatly enhance the lives of those with mobility limitations. Modern feeding systems can pick up and position food in front of a care recipient's mouth for a bite. However, many with severe mobility constraints cannot lean forward and need direct inside-mouth food placement. This demands precision, especially for those with restric

Jialu Shi, Zhiqiang Wei, Jie Nie, Lei Huang

The self-supervised contrastive learning strategy has attracted considerable attention due to its exceptional ability in representation learning. However, current contrastive learning tends to learn global coarse-grained representations of the image that benefit generic object recognition, whereas such coarse-grained features are insufficient for fine-graine

Carlos Barbero-Petrel, Peter Schmelcher, Rosario González-Férez

Recently Rydberg atom-ion bound states have been observed using a high resolution ion microscope (Nature 605, 453 (2022)) and the corresponding vibrational dynamics has been spectroscopically analyzed. The atom-ion bond is created by an avoided crossing, which involves a flipping molecular dipole. Motivated by the discovery of this binding mechanism we addre

Saikat Basu, Abir Malakar, Mohammad Mehedi Hasan Akash

Investigations on airborne transmission of pathogens constitute a rapidly expanding field, primarily focused on understanding the expulsion patterns of respiratory particulates from infected hosts and their dispersion in confined spaces. Largely overlooked has been the crucial role of fluid dynamics in guiding inhaled virus-laden particulates within the resp

Guilherme Ferraz de Arruda, Wan He, Nasimeh Heydaribeni, Tara Javidi

We propose a team assignment algorithm based on a hypergraph approach focusing on resilience and diffusion optimization. Specifically, our method is based on optimizing the algebraic connectivity of the Laplacian matrix of an edge-dependent vertex-weighted hypergraph. We used constrained simulated annealing, where we constrained the effort agents can exert t

Kenshiro Oguri

This paper presents a robust path-planning framework for safe spacecraft autonomy under uncertainty and develops a computationally tractable formulation based on convex programming. We utilize chance-constrained control to formulate the problem. It provides a mathematical framework to solve for a sequence of control policies that minimizes a probabilistic co

R. de la Fuente Marcos, J. de Leon, C. de la Fuente Marcos, M. R. Alarcon

Context. Known Mars trojans could be primordial small bodies that have remained in their present-day orbits for the age of the Solar System. Their orbital distribution is strongly asymmetric; there are over a dozen objects at the L5 point and just one at L4, (121514) 1999 UJ7. Most L5 trojans appear to form a collision-induced asteroid cluster, known as the

Neda Abbasi Taklimi, Franco Ferrari, Marcin Radosław Piątek, Luca Tubiana

Inspired by recent advances in the chromosome capture techniques, a method is proposed to study the structural organization of systems of polymers rings with topological constraints.To this purpose, the system is divided into compartments and a simple condition is provided in order to determine if two compartments are in contact or not. Next, a set of contac

Jean-Eric Wegrowe, Luqian Zhou, Sariah Al Saati

Two well-known Hall-like effects are occurring in ferromagnets: the Anomalous Hall effect and the Planar Hall effect. The former is analogous to the classical Hall effect and is defined by the Onsager reciprocity relation of the second kind (antisymmetric conductivity matrix), while the latter is defined by the Onsager reciprocity relation of the first kind

Yiran Wang, Martin Lysy, Audrey Béliveau

Plant-capture is a variant of classical capture-recapture methods used to estimate the size of a population. In this method, decoys referred to as "plants" are introduced into the population in order to estimate the capture probability. The method has shown considerable success in estimating population sizes from limited samples in many epidemiological, ecol

Ichiro Oda

We study the problem of how to derive conformal symmetry in the framework of quantum gravity. We start with a generic gravitational theory which is invariant under both the general coordinate transformation (GCT) and Weyl transformation (or equivalently, local scale transformation), and then construct its BRST formalism by fixing the gauge symmetries by the

Blake Bates, Zhanar Berikkyzy, Nick Chiem, Gabriel Elvin

We say a graph $H$ is $r$-rainbow-uncommon if the maximum number of rainbow copies of $H$ under an $r$-coloring of $E(K_n)$ is asymptotically (as $n \to \infty$) greater than what is expected from uniformly random $r$-colorings. Via explicit constructions, we show that for $H\in\{K_3,K_4, K_5\}$, $H$ is $r$-rainbow-uncommon for all $r\geq {|V(H)|\choose 2}$.

