cssi/SciSciGPT-SciSciNet huggingface dataset:
Ideal isotropic auxetic networks from random networks

Auxetic materials are characterized by a negative Poisson's ratio, ν. As the Poisson's ratio approaches the lower isotropic mechanical limit of ν = −1, materials show enhanced resistance to impact and shear, making them suitable for applications ranging from robotics to impact mitigation. Past experimental efforts aimed at reaching the ν = −1 limit have resulted in highly anisotropic materials, which show a negative Poisson's ratio only when subjected to deformations along specific directions. Isotropic designs have only attained moderately auxetic behavior or have led to solutions that cannot be manufactured in 3D. Here, we present a design strategy to create isotropic structures from disordered networks, which result in Poisson's ratios as low as ν = −0.98. The materials conceived through this approach are successfully fabricated in the laboratory and behave as predicted. ν depends on network structure and bond strengths; this sheds light on the motifs which lead to auxetic behavior. The ideas introduced here can be generalized to 3D, a wide range of materials, and a spectrum of length scales, thereby providing a general platform that could impact technology.
A RETROSPECTIVE STUDY ON MOLAR FURCATION ASSESSMENT VIA CLINICAL DETECTION, INTRAORAL RADIOGRAPHY, AND CONE BEAM COMPUTED TOMOGRAPHY

Background It is challenging to assess molar furcation bone loss by clinical detection and intraoral radiographs in many instances. Cone beam computed tomography (CBCT) is expected to open a new horizon in periodontal assessment. Objective(s) The aim of this study was to compare and correlate molar furcation assessment via clinical detection, intraoral radiography, and CBCT. Study Design Eighty-three patients, seen at the University of Texas School of Dentistry (UTSD), with chronic periodontitis who had existing CBCT scans were included. Furcation involvement was assessed on maxillary and mandibular first molars. Periodontal charts (modified Glickman classification), intraoral (periapical and/or bitewing) radiographs, and sagittal and axial CBCT reconstructions were used to identify furcation involvement on buccal and palatal/lingual sites. The correlation of furcation assessment by the 3 methods was evaluated by Pearson's analysis. Results There were significant correlations (P Discussion/Conclusions This study validates that CBCT is a valuable tool in molar furcation assessment in addition to clinical examination and intraoral radiography.
Binding Thermodynamics of Host–Guest Systems with SMIRNOFF99Frosst 1.0.5 from the Open Force Field Initiative

Designing ligands that bind their target biomolecules with high affinity and specificity is a key step in small-molecule drug discovery, but accurately predicting protein–ligand binding free energi...
Landslide Inventory (2001–2017) of Chittagong Hilly Areas, Bangladesh

Landslides are a frequent natural hazard in Chittagong Hilly Areas (CHA), Bangladesh, which causes the loss of lives and damage to the economy. Despite this, an official landslide inventory is still lacking in this area. In this paper, we present a landslide inventory of this area prepared using the visual interpretation of Google Earth images (Google Earth Mapping), field mapping, and a literature search. We mapped 730 landslides that occurred from January 2001 to March 2017. Different landslide attributes including type, size, distribution, state, water content, and triggers are presented in the dataset. In this area, slide and flow were the two dominant types of landslides. Out of the five districts (Bandarban, Chittagong, Cox’s Bazar, Khagrachari, and Rangamati), most (55%) of the landslides occurred in the Chittagong and Rangamati districts. About 45% of the landslides were small (<100 m2) in size, while the maximum size of the detected landslides was 85202 m2. This dataset will help to understand the characteristics of landslides in CHA and provide useful guidance for policy implementation.
Pygmy resonances and symmetry energy

I present a brief summary of the first three decades of studies of pygmy resonances in nuclei and their relation to the symmetry energy of nuclear matter. I discuss the first experiments and theories dedicated to study the electromagnetic response in halo nuclei and how a low energy peak was initially identified as a candidate for the pygmy resonance. This is followed by the description of a collective state in medium heavy and heavy nuclei which was identified as a pygmy resonance. The role of the slope parameter of the symmetry energy in determining the properties of neutron stars is stressed. The theoretical and experimental information collected on pygmy resonances, neutron skins, and the numerous correlations found with the slope parameter is briefly reviewed.
Influenza-associated mortality for circulatory and respiratory causes during the 2013-2014 through the 2018-2019 influenza seasons in Russia

