cssi/SciSciGPT-SciSciNet huggingface dataset:
Does Cosmological Evolution Select for Technology

If the parameters defining the physics of our universe departed from their present values, the observed rich structure and complexity would not be supported. This article considers whether similar fine-tuning of parameters applies to technology. The anthropic principle is one means of explaining the observed values of the parameters. This principle constrains physical theories to allow for our existence, yet the principle does not apply to the existence of technology. Cosmological natural selection has been proposed as an alternative to anthropic reasoning. Within this framework, fine-tuning results from selection of universes capable of prolific reproduction. It was originally proposed that reproduction occurs through singularities resulting from supernovae, and subsequently argued that life may facilitate the production of the singularities that become offspring universes. Here I argue technology is necessary for production of singularities by living beings, and ask whether the physics of our universe has been selected to simultaneously enable stars, intelligent life, and technology capable of creating progeny. Specific technologies appear implausibly equipped to perform tasks necessary for production of singularities, potentially indicating fine-tuning through cosmological natural selection. These technologies include silicon electronics, superconductors, and the cryogenic infrastructure enabled by the thermodynamic properties of liquid helium. Numerical studies are proposed to determine regions of physical parameter space in which the constraints of stars, life, and technology are simultaneously satisfied. If this overlapping parameter range is small, we should be surprised that physics allows technology to exist alongside us. The tests do not call for new astrophysical or cosmological observations. Only computer simulations of well-understood condensed matter systems are required.
Millimeter Wave Base Stations with Cameras: Vision-Aided Beam and Blockage Prediction

This paper investigates a novel research direction that leverages vision to help overcome the critical wireless communication challenges. In particular, this paper considers millimeter wave (mmWave) communication systems, which are principal components of 5G and beyond. These systems face two important challenges: (i) the large training overhead associated with selecting the optimal beam and (ii) the reliability challenge due to the high sensitivity to link blockages. Interestingly, most of the devices that employ mmWave arrays will likely also use cameras, such as 5G phones, self-driving vehicles, and virtual/augmented reality headsets. Therefore, we investigate the potential gains of employing cameras at the mmWave base stations and leveraging their visual data to help overcome the beam selection and blockage prediction challenges. To do that, this paper exploits computer vision and deep learning tools to predict mmWave beams and blockages directly from the camera RGB images and the sub-6GHz channels. The experimental results reveal interesting insights into the effectiveness of such solutions. For example, the deep learning model is capable of achieving over 90% beam prediction accuracy, which only requires snapping a shot of the scene and zero overhead.
Differential Effects of Pergolide and Bromocriptine on Working Memory Performance and Brain Activation after Mild Traumatic Brain Injury

Dopamine D1 and D2 receptors differ with respect to patterns of regional brain distribution and behavioral effects. Pre-clinical work suggests that D1 agonists enhance working memory, but the absen...
Leveraging Personal Navigation Assistant Systems Using Automated Social Media Traffic Reporting

Modern urbanization is demanding smarter technologies to improve a variety of applications in intelligent transportation systems to relieve the increasing amount of vehicular traffic congestion and incidents. Existing incident detection techniques are limited to the use of sensors in the transportation network and hang on human-inputs. Despite of its data abundance, social media is not well-exploited in such context. In this paper, we develop an automated traffic alert system based on Natural Language Processing (NLP) that filters this flood of information and extract important traffic-related bullets. To this end, we employ the fine-tuning Bidirectional Encoder Representations from Transformers (BERT) language embedding model to filter the related traffic information from social media. Then, we apply a question-answering model to extract necessary information characterizing the report event such as its exact location, occurrence time, and nature of the events. We demonstrate the adopted NLP approaches outperform other existing approach and, after effectively training them, we focus on real-world situation and show how the developed approach can, in real-time, extract traffic-related information and automatically convert them into alerts for navigation assistance applications such as navigation apps.
Topological origin of strain induced damage of multi-network elastomers by bond breaking

Abstract Elastomers that can sustain large reversible strain are essential components for stretchable electronics. The stretchability and mechanical robustness of unfilled elastomers can be enhanced by introducing easier-to-break cross-links, e.g. through the multi-network structure, which also causes stress–strain hysteresis indicating strain-induced damage. However, it remains unclear whether cross-link breakage follows a predictable pattern that can be used to understand the damage evolution with strain. Using coarse-grained molecular dynamics and topology analyses of the polymer network, we find that bond-breaking events are controlled by the evolution of the global shortest path length between well-separated cross-links, which is both anisotropic and hysteretic with strain. These findings establish an explicit connection between the molecular structure and the macroscopic mechanical behavior of elastomers, thereby providing guidelines for designing mechanically robust soft materials.
Toward Interoperable Cyberinfrastructure: Common Descriptions for Computational Resources and Applications

The user-facing components of the Cyberinfrastructure (CI) ecosystem, science gateways and scientific workflow systems, share a common need of interfacing with physical resources (storage systems and execution environments) to manage data and execute codes (applications). However, there is no uniform, platform-independent way to describe either the resources or the applications. To address this, we propose uniform semantics for describing resources and applications that will be relevant to a diverse set of stakeholders. We sketch a solution to the problem of a common description and catalog of resources: we describe an approach to implementing a resource registry for use by the community and discuss potential approaches to some long-term challenges. We conclude by looking ahead to the application description language.
On Learning Disentangled Representations for Gait Recognition.

