May 2022 arXiv papers — page 104

Robin Cockett, Amolak Ratan Kalra, Shiroman Prakash

It is known that the category of affine Lagrangian relations, AffLagRel_F, over a field, F, of integers modulo a prime p (with p > 2) is isomorphic to the category of stabilizer quantum circuits for p-dits. Furthermore, it is known that electrical circuits (generalized for the field F) occur as a natural subcategory of AffLagRel_F. The purpose of this paper

Yiming Zuo, Jia Deng

We address the task of view synthesis, generating novel views of a scene given a set of images as input. In many recent works such as NeRF (Mildenhall et al., 2020), the scene geometry is parameterized using neural implicit representations (i.e., MLPs). Implicit neural representations have achieved impressive visual quality but have drawbacks in computationa

Fanchen Bu, Dong Eui Chang

The optimization with orthogonality has been shown useful in training deep neural networks (DNNs). To impose orthogonality on DNNs, both computational efficiency and stability are important. However, existing methods utilizing Riemannian optimization or hard constraints can only ensure stability while those using soft constraints can only improve efficiency.

Steve Prabu, Paul J Hancock, Xiang Zhang, Steven J Tingay

Our previously reported survey of the Low Earth Orbit (LEO) environment using the Murchison Widefield Array (MWA) detected over 70 unique Resident Space Objects (RSOs) over multiple passes, from 20 hours of observations in passive radar mode. In this paper, we extend this work by demonstrating two methods that improve the detection sensitivity of the system.

Haibo Yang, Peiwen Qiu, Jia Liu, Aylin Yener

This paper considers over-the-air federated learning (OTA-FL). OTA-FL exploits the superposition property of the wireless medium, and performs model aggregation over the air for free. Thus, it can greatly reduce the communication cost incurred in communicating model updates from the edge devices. In order to fully utilize this advantage while providing compa

Mark Freyhof, George Grispos, Santosh Pitla, Cody Stolle

In today's modern farm, an increasing number of agricultural systems and vehicles are connected to the Internet. While the benefits of networked agricultural machinery are attractive, this technological shift is also creating an environment that is conducive to cyberattacks. While previous research has focused on general cybersecurity concerns in the farming

Guanjie Zhang, Hui Liu, Jun Chen, He Lin

A general spherical harmonics method is described for extracting anisotropic pair distribution functions (PDF) in this work. In the structural study of functional crystallized materials, the investigation of the local structures under the application of external stimuli, such as electric field and stress, is in urgent need. A well-established technique for l

Qifeng Zhang, Jiyuan Zhang, Zhi-zhong Sun

This is the second part of study on the optimal convergence rate of the explicit Euler discretization in time for the convection-diffusion equations [Appl. Math. Lett. \textbf{131} (2022) 108048] which focuses on high-dimensional linear/nonlinear cases under Dirichlet or Neumann boundary conditions. Several new corrected difference schemes are proposed based

Shin-ichi Sasa, Ken Hiura, Naoko Nakagawa, Akira Yoshida

For small thermodynamic systems in contact with a heat bath, we determine the free energy by imposing the following two conditions. First, the quasi-static work in any configuration change is equal to the free energy difference. Second, the temperature dependence of the free energy satisfies the Gibbs-Helmholtz relation. We find that these prerequisites uniq

Haoqin Tu, Zhongliang Yang, Jinshuai Yang, Yongfeng Huang

Variational Auto-Encoder (VAE) has become the de-facto learning paradigm in achieving representation learning and generation for natural language at the same time. Nevertheless, existing VAE-based language models either employ elementary RNNs, which is not powerful to handle complex works in the multi-task situation, or fine-tunes two pre-trained language mo

Ran Cheng, Xinyu Jiang, Yuan Chen, Lige Liu

Camera relocalization is the key component of simultaneous localization and mapping (SLAM) systems. This paper proposes a learning-based approach, named Sparse Spatial Scene Embedding with Graph Neural Networks (S3E-GNN), as an end-to-end framework for efficient and robust camera relocalization. S3E-GNN consists of two modules. In the encoding module, a trai

Gregory Eskin

We study the Lorentzian metric independent of the time variable in the cylinder $\mathbb{R}\times\Omega$ where $x_0\in\mathbb{R}$ is the time variable and $\Omega$ is a bounded smooth domain in $\mathbb{R}^n$. We consider forward null-geodesics in $\mathbb{R}\times \Omega$ starting on $\mathbb{R}\times\partial\Omega$ at $t=0$ and leaving $\mathbb{R}\times\Om

