May 2022 arXiv papers — page 117

Mohammad M. Allaham, Richard G. Forbes, Alexandr Knapek, Dinara Sobola

In field electron emission (FE) studies, to interpret current-voltage data and extract characterization parameters, we use smooth planar metal-like emitter (SPME) methodology and a data-analysis plot. Three types exist: Millikan-Lauritsen (ML), Fowler-Nordheim (FN) and Murphy-Good (MG) plots. In SPME methodology, ML and FN plots are slightly curved but a MG

Bo Wang, Sergey Nersesov, Hashem Ashrafiuon, Peiman Naseradinmousavi

We extend source seeking algorithms, in the absence of position and velocity measurements, and with tuning of the surge input, from velocity-actuated (unicycle) kinematic models to force-actuated generic Euler-Lagrange dynamic underactuated models. In the design and analysis, we employ a symmetric product approximation, averaging, passivity, and partial-stat

Clément Girod, Callum R. Stevens, Andrew Huxley, Eric D. Bauer

Despite intense experimental efforts, the nature of the unconventional superconducting order parameter of UTe$_2$ remains elusive. This puzzle stems from different reported numbers of superconducting transitions at ambient pressure, as well as a complex pressure-temperature phase diagram. To bring new insights into the superconducting properties of UTe$_2$,

S. J. Curran, J. P. Moss, Y. C. Perrott

With the aim of using machine learning techniques to obtain photometric redshifts based upon a source's radio spectrum alone, we have extracted the radio sources from the Million Quasars Catalogue. Of these, 44,119 have a spectroscopic redshift, required for model validation, and for which photometry could be obtained. Using the radio spectral properties as

Ian Frederick Vigogne Goodbody Hunter, Alessandro Palla, Sebastian Eusebiu Nagy, Richard Richmond

Calculating the most efficient schedule of work in a neural network compiler is a difficult task. There are many parameters to be accounted for that can positively or adversely affect that schedule depending on their configuration - How work is shared between distributed targets, the subdivision of tensors to fit in memory, toggling the enablement of optimiz

Rabindra Basnet, K. Kotur, M. Rybak, Cory Stephenson

Recent discoveries in two-dimensional (2D) magnetism have intensified the investigation of van der Waals (vdW) magnetic materials and further improved our ability to tune their magnetic properties. Tunable magnetism has been widely studied in antiferromagnetic metal thiophosphates MPX3. Substitution of metal ions M has been adopted as an important technique

Alejandro Tolcachier

In this article we study the relation between flat solvmanifolds and $G_2$-geometry. First, we give a classification of 7-dimensional flat splittable solvmanifolds using the classification of finite subgroups of $\mathsf{GL}(n,\mathbb{Z})$ for $n=5$ and $n=6$. Then, we look for closed, coclosed and divergence-free $G_2$-structures compatible with the flat me

Antonio Orvieto, Simon Lacoste-Julien, Nicolas Loizou

Recently, Loizou et al. (2021), proposed and analyzed stochastic gradient descent (SGD) with stochastic Polyak stepsize (SPS). The proposed SPS comes with strong convergence guarantees and competitive performance; however, it has two main drawbacks when it is used in non-over-parameterized regimes: (i) It requires a priori knowledge of the optimal mini-batch

Hasitha W. Suriya Arachchige, William R. Meier, Madalynn Marshall, Takahiro Matsuoka

Materials hosting kagome lattices have drawn interest for the diverse magnetic and electronic states generated by geometric frustration. In the $A$V$_3$Sb$_5$ compounds ($A$ = K, Rb, Cs), stacked vanadium kagome layers give rise to unusual charge density waves (CDW) and superconductivity. Here we report single-crystal growth and characterization of ScV$_6$Sn

Hamza Fawzi, James Saunderson

Quantum relative entropies are jointly convex functions of two positive definite matrices that generalize the Kullback-Leibler divergence and arise naturally in quantum information theory. In this paper, we prove self-concordance of natural barrier functions for the epigraphs of various quantum relative entropies and divergences. Furthermore we show that the

Shenglong Zhou

Hard-thresholding-based algorithms have seen various advantages for sparse optimization in controlling the sparsity and allowing for fast computation. Recent research shows that when techniques of the Newton-type methods are integrated, their numerical performance can be improved surprisingly. This paper develops a gradient projection Newton pursuit algorith

Joyce Byun, Elisabeth Krause

We extend the modal decomposition method, previously applied to compress the information in the real-space bispectrum, to the anisotropic redshift-space galaxy bispectrum. In the modal method approach, the bispectrum is expanded on a basis of smooth functions of triangles and their orientations, such that a set of modal expansion coefficients can capture the