Abstract Background Information on influenza-associated mortality in Russia is limited and largely related to deaths with influenza in the diagnosis that represent a small fraction of all influenza-associated deaths. Methods Using previously developed methodology (Goldstein et al., Epidemiology 2012), we regressed the monthly rates of mortality for respiratory causes, as well as circulatory causes (available from the Russian Federal State Statistics Service (Rosstat)) during the 2013/14 through the 2018/19 influenza seasons linearly against the monthly proxies for the incidence of influenza A/H3N2, A/H1N1 and B (obtained using data from the Smorodintsev Research Institute of Influenza (RII) on influenza/ARI consultations, testing of respiratory specimens and genetic/antigenic characterization of influenza viruses), adjusting for the baseline rates of mortality not associated with influenza circulation and temporal trends. Results For the 2013/14 through the 2018/19 seasons, influenza circulation was associated with an average annual 17636 (95% CI (9482,25790)) deaths for circulatory causes and 4179 (3250,5109) deaths for respiratory causes, with the largest number of deaths (32298 (18071,46525) for circulatory causes and 6689 (5019,8359) for respiratory causes) estimated during the 2014/15 influenza season. Influenza A/H3N2 was responsible for 51.8% of all circulatory influenza-associated deaths and 37.2% of all respiratory influenza-associated deaths. Influenza A/H1N1 was responsible for 23.4% of all circulatory influenza-associated deaths and 29.5% of all respiratory influenza-associated deaths. Influenza B was responsible for 24.9% of all circulatory influenza-associated deaths and 33.3% of all respiratory influenza-associated deaths, with the overwhelming majority of those deaths being caused by the B/Yamagata viruses. Compared to the 2013/14 through the 2015/16 seasons, during the 2016/17 through the 2018/19 seasons (when levels of influenza vaccination were significantly higher), the volume of influenza-associated mortality declined by about 16.1%, or 3809 annual respiratory and circulatory deaths. Conclusions Influenza circulation is associated with a substantial mortality burden in Russia, particularly for circulatory deaths, with some reduction in mortality rates observed following the major increase in influenza vaccination coverage. Those results support the potential utility of further extending the levels of influenza vaccination, the use of quadrivalent influenza vaccines, and extra efforts for protecting individuals with circulatory disease in Russia, including vaccination and the use of antiviral medications.
Linear and Nonlinear Electron Cyclotron Interaction in Open Resonators.

In this work the electron cyclotron maser oscillator interaction is analyzed in an open resonator configuration with a magnetostatic field along the resonator axis. One of the salient features of this configuration for generating high-power short-wavelength radiation is its ability to mode select. Both the linear and the nonlinear analysis are developed for an arbitrary value of the refractive index $n=\frac{\mathrm{kc}}{\ensuremath{\omega}}$. Specializing to luminous waves, $n\ensuremath{\rightarrow}1$, it is shown that the linear-gain curve is symmetric about synchronism and that instability can occur even at exact synchronism, for appropriate initial beam distributions. The nonlinear integrals of the motion are obtained, indicating that the total energy is available for conversion. The nonlinear evolution is described by a single differential equation, and the nonlinear efficiency is shown to depend on only four combinations of the beam, field, and resonator parameters. Nonlinear efficiencies up to 15% can be achieved. For a relativistic beam, a substantial frequency upshift is possible, with efficiencies up to 10%. Efficiency enhancement is possible, e.g., by the introduction of a two-stage resonator. In such a configuration, efficiencies exceeding 30% are calculated. Finally, the requirements for beam temperature and mode selectivity are found not to be particularly restrictive.
Added Mass of Lenses and Parallel Plates

Study of added mass of lens-shaped bodies, two parallel square plates, and two parallel, infinitely long rectangular plates of various thicknesses; added masses were determined from mass-frequency relationship obtained by immersing objects in water and accelerating them in oscillatory motion; experimental results are in good agreement with analytical studies of corresponding potential flows.
The Financial Costs of the U.S.-China Trade Tensions : Evidence from East Asian Stock Markets

This paper examines the impacts of United States-China trade tensions via the lens of East Asian stock markets. Studying 10 indices of the main East Asian stock markets, it finds that announcements of “trade war” escalation translated into 50 to 60 percent of the total declines in two major Chinese stock markets over the first eight months of 2018. In other words, in the absence of the “trade war” Asian stocks would have experienced half the decline, or they would have registered gains.
Quantifying Point-Prediction Uncertainty in Neural Networks via Residual Estimation with an I/O Kernel

Neural Networks (NNs) have been extensively used for a wide spectrum of real-world regression tasks, where the goal is to predict a numerical outcome such as revenue, effectiveness, or a quantitative result. In many such tasks, the point prediction is not enough: the uncertainty (i.e. risk or confidence) of that prediction must also be estimated. Standard NNs, which are most often used in such tasks, do not provide uncertainty information. Existing approaches address this issue by combining Bayesian models with NNs, but these models are hard to implement, more expensive to train, and usually do not predict as accurately as standard NNs. In this paper, a new framework (RIO) is developed that makes it possible to estimate uncertainty in any pretrained standard NN. The behavior of the NN is captured by modeling its prediction residuals with a Gaussian Process, whose kernel includes both the NN's input and its output. The framework is justified theoretically and evaluated in twelve real-world datasets, where it is found to (1) provide reliable estimates of uncertainty, (2) reduce the error of the point predictions, and (3) scale well to large datasets. Given that RIO can be applied to any standard NN without modifications to model architecture or training pipeline, it provides an important ingredient for building real-world NN applications.
GraphZoom: A Multi-level Spectral Approach for Accurate and Scalable Graph Embedding