Y. Zhang, T. An, A. Wang, S. Frey

The nature of jets in active galactic nuclei (AGN) in the early Universe and their feedback to the host galaxy remain a highly topical question. Observations of the radio structure of high-redshift AGNs enabled by very long baseline interferometry (VLBI) provide indispensable input into studies of their properties and role in the galaxies' evolution. Up to n

Xiaomin Zhang, Jiawei Sun, Huimin Yu

In this paper, we consider the one-dimensional isentropic compressible Euler equations with source term $\beta(t,x)\rho|u|^{\alpha}u$ in a bounded domain, which can be used to describe gas transmission in a nozzle.~The model is imposed a subsonic time-periodic boundary condition.~Our main results reveal that the time-periodic boundary can trigger an unique s

Abdullah Aldumaykhi, Saad Otai, Abdulkareem Alsudais

The main objective of this paper is to compare and evaluate the performances of three open Arabic NER tools: CAMeL, Hatmi, and Stanza. We collected a corpus consisting of 30 articles written in MSA and manually annotated all the entities of the person, organization, and location types at the article (document) level. Our results suggest a similarity between

Philip L. -F. Liu, Pablo Higuera

Both 1DH (dispersive and non-dispersive) and 2DH axisymmetric (approximate, non-dispersive) analytical solutions are derived for water waves generated by moving atmospheric pressures. In 1DH, three wave components can be identified: the locked wave propagating with the speed of the atmospheric pressure, $C_p$, and two free wave components propagating in oppo

Mingzhi Wang, Songbai Chen, Jiliang Jing

We give a brief review on the formation and the calculation of black hole shadow. Firstly, we introduce the conception of black hole shadow and the current works on a variety of black hole shadows. Secondly, we present main methods of calculating photon sphere radius and shadow radius, and then explain how the photon sphere affect the boundary of black hole

Shuo Yang, Xinxiao Wu

Language-driven action localization in videos is a challenging task that involves not only visual-linguistic matching but also action boundary prediction. Recent progress has been achieved through aligning language query to video segments, but estimating precise boundaries is still under-explored. In this paper, we propose entity-aware and motion-aware Trans

Zhong Sun, Daniele Ielmini

Matrix computation is ubiquitous in modern scientific and engineering fields. Due to the high computational complexity in conventional digital computers, matrix computation represents a heavy workload in many data-intensive applications, e.g., machine learning, scientific computing, and wireless communications. For fast, efficient matrix computations, analog

Shaozhu Xiao, Wen-He Jiao, Yu Lin, Qi Jiang

A Dirac nodal-line phase, as a quantum state of topological materials, usually occur in three-dimensional or at least two-dimensional materials with sufficient symmetry operations that could protect the Dirac band crossings. Here, we report a combined theoretical and experimental study on the electronic structure of the quasi-one-dimensional ternary tellurid

Wen Shi, Haoan Xu, Cong Sun, Jiwei Sun

Multi-slice magnetic resonance images of the fetal brain are usually contaminated by severe and arbitrary fetal and maternal motion. Hence, stable and robust motion correction is necessary to reconstruct high-resolution 3D fetal brain volume for clinical diagnosis and quantitative analysis. However, the conventional registration-based correction has a limite

Neil Irwin Bernardo, Jingge Zhu, Jamie Evans

The polar receiver architecture is a receiver design that captures the envelope and phase information of the signal rather than its in-phase and quadrature components. Several studies have demonstrated the robustness of polar receivers to phase noise and other nonlinearities. Yet, the information-theoretic limits of polar receivers with finite-precision quan

Li Du, Xiao Ding, Kai Xiong, Ting Liu

Understanding causality has vital importance for various Natural Language Processing (NLP) applications. Beyond the labeled instances, conceptual explanations of the causality can provide deep understanding of the causal facts to facilitate the causal reasoning process. However, such explanation information still remains absent in existing causal reasoning r

Ian Zieder, Alex Dytso, Martina Cardone

This paper studies the minimum mean squared error (MMSE) of estimating $\mathbf{X} \in \mathbb{R}^d$ from the noisy observation $\mathbf{Y} \in \mathbb{R}^k$, under the assumption that the noise (i.e., $\mathbf{Y}|\mathbf{X}$) is a member of the exponential family. The paper provides a new lower bound on the MMSE. Towards this end, an alternative representat

Jakob Povsic, Andrej Brodnik

In the thesis we focus on designing an authentication system to authenticate users over a network with a username and a password. The system uses the zero-knowledge proof (ZKP) system as a password verification mechanism. The ZKP protocol used is based on the quadratic residuosity problem. The authentication system is defined as a method in the extensible au

