October 2023 arXiv papers — page 39

Keiya Ishiguro, Takaaki Nomura, Hiroshi Okada, Yuta Orikasa

We study flavor phenomenologies in a basis of a double covering of modular $A_4$ group with a hidden $SU(2)$ symmetry, in which we work on regions at nearby two fixed points and three special points. These special points of $SL(2,\mathbb{Z})$ moduli space are statistically favored in flux compactifications of Type IIB string theory. Neutrino masses are appro

José Oscar González Cervantes

Recently, fiber bundle theory has been widely used in the study of the slice regular functions and continuing with this line of research, the present work shows that the quaternionic slice regular Bergman space is the base space of a coordinate sphere bundle and some properties of quaternionic slice regular Bergman theory are studied from the point of view o

Xuefeng Li, Elizabeth DeJong, Rob Armitage, Daniel Feezell

We introduce a multiple-carrier-lifetime model (MCLM) for light-emitting diodes (LEDs) with non-uniform carrier distribution, such as in multiple-quantum-well (MQW) structures. By employing the MCLM, we successfully explain the modulation response of V-pit engineered MQW LEDs, which exhibit an S21 roll-off slower than -20 dB/decade. Using the proposed model

Annu Jaiswal, Rajesh Kumar, Sudhir Kumar Srivastava, Megandhren Govender

In this work, we have investigated a novel aspect of black hole (BH) formation during the collapse of a self-gravitating configuration. The exact solution of the Einstein field equations is obtained in a model-independent way by considering a parametrization of the expansion scalar ($Θ$) in the background of spherically symmetric space-time geometry governed

Elisabetta Colombo, Paola Frediani, Juan Carlos Naranjo, Gian Pietro Pirola

We study the second fundamental form of the Siegel metric in $\mathcal A_5$ restricted to the locus of intermediate Jacobians of cubic threefolds. We prove that the image of this second fundamental form, which is known to be non-trivial, is contained in the kernel of a suitable multiplication map. Some ingredients are: the conic bundle structure of cubic thr

Caren Hasler

A solution to control for nonresponse bias consists of multiplying the design weights of respondents by the inverse of estimated response probabilities to compensate for the nonrespondents. Maximum likelihood and calibration are two approaches that can be applied to obtain estimated response probabilities. We consider a common framework in which these approa

Florian Frick, Jacob Lehmann Duke, Meenakshi McNamara, Hannah Park-Kaufmann

A cornerstone result of Erd\H os, Ginzburg, and Ziv (EGZ) states that any sequence of $2n-1$ elements in $\mathbb{Z}/n$ contains a zero-sum subsequence of length $n$. While algebraic techniques have predominated in deriving many deep generalizations of this theorem over the past sixty years, here we introduce topological approaches to zero-sum problems which

Hassen Saidi, Susmit Jha, Tuhin Sahai

As artificial intelligence (AI) gains greater adoption in a wide variety of applications, it has immense potential to contribute to mathematical discovery, by guiding conjecture generation, constructing counterexamples, assisting in formalizing mathematics, and discovering connections between different mathematical areas, to name a few. While prior work has

Naitong Chen, Trevor Campbell

A Bayesian coreset is a small, weighted subset of data that replaces the full dataset during inference in order to reduce computational cost. However, state of the art methods for tuning coreset weights are expensive, require nontrivial user input, and impose constraints on the model. In this work, we propose a new method -- Coreset MCMC -- that simulates a

Davide Villa, Imran Khan, Florian Kaltenberger, Nicholas Hedberg

The transition of fifth generation (5G) cellular systems to softwarized, programmable, and intelligent networks depends on successfully enabling public and private 5G deployments that are (i) fully software-driven and (ii) with a performance at par with that of traditional monolithic systems. This requires hardware acceleration to scale the Physical (PHY) la

T. Koide, F. Nicacio

We introduce a generalized classical model of Brownian motion for describing thermal relaxation processes which is thermodynamically consistent. Applying the canonical quantization to this model, a quantum equation for the density operator is obtained. This equation has a thermal equilibrium state as its stationary solution, but the time evolution is not nec

Hiago Sodre, Pablo Moraes, Monica Rodriguez, Victor Castelli

This article presents an application that evaluates the feasibility of humanoid robots as interactive guides in art museums. The application entailes programming a NAO robot and a chatbot to provide information about art pieces in a simulated museum environment. In this controlled scenario, the learning employees interact with the robot and the chatbot. The

