October 2022 arXiv papers — page 35

Xinyu Yang, Huaxiu Yao, Allan Zhou, Chelsea Finn

There is an inescapable long-tailed class-imbalance issue in many real-world classification problems. Current methods for addressing this problem only consider scenarios where all examples come from the same distribution. However, in many cases, there are multiple domains with distinct class imbalance. We study this multi-domain long-tailed learning problem

Zitao Yang, Elena F. Koslover

The function of many membrane-enclosed intracellular structures relies on release of diffusing particles that exit through narrow pores or channels in the membrane. The rate of release varies with pore size, density, and length of the channel. We propose a simple approximate model, validated with stochastic simulations, for estimating the effective release r

Marcel Dengler

In the first part of this doctoral thesis we develop a regularity theory for a polyconvex functional in compressible elasticity. In the second part, we will concentrate on uniqueness questions in various situations of finite elasticity. Here it is our main objective to establish uniqueness criteria, which when present, guarantee the uniqueness of the corresp

Jiujia Zhang, Ashok Cutkosky

We present new algorithms for online convex optimization over unbounded domains that obtain parameter-free regret in high-probability given access only to potentially heavy-tailed subgradient estimates. Previous work in unbounded domains considers only in-expectation results for sub-exponential subgradients. Unlike in the bounded domain case, we cannot rely

Christian Iliadis, Vimal Palanivelrajan, Rafael S. de Souza

The $^{16}$O(p,$\gamma$)$^{17}$F reaction is the slowest hydrogen-burning process in the CNO mass region. Its thermonuclear rate sensitively impacts predictions of oxygen isotopic ratios in a number of astrophysical sites, including AGB stars. The reaction has been measured several times at low bombarding energies using a variety of techniques. The most rece

Victor Zhong, Weijia Shi, Wen-tau Yih, Luke Zettlemoyer

We introduce RoMQA, the first benchmark for robust, multi-evidence, multi-answer question answering (QA). RoMQA contains clusters of questions that are derived from related constraints mined from the Wikidata knowledge graph. RoMQA evaluates robustness of QA models to varying constraints by measuring worst-case performance within each question cluster. Compa

William Krinsman

This survey provides an overview of common applications, both implicit and explicit, of "tensors" and "tensor products" in the fields of data science and statistics. One goal is to reconcile seemingly distinct usages of the term "tensor" in the literature, and to explain how these usages are manifestations of a common concept. Not all relevant topics are dis

Marcela Carena, Ying-Ying Li, Tong Ou, Yikun Wang

We investigate the electroweak phase transition patterns for a recently proposed baryogenesis model with CP violation originated in the dark sector. The model includes a complex scalar singlet-Higgs boson portal, a $U(1)_l$ gauge lepton symmetry with a $Z^\prime$ gauge boson portal and a fermionic dark matter particle. We find a novel thermal history of the

Sobhan Mohammadpour, Emma Frejinger

We address the problem of simultaneously estimating arc travel times in a network \emph{and} parameters of route choice models for strategic and tactical network planning purposes. Hitherto, these interdependent tasks have been approached separately in the literature on road traffic networks. We illustrate that ignoring this interdependence can lead to erron

Laura Pirovano, Roberto Armellin

Building and maintaining a catalog of resident space objects involves several tasks, ranging from observations to data analysis. Once acquired, the knowledge of a space object needs to be updated following a dedicated observing schedule. Dynamics mismodeling and unknown maneuvers can alter the catalog's accuracy, resulting in uncorrelated observations origin

Menghe Zhang, Weichen Liu, Nadir Weibel, Jurgen Schulze

Preoperative medical imaging is an essential part of surgical planning. The data from medical imaging devices, such as CT and MRI scanners, consist of stacks of 2D images in DICOM format. Conversely, advances in 3D data visualization provide further information by assembling cross-sections into 3D volumetric datasets. As Microsoft unveiled the HoloLens 2 (HL

Xiang Yue, Huseyin A. Inan, Xuechen Li, Girish Kumar

Privacy concerns have attracted increasing attention in data-driven products due to the tendency of machine learning models to memorize sensitive training data. Generating synthetic versions of such data with a formal privacy guarantee, such as differential privacy (DP), provides a promising path to mitigating these privacy concerns, but previous approaches

Xin Bing, Marten Wegkamp

This paper considers binary classification of high-dimensional features under a postulated model with a low-dimensional latent Gaussian mixture structure and non-vanishing noise. A generalized least squares estimator is used to estimate the direction of the optimal separating hyperplane. The estimated hyperplane is shown to interpolate on the training data.

