October 2023 arXiv papers — page 122

Wojciech Różowski, Alexandra Silva

We introduce Probabilistic Regular Expressions (PRE), a probabilistic analogue of regular expressions denoting probabilistic languages in which every word is assigned a probability of being generated. We present and prove the completeness of an inference system for reasoning about probabilistic language equivalence of PRE based on Salomaa's axiomatisation of

Maisy Lam, Laura Dodds, Aline Eid, Jimmy Hester

We present the design, implementation, and evaluation of MiFly, a self-localization system for autonomous drones that works across indoor and outdoor environments, including low-visibility, dark, and GPS-denied settings. MiFly performs 6DoF self-localization by leveraging a single millimeter-wave (mmWave) anchor in its vicinity - even if that anchor is visua

Alessandro Chiarini, Ianto Cannon, Marco Edoardo Rosti

We investigate the influence of dispersed solid spherical particles on the largest scales of the turbulent Arnold-Beltrami-Childress (ABC) flow. The ABC flow is an ideal instance of a complex flow: it does not have solid boundaries, but possesses an inhomogeneous and three-dimensional mean shear. By tuning the parameters of the suspension, we show that parti

Alan Chang, Alex McDonald, Krystal Taylor

Davies efficient covering theorem states that an arbitrary measurable set $W$ in the plane can be covered by full lines so that the measure of the union of the lines has the same measure as $W$. This result has an interesting dual formulation in the form of a prescribed projection theorem. In this paper, we formulate each of these results in a nonlinear sett

Victor Paredes, Ayonga Hereid

Complex robotic systems require whole-body controllers to deal with contact interactions, handle closed kinematic chains, and track task-space control objectives. However, for many applications, safety-critical controllers are important to steer away from undesired robot configurations to prevent unsafe behaviors. A prime example is legged robotics, where we

Manel Slokom, Peter-Paul de Wolf, Martha Larson

We investigate an attack on a machine learning model that predicts whether a person or household will relocate in the next two years, i.e., a propensity-to-move classifier. The attack assumes that the attacker can query the model to obtain predictions and that the marginal distribution of the data on which the model was trained is publicly available. The att

Mingyang Zhou, Zichao Yan, Elliot Layne, Esmeralda S. Whitammer

Phylogenetics is a branch of computational biology that studies the evolutionary relationships among biological entities. Its long history and numerous applications notwithstanding, inference of phylogenetic trees from sequence data remains challenging: the high complexity of tree space poses a significant obstacle for the current combinatorial and probabili

Karen D. Wang, Eric Burkholder, Carl Wieman, Shima Salehi

The study explores the capabilities of OpenAI's ChatGPT in solving different types of physics problems. ChatGPT (with GPT-4) was queried to solve a total of 40 problems from a college-level engineering physics course. These problems ranged from well-specified problems, where all data required for solving the problem was provided, to under-specified, real-wor

Zhao Ning Zou, Yuhang Zhang, Robert Wijaya

Transformer-based object detectors (DETR) have shown significant performance across machine vision tasks, ultimately in object detection. This detector is based on a self-attention mechanism along with the transformer encoder-decoder architecture to capture the global context in the image. The critical issue to be addressed is how this model architecture can

Nikita Sidorov

The notion of complex dimension of a one-dimensional Cantor set $C=\bigcap_{n=1}^\infty C_n$ dates back decades. It is defined as the set of poles of the meromorphic $\zeta$-function $\zeta(s)=\sum_{n=1}^{\infty}d_j^s$, where $\Re s>0$, and $d_j$ is the length of the $j$th interval in $C_n$. Following the trend, I switch from sets to measures, which will all

Leonardo R. Werneck, Cody Jessup, Austin Brandenberger, Tyler Knowles

Accurately measuring the translations of objects between images is essential in many fields, including biology, medicine, chemistry, and physics. One important application is tracking one or more particles by measuring their apparent displacements in a series of images. Popular methods, such as the center-of-mass, often require idealized scenarios to reach t

A. Uryson

We discuss the magnetic field influence on diffuse gamma-ray emission from extragalactic electromagnetic cascades initiated by ultra-high energy cosmic rays. Regions in space vary considerably in field strength: it is possibly of 10^(-12) G and lower in voids, of ~10^(-6) G inside galaxies, galactic clusters and groups, of ~10^(-7) G around them, and of ~ 10

Jennifer Li, Sebastián Torres

In arXiv:1008.3825, Totaro gave examples of a K3 surface such that its automorphism group is not commensurable with an arithmetic group, answering a question of Mazur. We give examples of rational surfaces with the same property. Our examples $Y$ are Looijenga pairs, i.e., there is a connected singular nodal curve $D \subset Y$ such that $K_{Y} + D = 0$.