H. Babaei-Aghbolagh, Mirmani Mirjalali, Davood Mahdavian Yekta, Komeil Babaei Velni

We study the thermodynamics of charged AdS black holes in deformed Jackiw-Teitelboim (dJT) gravity and their phase structures. In this regard, we will find some critical values for the temperature, entropy and charge of the corresponding black holes. We also compute the heat capacities, expansion coefficient and isothermal compressibility as thermodynamic re

Pablo Moraes, Hiago Sodre, Monica Rodriguez, Andre Kelbouscas

This article describes the design and development of robotic prototypes for robotic soccer competitions using Dynamixel motors. Although the prototypes are not aimed at world-class competitions, they represent a significant step in the development of sports robots. Model XL430-W250 Dynamixel motors were chosen and electronic circuits were implemented using c

Yuqi Li, Amy Bonsor, Oliver Shorttle

White dwarfs that have accreted planetary materials provide a powerful tool to probe the interiors and formation of exoplanets. In particular, the high Fe/Si ratio of some white dwarf pollutants suggests that they are fragments of bodies that were heated enough to undergo large-scale melting and iron core formation. In the solar system, this phenomenon is as

Mohammad Mohammadi, Jesse Thornburg

Exploring the convergence of electric vehicles (EVs), renewable energy, and smart grid technologies in the context of Texas, this study addresses challenges hindering the widespread adoption of EVs. Acknowledging their environmental benefits, the research focuses on grid stability concerns, uncoordinated charging patterns, and the complicated relationship be

Michael M. Shara, Kenneth M. Lanzetta, James T. Garland, Stefan Gromoll

Just 10 recurrent novae (RNe) - which erupt repeatedly on timescales shorter than one century - are known in our Galaxy. The most extreme RN known (located in the Andromeda galaxy), M31N 2008-12a, undergoes a nova eruption every year, and is surrounded by a vast nova "super-remnant", 134 pc in extent. Simulations predict that all RNe should be surrounded by

Yufei Tian, Felix Zhang, Nanyun Peng

Large language models (LLMs) such as GPT-3 have demonstrated a strong capability to generate coherent and contextually relevant text. However, amidst their successes, a crucial issue persists: their generated outputs still lack commonsense at times. Moreover, fine-tuning the entire LLM towards more commonsensical outputs is computationally expensive if not i

Shivam Arora, Alex Bihlo, Francis Valiquette

Physics-informed neural networks have emerged as a prominent new method for solving differential equations. While conceptually straightforward, they often suffer training difficulties that lead to relatively large discretization errors or the failure to obtain correct solutions. In this paper we introduce invariant physics-informed neural networks for ordina

Erkin Kirdan, Filip Rezabek, Nikolas Mülbauer, Georg Carle

OPC UA is an industry-standard machine-to-machine communication protocol in the Industrial Internet of Things. It relies on time-sensitive networking to meet the real-time requirements of various applications. Time-sensitive networking is implemented through various queueing disciplines (qdiscs), including Time Aware Priority, Multiqueue Priority, Earliest T

Ashish S. Newale, Pushan Sharma, Stephen B. Pope, Perrine Pepiot

LES/PDF approaches can be used for simulating challenging turbulent combustion configurations with strong turbulence chemistry interactions. Transported PDF methods are computationally expensive compared to flamelet-like turbulent combustion models. The pre-partitioned adaptive chemistry (PPAC) methodology was developed to address this cost differential. PPA

Zaid Khan, Vijay Kumar BG, Samuel Schulter, Manmohan Chandraker

Visual question answering (VQA) has traditionally been treated as a single-step task where each question receives the same amount of effort, unlike natural human question-answering strategies. We explore a question decomposition strategy for VQA to overcome this limitation. We probe the ability of recently developed large vision-language models to use human-

Chandra Suda

Tuberculosis (TB), a bacterial disease mainly affecting the lungs, is one of the leading infectious causes of mortality worldwide. To prevent TB from spreading within the body, which causes life-threatening complications, timely and effective anti-TB treatment is crucial. Cough, an objective biomarker for TB, is a triage tool that monitors treatment response

Jianwei Zhang, Suren Jayasuriya, Visar Berisha

A good supervised embedding for a specific machine learning task is only sensitive to changes in the label of interest and is invariant to other confounding factors. We leverage the concept of repeatability from measurement theory to describe this property and propose to use the intra-class correlation coefficient (ICC) to evaluate the repeatability of embed

Thierry Dufour

This comprehensive review begins by tracing the historical development and progress of cold plasma technology as an innovative approach to polymer engineering. The study emphasizes the versatility of cold plasma derived from a variety of sources including low-pressure glow discharges (e.g., radiofrequency capacitively coupled plasmas) and atmospheric pressur