Hasna Nhaila, Asma Elmaizi, Elkebir Sarhrouni, Ahmed Hammouch

Feature selection is one of the most important problems in hyperspectral images classification. It consists to choose the most informative bands from the entire set of input datasets and discard the noisy, redundant and irrelevant ones. In this context, we propose a new wrapper method based on normalized mutual information (NMI) and error probability (PE) us

Mimi Dai, Chao Wu

We consider the electron magnetohydrodynamics (MHD) with static background ion flow. A special situation of $B(x,y,t)=\nabla\times (a\vec e_z)+b \vec e_z$ with scalar-valued functions $a(x,y,t)$ and $b(x,y,t)$ was studied numerically in the physics paper [7]. The authors concluded from numerical simulations that there is no evidence of dissipation cutoff for

Alessio Corti, Giulia Gugiatti, Fernando Rodriguez Villegas

We study a hypergeometric local system that arises from the quantum Chen-Ruan cohomology of a family of weighted del Pezzo hypersurfaces. We prove that it is the anti-invariant variation of a pencil of genus-7 curves with respect to an involution having 4 fixed points.

Nicolas Bertin, Wei Cai, Sylvie Aubry, Athanasios Arsenlis

Understanding plastic deformation of crystals in terms of the fundamental physics of dislocations has remained a grand challenge in materials science for decades. To overcome this, the Discrete Dislocation Dynamics (DDD) method has been developed, but its lack of atomistic resolution leaves open the possibility that certain key mechanisms may be overlooked.

Bogdan A. Rodin, James Eills, Román Picazo-Frutos, Kirill F. Sheberstov

The field of magnetic resonance imaging with hyperpolarized contrast agents is rapidly expanding, and parahydrogen-induced polarization (PHIP) is emerging as an inexpensive and easy-to-implement method for generating the required hyperpolarized biomolecules. Hydrogenative PHIP delivers hyperpolarized proton spin order to a substrate via chemical addition of

Anas Maach, Jamila Elalami, Noureddine Elalami, El Houssine El Mazoudi

Coronary artery disease (CAD) is one of the most common cardiac diseases worldwide and causes disability and economic burden. It is the world's leading and most serious cause of mortality, with approximately 80% of deaths reported in low- and middle-income countries. The preferred and most precise diagnostic tool for CAD is angiography, but it is invasive, e

Leon Riesebos, Brad Bondurant, Jacob Whitlow, Junki Kim

Real-time control software and hardware is essential for operating quantum computers. In particular, the software plays a crucial role in bridging the gap between quantum programs and the quantum system. Unfortunately, current control software is often optimized for a specific system at the cost of flexibility and portability. We propose a systematic design

Max Nendel, Alessandro Sgarabottolo

In this paper, we explore a static setting for the assessment of risk in the context of mathematical finance and actuarial science that takes into account model uncertainty in the distribution of a possibly infinite-dimensional risk factor. We allow for perturbations around a baseline model, measured via Wasserstein distance, and we investigate to which exte

Fiona McCarthy, J. Colin Hill

Tensions between cosmological parameters (in particular the local expansion rate $H_0$ and the amplitude of matter clustering $S_8$) inferred from low-redshift data and data from the cosmic microwave background (CMB) and large-scale structure (LSS) experiments have inspired many extensions to the standard cosmological model, $\Lambda$CDM. Models which simult

Calvin W. Johnson, Oliver C. Gorton

We compute the proton-neutron entanglement entropy in the interacting nuclear shell model for a variety of nuclides and interactions. Some results make intuitive sense, for example that the shell structure, as governed by single-particle and monopole energies, strongly affects the energetically available space and thus the entanglement entropy. We also find

Francis Borceux, Maria Manuel Clementino

In a coherent category, the posets of subobjects have very strong properties. We emphasize the validity of these properties, in general categories, for well-behaved classes of subobjects. As an example of application, we investigate the problem of the various torsion theories which can be universally associated with a pretorsion one.