Peter Toma, Md Ali Muntaha, Joel B. Harley, Michael R. Tonks

Mesoscale simulations of fission gas release (FGR) in nuclear fuel provide a powerful tool for understanding how microstructure evolution impacts FGR, but they are computationally intensive. In this study, we present an alternate, data-driven approach, using deep learning to predict instantaneous FGR flux from 2D nuclear fuel microstructure images. Four conv

Rocco Martinazzo, Irene Burghardt

The quantum dynamics of electron-nuclear systems is analyzed from the perspective of the exact factorization of the wavefunction, with the aim of defining gauge invariant equations of motion for both the nuclei and the electrons. For pure states this is accomplished with a quantum hydrodynamical description of the nuclear dynamics and electronic density oper

Montie Avery, Paul Carter, Björn de Rijk, Arnd Scheel

We establish sharp nonlinear stability results for fronts that describe the creation of a periodic pattern through the invasion of an unstable state. The fronts we consider are critical, in the sense that they are expected to mediate pattern selection from compactly supported or steep initial data. We focus on pulled fronts, that is, on fronts whose propagat

Polina Zablotskaia, Misha Khalman, Rishabh Joshi, Livio Baldini Soares

Despite the recent advances in abstractive text summarization, current summarization models still suffer from generating factually inconsistent summaries, reducing their utility for real-world application. We argue that the main reason for such behavior is that the summarization models trained with maximum likelihood objective assign high probability to plau

Jessie Liu, Florian Schäfer, Spencer H. Bryngelson, Tamer A. Zaki

Reynolds-averaged Navier--Stokes (RANS) closure must be sensitive to the flow physics, including nonlocality and anisotropy of the effective eddy viscosity. Recent approaches used forced direct numerical simulations to probe these effects, including the macroscopic forcing method (MFM) of Mani and Park ($\textit{Phys. Rev. Fluids}$ $\textbf{6}$, 054607 (2021

Niklas Smedemark-Margulies, Ye Wang, Toshiaki Koike-Akino, Jing Liu

Classification models for electroencephalogram (EEG) data show a large decrease in performance when evaluated on unseen test sub jects. We reduce this performance decrease using new regularization techniques during model training. We propose several graphical models to describe an EEG classification task. From each model, we identify statistical relationship

K. Shu, Y. Tajima, R. Uozumi, N. Miyamoto

When laser radiation is skilfully applied, atoms and molecules can be cooled allowing precise measurements and control of quantum systems. This is essential in fundamental studies of physics as well as practical applications such as precision spectroscopy, quantum-statistical-property manifesting ultracold gases, and quantum computing. In laser cooling, repe

L. T. Glöggler, N. Gusakova, B. Rienäcker, A. Camper

We report on laser cooling of a large fraction of positronium (Ps) in free-flight by strongly saturating the $1^3S$-$2^3P$ transition with a broadband, long-pulsed 243 nm alexandrite laser. The ground state Ps cloud is produced in a magnetic and electric field-free environment. We observe two different laser-induced effects. The first effect is an increase i

Jayetri Bardhan, Kirk Roberts, Daisy Zhe Wang

Question Answering (QA) systems on patient-related data can assist both clinicians and patients. They can, for example, assist clinicians in decision-making and enable patients to have a better understanding of their medical history. Significant amounts of patient data are stored in Electronic Health Records (EHRs), making EHR QA an important research area.

Pedro Cañate

All the magnetically charged ultrastatic and spherically symmetric spacetime solutions in the framework of linear/nonlinear electrodynamics, with an arbitrary electromagnetic Lagrangian density $\mathcal{L}(\mathcal{F})$ depending only of the electromagnetic invariant $\mathcal{F}\!=\!F_{\alpha\beta}F^{\alpha\beta}\!/4$, minimally coupled to Einstein-scalar-

Samuel Kim, In Gu Sean Lee, Mijeong Irene Ban, Jane Chiang

We propose machine learning algorithms to automatically detect and predict clopidogrel treatment failure using longitudinal structured electronic health records (EHR). By drawing analogies between natural language and structured EHR, we introduce various machine learning algorithms used in natural language processing (NLP) applications to build models for tr