Quinn Taylor, Glenn D. Starkman, Michael Hinczewski, Deyan P. Mihaylov

The Hawking process results in a monotonic decrease of the black hole mass, but a biased random walk of the black hole angular momentum. We demonstrate that this stochastic process leads to a significant fraction of primordial black holes becoming extremal Kerr black holes (EKBHs) of one to a few Planck masses regardless of their initial mass. For these EKBH

Milos Micovic, Branko Malesevic

In this paper, two double Jordan-type inequalities are introduced that generalize some previously established inequalities. As a result, some new upper and lower bounds and approximations of the sinc function are obtained. This extension of Jordan's inequality is enabled by considering the corresponding inequalities through the concept of stratified families

Kun Chen

We present a time domain method to solve quantum scattering by an arbitrary potential of finite range. The scattering wave function in full space can be obtained, including the near field, the mid field (i.e. Fresnel region) and the far field. This is achieved by extending several techniques of FDTD computational electrodynamics into the quantum realm. The t

Sha Hu

In this letter, we proved a matrix identity of Hankel matrices that seems unrevealed before, generated from the moments of Gaussian distributions. In particular, we derived the Cholesky decompositions of the Hankel matrices in closed-forms, and showed some interesting connections between them. The results have potential applications in such as optimizing a n

Daniel Johnson, Ivan Polyakov, Tomasz Skwarnicki, Mengzhen Wang

It has been five years since the data sample from the LHCb detector, the first experiment optimized for heavy-flavor physics studies at a hadronic collider, was completed. These data led to many major discoveries in exotic hadron spectroscopy, which we review in this article. We supplement the experimental results with a selection of phenomenological interpr

Xiaolin Sun, Zizhan Zheng

Reinforcement learning (RL) has achieved phenomenal success in various domains. However, its data-driven nature also introduces new vulnerabilities that can be exploited by malicious opponents. Recent work shows that a well-trained RL agent can be easily manipulated by strategically perturbing its state observations at the test stage. Existing solutions eith

Matthias Jung, Sven O. Krumke, Christof Schroth, Elisabeth Lobe

The field of Electronic Design Automation (EDA) is crucial for microelectronics, but the increasing complexity of Integrated Circuits (ICs) poses challenges for conventional EDA: Corresponding problems are often NP-hard and are therefore in general solved by heuristics, not guaranteeing optimal solutions. Quantum computers may offer better solutions due to t

Richard Cushman

This paper constructs a Riemann surface associated to the icosahedron and discusses the geodesics associated to a flat metric on this surface. Because of the icosahedral symmetry, this is a distinguished special case of the example treated in [2].

M. Gil de Oliveira, L. J. Pereira, A. S. Santos, K. Dechoum

In this work we study the evolution of an optical vortex undergoing self phase modulation inside a nonlinear Kerr medium. The intensity dependent phase evolution couples the angular and radial degrees of freedom of the input vortex, giving rise to a rich dynamics where new radial modes are created. In the short propagation range, this dynamics is well descri

Christopher P. Porter

Reimann and Slaman initiated the study of sequences that are Martin-L\"of random with respect to a continuous measure, establishing fundamental facts about NCR, the collection of sequences that are not Martin-L\"of random with respect to any continuous measure. In the case of sequences that are random with respect to a computable, continuous measure, the pic