Maryam Olyaee, Juan M. Romero-Jerez, F. Javier López-Martínez, Andrea J. Goldsmith

We present a composite wireless fading model encompassing multipath fading and shadowing based on fluctuating two-ray (FTR) fading and inverse gamma (IG) shadowing. We first determine an alternative framework for the statistical characterization and performance evaluation of the FTR fading model, which is based on the fact that the FTR fading distribution ca

Go Ogiya, Daisuke Nagai

The rotation curves of some star forming massive galaxies at redshift two decline over the radial range of a few times the effective radius, indicating a significant deficit of dark matter (DM) mass in the galaxy centre. The DM mass deficit is interpreted as the existence of a DM density core rather than the cuspy structure predicted by the standard cosmolog

William D. Banks

For any real $\beta_0\in[\tfrac12,1)$, let ${\rm GRH}[\beta_0]$ be the assertion that for every Dirichlet character $\chi$ and all zeros $\rho=\beta+i\gamma$ of $L(s,\chi)$, one has $\beta\le\beta_0$ (in particular, ${\rm GRH}[\frac12]$ is the Generalized Riemann Hypothesis). In this paper, we show that the validity of ${\rm GRH}[\frac{9}{10}]$ depends only

Yicheng Gao, Giuliano Casale

With constrained resources, what, where, and how to cache at the edge is one of the key challenges for edge computing systems. The cached items include not only the application data contents but also the local caching of edge services that handle incoming requests. However, current systems separate the contents and services without considering the latency in

Nicola Garofalo, Giulio Tralli

In sub-Riemannian geometry there exist, in general, no known explicit representations of the heat kernels, and these functions fail to have any symmetry whatsoever. In particular, they are not a function of the control distance, nor they are for instance spherically symmetric in any of the layers of the Lie algebra. Despite these unfavourable aspects, in thi

Xiaoyu Cheng

When sample data are governed by an unknown sequence of independent but possibly non-identical distributions, the data-generating process (DGP) in general cannot be perfectly identified from the data. For making decisions facing such uncertainty, this paper presents a novel approach by studying how the data can best be used to robustly improve decisions. Tha

Kouki Nakata, Yuichi Ohnuma

The developments in the field of quantum optics raise expectations that laser-matter coupling is a promising building block for magnonics. Here, we propose a method for the generation of direct and alternating spin currents of magnons across the junction interface irradiated by linearly polarized laser. In a junction of ferromagnetic insulators with a large

Xinbo Ai

Pearson's r, the most widely-used correlation coefficient, is traditionally regarded as exclusively capturing linear dependence, leading to its discouragement in contexts involving nonlinear relationships. However, recent research challenges this notion, suggesting that Pearson's r should not be ruled out a priori for measuring nonlinear monotone relationshi

Wai Kin Lai, Xiaohui Liu, Manman Wang, Hongxi Xing

We reinterpret jet clustering as an axis-finding procedure which, along with the proton beam, defines the virtual-photon transverse momentum $q_T$ in deep inelastic scattering (DIS). In this way, we are able to probe the nucleon intrinsic structure using jet axes in a fully inclusive manner, similar to the Drell-Yan process. We present the complete list of a

Yongda Zhu, George D. Becker, Sarah E. I. Bosman, Laura C. Keating

We present a new investigation of the intergalactic medium (IGM) near reionization using dark gaps in the Lyman-$\beta$ (Ly$\beta$) forest. With its lower optical depth, Ly$\beta$ offers a potentially more sensitive probe to any remaining neutral gas compared to commonly used Ly$\alpha$ line. We identify dark gaps in the Ly$\beta$ forest using spectra of 42

Belén Acosta-Tripailao, Walter Max-Moerbeck, Denisse Pastén, Pablo S. Moya

{We focus on characterizing the high-energy emission mechanisms of blazars by analyzing the variability in the radio band of the light curves of more than a thousand sources. We are interested in assigning complexity parameters to these sources, modeling the time series of the light curves with the method of the Horizontal Visibility Graph (HVG), which allow

Michael Dikshtein, Nir Weinberger, Shlomo Shamai

We formulate and analyze the compound information bottleneck programming. In this problem, a Markov chain $ \mathsf{X} \rightarrow \mathsf{Y} \rightarrow \mathsf{Z} $ is assumed with fixed marginal distributions $\mathsf{P}_{\mathsf{X}}$ and $\mathsf{P}_{\mathsf{Y}}$, and the mutual information between $ \mathsf{X} $ and $ \mathsf{Z} $ is sought to be maximi