Zhenlin Zhang, Lixin Pu, Ang Li, Jun Zhang

Scoliosis is a three-dimensional spinal deformity, which may lead to abnormal morphologies, such as thoracic deformity, and pelvic tilt. Severe patients may suffer from nerve damage and urinary abnormalities. At present, the number of scoliosis patients in primary and secondary schools has exceeded five million in China, the incidence rate is about 3% to 5%

Sangwon Lim, Karim El-Basyouny, Yee Hong Yang

While recent advancements in deep-learning point cloud upsampling methods have improved the input to intelligent transportation systems, they still suffer from issues of domain dependency between synthetic and real-scanned point clouds. This paper addresses the above issues by proposing a new ray-based upsampling approach with an arbitrary rate, where a dept

Mehdi Ali, Michael Fromm, Klaudia Thellmann, Richard Rutmann

The recent success of Large Language Models (LLMs) has been predominantly driven by curating the training dataset composition, scaling of model architectures and dataset sizes and advancements in pretraining objectives, leaving tokenizer influence as a blind spot. Shedding light on this underexplored area, we conduct a comprehensive study on the influence of

Sreyan Ghosh, Ashish Seth, Sonal Kumar, Utkarsh Tyagi

A fundamental characteristic of audio is its compositional nature. Audio-language models (ALMs) trained using a contrastive approach (e.g., CLAP) that learns a shared representation between audio and language modalities have improved performance in many downstream applications, including zero-shot audio classification, audio retrieval, etc. However, the abil

Mohammadali Mohammadi, Le-Nam Tran, Zahra Mobini, Hien Quoc Ngo

This paper studies cell-free massive multiple-input multiple-output (CF-mMIMO) systems incorporating simultaneous wireless information and power transfer (SWIPT) for separate information users (IUs) and energy users (EUs) in Internet of Things (IoT) networks. To optimize both the spectral efficiency (SE) of IUs and harvested energy (HE) of EUs, we propose a

Hodaka Kikuchi, Shinichiro Asai, Taku J. Sato, Taro Nakajima

A new multiplex-type inelastic neutron scattering spectrometer, HOrizontally Defocusing Analyzer Concurrent data Acquisition spectrometer (HODACA), was recently developed and built at the C1-1 cold neutron beam port in JRR-3. The spectrometer is suitable for dynamics measurements in the energy range of $-1$ meV $\lesssim \hbar \omega \lesssim$ 7 meV, caterin

Fengxue Zhang, Zejie Zhu, Yuxin Chen

Optimizing objectives under constraints, where both the objectives and constraints are black box functions, is a common scenario in real-world applications such as scientific experimental design, design of medical therapies, and industrial process optimization. One popular approach to handling these complex scenarios is Bayesian Optimization (BO). In terms o

Jinsung Yoon, Sercan O Arik, Yanfei Chen, Tomas Pfister

Embeddings extracted by pre-trained Large Language Models (LLMs) have significant potential to improve information retrieval and search. Beyond the zero-shot setup in which they are being conventionally used, being able to take advantage of the information from the relevant query-corpus paired data can further boost the LLM capabilities. In this paper, we pr

Idris Assani, Ethan Ebbighausen

The Collatz Conjecture's connection to dynamical systems opens it to a variety of techniques aimed at recurrence and density results. First, we turn to density results and strengthen the result of Terras through finding a strict rate of convergence. This rate gives a preliminary result on the Triangle Conjecture, which describes a set nodes that would domina

Abdennour Boulesnane

Evolutionary Computation (EC) has emerged as a powerful field of Artificial Intelligence, inspired by nature's mechanisms of gradual development. However, EC approaches often face challenges such as stagnation, diversity loss, computational complexity, population initialization, and premature convergence. To overcome these limitations, researchers have integ

Megan F. Biggs, Brittany E. Knighton, Aldair Alejandro, Lauren M. Davis

To control structure-function relationships in solids with light, we must harness the shape of the potential energy surface, as expressed in anharmonic coupling coefficients. We use two-dimensional terahertz (THz) spectroscopy to identify trilinear coupling between sets of vibrational modes in CdWO$_4$. It is generally understood that efficient trilinear cou

Aakriti Agrawal, Rohith Aralikatti, Yanchao Sun, Furong Huang

Multi-agent reinforcement learning (MARL) plays a pivotal role in tackling real-world challenges. However, the seamless transition of trained policies from simulations to real-world requires it to be robust to various environmental uncertainties. Existing works focus on finding Nash Equilibrium or the optimal policy under uncertainty in one environment varia