Graph embedding techniques have been increasingly deployed in a multitude of different applications that involve learning on non-Euclidean data. However, existing graph embedding models either fail to incorporate node attribute information during training or suffer from node attribute noise, which compromises the accuracy. Moreover, very few of them scale to large graphs due to their high computational complexity and memory usage. In this paper we propose GraphZoom, a multi-level framework for improving both accuracy and scalability of unsupervised graph embedding algorithms. GraphZoom first performs graph fusion to generate a new graph that effectively encodes the topology of the original graph and the node attribute information. This fused graph is then repeatedly coarsened into a much smaller graph by merging nodes with high spectral similarities. GraphZoom allows any existing embedding methods to be applied to the coarsened graph, before it progressively refine the embeddings obtained at the coarsest level to increasingly finer graphs. We have evaluated our approach on a number of popular graph datasets for both transductive and inductive tasks. Our experiments show that GraphZoom increases the classification accuracy and significantly reduces the run time compared to state-of-the-art unsupervised embedding methods.
The effects of bandpass limiters on n-phase tracking systems

The combination of a bandpass limiter and an n(th) power tracking loop is considered. It is shown that the performance of the combination does not depend on the form of the nonlinear function used to create the n(th) harmonic signal so long as some energy in that zone is produced. Furthermore, the order in which the limiter and n(th) power nonlinearity occur is unimportant so that the results apply equally well to the n-phase Costas type of implementation of the n(th) power loops. Closed form expressions for the signal suppression factors are obtained and it is shown that the coherent and noncoherent SNR's out of the n(th) power-limiter combination can be expressed in terms of two of these suppression factors. This later point enables one to estimate the loop SNR of an n(th) power-limiter phase locked loop by simply calculating the suppression factors for n(th) and 2n(th) power loops. Finally, n(th) power phase locked loops without limiters are compared with similar loops with limiters where it is shown that the presence of the limiter can actually enhance the output SNR at moderate to large values of input SNR.
Inference for high-dimensional instrumental variables regression

Abstract This paper concerns statistical inference for the components of a high-dimensional regression parameter despite possible endogeneity of each regressor. Given a first-stage linear model for the endogenous regressors and a second-stage linear model for the dependent variable, we develop a novel adaptation of the parametric one-step update to a generic second-stage estimator. We provide conditions under which the scaled update is asymptotically normal. We then introduce a two-stage Lasso procedure and show that the second-stage Lasso estimator satisfies the aforementioned conditions. Using these results, we construct asymptotically valid confidence intervals for the components of the second-stage regression coefficients. We complement our asymptotic theory with simulation studies, which demonstrate the performance of our method in finite samples.
Making and Unmaking Citizens: Law and the Shaping of Civic Capacity

American democracy is more fragile today than in recent memory. As evidence of stubborn imbalances in political influence grow, so too does public skepticism concerning the relative benefits of our democratic institutions. Scholars have taken note, and two dominant camps have emerged to offer proposals for restoring democratic accountability and responsiveness. The first, like the public, identifies the flood of money into electoral politics as the primary source of our troubles whereas the second points to political parties as the root of the crisis. More recently, however, a nascent third approach has emerged. Looking beyond the usual suspects — money in politics or the state of our political parties — its focus is on legal reforms that would permit everyday Americans to exercise political power through organizations capable of providing a counterweight to the political influence of wealth. This Article seeks to further develop the efforts of this third approach. It argues that a more nuanced understanding of the recursive relationship between governance and civil society — one that appreciates the ways that public policy, as instantiated in legislation, inevitably influences the trajectory of civil society — permits us to envision a broader conception of law’s role in democratic reform, and that this broader conception is critical at this moment when several traditional routes have been effectively foreclosed by the Supreme Court. Toward that end, the Article identifies opportunities for law and politics — nudged perhaps by good governance philanthropists and technological advances — to make considerable strides toward rebuilding a participatory civil society capable of demanding the recognition of elected officials.
Striatal iron content is linked to reduced fronto-striatal brain function under working memory load.

