May 2022 arXiv papers — page 76

Kangil Lee, Junho Yim

Since the deep learning model is highly dependent on hyperparameters, hyperparameter optimization is essential in developing deep learning model-based applications, even if it takes a long time. As service development using deep learning models has gradually become competitive, many developers highly demand rapid hyperparameter optimization algorithms. In or

Dean Alvin L. Pablico, Eric A. Galapon

A time of arrival (TOA) operator that is conjugate with the system Hamiltonian was constructed by Galapon without canonical quantization in [J. Math. Phys. \textbf{45}, 3180 (2004)]. The constructed operator was expressed as an infinite series but only the leading term was investigated which was shown to be equal to the Weyl-quantized TOA-operator for entire

Ryan Sander, Wilko Schwarting, Tim Seyde, Igor Gilitschenski

Experience replay plays a crucial role in improving the sample efficiency of deep reinforcement learning agents. Recent advances in experience replay propose using Mixup (Zhang et al., 2018) to further improve sample efficiency via synthetic sample generation. We build upon this technique with Neighborhood Mixup Experience Replay (NMER), a geometrically-grou

Tamunonye Cheetham-West

We give the first examples of closed fibered hyperbolic 3-manifolds whose fundamental groups are distinguished from every other finitely generated, residually finite group by their finite quotients. One of the examples is also the first example of a non-orientable profinitely rigid hyperbolic 3-manifold.

Man Ho Chan, Chak Man Lee

Recent gamma-ray and radio studies have obtained some stringent constraints on annihilating dark matter properties. However, only a few studies have focussed on using X-ray data to constrain annihilating dark matter. In this article, we perform the X-ray analysis of annihilating dark matter using the data of the Omega Centauri cluster. If dark matter is the

Darren Creutz

We exhibit subshifts admitting weakly mixing (probability) measures, for arbitrary $\epsilon > 0$, with word complexity $p$ satisfying $\limsup \frac{p(q)}{q} < 1.5 + \epsilon$. For arbitrary $f(q) \to \infty$, said subshifts can be made to satisfy $p(q) < q + f(q)$ infinitely often. We establish that every subshift associated to a rank-one transformation (o

I. V. Solovyev, A. V. Ushakov, S. V. Streltsov

The on-site Coulomb repulsion $U$ is the key ingredient for describing the magnetic properties of Mott insulators, leading to a popular believe that many limitations of the density-functional theory based methods can be cured by artificially incorporating such on-site interactions for localized electrons in the model form. The layered antiferromagnet NaMnCl$

Aoyu Liu, Yaying Zhang

Accurate traffic forecasting is essential for smart cities to achieve traffic control, route planning, and flow detection. Although many spatial-temporal methods are currently proposed, these methods are deficient in capturing the spatial-temporal dependence of traffic data synchronously. In addition, most of the methods ignore the dynamically changing corre

Pratik Das, H. S. Udaykumar

Crystals of energetic materials, such as HMX, embedded in plastic binders are the building blocks of plastic-bonded explosives. Such heterogeneous energetic materials contain microstructural features such as sharp corners, interfaces between crystal and binder, intra- and extra-granular voids, and other defects. Energy localization or hotspots arise during s

Kunqi Wang, Daolin Si, Pu Wang, Jing Ge

Matching the rail cross-section profiles measured on site with the designed profile is a must to evaluate the wear of the rail, which is very important for track maintenance and rail safety. So far, the measured rail profiles to be matched usually have four features, that is, large amount of data, diverse section shapes, hardware made errors, and human exper

Jinsong Duan

$\alpha$-Sn (gray tin) is a group-IV, zero-gap semiconductor with potential use in infrared detectors, necessitating a clear understanding of its dielectric properties. We report the first-principles calculations of the band structure and dielectric function of $\alpha$-Sn using density functional theory, emphasizing the effects of strain, spin-orbit interac

Jinwei Xing, Takashi Nagata, Xinyun Zou, Emre Neftci

Although deep Reinforcement Learning (RL) has proven successful in a wide range of tasks, one challenge it faces is interpretability when applied to real-world problems. Saliency maps are frequently used to provide interpretability for deep neural networks. However, in the RL domain, existing saliency map approaches are either computationally expensive and t

Guy Casale, Matthew DeVilbiss, James Freitag, Joel Nagloo

In this manuscript, we give a new proof of strong minimality of certain automorphic functions, originally results of Freitag and Scanlon (2017), Casale, Freitag, and Nagloo (2020), Bl\'azquez-Sanz, Casale, Freitag, and Nagloo (2020). Our proof is shorter and conceptually different than those presently in the literature.