C. Y. Ma

Similarity analysis of microalgae growth is crucial for understanding microalgae cultivation, such analysis has not been considered before in previous studies. This letter considers the natural features of the microalgae growth phenomenon that are different from the inanimate process. Accordingly, the similarity of light transfer within microalgae suspension

A. Coleman, J. Eser, E. Mayotte, F. Sarazin

The present white paper is submitted as part of the "Snowmass" process to help inform the long-term plans of the United States Department of Energy and the National Science Foundation for high-energy physics. It summarizes the science questions driving the Ultra-High-Energy Cosmic-Ray (UHECR) community and provides recommendations on the strategy to answer t

Shenjian Gong, Shanshan Zhang, Jian Yang, Dengxin Dai

Recently, crowd density estimation has received increasing attention. The main challenge for this task is to achieve high-quality manual annotations on a large amount of training data. To avoid reliance on such annotations, previous works apply unsupervised domain adaptation (UDA) techniques by transferring knowledge learned from easily accessible synthetic

Vincent Y. F. Tan, Prashanth L. A., Krishna Jagannathan

In several applications such as clinical trials and financial portfolio optimization, the expected value (or the average reward) does not satisfactorily capture the merits of a drug or a portfolio. In such applications, risk plays a crucial role, and a risk-aware performance measure is preferable, so as to capture losses in the case of adverse events. This s

Zhenjie Liu

In February this year Google proposed a new Transformer variant called FLASH, which has a faster speed, lower VRAM footprint and better performance. This is achieved by designing a performant layer named GAU (Gated Attention Unit), which combines the Attention layer and FFN. In this paper, some implementation details are re-analyzed both theoretically and pr

Soufiane Belharbi, Jérôme Rony, Jose Dolz, Ismail Ben Ayed

Trained using only image class label, deep weakly supervised methods allow image classification and ROI segmentation for interpretability. Despite their success on natural images, they face several challenges over histology data where ROI are visually similar to background making models vulnerable to high pixel-wise false positives. These methods lack mechan

Duk-Soon Oh

We propose V--cycle multigrid methods for vector field problems arising from the lowest order hexahedral N\'{e}d\'{e}lec finite element. Since the conventional scalar smoothing techniques do not work well for the problems, a new type of smoothing method is necessary. We introduce new smoothers based on substructuring with nonoverlapping domain decomposition

Zhaopeng Guo, Junze Deng, Yue Xie, Zhijun Wang

Higher-order topological insulators have been introduced in the precursory Benalcazar-Bernevig-Hughes quadrupole model, but no electronic compound has been proposed to be a quadrupole topological insulator (QTI) yet. In this work, we predict that Ta$_2M_3$Te$_5$ ($M=$ Pd, Ni) monolayers can be 2D QTIs with second-order topology due to the double-band inversi

Hongwei Jin, Zishun Yu, Xinhua Zhang

Comparing structured data from possibly different metric-measure spaces is a fundamental task in machine learning, with applications in, e.g., graph classification. The Gromov-Wasserstein (GW) discrepancy formulates a coupling between the structured data based on optimal transportation, tackling the incomparability between different structures by aligning th

Florian Girelli, Matteo Laudonio, Adrian Tanasa, Panagiotis Tsimiklis

Group field theories are quantum field theories built on groups. They can be seen as a tool to generate topological state-sums or quantum gravity models. For four dimensional manifolds, different arguments have pointed towards 2-groups (such as crossed modules) as the relevant symmetry structure to probe four dimensional topological features. Here, we introd

Wei Tang, Margaret Martonosi

Quantum computing (QC) offers a new computing paradigm that has the potential to provide significant speedups over classical computing. Each additional qubit doubles the size of the computational state space available to a quantum algorithm. Such exponentially expanding reach underlies QC's power, but at the same time puts demanding requirements on the quant

Shahab Mirjalili, Makrand A Khanwale, Ali Mani

Second-order phase field models have emerged as an attractive option for capturing the advection of interfaces in two-phase flows. Prior to these, state-of-the-art models based on the Cahn-Hilliard equation, which is a fourth-order equation, allowed for the derivation of surface tension models through thermodynamic arguments. In contrast, the second-order ph

Roberto Gallotta, Kai Arulkumaran, L. B. Soros

When generating content for video games using procedural content generation (PCG), the goal is to create functional assets of high quality. Prior work has commonly leveraged the feasible-infeasible two-population (FI-2Pop) constrained optimisation algorithm for PCG, sometimes in combination with the multi-dimensional archive of phenotypic-elites (MAP-Elites)

