cometadata/datacite-rtg-text-other-reclassification-records huggingface dataset:
title: Stochastic modelling and analysis of wildfowl anatidae monitoring data from the wetland bird survey
Creators: name: Frost, Teresa Mary
Publisher: name: University of Kent
Year: 2022

description: British wetlands are used by many different resident and migratory wildfowl species for some or all of the winter months. The Wetland Bird Survey (WeBS) is a scheme set up with the objectives of assessing the size of waterbird populations, determining trends in numbers and distribution and assessing the importance of individual sites for waterbirds as part of the requirements of international conservation conventions and directives. Over three thousand sites around Britain are included in the survey, and volunteers have been undertaking counts of wildfowl species for over forty years. One of the key features of WeBS is that sites are surveyed at monthly intervals over the winter season. The analyses for the project used data for twenty-six wildfowl (sub-)species for winter seasons from 1966/67 to 2006/07. The limitations and bias of the sampling methods currently used in the Wetland Bird Survey, are examined. It is shown that post-selection of sites by the proportion of missing values, as is done currently, introduces an addi­tional bias that impacts on reported population trends. A new site selection criteria that minimises additional bias is proposed. WeBS wildfowl data comprise monthly counts of populations that change over each win­ter season due to short-term immigration and emigration. Ideas and methods from the held of Functional Data Analysis are used to explore phenological changes (spatiotem-poral variation in the seasonal patterns) due to changing species distributions and to select months where the seasonal patterns are most stable. The Underhill method, of imputing missing values using a site-year-month multiplicative model and the EM algorithm to generate an annual Underhill Index, is reviewed. It is shown that the model is a poor fit to most wildfowl WeBS data sets. Currently WeBS abundance indices are calculated using the Underhill Index, which treats each species’ seasonality as stationary, using the arithmetic mean over months and sites to derive a population index. Using ideas from economics, various alternative indexing approaches to constructing a single yearly index from the monthly counts are compared with simulated examples and WeBS data. The results have implications beyond the Wetland Bird Survey, to other wildlife moni­toring schemes; particularly those that monitor populations which show strong seasonal dynamics.
Quantum machine learning beyond kernel methods
Creators: name: Jerbi, Sofiene
name: Fiderer, Lukas J.
name: Nautrup, Hendrik Poulsen
name: Kübler, Jonas M.
name: Briegel, Hans J.
name: Dunjko, Vedran
Publisher: name: arXiv
Year: 2021
Subjects: subject: Quantum Physics (quant-ph)
subject: Artificial Intelligence (cs.AI)
subject: Machine Learning (cs.LG)
subject: Machine Learning (stat.ML)
subject: FOS: Physical sciences
subject: FOS: Computer and information sciences

description: Machine learning algorithms based on parametrized quantum circuits are prime candidates for near-term applications on noisy quantum computers. In this direction, various types of quantum machine learning models have been introduced and studied extensively. Yet, our understanding of how these models compare, both mutually and to classical models, remains limited. In this work, we identify a constructive framework that captures all standard models based on parametrized quantum circuits: that of linear quantum models. In particular, we show using tools from quantum information theory how data re-uploading circuits, an apparent outlier of this framework, can be efficiently mapped into the simpler picture of linear models in quantum Hilbert spaces. Furthermore, we analyze the experimentally-relevant resource requirements of these models in terms of qubit number and amount of data needed to learn. Based on recent results from classical machine learning, we prove that linear quantum models must utilize exponentially more qubits than data re-uploading models in order to solve certain learning tasks, while kernel methods additionally require exponentially more data points. Our results provide a more comprehensive view of quantum machine learning models as well as insights on the compatibility of different models with NISQ constraints.
description: 10+10 pages, 14 figures; significant changes in the main text, corrections in the numerical simulations
Data-aided Underwater Acoustic Ray Propagation Modeling
Creators: name: Li, Kexin
name: Chitre, Mandar
Publisher: name: arXiv
Year: 2022
Subjects: subject: Sound (cs.SD)
subject: Audio and Speech Processing (eess.AS)
subject: FOS: Computer and information sciences
subject: FOS: Electrical engineering, electronic engineering, information engineering

description: Acoustic propagation models are widely used in numerous oceanic and other underwater applications. Most conventional models are approximate solutions of the acoustic wave equation, and require accurate environmental knowledge to be available beforehand. Environmental parameters may not always be easily or accurately measurable. While data-driven techniques might allow us to model acoustic propagation without the need for extensive prior environmental knowledge, such techniques tend to be data-hungry and often infeasible in oceanic applications where data collection is difficult and expensive. We propose a data-aided ray physics based high frequency acoustic propagation modeling approach that enables us to train models with only a small amount of data. The proposed framework is not only data-efficient, but also offers flexibility to incorporate varying degrees of environmental knowledge, and generalizes well to permit extrapolation beyond the area where data was collected. We demonstrate the feasibility and applicability of our method through four numerical case studies, and one controlled experiment. We also benchmark our method's performance against classical data-driven techniques.
description: Accepted version in IEEE Journal of Oceanic Engineering
Uncertainty quantification in a mechanical submodel driven by a Wasserstein-GAN
Creators: name: Boukraichi, Hamza
name: Akkari, Nissrine
name: Casenave, Fabien
name: Ryckelynck, David
Publisher: name: arXiv
Year: 2021
Subjects: subject: Machine Learning (stat.ML)
subject: Machine Learning (cs.LG)
subject: FOS: Computer and information sciences
subject: G.3; G.1.8
subject: 68T07 (Primary) 35L05 (Secondary)