Jonas August, Thierry Dufour, Christophe Bailly

When mature Arabidopsis thaliana seeds are dormant, their germination is prevented in apparently favourable conditions. This primary dormancy can be released during seed dry storage through a process called after-ripening whose duration can last several months. To reduce this delay, cold atmospheric plasmas (CAP) can be used as sources of reactive oxygen spe

Thiem Hoang, Bao Truong

Magnetic fields are ubiquitous in the universe and are thought to play an important role in various astrophysical processes. Polarization of thermal dust emission from dust grains aligned with the magnetic field is widely used to measure the two-dimensional magnetic field projected onto the plane of the sky (POS), but the component along the line of sight (L

Andrew Knightly

The trace formula is a versatile tool for computing sums of spectral data across families of automorphic forms. Using specialized test functions, one can treat small families with refined spectral properties. This has proven fruitful in analytic applications. We detail such methodology here, with the aim of counting newforms in certain small families. The re

Scott Buckley, Robert Sison, Nils Wistoff, Curtis Millar

Microarchitectural timing channels are a major threat to computer security. A set of OS mechanisms called time protection was recently proposed as a principled way of preventing information leakage through such channels and prototyped in the seL4 microkernel. We formalise time protection and the underlying hardware mechanisms in a way that allows linking the

Chung T. Ma, Salinporn Kittiwatnakul, Apiprach Sittipongpittaya, Yuhan Wang

The ability to manipulate spins in magnetic materials is essential in designing spintronics devices. One method for magnetic switching is through strain. In VO2 on TiO2 thin films, while VO2 remains rutile across the metal-insulator transition, the in-plane lattice area expands going from low temperature insulating phase to high temperature conducting phase.

Zixin Ding, Si Chen, Ruoxi Jia, Yuxin Chen

Active learning is a promising paradigm to reduce the labeling cost by strategically requesting labels to improve model performance. However, existing active learning methods often rely on expensive acquisition function to compute, extensive modeling retraining and multiple rounds of interaction with annotators. To address these limitations, we propose a nov

William Waalkes, Zachory Berta-Thompson, Elisabeth Newton, Andrew Mann

AU Mic is an active 24 Myr pre-main sequence M dwarf in the stellar neighborhood (d$=$9.7 pc) with a rotation period of 4.86 days. The two transiting planets orbiting AU Mic, AU Mic b and c, are warm sub-Neptunes on 8.5 and 18.9 day periods and are targets of interest for atmospheric observations of young planets. Here we study AU Mic's unocculted starspots

Juyoung Yun

In this paper, we introduce StochGradAdam, a novel optimizer designed as an extension of the Adam algorithm, incorporating stochastic gradient sampling techniques to improve computational efficiency while maintaining robust performance. StochGradAdam optimizes by selectively sampling a subset of gradients during training, reducing the computational cost whil

Abhilasha Lodha, Gayatri Belapurkar, Saloni Chalkapurkar, Yuanming Tao

Fine-tuning all the layers of a pre-trained neural language encoder (either using all the parameters or using parameter-efficient methods) is often the de-facto way of adapting it to a new task. We show evidence that for different downstream language tasks, fine-tuning only a subset of layers is sufficient to obtain performance that is close to and often bet

F. Heydarizadmotlagh, T. D. G. Skinner, K. Kato, M. C. George

Increasing accuracy of the theory and experiment of the $n$$=$$2$ $^3$P fine structure of helium has allowed for increasingly-precise tests of quantum electrodynamics (QED), determinations of the fine-structure constant $\alpha$, and limitations on possible beyond-the-Standard-Model physics. Here we present a 2~part-per-billion (ppb) measurement of the $J$$=

Daniela Ben-David, Tzuf Paz-Argaman, Reut Tsarfaty

We propose a modular framework that leverages the expertise of different foundation models over different modalities and domains in order to perform a single, complex, multi-modal task, without relying on prompt engineering or otherwise tailor-made multi-modal training. Our approach enables decentralized command execution and allows each model to both contri

Javier Chico Vazquez, Andrew J. Horning

In this paper we present the result of successively applying a Chebyshev polynomial to a continuous random variable. In particular we show that under mild assumptions the limiting distribution will be the same as the weight with respect to which Chebyshev polynomials are orthogonal.

