July 2022 arXiv papers — page 29

Cailin Winston, Caleb Winston, Chloe Winston, Claris Winston

The 44th International Conference on Software Engineering (ICSE 2022) was held in person from May 22 to May 27, 2022 in Pittsburgh, PA, USA. Here, we summarize themes of research and the direction of research in the field of software engineering and testing that we observed at the conference.

Karuppasamy Pandian Soundarapandian, Domenico De Fazio, Sefaattin Tongay, Frank H. L. Koppens

High mobility is a crucial requirement for a large variety of electronic device applications. The state-of-the-art for high quality graphene devices is based on heterostructures made with graphene encapsulated in $>80\,$nm-thick flakes of hexagonal boron nitride (hBN). Unfortunately, scaling up multilayer hBN while precisely controlling the number of layers

Yushu Wu, Yifan Gong, Pu Zhao, Yanyu Li

Deep learning-based super-resolution (SR) has gained tremendous popularity in recent years because of its high image quality performance and wide application scenarios. However, prior methods typically suffer from large amounts of computations and huge power consumption, causing difficulties for real-time inference, especially on resource-limited platforms s

Yonatan Bitton, Nitzan Bitton Guetta, Ron Yosef, Yuval Elovici

While vision-and-language models perform well on tasks such as visual question answering, they struggle when it comes to basic human commonsense reasoning skills. In this work, we introduce WinoGAViL: an online game of vision-and-language associations (e.g., between werewolves and a full moon), used as a dynamic evaluation benchmark. Inspired by the popular

Tarannom Parhizkar

Simulation-based probabilistic risk assessment (SPRA) is a systematic and comprehensive methodology that has been used and refined over the past few decades to evaluate the risks associated with complex systems. SPRA models are well established for cases with considerable data and system behavior information available. In this regard, multiple statistical an

Ghayoor Shah, Shahriar Shahram, Yaser Fallah, Danyang Tian

Sensor data and Vehicle-to-Everything (V2X) communication can greatly assist Connected and Autonomous Vehicles (CAVs) in situational awareness and provide a safer driving experience. While sensor data recorded from devices such as radar and camera can assist in local awareness in the close vicinity of the Host Vehicle (HV), the information obtained is useful

Nelson Alvarado

We study degenerations of non-simple principally polarized abelian surfaces to the boundary in the toroidal compactification of $\mathcal{A}_2$, and describe the degenerate abelian surfaces as well as the degenerate elliptic curves that live inside them.

Ruocheng Wang, Yunzhi Zhang, Jiayuan Mao, Chin-Yi Cheng

We study the problem of translating an image-based, step-by-step assembly manual created by human designers into machine-interpretable instructions. We formulate this problem as a sequential prediction task: at each step, our model reads the manual, locates the components to be added to the current shape, and infers their 3D poses. This task poses the challe

Mandar Sharma, Ajay Gogineni, Naren Ramakrishnan

The neural boom that has sparked natural language processing (NLP) research through the last decade has similarly led to significant innovations in data-to-text generation (DTG). This survey offers a consolidated view into the neural DTG paradigm with a structured examination of the approaches, benchmark datasets, and evaluation protocols. This survey draws

Zhanghao Sun, Jian Wang, Yicheng Wu, Shree Nayar

Flash illumination is widely used in imaging under low-light environments. However, illumination intensity falls off with propagation distance quadratically, which poses significant challenges for flash imaging at a long distance. We propose a new flash technique, named ``patterned flash'', for flash imaging at a long distance. Patterned flash concentrates o

Xiaolei Zhang, Wei Qi

Let $R$ be a commutative ring with identity and $S$ a multiplicative subset of $R$. An $R$-module $M$ is said to be a uniformly $S$-Artinian ($u$-$S$-Artinian for abbreviation) module if there is $s\in S$ such that any descending chain of submodules of $M$ is $S$-stationary with respect to $s$. $u$-$S$-Artinian modules are characterized in terms of ($S$-MIN)

Aycil Cesmelioglu, Jeonghun J. Lee, Sander Rhebergen

We present and analyze a hybridizable discontinuous Galerkin (HDG) finite element method for the coupled Stokes--Biot problem. Of particular interest is that the discrete velocities and displacement are $H(\text{div})$-conforming and satisfy the compressibility equations pointwise on the elements. Furthermore, in the incompressible limit, the discretization

Danilo Obradovic, Mehrdad Ghandhari, Robert Eriksson

Frequency Containment Reserves might be insufficient to provide an appropriate response in the presence of large disturbances and low inertia scenarios. As a solution, this work assesses the supplementary droop frequency-based Emergency Power Control (EPC) from HVDC interconnections, applied in the detailed Nordic Power System model. EPC distribution and fac