Sara Maeda, Kohei Oiwake, Yukinori Nishigaki, Tetsuhiko Miyadera

One significant drawback of a spectroscopic ellipsometry (SE) technique is its time-consuming and often complicated analysis procedure necessary to assess the optical functions of thin-film and bulk samples. Here, to solve this inherent problem of a traditional SE method, we present a new general way that allows full automation of SE analyses for crystalline

Shuang Wu, Xiaoning Song, Zhenhua Feng, Xiao-Jun Wu

Recently, Flat-LAttice Transformer (FLAT) has achieved great success in Chinese Named Entity Recognition (NER). FLAT performs lexical enhancement by constructing flat lattices, which mitigates the difficulties posed by blurred word boundaries and the lack of word semantics. In FLAT, the positions of starting and ending characters are used to connect a matchi

Hongyu Wang, Eibe Frank, Bernhard Pfahringer, Michael Mayo

Cross-domain few-shot learning (CDFSL) addresses learning problems where knowledge needs to be transferred from one or more source domains into an instance-scarce target domain with an explicitly different distribution. Recently published CDFSL methods generally construct a universal model that combines knowledge of multiple source domains into one feature e

Zeying Zhang, Zhi-Ming Yu, Gui-Bin Liu, Zhenye Li

We propose an efficient algorithm to construct $\boldsymbol{k}\cdot \boldsymbol{p}$ effective Hamiltonians, which is much faster than the previously proposed algorithms. This algorithm is implemented in MagneticKP package. The package applies to both single-valued (spinless) and double-valued (spinful) cases, and it works for both magnetic and nonmagnetic sy

Si-xue Qin

The state-of-the-art physics consists of two irreconcilable branches, i.e., the quantum theory and the general relativity, which work well in their own territories, independently. However, what are quantum and spacetime after all? The key question was never addressed, satisfactorily. In this work, we describe a possibility to reformulate the quantum theory i

Hao Liu, Shingyu Leung, Jianliang Qian

We develop an efficient operator-splitting method for the eigenvalue problem of the Monge-Amp\`{e}re operator in the Aleksandrov sense. The backbone of our method relies on a convergent Rayleigh inverse iterative formulation proposed by Abedin and Kitagawa (Inverse iteration for the {M}onge-{A}mp{\`e}re eigenvalue problem, {\it Proceedings of the American Ma

Kashumi Madampe, Rashina Hoda, John Grundy

Background: Requirements Changes (RCs) -- the additions/modifications/deletions of functional/non-functional requirements in software products -- are challenging for software practitioners to handle. Handling some changes may significantly impact the emotions of the practitioners. Objective: We wanted to know the key challenges that make RC handling difficul

Yannan Nellie Wu, Po-An Tsai, Angshuman Parashar, Vivienne Sze

In recent years, many accelerators have been proposed to efficiently process sparse tensor algebra applications (e.g., sparse neural networks). However, these proposals are single points in a large and diverse design space. The lack of systematic description and modeling support for these sparse tensor accelerators impedes hardware designers from efficient a

Zoe L. Jiang, Jiajing Gu, Hongxiao Wang, Yulin Wu

With the development of machine learning, it is difficult for a single server to process all the data. So machine learning tasks need to be spread across multiple servers, turning the centralized machine learning into a distributed one. However, privacy remains an unsolved problem in distributed machine learning. Multi-key homomorphic encryption is one of th

Nobuhito Maru, Haruki Takahashi, Yoshiki Yatagai

Grand gauge-Higgs unification of five dimensional SU(6) gauge theory on an orbifold $S^1 / Z_2$ with localized gauge kinetic terms is discussed. The Standard model (SM) fermions on the boundaries and some massive bulk fermions coupling to the SM fermions are introduced. The number of the bulk fermions is reduced compared to the previous model, which reproduc

Vladimir Saveljev

We propose the multiview wavelets based on voxel patterns of autostereoscopic multiview displays. Direct and inverse continuous wavelet transforms of binary and gray-scale images were performed. The input to the inverse wavelet transform was the array of wavelet coefficients of the direct transform. A restored image reproduces the structure of the multiview

Rodrigo Angelo, Max Wenqiang Xu

We show that if $f$ is the random completely multiplicative function, the probability that $\sum_{n\le x}\frac{f(n)}{n}$ is positive for every $x$ is at least $1-10^{-45}$, while also strictly smaller than $1$. For large $x$, we prove an asymptotic upper bound of $O(\exp(-\exp( \frac{\log x}{C\log \log x })))$ on the exceptional probability that a particular