Abstract Non-heme iron accumulation contributes to age-related decline in brain structure and cognition via a cascade of oxidative stress and inflammation, although its effect on brain function is largely unexplored. Thus, we examine the impact of striatal iron on dynamic range of BOLD modulation to working memory load. N ​= ​166 healthy adults (age 20–94) underwent cognitive testing and an imaging session including n-back (0-, 2-, 3-, and 4-back fMRI), R2*-weighted imaging, and pcASL to measure cerebral blood flow. A statistical model was constructed to predict voxelwise BOLD modulation by age, striatal iron content and an age ​× ​iron interaction, controlling for cerebral blood flow, sex, and task response time. A significant interaction between age and striatal iron content on BOLD modulation was found selectively in the putamen, caudate, and inferior frontal gyrus. Greater iron was associated with reduced modulation to difficulty, particularly in middle-aged and younger adults with greater iron content. Further, iron-related decreases in modulation were associated with poorer executive function in an age-dependent manner. These results suggest that iron may contribute to differences in functional brain activation prior to older adulthood, highlighting the potential role of iron as an early factor contributing to trajectories of functional brain aging.
Role of Artificial Intelligence, Clinicians & Policymakers in Clinical Decision Making: A Systems Viewpoint

"What is a system?" Is one of those questions that is yet not clear to most individuals in this world. A system is an assemblage of interacting, interrelated and interdependent components forming a complex and integrated whole with an unambiguous and common goal. This paper emphasizes on the fact that all components of a complex system are interrelated and interdependent in some way and the behavior of that system depends on these interdependencies. A health care system as portrayed in this article is widespread and complex. This encompasses not only "hospitals" but also governing bodies like the FDA, technologies such as AI, biomedical devices, Cloud computing and many more. The interactions between all these components govern the behavior and existence of the overall healthcare system. In this paper, we focus on the interaction of artificial intelligence, care providers and policymakers and analyze using systems thinking approach, their impact on clinical decision making.
Control of Flying Robotic Insects: A Perspective and Unifying Approach

We discuss the problem of designing and implementing controllers for insect-scale flapping-wing micro air vehicles (FWMAVs), from a unifying perspective and employing two different experimental platforms; namely, a Harvard RoboBee-like two-winged robot and the four-winged USC Bee+. Through experiments, we demonstrate that a method that employs quaternion coordinates for attitude control, developed to control quadrotors, can be applied to drive both robotic insects considered in this work. The proposed notion that a generic strategy can be used to control several types of artificial insects with some common characteristics was preliminarily tested and validated using a set of experiments, which include position- and attitude-controlled flights. We believe that the presented results are interesting and valuable from both the research and educational perspectives.
On Landis’ conjecture in the plane for some equations with sign-changing potentials

In this article, we investigate the quantitative unique continuation properties of real-valued solutions to elliptic equations in the plane. Under a general set of assumptions on the operator, we establish quantitative forms of Landis’ conjecture. Of note, we prove a version of Landis’ conjecture for solutions to −Δu+Vu=0, where V is a bounded function whose negative part exhibits polynomial decay at infinity. The main mechanism behind the proofs is an order of vanishing estimate in combination with an iteration scheme. To prove the order of vanishing result, we present a new idea for constructing positive multipliers and use it reduce the equation to a Beltrami system. The resulting first-order equation is analyzed using the similarity principle and the Hadamard three-quasi-circle theorem.
From Shanghai to Sydney: Chinese stock market influences on Australia

Abstract We examine whether China's growing importance to Australia as both a trade partner and engine of growth has been accompanied by financial market interdependence. We consider effects on the overall Australian market as well as the iron ore sector, which has been accounting for over half of Australia's exports to China in the years since the global financial crisis. Markov-switching analysis yields evidence of the Shanghai Composite being connected not only with the Australian iron ore sector but also the broad market Australian All Ordinaries index. These ties are found to be significant only during low volatility periods, however.
The Adjoint Petrov–Galerkin method for non-linear model reduction

Abstract We formulate a new projection-based reduced-order modeling technique for non-linear dynamical systems. The proposed technique, which we refer to as the Adjoint Petrov–Galerkin (APG) method, is derived by decomposing the generalized coordinates of a dynamical system into a resolved coarse-scale set and an unresolved fine-scale set. A Markovian finite memory assumption within the Mori–Zwanzig formalism is then used to develop a reduced-order representation of the coarse scales. This procedure leads to a closed reduced-order model that displays commonalities with the adjoint stabilization method used in finite elements. The formulation is shown to be equivalent to a Petrov–Galerkin method with a non-linear, time-varying test basis, thus sharing some similarities with the Least-Squares Petrov–Galerkin method. Theoretical analysis examining a priori error bounds and computational cost is presented. Numerical experiments on the compressible Navier–Stokes equations demonstrate that the proposed method can lead to improvements in numerical accuracy, robustness, and computational efficiency over the Galerkin method on problems of practical interest. Improvements in numerical accuracy and computational efficiency over the Least-Squares Petrov–Galerkin method are observed in most cases.
Bumping into the Glass Ceiling: Exploring Women’s Experiences Seeking Positional Leadership Roles within the Student Affairs Profession in South Africa

