cssi/SciSciGPT-SciSciNet huggingface dataset:
Who Should We Count as Citizens? Categorizing People in Public Administration Research

Citizenship is a core concept in public administration research. This article examines how the concept was employed in twenty-nine research articles published in Public Administration Review since 2009. It finds two difficulties. The first is a tendency to omit an explicit definition of the concept, contrary to good practice. The second is a tendency to adopt an implied definition of citizenship that encompasses all of the general population. The paper considers possible justifications for current practice. Research would be improved by using the concept less frequently, defining it explicitly, adopting a definition that is closer to ordinary usage, and attending more carefully to the ways in which attitudes and behavior are influenced by a person's status within a jurisdiction.
Mitochondrial protein interaction landscape of SS-31

Mitochondrial dysfunction underlies the etiology of a broad spectrum of diseases including heart disease, cancer, neurodegenerative diseases, and the general aging process. Therapeutics that restore healthy mitochondrial function hold promise for treatment of these conditions. The synthetic tetrapeptide, elamipretide (SS-31), improves mitochondrial function, but mechanistic details of its pharmacological effects are unknown. Reportedly, SS-31 primarily interacts with the phospholipid cardiolipin in the inner mitochondrial membrane. Here we utilize chemical cross-linking with mass spectrometry to identify protein interactors of SS-31 in mitochondria. The SS-31-interacting proteins, all known cardiolipin binders, fall into two groups, those involved in ATP production through the oxidative phosphorylation pathway and those involved in 2-oxoglutarate metabolic processes. Residues cross-linked with SS-31 reveal binding regions that in many cases, are proximal to cardiolipin–protein interacting regions. These results offer a glimpse of the protein interaction landscape of SS-31 and provide mechanistic insight relevant to SS-31 mitochondrial therapy.
Magnetically Actuated Artificial Microswimmers as Mobile Microparticle Manipulators

Micro-scale swimming robots have been envisaged for many medical applications such as targeted drug delivery, where the microrobot will be expected to navigate in a fluid through channels carrying a payload. Alternatively, in many cases, such a payload does not have to be physically bound to the swimmer, but may be instead manipulated and steered through the channel by the microrobot. We investigate this problem of contactless manipulation of a microparticle by mobile microswimmer in a fluid at low Reynolds number. We consider a model of a magnetically actuated artificial microswimmer, whose locomotion through a fluid induces a disturbance velocity field in the fluid, that then acts to propel a cargo particle in its vicinity. The problem investigated in this paper is therefore one of coupled locomotion-manipulation of two bodies in a fluid. The magnetic swimmer's motion is actuated by an externally applied magnetic field of constant strength but whose direction rotates at a constant rate in a plane. The swimmer propels itself in the direction perpendicular to this plane if the frequency associated with the periodic magnetic field is above a critical frequency. Below this critical frequency, the swimmer tumbles in place without net locomotion. The coupled fluid-swimmer-cargo particle dynamics are solved numerically using the method of Stokesian dynamics. The induced motion of the cargo particle is shown to be controllable. This is achieved by switching the planes of rotation of the magnetic field and switching frequency of the magnetic field above and below the critical frequency. While a swimmer with a specific geometry has been used in the model, the results of this paper are applicable to swimmers with other geometries and means of propulsion. The results of this paper show that microswimmers can be utilized as mobile manipulators of microparticles in a fluid.
“Detaching” Courts from Local Politics? Assessing the Judicial Centralization Reforms in China

The local party-state has always been a major source of extrajudicial influence in China. Drawing on interviews with judges, this article examines the impact of Xi Jinping's ambitious judicial centralization reforms, which are aimed at enhancing judicial autonomy by transferring authority over local court personnel and finances from local to provincial level. It finds that the reforms have achieved limited results. Although many appointment and budgetary powers were formally transferred to the provincial level, the local party-state retains considerable influence in both areas owing to its superior manpower, local knowledge and, in the case of developed regions, financial resources. Moreover, the local party-state maintains significant informal influence over the courts, which require many forms of discretionary assistance from various state organs – ranging from appropriating land for new courthouses to providing police protection for remote tribunals – in order to function. This setback highlights the depth and complexity of the courts’ political and economic embeddedness and serves as a reminder of the inherent difficulty of institutionalizing judicial autonomy, however limited, in a large and diverse party-state.
NN-PARS: A Parallelized Neural Network Based Circuit Simulation Framework

The shrinking of transistor geometries as well as the increasing complexity of integrated circuits, significantly aggravate nonlinear design behavior. This demands accurate and fast circuit simulation to meet the design quality and time-to-market constraints. The existing circuit simulators which utilize lookup tables and/or closed-form expressions are either slow or inaccurate in analyzing the nonlinear behavior of designs with billions of transistors. To address these shortcomings, we present NN-PARS, a neural network (NN) based and parallelized circuit simulation framework with optimized event-driven scheduling of simulation tasks to maximize concurrency, according to the underlying GPU parallel processing capabilities. NN-PARS replaces the required memory queries in traditional techniques with parallelized NN-based computation tasks. Experimental results show that compared to a state-of-the-art current-based simulation method, NN-PARS reduces the simulation time by over two orders of magnitude in large circuits. NN-PARS also provides high accuracy levels in signal waveform calculations, with less than 2% error compared to HSPICE.
Inference of population admixture network from local gene genealogies: a coalescent-based maximum likelihood approach.