Florian Richoux

Terrain analysis in Real-Time Strategy games is a necessary step to allow spacial reasoning. The goal of terrain analysis is to gather and process data about the map topology and properties to have a qualitative spatial representation. On StarCraft games, all previous works on terrain analysis propose a crisp analysis based on connected component detection,

Zhaofeng Tian, Liangkai Liu, Weisong Shi

As the adaptive cruise control system (ACCS) on vehicles is well-developed today, vehicle manufacturers have increasingly employed this technology in new-generation intelligent vehicles. Pulse-and-glide (PnG) strategy is an efficacious driving strategy to diminish fuel consumption in traditional oil-fueled vehicles. However, current studies rarely focus on t

Xinmeng Xu, Jianjun Hao

For supervised speech enhancement, contextual information is important for accurate spectral mapping. However, commonly used deep neural networks (DNNs) are limited in capturing temporal contexts. To leverage long-term contexts for tracking a target speaker, this paper treats the speech enhancement as sequence-to-sequence mapping, and propose a novel monaura

Dong-Sheng Ding, Yi-Chen Yu, Zong-Kai Liu, Bao-Sen Shi

The collective effect of large amounts of atoms exhibit an enhanced interaction between light and atoms. This holds great interest in quantum optics, and quantum information. When a collective excited state of a group of atoms during Rabi oscillation is varying, the oscillation exhibits rich dynamics. Here, we experimentally observe a size-reduction effect o

Xu-Yang Hou, Zi-Wen Huang, Zheng Zhou, Xin Wang

Two representations of mixed states by state-vectors, known as purified state and thermal vacuum, have been realized on quantum computers. While the two representations look similar, they differ by a partial transposition in the ancilla space. While ordinary observables cannot discern the two representations, we generalize the Berry phase of pure quantum sta

Ge Zhang, Hongyi Xiao, Entao Yang, Robert J. S. Ivancic

Elastoplastic lattice models for the response of solids to deformation typically incorporate structure only implicitly via a local yield strain that is assigned to each site. However, the local yield strain can change in response to a nearby or even distant plastic event in the system. This interplay is key to understanding phenomena such as avalanches in wh

Jesse Bowers, Steve Culpepper

Latent Class Models (LCMs) are used to cluster multivariate categorical data (e.g. group participants based on survey responses). Traditional LCMs assume a property called conditional independence. This assumption can be restrictive, leading to model misspecification and overparameterization. To combat this problem, we developed a novel Bayesian model called

Yue Hu, Haiqi Li, Falong Tan

In this paper, we propose a new test for checking the parametric form of the conditional variance based on distance covariance in nonlinear and nonparametric regression models. Inherit from the nice properties of distance covariance, our test is very easy to implement in practice and less effected by the dimensionality of covariates. The asymptotic propertie

Kevin Yang, Olivia Deng, Charles Chen, Richard Shin

We introduce a novel setup for low-resource task-oriented semantic parsing which incorporates several constraints that may arise in real-world scenarios: (1) lack of similar datasets/models from a related domain, (2) inability to sample useful logical forms directly from a grammar, and (3) privacy requirements for unlabeled natural utterances. Our goal is to

Jason Gaitonde, Yingkai Li, Bar Light, Brendan Lucier

We study the aggregate welfare and individual regret guarantees of dynamic \emph{pacing algorithms} in the context of repeated auctions with budgets. Such algorithms are commonly used as bidding agents in Internet advertising platforms, adaptively learning to shade bids by a tunable linear multiplier in order to match a specified budget. We show that when ag

Zsombor Szádoczki, Sándor Bozóki

In preference modelling, it is essential to determine the number of questions and their arrangements to ask from the decision maker. We focus on incomplete pairwise comparison matrices, and provide the optimal filling in patterns, which result in the closest (LLSM) weight vectors on average to the complete case for at most six alternatives and for all possib

Christopher M. Drupieski, Jonathan R. Kujawa

This paper investigates cohomology and support varieties for Lie superalgebras and restricted Lie superalgebras over a field of characteristic 2. The existence of an underlying ordinary Lie algebra allows us to obtain results that are still open in odd characteristic, and also to establish results that have no non-super analogues in characteristic 2.