Chih-Wei Wang, Xiaohan Liu, Tian Qiao, Mohit Khurana

Hot electrons play a crucial role in enhancing the efficiency of photon-to-current conversion or photocatalytic reactions. In semiconductor nanocrystals, energetic hot electrons capable of photoemission can be generated via the upconversion process involving the dopant-originated intermediate state, currently known only in Mn-doped cadmium chalcogenide quant

HyunJun Jung, Patrick Ruhkamp, Guangyao Zhai, Nikolas Brasch

Depth estimation is a core task in 3D computer vision. Recent methods investigate the task of monocular depth trained with various depth sensor modalities. Every sensor has its advantages and drawbacks caused by the nature of estimates. In the literature, mostly mean average error of the depth is investigated and sensor capabilities are typically not discuss

Michael D. Higgins, Chris H. Greene

This work analyzes the three and four equal-mass fermionic systems near and at the $s$- and $p$-wave unitary limits using hyperspherical methods. The unitary regime addressed here is where the two-body dimer energy is at zero energy. For fermionic systems near the $s$-wave unitary limit, the hyperradial potentials in the $N$-body continuum exhibit a universa

Eric Luxenberg, Stephen Boyd

We consider the problem of choosing an optimal portfolio, assuming the asset returns have a Gaussian mixture (GM) distribution, with the objective of maximizing expected exponential utility. In this paper we show that this problem is convex, and readily solved exactly using domain-specific languages for convex optimization, without the need for sampling or s

Cheikh Birahim Ndiaye

In this paper, we study a natural optimal control problem associated to the Paneitz obstacle problem on closed 4-dimensional Riemannian manifolds. We show the existence of an optimal control which is an optimal state and induces also a conformal metric with prescribed Q-curvature. We show also C^{\infty}-regularity of optimal controls and some compactness re

Raymond Fok, Hita Kambhamettu, Luca Soldaini, Jonathan Bragg

Researchers need to keep up with immense literatures, though it is time-consuming and difficult to do so. In this paper, we investigate the role that intelligent interfaces can play in helping researchers skim papers, that is, rapidly reviewing a paper to attain a cursory understanding of its contents. After conducting formative interviews and a design probe

D. Crisan, D. D. Holm, P. Korn

A generic approach to stochastic climate modelling is developed for the example of an idealized Atmosphere-Ocean model that rests upon Hasselmann's paradigm for stochastic climate models. Namely, stochasticity is incorporated into the fast moving atmospheric component of an idealized coupled model by means of stochastic Lie transport, while the slow moving o

Michael Neely, Stefan F. Schouten, Maurits Bleeker, Ana Lucic

There has been significant debate in the NLP community about whether or not attention weights can be used as an explanation - a mechanism for interpreting how important each input token is for a particular prediction. The validity of "attention as explanation" has so far been evaluated by computing the rank correlation between attention-based explanations an

Eugenia Ellis, Emanuel Rodríguez Cirone

We relate the Davis-L\"uck homology with coefficients in Weibel's homotopy K-theory to the equivariant algebraic kk-theory using homotopy theory and adjointness theorems. We express the left hand side of the assembly map for the KH-isomorphism conjecture in terms of equivariant algebraic kk-groups.

Connor Scully-Allison, Ian Lumsden, Katy Williams, Jesse Bartels

Interactive visualization can support fluid exploration but is often limited to predetermined tasks. Scripting can support a vast range of queries but may be more cumbersome for free-form exploration. Embedding interactive visualization in scripting environments, such as computational notebooks, provides an opportunity to leverage the strengths of both direc

Claudio A. Buzzi, Yagor Romano Carvalho, Jaume Llibre

These last years an increasing interest appeared for studying the planar discontinuous piecewise differential systems motivated by the rich applications in modelling real phenomena. One of the difficulties for understanding the dynamics of these systems is the study their limit cycles. In this paper we study the maximum number of crossing limit cycles of som

M. V. Dolgopolik

We present a simple and efficient acceleration technique for an arbitrary method for computing the Euclidean projection of a point onto a convex polytope, defined as the convex hull of a finite number of points, in the case when the number of points in the polytope is much greater than the dimension of the space. The technique consists in applying any given

Nic Fishman, Leif Hancox-Li

"Attention is all you need" has become a fundamental precept in machine learning research. Originally designed for machine translation, transformers and the attention mechanisms that underpin them now find success across many problem domains. With the apparent domain-agnostic success of transformers, many researchers are excited that similar model architectu