C. Fougères, F. de Oliveira Santos, N. A. Smirnova, C. Michelagnoli

Simulations of explosive nucleosynthesis in novae predict the production of $^{22}$Na, a key astronomical observable to constrain nova models. Its gamma-ray line at 1.275 MeV has not yet been observed by the gamma-ray space telescopes. The $^{20}$Ne/$^{22}$Ne ratio in presolar grains, a possible tool to identify nova grains, also depends on $^{22}$Na produce

Debashis Ganguly, Wonsun Ahn

Amplitude amplification provides a quadratic speed-up for an array of quantum algorithms when run on a quantum machine perfectly isolated from its environment. However, the advantage is substantially diminished as the NISQ-era quantum machines lack the large number of qubits necessary to provide error correction. Noise in the computation grows with the numbe

Brandon Bauer, Daniel Pitonyak, Cody Shay

We provide the first rigorous numerical analysis of the longitudinal-transverse double-spin asymmetry $A_{LT}$ in electron-nucleon and proton-proton collisions for the case where only a single pion, jet, or photon is detected in the final state. Given recent extractions of certain, previously unknown, non-perturbative functions, we are able to compute contri

Nir Sharon, Rafael Sherbu Cohen, Holger Wendland

We address the problem of approximating an unknown function from its discrete samples given at arbitrarily scattered sites. This problem is essential in numerical sciences, where modern applications also highlight the need for a solution to the case of functions with manifold values. In this paper, we introduce and analyze a combination of kernel-based quasi

Maria Manuel Clementino, Andrea Montoli, Diana Rodelo

Our main focus concerns a possible lax version of the algebraic property of protomodularity for Ord-enriched categories. Our motivating example is the category OrdAb of preordered abelian groups; indeed, while abelian groups form a protomodular category, OrdAb does not. Having in mind the role of comma objects in the enriched context, we consider some of the

A. S. Kuznetsov, K. Biermann, A. Reynoso, A. Fainstein

Optomechanical systems provide a pathway for the bidirectional optical-to-microwave interconversion in (quantum) networks. We demonstrate the implementation of this functionality and non-adiabatic optomechanical control in a single, $\mu$m-sized potential trap for phonons and exciton-polariton condensates in a structured semiconductor microcavity. The excito

William Connell, Umair Khan, Michael J. Keiser

Gene regulation is a dynamic process that connects genotype and phenotype. Given the difficulty of physically mapping mammalian gene circuitry, we require new computational methods to learn regulatory rules. Natural language is a valuable analogy to the communication of regulatory control. Machine learning systems model natural language by explicitly learnin

Marcell Tibor Máthé, Bendegúz Farkas, László Péter, Ágnes Buka

Studies of sessile droplets and fluid bridges of a ferroelectric nematic liquid crystal in externally applied electric fields are presented. It is found that above a threshold, the interface of the fluid with air undergoes a fingering instability or ramification, resembling to Rayleigh-type instability observed in charged droplets in electric fields or circu

Aaron Mueller, Yu Xia, Tal Linzen

Structural probing work has found evidence for latent syntactic information in pre-trained language models. However, much of this analysis has focused on monolingual models, and analyses of multilingual models have employed correlational methods that are confounded by the choice of probing tasks. In this study, we causally probe multilingual language models

Thibault Jacquemin, Pratik Suchde, Stéphane P. A. Bordas

Computer Aided Design (CAD) is widely used in the creation and optimization of various industrial systems and processes. Transforming a CAD geometry into a computational discretization that be used to solve PDEs requires care and a deep knowledge of the selected computational method. In this article, we present a novel integrated collocation scheme based on

Alarith Uhde, Tim zum Hoff, Marc Hassenzahl

Nowadays, interactive technologies are used almost everywhere. As a result, designers need to increasingly make them "socially acceptable". Previous work recommends "subtle" forms of interaction to increase social acceptability and avoid negative experiences. Although often appropriate, such uniform recommendations neglect the variety of social situations. W

Nathan Gober, Gino Chacon, Lei Wang, Paul V. Gratz

Recent years have seen a dramatic increase in the microarchitectural complexity of processors. This increase in complexity presents a twofold challenge for the field of computer architecture. First, no individual architect can fully comprehend the complexity of the entire microarchitecture of the core. This leads to increasingly specialized architects, who t

Minseong Lee, Rico Schoenemann, Hao Zhang, David Dahlbom

We present a high-field study of the strongly anisotropic easy-plane square lattice $S$ = 2 quantum magnet Ba$_{2}$FeSi$_{2}$O$_{7}$. This compound is a rare high-spin antiferromagnetic system with very strong easy-plane anisotropy, such that the interplay between spin level crossings and antiferromagnetic order can be studied. We observe a magnetic field-in