Yi Lin, Yang-hao Chan, Woojoo Lee, Li-Syuan Lu

Optical excitation serves as a powerful approach to control the electronic structure of layered Van der Waals materials via many-body screening effects, induced by photoexcited free carriers, or via light-driven coherence, such as optical Stark and Bloch-Siegert effects. Although theoretical work has also pointed to an exotic mechanism of renormalizing band

Yuzhen Qin, Tommaso Menara, Samet Oymak, ShiNung Ching

Humans are capable of adjusting to changing environments flexibly and quickly. Empirical evidence has revealed that representation learning plays a crucial role in endowing humans with such a capability. Inspired by this observation, we study representation learning in the sequential decision-making scenario with contextual changes. We propose an online algo

T. M. Sitnova, S. A. Yakovleva, A. K. Belyaev, L. I. Mashonkina

We present a new model atom of Zn I-II based on the most up-to-date photoionisation cross-sections, electron-impact excitation rates, and rate coefficients for the Zn I + H I and Zn II + H- collisions. The latter were calculated using the multi-channel quantum asymptotic treatment based on the Born-Oppenheimer approach. Non-LTE analysis was performed for the

Nicole Lewis, Wendi Lv, Mason Alexander Ross, Chun Yuen Tsang

During the early development of Quantum Chromodynamics, it was proposed that baryon number could be carried by a non-perturbative Y-shaped topology of gluon fields, called the gluon junction, rather than by the valence quarks as in the QCD standard model. A puzzling feature of ultra-relativistic nucleus-nucleus collisions is the apparent substantial baryon e

Naoyuki Hirata, Tomokatsu Morota, Yuichiro Cho, Masanori Kanamaru

Asteroid 162173 Ryugu has numerous craters. The initial measurement of impact craters on Ryugu, by Sugita et al. (2019), is based on Hayabusa2 ONC images obtained during the first month after the arrival of Hayabusa2 in June 2018. Utilizing new images taken until February 2019, we constructed a global impact crater catalogue of Ryugu, which includes all crat

Steve M. Young, Mohan Sarovar, François Léonard

Single photon detectors play a key role across several basic science and technology applications. While progress has been made in improving performance, single photon detectors that can maintain high performance while also resolving the photon frequency are still lacking. By means of quantum simulations, we show that nanoscale elements cooperatively interact

Ren Ikeya, Naoyuki Hirata

The Japanese spacecraft Hayabusa 2 visited the asteroid (162173) Ryugu and provided many high-resolution images of its surface, revealing that Ryugu has a spinning-top shape with a prominent equatorial ridge, much like the shapes reported for some other asteroids. In this study, through dozens of numerical calculations, we demonstrate that during a period of

Hans Moritz Günther, Carl Melis, J. Robrade, P. C. Schneider

Cool stars on the main sequence generate X-rays from coronal activity, powered by a convective dynamo. With increasing temperature, the convective envelope becomes smaller and X-ray emission fainter. We present Chandra/HRC-I observations of four single stars with early A spectral types. Only the coolest star of this sample, $\tau^3$ Eri ($T_\mathrm{eff}\appr

Baisheng Yan

We provide some counterexamples concerning the uniqueness and regularity of weak solutions to the initial-boundary value problem for gradient flows of certain strongly polyconvex functionals by showing that such a problem can possess a trivial classical solution as well as infinitely many weak solutions that are nowhere smooth. Such polyconvex functions have

Narayan Khadka, Michal Zajaček, Swayamtrupta Panda, Mary Loli Martínez-Aldama

We use observations of 66 reverberation-measured \Mgii\ quasars (QSOs) in the redshift range $0.36 \leq z \leq 1.686$ -- a subset of the 78 QSOs we previously studied that also have \rfe\ (flux ratio parameter of UV \Feii\ to \Mgii\ that is used as an accretion-rate proxy) measurements -- to simultaneously constrain cosmological model parameters and QSO 2-pa

Daniel Simig, Fabio Petroni, Pouya Yanki, Kashyap Popat

The extreme multi-label classification (XMC) task aims at tagging content with a subset of labels from an extremely large label set. The label vocabulary is typically defined in advance by domain experts and assumed to capture all necessary tags. However in real world scenarios this label set, although large, is often incomplete and experts frequently need t

Quan Yu

A tensor nuclear norm (TNN) based method for solving the tensor recovery problem was recently proposed, and it has achieved state-of-the-art performance. However, it may fail to produce a highly accurate solution since it tends to treats each frontal slice and each rank component of each frontal slice equally. In order to get a recovery with high accuracy, w