Fabien H. Wagner, Ricardo Dalagnol, Alber H. Sánchez, Mayumi C. M. Hirye

Deep learning self-supervised algorithms that can segment an image in a fixed number of hard labels such as the k-means algorithm and relying only on deep learning techniques are still lacking. Here, we introduce the k-textures algorithm which provides self-supervised segmentation of a 4-band image (RGB-NIR) for a $k$ number of classes. An example of its app

Luca Franci, Emanuele Papini, Alfredo Micera, Giovanni Lapenta

We perform a high-resolution two-dimensional fully-kinetic numerical simulation of a turbulent plasma system with observation-driven conditions, in order to investigate the interplay between turbulence, magnetic reconnection, and particle heating from ion to sub-electron scales in the near-Sun solar wind. We find that the power spectra of the turbulent plasm

Zehua Tian, Longhao Wu, Liang Zhang, Jiliang Jing

The Unruh effect states an accelerated particle detector registers a thermal response when moving through the Minkowski vacuum, and its thermal feature is believed to be inseparable from Lorentz symmetry: Without the latter, the former disappears. Here we propose to observe analogue circular Unruh effect using an impurity atom in a quasi-two-dimensional Bose

Kyeongseob Song, Kuk-Jin Yoon

Monocular depth estimation has been extensively explored based on deep learning, yet its accuracy and generalization ability still lag far behind the stereo-based methods. To tackle this, a few recent studies have proposed to supervise the monocular depth estimation network by distilling disparity maps as proxy ground-truths. However, these studies naively d

Tristan Pollner, Mohammad Roghani, Amin Saberi, David Wajc

Motivated by applications in the gig economy, we study approximation algorithms for a \emph{sequential pricing problem}. The input is a bipartite graph $G=(I,J,E)$ between individuals $I$ and jobs $J$. The platform has a value of $v_j$ for matching job $j$ to an individual worker. In order to find a matching, the platform can consider the edges $(i j) \in E$

Haipeng You, Ning Ding, Jun Chen, Xiaoyan Yao

Two-dimensional (2D) magnets have great potentials for applications in next-generation information devices. Since the recent experimental discovery of intrinsic 2D magnetism in monolayer CrI$_3$ and few-layer Cr$_2$Ge$_2$Te$_6$, intensive studies have been stimulated in pursuing more 2D magnets and revealing their intriguing physical properties. In compariso

Eszter Molnár, Dénes Csala

The topology of production networks determines the propagation mechanisms of local shocks and thus the co-movement of industries. As a result, we need a more precisely defined production network to model economic growth accurately. In this study, we analyse Leontief&#39;s input-output model from a network theory perspective, aiming to construct a production

Lexing Ying

This note introduces a method for sampling Ising models with mixed boundary conditions. As an application of annealed importance sampling and the Swendsen-Wang algorithm, the method adopts a sequence of intermediate distributions that keeps the temperature fixed but turns on the boundary condition gradually. The numerical results show that the variance of th

Taro L. Saito, Naoki Takezoe, Yukihiro Okada, Takako Shimamoto

Treasure Data is processing millions of distributed SQL queries every day on the cloud. Upgrading the query engine service at this scale is challenging because we need to migrate all of the production queries of the customers to a new version while preserving the correctness and performance of the data processing pipelines. To ensure the quality of the query

Platon I. Karpov, Chengkun Huang, Iskandar Sitdikov, Chris L. Fryer

Turbulence plays an important role in astrophysical phenomena, including core-collapse supernovae (CCSN), but current simulations must rely on subgrid models since direct numerical simulation (DNS) is too expensive. Unfortunately, existing subgrid models are not sufficiently accurate. Recently, Machine Learning (ML) has shown an impressive predictive capabil

Adrianne Zhong, Michael R. DeWeese

Recent studies have explored finite-time dissipation-minimizing protocols for stochastic thermodynamic systems driven arbitrarily far from equilibrium, when granted full external control to drive the system. However, in both simulation and experimental contexts, systems often may only be controlled with a limited set of degrees of freedom. Here, going beyond

Sornette Didier, Sandro Lera, Jianhong Lin, Ke Wu

In social networks, bursts of activity often result from the imitative behavior between interacting agents. The Ising model, along with its variants in the social sciences, serves as a foundational framework to explain these phenomena through its critical properties. We propose an alternative generic mechanism for the emergence of collective exuberance withi

