January 2022 arXiv papers — page 63

Bobin Li

Phase transition is a central topic in condensed matter physics, all the time. In this paper, as a general representative of phase transition, the Cu crystallization is discussed. And some physical quantities is defined to quantificationally describe the structure property in Cu crystallization, such as diffusion property and symmetry so on. As a result, it

Jianye Hao, Jiawen Lu, Xijun Li, Xialiang Tong

The Dynamic Pickup and Delivery Problem (DPDP) is an essential problem within the logistics domain. So far, research on this problem has mainly focused on using artificial data which fails to reflect the complexity of real-world problems. In this draft, we would like to introduce a new benchmark from real business scenarios as well as a simulator supporting

Cristiana De Filippis, Giuseppe Mingione

Local Schauder estimates hold in the nonuniformly elliptic setting. Specifically, first derivatives of solutions to nonuniformly elliptic variational problems and elliptic equations are locally H\"older continuous, provided coefficients are locally H\"older continuous.

Gautam Rajendrakumar Gare, Wanwen Chen, Alex Ling Yu Hung, Edward Chen

In this paper, we study the significance of the pleura and adipose tissue in lung ultrasound AI analysis. We highlight their more prominent appearance when using high-frequency linear (HFL) instead of curvilinear ultrasound probes, showing HFL reveals better pleura detail. We compare the diagnostic utility of the pleura and adipose tissue using an HFL ultras

Yu Feng, Nathan Goulding-Hotta, Asif Khan, Hans Reyserhove

Gaze tracking is increasingly becoming an essential component in Augmented and Virtual Reality. Modern gaze tracking al gorithms are heavyweight; they operate at most 5 Hz on mobile processors despite that near-eye cameras comfortably operate at a r eal-time rate ($>$ 30 Hz). This paper presents a real-time eye tracking algorithm that, on average, operates a

Farzad Shahi

This is a review of the basic concepts of the theory of real and complex smooth vector bundles with finite rank. Besides, the concept of a tensor field is studied within the general framework of a smooth vector bundle rather than a smooth manifold.

Yue Ruan, Han-Hung Lee, Yiming Zhang, Ke Zhang

Text-to-shape retrieval is an increasingly relevant problem with the growth of 3D shape data. Recent work on contrastive losses for learning joint embeddings over multimodal data has been successful at tasks such as retrieval and classification. Thus far, work on joint representation learning for 3D shapes and text has focused on improving embeddings through

Liang Ding, Keqin Peng, Dacheng Tao

We present a simple and effective pretraining strategy {D}en{o}ising {T}raining DoT for neural machine translation. Specifically, we update the model parameters with source- and target-side denoising tasks at the early stage and then tune the model normally. Notably, our approach does not increase any parameters or training steps, requiring the parallel data

V. V. Barinov, B. T. Cleveland, S. N. Danshin, H. Ejiri

The Baksan Experiment on Sterile Transitions (BEST) probes the gallium anomaly and its possible connections to oscillations between active and sterile neutrinos. Based on the Gallium-Germanium Neutrino Telescope (GGNT) technology of the SAGE experiment, BEST employs two zones of liquid Ga target to explore neutrino oscillations on the meter scale. Oscillatio

Xinye Liu, Gad Licht, Xirui Wang, Stuart Licht

The electrolysis of CO$_2$ in molten carbonate has been introduced as an alternative mechanism to synthesize carbon nanomaterials inexpensively at high yield. Until recently, CO$_2$ was thought to be unreactive, making its removal a challenge. CO$_2$ is the main cause of anthropogenic global warming and its utilization and transformation into a stable, valua

Genya Ishigaki, Siddartha Devic, Riti Gour, Jason P. Jue

The emergence of diverse network applications demands more flexible and responsive resource allocation for networks. Network slicing is a key enabling technology that provides each network service with a tailored set of network resources to satisfy specific service requirements. The focus of this paper is the network slicing of access networks realized by Pa

Tsung-Cheng Lu, Sagar Vijay

Topologically-ordered phases of matter at non-zero temperature are conjectured to exhibit universal patterns of long-range entanglement which may be detected by a mixed-state entanglement measure known as entanglement negativity. We show that the entanglement negativity in certain topological orders can be understood through the properties of an emergent sym

Yuki Yanagi, Hiroaki Kusunose, Takuya Nomoto, Ryotaro Arita

We present a systematic method to automatically generate symmetry-adapted magnetic structures for given crystal structure and general propagation vector k as an efficient approach of the analysis of complex modulated magnetic structures. The method is developed as an extension of the generation scheme based on multipole expansion, which was demonstrated only