Despite women’s aspirations to obtain formal leadership positions within institutions of higher education in South Africa, they continue to face barriers that impede their ability to achieve senior management positions. The purpose of this phenomenological study is to explore the meaning women ascribe to the experience of seeking formal leadership positions within the field of student affairs in South Africa.
Hall viscosity in quantum systems with discrete symmetry: point group and lattice anisotropy

An expanded consideration of the nondissipative viscosity finds new relationships between anisotropy, internal angular momentum, and Hall viscosity.
Multi-Microphone Complex Spectral Mapping for Speech Dereverberation

This study proposes a multi-microphone complex spectral mapping approach for speech dereverberation on a fixed array geometry. In the proposed approach, a deep neural network (DNN) is trained to predict the real and imaginary (RI) components of direct sound from the stacked reverberant (and noisy) RI components of multiple microphones. We also investigate the integration of multi-microphone complex spectral mapping with beamforming and post-filtering. Experimental results on multi-channel speech dereverberation demonstrate the effectiveness of the proposed approach.
Quantum technology hype and national security

Technology hype is an important concept in business, marketing, and science and technology studies, but it is rarely related to security studies. What is technology hype? How does it relate to nati...
Superconductivity, antiferromagnetism, and neutron scattering

Abstract High-temperature superconductivity in both the copper-oxide and the iron–pnictide/chalcogenide systems occurs in close proximity to antiferromagnetically ordered states. Neutron scattering has been an essential technique for characterizing the spin correlations in the antiferromagnetic phases and for demonstrating how the spin fluctuations persist in the superconductors. While the nature of the spin correlations in the superconductors remains controversial, the neutron scattering measurements of magnetic excitations over broad ranges of energy and momentum transfers provide important constraints on the theoretical options. We present an overview of the neutron scattering work on high-temperature superconductors and discuss some of the outstanding issues.
Media Robustness and Scalability of Phosphate Regulated Promoters Useful for Two-Stage Autoinduction in E. coli.

A key challenge in synthetic biology is the successful utilization of characterized parts, such as promoters, in different biological contexts. We report the evaluation of the media robustness of a...
Lower and Upper Bounds for Positive Bases of Skein Algebras

We show that the if a sequence of normalized polynomials gives rise to a positive basis of the skein algebra of a surface, then it is sandwiched between the two types of Chebyshev polynomials. For the closed torus, we show that the normalized sequence of Chebyshev polynomials of type one $(\hat{T}_n)$ is the only one which gives a positive basis.
Chapter 28 – Business Associations

This chapter discusses the pros and cons of a community pharmacist undertaking his or her work as a sole trader, as a partnership or through a company. In each case, a pharmacist meets different legal concepts with which he or she will not be familiar. Issues such as fiduciary duties owed by a director of a company are considered, alongside the distinction between a limited company and a limited liability partnership.
Multivariate Distributions, Overview†

Multivariate distributions arise throughout statistics and applied probability and they are defined on finite-dimensional spaces. They serve as probabilistic models for dependent outcomes of random experiments. Biometric data typically comprises observations on multiple characteristics for each experimental subject, and joint distributions are central to the modeling and analyses of such data. From multivariate distributions, the distributions of various sample statistics of note in statistical inference can be derived. Multivariate distributions also characterize the behavior of stochastic processes through properties of their finite-dimensional projections. Keywords: correlation; regression; convergence; dispersion matrix; covariance matrix; multivariate Bartlett test; stochastic ordering; Wishart distribution; Dirichlet distribution; Weibull distribution; hypergeometric distributions
Retiree Health Insurance for Public School Employees: Does it Affect Retirement?

Despite the widespread provision of retiree health insurance for public sector workers, little attention has been paid to its effects on employee retirement. This is in contrast to the large literature on health-insurance-induced “job-lock” in the private sector. I use the introduction of retiree health insurance for public school employees in combination with administrative data on their retirement to identify the effects of retiree health insurance. As expected, the availability of retiree health insurance for older workers allows employees to retire earlier. These behavioral changes have budgetary implications, likely making the programs self-financing rather than costly to taxpayers.
Voluntary Regulation: Evidence from Medicare Payment Reform