Amir-Hossein Shahidzadeh, Seong Jong Yoo, Pavan Mantripragada, Chahat Deep Singh

Tactile exploration plays a crucial role in understanding object structures for fundamental robotics tasks such as grasping and manipulation. However, efficiently exploring such objects using tactile sensors is challenging, primarily due to the large-scale unknown environments and limited sensing coverage of these sensors. To this end, we present AcTExplore,

Jack Merullo, Carsten Eickhoff, Ellie Pavlick

Recent work in mechanistic interpretability has shown that behaviors in language models can be successfully reverse-engineered through circuit analysis. A common criticism, however, is that each circuit is task-specific, and thus such analysis cannot contribute to understanding the models at a higher level. In this work, we present evidence that insights (bo

Qiyuan Hu, Abbas A. Rizvi, Geoffery Schau, Kshitij Ingale

Microsatellite instability-high (MSI-H) is a tumor agnostic biomarker for immune checkpoint inhibitor therapy. However, MSI status is not routinely tested in prostate cancer, in part due to low prevalence and assay cost. As such, prediction of MSI status from hematoxylin and eosin (H&E) stained whole-slide images (WSIs) could identify prostate cancer patient

Sangyeon Cho, Nicola Martino, Seok-Hyun Yun

Nano-scale lasers harnessing metallic plasmons hold promise across physical sciences and industrial applications. Plasmons are categorized as surface plasmon polaritons (SPP) and localized surface plasmons (LSP). While SPP has gained popularity for nano-lasers by fitting a few cycles of SPP waves into resonators, achieving LSP lasing in single nanoparticles

Mathieu Le Provost, Ricardo Baptista, Jeff D. Eldredge, Youssef Marzouk

Heavy tails is a common feature of filtering distributions that results from the nonlinear dynamical and observation processes as well as the uncertainty from physical sensors. In these settings, the Kalman filter and its ensemble version - the ensemble Kalman filter (EnKF) - that have been designed under Gaussian assumptions result in degraded performance.

Tao Li, Gang Li, Zhiwei Deng, Bryan Wang

Large language models (LLMs) have shown increasing capacity at planning and executing a high-level goal in a live computer environment (e.g. MiniWoB++). To perform a task, recent works often require a model to learn from trace examples of the task via either supervised learning or few/many-shot prompting. Without these trace examples, it remains a challenge

Chao Feng, Alberto Huertas Celdran, Michael Vuong, Gerome Bovet

The growing concern over malicious attacks targeting the robustness of both Centralized and Decentralized Federated Learning (FL) necessitates novel defensive strategies. In contrast to the centralized approach, Decentralized FL (DFL) has the advantage of utilizing network topology and local dataset information, enabling the exploration of Moving Target Defe

Philip Fradkin, Ruian Shi, Bo Wang, Brendan Frey

In the face of rapidly accumulating genomic data, our understanding of the RNA regulatory code remains incomplete. Recent self-supervised methods in other domains have demonstrated the ability to learn rules underlying the data-generating process such as sentence structure in language. Inspired by this, we extend contrastive learning techniques to genomic da

Yuanwei Qu, Baifan Zhou, Arild Waaler, David Cameron

The petroleum industry is crucial for modern society, but the production process is complex and risky. During the production, accidents or failures, resulting from undesired production events, can cause severe environmental and economic damage. Previous studies have investigated machine learning (ML) methods for undesired event detection. However, the predic

Phil Attard

Botimer and Taborek (2016) measured the mass flux of superfluid $^4$He through a capillary into an evacuated chamber for various temperatures and pressures of the reservoir chamber. They found a sharp transition from low flux at low pressures to high flux at large pressures. Here it is shown that the superfluid condition of chemical potential equality predic

Jim Wu, David J. Schwab, Trevor GrandPre

In complex ecosystems such as microbial communities, there is constant ecological and evolutionary feedback between the residing species and the environment occurring on concurrent timescales. Species respond and adapt to their surroundings by modifying their phenotypic traits, which in turn alters their environment and the resources available. To study this

Abhijeet Melkani, Jayson Paulose

Linear mechanical systems with time-modulated parameters can harbor oscillations with amplitudes that grow or decay exponentially with time due to the phenomenon of parametric resonance. While the resonance properties of individual oscillators are well understood, those of systems of coupled oscillators remain challenging to characterize. Here, we determine

L. Martinez, M. C. Bersten, G. Folatelli, M. Orellana

SNe II show growing evidence of interaction with CSM surrounding their progenitor stars as a consequence of enhanced mass loss during the last years of the progenitor's life. We present an analysis of the progenitor mass-loss history of SN2023ixf, a nearby SN II showing signs of interaction. We calculate the early-time bolometric light curve (LC) for SN2023i