Xiaoyan Zhou, Adam S. Williams, Francisco R. Ortega

As augmented reality technology and hardware become more mature and affordable, researchers have been exploring more intuitive and discoverable interaction techniques for immersive environments. In this paper, we investigate multimodal interaction for 3D object manipulation in a multi-object virtual environment. To identify the user-defined gestures, we cond

Pouya Towhidi, Manouchehr Salehi

The most essential concept in concurrent multiscale methods involving atomistic-continuum coupling is how to define the relation between atomistic and continuum regions. A well-known coupling method that has been frequently employed in different concurrent multiscale methods such as Quasicontinuum is the strong compatibility coupling (SCC). Although the SCC

Joaquin Grefa, Mauricio Hippert, Jorge Noronha, Jacquelyn Noronha-Hostler

By using gravity/gauge correspondence, we employ an Einstein-Maxwell-Dilaton model to compute the equilibrium and out-of-equilibrium properties of a hot and baryon rich strongly coupled quark-gluon plasma. The family of 5-dimensional holographic black holes, which are constrained to mimic the lattice QCD equation of state at zero density, is used to investig

Guo Chen, Hongchi Wang, Roy van Boekel, Enric Palle

We present a new optical transmission spectrum of the hot Jupiter HAT-P-1b based on two transits observed with the Double Spectrograph (DBSP) on the Palomar 200-inch (P200) telescope. The DBSP transmission spectrum, covering a wavelength range from 3250 to 10007 \r{A}, is consistent with that observed with the Hubble Space Telescope (HST), but the former has

Hung-Hwa Lin, Wei-Ting Kuo, Daniel P. Arovas, Yi-Zhuang You

Band-touching Weyl points in Weyl semimetals give rise to many novel characteristics, one of which the presence of surface Fermi-arc states that is topologically protected. The number of such states can be computed by the Chern numbers at different momentum slices, which fluctuates with changing momentum and depends on the distribution of Weyl points in the

Yulia Gorginyan

An operator $I$ on a real Lie algebra $A$ is called a complex structure operator if $I^2=-Id$ and the $\sqrt{-1}$-eigenspace $A^{1,0}$ is a Lie subalgebra in the complexification of $A$. A hypercomplex structure on a Lie algebra $A$ is a triple of complex structures $I,J$ and $K$ on $A$ satisfying the quaternionic relations. We call a hypercomplex nilpotent

Pieter Gijsbers, Marcos L. P. Bueno, Stefan Coors, Erin LeDell

Comparing different AutoML frameworks is notoriously challenging and often done incorrectly. We introduce an open and extensible benchmark that follows best practices and avoids common mistakes when comparing AutoML frameworks. We conduct a thorough comparison of 9 well-known AutoML frameworks across 71 classification and 33 regression tasks. The differences

MohammadReza Mohammadi, Peyton Chandarana, James Seekings, Sara Hendrix

In this paper, we develop four spiking neural network (SNN) models for two static American Sign Language (ASL) hand gesture classification tasks, i.e., the ASL Alphabet and ASL Digits. The SNN models are deployed on Intel's neuromorphic platform, Loihi, and then compared against equivalent deep neural network (DNN) models deployed on an edge computing device

Adam S. Williams, Xiaoyan Zhou, Michel Pahud, Francisco R. Ortega

Immersive Analytics (IA) and consumer adoption of augmented reality (AR) and virtual reality (VR) head-mounted displays (HMDs) are both rapidly growing. When used in conjunction, stereoscopic IA environments can offer improved user understanding and engagement; however, it is unclear how the choice of stereoscopic display impacts user interactions within an

Aycil Cesmelioglu, Jeonghun J. Lee, Sander Rhebergen

We present an embedded-hybridizable discontinuous Galerkin finite element method for the total pressure formulation of the quasi-static poroelasticity model. Although the displacement and the Darcy velocity are approximated by discontinuous piece-wise polynomials, $H(\text{div})$-conformity of these unknowns is enforced by Lagrange multipliers. The semi-disc

Luís Fernando C. Pereira, Edilberto O. Silva

We investigate the properties of a two-dimensional quantum ring under rotating and external magnetic field effects. We initially analyse the Landau levels and inertial effects on them. Among the results obtained, we emphasize that the rotation lifted the degeneracy of Landau levels. When electrons are confined in a two-dimensional ring, which is modeled by a