Gait, the walking pattern of individuals, is one of the important biometrics modalities. Most of the existing gait recognition methods take silhouettes or articulated body models as gait features. These methods suffer from degraded recognition performance when handling confounding variables, such as clothing, carrying and view angle. To remedy this issue, we propose a novel AutoEncoder framework, GaitNet, to explicitly disentangle appearance, canonical and pose features from RGB imagery. The LSTM integrates pose features over time as dynamic gait feature while canonical features are averaged as static gait feature. Both of them are utilized as classification features. In addition, we collect a Frontal-View Gait (FVG) dataset to focus on gait recognition from frontal-view walking, which is a challenging problem since it contains minimal gait cues compared to other views. FVG also includes other important variations, e.g., walking speed, carrying, and clothing. With extensive experiments on CASIA-B, USF, and FVG datasets, our method demonstrates superior performance to the SOTA quantitatively, the ability of feature disentanglement qualitatively, and promising computational efficiency. We further compare our GaitNet with state of the art face recognition to demonstrate the advantages of gait biometrics identification under certain scenarios, e.g., long distance/ lower resolutions, cross view angles.
Convergence rates for an inexact ADMM applied to separable convex optimization

Convergence rates are established for an inexact accelerated alternating direction method of multipliers (I-ADMM) for general separable convex optimization with a linear constraint. Both ergodic and non-ergodic iterates are analyzed. Relative to the iteration number k, the convergence rate is $$\mathcal{{O}}(1/k)$$ in a convex setting and $$\mathcal{{O}}(1/k^2)$$ in a strongly convex setting. When an error bound condition holds, the algorithm is 2-step linearly convergent. The I-ADMM is designed so that the accuracy of the inexact iteration preserves the global convergence rates of the exact iteration, leading to better numerical performance in the test problems.
Non-Uniform Windowed Decoding For Multi-Dimensional Spatially-Coupled LDPC Codes

In this paper, we propose a non-uniform windowed decoder for multi-dimensional spatially-coupled LDPC (MD-SCLDPC) codes over the binary erasure channel. An MD-SC-LDPC code is constructed by connecting together several SC-LDPC codes into one larger code that provides major benefits over a variety of channel models. In general, SC codes allow for lowlatency windowed decoding. While a standard windowed decoder can be naively applied, such an approach does not fully utilize the unique structure of MD-SC-LDPC codes. In this paper, we propose and analyze a novel non-uniform decoder to provide more flexibility between latency and reliability. Our theoretical derivations and empirical results show that our non-uniform decoder greatly improves upon the standard windowed decoder in terms of design flexibility, latency, and complexity.
Investigating the Effect of Deictic Movements of a Multi-Robot

While research on human-robot interaction is ongoing as robots become more readily available and easier to use, the study of interactions between a human and a team of multiple robots represents a ...
Body-Worn Cameras and Transparency: Experimental Evidence of Inconsistency in Police Executive Decision-Making

Body-worn cameras (BWC) have diffused rapidly throughout policing as a means of promoting transparency and accountability. Yet, whether to release BWC footage to the public remains largely up to th...
Surfaces generating the even primal cohomology of an abelian fivefold

Given a very general abelian fivefold A and a principal polarization $$\Theta \subset A$$ , we construct surfaces generating the algebraic part of the middle cohomology $$H^4(\Theta , {\mathbb Q})$$ , and determine the intersection pairing between these surfaces. In particular, we obtain a new proof of the Hodge conjecture for $$H^4(\Theta , {\mathbb Q})$$ and show that it contains a copy of the root lattice of $$E_6$$ .
Impedance Control of a Transfemoral Prosthesis using Continuously Varying Ankle Impedances and Multiple Equilibria

Impedance controllers are popularly used in the field of lower limb prostheses and exoskeleton development. Such controllers assume the joint to be a spring-damper system described by a discrete set of equilibria and impedance parameters. These parameters are estimated via a least squares optimization that minimizes the difference between the controller's output torque and human joint torque. Other researchers have used perturbation studies to determine empirical values for ankle impedance. The resulting values vary greatly from the prior least squares estimates. While perturbation studies are more credible, they require immense investment. This paper extended the least squares approach to reproduce the results of perturbation studies. The resulting ankle impedance parameters were successfully tested on a powered transfemoral prosthesis, AMPRO II. Further, the paper investigated the effect of multiple equilibria on the least squares estimation and the performance of the impedance controller. Finally, the paper uses the proposed least squares optimization method to estimate knee impedance.
The Rise and Fall of Import Substitution

Abstract In the 1950s, many economists believed that import substitution – policies to restrict imports of manufactured goods – was the best trade strategy to promote industrialization and economic growth in developing countries. By the mid-1960s, there was widespread disenchantment with the results of such policies, even among its proponents. This paper traces the rise and fall of import substitution as a development idea. Perhaps surprisingly, early advocates of import substitution were quite cautious in their support for the policy and were also among the first to question it based on evidence derived from country experiences.
Generating anthropomorphic phantoms using fully unsupervised deformable image registration with convolutional neural networks.