Connor Robertson, Jared L. Wilmoth, Scott Retterer, Miguel Fuentes-Cabrera

A Recurrent Neural Network (RNN) was used to perform video frame prediction of microbial growth for a population of two mutants of Pseudomonas aeruginosa. The RNN was trained on videos of 20 frames that were acquired using fluorescence microscopy and microfluidics. The network predicted the last 10 frames of each video, and the accuracy's of the predictions

Victor Churchill, Dongbin Xiu

Recently, a general data driven numerical framework has been developed for learning and modeling of unknown dynamical systems using fully- or partially-observed data. The method utilizes deep neural networks (DNNs) to construct a model for the flow map of the unknown system. Once an accurate DNN approximation of the flow map is constructed, it can be recursi

Claudia Felser, Johannes Gooth

Topology, a well-established concept in mathematics, has nowadays become essential to describe condensed matter. At its core are chiral electron states on the bulk, surfaces and edges of the condensed matter systems, in which spin and momentum of the electrons are locked parallel or anti-parallel to each other. Magnetic and non-magnetic Weyl semimetals, for

Jose Alfredo de Leon, Alejandro Fonseca, Francois Leyvraz, David Davalos

Decoherence of quantum systems is described by quantum channels. However, a complete understanding of such channels, especially in the multi-particle setting, is still an ongoing difficult task. We propose the family of quantum maps that preserve or completely erase the components of a multi-qubit system in the basis of Pauli strings, which we call Pauli com

Patrick Wilken, Evgeny Matusov

To participate in the Isometric Spoken Language Translation Task of the IWSLT 2022 evaluation, constrained condition, AppTek developed neural Transformer-based systems for English-to-German with various mechanisms of length control, ranging from source-side and target-side pseudo-tokens to encoding of remaining length in characters that replaces positional e

Tarek Mealy, Robert Marosi, Filippo Capolino

We propose a scheme to calculate the reduced plasma frequency of a cylindrical-shaped electron beam flowing inside of a cylindrical tunnel, based on results obtained from Particle-in-cell (PIC) simulations. In PIC simulations, we modulate the electron beam using two parallel, non-intercepting, closely-spaced grids which are electrically connected together by

Patrick Wilken, Panayota Georgakopoulou, Evgeny Matusov

This paper addresses the problem of evaluating the quality of automatically generated subtitles, which includes not only the quality of the machine-transcribed or translated speech, but also the quality of line segmentation and subtitle timing. We propose SubER - a single novel metric based on edit distance with shifts that takes all of these subtitle proper

Sanjaya Lohani, Sangita Regmi, Joseph M. Lukens, Ryan T. Glasser

We introduce an approach for performing quantum state reconstruction on systems of $n$ qubits using a machine-learning-based reconstruction system trained exclusively on $m$ qubits, where $m\geq n$. This approach removes the necessity of exactly matching the dimensionality of a system under consideration with the dimension of a model used for training. We de

Rajan Gupta, Tanmoy Bhattacharya, Boram Yoon

Theoretical particle physicists continue to push the envelope in both high performance computing and in managing and analyzing large data sets. For example, the goals of sub-percent accuracy in predictions of quantum chromodynamics (QCD) using large scale simulations of lattice QCD and in finding signals of rare events and new physics in exabytes of data pro

Prakash Mishra, Yoh Yamamoto, J. Karl Johnson, Koblar A. Jackson

We study the effect of self-interaction errors on the barrier heights of chemical reactions. For this purpose we use the well-known Perdew-Zunger [J. P. Perdew and A. Zunger, Phys. Rev. B, {\bf 23}, 5048 (1981)] self-interaction-correction (PZSIC), as well as two variations of the recently developed, locally scaled self-interaction correction (LSIC) [R. R. Z

Prakash Mishra, Yoh Yamamoto, Po-Hao Chang, Duyen B. Nguyen

We examine the role of self-interaction errors (SIE) removal on the evaluation of magnetic exchange coupling constants. In particular we analyze the effect of scaling down the self-interaction-correction (SIC) for three {\em non-empirical} density functional approximations (DFAs) namely, the local spin density approximation, the Perdew-Burke-Ernzerhof genera

Tianjiao Li, Feiyang Wu, Guanghui Lan

We study average-reward Markov decision processes (AMDPs) and develop novel first-order methods with strong theoretical guarantees for both policy optimization and policy evaluation. Compared with intensive research efforts in finite sample analysis of policy gradient methods for discounted MDPs, existing studies on policy gradient methods for AMDPs mostly f