Alcides Buss, Luiz Felipe Garcia, Devarshi Mukherjee

We introduce $p$-adic operator algebras, which are nonarchimedean analogues of $C^*$-algebras. We demonstrate that various classical examples of operator algebras - such as group(oid) $C^*$-algebras - have nonarchimedean counterparts. The category of $p$-adic operator algebras exhibits similar properties to those of the category of real and complex $C^*$-alg

Laurent Bienvenu, Christopher P. Porter

In this article, we study the relationship between notions of depth for sequences, namely, Bennett's notions of strong and weak depth, and deep $\Pi^0_1$ classes, introduced by the authors and motivated by previous work of Levin. For the first main result of the study, we show that every member of a $\Pi^0_1$ class is order-deep, a property that implies stro

Erik Gengel, Zafrir Kuplik, Dror Angel, Eyal Heifetz

We present a theory of jellyfish swarm formation and exemplify it with simulations of active Brownian particles. The motivation for our analysis is the phenomenon of jellyfish blooms in the ocean and clustering of jellyfish in tank experiments. We argue that such clusters emerge due to an externally induced phase transition of jellyfish density, such as conv

Christopher P. Porter

In this paper, we study the effective dimension of points in infinite fractal trees generated recursively by a finite tree over some alphabet. Using unequal costs coding, we associate a length function with each such fractal tree and show that the channel capacity of the length function is equal to the similarity dimension of the fractal tree (up to a multip

Fengtao Jin, Alexander Borovik, B. Michel Döhring, Benjamin Ebinger

We present new experimental and theoretical cross sections for electron-impact single ionization of Xe$^{12+}$ and Xe$^{13+}$ ions, and double ionization of Xe$^{12+}$, Xe$^{13+}$ and Xe$^{14+}$ ions for collision energies from the respective ionization thresholds up to 3500 eV. The calculations use the fully relativistic subconfiguration-averaged distorted-

Hanqi Wang, Tao Chen, Liang Song

EEG-based Emotion recognition holds significant promise for applications in human-computer interaction, medicine, and neuroscience. While deep learning has shown potential in this field, current approaches usually rely on large-scale high-quality labeled datasets, limiting the performance of deep learning. Self-supervised learning offers a solution by automa

M. Gil de Oliveira, A. L. S. Santos Junior, A. C. Barbosa, B. Pinheiro da Silva

We investigate theoretically and experimentally the optical second harmonic generation (SHG) with a twisted Gaussian Schell model (TGSM) beam as the fundamental field. We use Type-II phase matching and analyze the cross spectral density (CSD) of the SHG output beam when the input fundamental is prepared with a TGSM structure. We analyze two synthetization me

Gabriel Ruiz

We cover how to determine a sufficiently large sample size for a $K$-armed randomized experiment in order to estimate conditional counterfactual expectations in data-driven subgroups. The sub-groups can be output by any feature space partitioning algorithm, including as defined by binning users having similar predictive scores or as defined by a learned poli

Abdul Rasak Zubair, Oluwaseun Adewunmi Alo

Grey Level Co-occurrence Matrix and Grey Level Difference Vector are described and computed for twenty four 128 x 128 x 3 test images along horizontal, vertical and diagonal directions. Second order image statistics such as Contrast, Dissimilarity, Homogeneity (Inverse Difference Moment), Angular Second Moment, Energy, Maximum Probability, Entropy, Mean, Sta

Nizar Masmoudi, Wael Jaafar

The conjunction of edge intelligence and the ever-growing Internet-of-Things (IoT) network heralds a new era of collaborative machine learning, with federated learning (FL) emerging as the most prominent paradigm. With the growing interest in these learning schemes, researchers started addressing some of their most fundamental limitations. Indeed, convention

Jun Chen, Weng-Keen Wong, Bechir Hamdaoui

Radio Frequency (RF) device fingerprinting has been recognized as a potential technology for enabling automated wireless device identification and classification. However, it faces a key challenge due to the domain shift that could arise from variations in the channel conditions and environmental settings, potentially degrading the accuracy of RF-based devic