Purpose Computerized phantoms have been widely used in nuclear medicine imaging for imaging system optimization and validation. Although the existing computerized phantoms can model anatomical variations through organ and phantom scaling, they do not provide a way to fully reproduce the anatomical variations and details seen in humans. In this work, we present a novel registration-based method for creating highly anatomically detailed computerized phantoms. We experimentally show substantially improved image similarity of the generated phantom to a patient image. Methods We propose a deep-learning-based unsupervised registration method to generate a highly anatomically detailed computerized phantom by warping an XCAT phantom to a patient computed tomography (CT) scan. We implemented and evaluated the proposed method using the NURBS-based XCAT phantom and a publicly available low-dose CT dataset from TCIA. A rigorous tradeoff analysis between image similarity and deformation regularization was conducted to select the loss function and regularization term for the proposed method. A novel SSIM-based unsupervised objective function was proposed. Finally, ablation studies were conducted to evaluate the performance of the proposed method (using the optimal regularization and loss function) and the current state-of-the-art unsupervised registration methods. Results The proposed method outperformed the state-of-the-art registration methods, such as SyN and VoxelMorph, by more than 8%, measured by the SSIM and less than 30%, by the MSE. The phantom generated by the proposed method was highly detailed and was almost identical in appearance to a patient image. Conclusions A deep-learning-based unsupervised registration method was developed to create anthropomorphic phantoms with anatomies labels that can be used as the basis for modeling organ properties. Experimental results demonstrate the effectiveness of the proposed method. The resulting anthropomorphic phantom is highly realistic. Combined with realistic simulations of the image formation process, the generated phantoms could serve in many applications of medical imaging research.
Current Cognition Tests, Potential Virtual Reality Applications, and Serious Games in Cognitive Assessment and Non-Pharmacological Therapy for Neurocognitive Disorders

As the global population ages, the incidence of major neurocognitive disorders (major NCDs), such as the most common geriatric major NCD, Alzheimer’s disease (AD), has grown. Thus, the need for more definitive cognitive assessment or even effective non-pharmacological intervention for age-related NCDs is becoming more and more pressing given that no definitive diagnostics or efficacious therapeutics are currently unavailable for them. We evaluate the current state of the art of cognitive assessment for major NCDs, and then briefly glance ahead at potential application of virtual reality (VR) technologies in major NCD assessment and in cognition training of visuospatial reasoning in a 3D environment, as well as in the alleviation of depression and other symptoms of cognitive disorders. We believe that VR-based technologies have tremendous potentials in cognitive assessment and non-pharmacological therapy for major NCDs.
Differences in COVID-19-Related Testing and Healthcare Utilization by Race and Ethnicity in the Veterans Health Administration.

Recent reports indicate differences in COVID-19-related care and outcomes between Black and White Americans. We examine the COVID-19-related healthcare utilization and mortality by race and ethnicity of patients tested for SARS-CoV-2 in the Veterans Health Administration (VHA). A retrospective cohort study. We used the VHA COVID-19 shared data resources between February 1 and June 30, 2020. Veterans tested for SARS-CoV-2 virus by VHA. Three racial-ethnicity groups of Black, Hispanic, and White (as reference) veterans. Main outcomes are testing rate, positivity rate, hospitalization rate, ICU admission rate, and in-hospital mortality. Controlling for sex, age, and Elixhauser comorbidity index, we report adjusted odds ratios (aOR) and 95% confidence intervals (95% CI) from logistic regression models. Of the 8,667,996 active veteran enrollees, 252,702 were tested by VHA from February to June, 2020, with 20,500 positive results and 4,790 hospitalizations. The testing rate was 4.4% among Black and 4.7% among Hispanic veterans compared to White veterans, 2.8%. The testing positivity rate was similarly elevated among Black (12.2%) and Hispanic (11.6%) veterans compared to White veterans (6.0%). The aORs of hospitalization in Black veterans (1.88; 95% CI 1.74, 2.03) and Hispanic veterans (1.41; 95% CI 1.25, 1.60) were higher compared to White veterans. No significant differences by race and ethnicity were observed in OR or aOR of ICU admission and in-hospital death among hospitalized patients. On a national level, the VHA was more likely to test and hospitalize Black and Hispanic veterans compared to White veterans, but there were no significant differences in ICU admission or in-hospital mortality among those hospitalized. This pattern of differences may relate to social determinants of health, factors affecting access to non-VHA care, or preferences for VHA care affecting initial care seeking, but not in-hospital outcomes.
Economic Evaluation in Opioid Modeling: Systematic Review.