Government programs are often offered on an optional basis to market participants. We explore the economics of such voluntary regulation in the context of a Medicare payment reform, in which one medical provider receives a single, predetermined payment for a sequence of related healthcare services, instead of separate service-specific payments. This “bundled payment” program was originally implemented as a 5-year randomized trial, with mandatory participation by hospitals assigned to the new payment model, but after two years participation was unexpectedly made voluntary for half of these hospitals. Using detailed claim-level data, we document that voluntary participation is more likely for hospitals who can increase revenue without changing behavior (“selection on levels”) and for hospitals that had large changes in behavior when participation was mandatory (“selection on slopes”). To assess outcomes under counterfactual regimes, we estimate a simple model of responsiveness to and selection into the program. We find that the current voluntary regime generates inefficient transfers to hospitals and reduces social welfare compared to the status quo, but that alternative (feasible) designs could substantially reduce these inefficient transfers. Our analysis highlights key design elements to consider under voluntary regulation.
Substituting Gadolinium in Brain MRI Using DeepContrast

Cerebral blood volume (CBV) is a hemodynamic correlate of oxygen metabolism and reflects brain activity and function. High-resolution CBV maps can be generated using the steady-state gadolinium-enhanced MRI technique. Such a technique requires an intravenous injection of exogenous gadolinium based contrast agent (GBCA) and recent studies suggest that the GBCA can accumulate in the brain after frequent use. We hypothesize that endogenous sources of contrast might exist within the most conventional and commonly acquired structural MRI, potentially obviating the need for exogenous contrast. Here, we test this hypothesis by developing and optimizing a deep learning algorithm, which we call DeepContrast, in mice. We find that DeepContrast performs equally well as exogenous GBCA in mapping CBV of the normal brain tissue and enhancing glioblastoma. Together, these studies validate our hypothesis that a deep learning approach can potentially replace the need for GBCAs in brain MRI.
Deep Non-Rigid Structure From Motion With Missing Data

Non-rigid structure from motion (NRSfM) refers to the problem of reconstructing cameras and the 3D point cloud of a non-rigid object from an ensemble of images with 2D correspondences. Current NRSfM algorithms are limited from two perspectives: (i) the number of images, and (ii) the type of shape variability they can handle. These difficulties stem from the inherent conflict between the condition of the system and the degrees of freedom needing to be modeled – which has hampered its practical utility for many applications within vision. In this paper we propose a novel hierarchical sparse coding model for NRSFM which can overcome (i) and (ii) to such an extent, that NRSFM can be applied to problems in vision previously thought too ill posed. Our approach is realized in practice as the training of an unsupervised deep neural network (DNN) auto-encoder with a unique architecture that is able to disentangle pose from 3D structure. Using modern deep learning computational platforms allows us to solve NRSfM problems at an unprecedented scale and shape complexity. Our approach has no 3D supervision, relying solely on 2D point correspondences. Further, our approach is also able to handle missing/occluded 2D points without the need for matrix completion. Extensive experiments demonstrate the impressive performance of our approach where we exhibit superior precision and robustness against all available state-of-the-art works in some instances by an order of magnitude. We further propose a new quality measure (based on the network weights) which circumvents the need for 3D ground-truth to ascertain the confidence we have in the reconstructability. We believe our work to be a significant advance over state-of-the-art in NRSFM.
Continuous and Discrete Phasor Analysis of Binned or Time-Gated Periodic Decays

Time-resolved analysis of periodically excited luminescence decays by the phasor method in the presence of time-gating or binning is revisited. Analytical expressions for discrete configurations of square gates are derived and the locus of the phasors of such modified periodic single-exponential decays is compared to the canonical uni-versal semicircle. The effects of IRF offset, decay truncation and gate shape are also discussed. Finally, modified expressions for the phase and modulus lifetimes are pro-vided for some simple cases. A discussion of a modified phasor calibration approach is presented, and illustration of the new concepts with examples from the literature conclude this work.
The transition state and regulation of γ-TuRC-mediated microtubule nucleation revealed by single molecule microscopy

Determining how microtubules (MTs) are nucleated is essential for understanding how the cytoskeleton assembles. While the MT nucleator, γ-tubulin ring complex (γ-TuRC) has been identified, precisely how γ-TuRC nucleates a MT remains poorly understood. Here, we developed a single molecule assay to directly visualize nucleation of a MT from purified Xenopus laevis γ-TuRC. We reveal a high γ-/αβ-tubulin affinity, which facilitates assembly of a MT from γ-TuRC. Whereas spontaneous nucleation requires assembly of 8 αβ-tubulins, nucleation from γ-TuRC occurs efficiently with a cooperativity of 4 αβ-tubulin dimers. This is distinct from pre-assembled MT seeds, where a single dimer is sufficient to initiate growth. A computational model predicts our kinetic measurements and reveals the rate-limiting transition where laterally associated αβ-tubulins drive γ-TuRC into a closed conformation. NME7, TPX2, and the putative activation domain of CDK5RAP2 h γ-TuRC-mediated nucleation, while XMAP215 drastically increases the nucleation efficiency by strengthening the longitudinal γ-/αβ-tubulin interaction.
Learning Implicit Text Generation via Feature Matching