Júlia Pareto Boada, Begoña Román Maestre, Carme Torras

Social robotics development for the practice of care and European prospects to incorporate these AI-based systems in institutional healthcare contexts call for an urgent ethical reflection to (re)configurate our practical life according to human values and rights. Despite the growing attention to the ethical implications of social robotics, the current debat

Anique Akhtar, Zhu Li, Geert Van der Auwera

Efficient point cloud compression is essential for applications like virtual and mixed reality, autonomous driving, and cultural heritage. This paper proposes a deep learning-based inter-frame encoding scheme for dynamic point cloud geometry compression. We propose a lossy geometry compression scheme that predicts the latent representation of the current fra

Oliver Edtmair

Inspired by Hutchings' elementary alternative to ECH capacities, we introduce an elementary alternative to spectral invariants defined via periodic Floer homology (PFH). We use these spectral invariants to provide more elementary proofs of a number of results which have recently been obtained using PFH spectral invariants. Among these results are quantitativ

Vishnu Rajendran S, Soran Parsa, Simon Parsons, Amir Ghalamzan Esfahani

Robotic harvesting of strawberries has gained much interest in the recent past. Although there are many innovations, they haven't yet reached a level that is comparable to an expert human picker. The end effector unit plays a major role in defining the efficiency of such a robotic harvesting system. Even though there are reports on various end effectors for

Jessica Huynh, Ting-Rui Chiang, Jeffrey Bigham, Maxine Eskenazi

Dialog system developers need high-quality data to train, fine-tune and assess their systems. They often use crowdsourcing for this since it provides large quantities of data from many workers. However, the data may not be of sufficiently good quality. This can be due to the way that the requester presents a task and how they interact with the workers. This

Noor Sajid, Panagiotis Tigas, Zafeirios Fountas, Qinghai Guo

How can artificial agents learn non-reinforced preferences to continuously adapt their behaviour to a changing environment? We decompose this question into two challenges: ($i$) encoding diverse memories and ($ii$) selectively attending to these for preference formation. Our proposed \emph{no}n-\emph{re}inforced preference learning mechanism using selective

Sreetama Das, Filippo Caruso

Quantum image processing is an emerging topic in the field of quantum information and technology. In this paper, we propose a new quantum image representation of RGB images, which is an improvement to all the existing representations in terms of using minimum resource. We use two entangled quantum registers constituting of total 7 qutrits to encode the color

D. F. Tamayo Agudelo, A. Mariano, D. E. Jaramillo Arango

In this work we report the calculation of the charged current total and differential cross sections for weak pion-production with neutrinos and antineutrinos, with the final pion-nucleon pair invariant mass $W_{\pi N}\lesssim2$GeV. Our results are compared with the recent reanalyzed data from the old bubble chamber experiments, that solved the discrepancy be

Qiang Zhu, Weilun Tang, Shinnosuke Hattori

In this work, we present a new computational approach to characterize and classify molecular packing in the solid states. The key idea is to project each neighboring molecule (or short contact) from the centered molecule into a unit sphere according to the interaction energy. Consequently, the similarity between two spherical images can be evaluated from the

Charles Edison Tripp, Jordan Perr-Sauer, Lucas Hayne, Monte Lunacek

We present an empirical dataset surveying the deep learning phenomenon on fully-connected feed-forward multilayer perceptron neural networks. The dataset, which is now freely available online, records the per-epoch training and generalization performance of 483 thousand distinct hyperparameter choices of architectures, tasks, depths, network sizes (number of

Wai Tong Chung, Ki Sung Jung, Jacqueline H. Chen, Matthias Ihme

In general, large datasets enable deep learning models to perform with good accuracy and generalizability. However, massive high-fidelity simulation datasets (from molecular chemistry, astrophysics, computational fluid dynamics (CFD), etc. can be challenging to curate due to dimensionality and storage constraints. Lossy compression algorithms can help mitiga

Adam Dziedzic, Stephan Rabanser, Mohammad Yaghini, Armin Ale

The lack of well-calibrated confidence estimates makes neural networks inadequate in safety-critical domains such as autonomous driving or healthcare. In these settings, having the ability to abstain from making a prediction on out-of-distribution (OOD) data can be as important as correctly classifying in-distribution data. We introduce $p$-DkNN, a novel inf

Hongyu Wang, Nikolas Martelaro

The end-user programming of social robot behavior is usually limited by a predefined set of movements. We are proposing a puppeteering robotic interface that provides a more intuitive method of programming robot expressive movements. As the user manipulates the puppet of a robot, the actual robot replicates the movements, providing real-time visual feedback.