description: The analysis of parametric and non-parametric uncertainties of very large dynamical systems requires the construction of a stochastic model of said system. Linear approaches relying on random matrix theory and principal componant analysis can be used when systems undergo low-frequency vibrations. In the case of fast dynamics and wave propagation, we investigate a random generator of boundary conditions for fast submodels by using machine learning. We show that the use of non-linear techniques in machine learning and data-driven methods is highly relevant. Physics-informed neural networks is a possible choice for a data-driven method to replace linear modal analysis. An architecture that support a random component is necessary for the construction of the stochastic model of the physical system for non-parametric uncertainties, since the goal is to learn the underlying probabilistic distribution of uncertainty in the data. Generative Adversarial Networks (GANs) are suited for such applications, where the Wasserstein-GAN with gradient penalty variant offers improved convergence results for our problem. The objective of our approach is to train a GAN on data from a finite element method code (Fenics) so as to extract stochastic boundary conditions for faster finite element predictions on a submodel. The submodel and the training data have both the same geometrical support. It is a zone of interest for uncertainty quantification and relevant to engineering purposes. In the exploitation phase, the framework can be viewed as a randomized and parametrized simulation generator on the submodel, which can be used as a Monte Carlo estimator.
Vacancy Formation and Oxidation Characteristics of Single Layer TiS3
Creators: name: Iyikanat, F.
name: Sahin, H.
name: Senger, R. T.
name: Peeters, F. M.
Publisher: name: arXiv
Year: 2015
Subjects: subject: Mesoscale and Nanoscale Physics (cond-mat.mes-hall)
subject: FOS: Physical sciences

description: The structural, electronic, and magnetic properties of pristine, defective, and oxidized monolayer TiS3 are investigated using first-principles calculations in the framework of density functional theory. We found that a single layer of TiS3 is a direct band gap semiconductor, and the bonding nature of the crystal is fundamentally different from other transition metal chalcogenides. The negatively charged surfaces of single layer TiS3 makes this crystal a promising material for lubrication applications. The formation energies of possible vacancies, i.e. S, Ti, TiS, and double S, are investigated via total energy optimization calculations. We found that the formation of a single S vacancy was the most likely one among the considered vacancy types. While a single S vacancy results in a nonmagnetic, semiconducting character with an enhanced band gap, other vacancy types induce metallic behavior with spin polarization of 0.3-0.8 μB. The reactivity of pristine and defective TiS3 crystals against oxidation was investigated using conjugate gradient calculations where we considered the interaction with atomic O, O2, and O3. While O2 has the lowest binding energy with 0.05-0.07 eV, O3 forms strong bonds stable even at moderate temperatures. The strong interaction (3.9-4.0 eV) between atomic O and TiS3 results in dissociative adsorption of some O-containing molecules. In addition, the presence of S-vacancies enhances the reactivity of the surface with atomic O, whereas it had a negative effect on the reactivity with O2 and O3 molecules.
description: accepted, J. Phys. Chem. C
On orthogonal polynomials with respect to a class of differential operators
Creators: name: Borrego-Morell, Jorge A.
Publisher: name: arXiv
Year: 2022
Subjects: subject: Classical Analysis and ODEs (math.CA)
subject: FOS: Mathematics

description: We consider orthogonal polynomials with respect to a linear differential operator $$\mathcal{L}^{(M)}=\sum_{k=0}^{M}ρ_{k}(z)\frac{d^k}{dz^k}, $$ where $\{ρ_k\}_{k=0}^{M}$ are complex polynomials such that $deg[ρ_k]\leq k, 0\leq k \leq M$, with equality for at least one index. We analyze the uniqueness and zero location of these polynomials. An interesting phenomenon occurring in this kind of orthogonality is the existence of operators for which the associated sequence of orthogonal polynomials reduces to a finite set. For a given operator, we find a classification of the measures for which it is possible to guarantee the existence of an infinite sequence of orthogonal polynomials, in terms of a linear system of difference equations with varying coefficients. Also, for the case of a first-order differential operator, we locate the zeros and establish the strong asymptotic behavior of these polynomials.
Leveraging Global Binary Masks for Structure Segmentation in Medical Images
Creators: name: Kazemimoghadam, Mahdieh
name: Yang, Zi
name: Ma, Lin
name: Chen, Mingli
name: Lu, Weiguo
name: Gu, Xuejun
Publisher: name: arXiv
Year: 2022
Subjects: subject: Image and Video Processing (eess.IV)
subject: Artificial Intelligence (cs.AI)
subject: Machine Learning (cs.LG)
subject: FOS: Electrical engineering, electronic engineering, information engineering
subject: FOS: Computer and information sciences