Loris Di Cairano

We develop a geometric theory of phase transitions (PTs) for Hamiltonian systems in the microcanonical ensemble. This theory allows to reformulate Bachmann's classification of PTs for finite-size systems in terms of geometric properties of the energy level sets (ELSs) associated to the Hamiltonian function. Specifically, by defining the microcanonical entrop

Aycil Cesmelioglu, Dinh Dong Pham, Sander Rhebergen

We present a high-order hybridized discontinuous Galerkin (HDG) method for the fully coupled time-dependent Stokes-Darcy-transport problem where the fluid viscosity and source/sink terms depend on the concentration and the dispersion/diffusion tensor depends on the fluid velocity. This HDG method is such that the discrete flow equations are compatible with t

Ivan Ezhov, Marcel Rosier, Lucas Zimmer, Florian Kofler

Solving the inverse problem is the key step in evaluating the capacity of a physical model to describe real phenomena. In medical image computing, it aligns with the classical theme of image-based model personalization. Traditionally, a solution to the problem is obtained by performing either sampling or variational inference based methods. Both approaches a

CUORE Collaboration, D. Q. Adams, C. Alduino, K. Alfonso

The Cryogenic Underground Observatory for Rare Events (CUORE) is the most sensitive experiment searching for neutrinoless double-beta decay ($0\nu\beta\beta$) in $^{130}\text{Te}$. CUORE uses a cryogenic array of 988 TeO$_2$ calorimeters operated at $\sim$10 mK with a total mass of 741 kg. To further increase the sensitivity, the detector response must be we

Nikhil Chandak, Kenny Chour, Sivakumar Rathinam, R. Ravi

We interleave sampling based motion planning methods with pruning ideas from minimum spanning tree algorithms to develop a new approach for solving a Multi-Goal Path Finding (MGPF) problem in high dimensional spaces. The approach alternates between sampling points from selected regions in the search space and de-emphasizing regions that may not lead to good

Kevin Ryczko, Jaron T. Krogel, Isaac Tamblyn

We present two machine learning methodologies that are capable of predicting diffusion Monte Carlo (DMC) energies with small datasets (~60 DMC calculations in total). The first uses voxel deep neural networks (VDNNs) to predict DMC energy densities using Kohn-Sham density functional theory (DFT) electron densities as input. The second uses kernel ridge regre

Manfred Bucher

The rotated stripes are a consequence of the orthorhombic crystal lattice and the isotropy of Coulomb repulsion between pairs of doped holes, residing at oxygen lattice sites. With stripe slanting, the doped-hole pairs come closer to equidistance than without. The slant ratio depends on the orthorhombicity $o$ as $s = \sqrt{o/2}$.

Iain Mackie, Paul Owoicho, Carlos Gemmell, Sophie Fischer

CODEC is a document and entity ranking benchmark that focuses on complex research topics. We target essay-style information needs of social science researchers, i.e. "How has the UK's Open Banking Regulation benefited Challenger Banks?". CODEC includes 42 topics developed by researchers and a new focused web corpus with semantic annotations including entity

Eli Sennesh, Tom Xu, Yoshihiro Maruyama

Applied category theory has recently developed libraries for computing with morphisms in interesting categories, while machine learning has developed ways of learning programs in interesting languages. Taking the analogy between categories and languages seriously, this paper defines a probabilistic generative model of morphisms in free monoidal categories ov

Jens Helge Reelfs, Oliver Hohlfeld, Markus Strohmaier, Niklas Henckell

Social media is subject to constant growth and evolution, yet little is known about their early phases of adoption. To shed light on this aspect, this paper empirically characterizes the initial and country-wide adoption of a new type of social media in Saudi Arabia that happened in 2017. Unlike established social media, the studied network Jodel is anonymou

Jacek Gulgowski, Piotr Kasprzak, Piotr Maćkowiak

The aim of the paper is to characterize (pre)compactness in the spaces of Lipschitz/H\"older continuous mappings acting from a compact metric space to a normed space. To this end some extensions and generalizations of already existing compactness criteria for the spaces of bounded and continuous mappings with values in normed spaces needed to be established.