Andrew J. Hanson, Sonya M. Hanson

Quaternions are important for a wide variety of rotation-related problems in computer graphics, machine vision, and robotics. We study the nontrivial geometry of the relationship between quaternions and rotation matrices by exploiting the adjugate matrix of the characteristic equation of a related eigenvalue problem to obtain the manifold of the space of a q

Mahnaz Asghari, Ahmad Sheykhi

In this paper, we explore a fractal model of the universe proposed by Calcagni [JHEP{\bf03}(2010)120] for a power-counting renormalizable field theory living in a fractal spacetime. Considering a timelike fractal profile, we derived field equations in fractal cosmology, in order to explore the structure formation and the expansion history in fractal universe

Tristan Frizza, Donald G. Dansereau, Nagita Mehr Seresht, Michael Bewley

This work addresses the problems of semantic segmentation and image super-resolution by jointly considering the performance of both in training a Generative Adversarial Network (GAN). We propose a novel architecture and domain-specific feature loss, allowing super-resolution to operate as a pre-processing step to increase the performance of downstream comput

Kevin Carter-Fenk, Leonardo A. Cunha, Juan E. Arias-Martinez, Martin Head-Gordon

The particle-hole interaction problem is longstanding within time-dependent density functional theory (TDDFT) and leads to extreme errors in the prediction of K-edge X-ray absorption spectra (XAS). We derive a linear-response formalism that uses optimized orbitals of the n-1-electron system as reference, building orbital relaxation and a proper hole into the

Javier Felip Leon, David Gonzalez-Aguirre, Lama Nachman

The combination of collaborative robots and end-to-end AI, promises flexible automation of human tasks in factories and warehouses. However, such promise seems a few breakthroughs away. In the meantime, humans and cobots will collaborate helping each other. For these collaborations to be effective and safe, robots need to model, predict and exploit human's i

Erhan Bayraktar, Zhenhua Wang, Zhou Zhou

We investigate the stability of equilibrium-induced optimal values with respect to (w.r.t.) reward functions $f$ and transition kernels $Q$ for time-inconsistent stopping problems under nonexponential discounting in discrete time. First, with locally uniform convergence of $f$ and $Q$ equipped with total variation distance, we show that the optimal value is

Michael Cook

In this paper we survey the motivations behind contemporary game AI research by analysing individual publications, the researchers themselves, and the institutions that influence them. In doing so, we identify some negative effects on our field, caused both by external forces outside of our control as well as institutionalised behaviours that are easily over

M. M. Majczak, F. Cajiao Vélez, J. Z. Kamiński, K. Krajewska

Formation of electron vortices and momentum spirals in photodetachment of the H$^-$ anion driven by isolated ultrashort laser pulses of circular polarization or by pairs of such pulses (of either corotating or counterrotating polarizations) are analyzed under the scope of the strong-field approximation. It is demonstrated that the carrier-envelope phase (CEP

Shanzhong Han

We examine the Weyl double copy relation for vacuum solutions of the Einstein equations with a cosmological constant using the approach we previously described, in which the spin-1/2 massless free-field spinors (Dirac-Weyl fields) are regarded as basic units. Based on the exact non-twisting vacuum type N and vacuum type D solutions, the finding explicitly sh

Matteo Sesia, Tianshu Sun

This paper introduces an innovative method for conducting conditional independence testing in high-dimensional data, facilitating the automated discovery of significant associations within distinct subgroups of a population, all while controlling the false discovery rate. This is achieved by expanding upon the model-X knockoff filter to provide more informat

Todd Ely, Shyam Bhaskaran, Nicholas Bradley, T. Joseph W. Lazio

Recent advances with space navigation technologies developed by NASA in space-based atomic clocks and pulsar X-ray navigation combined with past successes in autonomous navigation using optical imaging, brings to the forefront the need to compare space navigation using optical, radiometric, and pulsar-based measurements using a common set of assumptions and

Welberth Kennedy, Sebastião dos Anjos Sousa Júnior, Natanael C. Costa, Raimundo R. dos Santos

The nature of metal-insulator and magnetic transitions is still a subject under intense debate in condensed matter physics. Amongst the many possible mechanisms, the interplay between electronic correlations and spin-orbit couplings is an issue of a great deal of interest, in particular when dealing with quasi-2D compounds. In view of this, here we use a Har