description: Deep learning (DL) models for medical image segmentation are highly influenced by intensity variations of input images and lack generalization due to primarily utilizing pixels' intensity information for inference. Acquiring sufficient training data is another challenge limiting models' applications. We proposed to leverage the consistency of organs' anatomical shape and position information in medical images. We introduced a framework leveraging recurring anatomical patterns through global binary masks for organ segmentation. Two scenarios were studied.1) Global binary masks were the only model's (i.e. U-Net) input, forcing exclusively encoding organs' position and shape information for segmentation/localization.2) Global binary masks were incorporated as an additional channel functioning as position/shape clues to mitigate training data scarcity. Two datasets of the brain and heart CT images with their ground-truth were split into (26:10:10) and (12:3:5) for training, validation, and test respectively. Training exclusively on global binary masks led to Dice scores of 0.77(0.06) and 0.85(0.04), with the average Euclidian distance of 3.12(1.43)mm and 2.5(0.93)mm relative to the center of mass of the ground truth for the brain and heart structures respectively. The outcomes indicate that a surprising degree of position and shape information is encoded through global binary masks. Incorporating global binary masks led to significantly higher accuracy relative to the model trained on only CT images in small subsets of training data; the performance improved by 4.3-125.3% and 1.3-48.1% for 1-8 training cases of the brain and heart datasets respectively. The findings imply the advantages of utilizing global binary masks for building generalizable models and to compensate for training data scarcity.
Alfaxalone Anaesthesia Facilitates Electrophysiological Recordings of Nociceptive Withdrawal Reflexes in Dogs (Canis familiaris)
Creators: name: Hunt, James
name: Lascelles, B. Duncan X.
name: Kelly, Sara
name: Knazovicky, David
name: Knowles, Toby G.
name: Harris, John
name: Murrell, Jo
Publisher: name: The University of North Carolina at Chapel Hill University Libraries
Year: 2016

description: Naturally occurring canine osteoarthritis represents a welfare issue for affected dogs (Canis familiaris), but is also considered very similar to human osteoarthritis and has therefore been proposed as a model of disease in humans. Central sensitisation is recognized in human osteoarthritis sufferers but identification in dogs is challenging. Electromyographic measurement of responses to nociceptive stimulation represents a potential means of investigating alterations in central nociceptive processing, and has been evaluated in conscious experimental dogs, but is likely to be aversive. Development of a suitable anaesthetic protocol in experimental dogs, which facilitated electrophysiological nociceptive withdrawal reflex assessment, may increase the acceptability of using the technique in owned dogs with naturally occurring osteoarthritis. Seven purpose bred male hound dogs underwent electromyographic recording sessions in each of three states: acepromazine sedation, alfaxalone sedation, and alfaxalone anaesthesia. Electromyographic responses to escalating mechanical and electrical, and repeated electrical, stimuli were recorded. Subsequently the integral of both early and late rectified responses was calculated. Natural logarithms of the integral values were analysed within and between the three states using multi level modeling. Alfaxalone increased nociceptive thresholds and decreased the magnitude of recorded responses, but characteristics of increasing responses with increasing stimulus magnitude were preserved. Behavioural signs of anxiety were noted in two out of seven dogs during recordings in the acepromazine sedated state. There were few significant differences in response magnitude or nociceptive threshold between the two alfaxalone states. Following acepromazine premedication, induction of anaesthesia with 1–2 mg kg-1 alfaxalone, followed by a continuous rate infusion in the range 0.075–0.1 mg kg-1 min-1 produced suitable conditions to enable assessment of spinal nociceptive processing in dogs, without subjecting them to potentially aversive experiences. This methodology may be appropriate for obtaining electrophysiological nociceptive withdrawal reflex data in client-owned dogs with naturally occurring osteoarthritis.
Adaptive estimation of quantum observables
Creators: name: Shlosberg, Ariel
name: Jena, Andrew J.
name: Mukhopadhyay, Priyanka
name: Haase, Jan F.
name: Leditzky, Felix
name: Dellantonio, Luca
Publisher: name: arXiv
Year: 2021
Subjects: subject: Quantum Physics (quant-ph)
subject: FOS: Physical sciences