Brandon Legried, Sebastien Roch

Ancestral sequence reconstruction is a key task in computational biology. It consists in inferring a molecular sequence at an ancestral species of a known phylogeny, given descendant sequences at the tip of the tree. In addition to its many biological applications, it has played a key role in elucidating the statistical performance of phylogeny estimation me

Salman Ahmadi-Asl, Anh-Huy Phan, Andrzej Cichocki

Efficient and fast computation of a tensor singular value decomposition (t-SVD) with a few passes over the underlying data tensor is crucial because of its many potential applications. The current/existing subspace randomized algorithms need (2q+2) passes over the data tensor to compute a t-SVD, where q is a non-negative integer number (power iteration param

Francesca Carocci, Navid Nabijou

A tropical expansion is a degeneration of a toroidal embedding, induced by a polyhedral subdivision of its tropicalisation. Each irreducible component of a tropical expansion admits a collapsing map down to a stratum of the original variety. We study the relative geometry of this map. We give a polyhedral criterion for the map to have the structure of a tori

Fabien Malbet, Lucas Labadie, Alessandro Sozzetti, Gary A. Mamon

High-precision astrometry well beyond the capacities of Gaia will provide a unique way to achieve astrophysical breakthroughs, in particular on the nature of dark matter, and a complete survey of nearby habitable exoplanets. In this contribution, we present the scientific cases that require a flexibly-pointing instrument capable of high astrometric accuracy

Lukas Neumeier, Mario A. Ciampini, Oriol Romero-Isart, Markus Aspelmeyer

We introduce and theoretically analyze a scheme to prepare and detect non-Gaussian quantum states of an optically levitated particle via the interaction with a light pulse that generates cubic and inverted potentials. We show that this allows operating on short time- and lengthscales, which significantly reduces the demands on decoherence rates in such exper

Onuralp Soylemez

The approval success rate of drug candidates is very low with the majority of failure due to safety and efficacy. Increasingly available high dimensional information on targets, drug molecules and indications provides an opportunity for ML methods to integrate multiple data modalities and better predict clinically promising drug targets. Notably, drug target

Zhouping Wang, Sarah Ostadabbas

3D Human body pose and shape estimation within a temporal sequence can be quite critical for understanding human behavior. Despite the significant progress in human pose estimation in the recent years, which are often based on single images or videos, human motion estimation on live stream videos is still a rarely-touched area considering its special require

James Avery, Mark Runciman, Cristina Fiani, Elena Monfort Sanchez

Incorrectly sized balloon catheters can lead to increased post-surgical complications, yet even with preoperative imaging, correct selection remains a challenge. With limited feedback during surgery, it is difficult to verify correct deployment. We propose the use of integrated impedance measurements and Electrical Impedance Tomography (EIT) imaging to asses

Xinlei He, Hongbin Liu, Neil Zhenqiang Gong, Yang Zhang

Semi-supervised learning (SSL) leverages both labeled and unlabeled data to train machine learning (ML) models. State-of-the-art SSL methods can achieve comparable performance to supervised learning by leveraging much fewer labeled data. However, most existing works focus on improving the performance of SSL. In this work, we take a different angle by studyin

Huan Wang, Yun Fu

Many recent works have shown trainability plays a central role in neural network pruning -- unattended broken trainability can lead to severe under-performance and unintentionally amplify the effect of retraining learning rate, resulting in biased (or even misinterpreted) benchmark results. This paper introduces trainability preserving pruning (TPP), a scala

Martin Figura, Yixuan Lin, Ji Liu, Vijay Gupta

In decentralized cooperative multi-agent reinforcement learning, agents can aggregate information from one another to learn policies that maximize a team-average objective function. Despite the willingness to cooperate with others, the individual agents may find direct sharing of information about their local state, reward, and value function undesirable due

Kai-Jia Sun, Rui Wang, Che Ming Ko, Yu-Gang Ma

Like nucleosynthesis during the early universe, light nuclei are also produced in relativistic heavy-ion collisions. Although the deuteron ($d$) yields in these collisions can be well described by the statistical hadronization model (SHM), which assumes that particle yields are fixed at a common chemical freezeout near the phase boundary between the quark-gl

Alperen A. Ergür, Jesus Rebollo Bueno, Petros Valettas

We investigate the effect of an $\varepsilon$-room of perturbation tolerance on symmetric tensor decomposition. To be more precise, suppose a real symmetric $d$-tensor $f$, a norm $||.||$ on the space of symmetric $d$-tensors, and $\varepsilon >0$ are given. What is the smallest symmetric tensor rank in the $\varepsilon$-neighborhood of $f$? In other words,