Motivation Population admixture is an important subject in population genetics. Inferring population demographic history with admixture under the so-called admixture network model from population genetic data is an established problem in genetics. Existing admixture network inference approaches work with single genetic polymorphisms. While these methods are usually very fast, they do not fully utilize the information [e.g. linkage disequilibrium (LD)] contained in population genetic data. Results In this article, we develop a new admixture network inference method called GTmix. Different from existing methods, GTmix works with local gene genealogies that can be inferred from population haplotypes. Local gene genealogies represent the evolutionary history of sampled haplotypes and contain the LD information. GTmix performs coalescent-based maximum likelihood inference of admixture networks with inferred local genealogies based on the well-known multispecies coalescent (MSC) model. GTmix utilizes various techniques to speed up the likelihood computation on the MSC model and the optimal network search. Our simulations show that GTmix can infer more accurate admixture networks with much smaller data than existing methods, even when these existing methods are given much larger data. GTmix is reasonably efficient and can analyze population genetic datasets of current interests. Availability and implementation The program GTmix is available for download at: https://github.com/yufengwudcs/GTmix. Supplementary information Supplementary data are available at Bioinformatics online.
UNCONVENTIONAL FERMIONS: THE PRICE OF QUARK-LEPTON UNIFICATION AT TEV SCALES

The early petite unification (PUT) of quarks and leptons at TeV scales with used as a constraint, necessitates the introduction of extra quarks and leptons with unconventional electric charges (up to 4/3 for the quarks and 2 for the leptons). This talk, in honor of Paul Frampton's 60th birthday, will be devoted to the motivation and construction of models of early unification and to their implications, including the issues of rare decays and unconventional fermions.
The Price of Capital Goods: A Driver of Investment Under Threat

Over the past three decades, the price of machinery and equipment fell dramatically relative to other prices in advanced and emerging market and developing economies. Using cross-country and sectoral data, we show that the decline in the relative price of tangible tradable capital goods provided a significant impetus to the capital deepening that took place during the same time period. The broad-based decline in the relative price of machinery and equipment, in turn, was driven by the faster productivity growth in the capital goods producing sectors relative to the rest of the economy, and deeper trade integration, which induced domestic producers to lower prices and increase their efficiency. Our findings suggest an additional channel through which rising trade tensions and sluggish productivity could threaten real investment growth going forward.
Robotic Exploration of Unknown 2D Environment Using a Frontier-based Automatic-Differentiable Information Gain Measure

At the heart of path-planning methods for autonomous robotic exploration is a heuristic which encourages exploring unknown regions of the environment. Such heuristics are typically computed using frontier-based or information-theoretic methods. Frontier-based methods define the information gain of an exploration path as the number of boundary cells, or frontiers, which are visible from the path. However, the discrete and non-differentiable nature of this measure of information gain makes it difficult to optimize using gradient-based methods. In contrast, information-theoretic methods define information gain as the mutual information between the sensor’s measurements and the explored map. However, computation of the gradient of mutual information involves finite differencing and is thus computationally expensive. In this work, we propose an exploration planning framework which combines sampling-based path planning and gradient-based path optimization. The main contribution of this framework is a novel reformulation of information gain as a differentiable function. This allows us to simultaneously optimize information gain with other differentiable quality measures, such as smoothness. The effectiveness of the proposed planning framework is verified both in simulation and in hardware experiments using a Turtlebot3 Burger robot.
The price and volume response to earnings announcements in the corporate bond market

We examine abnormal returns and trading activity in bond markets around earnings announcements. Previous work provides mixed evidence on the relative impact of positive and negative surprises and the degree of response in investment‐grade and speculative‐grade bonds. We find that these announcements convey value‐relevant information for both positive and negative earnings surprises in both investment and speculative‐grade bonds. We also document significant heterogeneity in the response across industries, with muted responses in both abnormal returns and trading activity for bonds of firms in the financial and utilities industries.
Mechanisms for Hiding Sensitive Genotypes with Information-Theoretic Privacy

The growing availability of personal genomics services comes with increasing concerns for genomic privacy. Individuals may wish to withhold sensitive genotypes that contain critical health-related information when sharing their data with such services. A straightforward solution that masks only the sensitive genotypes does not ensure privacy due to the correlation structure within the genome. Here, we develop an informationtheoretic mechanism for masking sensitive genotypes, which ensures no information about the sensitive genotypes is leaked. We also propose an efficient algorithmic implementation of our mechanism for genomic data governed by hidden Markov models. Our work is a step towards more rigorous control of privacy in genomic data sharing.
On Tractable Representations of Binary Neural Networks.