description: The accurate estimation of quantum observables is a critical task in science. With progress on the hardware, measuring a quantum system will become increasingly demanding, particularly for variational protocols that require extensive sampling. Here, we introduce a measurement scheme that adaptively modifies the estimator based on previously obtained data. Our algorithm, which we call AEQuO, continuously monitors both the estimated average and the associated error of the considered observable, and determines the next measurement step based on this information. We allow both for overlap and non-bitwise commutation relations in the subsets of Pauli operators that are simultaneously probed, thereby maximizing the amount of gathered information. AEQuO comes in two variants: a greedy bucket-filling algorithm with good performance for small problem instances, and a machine learning-based algorithm with more favorable scaling for larger instances. The measurement configuration determined by these subroutines is further post-processed in order to lower the error on the estimator. We test our protocol on chemistry Hamiltonians, for which AEQuO provides error estimates that improve on all state-of-the-art methods based on various grouping techniques or randomized measurements, thus greatly lowering the toll of measurements in current and future quantum applications.
description: 10+14 pages, 6 figures, comments welcome
Relational representation learning with spike trains
Creators: name: Dold, Dominik
Publisher: name: arXiv
Year: 2022
Subjects: subject: Neural and Evolutionary Computing (cs.NE)
subject: Machine Learning (cs.LG)
subject: Neurons and Cognition (q-bio.NC)
subject: FOS: Computer and information sciences
subject: FOS: Biological sciences

description: Relational representation learning has lately received an increase in interest due to its flexibility in modeling a variety of systems like interacting particles, materials and industrial projects for, e.g., the design of spacecraft. A prominent method for dealing with relational data are knowledge graph embedding algorithms, where entities and relations of a knowledge graph are mapped to a low-dimensional vector space while preserving its semantic structure. Recently, a graph embedding method has been proposed that maps graph elements to the temporal domain of spiking neural networks. However, it relies on encoding graph elements through populations of neurons that only spike once. Here, we present a model that allows us to learn spike train-based embeddings of knowledge graphs, requiring only one neuron per graph element by fully utilizing the temporal domain of spike patterns. This coding scheme can be implemented with arbitrary spiking neuron models as long as gradients with respect to spike times can be calculated, which we demonstrate for the integrate-and-fire neuron model. In general, the presented results show how relational knowledge can be integrated into spike-based systems, opening up the possibility of merging event-based computing and relational data to build powerful and energy efficient artificial intelligence applications and reasoning systems.
description: Accepted for publication at the WCCI 2022 (IJCNN)
Mellin-Barnes Representation of the Topological String
Creators: name: Krefl, Daniel
Publisher: name: arXiv
Year: 2015
Subjects: subject: High Energy Physics - Theory (hep-th)
subject: FOS: Physical sciences

description: We invoke integrals of Mellin-Barnes type to analytically continue the Gopakumar-Vafa resummation of the topological string free energy in the string coupling constant, leading to additional non-perturbative terms. We also discuss in a similar manner the refined and Nekrasov-Shatashvili limit version thereof. The derivation is straight-forward and essentially boils down to taking residue. This allows us to confirm some related conjectures in the literature at tree-level.
description: 16 pages
An Activity Index for Raw Accelerometry Data and Its Comparison with Other Activity Metrics
Creators: name: Di, Chongzhi
name: Crainiceanu, Ciprian M.
name: LaCroix, Andrea Z.
name: Buchner, David M.
name: Evenson, Kelly R.
name: Xiao, Luo
name: Bai, Jiawei
Publisher: name: The University of North Carolina at Chapel Hill University Libraries
Year: 2016

description: Accelerometers have been widely deployed in public health studies in recent years. While they collect high-resolution acceleration signals (e.g., 10–100 Hz), research has mainly focused on summarized metrics provided by accelerometers manufactures, such as the activity count (AC) by ActiGraph or Actical. Such measures do not have a publicly available formula, lack a straightforward interpretation, and can vary by software implementation or hardware type. To address these problems, we propose the physical activity index (AI), a new metric for summarizing raw tri-axial accelerometry data. We compared this metric with the AC and another recently proposed metric for raw data, Euclidean Norm Minus One (ENMO), against energy expenditure. The comparison was conducted using data from the Objective Physical Activity and Cardiovascular Health Study, in which 194 women 60–91 years performed 9 lifestyle activities in the laboratory, wearing a tri-axial accelerometer (ActiGraph GT3X+) on the hip set to 30 Hz and an Oxycon portable calorimeter, to record both tri-axial acceleration time series (converted into AI, AC, and ENMO) and oxygen uptake during each activity (converted into metabolic equivalents (METs)) at the same time. Receiver operating characteristic analyses indicated that both AI and ENMO were more sensitive to moderate and vigorous physical activities than AC, while AI was more sensitive to sedentary and light activities than ENMO. AI had the highest coefficients of determination for METs (0.72) and was a better classifier of physical activity intensity than both AC (for all intensity levels) and ENMO (for sedentary and light intensity). The proposed AI provides a novel and transparent way to summarize densely sampled raw accelerometry data, and may serve as an alternative to AC. The AI’s largely improved sensitivity on sedentary and light activities over AC and ENMO further demonstrate its advantage in studies with older adults.
Code2Snapshot: Using Code Snapshots for Learning Representations of Source Code
Creators: name: Rabin, Md Rafiqul Islam
name: Alipour, Mohammad Amin
Publisher: name: arXiv
Year: 2021
Subjects: subject: Software Engineering (cs.SE)
subject: Machine Learning (cs.LG)
subject: Programming Languages (cs.PL)
subject: FOS: Computer and information sciences