Tim Menke, William P. Banner, Thomas R. Bergamaschi, Agustin Di Paolo

Nonpairwise multi-qubit interactions present a useful resource for quantum information processors. Their implementation would facilitate more efficient quantum simulations of molecules and combinatorial optimization problems, and they could simplify error suppression and error correction schemes. Here we present a superconducting circuit architecture in whic

Simon Marynissen, Jesse Heyninck, Bart Bogaerts, Marc Denecker

Justification theory is a general framework for the definition of semantics of rule-based languages that has a high explanatory potential. Nested justification systems, first introduced by Denecker et al. (2015), allow for the composition of justification systems. This notion of nesting thus enables the modular definition of semantics of rule-based languages

Alexandru Ionescu, Benoit Pausader, Xuecheng Wang, Klaus Widmayer

We prove asymptotic stability of the Poisson homogeneous equilibrium among solutions of the Vlassov-Poisson system in the Euclidean space $\mathbb{R}^3$. More precisely, we show that small, smooth, and localized perturbations of the Poisson equilibrium lead to global solutions of the Vlasov-Poisson system, which scatter to linear solutions at a polynomial ra

Bo Zhang

Many recent efforts center on assessing the ability of real-world evidence (RWE) generated from non-randomized, observational data to produce results compatible with those from randomized controlled trials (RCTs). One noticeable endeavor is the RCT DUPLICATE initiative (Franklin et al., 2020, 2021). To better reconcile findings from an observational study an

Ramis Movassagh, Mario Szegedy, Guanyang Wang

Sourav Chatterjee, Persi Diaconis, Allan Sly and Lingfu Zhang, prompted by a question of Ramis Movassagh, renewed the study of a process proposed in the early 1980s by Jean Bourgain. A state vector $v \in \mathbb R^n$, labeled with the vertices of a connected graph, $G$, changes in discrete time steps following the simple rule that at each step a random edge

Mohammad Javad Amiri, Chenyuan Wu, Divyakant Agrawal, Amr El Abbadi

Byzantine Fault-Tolerant (BFT) protocols have recently been extensively used by decentralized data management systems with non-trustworthy infrastructures, e.g., permissioned blockchains. BFT protocols cover a broad spectrum of design dimensions from infrastructure settings such as the communication topology, to more technical features such as commitment str

Alvin Chan, Yew-Soon Ong, Clement Tan

Model robustness is vital for the reliable deployment of machine learning models in real-world applications. Recent studies have shown that data augmentation can result in model over-relying on features in the low-frequency domain, sacrificing performance against low-frequency corruptions, highlighting a connection between frequency and robustness. Here, we

Stefano Lanza, Fernando Marchesano, Luca Martucci, Irene Valenzuela

In any consistent effective field theory of quantum gravity limits of infinite field distance are expected to lead to the EFT breakdown due to the appearance of an infinite tower of light states, as predicted by the Distance Conjecture. We review the Distant Axionic String Conjecture, which proposes that any 4d EFT infinite-field-distance limit can be realiz

Sergei A. Plotnikov

The problem of synchronization in heterogeneous networks of linear systems with nonlinear delayed diffusive coupling is considered. The network is presented in new coordinates mean-field dynamics and synchronization errors. Thus the problem of network synchronization is reduced to the studying of synchronization-error system stability. The circle criterion f

P. Blumer, M. Charlton, M. Chung, P. Clade

We present a description of the GBAR positron (e+) trapping apparatus, which consists of a three stage Buffer Gas Trap (BGT) followed by a High Field Penning Trap (HFT), and discuss its performance. The overall goal of the GBAR experiment is to measure the acceleration of the neutral antihydrogen (H) atom in the terrestrial gravitational field by neutralisin

Susmija Jabbireddy, Xuetong Sun, Xiaoxu Meng, Amitabh Varshney

With the recent interest in virtual reality and augmented reality, there is a newfound demand for displays that can provide high resolution with a wide field of view (FOV). However, such displays incur significantly higher costs for rendering the larger number of pixels. This poses the challenge of rendering realistic real-time images that have a wide FOV an

Claudia Roberts, Maria Dimakopoulou, Qifeng Qiao, Ashok Chandrashekhar

Contextual bandits are widely used in industrial personalization systems. These online learning frameworks learn a treatment assignment policy in the presence of treatment effects that vary with the observed contextual features of the users. While personalization creates a rich user experience that reflect individual interests, there are benefits of a shared

Damanvir Singh Binner

In this note, we obtain a formula which leads to a practical and efficient method to calculate the number of partitions of n into parts not divisible by m for given natural numbers n and m.