Chris Duffy, Gary MacGillivray, Ben Tremblay

Let $G$ be a graph whose edges are each assigned one of the $m$-colours $1, 2, \ldots, m$, and let $\Gamma$ be a subgroup of $S_m$. The operation of switching at a vertex $x$ with respect $\pi \in \Gamma$ permutes the colours of the edges incident with $x$ according to $\pi$. There is a well-developed theory of switching when $\Gamma$ is Abelian. Much less i

J. -M. Delouis, E. Allys, E. Gauvrit, F. Boulanger

Scattering transforms have been successfully used to describe dust polarization for flat-sky images. This paper expands this framework to noisy observations on the sphere with the aim of obtaining denoised Stokes Q and U all-sky maps at 353GHz, as well as a non-Gaussian model of dust polarization, from the Planck data. To achieve this goal, we extend the com

Roger J. A. Laeven, Emanuela Rosazza Gianin

This paper introduces and fully characterizes the novel class of quasi-logconvex measures of risk, to stand on equal footing with the rich class of quasi-convex measures of risk. Quasi-logconvex risk measures naturally generalize logconvex return risk measures, just like quasi-convex risk measures generalize convex monetary risk measures. We establish their

Russell J. Campbell, Nancy E. Clarke, Gary MacGillivray

Several possible definitions of local injectivity for a homomorphism of an oriented graph $G$ to an oriented graph $H$ are considered. In each case, we determine the complexity of deciding whether there exists such a homomorphism when $G$ is given and $H$ is a fixed tournament on three or fewer vertices. Each possible definition leads to a locally-injective

Heidy Khlaaf, Pamela Mishkin, Joshua Achiam, Gretchen Krueger

Codex, a large language model (LLM) trained on a variety of codebases, exceeds the previous state of the art in its capacity to synthesize and generate code. Although Codex provides a plethora of benefits, models that may generate code on such scale have significant limitations, alignment problems, the potential to be misused, and the possibility to increase

Kyle P. Kelley, Anna N. Morozovska, Eugene A. Eliseev, Yongtao Liu

Ferroelectricity in binary oxides including hafnia and zirconia have riveted the attention of the scientific community due to highly unconventional physical mechanisms and the potential for integration of these materials into semiconductor workflows. Over the last decade, it has been argued that behaviors such as wake-up phenomena and an extreme sensitivity

Youssef Azouzi, Mohamed Amine Ben Amor, Jonathan Homann, Marwa Masmoudi

Riesz space (non-pointwise) generalizations for iterative processes are given for the concepts of recurrence, first recurrence and conditional ergodicity. Riesz space conditional versions of the Poincar\'{e} Recurrence Theorem and the Kac formula are developed. Under mild assumptions, it is shown that every conditional expectation preserving process is condi

Russell Campbell, Nancy E. Clarke, Gary MacGillivray

Each of several possible definitions of local injectivity for a homomorphism of an oriented graph $G$ to an oriented graph $H$ leads to an injective oriented colouring problem. For each case in which such a problem is solvable in polynomial time, we identify a set $\mathcal{F}$ of oriented graphs such that an oriented graph $G$ has an injective oriented colo

Maya Beleznay, Michelle Kunimoto

We present estimates for the occurrence rates of hot Jupiters around dwarf stars based on data from the Transiting Exoplanet Survey Satellite (TESS) Prime Mission. We take 97 hot Jupiters orbiting 198,721 AFG dwarf stars (ranging in mass from $0.8M_{\odot}$ to $2.3M_{\odot}$) from an independent search for hot Jupiters using TESS Prime Mission data. We estim

Albert Swiecicki, Nianyi Li, Jonathan O'Donnell, Nicholas Said

A fully-automated deep learning algorithm matched performance of radiologists in assessment of knee osteoarthritis severity in radiographs using the Kellgren-Lawrence grading system. To develop an automated deep learning-based algorithm that jointly uses Posterior-Anterior (PA) and Lateral (LAT) views of knee radiographs to assess knee osteoarthritis severit

Yifu Tao, Marija Popović, Yiduo Wang, Sundara Tejaswi Digumarti

Safe motion planning in robotics requires planning into space which has been verified to be free of obstacles. However, obtaining such environment representations using lidars is challenging by virtue of the sparsity of their depth measurements. We present a learning-aided 3D lidar reconstruction framework that upsamples sparse lidar depth measurements with

Steven H. Low

First we present an approach to formulate unbalanced three-phase power flow problems for general networks that explicitly separates device models and network models. A device model consists of (i) an internal model and (ii) a conversion rule. The conversion rule relates the internal variables (voltage, current, and power) of a device to its terminal variable

George Domat, Hannah Hoganson, Sanghoon Kwak

We prove that the full automorphism group and the outer automorphism group of the free group of countably infinite rank are coarsely bounded. That is, these groups admit no continuous actions on a metric space with unbounded orbits, and have the quasi-isometry type of a point.