Margarita Colberg, Jeremy Schofield

Under certain conditions, the dynamics of coarse-grained models of solvated proteins can be described using a Markov state model, which tracks the evolution of populations of configurations. The transition rates among states that appear in the Markov model can be determined by computing the relative entropy of states and their mean first passage times. In th

Bryce Manifold, Gabriel F. Dorlhiac, Markita P. Landry, Aaron Streets

Chemical neurotransmission is central to neurotypical brain function but also implicated in a variety of psychiatric neurodegenerative diseases. The release dynamics of neurotransmitters is correlated with but distinct from neuronal electrical signal propagation. It is therefore necessary to track neurotransmitter modulation separately from neuron electrical

R. Jorge, G. G. Plunk, M. Drevlak, M. Landreman

A single-field-period quasi-isodynamic stellarator configuration is presented. This configuration, which resembles a twisted strip, is obtained by the method of direct construction, that is, it is found via an expansion in the distance from the magnetic axis. Its discovery, however, relied on an additional step involving numerical optimization, performed wit

David A. Neufeld

An infrared L- and M-band spectral survey, performed toward the young planetary nebula NGC 7027 with the iSHELL instrument on NASA's Infrared Telescope Facility (IRTF), has revealed more than 20 vibrational lines of the molecules HeH$^+$, H$_2$, and CH$^+$ and more than 50 spectral lines of atoms and atomic ions. The present paper focuses on the atomic line

Ezzeddine El Sai, Parker Gara, Markus J. Pflaum

As datasets used in scientific applications become more complex, studying the geometry and topology of data has become an increasingly prevalent part of the data analysis process. This can be seen for example with the growing interest in topological tools such as persistent homology. However, on the one hand, topological tools are inherently limited to provi

Francis Ogoke, Kyle Johnson, Michael Glinsky, Chris Laursen

Laser Powder Bed Fusion has become a widely adopted method for metal Additive Manufacturing (AM) due to its ability to mass produce complex parts with increased local control. However, AM produced parts can be subject to undesirable porosity, negatively influencing the properties of printed components. Thus, controlling porosity is integral for creating effe

Hjalmar Wijk, Benjie Wang, Marta Kwiatkowska

In many domains, worst-case guarantees on the performance (e.g., prediction accuracy) of a decision function subject to distributional shifts and uncertainty about the environment are crucial. In this work we develop a method to quantify the robustness of decision functions with respect to credal Bayesian networks, formal parametric models of the environment

Ferdinand Ihringer

We give variants of the Krein bound and the absolute bound for graphs with a spectrum similar to that of a strongly regular graph. In particular, we investigate what we call approximately strongly regular graphs. We apply our results to extremal problems. Among other things, we show the following: (1) Caps in $\mathrm{PG}(n, q)$ for which the number of secan

Xinxing Tang, Junrong Yan

The aim of this paper is to rigorously establish the Calabi-Yau/Landau-Ginzburg (CY/LG) correspondence for the $tt^*$ geometry structure--a generalized version of variation of Hodge structures. Although it is well-known that there exists a map between Hodge structures on the LG and CY's sides that preserves the Hodge filtration and bilinear form, it remains

F. M. R. Safara, H. P. Melo, M. M. Telo da Gama, N. A. M. Araújo

We propose a minimal model, based on active Brownian particles, for the dynamics of cells confined in a two-state micropattern, composed of two rectangular boxes connected by a bridge, and investigate the transition statistics. A transition between boxes occurs when the active particle crosses the center of the bridge, and the time between subsequent transit

Daniel Hesslow, Niccoló Zanichelli, Pascal Notin, Iacopo Poli

In this work we introduce RITA: a suite of autoregressive generative models for protein sequences, with up to 1.2 billion parameters, trained on over 280 million protein sequences belonging to the UniRef-100 database. Such generative models hold the promise of greatly accelerating protein design. We conduct the first systematic study of how capabilities evol

Matthew Heine, Olle Hellman, David Broido

A hybrid ab initio theoretical approach for examining thermal properties in magnetic systems of unknown entropy is presented. Commonly used theoretical approaches interrogate thermal properties from Gibbs/Helmholtz free energies, which require an accurate model of magnetic interactions. The present approach avoids this requirement by instead calculating syst

Zhongyu Li, Jun Zeng, Akshay Thirugnanam, Koushil Sreenath

Bridging model-based safety and model-free reinforcement learning (RL) for dynamic robots is appealing since model-based methods are able to provide formal safety guarantees, while RL-based methods are able to exploit the robot agility by learning from the full-order system dynamics. However, current approaches to tackle this problem are mostly restricted to