Theodore Steele, Kinwah Wu

Fronts are regions of transition from one state to another in a medium. They are present in many areas of science and applied mathematics, and modelling them and their evolution is often an effective way of treating the underlying phenomena responsible for them. In this paper, we propose a new approach to modelling front propagation, which characterises the

Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, Alessandro Bozzon

In an effort to regulate Machine Learning-driven (ML) systems, current auditing processes mostly focus on detecting harmful algorithmic biases. While these strategies have proven to be impactful, some values outlined in documents dealing with ethics in ML-driven systems are still underrepresented in auditing processes. Such unaddressed values mainly deal wit

Adrien Rosuel, Christophe Marcenat, Georg Knebel, Thierry Klein

The recently discovered superconductor UTe$_2$, with a T$_c$ between 1.5~K and 2~K, is attracting much attention due to strong suspicion of spin-triplet and topological superconductivity. Its properties under magnetic field are also remarkable, with field-reinforced and field-induced superconducting phases. Here, we report the first complete thermodynamic de

Zhijian Yang, Junhao Wen, Christos Davatzikos

A plethora of machine learning methods have been applied to imaging data, enabling the construction of clinically relevant imaging signatures of neurological and neuropsychiatric diseases. Oftentimes, such methods don't explicitly model the heterogeneity of disease effects, or approach it via nonlinear models that are not interpretable. Moreover, unsupervise

Iman Askari, Mulugeta A. Haile, Xuemin Tu, Huazhen Fang

The implicit particle filter seeks to mitigate particle degeneracy by identifying particles in the target distribution's high-probability regions. This study is motivated by the need to enhance computational tractability in implementing this approach. We investigate the connection of the particle update step in the implicit particle filter with that of the K

Dixon Domfeh, Arpita Chatterjee, Matthew Dixon

Catastrophe (CAT) bond markets are incomplete and hence carry uncertainty in instrument pricing. As such various pricing approaches have been proposed, but none treat the uncertainty in catastrophe occurrences and interest rates in a sufficiently flexible and statistically reliable way within a unifying asset pricing framework. Consequently, little is known

Jurijs Nazarovs, Zhichun Huang

Generating smooth animations from a limited number of sequential observations has a number of applications in vision. For example, it can be used to increase number of frames per second, or generating a new trajectory only based on first and last frames, e.g. a motion of face emotions. Despite the discrete observed data (frames), the problem of generating a

Dandan Ma, Yong Wang, Mueed Azhar, Ansgar Adler

Stent intervention is a recommended therapy to reduce the pressure gradient and restore blood flow for patients with coarctation of the aorta (CoA). In this work, we developed a framework for personalized stent intervention in CoA using in silico modeling, combining computational fluid dynamics (CFD) and image-based prediction of the geometry of the aorta af

Md. Mashih Ibn Yasin Adan, Md. Kamrujjaman, Md. Mamun Molla, Muhammad Mohebujjaman

This paper investigates the competition of two species in a heterogeneous environment subject to the effect of harvesting. The most realistic harvesting case is connected with the intrinsic growth rate, and the harvesting functions are developed based on this clause instead of random choice. We prove the existence and uniqueness of the solution to the model

Gustavo Rigolin

We derive a Dirac-like equation, the asymmetric Dirac equation, where particles and antiparticles sharing the same wave number have different energies and momenta. We show that this equation is Lorentz covariant under proper Lorentz transformations (boosts and spatial rotations) and also determine the corresponding transformation law for its wave function. W

Dian Yu, Mingqiu Wang, Yuan Cao, Izhak Shafran

Carefully-designed schemas describing how to collect and annotate dialog corpora are a prerequisite towards building task-oriented dialog systems. In practical applications, manually designing schemas can be error-prone, laborious, iterative, and slow, especially when the schema is complicated. To alleviate this expensive and time consuming process, we propo

S. Mironov, An. Morozov

We study the Mana and Magic for quantum states. They have a standard definition through the Clifford group, which is finite and thus classically computable. We introduce a modified Mana and Magic, which keep their main property of classical computability, while making other states classically computable. We also apply these new definitions to the studies of

Li Chen, Jinyeop Lee, Yue Li, Matthew Liew

We re-examine the combined semi-classical and mean-field limit in the $N$-body fermionic Schr\"odinger equation with pure state initial data using the Husimi measure framework. The Husimi measure equation involves three residue types: kinetic, semiclassical, and mean-field. The main result of this paper is to provide better estimates for the kinetic and mean

Mario Santilli, Bernd Schmidt

We consider a two phase elastic thin film with soft inclusions subject to bending dominated deformations. The soft (void) phase may comprise asymptotically small droplets within the elastic matrix. We perform a dimension reduction analysis and obtain a novel `Blake-Zisserman-Kirchhoff' model that combines Kirchhoff's classical plate theory with Blake-Zisserm

Nikil Pancha, Andrew Zhai, Jure Leskovec, Charles Rosenberg

Sequential models have become increasingly popular in powering personalized recommendation systems over the past several years. These approaches traditionally model a user's actions on a website as a sequence to predict the user's next action. While theoretically simplistic, these models are quite challenging to deploy in production, commonly requiring strea