Kaustubh Sridhar, Radoslav Ivanov, Vuk Lesi, Marcio Juliato

This paper tackles the problem of making complex resource-constrained cyber-physical systems (CPS) resilient to sensor anomalies. In particular, we present a framework for checkpointing and roll-forward recovery of state-estimates in nonlinear, hierarchical CPS with anomalous sensor data. We introduce three checkpointing paradigms for ensuring different leve

Lewis Coburn, Michael Hitrik, Johannes Sjoestrand

We study Toeplitz operators on the Bargmann space, with Toeplitz symbols that are exponentials of complex quadratic forms, from the point of view of Fourier integral operators in the complex domain. Sufficient conditions are established for the composition of two such operators to be a Toeplitz operator.

Ahmet Inci, Siri Garudanagiri Virupaksha, Aman Jain, Venkata Vivek Thallam

As the machine learning and systems community strives to achieve higher energy-efficiency through custom DNN accelerators and model compression techniques, there is a need for a design space exploration framework that incorporates quantization-aware processing elements into the accelerator design space while having accurate and fast power, performance, and a

Kiran Ramesh

Unsteady thin-aerofoil theory is a low-order method for solving potential-flow aerodynamics on a camber-line undergoing arbitrary motion. In this method, a Kutta condition must be applied at the trailing edge to uniquely specify the net circulation about the aerofoil. This article provides a critical discussion on applying the Kutta condition in unsteady flo

Raffael Stenzel

We introduce the notion of a higher covering diagram in a base $\infty$-category $\mathcal{C}$. The theory of higher covering diagrams in $\mathcal{C}$ will be shown to recover various descent conditions known from the $\infty$-categorical literature in a uniform manner. In fact, higher covering diagrams always assemble to what we refer to as a structured co

Kingson Man, Antonio Damasio, Hartmut Neven

In living organisms, homeostasis is the natural regulation of internal states aimed at maintaining conditions compatible with life. Typical artificial systems are not equipped with comparable regulatory features. Here, we introduce an artificial neural network that incorporates homeostatic features. Its own computing substrate is placed in a needful and vuln

Dae Woong Ham, Luke Miratrix

The Difference in Difference (DiD) estimator is a popular estimator built on the "parallel trends" assumption, which is an assertion that the treatment group, absent treatment, would change "similarly" to the control group over time. To bolster such a claim, one might generate a comparison group, via matching, that is similar to the treated group with respec

Susan Gruber, Rachael V. Phillips, Hana Lee, Martin Ho

The 21st Century Cures Act of 2016 includes a provision for the U.S. Food and Drug Administration (FDA) to evaluate the potential use of real-world evidence (RWE) to support new indications for use for previously approved drugs, and to satisfy post-approval study requirements. Extracting reliable evidence from real-world data (RWD) is often complicated by a

Yi-Jun Chang, Shunhua Jiang

We consider the time and energy complexities of randomized leader election in a multiple-access channel, where the number of devices $n\geq 2$ is unknown. It is well-known that for polynomial-time randomized leader election algorithms with success probability $1-1/poly(n)$, the optimal energy complexity is $\Theta(\log\log^*n)$ if receivers can detect collis

Vipindev Adat Vasudevan, Muhammad Tayyab, George P. Koudouridis, Xavier Gelabert

Future wireless networks must serve dense mobile networks with high data rates, keeping energy requirements to a possible minimum. The small cell-based network architecture and device-to-device (D2D) communication are already being considered part of 5G networks and beyond. In such environments, network coding (NC) can be employed to achieve both higher thro

Rikaz Rameez, Hossein A. Rahmani, Emine Yilmaz

Recently, Twitter has become the social network of choice for sharing and spreading information to a multitude of users through posts called 'tweets'. Users can easily re-share these posts to other users through 'retweets', which allow information to cascade to many more users, increasing its outreach. Clearly, being able to know the extent to which a post c

Erik K. Antonsson, Ph. D., P. E., N. A. E

A collision hazard measure that has the essential characteristics to provide a measurement of safety that will be useful to AV developers, traffic infrastructure developers and managers, regulators and the public is introduced here. The Streetscope Collision Hazard Measure (SHM) overcomes the limitations of existing measures, and provides an independent lead

Simon Donaldson

This is an expository article on the 1978 Atiyah-Drinfeld-Hitchin-Manin construction of Yang-Mills instanton connections over the 4-sphere.