Thomas Kleine Buening, Aadirupa Saha

We study the problem of non-stationary dueling bandits and provide the first adaptive dynamic regret algorithm for this problem. The only two existing attempts in this line of work fall short across multiple dimensions, including pessimistic measures of non-stationary complexity and non-adaptive parameter tuning that requires knowledge of the number of prefe

Zexin Fang, Bin Han, C. Clark Cao, Hans. D. Schotten

The popularity of Autonomous Sensory Meridian Response (ASMR) has skyrockted over the past decade, but scientific studies on what exactly triggered ASMR effect remain few and immature, one most commonly acknowledged trigger is that ASMR clips typically provide rich semantic information. With our attention caught by the common acoustic patterns in ASMR audios

Rini J. Gladstone, Mohammad A. Nabian, N. Sukumar, Ankit Srivastava

Physics-Informed Neural Networks (PINNs) are a class of deep learning neural networks that learn the response of a physical system without any simulation data, and only by incorporating the governing partial differential equations (PDEs) in their loss function. While PINNs are successfully used for solving forward and inverse problems, their accuracy decreas

Xiangyu Chen, Ying Qin, Wenju Xu, Andrés M. Bur

Vision Transformers have attracted a lot of attention recently since the successful implementation of Vision Transformer (ViT) on vision tasks. With vision Transformers, specifically the multi-head self-attention modules, networks can capture long-term dependencies inherently. However, these attention modules normally need to be trained on large datasets, an

Aditya Dutt, Alina Zare, Paul Gader

Heterogeneous data fusion can enhance the robustness and accuracy of an algorithm on a given task. However, due to the difference in various modalities, aligning the sensors and embedding their information into discriminative and compact representations is challenging. In this paper, we propose a Contrastive learning based MultiModal Alignment Network (CoMMA

Disen Hu, Nantheera Anantrasirichai

The influence of atmospheric turbulence on acquired surveillance imagery poses significant challenges in image interpretation and scene analysis. Conventional approaches for target classification and tracking are less effective under such conditions. While deep-learning-based object detection methods have shown great success in normal conditions, they cannot

Michael Thomas Hedges

The Mu2e experiment will search for a Standard Model violating rate of neutrinoless conversion of a muon into an electron in the presence of an aluminum nucleus. Observation of this charged lepton flavor violating process would be an unambiguous sign of new physics. Mu2e will improve upon previous searches for this process by four orders of magnitude. This r

W. Tornow, A. P. Tonchev, S. W. Finch, Krishichayan

In view of the great interest in liquid argon neutrino detectors, the $^{40}$Ar($\gamma,\gamma'$)$^{40}$Ar$^{*}$ reaction was revisited to guide a calculation of the neutral current neutrino cross section at supernova energies. Using the nuclear resonance fluorescence technique with a monoenergetic, 99% linearly polarized photon beam, we report a three-fold

Anamay Chaturvedi, Huy Lê Nguyen, Thy Nguyen

In this work, we study the problem of privately maximizing a submodular function in the streaming setting. Extensive work has been done on privately maximizing submodular functions in the general case when the function depends upon the private data of individuals. However, when the size of the data stream drawn from the domain of the objective function is la

A. Rosenthal, K. Lüdemann, A. Tilgner

Double diffusive staircases are investigated experimentally in a fluid layer with a stabilizing temperature gradient and a destabilizing gradient of ion concentration. Gradients of temperature and ion concentration are maintained in a steady state within an electrochemical system. Staircases are observed even if the density stratification is unstable. None o

Huicong Zhong, Xiaobing Feng

This paper is concerned with developing an efficient numerical algorithm for fast implementation of the sparse grid method for computing the $d$-dimensional integral of a given function. The new algorithm, called the MDI-SG ({\em multilevel dimension iteration sparse grid}) method, implements the sparse grid method based on a dimension iteration/reduction pr

Pouria Mistani, Samira Pakravan, Rajesh Ilango, Frederic Gibou

We present a scalable strategy for development of mesh-free hybrid neuro-symbolic partial differential equation solvers based on existing mesh-based numerical discretization methods. Particularly, this strategy can be used to efficiently train neural network surrogate models of partial differential equations by (i) leveraging the accuracy and convergence pro

Matthew Satriano, Andrew P. Staal

Bhargava and the first-named author of this paper introduced a functorial Galois closure operation for finite-rank ring extensions, generalizing constructions of Grothendieck and Katz--Mazur. In this paper, we generalize Galois closures and apply them to construct a new infinite family of irreducible components of Hilbert schemes of points. We show that thes