description: There are several approaches for encoding source code in the input vectors of neural models. These approaches attempt to include various syntactic and semantic features of input programs in their encoding. In this paper, we investigate Code2Snapshot, a novel representation of the source code that is based on the snapshots of input programs. We evaluate several variations of this representation and compare its performance with state-of-the-art representations that utilize the rich syntactic and semantic features of input programs. Our preliminary study on the utility of Code2Snapshot in the code summarization and code classification tasks suggests that simple snapshots of input programs have comparable performance to state-of-the-art representations. Interestingly, obscuring input programs have insignificant impacts on the Code2Snapshot performance, suggesting that, for some tasks, neural models may provide high performance by relying merely on the structure of input programs.
description: The 21st IEEE International Conference on Machine Learning and Applications (ICMLA'22)
Artificial Open World for Evaluating AGI: a Conceptual Design
Creators: name: Xu, Bowen
name: Ren, Quansheng
Publisher: name: arXiv
Year: 2022
Subjects: subject: Artificial Intelligence (cs.AI)
subject: FOS: Computer and information sciences

description: How to evaluate Artificial General Intelligence (AGI) is a critical problem that is discussed and unsolved for a long period. In the research of narrow AI, this seems not a severe problem, since researchers in that field focus on some specific problems as well as one or some aspects of cognition, and the criteria for evaluation are explicitly defined. By contrast, an AGI agent should solve problems that are never-encountered by both agents and developers. However, once a developer tests and debugs the agent with a problem, the never-encountered problem becomes the encountered problem, as a result, the problem is solved by the developers to some extent, exploiting their experience, rather than the agents. This conflict, as we call the trap of developers' experience, leads to that this kind of problems is probably hard to become an acknowledged criterion. In this paper, we propose an evaluation method named Artificial Open World, aiming to jump out of the trap. The intuition is that most of the experience in the actual world should not be necessary to be applied to the artificial world, and the world should be open in some sense, such that developers are unable to perceive the world and solve problems by themselves before testing, though after that they are allowed to check all the data. The world is generated in a similar way as the actual world, and a general form of problems is proposed. A metric is proposed aiming to quantify the progress of research. This paper describes the conceptual design of the Artificial Open World, though the formalization and the implementation are left to the future.
Logic and Branching Automata
Creators: name: Nicolas, Bedon
Publisher: name: arXiv
Year: 2015
Subjects: subject: Formal Languages and Automata Theory (cs.FL)
subject: Logic in Computer Science (cs.LO)
subject: FOS: Computer and information sciences

description: In this paper we study the logical aspects of branching automata, as defined by Lodaya and Weil. We first prove that the class of languages of finite N-free posets recognized by branching automata is closed under complementation. Then we define a logic, named P-MSO as it is a extension of monadic second-order logic with Presburger arithmetic, and show that it is precisely as expressive as branching automata. As a consequence of the effectiveness of the construction of one formalism from the other, the P-MSO theory of the class of all finite N-free posets is decidable.
Gray/dark soliton behavior and population under a symmetric and asymmetric potential trap
Creators: name: Hussain, Jameel
name: Akram, Javed
name: Saif, Farhan
Publisher: name: arXiv
Year: 2022
Subjects: subject: Pattern Formation and Solitons (nlin.PS)
subject: Quantum Gases (cond-mat.quant-gas)
subject: Quantum Physics (quant-ph)
subject: FOS: Physical sciences

description: We numerically study the impact of Gaussian barrier height and width on the gray solitons population in a symmetric and asymmetric potential trap. The gray solitons are created in a double-well potential by the density engineering method. Two identical Bose-Einstein condensate fragments are confined and made to collide by switching off the Gaussian barrier in a double-well potential. We find that the gray solitons population can be manipulated by Gaussian barrier height and width. We also study the gray solitons population dependence on the coupling strength. Moreover, we also study the impact of an asymmetry present in the double-well potential. We observe that such an asymmetry always swings the point of collision of the gray solitons. Later, a stationary dark soliton is created by the phase imprinting method and we observe that the initial asymmetry in the double-well potential trap sets the dark soliton into oscillation.
Additional file 3 of The clinical efficacy and safety of platelet-rich plasma on frozen shoulder: a systematic review and meta-analysis of randomized controlled trials
Creators: name: Zhang, Wen-Bin
name: Ma, Yu-Lin
name: Lu, Fei-Long
name: Guo, Hai-Rui
name: Song, Hao
name: Hu, Yi-Mei
Publisher: name: figshare
Year: 2024
Subjects: subject: Medicine
subject: Biotechnology
subject: Chemical Sciences not elsewhere classified
subject: Sociology
subject: FOS: Sociology
subject: Biological Sciences not elsewhere classified
subject: Science Policy
subject: Infectious Diseases
subject: FOS: Health sciences

description: Supplementary Material 3
Black Hole Images as Tests of General Relativity: Effects of Spacetime Geometry
Creators: name: Younsi, Ziri
name: Psaltis, Dimitrios
name: Özel, Feryal
Publisher: name: arXiv
Year: 2021
Subjects: subject: High Energy Astrophysical Phenomena (astro-ph.HE)
subject: General Relativity and Quantum Cosmology (gr-qc)
subject: FOS: Physical sciences