Francesco Micheli, John Lygeros

Model Predictive Control is an extremely effective control method for systems with input and state constraints. Model Predictive Control performance heavily depends on the accuracy of the open-loop prediction. For systems with uncertainty this in turn depends on the information that is available about the properties of the model and disturbance uncertainties

B. Harlech-Jones, S. J. Waddy, J. D. S. Witt, D. Govender

We report microwave impedance measurements of a superconductor-semiconductor hybrid nanowire device with three terminals (3T). Our technique makes use of transmission line resonators to acquire the nine complex scattering matrix parameters (S-parameters) of the device on fast timescales and across a spectrum of frequencies spanning 0.3 - 7 GHz. Via compariso

Yingqiang Ge, Shuchang Liu, Zuohui Fu, Juntao Tan

Recommender systems (RS), serving at the forefront of Human-centered AI, are widely deployed in almost every corner of the web and facilitate the human decision-making process. However, despite their enormous capabilities and potential, RS may also lead to undesired effects on users, items, producers, platforms, or even the society at large, such as compromi

Sourav Chakraborty, Eldar Fischer, Arijit Ghosh, Gopinath Mishra

The study of distribution testing has become ubiquitous in the area of property testing, both for its theoretical appeal, as well as for its applications in other fields of Computer Science. The original distribution testing model relies on samples drawn independently from the distribution to be tested. However, when testing distributions over the $n$-dimens

Rouzbeh Modarresi Yazdi, Shuzhe Shi, Charles Gale, Sangyong Jeon

Photons resulting from jet-medium interactions offer the opportunity of studying the evolving quark distribution in a heavy ion collision. The spectra of jet-medium photons is presented within the JETSCAPE framework for two different energy loss models, MARTINI and CUJET. Jet-medium photons can contribute significantly to the spectrum of direct photons in th

Felix Ahlborn, Friedrich Kupka, Achim Weiss, Martin Flaskamp

Convective overshoot mixing is a critical ingredient of stellar structure models, but is treated in most cases by ad hoc extensions of the mixing-length theory for convection. Advanced theories which are both more physical and numerically treatable are needed. Convective flows in stellar interiors are highly turbulent. This poses a number of numerical challe

Christopher J. Shallue, Daniel J. Eisenstein

We present a method to reconstruct the initial linear-regime matter density field from the late-time non-linearly evolved density field in which we channel the output of standard first-order reconstruction to a convolutional neural network (CNN). Our method shows dramatic improvement over the reconstruction of either component alone. We show why CNNs are not

Ramya Gurunathan, Kamal Choudhary, Francesca Tavazza

The phonon density-of-states (DOS) summarizes the lattice vibrational modes supported by a structure, and gives access to rich information about the material's stability, thermodynamic constants, and thermal transport coefficients. Here, we present an atomistic line graph neural network (ALIGNN) model for the prediction of the phonon density of states and th

Riccardo Poiani, Ciprian Stirbu, Alberto Maria Metelli, Marcello Restelli

With the continuous growth of the global economy and markets, resource imbalance has risen to be one of the central issues in real logistic scenarios. In marine transportation, this trade imbalance leads to Empty Container Repositioning (ECR) problems. Once the freight has been delivered from an exporting country to an importing one, the laden will turn into

Suhas Rao Tamvada, Saeed Moghaddam

This paper presents the latest progress on characterization of our membrane assisted phase-change heat sink (MHS) at conditions suitable for implementation in data centers (DCs). Experiments are conducted using water as the working fluid at a vapor space pressure ($P_{vapor}$) of 16 kPa, corresponding to a saturation temperature of $\sim$ 55$^{\circ}$C. This

Nairen Cao, Jeremy T. Fineman, Shi Li, Julián Mestre

The active-time scheduling problem considers the problem of scheduling preemptible jobs with windows (release times and deadlines) on a parallel machine that can schedule up to $g$ jobs during each timestep. The goal in the active-time problem is to minimize the number of active steps, i.e., timesteps in which at least one job is scheduled. In this way, the

A. Kume, C. Villa, S. G. Walker

The pooling of prior opinions is an important area of research and has been for a number of decades. The idea is to obtain a single belief probability distribution from a set of expert opinion belief distributions. The paper proposes a new way to provide a resultant prior opinion based on a minimization of information principle. This is done in the square-ro