Yin Zhang, Ruoxi Wang, Tiansheng Yao, Xinyang Yi

Industry recommender systems usually suffer from highly-skewed long-tail item distributions where a small fraction of the items receives most of the user feedback. This skew hurts recommender quality especially for the item slices without much user feedback. While there have been many research advances made in academia, deploying these methods in production

Lyndon R. Duong, Bohan Li, Cheng Chen, Jingning Han

Contemporary lossy image and video coding standards rely on transform coding, the process through which pixels are mapped to an alternative representation to facilitate efficient data compression. Despite impressive performance of end-to-end optimized compression with deep neural networks, the high computational and space demands of these models has prevente

E. Toler, A. J. Cerfon, D. Malhotra

Many applications in magnetic confinement fusion require the efficient calculation of surface integrals with singular integrands. The singularity subtraction approaches typically used to handle such singularities are complicated to implement and low order accurate. In contrast, we demonstrate that the Kapur-Rokhlin quadrature scheme is well-suited for the lo

Karan Praharaj, Irina Matveeva

Recent work in incremental learning has introduced diverse approaches to tackle catastrophic forgetting from data augmentation to optimized training regimes. However, most of them focus on very few training steps. We propose a method for robust incremental learning over dozens of fine-tuning steps using data from a variety of languages. We show that a combin

Hussein Mozannar, Gagan Bansal, Adam Fourney, Eric Horvitz

Code-recommendation systems, such as Copilot and CodeWhisperer, have the potential to improve programmer productivity by suggesting and auto-completing code. However, to fully realize their potential, we must understand how programmers interact with these systems and identify ways to improve that interaction. To seek insights about human-AI collaboration wit

Runze Zhang

We prove that the boundary of every parabolic component in the cubic polynomial slice $Per_1(1)$ is a Jordan curve by adapting the technique of para-puzzles presented in \cite{Roesch1}. We also give a global description of the connected locus $\mathcal{C}_1$: it is the union of two main parabolic components and the limbs attached on their boundaries.

Xianzhi Li, Will Aitken, Xiaodan Zhu, Stephen W. Thomas

With the recent surge of NLP technologies in the financial domain, banks and other financial entities have adopted virtual agents (VA) to assist customers. A challenging problem for VAs in this domain is determining a user's reason or intent for contacting the VA, especially when the intent was unseen or open during the VA's training. One method for handling

Youngin Cho, Daejin Kim, Dongmin Kim, Mohammad Azam Khan

Time series forecasting has become a critical task due to its high practicality in real-world applications such as traffic, energy consumption, economics and finance, and disease analysis. Recent deep-learning-based approaches have shown remarkable success in time series forecasting. Nonetheless, due to the dynamics of time series data, deep networks still s

Mustapha Kaci, Sonia Radjef

This paper is a follow-up to a previous work where we defined and generated the set of all possible compromises of multilevel multiobjective linear programming problems (ML-MOLPP). In this paper, we introduce a new algorithm to solve ML-MOLPP in which the adaptive method of linear programming is nested. First, we start by generating the set of all possible c

Adam Hoyt, Brett Stevens

A complementary Gray code for binary n-tuples is one that, when all the tuples are complemented, is identical to itself; this is equivalent to the complement of the first half of the code being identical to the second half. We generalize the notion of complementary to q-ary n-tuples, fixed size combinations of an n-set and permutations and, in each case, con

Matthew Bowen, Antoine Poulin, Jenna Zomback

Let $(X,\tau)$ be a Polish space with Borel probability measure $\mu,$ and $G$ a locally finite one-ended Borel graph on $X.$ We show that $G$ admits a Borel one-ended spanning tree generically. If $G$ is induced by a free Borel action of an amenable (resp., polynomial growth) group then we show the same result $\mu$-a.e. (resp., everywhere). Our results gen

Hanzi Xu, Slobodan Vucetic, Wenpeng Yin

Prior studies of zero-shot stance detection identify the attitude of texts towards unseen topics occurring in the same document corpus. Such task formulation has three limitations: (i) Single domain/dataset. A system is optimized on a particular dataset from a single domain; therefore, the resulting system cannot work well on other datasets; (ii) the model i

Katy Craig, Braxton Osting, Dong Wang, Yiming Xu

Archetypal analysis is an unsupervised machine learning method that summarizes data using a convex polytope. In its original formulation, for fixed k, the method finds a convex polytope with k vertices, called archetype points, such that the polytope is contained in the convex hull of the data and the mean squared Euclidean distance between the data and the