Henric Krawczynski, Banafsheh Beheshtipour

We present a new analysis of NuSTAR and Suzaku observations of the black hole Cygnus X-1 in the intermediate state. The analysis uses kerrC, a new model for analyzing spectral and spectropolarimetric X-ray observations of black holes. kerrC builds on a large library of simulated black holes in X-ray binaries. The model accounts for the X-ray emission from a

M. Andrecut

Behavioral Indicators of Compromise are associated with various automated methods used to extract the sample behavior by observing the system function calls performed in a virtual execution environment. Thus, every sample is described by a set of BICs triggered by the sample behavior in the sandbox environment. Here we discuss a Machine Learning approach to

Jonathan D. Sterk, Henry Coakley, Joshua Goldberg, Vincent Hietala

Shuttling ions at high speed and with low motional excitation is essential for realizing fast and high-fidelity algorithms in many trapped-ion based quantum computing architectures. Achieving such performance is challenging due to the sensitivity of an ion to electric fields and the unknown and imperfect environmental and control variables that create them.

Gautam Rajendrakumar Gare, Hai V. Tran, Bennett P deBoisblanc, Ricardo Luis Rodriguez

With the onset of the COVID-19 pandemic, ultrasound has emerged as an effective tool for bedside monitoring of patients. Due to this, a large amount of lung ultrasound scans have been made available which can be used for AI based diagnosis and analysis. Several AI-based patient severity scoring models have been proposed that rely on scoring the appearance of

Mason A. Porter

I give a short introduction to data ethics. I begin with some background information and societal context for data ethics. I then discuss data ethics in mathematical-science education and indicate some available course material. I briefly highlight a few efforts -- at my home institution and elsewhere -- on data ethics, society, and social good. I then discu

Chengxi Zhao, Kisung Kang, Joerg C. Neuefeind, Andre Schleife

The ordered tetragonal intermetallic Cr$_2$Al forms the same structure type as Mn$_2$Au, and the latter has been heavily investigated for its potential in antiferromagnetic spintronics due to its degenerate in-plane N\'{e}el vector. We present the single crystal flux growth of Cr$_2$Al and orientation-dependent magnetic properties. Powder neutron diffraction

Taro Matsuo, Satoshi Ikari, Hirotaka Kondo, Sho Ishiwata

Infrared space interferometers can surpass the spatial resolution limitations of single-dish space telescopes. However, stellar interferometers from space have not been realized because of technical difficulties. Two beams coming from individual satellites separated by more than a few tens of meters should precisely interfere such that the optical-path and a

Ichiro Oda

We perform the manifestly covariant quantization of a scale invariant gravity with a scalar field, which is equivalent to the well-known Brans-Dicke gravity via a field redefinition of the scalar field, in the de Donder gauge condition (or harmonic gauge condition) for general coordinate invariance. First, without specifying the expression of a gravitational

Runpeng Li, Prativa Sahoo, Dongrui Wang, Qixuan Wang

Chimeric antigen receptor (CAR) T-cell based immunotherapy has shown its potential in treating blood cancers, and its application to solid tumors is currently being extensively investigated. For glioma brain tumors, various CAR T-cell targets include IL13Ra2, EGFRvIII, HER2, EphA2, GD2, B7-H3, and chlorotoxin. In this work, we are interested in developing a

Minzhao Lyu, Hassan Habibi Gharakheili, Craig Russell, Vijay Sivaraman

The Domain Name System (DNS) is a critical service that enables domain names to be converted to IP addresses (or vice versa); consequently, it is generally permitted through enterprise security systems (e.g., firewalls) with little restriction. This has exposed organizational networks to DDoS, exfiltration, and reflection attacks, inflicting significant fina

Finlay Macklon, Markos Viggiato, Natalia Romanova, Chris Buzon

The HTML5 <canvas> is widely used to display high quality graphics in web applications. However, the combination of web, GUI, and visual techniques that are required to build <canvas> applications, together with the lack of testing and debugging tools, makes developing such applications very challenging. To help direct future research on testing <canvas> app

Yi Huang, Brian Skinner, B. I. Shklovskii

We are honored to dedicate this article to Emmanuel Rashba on the occasion of his 95 birthday. In the ideal disorder-free situation, a two-dimensional band gap insulator has an activation energy for conductivity equal to half the band gap, $\Delta$. But transport experiments usually exhibit a much smaller activation energy at low temperature, and the relatio