Phillip Popp, Walter R. L. Lambrecht

A first-principles computational study is presented of various phases of LiAlO$_2$.The $\beta$ and $\gamma$ tetrahedral phases are found to be very close in energy with the $\gamma$ phase having the lowest energy. The octahedral $\alpha$ phase is a high-pressure phase and the transition pressure from the $\gamma$ and $\beta$ phases to $\alpha$ is determined

J. Greiner, U. Hugentobler, J. M. Burgess, F. Berlato

The accurate localization of gamma-ray bursts remains a crucial task. While historically, improved localization have led to the discovery of afterglow emission and the realization of their cosmological distribution via redshift measurements, a more recent requirement comes with the potential of studying the kilonovae of neutron star mergers. Gravitational wa

Paarth Gulati, Suraj Shankar, M. Cristina Marchetti

Boundary conditions dictate how fluids, including liquid crystals, flow when pumped through a channel. Can boundary conditions also be used to control internally driven active fluids that generate flows spontaneously? By using numerical simulations and stability analysis we explore how surface anchoring of active agents at the boundaries and substrate drag c

Gino Isidori, Davide Lancerini, Saad Nabeebaccus, Roman Zwicky

Using analytic results obtained in a meson effective theory, that includes all infrared sensitive logs, we build a dedicated Monte Carlo framework to describe QED corrections in $\bar B \to \bar K \ell^+\ell^-$ for a generic form factor. For the neutral mode $\bar B^0 \to \bar K^0 \ell^+\ell^-$, we perform a detailed numerical comparison versus those obtaine

Suhas Vijaykumar

The Frank-Wolfe algorithm has seen a resurgence in popularity due to its ability to efficiently solve constrained optimization problems in machine learning and high-dimensional statistics. As such, there is much interest in establishing when the algorithm may possess a "linear" $O(\log(1/\epsilon))$ dimension-free iteration complexity comparable to projected

Suhas Vijaykumar, Claire Lazar Reich

Modern algorithms for binary classification rely on an intermediate regression problem for computational tractability. In this paper, we establish a geometric distinction between classification and regression that allows risk in these two settings to be more precisely related. In particular, we note that classification risk depends only on the direction of t

Vu H. N. Phan, Moshe Y. Vardi

In Bayesian inference, the most probable explanation (MPE) problem requests a variable instantiation with the highest probability given some evidence. Since a Bayesian network can be encoded as a literal-weighted CNF formula $\varphi$, we study Boolean MPE, a more general problem that requests a model $\tau$ of $\varphi$ with the highest weight, where the we

Simon Donaldson

This article is a review of parts of Atiyah's work, mainly from the periods 1952-1958 and 1976-1990.

Anders S. Buch, Pierre-Emmanuel Chaput, Leonardo C. Mihalcea, Nicolas Perrin

We prove that the Schubert structure constants of the quantum $K$-theory ring of any minuscule flag variety or quadric hypersurface have signs that alternate with codimension. We also prove that the powers of the deformation parameter $q$ that occur in the product of two Schubert classes in the quantum cohomology or quantum $K$-theory ring of a cominuscule f

Angel Abusleme, Thomas Adam, Shakeel Ahmad, Rizwan Ahmed

Main goal of the JUNO experiment is to determine the neutrino mass ordering using a 20kt liquid-scintillator detector. Its key feature is an excellent energy resolution of at least 3 % at 1 MeV, for which its instruments need to meet a certain quality and thus have to be fully characterized. More than 20,000 20-inch PMTs have been received and assessed by JU

John Rushby

We use a mechanized verification system, PVS, to examine the argument from Anselm's Proslogion Chapter III, the so-called "Modal Ontological Argument." We consider several published formalizations for the argument and show they are all essentially similar. Furthermore, we show that the argument is trivial once the modal axioms are taken into account. This wo

Thomas B Berrett, Richard J Samworth

Given a set of incomplete observations, we study the nonparametric problem of testing whether data are Missing Completely At Random (MCAR). Our first contribution is to characterise precisely the set of alternatives that can be distinguished from the MCAR null hypothesis. This reveals interesting and novel links to the theory of Fr\'echet classes (in particu

Julio C. Rebelo, Helena Reis

Let $\mathcal{F}$ be a foliation defined on a complex projective manifold $M$ of dimension $n$ and admitting a holomorphic vector field $X$ tangent to it along some non-empty Zariski-open set. In this paper we prove that if $X$ has sufficiently many integral curves that are given by meromorphic functions defined on $\mathbb{C}$ then the restriction of $\math