Iman Askari, Babak Badnava, Thomas Woodruff, Shen Zeng

Control of machine learning models has emerged as an important paradigm for a broad range of robotics applications. In this paper, we present a sampling-based nonlinear model predictive control (NMPC) approach for control of neural network dynamics. We show its design in two parts: 1) formulating conventional optimization-based NMPC as a Bayesian state estim

Courtney Mansfield, Amandalynne Paullada, Kristen Howell

Many datasets contain personally identifiable information, or PII, which poses privacy risks to individuals. PII masking is commonly used to redact personal information such as names, addresses, and phone numbers from text data. Most modern PII masking pipelines involve machine learning algorithms. However, these systems may vary in performance, such that in

Allard Oelen, Markus Stocker, Sören Auer

As the number of published scholarly articles grows steadily each year, new methods are needed to organize scholarly knowledge so that it can be more efficiently discovered and used. Natural Language Processing (NLP) techniques are able to autonomously process scholarly articles at scale and to create machine readable representations of the article content.

Ted C. Rogers

I propose a method for tracking and assessing scientific progress using a prediction consensus algorithm designed for the purpose. The protocol obviates the need for centralized referees to generate scientific questions, gather predictions, and assess the accuracy or success of those predictions. It relies instead on crowd wisdom and a system of checks and b

Zeyu Ma, Zachary Teed, Jia Deng

We address multiview stereo (MVS), an important 3D vision task that reconstructs a 3D model such as a dense point cloud from multiple calibrated images. We propose CER-MVS (Cascaded Epipolar RAFT Multiview Stereo), a new approach based on the RAFT (Recurrent All-Pairs Field Transforms) architecture developed for optical flow. CER-MVS introduces five new chan

Janet Y Sheung, Jonathan Garamella, Stella K Kahl, Brian Y Lee

The cytoskeleton -- a composite network of biopolymers, molecular motors, and associated binding proteins -- is a paradigmatic example of active matter. Particle transport through the cytoskeleton can range from anomalous and heterogeneous subdiffusion to superdiffusion and advection. Yet, recapitulating and understanding these properties -- ubiquitous to th

Ruoshui Wang, Xiangdong Zeng, Ce Shen, Ling-Yan Hung

In this paper, we propose a general implementation of the Virasoro generators and Kac-Moody currents in generic tensor network representations of 2-dimensional critical lattice models. Our proposal works even when a quantum Hamiltonian of the lattice model is not available, which is the case in many numerical computations involving numerical blockings. We te

Gebhard Böckle, Tony Feng, Michael Harris, Chandrashekhar Khare

Let $G$ be a split semi-simple group over a global function field $K$. Given a cuspidal automorphic representation $\Pi$ of $G$ satisfying a technical hypothesis, we prove that for almost all primes $\ell$, there is a cyclic base change lifting of $\Pi$ along any $\mathbb{Z}/\ell\mathbb{Z}$-extension of $K$. Our proof does not rely on any trace formulas; ins

Shohre Janjan, Fardin Kheirandish

In the present work, we have analyzed the motion of a structured matter wave in the presence of a constant magnetic field and under the influence of a time-dependent external force. We have introduced exact propagator kernels obtained from partial differential equations based on the Heisenberg equations of motion. The initial wave function is assumed as a Ga

Iman Askari, Shen Zeng, Huazhen Fang

Nonlinear model predictive control (NMPC) has gained widespread use in many applications. Its formulation traditionally involves repetitively solving a nonlinear constrained optimization problem online. In this paper, we investigate NMPC through the lens of Bayesian estimation and highlight that the Monte Carlo sampling method can offer a favorable way to im

Yijie Zhu, Benjamin Beringue, Steve K. Choi, Nicholas Battaglia

Rayleigh scattering of the cosmic microwave background (CMB) by neutral hydrogen shortly after recombination leaves frequency-dependent imprints on intensity and polarization fluctuations. High signal-to-noise observations of CMB Rayleigh scattering would provide additional insight into the physics of recombination, including greater constraining power for p

Marcos Marino, Ramon Miravitllas, Tomas Reis

Some sigma models which admit a theta angle are integrable at both $\vartheta=0$ and $\vartheta=\pi$. This includes the well-known $O(3)$ sigma model and two families of coset sigma models studied by Fendley. We consider the ground state energy of these models in the presence of a magnetic field, which can be computed with the Bethe ansatz. We obtain explici