John Kuszmaul

The bamboo trimming problem considers $n$ bamboo with growth rates $h_1, h_2, \ldots, h_n$ satisfying $\sum_i h_i = 1$. During a given unit of time, each bamboo grows by $h_i$, and then the bamboo-trimming algorithm gets to trim one of the bamboo back down to height zero. The goal is to minimize the height of the tallest bamboo, also known as the backlog. Th

Pallavi Patil, Mark Whittle, Kristina Nyland, Carol Lonsdale

We present radio spectra spanning $0.1 - 10$ GHz for the sample of heavily obscured luminous quasars with extremely red mid-infrared-optical colors and compact radio emission. The spectra are constructed from targeted 10 GHz observations and archival radio survey data, which together yield $6-11$ flux density measurements for each object. Our suite of Python

Samuel Deng, Yilin Guo, Daniel Hsu, Debmalya Mandal

We introduce a tensor-based model of shared representation for meta-learning from a diverse set of tasks. Prior works on learning linear representations for meta-learning assume that there is a common shared representation across different tasks, and do not consider the additional task-specific observable side information. In this work, we model the meta-par

Yifei Guan, Adam Subel, Ashesh Chattopadhyay, Pedram Hassanzadeh

We demonstrate how incorporating physics constraints into convolutional neural networks (CNNs) enables learning subgrid-scale (SGS) closures for stable and accurate large-eddy simulations (LES) in the small-data regime (i.e., when the availability of high-quality training data is limited). Using several setups of forced 2D turbulence as the testbeds, we exam

Lei Zhou, Joseph Bae, Huidong Liu, Gagandeep Singh

Well-labeled datasets of chest radiographs (CXRs) are difficult to acquire due to the high cost of annotation. Thus, it is desirable to learn a robust and transferable representation in an unsupervised manner to benefit tasks that lack labeled data. Unlike natural images, medical images have their own domain prior; e.g., we observe that many pulmonary diseas

Vicente Cortés, Jeremias Ehlert, Alexander S. Haupt, David Lindemann

We classify all left-invariant pseudo-Riemannian Einstein metrics on $\mathrm{SL}(2,\mathbb{R})\times \mathrm{SL}(2,\mathbb{R})$ that are bi-invariant under a one-parameter subgroup. We find that there are precisely two such metrics up to homothety, the Killing form and a nearly pseudo-K\"ahler metric.

Arda Genc, Libor Kovarik, Hamish L. Fraser

Heterogeneous catalysts possess complex surface and bulk structures, relatively poor intrinsic contrast, and often a sparse distribution of the catalytic nanoparticles (NPs), posing a significant challenge for image segmentation, including the current state-of-the-art deep learning methods. To tackle this problem, we apply a deep learning-based approach for

Sean Deyo, Veit Elser

We implement a divide-and-concur iterative projection approach to context-free grammar inference. Unlike most state-of-the-art models of natural language processing, our method requires a relatively small number of discrete parameters, making the inferred grammar directly interpretable -- one can read off from a solution how to construct grammatically valid

Yogesh S. S. Patil, Jiaxin Yu, Sean Frazier, Yiqi Wang

We use single photon detectors to probe the motional state of a superfluid $^4$He resonator of mass $\sim1$ ng. The arrival times of Stokes and anti-Stokes photons (scattered by the resonator's acoustic mode) are used to measure the resonator's phonon coherences up to the fourth order. By post-selecting on photon detection events, we also measure coherences

Laura Congreve Hunter, Liese van Zee, Kristen B. W. McQuinn, Ray Garner

Stellar feedback is fundamental to the modeling of galaxy evolution as it drives turbulence and outflows in galaxies. Understanding the timescales involved are critical for constraining the impact of stellar feedback on the interstellar medium (ISM). We analyzed the resolved star formation histories along with the spatial distribution and kinematics of the a

Noelia Ferruz, Birte Höcker

The 21st century is presenting humankind with unprecedented environmental and medical challenges. The ability to design novel proteins tailored for specific purposes could transform our ability to respond timely to these issues. Recent advances in the field of artificial intelligence are now setting the stage to make this goal achievable. Protein sequences a

Alex Debrecht, Jonathan Carroll-Nellenback, Adam Frank, Eric G. Blackman

The role of charge exchange in shaping exoplanet photoevaporation remains a topic of contention. Exchange of electrons between stellar wind protons from the exoplanet's host star and neutral hydrogen from the planet's wind has been proposed as a mechanism to create "energetic neutral atoms" (ENAs), which could explain the high absorption line velocities obse

Siu Wun Cheung, Youngsoo Choi, Dylan Matthew Copeland, Kevin Huynh

Rayleigh-Taylor instability is a classical hydrodynamic instability of great interest in various disciplines of science and engineering, including astrophyics, atmospheric sciences and climate, geophysics, and fusion energy. Analytical methods cannot be applied to explain the long-time behavior of Rayleigh-Taylor instability, and therefore numerical simulati