Yuan Xin, Dingfan Chen, Michael Backes, Xiao Zhang

As ML models are increasingly deployed in critical applications, robustness against adversarial perturbations is crucial. While numerous defenses have been proposed to counter such attacks, they typically assume that all adversarial transformations are equally important, an assumption that rarely aligns with real-world applications. To address this, we study

Geigh Zollicoffer, Kenneth Eaton, Jonathan Balloch, Julia Kim

Reinforcement learning (RL) using world models has found significant recent successes. However, when a sudden change to world mechanics or properties occurs then agent performance and reliability can dramatically decline. We refer to the sudden change in visual properties or state transitions as novelties. Implementing novelty detection within generated worl

Zeyu Zhou, Hsing-Ta Chen, Maxim Sukharev, Joseph E. Subotnik

We investigate the effect of a cavity on nonlinear two-photon transitions of a molecular system and how such an effect depends on the cavity quality factor, the field enhancement and the possibility of dephasing. We find that the molecular response to strong light fields in a cavity with variable quality factor can be understood as arising from a balance bet

Marcel Wagner, Rafał Ołdziejewski, Félix Rose, Verena Köder

Feshbach resonances play a vital role in the success of cold atoms investigating strongly-correlated physics. The recent observation of their solid-state analog in the scattering of holes and intralayer excitons in transition metal dichalcogenides [Schwartz et al., Science 374, 336 (2021)] holds compelling promise for bringing fully controllable interactions

Michal Krelina

Quantum communication, particularly quantum key distribution, is poised to play a pivotal role in our communication system in the near future. Consequently, it is imperative to not only assess the vulnerability of quantum communication to eavesdropping (one aspect of quantum hacking), but also to scrutinise the feasibility of executing a denial-of-service at

Kevin L. Schulte, Daniel J. Friedman, Titilope Dada, Harvey L. Guthrey

We present inverted metamorphic Ga0.3In0.7As photovoltaic converters with sub-0.60 eV bandgaps grown on InP and GaAs substrates. The compositionally graded buffers in these devices have threading dislocation densities of 1.3x10^6 cm^-2 and 8.9x10^6 cm^-2 on InP and GaAs, respectively. The devices generate open-circuit voltages of 0.386 V and 0.383 V, respect

Peter Z. Schochet

There is a growing literature on design-based methods to estimate average treatment effects (ATEs) for randomized controlled trials (RCTs) for full sample analyses. This article extends these methods to estimate ATEs for discrete subgroups defined by pre-treatment variables, with an application to an RCT testing subgroup effects for a school voucher experime

Zirui Liang, Yuntao Li, Tianjin Huang, Akrati Saxena

Graph neural networks (GNNs) have shown promise in addressing graph-related problems, including node classification. However, conventional GNNs assume an even distribution of data across classes, which is often not the case in real-world scenarios, where certain classes are severely underrepresented. This leads to suboptimal performance of standard GNNs on i

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

Context. The Arjuna asteroid belt is loosely defined as a diverse group of small asteroids that follow dynamically cold, Earth-like orbits. Most of them are not actively engaged in resonant, co-orbital behavior with Earth. Some of them experience temporary but recurrent horseshoe episodes. Objects in horseshoe paths tend to approach Earth at a low velocity,

André Carvalho

We prove that centralizers of elements in [f.g. free]-by-cyclic groups are computable. As a corollary we get that, given two conjugate elements in a [f.g. free]-by-cyclic group, the set of conjugators can be computed and that the conjugacy problem with context-free constraints is decidable. In the end, we pose several problems arising naturally from this wor

Yijiang Yu, Michael Graham

We present a numerical study of a thin elastic sheet with small extensibility freely sedimenting in a viscous fluid. Two scenarios are investigated: sedimentation in free space and near an infinite wall, where the wall may be vertical or tilted. Elastic sheets with a rest shape of a square are modeled with a finite-element-based continuum model that accounts

Jincheng Pang, Hong Yan, Zoe Hua

With increasing interest in adaptive clinical trial designs, challenges are present to drug supply chain management which may offset the benefit of adaptive designs. Thus, it is necessary to develop an optimization tool to facilitate the decision making and analysis of drug supply chain planning. The challenges include the uncertainty of maximum drug supply