Paul Norridge

Recent work has suggested that the generalisation performance of a DNN is related to the extent to which the Signal-to-Noise Ratio is optimised at each of the nodes. In contrast, Gradient Descent methods do not always lead to SNR-optimal weight configurations. One way to improve SNR performance is to suppress large weight updates and amplify small weight upd

M. Iftekhar Tanveer, Diego Casabuena, Jussi Karlgren, Rosie Jones

Podcasts are conversational in nature and speaker changes are frequent -- requiring speaker diarization for content understanding. We propose an unsupervised technique for speaker diarization without relying on language-specific components. The algorithm is overlap-aware and does not require information about the number of speakers. Our approach shows 79% im

Philip Darke, Paolo Missier, Jaume Bacardit

The development of models for Electronic Health Record data is an area of active research featuring a small number of public benchmark data sets. Researchers typically write custom data processing code but this hinders reproducibility and can introduce errors. The Python package torchtime provides reproducible implementations of commonly used PhysioNet and U

Marina G. Rachid, Will Rocha, Harold Linnartz

The increasing sensitivity and resolution of ground-based telescopes have enabled the detection of gas-phase complex organic molecules (COMs) across a variety of environments. Many of the detected species are expected to form on the icy surface of interstellar grains and transfer later into the gas phase. Therefore, icy material is regarded as a primordial s

D. M. Stull

In this paper, we use algorithmic tools, effective dimension and Kolmogorov complexity, to study the fractal dimension of distance sets. We show that, for any analytic set $E\subseteq\R^2$ of Hausdorff dimension strictly greater than one, the \textit{pinned distance set} of $E$, $\Delta_x E$, has Hausdorff dimension of at least $\frac{3}{4}$, for all points

Daniel Carranza, Chris Kapulkin, Andrew Tonks

We give an elementary proof of the Hurewicz theorem relating homotopy and homology groups of a cubical Kan complex. Our approach is based on the notion of a loop space of a cubical set, developed in a companion paper ``Homotopy groups of cubical sets'' by the first two authors.

Sayak Chakraborti, Brian Coutinho, Sandhya Dwarkadas, Parth Malani

Datacenters suffer from resource utilization inefficiencies due to the conflicting goals of service owners and platform providers. Service owners intending to maintain Service Level Objectives (SLO) for themselves typically request a conservative amount of resources. Platform providers want to increase operational efficiency to reduce capital and operating c

F. V. Lovos, R. F. Díaz, L. A. Nieto

In recent years the number of exoplanets has grown considerably. The most successful techniques in these detections are the radial velocity (RV) and planetary transits techniques, the latter significantly advanced by the Kepler, K2 and, more recently, the TESS missions. The detection of exoplanets both by means of transit and by RVs is of importance, because

Beepul Bharti, Paul Yi, Jeremias Sulam

As the use of machine learning models in real world high-stakes decision settings continues to grow, it is highly important that we are able to audit and control for any potential fairness violations these models may exhibit towards certain groups. To do so, one naturally requires access to sensitive attributes, such as demographics, gender, or other potenti

Bandhav Veluri, Collin Pernu, Ali Saffari, Joshua Smith

We present NeuriCam, a novel deep learning-based system to achieve video capture from low-power dual-mode IoT camera systems. Our idea is to design a dual-mode camera system where the first mode is low-power (1.1 mW) but only outputs grey-scale, low resolution, and noisy video and the second mode consumes much higher power (100 mW) but outputs color and high

Jaeyel Lee, Zuhawn Sung, Akshay A. Murthy, Anna Grassellino

We report the observation of omega phase formation in Nb thin films deposited by high-power impulse magnetron sputtering (HiPIMS) for superconducting qubits using transmission electron microscopy (TEM). We hypothesize that this phase transformation to the omega phase with hexagonal structure from bcc phase as well as the formation of {111}<112> mechanical tw

Sergio Ocampo, Raphael Schoenle, Dominic A. Smith

Robust inflation measures gauge inflation behavior by excluding volatile expenditure categories from headline inflation. We evaluate the forecasting performance of a wide set of such measures between 1970 and 2024, including core, median, and trimmed mean personal-consumption-expenditure (PCE) inflation. Core inflation performs significantly worse than offic

Lior Bary-Soroker, Or Ben-Porath, Vlad Matei

The paper studies the probability for a Galois group of a random polynomial to be $A_n$. We focus on the so-called large box model, where we choose the coefficients of the polynomial independently and uniformly from $\{-L,\ldots, L\}$. The state-of-the-art upper bound is $O(L^{-1})$, due to Bhargava. We conjecture a much stronger upper bound $L^{-n/2 +\epsil