Abstract Objectives The rapid increase in opioid overdose and opioid use disorder (OUD) over the past 20 years is a complex problem associated with significant economic costs for healthcare systems and society. Simulation models have been developed to capture and identify ways to manage this complexity and to evaluate the potential costs of different strategies to reduce overdoses and OUD. A review of simulation-based economic evaluations is warranted to fully characterize this set of literature. Methods A systematic review of simulation-based economic evaluation (SBEE) studies in opioid research was initiated by searches in PubMed, EMBASE, and EbscoHOST. Extraction of a predefined set of items and a quality assessment were performed for each study. Results The screening process resulted in 23 SBEE studies ranging by year of publication from 1999 to 2019. Methodological quality of the cost analyses was moderately high. The most frequently evaluated strategies were methadone and buprenorphine maintenance treatments; the only harm reduction strategy explored was naloxone distribution. These strategies were consistently found to be cost-effective, especially naloxone distribution and methadone maintenance. Prevention strategies were limited to abuse-deterrent opioid formulations. Less than half (39%) of analyses adopted a societal perspective in their estimation of costs and effects from an opioid-related intervention. Prevention strategies and studies’ accounting for patient and physician preference, changing costs, or result stratification were largely ignored in these SBEEs. Conclusion The review shows consistently favorable cost analysis findings for naloxone distribution strategies and opioid agonist treatments and identifies major gaps for future research.
Dielectric Breakdown by Electric-field Induced Phase Separation

The control of the dielectric and conductive properties of device-level systems is important for increasing the efficiency of energy- and information-related technologies. In some cases, such as neuromorphic computing, it is desirable to increase the conductivity of an initially insulating medium by several orders of magnitude, resulting in effective dielectric breakdown. Here, we show that by tuning the value of the applied electric field in systems { with variable permittivity and electric conductivity}, e.g. ion intercalation materials, we can vary the device-level electrical conductivity by orders of magnitude. We attribute this behavior to the formation of filament-like conductive domains that percolate throughout the system, { which form only when the electric conductivity depends on the concentration}. We conclude by discussing the applicability of our results in neuromorphic computing devices and Li-ion batteries.
Analyses of Scissors Cutting Paper at Superluminal Speeds.

A popular physics legend holds that scissors can cut paper with a speed faster than light. Here this counter-intuitive myth is investigated theoretically using four simple examples of scissors. For simplicity, all cases will involve a static lower scissors blade that remains horizontal just under the paper. In the first case, the upper blade will be considered perfectly rigid as it rotates around and through the paper, while in the second case, a rigid upper blade will drop down to cut the paper like a guillotine. In the third case, the paper is cut with a laser rotating with a constant angular speed that is pointed initially perpendicular to the paper at the closest point, while in the fourth case, the uniformly rotating laser is pointed initially parallel to the paper. Although details can be surprising and occasionally complex, all cases allow sections of the paper to be cut faster than light without violating special relativity. Therefore, the popular legend is confirmed, in theory, to be true.
NEW LIMITS ON AN INTERMEDIATE-MASS BLACK HOLE IN OMEGA CENTAURI. II. DYNAMICAL MODELS

We present a detailed dynamical analysis of the projected density and kinematical data available for the globular cluster ω Centauri. We solve the spherical anisotropic Jeans equation for a given density profile to predict the projected profiles of the rms velocity , in each of the three orthogonal coordinate directions (line of sight, proper motion radial, and proper motion tangential). The models allow for the presence of a central dark mass, such as a possible intermediate-mass black hole (IMBH). We fit the models to new Hubble Space Telescope star count and proper motion data near the cluster center presented in the companion paper, which is Paper I in this series, combined with existing ground-based measurements at larger radii. The projected density profile is consistent with being flat near the center, with an upper limit γ 0.07 on the central logarithmic slope. The rms proper motion profile is also consistent with being flat near the center. The velocity anisotropy profile, distance, and stellar mass-to-light ratio are all tightly constrained by the data and found to be in good agreement with previous determinations by van de Ven et al. To fit the kinematics, we consider anisotropic models with either a flat core (γ = 0) or a shallow cusp (γ = 0.05). Core models provide a good fit to the data with M BH = 0; cusp models require a dark mass. If the dark mass in cusp models is an IMBH, then M BH = (8.7 ± 2.9) × 103 M ☉; if it is a dark cluster, then its extent must be 0.16 pc. Isotropic models do not fit the observed proper motion anisotropy and yield spuriously high values for any central dark mass. These models do provide a good fit to the Gauss-Hermite moments of the observed proper motion distributions (h 4 = –0.023 ± 0.004, h 6 = 0.001 ± 0.004). There are no unusually fast-moving stars observed in the wings of the proper motion distribution, but we show that this does not strongly constrain the mass of any possible IMBH. The overall end result of the modeling is an upper limit to the mass of any possible IMBH in ω Centauri: M BH 1.2 × 104 M ☉ at ~1σ confidence (or 1.8 × 104 M ☉ at ~3σ confidence). The 1σ limit corresponds to M BH/M tot 0.43%. We combine this with results for other clusters and discuss the implications for globular cluster IMBH demographics. Tighter limits will be needed to rule out or establish whether globular clusters follow the same black hole demographics correlations as galaxies. The arguments put forward by Noyola et al. to suspect an IMBH in ω Centauri are not confirmed by our study; the value of M BH they suggested is firmly ruled out.
Characterizing Dust Attenuation in Local Star-forming Galaxies: Near-infrared Reddening and Normalization