Julian Kern

We provide a short proof for the exponential equivalence between misanthrope processes in contact with weak reservoirs and those with impermeable boundaries. As a consequence, we can derive both the hydrodynamic limit and the large deviations of the totally asymmetric simple exclusion process (TASEP) in contact with weak reservoirs. This extends a recent res

Mathias Backes, Anja Butter, Monica Dunford, Bogdan Malaescu

The unfolding of detector effects is a key aspect of comparing experimental data with theoretical predictions. In recent years, different Machine-Learning methods have been developed to provide novel features, e.g. high dimensionality or a probabilistic single-event unfolding based on generative neural networks. Traditionally, many analyses unfold detector e

Neco Kriel, James R. Beattie, Christoph Federrath, Mark R. Krumholz

Many astrophysical small-scale dynamos (SSDs) amplify weak magnetic fields via highly compressible, supersonic turbulence, but established SSD theories have overlooked these compressible effects. To address this, we perform visco-resistive SSD simulations across a range of sonic Mach numbers ($\mathcal{M}$), hydrodynamic Reynolds numbers ($\mathrm{Re}$), and

Shangbang Long, Siyang Qin, Yasuhisa Fujii, Alessandro Bissacco

We propose Hierarchical Text Spotter (HTS), a novel method for the joint task of word-level text spotting and geometric layout analysis. HTS can recognize text in an image and identify its 4-level hierarchical structure: characters, words, lines, and paragraphs. The proposed HTS is characterized by two novel components: (1) a Unified-Detector-Polygon (UDP) t

Felipe S. Abrahão, Raoni Arroyo

We offer an argument against simplicity as a sole intrinsic criterion for nomic realism. The argument is based on the simplicity bubble effect. Underdetermination in quantum foundations illustrates the case.

Besnik Fetahu, Pedro Faustini, Giuseppe Castellucci, Anjie Fang

The adoption of voice assistants like Alexa or Siri has grown rapidly, allowing users to instantly access information via voice search. Query suggestion is a standard feature of screen-based search experiences, allowing users to explore additional topics. However, this is not trivial to implement in voice-based settings. To enable this, we tackle the novel t

Duarte J. Antunes, J. P. Hespanha

We consider a standard discrete-time event-triggered control setting by which a scheduler collocated with the plant's sensors decides when to transmit sensor data to a remote controller collocated with the plant's actuators. When the scheduler transmits periodically with period larger than or equal to one, the $h_\infty$ optimal controller guarantees an opti

Saad Zafar Khan, Nazeefa Muzammil, Salman Ghafoor, Haibat Khan

Accurate solar power forecasting is pivotal for the global transition towards sustainable energy systems. This study conducts a meticulous comparison between Quantum Long Short-Term Memory (QLSTM) and classical Long Short-Term Memory (LSTM) models for solar power production forecasting. The primary objective is to evaluate the potential advantages of QLSTMs,

Duarte J. Antunes, J. P. Hespanha

We consider a discrete-time linear system for which the control input is updated at every sampling time, but the state is measured at a slower rate. We allow the state to be sampled according to a periodic schedule, which dictates when the state should be sampled along a period. Given a desired average sampling interval, our goal is to determine sampling sch

Antoine Leymonerie, Jean-Sebastien Boisvert, Leonie Juszczak, Sebastien Loranger

We show that the use of Random Optical Grating using UV Exposure (ROGUE) can significantly reduce the noise floor of an optical frequency domain reflectometry (OFDR) measurement of Faraday rotation in the polarization. We compare it with unexposed spun fiber which shows a S/P minimum ratio (signal noise floor) 20 dB higher than when using our ROGUE. High sen

Etienne Ménager, Quentin Peyron, Christian Duriez

Soft robots are naturally designed to perform safe interactions with their environment, like locomotion and manipulation. In the literature, there are now many concepts, often bio-inspired, to propose new modes of locomotion or grasping. However, a methodology for implementing motion planning of these tasks, as exists for rigid robots, is still lacking. One

Maxim Lyutikov, Henry Freund

Using the ONEDFEL code we perform Free Electron Laser simulations in the astrophysically important guide-field dominated regime. For wigglers' (Alfven waves) wavelengths of tens of meters and beam Lorentz factor $\sim 10^3$, the resulting coherently emitted waves are in the centimeter range. Our simulations show a growth of the wave intensity over fourteen o

Garth Warner

These notes are a chapter in Real Analysis. While primarily standard, the reader will find a discussion of certain topics that are ordinarily not covered in the usual accounts. For example, the notion of bounded variation in the sense of Cesari is introduced. This is Volume 1 of 4, to be followed by Curves and Length, Functions of Several Variables, and Surf