Els Peeters, Emilie Habart, Olivier Berne, Ameek Sidhu

(Abridged) We investigate the impact of radiative feedback from massive stars on their natal cloud and focus on the transition from the HII region to the atomic PDR (crossing the ionisation front (IF)), and the subsequent transition to the molecular PDR (crossing the dissociation front (DF)). We use high-resolution near-IR integral field spectroscopic data f

Monnie McGee

Central to diving competitions is the diver's ``dive list'', which is the list of dives an athlete will perform during a competition. Creating a dive list that contains enough difficulty to be competitive yet not beyond the capability of the diver is an important consideration in diving. In this work, we examine the discrepancy between a diver's ability and

Akbar Sayeed, Damla Guven, Michael Doebereiner, Sebastian Semper

A framework is proposed for developing and evaluating algorithms for extracting multipath propagation components (MPCs) from measurements collected by channel sounders at millimeter-wave frequencies. Sounders equipped with an omnidirectional transmitter and a receiver with a uniform planar array (UPA) are considered. An accurate mathematical model is develop

Owen Long, Benjamin Nachman

Many analyses in particle and nuclear physics use simulations to infer fundamental, effective, or phenomenological parameters of the underlying physics models. When the inference is performed with unfolded cross sections, the observables are designed using physics intuition and heuristics. We propose to design targeted observables with machine learning. Unfo

Hanzhao Wang, Xiaocheng Li, Kalyan Talluri

Discrete-choice models, such as Multinomial Logit, Probit, or Mixed-Logit, are widely used in Marketing, Economics, and Operations Research: given a set of alternatives, the customer is modeled as choosing one of the alternatives to maximize a (latent) utility function. However, extending such models to situations where the customer chooses more than one ite

Ju-Chieh Chou, Chung-Ming Chien, Wei-Ning Hsu, Karen Livescu

Speech and text are two major forms of human language. The research community has been focusing on mapping speech to text or vice versa for many years. However, in the field of language modeling, very little effort has been made to model them jointly. In light of this, we explore joint language modeling for speech units and text. Specifically, we compare dif

Gustavo A. Cardona, Kevin Leahy, Makai Mann, Cristian-Ioan Vasile

Temporal logic is an important tool for specifying complex behaviors of systems. It can be used to define properties for verification and monitoring, as well as goals for synthesis tools, allowing users to specify rich missions and tasks. Some of the most popular temporal logics include Metric Temporal Logic (MTL), Signal Temporal Logic (STL), and weighted S

Ollie Jackson, Maaike A. M. van Kooten, Saavidra Perera, Rebecca Jensen-Clem

Optimal atmospheric conditions are beneficial for detecting exoplanets via high contrast imaging (HCI), as speckles from adaptive optics' (AO's) residuals can make it difficult to identify exoplanets. While AO systems greatly improve our image quality, having access to real-time estimates of atmospheric conditions could also help astronomers use their telesc

Mohsen Banaei, Majid Oloomi Buygi, Hani Raouf-Sheybani, Razgar Ebrahimy

Uncertainty in the output power of large-scale wind power plants (WPPs) can face the electricity market players with undesirable profit variations. Market players can hedge themselves against these risks by participating in forward contracts markets alongside the day-ahead markets. The participation of market players in these two markets affects their profit

Iztok Banic, Judy Kennedy, Chris Mouron, Van Nall

Recently, many examples of smooth fans that admit a transitive homeomorphism have been constructed. For example, a family of uncountably many pairwise non-homeomorphic smooth fans that admit transitive homeomorphisms was constructed. In this paper, we construct a family of uncountably many pairwise non-homeomorphic non-smooth fans that admit transitive homeo

Cole Gulino, Justin Fu, Wenjie Luo, George Tucker

Simulation is an essential tool to develop and benchmark autonomous vehicle planning software in a safe and cost-effective manner. However, realistic simulation requires accurate modeling of nuanced and complex multi-agent interactive behaviors. To address these challenges, we introduce Waymax, a new data-driven simulator for autonomous driving in multi-agen

Ryan Hynd

A ball polyhedron is the intersection of a finite number of closed balls in $\mathbb{R}^3$ with the same radius. In this note, we study ball polyhedra in which the set of centers defining the balls have the maximum possible number of diametric pairs. We explain how to compute the perimeter and volume of these shapes by employing the Gauss-Bonnet theorem and

Adi Shamir, Isaac Canales-Martinez, Anna Hambitzer, Jorge Chavez-Saab

Billions of dollars and countless GPU hours are currently spent on training Deep Neural Networks (DNNs) for a variety of tasks. Thus, it is essential to determine the difficulty of extracting all the parameters of such neural networks when given access to their black-box implementations. Many versions of this problem have been studied over the last 30 years,