We characterize the near-infrared dust attenuation for a sample of ~5500 local (z ≾ 0.1) star-forming galaxies and obtain an estimate of their average total-to-selective attenuation k(λ). We utilize data from the United Kingdom Infrared Telescope and the Two-Micron All Sky Survey, which is combined with previously measured UV–optical data for these galaxies. The average attenuation curve is slightly lower in the far-ultraviolet than in local starburst galaxies by roughly 15% but appears similar at longer wavelengths with a total-to-selective normalization of R_V = 3.67_(-0.35)^(+0.44). Under the assumption of energy balance, the total attenuated energy inferred from this curve is found to be broadly consistent with the observed infrared dust emission (L_(TIR)) in a small sample of local galaxies for which far-infrared measurements are available. However, the significant scatter in this quantity among the sample may reflect large variations in the attenuation properties of individual galaxies. We also derive the attenuation curve for subpopulations of the main sample, separated according to mean stellar population age (via D_n4000), specific star formation rate, stellar mass, and metallicity, and find that they show only tentative trends with low significance, at least over the range that is probed by our sample. These results indicate that a single curve is reasonable for applications seeking to broadly characterize large samples of galaxies in the local universe, while applications to individual galaxies would yield large uncertainties and is not recommended.
Optimal Error Estimates for Analytic Continuation in the Upper Half-Plane

Analytic functions in the Hardy class $H^2$ over the upper half-plane $\mathbb{H}_+$ are uniquely determined by their values on any curve $\Gamma$ lying in the interior or on the boundary of $\mathbb{H}_+$. The goal of this paper is to provide a quantitative version of this statement. Given that $f$ from a unit ball in $H^2$ is small on $\Gamma$ (say, its $L^2$ norm is of order $\epsilon$), how does this affect the magnitude of $f$ at a point $z$ away from the curve? When $\Gamma \subset \partial \mathbb{H}_+$, we give a sharp upper bound on $|f(z)|$ of the form $\epsilon^\gamma$, with an explicit exponent $\gamma=\gamma(z) \in (0,1)$ and describe the maximizer function attaining the upper bound. When $\Gamma \subset \mathbb{H}_+$ we give an implicit sharp upper bound in terms of a solution of an integral equation on $\Gamma$. We conjecture and give evidence that this bound also behaves like $\epsilon^\gamma$ for some $\gamma=\gamma(z) \in (0,1)$. These results can also be transplanted to other domains conformally equivalent to the upper half-plane.
Running Masses in the Nucleon and its Resonances

An overarching scientific challenge for the coming decade is to discover the meaning of confinement, its relationship to dynamical chiral symmetry breaking (DCSB) – the origin of visible mass – and the connection between them. In progressing toward meeting this challenge, significant progress has been made using continuum methods in QCD. For example, a novel understanding of gluon and quark confinement and its consequences has begun to emerge from quantum field theory; a clear picture is being drawn of how hadron masses emerge dynamically in a universe with light quarks; and ground-state hadron wave functions with a direct connection to QCD are becoming available, which reveal that quark-quark correlations are crucial in hadron structure. There is growing experimental support for this body of predictions in both elastic and nucleon-to-resonance-transition form factors.
Monte Carlo simulations of globular cluster evolution. I. Method and test calculations

We present a new parallel supercomputer implementation of the Monte Carlo method for simulating the dynamical evolution of globular star clusters. Our method is based on a modified version of Henon's Monte Carlo algorithm for solving the Fokker-Planck equation. Our code allows us to follow the evolution of a cluster containing up to 5 × 105 stars to core collapse in 40 hours of computing time. In this paper we present the results of test calculations for clusters with equal-mass stars, starting from both Plummer and King model initial conditions. We consider isolated as well as tidally truncated clusters. Our results are compared to those obtained from approximate, self-similar analytic solutions, from direct numerical integrations of the Fokker-Planck equation, and from direct N-body integrations performed on a GRAPE-4 special-purpose computer with N = 16384. In all cases we find excellent agreement with other methods, establishing our new code as a robust tool for the numerical study of globular cluster dynamics using a realistic number of stars.
Co-generation of game levels and game-playing agents