description: The images of supermassive black holes surrounded by optically-thin, radiatively-inefficient accretion flows, like those observed with the Event Horizon Telescope, are characterized by a bright ring of emission surrounding the black-hole shadow. In the Kerr spacetime this bright ring, when narrow, closely traces the boundary of the shadow and can, with appropriate calibration, serve as its proxy. The present paper expands the validity of this statement by considering two particular spacetime geometries: a solution to the field equations of a modified gravity theory and another that parametrically deviates from Kerr but recovers the Kerr spacetime when its deviation parameters vanish. A covariant, axisymmetric analytic model of the accretion flow based on conservation laws and spanning a broad range of plasma conditions is utilized to calculate synthetic non-Kerr black-hole images, which are then analysed and characterized. We find that in all spacetimes: (i) it is the gravitationally-lensed unstable photon orbit that plays the critical role in establishing the diameter of the rings observed in black-hole images, not the event horizon or the innermost stable circular orbit, (ii) bright rings in these images scale in size with, and encompass, the boundaries of the black-hole shadows, even when deviating significantly from Kerr, and (iii) uncertainties in the physical properties of the accreting plasma introduce subdominant corrections to the relation between the diameter of the image and the diameter of the black-hole shadow. These results provide important new theoretical justification for using black-hole images to probe and test the spacetimes of supermassive black holes.
description: 30 pages, 18 figures, 2 tables. Accepted for publication in ApJ
Wildebeest Herds on Rolling Hills: Flocking on Arbitrary Curved Surfaces
Creators: name: Hueschen, Christina L.
name: Dunn, Alexander R.
name: Phillips, Rob
Publisher: name: arXiv
Year: 2022
Subjects: subject: Soft Condensed Matter (cond-mat.soft)
subject: Biological Physics (physics.bio-ph)
subject: Quantitative Methods (q-bio.QM)
subject: FOS: Physical sciences
subject: FOS: Biological sciences

description: The collective behavior of active agents, whether herds of wildebeest or microscopic actin filaments propelled by molecular motors, is an exciting frontier in biological and soft matter physics. Almost three decades ago, Toner and Tu developed a continuum theory of the collective action of flocks, or herds, that helped launch the modern field of active matter. One challenge faced when applying continuum active matter theories to living phenomena is the complex geometric structure of biological environments. Both macroscopic and microscopic herds move on asymmetric curved surfaces, like undulating grass plains or the surface layers of cells or embryos, which can render problems analytically intractable. In this work, we present a formulation of the Toner-Tu flocking theory that uses the finite element method to solve the governing equations on arbitrary curved surfaces. First, we test the developed formalism and its numerical implementation in channel flow with scattering obstacles and on cylindrical and spherical surfaces, comparing our results to analytical solutions. We then progress to surfaces with arbitrary curvature, moving beyond previously accessible problems to explore herding behavior on a variety of landscapes. Our approach allows the investigation of transients and dynamic solutions not revealed by analytic methods. It also enables versatile incorporation of new geometries and boundary conditions and efficient sweeps of parameter space. Looking forward, the work presented here lays the groundwork for a dialogue between Toner-Tu theory and data on collective motion in biologically-relevant geometries, from drone footage of migrating animal herds to movies of microscopic cytoskeletal flows within cells.
description: 49 pages, 17 figures, 4 videos
Assessing Progress towards Public Health, Human Rights, and International Development Goals Using Frontier Analysis
Creators: name: Bartram, Jamie
name: Cronk, Ryan
name: Luh, Jeanne
Publisher: name: The University of North Carolina at Chapel Hill University Libraries
Year: 2016

description: Indicators to measure progress towards achieving public health, human rights, and international development targets, such as 100% access to improved drinking water or zero maternal mortality ratio, generally focus on status (i.e., level of attainment or coverage) or trends in status (i.e., rates of change). However, these indicators do not account for different levels of development that countries experience, thus making it difficult to compare progress between countries. We describe a recently developed new use of frontier analysis and apply this method to calculate country performance indices in three areas: maternal mortality ratio, poverty headcount ratio, and primary school completion rate. Frontier analysis is used to identify the maximum achievable rates of change, defined by the historically best-performing countries, as a function of coverage level. Performance indices are calculated by comparing a country’s rate of change against the maximum achievable rate at the same coverage level. A country’s performance can be positive or negative, corresponding to progression or regression, respectively. The calculated performance indices allow countries to be compared against each other regardless of whether they have only begun to make progress or whether they have almost achieved the target. This paper is the first to use frontier analysis to determine the maximum achievable rates as a function of coverage level and to calculate performance indices for public health, human rights, and international development indicators. The method can be applied to multiple fields and settings, for example health targets such as cessation in smoking or specific vaccine immunizations, and offers both a new approach to analyze existing data and a new data source for consideration when assessing progress achieved.
Retroreflection and diffraction of a Bose-Einstein condensate by evanescent standing wave potential
Creators: name: Akram, Javed
name: Qasim, Khan
name: Wei, Lei
Publisher: name: arXiv
Year: 2022
Subjects: subject: Quantum Physics (quant-ph)
subject: Quantum Gases (cond-mat.quant-gas)
subject: FOS: Physical sciences