Jue Jiang, Jun Hong, Kathryn Tringale, Marsha Reyngold

Method: ProRSeg was trained using 5-fold cross-validation with 110 T2-weighted MRI acquired at 5 treatment fractions from 10 different patients, taking care that same patient scans were not placed in training and testing folds. Segmentation accuracy was measured using Dice similarity coefficient (DSC) and Hausdorff distance at 95th percentile (HD95). Registr

Twesh Upadhyaya, Thomas van Himbeeck, Norbert Lütkenhaus

The dimension reduction method enables security proofs of quantum key distribution (QKD) protocols that are originally formulated in infinite dimensions via reduction to a tractable finite-dimensional optimization. The reduction of dimensions is associated with a correction term in the secret key rate calculation. The previously derived correction term is lo

Xiaohan Zhang, Waqas Sultani, Safwan Wshah

Cross-view geo-localization aims to estimate the GPS location of a query ground-view image by matching it to images from a reference database of geo-tagged aerial images. To address this challenging problem, recent approaches use panoramic ground-view images to increase the range of visibility. Although appealing, panoramic images are not readily available c

Basem Aref Frasin

Let $(\mathbb{R}_{\alpha ,\beta ,\gamma }(z))_{m}(z)=z+\sum_{n=1}^{m}A_{n}z^{n+1}$ be the sequence of partial sums of the normalized Rabotnov functions $\mathbb{R}_{\alpha ,\beta ,\gamma }(z)=z+\sum_{n=1}^{\infty }A_{n}z^{n+1}$ where $A_{n}=\frac{\beta ^{n}\Gamma \left( \gamma +\alpha \right) }{\Gamma \left( \left( \gamma +\alpha \right) (n+1)\right) }.$ The

Victoria Meadows, Heather Graham, Victor Abrahamsson, Zach Adam

The search for life beyond the Earth is the overarching goal of the NASA Astrobiology Program, and it underpins the science of missions that explore the environments of Solar System planets and exoplanets. However, the detection of extraterrestrial life, in our Solar System and beyond, is sufficiently challenging that it is likely that multiple measurements

Manuel Hentschel, Sebastian Engelke, Johan Segers

The severity of multivariate extreme events is driven by the dependence between the largest marginal observations. The H\"usler-Reiss distribution is a versatile model for this extremal dependence, and it is usually parameterized by a variogram matrix. In order to represent conditional independence relations and obtain sparse parameterizations, we introduce

Ahsan Ullah, Xin Li, Yunlong Jin, Rabindra Pahari

Topological phase in magnetic materials yields a quantized contribution to the Hall effect known as the topological Hall effect, which is often caused by skyrmions, with each skyrmion creating a magnetic flux quantum h/e. The control and understanding of topological properties in nanostructured materials is the subject of immense interest for both fundamenta

Bin Guo, Emil Sekerinski

The core numbers of vertices in a graph are one of the most well-studied cohesive subgraph models because of the linear running time. In practice, many data graphs are dynamic graphs that are continuously changing by inserting or removing edges. The core numbers are updated in dynamic graphs with edge insertions and deletions, which is called core maintenanc

Seulbae Kim, Major Liu, Junghwan "John" Rhee, Yuseok Jeon

Autonomous driving has become real; semi-autonomous driving vehicles in an affordable price range are already on the streets, and major automotive vendors are actively developing full self-driving systems to deploy them in this decade. Before rolling the products out to the end-users, it is critical to test and ensure the safety of the autonomous driving sys

Marta Dell'Atti, Pierandrea Vergallo

In this paper, the authors investigate non-homogeneous Hamiltonian operators composed of a first-order Dubrovin-Novikov operator and an ultralocal one. The study of such operators turns out to be fundamental for the inverted system of equations associated with a class of Hamiltonian scalar equations. Often, the involved operators are degenerate in the first-

A. C. Mesquita, M. F. A. da Silva

We model a compact radiant star that undergoes gravitational collapse from a certain initial static configuration until it becomes a black hole. The star consists of a fluid with anisotropy in pressures, bulk viscosity, in addition to the radial heat flow. A solution of Einstein's field equations with temporal dependence was presented to study the dynamic ev

Georgios Georgalis, Kolos Retfalvi, Paul E. DesJardin, Abani Patra

In complex physical process characterization, such as the measurement of the regression rate for solid hybrid rocket fuels, where both the observation data and the model used have uncertainties originating from multiple sources, combining these in a systematic way for quantities of interest(QoI) remains a challenge. In this paper, we present a forward propag