Tigran Bakaryan, Giuseppe Di Fazio, Diogo A. Gomes

This paper investigates stationary mean-field games (MFGs) on the torus with Lipschitz non-homogeneous diffusion and logarithmic-like couplings. The primary objective is to understand the existence of $C^{1,\alpha}$ solutions to address the research gap between low-regularity results for bounded and measurable diffusions and the smooth results modeled by the

AJ Bu

In this paper, we first describe how to find the generating function for the sum of the areas under generalized Dyck paths (with an arbitrary set of steps) using Motzkin paths as a motivating example. We then focus on Motzkin and Dyck paths, deriving functional equations for them. We then describe an algorithm to manipulate these functional equations for fin

Sylee Beltiukov, Satyandra Guthula, Haarika Manda, Jaber Daneshamooz

Network foundation models promise reusable representations for diverse traffic analysis tasks, but recent diagnostic works have revealed fundamental problems: models exploit dataset shortcuts rather than learning genuine traffic patterns, produce collapsed embedding spaces, and fail to capture the exogenous network conditions that shape real-world behavior.

Vinicius M. Placco, Felipe Almeida-Fernandes, Erika M. Holmbeck, Ian U. Roederer

We report on the chemo-dynamical analysis of SPLUS J142445.34-254247.1, an extremely metal-poor halo star enhanced in elements formed by the rapid neutron-capture process. This star was first selected as a metal-poor candidate from its narrow-band S-PLUS photometry and followed up spectroscopically in medium-resolution with Gemini South/GMOS, which confirmed

Jiawen Chen, Wancen Mu, Yun Li, Didong Li

In this paper, we critically examine the prevalent practice of using additive mixtures of Mat\'ern kernels in single-output Gaussian process (GP) models and explore the properties of multiplicative mixtures of Mat\'ern kernels for multi-output GP models. For the single-output case, we derive a series of theoretical results showing that the smoothness of a mi

Sidharth Mudgal, Jong Lee, Harish Ganapathy, YaGuang Li

KL-regularized reinforcement learning (RL) is a popular alignment framework to control the language model responses towards high reward outcomes. We pose a tokenwise RL objective and propose a modular solver for it, called controlled decoding (CD). CD exerts control through a separate prefix scorer module, which is trained to learn a value function for the r

Shikai Fang, Xin Yu, Shibo Li, Zheng Wang

Practical tensor data is often along with time information. Most existing temporal decomposition approaches estimate a set of fixed factors for the objects in each tensor mode, and hence cannot capture the temporal evolution of the objects' representation. More important, we lack an effective approach to capture such evolution from streaming data, which is c

A. S. Sanz, R. Martínez-Herrero

In practice, Airy beams can only be reproduced in an approximate manner, with a limited spatial extension and hence a finite energy content. To this end, different procedures have been reported in the literature, based on a convenient tuning of the transmission properties of aperture functions. In order to investigate the effects generated by the truncation

K. R. Zentner, Ryan Julian, Brian Ichter, Gaurav S. Sukhatme

This paper combines two contributions. First, we introduce an extension of the Meta-World benchmark, which we call "Language-World," which allows a large language model to operate in a simulated robotic environment using semi-structured natural language queries and scripted skills described using natural language. By using the same set of tasks as Meta-World

Johannes Voß, Raphael Wittkowski

We study nonhelical nano- and microparticles that, through a particular shape, rotate when they are exposed to ultrasound. Employing acoustofluidic computer simulations, we investigate the flow field that is generated around these particles in the presence of a planar traveling ultrasound wave as well as the resulting propulsion force and torque of the parti

Young Min Cho, Sunny Rai, Lyle Ungar, João Sedoc

Mental health conversational agents (a.k.a. chatbots) are widely studied for their potential to offer accessible support to those experiencing mental health challenges. Previous surveys on the topic primarily consider papers published in either computer science or medicine, leading to a divide in understanding and hindering the sharing of beneficial knowledg

Teresa Cheng, Han Hu, Navid Valizadeh, Qiong Liu

Triboelectric nanogenerators (TENGs) are promising self-powering supplies for a diverse range of intelligent sensing and monitoring devices, especially due to their capability of harvesting electric energy from low frequency and small-scale mechanical motions. Inspired by the fact that contact-separation mode TENGs with small contact areas harvest high elect

Anna Koufakou, Diego Grisales, Ragy Costa de jesus, Oscar Fox

Emotion recognition in text, the task of identifying emotions such as joy or anger, is a challenging problem in NLP with many applications. One of the challenges is the shortage of available datasets that have been annotated with emotions. Certain existing datasets are small, follow different emotion taxonomies and display imbalance in their emotion distribu