description: The characteristic of the angular distributions of accelerated Bose-Einstein condensate (BEC) atoms incidence on the surface is designed using the mathematical modeling method. Here, we proposed the idea to study the retroreflection and diffraction of a BEC from an evanescent standing wave potential (ESWP). The ESWP is formed by multiple reflections of the laser beam from the surface of the prism under the influence of gravity. After BEC's reflection and diffraction, the so-called BEC's density rainbow patterns develop due to the interference which depends on the surface structure which we model with the periodic decaying evanescent field. The interaction of accelerated bosonic atoms with a surface can help to demonstrate surface structures or to determine surface roughness, or to build future high spatial resolution and high sensitivity magnetic-field sensors in two-dimensional systems.
Weak product spaces of Dirichlet series
Creators: name: Brevig, Ole Fredrik
name: Perfekt, Karl-Mikael
Publisher: name: arXiv
Year: 2015
Subjects: subject: Functional Analysis (math.FA)
subject: FOS: Mathematics
subject: Primary 47B35, Secondary 30B50

description: Let $\mathscr{H}^2$ denote the space of ordinary Dirichlet series with square summable coefficients, and let $\mathscr{H}^2_0$ denote its subspace consisting of series vanishing at $+\infty$. We investigate the weak product spaces $\mathscr{H}^2\odot\mathscr{H}^2$ and $\mathscr{H}^2_0\odot\mathscr{H}^2_0$, finding that several pertinent problems are more tractable for the latter space. This surprising phenomenon is related to the fact that $\mathscr{H}^2_0\odot\mathscr{H}^2_0$ does not contain the infinite-dimensional subspace of $\mathscr{H}^2$ of series which lift to linear functions on the infinite polydisc. The problems considered stem from questions about the dual spaces of these weak product spaces, and are therefore naturally phrased in terms of multiplicative Hankel forms. We show that there are bounded, even Schatten class, multiplicative Hankel forms on $\mathscr{H}^2_0 \times \mathscr{H}^2_0$ whose analytic symbols are not in $\mathscr{H}^2$. Based on this result we examine Nehari's theorem for such Hankel forms. We define also the skew product spaces associated with $\mathscr{H}^2\odot\mathscr{H}^2$ and $\mathscr{H}^2_0\odot\mathscr{H}^2_0$, with respect to both half-plane and polydisc differentiation, the latter arising from Bohr's point of view. In the process we supply square function characterizations of the Hardy spaces $\mathscr{H}^p$, for $0 < p < \infty$, from the viewpoints of both types of differentiation. Finally we compare the skew product spaces to the weak product spaces, leading naturally to an interesting Schur multiplier problem.
description: This paper has been accepted for publication in Integral Equations and Operator Theory
Hamiltonian simulation with random inputs
Creators: name: Zhao, Qi
name: Zhou, You
name: Shaw, Alexander F.
name: Li, Tongyang
name: Childs, Andrew M.
Publisher: name: arXiv
Year: 2021
Subjects: subject: Quantum Physics (quant-ph)
subject: FOS: Physical sciences

description: The algorithmic error of digital quantum simulations is usually explored in terms of the spectral norm distance between the actual and ideal evolution operators. In practice, this worst-case error analysis may be unnecessarily pessimistic. To address this, we develop a theory of average-case performance of Hamiltonian simulation with random initial states. We relate the average-case error to the Frobenius norm of the multiplicative error and give upper bounds for the product formula (PF) and truncated Taylor series methods. As applications, we estimate average-case error for digital Hamiltonian simulation of general lattice Hamiltonians and $k$-local Hamiltonians. In particular, for the nearest-neighbor Heisenberg chain with $n$ spins, the error is quadratically reduced from $\mathcal O(n)$ in the worst case to $\mathcal O(\sqrt{n})$ on average for both the PF method and the Taylor series method. Numerical evidence suggests that this theory accurately characterizes the average error for concrete models. We also apply our results to error analysis in the simulation of quantum scrambling.
Photoelectric Factor Prediction Using Automated Learning and Uncertainty Quantification
Creators: name: Alsamadony, Khalid L.
name: Ibrahim, Ahmed Farid
name: Elkatatny, Salaheldin
name: Abdulraheem, Abdulazeez
Publisher: name: arXiv
Year: 2022
Subjects: subject: Machine Learning (cs.LG)
subject: Geophysics (physics.geo-ph)
subject: Applications (stat.AP)
subject: FOS: Computer and information sciences
subject: FOS: Physical sciences