Lee J. Rosenthal, Andrew W. Howard, Heather A. Knutson, Benjamin J. Fulton

We compare systems with single giant planets to systems with multiple giant planets using a catalog of planets from a high-precision radial velocity survey of FGKM stars. Our comparison focuses on orbital properties, planet masses, and host star properties. We use hierarchical methods to model the orbital eccentricity distributions of giant singles and giant

Gregory Lupton, Oleg Musin, Nicholas A. Scoville, P. Christopher Staecker

We define a second (higher) homotopy group for digital images. Namely, we construct a functor from digital images to abelian groups, which closely resembles the ordinary second homotopy group from algebraic topology. We illustrate that our approach can be effective by computing this (digital) second homotopy group for a digital 2-sphere.

Anamaria Crisan, Maddie Shang, Eric Brochu

Domain experts increasingly use automated data science tools to incorporate machine learning (ML) models in their work but struggle to "debug" these models when they are incorrect. For these experts, semantic interactions can provide an accessible avenue to guide and refine ML models without having to programmatically dive into its technical details. In this

Alisha Ukani

People are becoming increasingly concerned with their online privacy, especially with how advertising companies track them across websites (a practice called cross-site tracking), as reconstructing a user's browser history can reveal sensitive information. Recent legislation like the General Data Protection Regulation (GDPR) and the California Consumer Priva

Kangqing Shen, Gemine Vivone, Xiaoyuan Yang, Simone Lolli

Synthetic aperture radar (SAR) images are widely used in remote sensing. Interpreting SAR images can be challenging due to their intrinsic speckle noise and grayscale nature. To address this issue, SAR colorization has emerged as a research direction to colorize gray scale SAR images while preserving the original spatial information and radiometric informati

Sukwoong Choi, Hyo Kang, Namil Kim, Junsik Kim

We study how humans learn from AI, leveraging an introduction of an AI-powered Go program (APG) that unexpectedly outperformed the best professional player. We compare the move quality of professional players to APG's superior solutions around its public release. Our analysis of 749,190 moves demonstrates significant improvements in players' move quality, es

Ravi Kumar, Saurabh Kumar Srivastav, Ujjal Roy, Jinhong Park

Collective spin-wave excitations-magnons-in a quantum Hall ferromagnet are promising quasi-particles for next-generation spintronics devices, including platforms for information transfer. Detection of these charge-neutral excitations relies on the conversion of magnons into electrical signals in the form of excess electrons and holes, but if these signals ar

Jiaheng Hu, Zizhao Wang, Peter Stone, Roberto Martin-Martin

Tasks with large state space and sparse rewards present a longstanding challenge to reinforcement learning. In these tasks, an agent needs to explore the state space efficiently until it finds a reward. To deal with this problem, the community has proposed to augment the reward function with intrinsic reward, a bonus signal that encourages the agent to visit

Gebrehiwet Gebrekrstos Lema

Free Space Optics (FSO) communication provides attractive bandwidth enhancement with unlicensed bands worldwide spectrum. However, the link capacity and availability are the major concern in the different atmospheric conditions. The reliability of the link is highly dependent on weather conditions that attenuate the signal strength. Hence, this study focuses

Sofía M del Pozo, Sebastián Pinto, Matteo Serafino, Lucio Garcia

The extensive data generated on social media platforms allow us to gain insights over trending topics and public opinions. Additionally, it offers a window into user behavior, including their content engagement and news sharing habits. In this study, we analyze the relationship between users' political ideologies and the news they share during Argentina's 20

Shih-Yu Chang

Random hypergraph is a broad concept used to describe probability distributions over hypergraphs, which are mathematical structures with applications in various fields, e.g., complex systems in physics, computer science, social sciences, and network science. Ensemble methods, on the other hand, are crucial both in physics and machine learning. In physics, en

Ryan Yen, Jiawen Zhu, Sangho Suh, Haijun Xia

Programmers increasingly rely on Large Language Models (LLMs) for code generation. However, misalignment between programmers' goals and generated code complicates the code evaluation process and demands frequent switching between prompt authoring and code evaluation. Yet, current LLM-driven code assistants lack sufficient scaffolding to help programmers form

Matthäus Krantz, Francesco Toschi, Benedikt Maier, Greta Heine

We present two variants of a magnetic microcalorimeter with paramagnetic temperature sensors and integrated dc-SQUID readout for high-resolution X-ray emission spectroscopy. Each variant employs two overhanging gold absorbers with a sensitive area of 150$\mu$m x 150$\mu$m and a thickness of 3$\mu$m, thus providing a quantum efficiency of 98% for photons up t