Generative feature matching network (GFMN) is an approach for training state-of-the-art implicit generative models for images by performing moment matching on features from pre-trained neural networks. In this paper, we present new GFMN formulations that are effective for sequential data. Our experimental results show the effectiveness of the proposed method, SeqGFMN, for three distinct generation tasks in English: unconditional text generation, class-conditional text generation, and unsupervised text style transfer. SeqGFMN is stable to train and outperforms various adversarial approaches for text generation and text style transfer.
d-MABE: Distributed Multilevel Attribute-Based EMR Management and Applications

Current systems used by medical institutions for the management and transfer of Electronic Medical Records (EMRs) can be vulnerable to security and privacy threats. In addition, these systems are centralized, often lack interoperability, and give patients limited or no access to their own EMRs. In this paper, we propose a novel distributed data sharing scheme that applies the security benefits of blockchain to address these concerns. We deploy smart contracts on Ethereum blockchain and utilize a distributed storage system to alleviate the dependence on the record-generating institutions to manage and share patient records. To preserve privacy of patient records, we implement our smart contracts as a method to allow patients to verify attributes prior to granting access rights. Our proposed scheme also facilitates selective sharing of medical records among staff members that belong to different levels of a hierarchical institution. We provide extensive security, privacy, and evaluation analyses to show that our proposed scheme is both efficient and practical.
Artificial versus biological intelligence in the Cosmos: clues from a stochastic analysis of the Drake equation

The Drake equation has been used many times to estimate the number of observable civilizations in the galaxy. However, the uncertainty of the outcome is so great that any individual result is of limited use, as predictions can range from a handful of observable civilizations in the observable universe to tens of millions per Milky Way-sized galaxy. A statistical investigation shows that the Drake equation, despite its uncertainties, delivers robust predictions of the likelihood that the prevalent form of intelligence in the universe is artificial rather than biological. The likelihood of artificial intelligence far exceeds the likelihood of biological intelligence in all cases investigated. This conclusion is contingent upon a limited number of plausible assumptions. The significance of this outcome for the Fermi paradox is discussed.
Consensus Finder web tool to predict stabilizing substitutions in proteins.

Abstract The consensus sequence approach to predicting stabilizing substitutions in proteins rests on the notion that conserved amino acids are more likely to contribute to the stability of a protein fold than non-conserved amino acids. To implement a prediction for a target protein sequence, one finds homologous sequences and aligns them in a multiple sequence alignment. The sequence of the most frequently occurring amino acid at each position is the consensus sequence. Replacement of a rarely occurring amino acid in the target with a frequently occurring amino acid from the consensus sequence is predicted to be stabilizing. Consensus Finder is an open-source web tool that automates this prediction. This chapter reviews the rationale for the consensus sequence approach and explains the options for fine-tuning this approach using Staphylococcus nuclease A as an example.
An open database of resting-state fMRI in awake rats

Rodent models are essential to translational research in health and disease. Investigation in rodent brain function and organization at the systems level using resting-state functional magnetic resonance imaging (rsfMRI) has become increasingly popular. Due to this rapid progress, publicly shared rodent rsfMRI databases can be of particular interest and importance to the scientific community, as inspired by human neuroscience and psychiatric research that are substantially facilitated by open human neuroimaging datasets. However, such databases in rats are still rare. In this paper, we share an open rsfMRI database acquired in 90 rats with a well-established awake imaging paradigm that avoids anesthesia interference. Both raw and preprocessed data are made publicly available. Procedures in data preprocessing to remove artefacts induced by the scanner, head motion and non-neural physiological noise are described in details. We also showcase inter-regional functional connectivity and functional networks obtained from the database.
Direct Observation of Repeated Infections With Endemic Coronaviruses.

Background Although the mechanisms of adaptive immunity to pandemic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are still unknown, the immune response to the widespread endemic coronaviruses HKU1, 229E, NL63, and OC43 provide a useful reference for understanding repeat infection risk. Methods Here we used data from proactive sampling carried out in New York City from fall 2016 to spring 2018. We combined weekly nasal swab collection with self-reports of respiratory symptoms from 191 participants to investigate the profile of recurring infections with endemic coronaviruses. Results During the study, 12 individuals tested positive multiple times for the same coronavirus. We found no significant difference between the probability of testing positive at least once and the probability of a recurrence for the betacoronaviruses HKU1 and OC43 at 34 weeks after enrollment/first infection. We also found no significant association between repeat infections and symptom severity, but found strong association between symptom severity and belonging to the same family. Conclusions This study provides evidence that reinfections with the same endemic coronavirus are not atypical in a time window shorter than 1 year and that the genetic basis of innate immune response may be a greater determinant of infection severity than immune memory acquired after a previous infection.
Model Interpretation Through Lower-Dimensional Posterior Summarization