description: The photoelectric factor (PEF) is an important well logging tool to distinguish between different types of reservoir rocks because PEF measurement is sensitive to elements with high atomic number. Furthermore, the ratio of rock minerals could be determined by combining PEF log with other well logs. However, PEF log could be missing in some cases such as in old well logs and wells drilled with barite-based mud. Therefore, developing models for estimating missing PEF log is essential in those circumstances. In this work, we developed various machine learning models to predict PEF values using the following well logs as inputs: bulk density (RHOB), neutron porosity (NPHI), gamma ray (GR), compressional and shear velocity. The predictions of PEF values using adaptive-network-fuzzy inference system (ANFIS) and artificial neural network (ANN) models have errors of about 16% and 14% average absolute percentage error (AAPE) in the testing dataset, respectively. Thus, a different approach was proposed that is based on the concept of automated machine learning. It works by automatically searching for the optimal model type and optimizes its hyperparameters for the dataset under investigation. This approach selected a Gaussian process regression (GPR) model for accurate estimation of PEF values. The developed GPR model decreases the AAPE of the predicted PEF values in the testing dataset to about 10% AAPE. This error could be further decreased to about 2% by modeling the potential noise in the measurements using the GPR model.
Is entanglement a unique resource in quantum illumination?
Creators: name: Kim, MuSeong
name: Hwang, Mi-Ra
name: Jung, Eylee
name: Park, DaeKil
Publisher: name: arXiv
Year: 2021
Subjects: subject: Quantum Physics (quant-ph)
subject: FOS: Physical sciences

description: It is well-known that quantum illumination with a two-mode squeezed vacuum state as an initial entangled bipartite state achieves $6$ dB quantum advantage in the error probability compared to classical coherent-state illumination. Is entanglement the only resource responsible for the quantum advantage? We explore this question by making use of squeezing operations. Finally, we conclude that the answer to the question is negative.
description: 16 pages, no figure. arXiv admin note: substantial text overlap with arXiv:2107.05203 V2: completely changed including title, abstract, main body, and conclusion. 18 pages, 3 figures
The intrinsic dynamics of optimal transport
Creators: name: McCann, Robert J.
name: Rifford, Ludovic
Publisher: name: arXiv
Year: 2015
Subjects: subject: Optimization and Control (math.OC)
subject: Dynamical Systems (math.DS)
subject: FOS: Mathematics
subject: 49N05

description: The question of which costs admit unique optimizers in the Monge-Kantorovich problem of optimal transportation between arbitrary probability densities is investigated. For smooth costs and densities on compact manifolds, the only known examples for which the optimal solution is always unique require at least one of the two underlying spaces to be homeomorphic to a sphere. We introduce a (multivalued) dynamics which the transportation cost induces between the target and source space, for which the presence or absence of a sufficiently large set of periodic trajectories plays a role in determining whether or not optimal transport is necessarily unique. This insight allows us to construct smooth costs on a pair of compact manifolds with arbitrary topology, so that the optimal transportation between any pair of probility densities is unique.
description: 33 pages, 4 figures
Association of Childhood Economic Hardship with Adult Height and Adult Adiposity among Hispanics/Latinos. The HCHS/SOL Socio-Cultural Ancillary Study
Creators: name: Kim, Ryung
name: Perreira, Krista M.
name: Parrinello, Christina M.
name: Gallo, Linda C.
name: Isasi, Carmen R.
name: Jung, Molly
name: Kaplan, Robert C.
name: Sotres-Alvarez, Daniela
name: Gouskova, Natalia A.
name: Crespo, Noe C.
name: Penedo, Frank J.
name: Gonzalez, Patricia
name: Van Horn, Linda
name: Perrino, Tatiana
Publisher: name: The University of North Carolina at Chapel Hill University Libraries
Year: 2016

description: The study examined the association of childhood and current economic hardship with anthropometric indices in Hispanic/Latino adults, using data from the HCHS/SOL Socio-cultural ancillary study (N = 5,084), a community-based study of Hispanic/Latinos living in four urban areas (Bronx, NY, Chicago, IL, Miami, FL, and San Diego, CA). Childhood economic hardship was defined as having experienced a period of time when one’s family had trouble paying for basic needs (e.g., food, housing), and when this economic hardship occurred: between 0–12, 13–18 years old, or throughout both of those times. Current economic hardship was defined as experiencing trouble paying for basic needs during the past 12 months. Anthropometry included height, body mass index (BMI), waist circumference (WC), and percentage body fat (%BF). Complex survey linear regression models were used to test the associations of childhood economic hardship with adult anthropometric indices, adjusting for potential confounders (e.g., age, sex, Hispanic background). Childhood economic hardship varied by Hispanic background, place of birth, and adult socio-economic status. Childhood economic hardship during both periods, childhood and adolescence, was associated with shorter height. Childhood economic hardship was associated with greater adiposity among US born individuals only. Current economic hardship was significantly associated with all three measures of adiposity (BMI, WC, %BF). These findings suggest that previous periods of childhood economic hardship appear to influence adult height more than adiposity, whereas current economic hardship may be a better determinant of adult adiposity in Hispanics.