Satoshi Ohashi, Claudio Codella, Nami Sakai, Claire J. Chandler

We report a study of the low-mass Class-0 multiple system VLA 1623AB in the Ophiuchus star-forming region, using H$^{13}$CO$^+$ ($J=3-2$), CS ($J=5-4$), and CCH ($N=3-2$) lines as part of the ALMA Large Program FAUST. The analysis of the velocity fields revealed the rotation motion in the envelope and the velocity gradients in the outflows (about 2000 au dow

Jacob P. Matherne, Alejandro H. Morales, Jesse Selover

Chromatic symmetric functions are well-studied symmetric functions in algebraic combinatorics that generalize the chromatic polynomial and are related to Hessenberg varieties and diagonal harmonics. Motivated by the Stanley--Stembridge conjecture, we show that the allowable coloring weights for indifference graphs of Dyck paths are the lattice points of a pe

Yinghe Qi, Shiyong Tan, Noah Corbitt, Carl Urbanik

From air-sea gas exchange, oil pollution, to bioreactors, the ubiquitous fragmentation of bubbles/drops in turbulence has been modelled by relying on the classical Kolmogorov-Hinze paradigm since the 1950s. This framework hypothesizes that bubbles/drops are broken solely by eddies of the same size, even though turbulence is well known for its wide spectrum o

Andreas Ekstedt

This paper extends classical results by Langer and Kramers and combines them with modern methods from high-temperature field theory. Assuming Langevin dynamics, the end-product is an all-orders description of bubble-nucleation at high temperatures. Specifically, it's shown that equilibrium and non-equilibrium effects factorize to all orders, and that the nuc

Natalia G. Berloff

We develop a universal model based on the classical complex matter fields that allow the optimal mapping of many real-life NP-hard combinatorial optimisation problems into the problem of minimising a spin Hamiltonian. We explicitly formulate one-to-one mapping for three famous problems: graph colouring, the travelling salesman, and the modular N-queens probl

C. A. Holliman, M. Fan, A. Contractor, S. M. Brewer

We report the first operation of a Ra$^{+}$ optical clock, a promising high-performance clock candidate. The clock uses a single trapped $^{226}$Ra$^{+}$ ion and operates on the $7s\ ^2S_{1/2}\rightarrow$ $6d\ ^2D_{5/2}$ electric quadrupole transition. By self-referencing three pairs of symmetric Zeeman transitions, we demonstrate a frequency instability of

Matey Neykov

We determine the exact minimax rate of a Gaussian sequence model under bounded convex constraints, purely in terms of the local geometry of the given constraint set $K$. Our main result shows that the minimax risk (up to constant factors) under the squared $\ell_2$ loss is given by $\epsilon^{*2} \wedge \operatorname{diam}(K)^2$ with \begin{align*} \epsilon^

Harsh Sharma, Mayuresh Patil, Craig Woolsey

In this paper, we present two Hermite polynomial based approaches to derive one-step numerical integrators for mechanical systems. These methods are based on discretizing the configuration using Hermite polynomials which leads to numerical trajectories continuous in both configuration and velocity. First, we incorporate Hermite polynomials for time-discretiz

Shalosh B. Ekhad, Doron Zeilberger

Arthur Cayley famously proved that there are n to the power n-2 labeled trees on n vertices. Here we go much further and show how to enumerate, fully automatically, labeled trees such that every vertex has a number of neighbors that belongs to a specified finite set, and also count trees where the number of neighbors is not allowed to be in a given finite se

Daria Sushnikova, Leslie Greengard, Michael O'Neil, Manas Rachh

We present a fast direct solver for boundary integral equations on complex surfaces in three dimensions using an extension of the recently introduced recursive strong skeletonization scheme. For problems that are not highly oscillatory, our algorithm computes an ${LU}$-like hierarchical factorization of the dense system matrix, permitting application of the

Theo de Reus

Recent theoretical investigations of pair-production probabilities for given sets of nuclear trajectories predict the observability of spontaneous positron emission in elastic collisions of heavy ions. These calculations are extended to the investigation of ratios $P_{y}(\eta)/P_{y}(1)$ for partial probabilities of positron emission at small positron energie

Salem AlJanah, Ning Zhang, Siok Wah Tay

Existing authentication solutions proposed for Internet of Things (IoT) provide a single Level of Assurance (LoA) regardless of the sensitivity levels of the resources or interactions between IoT devices being protected. For effective (with adequate level of protection) and efficient (with as low overhead costs as possible) protections, it may be desirable t