Open-endedness, a longstanding cornerstone of artificial life research, is the ability of systems to generate potentially unbounded ontologies of increasing novelty and complexity. Engineering generative systems displaying at least some degree of this ability is a goal with clear applications to procedural content generation in games. The Paired Open-Ended Trailblazer (POET) algorithm, heretofore explored only in a biped walking domain, is a coevolutionary system that simultaneously generates environments and agents that can solve them. This paper introduces a POET-Inspired Neuroevolutionary System for KreativitY (PINSKY) in games, which co-generates levels for multiple video games and agents that play them. This system leverages the General Video Game Artificial Intelligence (GVGAI) framework to enable co-generation of levels and agents for the 2D Atari-style games Zelda and Solar Fox. Results demonstrate the ability of PINSKY to generate curricula of game levels, opening up a promising new avenue for research at the intersection of procedural content generation and artificial life. At the same time, results in these challenging game domains highlight the limitations of the current algorithm and opportunities for improvement.
On the role of coherent states in quantum foundations

Coherent states, and the Hilbert space representations they generate, provide ideal tools to discuss classical/quantum relationships. In this paper we analyze three separate classical/quantum problems using coherent states, and show that useful connections arise among them. The topics discussed are: (1) a truly natural formulation of phase space path integrals; (2) how this analysis implies that the usual classical formalism is “simply a subset” of the quantum formalism, and thus demonstrates a universal coexistence of both the classical and quantum formalisms; and (3) how these two insights lead to a complete analytic solution of a formerly insoluble family of nonlinear quantum field theory models.
Hydrodynamic flow in small systems or: “How the heck is it possible that a system emitting only a dozen particles can be described by fluid dynamics?”

The "unreasonable effectiveness" of relativistic fluid dynamics in describing high energy heavy-ion and even proton-proton collisions are demonstrated and discussed. Several recent ideas of optimizing relativistic fluid dynamics for the specific challenges posed by such collisions will be presented, and some thoughts will be offered why the framework works better than originally expected. I will also address the unresolved question where exactly hydrodynamics breaks down, and why.
Constant-Time Filtering Using Shiftable Kernels

It was recently demonstrated in that the nonlinear bilateral filter can be efficiently implemented using a constant-time or O(1) algorithm. At the heart of this algorithm was the idea of approximating the Gaussian range kernel of the bilateral filter using trigonometric functions. In this letter, we explain how the idea in can be extended to few other linear and nonlinear filters . While some of these filters have received a lot of attention in recent years, they are known to be computationally intensive. To extend the idea in , we identify a central property of trigonometric functions, called shiftability, that allows us to exploit the redundancy inherent in the filtering operations. In particular, using shiftable kernels, we show how certain complex filtering can be reduced to simply that of computing the moving sum of a stack of images. Each image in the stack is obtained through an elementary pointwise transform of the input image. This has a two-fold advantage. First, we can use fast recursive algorithms for computing the moving sum , , and, secondly, we can use parallel computation to further speed up the computation. We also show how shiftable kernels can also be used to approximate the (nonlinearshiftable) Gaussian kernel that is ubiquitously used in image filtering.
Sparse signal recovery under Poisson statistics

We are motivated by problems that arise in a number of applications such as explosives detection and online Marketing, where the observations are governed by Poisson statistics. Here each observation is a Poisson random variable whose mean is a sparse linear superposition of known patterns. Unlike many conventional problems observations here are not identically distributed since they are associated with different sensing modalities. We analyse the performance of a Maximum Likelihood (ML) decoder, which for our Poisson setting is computationally tractable. We derive fundamental sample complexity bounds for sparse recovery in the high-dimensional setting. We show that when the sensing matrix satisfies the so-called Restricted Eigenvalue (RE) condition the l1 regularized ML decoder is consistent. Moreover, it converges exponentially fast in terms of number of observations. Our results apply to both deterministic and random sensing matrices and we present several results for both cases.
Universal limit linear series and descent of moduli spaces

We introduce a formalism of descent of moduli spaces, and use it to produce limit linear series moduli spaces for families of curves in which the components of geometric fibers may have nontrivial monodromy. We then construct a universal stack of limit linear series over the stack of semistable curves of compact type, and produce new results on existence of real curves with few real linear series.
TWO-DIMENSIONAL QUANTUM CHROMODYNAMICS ON A CYLINDER

We study two-dimensional quantum chromodynamics with massive quarks on a cylinder in a light-cone formalism. We eliminate the non-dynamical degrees of freedom and express the theory in terms of the quark and Wilson loop variables. It is possible to perform this reduction without gauge fixing. The fermionic Fock space can be defined independent of the gauge field in this light-cone formalism.
PointAR : Efficient Lighting Estimation for Mobile Augmented Reality