A. Olivier, T. Cai, S. Akhter, Z. Ahmad Dar

Neutron production in antineutrino interactions can lead to bias in energy reconstruction in neutrino oscillation experiments, but these interactions have rarely been studied. MINERvA previously studied neutron production at an average antineutrino energy of ~3 GeV in 2016 and found deficiencies in leading models. In this paper, the MINERvA 6 GeV average ant

Gregor von Laszewski, Wo Chang, Russell Reinsch, Olivera Kotevska

Over the last several years, the computation landscape for conducting data analytics has completely changed. While in the past, a lot of the activities have been undertaken in isolation by companies, and research institutions, today's infrastructure constitutes a wealth of services offered by a variety of providers that offer opportunities for reuse, and int

Patrick Sattler, Johannes Zirngibl, Mattijs Jonker, Oliver Gasser

Internet-wide scans are an important tool to evaluate the deployment of services. To enable large-scale application layer scans, a fast, stateless port scan (e.g., using ZMap) is often performed ahead of time to collect responsive targets. It is a common expectation that port scans on the entire IPv4 address space provide a relatively unbiased view as they c

Elif Bozkurt

Different people have different facial expressions while speaking emotionally. A realistic facial animation system should consider such identity-specific speaking styles and facial idiosyncrasies to achieve high-degree of naturalness and plausibility. Existing approaches to personalized speech-driven 3D facial animation either use one-hot identity labels or

Claudio Fanconi, Moritz Vandenhirtz, Severin Husmann, Julia E. Vogt

Prototype learning, a popular machine learning method designed for inherently interpretable decisions, leverages similarities to learned prototypes for classifying new data. While it is mainly applied in computer vision, in this work, we build upon prior research and further explore the extension of prototypical networks to natural language processing. We in

Yuling Yao, Bruno Régaldo-Saint Blancard, Justin Domke

Simulation-based inference has been popular for amortized Bayesian computation. It is typical to have more than one posterior approximation, from different inference algorithms, different architectures, or simply the randomness of initialization and stochastic gradients. With a consistency guarantee, we present a general posterior stacking framework to make

Mitia Duerinckx, Lucas Ertzbischoff, Alexandre Girodroux-Lavigne, Richard M. Höfer

We study the multiscale viscoelastic Doi model for suspensions of Brownian rigid rod-like particles, as well as its generalization by Saintillan and Shelley for self-propelled particles. We consider the regime of a small Weissenberg number, which corresponds to a fast rotational diffusion compared to the fluid velocity gradient, and we analyze the resulting

Graham G. Brown, Rui E. F. Silva, Álvaro Jiménez-Galán, Eleftherios Goulielmakis

Using real-space view of high harmonic generation (HHG) in solids, we develop a physically transparent and gauge-invariant approach for distinguishing intraband and interband HHG mechanisms. Our approach relies on resolving the harmonic emission according to the separation between Wannier states involved in radiative transitions. We show that the intra- and

William W. Howard, Samuel R. Shebert, Benjamin H. Kirk, R. Michael Buehrer

Cognitive Radar Networks were proposed by Simon Haykin in 2006 to address problems with large legacy radar implementations - primarily, single-point vulnerabilities and lack of adaptability. This work proposes to leverage the adaptability of cognitive radar networks to trade between active radar observation, which uses high power and risks interception, and

Graham G. Brown, Álvaro Jiménez-Galán, Rui E. F. Silva, Misha Ivanov

Using a fully real-space perspective on high harmonic generation (HHG) in solids, we examine the relationship between microscopic response, macroscopic propagation of this response to the far field, and the extremely short dephasing times routinely used in the theoretical simulations of experimentally measured solid-state HHG spectra. We find that far field

Jan-Hendrik Hanschke, Daniel Arteaga, Giulio Cengarle, Joshua Lando

Loudspeaker rendering techniques that create phantom sound sources often assume an equidistant loudspeaker layout. Typical home setups might not fulfill this condition as loudspeakers deviate from canonical positions, thus requiring a corresponding calibration. The standard approach is to compensate for delays and to match the loudness of each loudspeaker at

Haotian Tang, Shang Yang, Zhijian Liu, Ke Hong

Sparse convolution plays a pivotal role in emerging workloads, including point cloud processing in AR/VR, autonomous driving, and graph understanding in recommendation systems. Since the computation pattern is sparse and irregular, specialized high-performance kernels are required. Existing GPU libraries offer two dataflow types for sparse convolution. The g