Siyuan Shan, Vishal Baskaran, Haidong Yi, Jolene Ranek

Modern single-cell flow and mass cytometry technologies measure the expression of several proteins of the individual cells within a blood or tissue sample. Each profiled biological sample is thus represented by a set of hundreds of thousands of multidimensional cell feature vectors, which incurs a high computational cost to predict each biological sample's a

Navpreet Kaur, Jason Hughes, Juntao Chen

With the continuous rise of the COVID-19 cases worldwide, it is imperative to ensure that all those vulnerable countries lacking vaccine resources can receive sufficient support to contain the risks. COVAX is such an initiative operated by the WHO to supply vaccines to the most needed countries. One critical problem faced by the COVAX is how to distribute th

Elizabeth Medina, Sarah Peterson, Kristan Singletary, Lucia Peixoto

Brain development relies on both experience and genetically defined programs. Time windows where certain brain circuits are particularly receptive to external stimuli, resulting in heightened plasticity, are referred to as critical periods. Sleep is thought to be essential for normal brain development. Importantly, studies have shown that sleep enhances crit

Kenichi Shimizu

This paper studies large sample properties of a Bayesian approach to inference about slope parameters $\gamma$ in linear regression models with a structural break. In contrast to the conventional approach to inference about $\gamma$ that does not take into account the uncertainty of the unknown break location $\tau$, the Bayesian approach that we consider in

Gang Chen

Although it has been shown experimentally that a temperature discontinuity exists at the liquid-vapor interface during evaporation and condensation, quantitatively modeling this temperature jump has been difficult. The classical Schrage equation does not give enough information to determine the interfacial temperature jump. Starting from the Boltzmann transp

Qiyuan An, Ruijiang Li, Lin Gu, Hao Zhang

Unsupervised domain adaptation (UDA) generally aligns the unlabeled target domain data to the distribution of the source domain to mitigate the distribution shift problem. The standard UDA requires sharing the source data with the target, having potential data privacy leaking risks. To protect the source data's privacy, we first propose to share the source f

Morris Stallmann, Anna Wilbik

Federated Learning (FL) is a setting where multiple parties with distributed data collaborate in training a joint Machine Learning (ML) model while keeping all data local at the parties. Federated clustering is an area of research within FL that is concerned with grouping together data that is globally similar while keeping all data local. We describe how th

B. Riaz, W. -F. Thi

We have conducted the first extensive observational survey of several deuterated species in 16 Class 0/I proto-brown dwarfs (proto-BDs) and 4 Class Flat/Class II brown dwarfs. Observations were obtained with the IRAM 30m telescope in the DCO$^{+}$ (3-2), DCN (3-2), DNC (3-2), and N$_{2}$D$^{+}$ (3-2) lines. The DCO$^{+}$/H$^{13}$CO$^{+}$, DCN/H$^{13}$CN, and

Maarten Van Damme, Laurens Vanderstraeten

We introduce a method based on matrix product states (MPS) for computing spectral functions of (quasi) one-dimensional spin chains, working directly in momentum space in the thermodynamic limit. We simulate the time evolution after applying a momentum operator to an MPS ground state by working with the momentum superposition of a window MPS. We show explicit

The ANTARES collaboration, A. Albert, S. Alves, M. André

For several decades, the origin of ultra-high-energy cosmic rays (UHECRs) has been an unsolved question of high-energy astrophysics. One approach for solving this puzzle is to correlate UHECRs with high-energy neutrinos, since neutrinos are a direct probe of hadronic interactions of cosmic rays and are not deflected by magnetic fields. In this paper, we pres

Qianlin Liang, Walid A. Hanafy, Ahmed Ali-Eldin, Prashant Shenoy

Since emerging edge applications such as Internet of Things (IoT) analytics and augmented reality have tight latency constraints, hardware AI accelerators have been recently proposed to speed up deep neural network (DNN) inference run by these applications. Resource-constrained edge servers and accelerators tend to be multiplexed across multiple IoT applicat

Radhakrishnan Balu

We clarify the relations between the mathematical structures that enable fashioning quantum walks on regular graphs and their realizations in anyonic systems. Our protagonist is association schemes that may be synthesized from type-II matrices which have a canonical construction of subfactors. This way we set up quantum walks on growing distance-regular grap

Qiao Gu, Brian Okorn, David Held

Real-time object pose estimation is necessary for many robot manipulation algorithms. However, state-of-the-art methods for object pose estimation are trained for a specific set of objects; these methods thus need to be retrained to estimate the pose of each new object, often requiring tens of GPU-days of training for optimal performance. In this paper, we p