Nonparametric regression models have recently surged in their power and popularity, accompanying the trend of increasing dataset size and complexity. While these models have proven their predictive...
Online Algorithms for Dynamic Matching Markets in Power Distribution Systems

This letter proposes online algorithms for dynamic matching markets in power distribution systems. These algorithms address the problem of matching flexible loads with renewable generation, with the objective of maximizing social welfare of the exchange in the system. More specifically, two online matching algorithms are proposed for two generation-load scenarios: (i) when the mean of renewable generation is greater than the mean of the flexible load, and (ii) when the condition (i) is reversed. With the intuition that the performance of such algorithms degrades with increasing randomness of the supply and demand, two properties are proposed for assessing the performance of the algorithms. First property is convergence to optimality (CO) as the underlying randomness of renewable generation and customer loads goes to zero. The second property is deviation from optimality, which is measured as a function of the standard deviation of the underlying randomness of renewable generation and customer loads. The algorithm proposed for the first scenario is shown to satisfy CO and a deviation from optimality that varies linearly with the variation in the standard deviation. We then show that the algorithm proposed for the second scenario satisfies CO and a deviation from optimality that varies linearly with the variation in standard deviation plus an offset under certain condition.
Inequality of opportunity, inequality of income and economic growth

Abstract Despite the firm consensus that income inequality is intrinsically undesirable, its impact on economic growth is much disputed. We posit that the relationship between income inequality and economic growth is conditioned by the level of equality of opportunity. Our testable hypothesis is simple: in societies where opportunities are unequally distributed – where the material circumstances of parents act as binding constraints on the opportunities available to their children – income inequality exerts a greater drag on future growth. In contrast, in societies with a more equal distribution of opportunities, an increase in income inequality can be more easily reversed and need not constrain investment opportunities and growth. We identify equality of opportunity with intergenerational mobility, that is, the degree of the correlation between parents’ attainments (income and education) and childrens’ attainments. We use several recently developed internationally comparable measures of intergenerational mobility—including the World Bank’s comprehensive GDIM database—to confirm that the negative impact of income inequality on growth is higher the lower is intergenerational mobility. Our results suggest that omitting intergenerational mobility leads to misspecification, shedding light on why the empirical literature on income inequality and growth has been so inconclusive.
Error-correcting Codes for Short Tandem Duplication and Substitution Errors

Due to its high data density and longevity, DNA is considered a promising storage medium for satisfying ever-increasing data storage needs. However, the diversity of errors that occur in DNA sequences makes efficient error-correction a challenging task. This paper aims to address simultaneously correcting two types of errors, namely, short tandem duplication and substitution errors. We focus on tandem repeats of length at most 3 and design codes for correcting an arbitrary number of duplication errors and one substitution error. Because a substituted symbol can be duplicated many times (possibly as part of longer substrings), a single substitution can affect an unbounded substring of the retrieved word. However, we show that with appropriate preprocessing, the effect may be limited to a substring of finite length, thus making efficient error-correction possible. We construct a code for correcting the aforementioned errors and provide lower bounds for its rate. In particular, compared to optimal codes correcting only duplication errors, numerical results show that the asymptotic cost of protecting against an additional substitution is only 0.003 bits/symbol when the alphabet has size 4, an important case corresponding to data storage in DNA.
Sampling in thermoacoustic tomography

We explore the effect of sampling rates when measuring data given by $Mf$ for special operators $M$ arising in Thermoacoustic Tomography. We start with sampling requirements on $Mf$ given $f$ satisfying certain conditions. After this we discuss the resolution limit on $f$ posed by the sampling rate of $Mf$ without assuming any conditions on these sampling rates. Next we discuss aliasing artifacts when $Mf$ is known to be under sampled in one or more of its variables. Finally, we discuss averaging of measurement data and resulting aliasing and artifacts, along with a scheme for anti-aliasing.
Spectral clustering of risk score trajectories stratifies sepsis patients by clinical outcome and interventions received

Sepsis is not a monolithic disease, but a loose collection of symptoms with diverse outcomes. Thus, stratification and subtyping of sepsis patients is of great importance. We examine the temporal evolution of patient state using our previously-published method for computing risk of transition from sepsis into septic shock. Risk trajectories diverge into four clusters following early prediction of septic shock, stratifying by outcome: the highest-risk and lowest-risk groups have a 76.5% and 10.4% prevalence of septic shock, and 43% and 18% mortality, respectively. These clusters differ also in treatments received and median time to shock onset. Analyses reveal the existence of a rapid (30-60 min) transition in risk at the time of threshold crossing. We hypothesize that this transition occurs as a result of the failure of compensatory biological systems to cope with infection, resulting in a bifurcation of low to high risk. Such a collapse, we believe, represents the true onset of septic shock. Thus, this rapid elevation in risk represents a potential new data-driven definition of septic shock.