Dipankar Mazumdar, Jason Hughes, JB Onofre

Relational Database Management Systems designed for Online Analytical Processing (RDBMS-OLAP) have been foundational to democratizing data and enabling analytical use cases such as business intelligence and reporting for many years. However, RDBMS-OLAP systems present some well-known challenges. They are primarily optimized only for relational workloads, lea

Weiqing Wang, Ming Li

This paper proposes an online target speaker voice activity detection system for speaker diarization tasks, which does not require a priori knowledge from the clustering-based diarization system to obtain the target speaker embeddings. By adapting the conventional target speaker voice activity detection for real-time operation, this framework can identify sp

Rory O'Dwyer

In previous work, a lattice scalar propagator was rigorously defined in $d=1$ flat space and shown to equal the known Klein-Gordon propagator of QFT. This work generalizes this lattice propagator to manifolds whose universal cover is the hyperbolic half plane as well as a broad class of higher dimensional manifolds. We motivate a conjecture for the power spe

S. Martinet, A. Choplin, S. Goriely, L. Siess

We investigate both the systematic and statistical uncertainties associated with theoretical nuclear reaction rates of relevance during the i-process and explore their impact on the i-process elemental production, and subsequently on the surface enrichment, for a low-mass low-metallicity star during the early AGB phase. We use the TALYS reaction code (Koning

Paulinho Demeneghi, Felipe Augusto Tasca

We prove that every partial action of an inverse semigroupoid on a set admits a universal globalization. Moreover, we show that our construction gives a reflector from the category of partial actions on the full subcategory of global actions. Finally, we investigate if the mediating function given by the universal property of our construction is injective.

Renwen Yu, Shanhui Fan

We explore near-field radiative heat transfer between two bodies under time modulation by developing a rigorous fluctuational electrodynamics formalism. We demonstrate that time modulation can results in the enhancement, suppression, elimination, or reversal of radiative heat flow between the two bodies, and can be used to create a radiative thermal diode wi

Omid Mirzapour, Xinyang Rui, Brittany Pruneau, Mostafa Sahraei-Ardakani

As global concerns regarding climate change are increasing worldwide, the transition towards clean energy sources has accelerated. Accounting for a large share of energy consumption, the electricity sector is experiencing a significant shift towards renewable energy sources. To accommodate this rapid shift, the transmission system requires major upgrades. Al

Gabor Lippner, Yujia Shi

This paper discusses continuous-time quantum walks and asymptotic state transfer in graphs with an involution. By providing quantitative bounds on the eigenvectors of the Hamiltonian, it provides an approach to achieving high-fidelity state transfer by strategically selecting energy potentials based on the maximum degrees of the graphs. The study also involv

Balder ten Cate, Jesse Comer

We show that the guarded-negation fragment is, in a precise sense, the smallest extension of the guarded fragment with Craig interpolation. In contrast, we show that full first-order logic is the smallest extension of both the two-variable fragment and the forward fragment with Craig interpolation. Similarly, we also show that all extensions of the two-varia

Qi Sun, Vladimir Yushutin

Balance laws of coupled bulk-surface-bulk fluid flows are considered. We carefully investigate how do the interface conditions influence the total mass, total momentum and total energy of the system.

Yuanyuan Feng, Abhilasha Ravichander, Yaxing Yao, Shikun Zhang

Understanding and managing data privacy in the digital world can be challenging for sighted users, let alone blind and low-vision (BLV) users. There is limited research on how BLV users, who have special accessibility needs, navigate data privacy, and how potential privacy tools could assist them. We conducted an in-depth qualitative study with 21 US BLV par

Siena Hurwitz, Matt Landreman, Thomas M. Antonsen

The design of electromagnetic coils may require evaluation of several quantities that are challenging to compute numerically. These quantities include Lorentz forces, which may be a limiting factor due to stresses; the internal magnetic field, which is relevant for determining stress as well as a superconducting coil's proximity to its quench limit; and the

Ergun Akleman, Akhilesh Vijaykumar, Richard Furuta, Derya Akleman

The creation of cartoon-based stories (comics) requires a lot of creativity and hard work for naive users. We observe that single-panel cartoons are the building blocks of any comic story. To develop a strong comic story, it is critical to obtain visually valid single panels. In this work, we have developed a methodology to guarantee the placement of charact