Tong Mu, Stephan Zheng, Alexander Trott

Principal-Agent (PA) problems describe a broad class of economic relationships characterized by misaligned incentives and asymmetric information. The Principal's problem is to find optimal incentives given the available information, e.g., a manager setting optimal wages for its employees. Whereas the Principal is often assumed rational, comparatively little

Jernej Hribar, Ivana Dusparic

Deep Reinforcement Learning (DRL) solutions are becoming pervasive at the edge of the network as they enable autonomous decision-making in a dynamic environment. However, to be able to adapt to the ever-changing environment, the DRL solution implemented on an embedded device has to continue to occasionally take exploratory actions even after initial converge

Xinan Chen, Anh Phong Tran, Rena Elkin, Helene Benveniste

Regularized optimal mass transport (rOMT) problem adds a diffusion term to the continuity equation in the original dynamic formulation of the optimal mass transport (OMT) problem proposed by Benamou and Brenier. We show that the rOMT model serves as a powerful tool in computational fluid dynamics (CFD) for visualizing fluid flows in the glymphatic system. In

Sayak Ray Chowdhury, Patrick Saux, Odalric-Ambrym Maillard, Aditya Gopalan

We revisit the method of mixture technique, also known as the Laplace method, to study the concentration phenomenon in generic exponential families. Combining the properties of Bregman divergence associated with log-partition function of the family with the method of mixtures for super-martingales, we establish a generic bound controlling the Bregman diverge

Ariel Rosenfeld, Ehud Shapiro, Nimrod Talmon

Many democratic political parties hold primary elections, which nicely reflects their democratic nature and promote, among other things, the democratic value of inclusiveness. However, the methods currently used for holding such primary elections may not be the most suitable, especially if some form of proportional ranking is desired. In this paper, we compa

A. G. Alarcon Jara, M. Fellhauer, J. Simon, A. del Pino

Context: A variety of formation models for dwarf spheroidal (dSph) galaxies have been proposed in the literature, but generally they have not been quantitatively compared with observations. Aims: We search for chemodynamical patterns in our observational data set and compare the results with mock galaxies consisting of pure random motions, and simulated dwar

Joshua C. C. Chan

Time-varying parameter VARs with stochastic volatility are routinely used for structural analysis and forecasting in settings involving a few endogenous variables. Applying these models to high-dimensional datasets has proved to be challenging due to intensive computations and over-parameterization concerns. We develop an efficient Bayesian sparsification me

Dananjaya Liyanage, Yi Ji, Derek Everett, Matthew Heffernan

Measurements from the Large Hadron Collider (LHC) and the Relativistic Heavy Ion Collider (RHIC) can be used to study the properties of quark-gluon plasma. Systematic constraints on these properties must combine measurements from different collision systems and methodically account for experimental and theoretical uncertainties. Such studies require a vast n

CMS Collaboration

The production cross section of a top quark pair in association with a photon is measured in proton-proton collisions in the decay channel with two oppositely charged leptons (e$^\pm\mu^\mp$, e$^+$e$^-$, or $\mu^+\mu^-$). The measurement is performed using 138 fb$^{-1}$ of proton-proton collision data recorded by the CMS experiment at $\sqrt{s} =$ 13 TeV dur

Md Faizul Bari, Parv Agrawal, Baibhab Chatterjee, Shreyas Sen

Due to the diverse and mobile nature of the deployment environment, smart commodity devices are vulnerable to various attacks which can grant unauthorized access to a rogue device in a large, connected network. Traditional digital signature-based authentication methods are vulnerable to key recovery attacks, CSRF, etc. To circumvent this, RF-PUF had been pro

Benoit Vicedo, Jennifer Winstone

The affine Gaudin model, associated with an untwisted affine Kac-Moody algebra, is known to arise from a certain gauge fixing of 4-dimensional mixed topological-holomorphic Chern-Simons theory in the Hamiltonian framework. We show that the finite Gaudin model, associated with a finite-dimensional semisimple Lie algebra, or more generally the tamely ramified

Abhishek Mallick

Given an equivariant knot $K$ of order $2$, we study the induced action of the symmetry on the knot Floer homology. We relate this action with the induced action of the symmetry on the Heegaard Floer homology of large surgeries on $K$. This surgery formula can be thought of as an equivariant analog of the involutive large surgery formula proved by Hendricks