Julius Baldauf, Tang-Kai Lee

This paper defines a parabolic frequency for solutions of the heat equation along homothetically shrinking mean curvature flows and proves its monotonicity along such flows. As a corollary, frequency monotonicity provides a proof of backwards uniqueness. Additionally, for solutions of more general parabolic equations on mean curvature flow shrinkers, this pa

O. Moreno, P. Sarriguren, A. Algora, L. M. Fraile

Ground and isomeric states of the neutron-deficient odd-$A$ isotopes $^{183}$Hg, $^{185}$Hg, and $^{187}$Hg are described from a microscopic calculation based on a self-consistent, axially-deformed Hartree-Fock mean field with the Skyrme functional and pairing within BCS approximation. For each equilibrium shape and different odd-neutron states, results on m

Hanyuan Wang, Majid Mirmehdi, Dima Damen, Toby Perrett

Current one-stage action detection methods, which simultaneously predict action boundaries and the corresponding class, do not estimate or use a measure of confidence in their boundary predictions, which can lead to inaccurate boundaries. We incorporate the estimation of boundary confidence into one-stage anchor-free detection, through an additional predicti

Kyoungseok Jang, Chicheng Zhang, Kwang-Sung Jun

In sparse linear bandits, a learning agent sequentially selects an action and receive reward feedback, and the reward function depends linearly on a few coordinates of the covariates of the actions. This has applications in many real-world sequential decision making problems. In this paper, we propose a simple and computationally efficient sparse linear esti

Pratik Vaishnavi, Kevin Eykholt, Amir Rahmati

Training deep neural network classifiers that are certifiably robust against adversarial attacks is critical to ensuring the security and reliability of AI-controlled systems. Although numerous state-of-the-art certified training methods have been developed, they are computationally expensive and scale poorly with respect to both dataset and network complexi

Rakesh Arora, Alessio Fiscella, Tuhina Mukherjee, Patrick Winkert

In this paper we study quasilinear elliptic equations driven by the double phase operator involving a Choquard term of the form \begin{align*} -\mathcal{L}_{p,q}^{a}(u) + |u|^{p-2}u+ a(x) |u|^{q-2}u = \left( \int_{\mathbb{R}^N} \frac{F(y, u)}{|x-y|^\mu}\,\mathrm{d} y\right)f(x,u) \quad\text{in } \mathbb{R}^N, \end{align*} where $\mathcal{L}_{p,q}^{a}$ is the

Viacheslav Krivorol, Michail Nalimov

We discuss the microscopical justification of dissipation in the model nonrelativistic Fermi and Bose systems with weak local interactions above phase transitions. The dynamics of equilibrium fluctuations are considered in Keldysh - Schwinger framework. We show that the dissipation is related to pinch singularities of the diagram technique. Using Dyson - Sch

William Brown

We consider the problem of simultaneous learning in stochastic games with many players in the finite-horizon setting. While the typical target solution for a stochastic game is a Nash equilibrium, this is intractable with many players. We instead focus on variants of {\it correlated equilibria}, such as those studied for extensive-form games. We begin with a

Longke Tang

We introduce $F$-gauges over a prism, construct syntomic cycle classes, and prove the prismatic Poincar\'e duality for proper smooth schemes.

Weiquan Meng, Zhenyu Xu

Quantum speed limits (QSLs) provide an upper bound for the speed of evolution of quantum states in any physical process. Based on the Stratonovich-Weyl correspondence, we derive a universal QSL bound in arbitrary phase spaces that is applicable for both continuous variable systems and finite-dimensional discrete quantum systems. This QSL bound allows the det

Eric C. Peterson, Peter J. Karalekas

We describe a distributed, asynchronous variant of Edmonds's exact algorithm for producing perfect matchings of minimum weight. The development of this algorithm is driven by an application to online error correction in quantum computing, first envisioned by Fowler; we analyze the performance of our algorithm as applied to this domain in a sequel.