Urvashi Verma, Aniket Banerjee, Rana D. Parshad

A number of top down bio-control models have been proposed where the introduced predators' efficacy is enhanced via the provision of additional food (AF). However, if the predator has a pest dependent monotone functional response, pest extinction is unattainable. In the current manuscript, we propose a model where a predator with pest dependent monotone func

Princewill Okoroafor, Robert Kleinberg, Wen Sun

Predictive models in ML need to be trustworthy and reliable, which often at the very least means outputting calibrated probabilities. This can be particularly difficult to guarantee in the online prediction setting when the outcome sequence can be generated adversarially. In this paper we introduce a technique using Blackwell's approachability theorem for ta

Sho Katayama

This paper concerns positive solutions to the boundary value problems of the scalar field equation in the half space with a Sobolev supercritical nonlinearity and an inhomogeneous Dirichlet boundary condition, admitting a nontrivial nonnegative Radon measure as the boundary data. Under a suitable integrability assumption on the boundary data and the Joseph--

Amineh Mohseni, Mahdi Torabian

We argue that, in a theory of quantum gravity, the gauge coupling and the confinement scale of a gauge theory are related to distance in the space of metric configurations, and in turn to the cosmological constant. To support the argument, we compute the gauge kinetic functions in variuos supersymmetric Heterotic and type II string compactifications and show

Fahim Ahmed Zaman, Xiaodong Wu, Weiyu Xu, Milan Sonka

We describe a method for verifying the output of a deep neural network for medical image segmentation that is robust to several classes of random as well as worst-case perturbations i.e. adversarial attacks. This method is based on a general approach recently developed by the authors called "Trust, but Verify" wherein an auxiliary verification network produc

Michael A. Lively, Danny Perez, Blas P. Uberuaga, Yanzeng Zhang

Stand-off runaway electron termination by injected tungsten particulates offers a plausible option in the toolbox of disruption mitigation. Tungsten is an attractive material choice for this application due to large electron stopping power and high melting point. To assess the feasibility of this scheme, we simulate runaway collisions with tungsten particula

Gabriel Jarry-Bolduc, Chayne Planiden

This paper presents two methods for approximating a proper subset of the entries of a Hessian using only function evaluations. These approximations are obtained using the techniques called \emph{generalized simplex Hessian} and \emph{generalized centered simplex Hessian}. We show how to choose the matrices of directions involved in the computation of these t

Ray A. O. Sinurat, Anurag Daram, Haryadi S. Gunawi, Robert B. Ross

Machine learning-based performance models are increasingly being used to build critical job scheduling and application optimization decisions. Traditionally, these models assume that data distribution does not change as more samples are collected over time. However, owing to the complexity and heterogeneity of production HPC systems, they are susceptible to

Saptarshi Sengupta, Connor Heaton, Shreya Ghosh, Wenpeng Yin

We study extractive question-answering in the medical domain (Medical-EQA). This problem has two main challenges: (i) domain specificity, as most AI models lack necessary domain knowledge, and (ii) extraction-based answering style, which restricts most autoregressive LLMs due to potential hallucinations. To handle those challenges, we propose TOP-Training, a

Vladimir Narovlansky, Herman Verlinde

We propose a new model of low dimensional de Sitter holography in the form of a pair of double-scaled SYK models at infinite temperature coupled via an equal energy constraint $H_L=H_R$. As a test of the duality, we compute the two-point function between two dressed SYK operators ${\cal O}_\Delta$ that preserve the constraint. We find that in the large $N$ l

Vašek Chvátal, Guillermo A. Gamboa Quintero., Ida Kantor

Given a metric space $(X, \rho)$, we say $y$ is between $x$ and $z$ if $\rho(x,z) = \rho(x,y) + \rho(y,z)$. A metric space gives rise to a 3-uniform hypergraph that has as hyperedges those triples $\{ x,y,z \}$ where $y$ is between $x$ and $z$. Such hypergraphs are called metric and understanding them is key to the study of metric spaces. In this paper, we p

Sinclair Schneider, Florian Steuber, Joao A. G. Schneider, Gabi Dreo Rodosek

With the rise of generative pre-trained transformer models such as GPT-3, GPT-NeoX, or OPT, distinguishing human-generated texts from machine-generated ones has become important. We refined five separate language models to generate synthetic tweets, uncovering that shallow learning classification algorithms, like Naive Bayes, achieve detection accuracy betwe

Mohtasim Hadi Rafi, Mohammad Ratul Mahjabin, Md Sabbir Rahman

One of the biggest challenges that the farmers go through is to fight insect pests during agricultural product yields. The problem can be solved easily and avoid economic losses by taking timely preventive measures. This requires identifying insect pests in an easy and effective manner. Most of the insect species have similarities between them. Without prope