Vedant Bahel, Harshinee Sriram, Cristina Conati

The paper extends an existing Intelligent Tutoring System (ITS) that supports students' learning via AI-driven personalized hints and can generate explanations to justify why/how the hints were generated. In this work, we investigate personalizing these hint explanations to students with low levels of two traits, Need for Cognition and Conscientiousness in o

Rodrigo Avalos

In this paper, we address the existence of preferred asymptotic coordinates on asymptotically Euclidean (AE) manifolds $(M^3,g)$ such that $g$ admits an asymptotically Schwarzschildian first order expansion, based purely on a priori geometric conditions, which will then be used to establish geometric criteria guaranteeing the convergence of the ADM center of

Karthik Sridharan, Seung Won Wilson Yoo

We consider the problem of online learning where the sequence of actions played by the learner must adhere to an unknown safety constraint at every round. The goal is to minimize regret with respect to the best safe action in hindsight while simultaneously satisfying the safety constraint with high probability on each round. We provide a general meta-algorit

Alva V. I. Kinman, Maya A. Petkova, Jonathan C. Tan, Giuliana Cosentino

The origin of the stellar Initial Mass Function (IMF) and how it may vary with galactic environment is a matter of debate. Certain star formation theories involve a close connection between the IMF and the Core Mass Function (CMF) so it is important to measure this CMF in a range of Milky Way locations. Here we study the CMF of three Galactic Center clouds:

Yebowen Hu, Kaiqiang Song, Sangwoo Cho, Xiaoyang Wang

This paper explores the cutting-edge Large Language Model with analytical reasoning on sports. Our analytical reasoning embodies the tasks of letting large language models count how many points each team scores in a quarter in the NBA and NFL games. Our major discoveries are in two folds. Firstly, we find among all the models we employed, GPT-4 stands out in

Paulo Areyuna C., Jilberto Zamora-Saa, Alfonso R. Zerwekh

In this work, we have studied the Vector Scotogenic Model in the context of the Dark Matter problem. Due to unitarity considerations, we have focused on the scenario with fermion dark matter, finding out that co-annihilations play a fundamental role in achieving dark matter relic abundance. Moreover, the coannihilation effects allow to separate the parameter

M. Ali Khan, Arthur Paul Pedersen, David Schrittesser

The observation that every two-person adversarial game is an affine transformation of a zero-sum game is traceable to Luce & Raiffa (1957) and made explicit in Aumann (1987). Recent work of (ADP) Adler et al. (2009), and of Raimondo (2023) in increasing generality, proves what has so far remained a conjecture. We present two proofs of an even more general fo

Bile Peng, Karl-Ludwig Besser, Shanpu Shen, Finn Siegismund-Poschmann

Space-division multiple access (SDMA) plays an important role in modern wireless communications. Its performance depends on the channel properties, which can be improved by reconfigurable intelligent surfaces (RISs). In this work, we jointly optimize SDMA precoding at the base station (BS) and RIS configuration. We tackle difficulties of mutual coupling betw

Marc Sulliger, Jaime Ortega Arroyo, Romain Quidant

Droplet microfluidics offers a versatile platform for analyzing liquid samples. Despite its potential, there is a lack of techniques that allow to reliably probe individual circulating droplets. The prospect of combining droplet microfluidics with sensitive, broadband spectroscopic techniques would therefore unlock new capabilities for various disciplines, i

Yesdaulet Izenov, Asoke Datta, Brian Tsan, Abylay Amanbayev

In this work, we define the problem of finding an optimal query plan as finding spanning trees with low costs. This approach empowers the utilization of a series of spanning tree algorithms, thereby enabling systematic exploration of the plan search space over a join graph. Capitalizing on the polynomial time complexity of spanning tree algorithms, we presen

Pablo Serna, Andrés M. Somoza, Adam Nahum

The analysis of phase transitions of gauge theories has relied heavily on simplifications that arise at the boundaries of phase diagrams, where certain excitations are forbidden. Taking 2+1 dimensional $\mathbb{Z}_2$ gauge theory as an example, the simplification can be visualized geometrically: on the phase diagram boundaries the partition function is an en