We propose an efficient lighting estimation pipeline that is suitable to run on modern mobile devices, with comparable resource complexities to state-of-the-art mobile deep learning models. Our pipeline, PointAR, takes a single RGB-D image captured from the mobile camera and a 2D location in that image, and estimates 2nd order spherical harmonics coefficients. This estimated spherical harmonics coefficients can be directly utilized by rendering engines for supporting spatially variant indoor lighting, in the context of augmented reality. Our key insight is to formulate the lighting estimation as a point cloud-based learning problem directly from point clouds, which is in part inspired by the Monte Carlo integration leveraged by real-time spherical harmonics lighting. While existing approaches estimate lighting information with complex deep learning pipelines, our method focuses on reducing the computational complexity. Through both quantitative and qualitative experiments, we demonstrate that PointAR achieves lower lighting estimation errors compared to state-of-the-art methods. Further, our method requires an order of magnitude lower resource, comparable to that of mobile-specific DNNs.
Numerical metrics, curvature expansions and Calabi-Yau manifolds

We discuss the extent to which numerical techniques for computing approximations to Ricci-flat metrics can be used to investigate hierarchies of curvature scales on Calabi-Yau manifolds. Control of such hierarchies is integral to the validity of curvature expansions in string effective theories. Nevertheless, for seemingly generic points in moduli space it can be difficult to analytically determine if there might be a highly curved region localized somewhere on the Calabi-Yau manifold. We show that numerical techniques are rather efficient at deciding this issue.
Fluvial seeding of cyanobacterial blooms in oligotrophic Lake Superior.

Lake Superior has recently begun experiencing cyanobacterial blooms comprised of Dolichospermum lemmermannii near the Apostle Islands and along the southern shore of the western arm. Little is known about the origin of these blooms. Experiments were conducted during the summers of 2017 and 2018 to identify sources of propagules and characteristics of sites that were potential sources. The 2017 experiments were conducted using a factorial design with three source zones ('River', 'Lake', and 'Harbor'), two nutrient conditions (high and low N:P), and three temperatures (15, 20, and 25°C). At the end of the experiment, cyanobacteria were most abundant from the 'River' and 'Harbor' zones at low N:P and 20 and 25°C, with D. lemmermannii most abundant at 20°C. Subsequently, in 2018 we evaluated 26 specific inland locations from three waterbody types ('River', 'Lake/Pond', and 'Coastal') and explored similarities among those sites that produced cyanobacteria in high abundance when samples were incubated under optimal conditions (low N:P and 25°C). Under these growing conditions, we found high cyanobacteria abundance developed in samples from river sites with low ambient temperatures and high conductivity. Field monitoring showed that Lake Superior nearshore temperatures were higher than rivers. These observations suggest that blooms of D. lemmermannii in Lake Superior are initiated by fluvial seeding of propagules and highlight the importance of warmer temperatures and favorable nutrient and light conditions for subsequent extensive cyanobacterial growth. We argue that the watershed is an important source of biological loading of D. lemmermannii to Lake Superior and that when those cells reach the nearshore where there are warmer water temperatures and increased light, they can grow in abundance to produce blooms.
Modeling heavy ion collisions in AdS/CFT

We construct a model of high energy heavy ion collisions as two ultrarelativistic shock waves colliding in AdS5. We point out that shock waves corresponding to physical energy-momentum tensors of the nuclei completely stop almost immediately after the collision in AdS5, which, on the field theory side, corresponds to complete nuclear stopping due to strong coupling effects, likely leading to Landau hydrodynamics. Since in real-life heavy ion collisions the large Bjorken x part of nuclear wave functions continues to move along the light cone trajectories of the incoming nuclei leaving the small-x partons behind, we conclude that a pure large coupling approach is not likely to adequately model nuclear collisions. We show that to account for small-coupling effects one can model the colliding nuclei by two (unphysical) ultrarelativistic shock waves with zero net energy each (but with non-zero energy density). We use this model to study the energy density of the strongly-coupled matter created immediately after the collision. We argue that expansion of the energy density in the powers of proper time τ squared corresponds on the gravity side to a perturbative expansion of the metric in graviton exchanges. Using such expansion we reproduce our earlier result [1] that the energy density of produced matter at mid-rapidity starts out as a constant (of time) in heavy ion collisions at large coupling.
Geometric Correction in Diffusive Limit of Neutron Transport Equation in 2D Convex Domains

Consider the steady neutron transport equation with diffusive boundary condition. In Wu and Guo (Commun Math Phys 336:1473–1553, 2015) and Wu et al. (J Stat Phys 165:585–644, 2016), it was discovered that geometric correction is necessary for the Milne problem of Knudsen-layer construction in a disk or annulus. In this paper, we establish the diffusive limit for a 2D convex domain. Our contribution relies on novel weighted $${W^{1,\infty}}$$ estimates for the Milne problem with geometric correction in the presence of a convex domain, as well as an $${L^{2m}-L^{\infty}}$$ framework which yields stronger remainder estimates.
Studying the Pulsation of Mira Variables in the Ultraviolet