Karia R. Dibert, Adam J. Anderson, Amy N. Bender, Bradford A. Benson

The Rayleigh scattering of cosmic microwave background (CMB) photons off the neutral hydrogen produced during recombination effectively creates an additional scattering surface after recombination that encodes new cosmological information, including the expansion and ionization history of the universe. A first detection of Rayleigh scattering is a tantalizin

Claude Duhr, Bernhard Mistlberger, Gherardo Vita

We calculate soft phase-space and loop master integrals tor the computation of color-singlet cross sections through N$^3$LO in perturbative QCD. Our results are functions of homogeneous transcendental weight and include the first nine terms in the expansion in the dimensional regulator $\epsilon$. We discuss the application of our results to the computation

Farhan Islam, Thaís V. Trevisan, Thomas Heitmann, Santanu Pakhira

We report neutron-diffraction results on single-crystal CaMn$_2$P$_2$ containing corrugated Mn honeycomb layers and determine its ground-state magnetic structure. The diffraction patterns consist of prominent (1/6, 1/6, $L$) reciprocal lattice unit (r.l.u.; $L$ = integer) magnetic Bragg reflections, whose temperature-dependent intensities are consistent with

Mahsa Taheri, Fang Xie, Johannes Lederer

Since statistical guarantees for neural networks are usually restricted to global optima of intricate objective functions, it is unclear whether these theories explain the performances of actual outputs of neural network pipelines. The goal of this paper is, therefore, to bring statistical theory closer to practice. We develop statistical guarantees for shal

Artyom Nikitin, Andrei Chertkov, Rafael Ballester-Ripoll, Ivan Oseledets

Collaborative filtering models generally perform better than content-based filtering models and do not require careful feature engineering. However, in the cold-start scenario collaborative information may be scarce or even unavailable, whereas the content information may be abundant, but also noisy and expensive to acquire. Thus, selection of particular fea

Xiaoqi Huang, Christopher D. Sogge, Michael E. Taylor

We show that if $Y$ is a compact Riemannian manifold with improved $L^q$ eigenfunction estimates then, at least for large enough exponents, one always obtains improved $L^q$ bounds on the product manifold $X\times Y$ if $X$ is another compact manifold. Similarly, improved Weyl remainder term bounds on the spectral counting function of $Y$ lead to correspondi

Trystyn A. M. Berg, Guido Cupani, Pedro Figueira, Andrea Mehner

As of October 2021 (Period 108), the European Southern Observatory (ESO) offers a new mode of the ESPRESSO spectrograph designed to use the High Resolution grating with 4x2 binning (spatial by spectral; HR42 mode) with the specific objective of observing faint targets with a single Unit Telescope at Paranal. We validated the new HR42 mode using four hours of

Kenta Kiuchi, Loren E. Held, Yuichiro Sekiguchi, Masaru Shibata

We implement advanced Riemann solvers HLLC and HLLD \cite{Mignone:2005ft,MUB:2009} together with an advanced constrained transport scheme \cite{Gardiner:2007nc} in a numerical-relativity neutrino-radiation magnetohydrodynamics code. We validate our implementation by performing a series of one- and multi-dimensional test problems for relativistic hydrodynamic

Salvatore Bottaro, Dario Buttazzo, Marco Costa, Roberto Franceschini

We continue the study of weakly interacting massive particles (WIMP) started in [arXiv:2107.09688], focusing on a single complex electroweak $n$-plet with non-zero hypercharge added to the Standard Model. The minimal splitting between the Dark Matter and its electroweak neutral partner required to circumvent direct detection constraints allows only multiplet

Adam R. Brown

This paper proves the polynomial equivalence of a broad class of definitions of quantum computational complexity. We study right-invariant metrics on the unitary group -- often called `complexity geometries' following the definition of quantum complexity proposed by Nielsen -- and delineate the equivalence class of metrics that have the same computational po

Joakim Argillander, Alvaro Alarcón, Guilherme B. Xavier

Quantum random number generators (QRNG) are based on the naturally random measurement results performed on individual quantum systems. Here, we demonstrate a branching-path photonic QRNG implemented with a Sagnac interferometer with a tunable splitting ratio. The fine-tuning of the splitting ratio allows us to maximize the entropy of the generated sequence o

Gopal-Krishna, Ravi Joshi, Dusmanta Patra, Xiaolong Yang

We report observations of a bright dumb-bell system of galaxies ($z =0.162$) with the upgraded Giant Metrewave Radio Telescope ($u$GMRT), which show that each member of this gravitationally bound pair of galaxies hosts bipolar radio jets extended on 100 kiloparsec scales. Only two cases of such radio morphology have been reported previously, both being dumb-