Christopher Boudreaux, Anand Jha, Monica Escaleras

The purpose of this paper is to investigate if a country quality of governance moderates the effect of natural disasters on startup activity within that country. We test our hypotheses using a panel of 95 countries from 2006 to 2016. Our findings suggest that natural disasters discourage startup activity in countries that have low quality governance but enco

Keunho J. Kim, Matthew B. Bayliss, Allison G. Noble, Gourav Khullar

The environments where galaxies reside crucially shape their star formation histories. We investigate a large sample of 1626 cluster galaxies located within 105 galaxy clusters spanning a large range in redshift ($0.26 < z < 1.13)$. The galaxy clusters are massive (M$_{500} \gtrsim 2\times10^{14}$M$_{\odot}$), and are uniformly selected from the SPT and ACT

Leonardo Fiore, Jeffrey Yelton

Given a Galois cover $Y \to X$ of smooth projective geometrically connected curves over a complete discrete valuation field $K$ with algebraically closed residue field, we define a semistable model of $Y$ over the ring of integers of a finite extension of $K$, which we call the relatively stable model $\mathcal{Y}^{\mathrm{rst}}$ of $Y$, and we discuss its p

Guillermo Hinojosa

The objective of our proposal is to study the photoionization of Chlorine, Sulfur, and Phosphorus ions using the unique high resolution capability of 10.0.1 beamline in the ALS. These species are relevant in astrophysics and as a benchmark for state of the art theoretical models of the quantum properties of ions. We also propose to test a technique to quench

Somnath Jha, Dipramit Majumdar, Pratiksha Shingavekar

Consider a Mordell curve $E_a:y^2=x^3+a$ with $a \in \mathbb Z$. These curves have a rational $3$-isogeny, say $\varphi$. We give an upper and a lower bound on the rank of the $\varphi$-Selmer group of $E_a$ over $\mathbb Q(\zeta_3)$ in terms of the $3$-part of the ideal class group of certain quadratic extension of $\mathbb Q(\zeta_3)$. Using our bounds on

Dmitry Gromov, Tadashi Shigoka, Anton Bondarev

In this paper, we consider the problem of pollution control in a system that undergoes regular regime shifts. We first show that the optimal policy of pollution abatement is periodic as well, and is described by the unique hybrid limit cycle. We next introduce the notion of an environmentally sustainable solution, and demonstrate that such a policy is the on

Emery Pierson, Mohamed Daoudi, Sylvain Arguillere

In this paper we address the task of the comparison and the classification of 3D shape sequences of human. The non-linear dynamics of the human motion and the changing of the surface parametrization over the time make this task very challenging. To tackle this issue, we propose to embed the 3D shape sequences in an infinite dimensional space, the space of va

Peter Hoff, Andrew McCormack, Anru R. Zhang

A separable covariance model for a random matrix provides a parsimonious description of the covariances among the rows and among the columns of the matrix, and permits likelihood-based inference with a very small sample size. However, in many applications the assumption of exact separability is unlikely to be met, and data analysis with a separable model may

Jennifer Li

We consider log Calabi-Yau surfaces $(Y, D)$ with singular boundary. In each deformation type, there is a distinguished surface $(Y_e,D_e)$ such that the mixed Hodge structure on $H_2(Y \setminus D)$ is split. We prove that (1) the action of the automorphism group of $(Y_e,D_e)$ on its nef effective cone admits a rational polyhedral fundamental domain; and (

Servio Palacios, Aaron Ault, James V. Krogmeier, Bharat Bhargava

This paper introduces AGAPECert, an Auditable, Generalized, Automated, Privacy-Enabling, Certification framework capable of performing auditable computation on private data and reporting real-time aggregate certification status without disclosing underlying private data. AGAPECert utilizes a novel mix of trusted execution environments, blockchain technologie

Zhaidary Myrzakulova, Gulgassyl Nugmanova, Kuralay Yesmakhanova, Nurzhan Serikbayev

We consider some nonlinear models describing interactions of long and short (LS) waves. Such LS models have been derived and proposed with various motivations, which mainly come from fluid and plasma physics. In this paper, we study some of integrable LS models, namely, the Yajima-Oikawa equation, the Newell equation, the Ma equation, the Geng-Li equation an

Michael Anshelevich, Jacob Mashburn

Let $B$ be a star-algebra with a state $\phi$, and $t > 0$. Through a Fock space construction, we define two states $\Phi_t$ and $\Psi_t$ on the tensor algebra $T(B, \phi)$ such that under the natural map $(B, \phi) \rightarrow (T(B, \phi), \Phi_t, \Psi_t)$, free independence of arguments leads to free independence, while Boolean independence of centered arg