Nidhi Vakil, Hadi Amiri

We present a generic and trend-aware curriculum learning approach for graph neural networks. It extends existing approaches by incorporating sample-level loss trends to better discriminate easier from harder samples and schedule them for training. The model effectively integrates textual and structural information for relation extraction in text graphs. Expe

Simon Donaldson

The first part of this article is a short and selective survey of developments in differential and algebraic geometry from the 1980's involving enumerative questions and nonlinear elliptic partial differential equations. In the second part we discuss possible extensions of these ideas to structures on 4-manifolds, in particular to complex structures on surfa

Jarod Alper, Daniel Halpern-Leistner, Jack Hall, David Rydh

We give a variant of Artin algebraization along closed subschemes and closed substacks. Our main application is the existence of \'etale, smooth, or syntomic neighborhoods of closed subschemes and closed substacks. In particular, we prove local structure theorems for stacks and their derived counterparts and the existence of henselizations along linearly fun

Wei Hu, Jihao Long, Yaohua Zang, Weinan E

Collisions are common in many dynamical systems with real applications. They can be formulated as hybrid dynamical systems with discontinuities automatically triggered when states transverse certain manifolds. We present an algorithm for the optimal control problem of such hybrid dynamical systems based on solving the equations derived from the hybrid minimu

Muhammad Umair Nasir, Innocent Amos Mchechesi

Stemming from the limited availability of datasets and textual resources for low-resource languages such as isiZulu, there is a significant need to be able to harness knowledge from pre-trained models to improve low resource machine translation. Moreover, a lack of techniques to handle the complexities of morphologically rich languages has compounded the une

Maria C. Schutte, Leslie Hebb, Simon Lowry, John Wisniewski

Understanding magnetic activity on the surface of stars other than the Sun is important for exoplanet analyses to properly characterize an exoplanet's atmosphere and to further characterize stellar activity on a wide range of stars. Modeling stellar surface features of a variety of spectral types and rotation rates are key to understanding of the magnetic ac

Matteo Aldeghi, Connor W. Coley

Synthetic polymers are versatile and widely used materials. Similar to small organic molecules, a large chemical space of such materials is hypothetically accessible. Computational property prediction and virtual screening can accelerate polymer design by prioritizing candidates expected to have favorable properties. However, in contrast to organic molecules

Khristo N. Boyadzhiev

In this paper we present a special formula for transforming integrals to series. The resulting series involves binomial transforms with the Taylor coefficients of the integrand. Five applications are provided for evaluating challenging integrals.

Jean-Christophe Pain

We provide a family of expressions of $\pi$ in terms of the golden ratio $\phi$ in the same spirit of the formula obtained by Bailey, Borwein and Plouffe for $\pi$. Connection with cyclotomic polynomials is outlined.

Anupam Das, Marianna Girlando

We propose a cut-free cyclic system for Transitive Closure Logic (TCL) based on a form of hypersequents, suitable for automated reasoning via proof search. We show that previously proposed sequent systems are cut-free incomplete for basic validities from Kleene Algebra (KA) and Propositional Dynamic Logic (PDL), over standard translations. On the other hand,

Sara Aghajanzadeh, David Forsyth

In this paper, we study the problem of making brighter images from dark images found in the wild. The images are dark because they are taken in dim environments. They suffer from color shifts caused by quantization and from sensor noise. We don't know the true camera reponse function for such images and they are not RAW. We use a supervised learning method,

Lucian Nita, Eduardo M. G. Vila, Marta A. Zagorowska, Eric C. Kerrigan

Introducing flexibility in the time-discretisation mesh can improve convergence and computational time when solving differential equations numerically, particularly when the solutions are discontinuous, as commonly found in control problems with constraints. State-of-the-art methods use fixed mesh schemes, which cannot achieve superlinear convergence in the

Zachary Forrest, Robert D. Freeman

We present a new proof for the equivalence of potential theoretic weak solutions and viscosity solutions to the $\texttt{p}(\cdot)$-Laplace equation in $\mathbb{R}^n$. The proof of the equivalence in the variable exponent case in Euclidean space was first given by Juutinen, Lukkari, and Parviainen (2010) and extended the equivalence of potential theoretic we