G. Hagen, S. J. Novario, Z. H. Sun, T. Papenbrock

Single-reference coupled-cluster theory is an accurate and affordable computational method for the nuclear many-body problem. For open-shell nuclei, the reference state typically breaks rotational invariance and angular momentum must be restored as a good quantum number. We perform angular-momentum projection after variation and employ the disentangled coupl

Benjamin Gregory, Jörg Strempfer, Daniel Weinstock, Jacob Ruf

In its ground state, RuO2 was long thought to be an ordinary metallic paramagnet. Recent neutron and x-ray diffraction revealed that bulk RuO2 is an antiferromagnet (AFM) with TN above 300 K. Furthermore, epitaxial strain induces novel superconductivity in thin films of RuO2 below 2 K. Here, we present a resonant elastic x-ray scattering (REXS) study at the

Bekzhan Kerimkulov, James-Michael Leahy, David Šiška, Lukasz Szpruch

We study the global convergence of policy gradient for infinite-horizon, continuous state and action space, and entropy-regularized Markov decision processes (MDPs). We consider a softmax policy with (one-hidden layer) neural network approximation in a mean-field regime. Additional entropic regularization in the associated mean-field probability measure is a

Jørgen Olsen Lye

This article takes a detailed look at the Ricci-flat metrics introduced by Eguchi-Hanson and Calabi on the canonical line bundle of complex projective space. We give a description of these spaces as resolutions of certain orbifold singularities. We then compute the curvature explicitly and show that all compact, minimal submanifolds are contained in the zero

Thorsten Simon, Georg J. Mayr

Lightning flashes are rare albeit hazardous events. Despite this scarcity, generalized additive models (GAMs) succeed in producing a climatology of lightning occurrence for the eastern Alps and surrounding lowlands at an unprecedented resolution of 1\,km$^2$ for each day of April through September with data from the ALDIS lightning location system. The GAM a

Maxim V. Polyakov, Michal Praszalowicz

We employ the chiral quark-soliton model and the heavy quark symmetry to describe spectra of charm and beauty baryons. Heavy baryons can be classified according to the SU(3) representations of the light sector. We argue that recently discovered $\Xi_b$ states can be interpreted as negative parity excited anti-triplets or sextets, and the $\Sigma_b$ states as

Modou Ngom, Moumouni Diallo, Adja Mbarka Fall, Gane Samb Lo

We introduce a new generalization of the Pseudo-Lindley distribution by applying alpha power transformation. The obtained distribution is referred as the Pseudo-Lindley alpha power transformed distribution (\textit{PL-APT}). Some tractable mathematical properties of the \textit{PL-APT} distribution as reliability, hazard rate, order statistics and entropies

Zhenyu Zhang, Akihiko Nishimura, Nídia S. Trovão, Joshua L. Cherry

Inferring dependencies between complex biological traits while accounting for evolutionary relationships between specimens is of great scientific interest yet remains infeasible when trait and specimen counts grow large. The state-of-the-art approach uses a phylogenetic multivariate probit model to accommodate binary and continuous traits via a latent variab

Yamini K. Rao, Giulio Del Zanna, Helen E. Mason

We study the non-thermal velocities in the quiet-sun using various high spatial, temporal, and spectral resolution observations from the Interface Region Imaging Spectrograph (IRIS). We focus our analysis on the transition region using the optically thin line (Si IV 1393.7 \AA), and select line profiles that are nearly Gaussian. We find evidence of a centre-

Akbar Rafiey, Yuichi Yoshida

Submodular functions are at the core of many machine learning and data mining tasks. The underlying submodular functions for many of these tasks are decomposable, i.e., they are sum of several simple submodular functions. In many data intensive applications, however, the number of underlying submodular functions in the original function is so large that we n

Tijmen Vermeij, Jorn Verstijnen, Tim Ramirez y Cantador, Benoit Blaysat

The continuous development of new multiphase alloys with improved mechanical properties requires quantitative microstructure-resolved observation of the nanoscale deformation mechanisms at, e.g., multiphase interfaces. This calls for a combinatory approach beyond advanced testing methods such as microscale strain mapping on bulk material and micrometer sized

Thomas Zacharias, Ashutosh Taklikar, Raja Giryes

Fictional languages have become increasingly popular over the recent years appearing in novels, movies, TV shows, comics, and video games. While some of these fictional languages have a complete vocabulary, most do not. We propose a deep learning solution to the problem. Using style transfer and machine translation tools, we generate new words for a given ta

Debarnab Mitra, Lev Tauz, Lara Dolecek

Light nodes in blockchain systems are known to be vulnerable to data availability (DA) attacks where they accept an invalid block with unavailable portions. Previous works have used LDPC and 2-D Reed Solomon (2D-RS) codes with Merkle Trees to mitigate DA attacks. While these codes have demonstrated improved performance across a variety of metrics such as DA