Mehrdad Mirbabayi

The interface between a big bubble of true AdS$_D$ vacuum expanding inside a false AdS$_D$ vacuum is a model of an inflating $D-1$ dimensional universe. It looks like an interesting setup to study fundamentals of inflation. A recent computation shows that the prediction of this model for the wavefunction of the universe disagrees with that of Hartle and Hawk

Wenchuan Mu

Capturing the similarities between human language units is crucial for explaining how humans associate different objects, and therefore its computation has received extensive attention, research, and applications. With the ever-increasing amount of information around us, calculating similarity becomes increasingly complex, especially in many cases, such as l

Inwon Kim, Yuming Paul Zhang

In this paper we study the regularity property of Hele-Shaw flow, where source and drift are present in the evolution. More specifically we consider H\"{o}lder continuous source and Lipschitz continuous drift. We show that if the free boundary of the solution is locally close to a Lipschitz graph, then it is indeed Lipschitz, given that the Lipschitz constan

Yu-Heng Lin, Sultan Hassan, Bruno Régaldo-Saint Blancard, Michael Eickenberg

High-dimensional data sets are expected from the next generation of large-scale surveys. These data sets will carry a wealth of information about the early stages of galaxy formation and cosmic reionization. Extracting the maximum amount of information from the these data sets remains a key challenge. Current simulations of cosmic reionization are computatio

Nicolas Nessi, Lucas Sourrouille

We study two quantum mechanical systems on the noncommutative plane using a representation independent approach. First, in the context of the Landau problem, we obtain an explicit expression for the gauge transformation that connects the Landau and the symmetric gauge in noncommutative space. This lead us to conclude that the usual form of the symmetric gaug

Tingwei Wang, Da Li, Kaiyang Zhou, Tao Xiang

Machine learning models are intrinsically vulnerable to domain shift between training and testing data, resulting in poor performance in novel domains. Domain generalization (DG) aims to overcome the problem by leveraging multiple source domains to learn a domain-generalizable model. In this paper, we propose a novel augmentation-based DG approach, dubbed Au

Youngin Cho, Junsoo Lee, Soyoung Yang, Juntae Kim

Existing deep interactive colorization models have focused on ways to utilize various types of interactions, such as point-wise color hints, scribbles, or natural-language texts, as methods to reflect a user's intent at runtime. However, another approach, which actively informs the user of the most effective regions to give hints for sketch image colorizatio

Mustapha Kaci, Sonia Radjef

In this paper, an algorithm is developed to solve a multilevel mono-objective linear programming problem (ML(MO)LPP), where the constructive adaptive method of linear programming is nested. This procedure is the modified version of the SB. Sinha and S. Sinha's linear programming approach. First, we build a map that reduces the ranges of decision variables th

Popov S. B.

Presently, it is broadly assumed that fast radio bursts (FRBs) are sources of coherent emission powered by the magnetic energy release in magnetars. However, the exact emission mechanism is not known, yet. Two main frameworks exist: magnetospheric emission and radiation from external relativistic shocks. In this brief review, I describe basics of both approa

Weihao Xia, Jing-Hao Xue

Recent years have seen remarkable progress in deep learning powered visual content creation. This includes deep generative 3D-aware image synthesis, which produces high-idelity images in a 3D-consistent manner while simultaneously capturing compact surfaces of objects from pure image collections without the need for any 3D supervision, thus bridging the gap

Jason R. Bailey, Davide Lauria, W. Brent Lindquist, Stefan Mittnik

We consider the use of P-spline generalized additive hedonic models for real estate prices in large U.S. cities, contrasting their predictive efficiency against linear and polynomial based generalized linear models. Using intrinsic and extrinsic factors available from Redfin, we show that GAM models are capable of describing 84% to 92% of the variance in the

Or Sattath

The no-cloning theorem asserts that, unlike classical information, quantum information cannot be copied. This seemingly undesirable phenomenon is harnessed in quantum cryptography. Uncloneable cryptography studies settings in which the impossibility of copying is a desired property, and achieves forms of security that are classically unattainable. The first

Rahul Yadav, Maria D. Kazachenko

We analyze high-cadence vector magnetograms (135~s) and flare-ribbon observations of 37 flares from the Solar Dynamics Observatory to understand the spatial and temporal properties of changes in the photospheric vector magnetic field and their relationship to footpoints of reconnected fields. Confirming previous studies, we find that the largest permanent ch

Yuejiang Li, Hong Vicky Zhao, Gene Cheung

Graph sampling is the problem of choosing a node subset via sampling matrix $\mathbf{H} \in \{0,1\}^{K \times N}$ to collect samples $\mathbf{y} = \mathbf{H} \mathbf{x} \in \mathbb{R}^K$, $K < N$, so that the target signal $\mathbf{x} \in \mathbb{R}^N$ can be reconstructed in high fidelity. While sampling on undirected graphs is well studied, we propose the