Ricardo Bigolin Lanfredi, Pritam Mukherjee, Ronald Summers

In chest X-ray (CXR) image analysis, rule-based systems are usually employed to extract labels from reports for dataset releases. However, there is still room for improvement in label quality. These labelers typically output only presence labels, sometimes with binary uncertainty indicators, which limits their usefulness. Supervised deep learning models have

Chuan-Ning Luo, Shao-Peng Tang, Jing-Liang Jiang, Wei-Hong Gao

The measurements of the bulk properties of most isolated neutron stars (INSs) are challenging tasks. Tang et al. (2020) have developed a new method, based on the equation of state (EoS) of neutron star (NS) material constrained by the observational data, to infer the gravitational masses of a few INSs whose gravitational redshifts are available. However, in

Thomas Young, Dimitri M. Gangardt, Curt von Keyserlingk

We study entanglement growth in a harmonic oscillator chain subjected to the weak measurement of observables which have been smeared-out over a length scale $R$. We find that entanglement grows diffusively ($S \sim t^{1/2}$) for a large class of initial Gaussian states provided the measurement scale $R$ is sufficiently large. At late times $t \gtrsim \mathca

Yewei Huang, Xi Lin, Brendan Englot

We propose an autonomous exploration algorithm designed for decentralized multi-robot teams, which takes into account map and localization uncertainties of range-sensing mobile robots. Virtual landmarks are used to quantify the combined impact of process noise and sensor noise on map uncertainty. Additionally, we employ an iterative expectation-maximization

Gagik Gavalian

This paper presents the results of charged particle track reconstruction in CLAS12 using artificial intelligence. In our approach, we use machine learning algorithms to reconstruct tracks, including their momentum and direction, with high accuracy from raw hits of the CLAS12 drift chambers. The reconstruction is performed in real-time, with the rate of data

M. A. T. Patricio, G. M. Jacobsen, M. D. Teodoro, G. M. Gusev

The dynamics of the diffusion flow of holes photoinjected into a mesoscopic GaAs channel of variable width, where they, together with background electrons, form a hydrodynamic electron-hole fluid, is studied using time-resolved microphotoluminescence. It is found that the rate of recombination of photoinjected holes, which is proportional to the rate of thei

Michael P. Wellman, Karl Tuyls, Amy Greenwald

In the empirical approach to game-theoretic analysis (EGTA), the model of the game comes not from declarative representation, but is derived by interrogation of a procedural description of the game environment. The motivation for developing this approach was to enable game-theoretic reasoning about strategic situations too complex for analytic specification

Lasse Blaauwbroek, David Cerna, Thibault Gauthier, Jan Jakubův

Automated theorem provers and formal proof assistants are general reasoning systems that are in theory capable of proving arbitrarily hard theorems, thus solving arbitrary problems reducible to mathematics and logical reasoning. In practice, such systems however face large combinatorial explosion, and therefore include many heuristics and choice points that

Victor Magron, Yoshio Ebihara, Shingo Nishinaka, Dimitri Peaucelle

We consider the stability analysis of feedback systems with rectified linear unit (ReLU) activations, and model this problem with polynomial optimization. Stability can be certified by means of copositive multipliers in the framework of integral quadratic constraints. Based on a duality argument, we show how to certify instability by considering a complete h

Xinyuan Wang, Dongjie Wang, Wangyang Ying, Rui Xie

Feature selection prepares the AI-readiness of data by eliminating redundant features. Prior research falls into two primary categories: i) Supervised Feature Selection, which identifies the optimal feature subset based on their relevance to the target variable; ii) Unsupervised Feature Selection, which reduces the feature space dimensionality by capturing t