Stephen Vintskevich, Dmitry Grigoriev

Currently, quantum reservoir computing is one of the most promising and experimentally accessible techniques for hybrid, quantum-classical machine learning. However, its applications are limited due to practical restrictions on the size of quantum systems and the influence of noise. Here we propose a novel approach to connect two quantum reservoirs in a netw

Hengrui Zhang, Youfang Lin, Sheng Han, Shuo Wang

Safety exploration can be regarded as a constrained Markov decision problem where the expected long-term cost is constrained. Previous off-policy algorithms convert the constrained optimization problem into the corresponding unconstrained dual problem by introducing the Lagrangian relaxation technique. However, the cost function of the above algorithms provi

Junyi Lu, Sebastian Meyer

Count data with excessive zeros are often encountered when modelling infectious disease occurrence. The degree of zero inflation can vary over time due to non-epidemic periods as well as by age group or region. The existing endemic-epidemic modelling framework (aka HHH) lacks a proper treatment for surveillance data with excessive zeros as it is limited to P

Shreshth Tuli, Giuliano Casale, Nicholas R. Jennings

Efficient anomaly detection and diagnosis in multivariate time-series data is of great importance for modern industrial applications. However, building a system that is able to quickly and accurately pinpoint anomalous observations is a challenging problem. This is due to the lack of anomaly labels, high data volatility and the demands of ultra-low inference

Ushasi Roy, Tyler Collins, Mohit K. Jolly, Parag Katira

Multicellular collective migration is a ubiquitous strategy of cells to translocate spatially in diverse tissue environments to accomplish a wide variety of biological phenomena, viz. embryonic development, wound healing, and tumor progression. Diverse cellular functions and behaviors, for instance, cell protrusions, active contractions, cell-cell adhesion,

Tobias Mistele, Stacy McGaugh, Sabine Hossenfelder

We make rotation curve fits to test the superfluid dark matter model. In addition to verifying that the resulting fits match the rotation curve data reasonably well, we aim to evaluate how satisfactory they are with respect to two criteria, namely, how reasonable the resulting stellar mass-to-light ratios are and whether the fits end up in the regime of supe

Hang Jiang, Yining Hua, Doug Beeferman, Deb Roy

Social media data such as Twitter messages ("tweets") pose a particular challenge to NLP systems because of their short, noisy, and colloquial nature. Tasks such as Named Entity Recognition (NER) and syntactic parsing require highly domain-matched training data for good performance. To date, there is no complete training corpus for both NER and syntactic ana

Clemens Dubslaff, Kallistos Weis, Christel Baier, Sven Apel

Detecting and understanding reasons for defects and inadvertent behavior in software is challenging due to their increasing complexity. In configurable software systems, the combinatorics that arises from the multitude of features a user might select from adds a further layer of complexity. We introduce the notion of feature causality, which is based on coun

Neil Turok, Latham Boyle

We suggest a new explanation for the observed large scale flatness, homogeneity and isotropy of the universe. The basic ingredients are elementary and well-known, namely Einstein's theory of gravity and Hawking's method of computing gravitational entropy. The new twist is provided by the boundary conditions we recently proposed for "big bang" type singularit

Aleksei Aleksandrov, Vladimir Peller

Let $f$ be a function in the inhomogeneous analytic Besov space $B_{\infty,1}^1$. For a pair $(L,M)$ of not necessarily commuting maximal dissipative operators, we define the function $f(L,M)$ of $L$ and $M$ as a densely defined linear operator. We prove for $p\in[1,2]$ that if $(L_1,M_1)$ and $(L_2,M_2)$ are pairs of not necessarily commuting maximal dissip

Youssef Aziz Alaoui, Bihui Zhu, Sean Robert Muleady, William Dubosclard

We perform collective spin measurements to study the buildup of two-body correlations between $\approx10^4$ spin $s=3$ chromium atoms pinned in a 3D optical lattice. The spins interact via long range and anisotropic dipolar interactions. From the fluctuations of total magnetization, measured at the standard quantum limit, we estimate the dynamical growth of

Christian Hirsch, Takashi Owada

We prove a large deviation principle for the point process associated to $k$-element connected components in $\mathbb R^d$ with respect to the connectivity radii $r_n\to\infty$. The random points are generated from a homogeneous Poisson point process, so that $(r_n)_{n\ge1}$ satisfies $n^kr_n^{d(k-1)}\to\infty$ and $nr_n^d\to0$ as $n\to\infty$ (i.e., sparse