We present results from an empirical study of the Mg II h and k emission lines of selected Mira variable stars, using spectra from the International Ultraviolet Explorer (IUE). The stars all exhibit similar Mg II behavior during the course of their pulsation cycles. The Mg II flux always peaks after optical maximum near pulsation phase = 0.2-0.5, although the Mg II flux can vary greatly from one cycle to the next. The lines are highly blueshifted, with the magnitude of the blueshift decreasing with phase. The widths of the Mg II lines are also phase dependent, decreasing from about 70 km s-1 to 40 km s-1 between = 0.2 and = 0.6. We also study other UV emission lines apparent in the IUE spectra, most of them Fe II lines. These lines are much narrower and not nearly as blueshifted as the Mg II lines. They exhibit the same phase-dependent flux behavior as Mg II, but they do not show similar velocity or width variations.
LATE-TIME DUST EMISSION FROM THE TYPE IIn SUPERNOVA 1995N

Type IIn supernovae (SNe IIn) have been found to be associated with significant amounts of dust. These core-collapse events are generally expected to be the final stage in the evolution of highly massive stars, either while in an extreme red supergiant phase or during a luminous blue variable phase. Both evolutionary scenarios involve substantial pre-supernova mass loss. I have analyzed the SN IIn 1995N in MCG –02–38–017 (Arp 261), for which mid-infrared archival data obtained with the Spitzer Space Telescope in 2009 (~14.7 yr after explosion) and with the Wide-field Infrared Survey Explorer in 2010 (~15.6-16.0 yr after explosion) reveal a luminous (~2 × 10^7 L_☉) source detected from 3.4 to 24 μm. These observations probe the circumstellar material, set up by pre-SN mass loss, around the progenitor star and indicate the presence of ~0.05-0.12 M_☉ of pre-existing, cool dust at ~240 K. This is at least a factor ~10 lower than the dust mass required to be produced from SNe at high redshift, but the case of SN 1995N lends further evidence that highly massive stars could themselves be important sources of dust.
Topological semantics for lumped parameter systems modeling

Abstract Behaviors of many engineering systems are described by lumped parameter models that encapsulate the spatially distributed nature of the system into networks of lumped elements; the dynamics of such a network is governed by a system of ordinary differential and algebraic equations. Languages and simulation tools for modeling such systems differ in syntax, informal semantics, and in the methods by which such systems of equations are generated and simulated, leading to numerous interoperability challenges. Logical extensions of SysML aim specifically at unifying a subset of the underlying concepts in such languages. We propose to unify semantics of all such systems using standard notions from algebraic topology. In particular, Tonti diagrams classify all physical theories in terms of physical laws (topological and constitutive) defined over a pair of dual cochain complexes and may be used to describe different types of lumped parameter systems. We show that all possible methods for generating the corresponding state equations within each physical domain correspond to paths over Tonti diagrams. We further propose a generalization of Tonti diagram that captures the behavior and supports canonical generation of state equations for multi-domain lumped parameter systems. The unified semantics provides a basis for greater interoperability in systems modeling, supporting automated translation, integration, reuse, and numerical simulation of models created in different authoring systems and applications. Notably, the proposed algebraic topological semantics is also compatible with spatially and temporally distributed models that are at the core of modern CAD and CAE systems.
Recent developments for the optical model of nuclei

Abstract A brief overview of various approaches to the optical-model description of nuclei is presented. A survey of some of the formal aspects is given which links the Feshbach formulation for either the hole or particle Green’s function to the time-ordered quantity of many-body theory. The link between the reducible self-energy and the elastic nucleon–nucleus scattering amplitude is also presented using the development of Villars. A brief summary of the essential elements of the multiple-scattering approach is also included. Several ingredients contained in the time-ordered Green’s function are summarized for the formal framework of the dispersive optical model (DOM). Empirical approaches to the optical potential are reviewed with emphasis on the latest global parametrizations for nucleons and composites. Various calculations that start from an underlying realistic nucleon–nucleon interaction are discussed with emphasis on more recent work. The efficacy of the DOM is illustrated in relating nuclear structure and nuclear reaction information. Its use as an intermediate between experimental data and theoretical calculations is advocated. Applications of the use of optical models are pointed out in the context of the description of nuclear reactions other than elastic nucleon–nucleus scattering.
Morphology of Epitaxial Core−Shell Nanowires

We analyze the morphological stability against azimuthal, axial, and general helical perturbations for epitaxial core-shell nanowires in the growth regimes limited by either surface diffusion or evaporation-condensation surface kinetics. For both regimes, we find that geometric parameters (i.e., core radius and shell thickness) play a central role in determining whether the nanowire remains cylindrical or its shell breaks up into epitaxial islands similar to those observed during Stranski-Krastanow growth in thin epilayers. The combination of small cores and rapid growth of the shell emerges as the key factor leading to stable shell growth. Our results provide an explanation for the different core-shell morphologies reported in the Si-Ge system experimentally and also identify a growth-induced intrinsic mechanism for the formation of helical nanowires.