Leon Liyang Zhang, Jiarui Lu, Joel Ruben Antony Moniz, Aditya Kulkarni

In the context of a voice assistant system, steering refers to the phenomenon in which a user issues a follow-up command attempting to direct or clarify a previous turn. We propose STEER, a steering detection model that predicts whether a follow-up turn is a user's attempt to steer the previous command. Constructing a training dataset for steering use cases

Tengyuan Liang, Benjamin Recht

N-of-1 experiments, where a unit serves as its own control and treatment in different time windows, have been used in certain medical contexts for decades. However, due to effects that accumulate over long time windows and interventions that have complex evolution, a lack of robust inference tools has limited the widespread applicability of such N-of-1 desig

Bartlomiej Czech, Sirui Shuai, Haifeng Tang

We revisit information retrieval from evaporating black holes in the Hayden-Preskill protocol, treating the black hole dynamics as Haar-random. We compute, down to the first exponentially suppressed terms, all integer-indexed R\'enyi mutual informations between a black hole, its radiation, and a reference that catalogues Alice's diaries. We find that droppin

Jay Shah, Md Mahfuzur Rahman Siddiquee, Yi Su, Teresa Wu

Age is one of the major known risk factors for Alzheimer's Disease (AD). Detecting AD early is crucial for effective treatment and preventing irreversible brain damage. Brain age, a measure derived from brain imaging reflecting structural changes due to aging, may have the potential to identify AD onset, assess disease risk, and plan targeted interventions.

Jiaming Chen, Alex Küronya, Yusuf Mustopa, Jakob Stix

We contemplate the range of convex Fujita numbers for minimal smooth projective surfaces according to their position in the Kodaira-Enriques classification.

Gennaro Gala, Cassio de Campos, Robert Peharz, Antonio Vergari

Continuous latent variables (LVs) are a key ingredient of many generative models, as they allow modelling expressive mixtures with an uncountable number of components. In contrast, probabilistic circuits (PCs) are hierarchical discrete mixtures represented as computational graphs composed of input, sum and product units. Unlike continuous LV models, PCs prov

Anoushka Alavilli, Khai Nguyen, Sam Schoedel, Brian Plancher

Model-predictive control (MPC) is a powerful tool for controlling highly dynamic robotic systems subject to complex constraints. However, MPC is computationally demanding, and is often impractical to implement on small, resource-constrained robotic platforms. We present TinyMPC, a high-speed MPC solver with a low memory footprint targeting the microcontrolle

Brad Sheese, Mark Liffiton, Jaromir Savelka, Paul Denny

Providing personalized assistance at scale is a long-standing challenge for computing educators, but a new generation of tools powered by large language models (LLMs) offers immense promise. Such tools can, in theory, provide on-demand help in large class settings and be configured with appropriate guardrails to prevent misuse and mitigate common concerns ar

Simon F. Müller-Cleve, Fernando M. Quintana, Vittorio Fra, Pedro L. Galindo

Neuromorphic computing relies on spike-based, energy-efficient communication, inherently implying the need for conversion between real-valued (sensory) data and binary, sparse spiking representation. This is usually accomplished using the real valued data as current input to a spiking neuron model, and tuning the neuron's parameters to match a desired, often

Guanyu Zhu, Shehryar Sikander, Elia Portnoy, Andrew W. Cross

We study parallel fault-tolerant quantum computing for families of homological quantum low-density parity-check (LDPC) codes defined on 3-manifolds with constant or almost-constant encoding rate. We derive generic formula for a transversal $T$ gate of color codes on general 3-manifolds, which acts as collective non-Clifford logical CCZ gates on any triplet o

Lasse Hansen, Nabeel Seedat, Mihaela van der Schaar, Andrija Petrovic

Synthetic data serves as an alternative in training machine learning models, particularly when real-world data is limited or inaccessible. However, ensuring that synthetic data mirrors the complex nuances of real-world data is a challenging task. This paper addresses this issue by exploring the potential of integrating data-centric AI techniques which profil

Shrobana Ghosh, Panagiota Kolitsidou, Mark Hannam

Gravitational-wave signals from binaries that contain spinning black holes in general include an asymmetry between the $+m$ and $-m$ multipoles that is not included in most signal models used in LIGO-Virgo-KAGRA (LVK) analysis to date. This asymmetry manifests itself in out-of-plane recoil of the final black hole and its inclusion in signal models is necessa