Zhijie Wang, Yuheng Huang, Da Song, Lei Ma

The recent advancements in Generative AI have significantly advanced the field of text-to-image generation. The state-of-the-art text-to-image model, Stable Diffusion, is now capable of synthesizing high-quality images with a strong sense of aesthetics. Crafting text prompts that align with the model's interpretation and the user's intent thus becomes crucia

Kongyang Chen, Wenfeng Wang, Zixin Wang, Wangjun Zhang

Federated Contrastive Learning (FCL) represents a burgeoning approach for learning from decentralized unlabeled data while upholding data privacy. In FCL, participant clients collaborate in learning a global encoder using unlabeled data, which can serve as a versatile feature extractor for diverse downstream tasks. Nonetheless, FCL is susceptible to privacy

Oseremen Joy Idialu, Noble Saji Mathews, Rungroj Maipradit, Joanne M. Atlee

Artificial intelligence (AI) assistants such as GitHub Copilot and ChatGPT, built on large language models like GPT-4, are revolutionizing how programming tasks are performed, raising questions about whether code is authored by generative AI models. Such questions are of particular interest to educators, who worry that these tools enable a new form of academ

Yingbo Ma, Suraj Kolla, Dhruv Kaliraman, Victoria Nolan

The breadth, scale, and temporal granularity of modern electronic health records (EHR) systems offers great potential for estimating personalized and contextual patient health trajectories using sequential deep learning. However, learning useful representations of EHR data is challenging due to its high dimensionality, sparsity, multimodality, irregular and

Sebastián Echeverría-Veas, Pablo S. Moya, Marian Lazar, Stefaan Poedts

Different in situ satellite observations within 0.3 to 1 AU from the Sun reveal deviations in the thermodynamics of solar wind expansion. Specifically, these deviations challenge the applicability of the double adiabatic or CGL theory, indicating potential influences such as perpendicular heating and/or parallel cooling of ions. The study aims to investigate

Jing Gu, Dongmian Zou

Graph anomaly detection plays a vital role for identifying abnormal instances in complex networks. Despite advancements of methodology based on deep learning in recent years, existing benchmarking approaches exhibit limitations that hinder a comprehensive comparison. In this paper, we revisit datasets and approaches for unsupervised node-level graph anomaly

Bohan Jiang, Lu Cheng, Zhen Tan, Ruocheng Guo

News media has been utilized as a political tool to stray from facts, presenting biased claims without evidence. Amid the COVID-19 pandemic, politically biased news (PBN) has significantly undermined public trust in vaccines, despite strong medical evidence supporting their efficacy. In this paper, we analyze: (i) how inherent vaccine stances subtly influenc

Xingyu Bruce Liu, Jiahao Nick Li, David Kim, Xiang 'Anthony' Chen

Situationally Induced Impairments and Disabilities (SIIDs) can significantly hinder user experience in contexts such as poor lighting, noise, and multi-tasking. While prior research has introduced algorithms and systems to address these impairments, they predominantly cater to specific tasks or environments and fail to accommodate the diverse and dynamic nat

Wesley A. Suttle, Vipul K. Sharma, Krishna C. Kosaraju, S. Sivaranjani

We develop provably safe and convergent reinforcement learning (RL) algorithms for control of nonlinear dynamical systems, bridging the gap between the hard safety guarantees of control theory and the convergence guarantees of RL theory. Recent advances at the intersection of control and RL follow a two-stage, safety filter approach to enforcing hard safety

Teresa Faria

A nonautonomous periodic chemostat model with delays modelling $n$ species in competition is considered. Sufficient conditions on the coefficients and consumption functions for the species are given, for both the extinction of the species and for the existence of $n$ nontrivial and nonnegative periodic solutions. Further criteria guarantee that the system ad

Zoltán Kolarovszki, Tomasz Rybotycki, Péter Rakyta, Ágoston Kaposi

We introduce the Piquasso quantum programming framework, a full-stack open-source software platform for the simulation and programming of photonic quantum computers. Piquasso can be programmed via a high-level Python programming interface enabling users to perform efficient quantum computing with discrete and continuous variables. Via optional high-performan