Eric R. Anschuetz, Bobak T. Kiani

One of the most important properties of classical neural networks is how surprisingly trainable they are, though their training algorithms typically rely on optimizing complicated, nonconvex loss functions. Previous results have shown that unlike the case in classical neural networks, variational quantum models are often not trainable. The most studied pheno

Jean-Marc Valin, Ahmed Mustafa, Christopher Montgomery, Timothy B. Terriberry

As deep speech enhancement algorithms have recently demonstrated capabilities greatly surpassing their traditional counterparts for suppressing noise, reverberation and echo, attention is turning to the problem of packet loss concealment (PLC). PLC is a challenging task because it not only involves real-time speech synthesis, but also frequent transitions be

Nicholas Waytowich, James Hare, Vinicius G. Goecks, Mark Mittrick

Traditionally, learning from human demonstrations via direct behavior cloning can lead to high-performance policies given that the algorithm has access to large amounts of high-quality data covering the most likely scenarios to be encountered when the agent is operating. However, in real-world scenarios, expert data is limited and it is desired to train an a

Trenton W. Ford, William Theisen, Michael Yankoski, Tom Henry

This article presents a beta-version of MEWS (Misinformation Early Warning System). It describes the various aspects of the ingestion, manipulation detection, and graphing algorithms employed to determine--in near real-time--the relationships between social media images as they emerge and spread on social media platforms. By combining these various technolog

Olivier Hudry, Antoine Lobstein

The decision problems of the existence of a Hamiltonian cycle or of a Hamiltonian path in a given graph, and of the existence of a truth assignment satisfying a given Boolean formula $C$, are well-known {\it NP}-complete problems. Here we study the problems of the {\it uniqueness} of a Hamiltonian cycle or path in an undirected, directed or oriented graph, a

Adrian Lehmann, Ben Caldwell, Robert Rand

Optimizing quantum circuits is a key challenge for quantum computing. The PyZX compiler broke new ground by optimizing circuits via the ZX calculus, a powerful graphical alternative to the quantum circuit model. Still, it carries no guarantee of its correctness. To address this, we developed VyZX, a verified ZX-calculus in the Coq proof assistant. VyZX provi

Vincenzo Ferone, Bruno Volzone

In this note we prove a new symmetrization result, in the form of mass concentration comparison, for solutions of nonlocal nonlinear Dirichlet problems involving fractional p Laplacians. Some regularity estimates of solutions will be established as a direct application of the main result.

Tatiana Komarova, William Matcham

When individuals make simultaneous decisions across multiple ordered dimensions, standard multivariate ordered choice models impose narrow bracketing: each dimension is decided as if the others did not exist. We develop a general rectangular structure model capturing broad bracketing while nesting the standard model. The framework introduces two layers of de

Lifeng Wang

This paper provides equivalence characterizations of homogeneous Triebel-Lizorkin and Besov-Lipschitz spaces, denoted by $\dot{F}^s_{p,q}(\mathbb{R}^n)$ and $\dot{B}^s_{p,q}(\mathbb{R}^n)$ respectively, in terms of maximal functions of the mean values of iterated difference. It also furnishes the reader with inequalities in $\dot{F}^s_{p,q}(\mathbb{R}^n)$ in

Nima S. Hejazi, David Benkeser, Iván Díaz, Mark J. van der Laan

Continuous treatments have posed a significant challenge for causal inference, both in the formulation and identification of scientifically meaningful effects and in their robust estimation. Traditionally, focus has been placed on techniques applicable to binary or categorical treatments with few levels, allowing for the application of propensity score-based

Steven De Keninck, Martin Roelfs

Geometric algebras of dimension $n < 6$ are becoming increasingly popular for the modeling of 3D and 3+1D geometry. With this increased popularity comes the need for efficient algorithms for common operations such as normalization, square roots, and exponential and logarithmic maps. The current work presents a signature agnostic analysis of these common oper

Nicolas Zilberstein, Chris Dick, Rahman Doost-Mohammady, Ashutosh Sabharwal

Solving the optimal symbol detection problem in multiple-input multiple-output (MIMO) systems is known to be NP-hard. Hence, the objective of any detector of practical relevance is to get reasonably close to the optimal solution while keeping the computational complexity in check. In this work, we propose a MIMO detector based on an annealed version of Lange

Bowen Zhang, Houssem Sifaou, Geoffrey Ye Li

Deep learning has been widely adopted for channel state information (CSI)-fingerprinting indoor localization systems. These systems usually consist of two main parts, i.e., a positioning network that learns the mapping from high-dimensional CSI to physical locations and a tracking system that utilizes historical CSI to reduce the positioning error. This pape