Bryan Liu, Toshiaki Koike-Akino, Ye Wang, Kieran Parsons

This paper investigates a turbo receiver employing a variational quantum circuit (VQC). The VQC is configured with an ansatz of the quantum approximate optimization algorithm (QAOA). We propose a 'learning to learn' (L2L) framework to optimize the turbo VQC decoder such that high fidelity soft-decision output is generated. Besides demonstrating the proposed

Ali H. Al-Nuaimi, Emmanuel Jammeh, Lingfen Sun, Emmanuel Ifeachor

Alzheimers disease (AD) is a progressive disorder that affects cognitive brain functions and starts many years before its clinical manifestations. A biomarker that provides a quantitative measure of changes in the brain due to AD in the early stages would be useful for early diagnosis of AD, but this would involve dealing with large numbers of people because

Jasvith Raj Basani, Mikkel Heuck, Dirk R. Englund, Stefan Krastanov

Optics and photonics has recently captured interest as a platform to accelerate linear matrix processing, that has been deemed as a bottleneck in traditional digital electronic architectures. In this paper, we propose an all-photonic artificial neural network processor wherein information is encoded in the amplitudes of frequency modes that act as neurons. T

J. C. Bernauer, C. T. Dean, C. Fanelli, J. Huang

The Electron Ion Collider (EIC) is the next generation of precision QCD facility to be built at Brookhaven National Laboratory in conjunction with Thomas Jefferson National Laboratory. There are a significant number of software and computing challenges that need to be overcome at the EIC. During the EIC detector proposal development period, the ECCE consorti

Hasibul Jamil, Ning Weng

High performance packet classification is a key component to support scalable network applications like firewalls, intrusion detection, and differentiated services. With ever increasing in the line-rate in core networks, it becomes a great challenge to design a scalable and high performance packet classification solution using hand-tuned heuristics approache

Tong Niu, Kazuma Hashimoto, Yingbo Zhou, Caiming Xiong

Aligning parallel sentences in multilingual corpora is essential to curating data for downstream applications such as Machine Translation. In this work, we present OneAligner, an alignment model specially designed for sentence retrieval tasks. This model is able to train on only one language pair and transfers, in a cross-lingual fashion, to low-resource lan

Mario W. Barela, J. Montaño-Domínguez

Together with any Beyond the Standard Model ultraviolet complete, renormalizable model, one or several exotic particles are usually hypothesized. $C\!P$-even neutral and doubly-charged scalars are common, well known examples which can contribute to the seven 3-body Charged Lepton Flavor Violating decays. The experimental bounds on each Branching Ratio within

Bryan Liu, Toshiaki Koike-Akino, Ye Wang, Kieran Parsons

This paper introduces a new quantum computing framework integrated with a two-step compressed sensing technique, applied to a joint channel estimation and user identification problem. We propose a variational quantum circuit (VQC) design as a new denoising solution. For a practical grant-free communications system having correlated device activities, variati

Jerome Guilet, Alexis Reboul-Salze, Raphael Raynaud, Matteo Bugli

The dynamo driven by the magnetorotational instability (MRI) is believed to play an important role in the dynamics of accretion discs and may also explain the origin of the extreme magnetic fields present in magnetars. Its saturation level is an important open question known to be particularly sensitive to the diffusive processes through the magnetic Prandtl

Nicholas P Baskerville, Jonathan P Keating, Francesco Mezzadri, Joseph Najnudel

This paper considers several aspects of random matrix universality in deep neural networks. Motivated by recent experimental work, we use universal properties of random matrices related to local statistics to derive practical implications for deep neural networks based on a realistic model of their Hessians. In particular we derive universal aspects of outli

Grant Johnson, Patrick Kilian, Fan Guo, Xiaocan Li

Particle acceleration during magnetic reconnection is a long-standing topic in space, solar and astrophysical plasmas. Recent 3D particle-in-cell simulations of magnetic reconnection show that particles can leave flux ropes due to 3D field-line chaos, allowing particles to access additional acceleration sites, gain more energy through Fermi acceleration, and

Toshiaki Koike-Akino, Pu Wang, Ye Wang

Commercial Wi-Fi devices can be used for integrated sensing and communications (ISAC) to jointly exchange data and monitor indoor environment. In this paper, we investigate a proof-of-concept approach using automated quantum machine learning (AutoQML) framework called AutoAnsatz to recognize human gesture. We address how to efficiently design quantum circuit