We consider the compilation of a binary neural network's decision function into tractable representations such as Ordered Binary Decision Diagrams (OBDDs) and Sentential Decision Diagrams (SDDs). Obtaining this function as an OBDD/SDD facilitates the explanation and formal verification of a neural network's behavior. First, we consider the task of verifying the robustness of a neural network, and show how we can compute the expected robustness of a neural network, given an OBDD/SDD representation of it. Next, we consider a more efficient approach for compiling neural networks, based on a pseudo-polynomial time algorithm for compiling a neuron. We then provide a case study in a handwritten digits dataset, highlighting how two neural networks trained from the same dataset can have very high accuracies, yet have very different levels of robustness. Finally, in experiments, we show that it is feasible to obtain compact representations of neural networks as SDDs.
Verkade Base in FLP Chemistry–From Stoichiometric C–H Bond Cleavage to the Catalytic Dimerization of Alkynes

Stoichiometric and catalytic reactions of terminal alkynes with various FLPs and Lewis acid–base adducts have been investigated. Reactions of phenylacetylene with FLPs composed of the Verkade base ...
Management Tenure and the Quality of Corporate Bond Ratings

We examine the implication of management for credit rating quality by focusing on the relation between management tenure and rating quality. Using a large sample of corporate bond issues in the U.S., we find robust evidence that firms with longer-tenured CEOs have lower rating quality, as reflected in lower rating accuracy, informativeness, and timeliness. Further analyses uncover two channels that underlie this relation. One channel is through learned confidence: as CEO tenure increases, rating agencies learn about how the CEO influences firm value, which leads agencies to reduce their caution and effort in management assessment. The other channel is through developed relationships: as CEO tenure increases, rating agencies develop relationships with the CEO, which leads agencies to reduce scrutiny of or cater to the CEO and her firm. Overall, we show that management tenure has important implications for the external oversight of rating agencies.
Chronic activation of fear engrams induces extinction‐like behavior in ethanol‐exposed mice

Alcohol withdrawal directly impacts the brain's stress and memory systems, which may underlie individual susceptibility to persistent drug and alcohol-seeking behaviors. Numerous studies demonstrate that forced alcohol abstinence, which may lead to withdrawal, can impair fear-related memory processes in rodents such as extinction learning; however, the underlying neural circuits mediating these impairments remain elusive. Here, we tested an optogenetic strategy aimed at mitigating fear extinction retrieval impairments in male c57BL/6 mice following exposure to alcohol (i.e., ethanol) and forced abstinence. In the first experiment, extensive behavioral extinction training in a fear-conditioned context was impaired in ethanol-exposed mice compared to controls. In the second experiment, neuronal ensembles processing a contextual fear memory in the dorsal hippocampus were tagged and optogenetically reactivated repeatedly in a distinct context in ethanol-exposed and control mice. Chronic activation of these cells resulted in a context-specific, extinction-like reduction in fear responses in both control and ethanol-exposed mice. These findings suggest that while ethanol can impair the retrieval an extinction memory, optogenetic manipulation of a fear engram is sufficient to induce an extinction-like reduction in fear responses.
Knockoff boosted tree for model-free variable selection.

Motivation The recently proposed knockoff filter is a general framework for controlling the false discovery rate (FDR) when performing variable selection. This powerful new approach generates a 'knockoff' of each variable tested for exact FDR control. Imitation variables that mimic the correlation structure found within the original variables serve as negative controls for statistical inference. Current applications of knockoff methods use linear regression models and conduct variable selection only for variables existing in model functions. Here, we extend the use of knockoffs for machine learning with boosted trees, which are successful and widely used in problems where no prior knowledge of model function is required. However, currently available importance scores in tree models are insufficient for variable selection with FDR control. Results We propose a novel strategy for conducting variable selection without prior model topology knowledge using the knockoff method with boosted tree models. We extend the current knockoff method to model-free variable selection through the use of tree-based models. Additionally, we propose and evaluate two new sampling methods for generating knockoffs, namely the sparse covariance and principal component knockoff methods. We test and compare these methods with the original knockoff method regarding their ability to control type I errors and power. In simulation tests, we compare the properties and performance of importance test statistics of tree models. The results include different combinations of knockoffs and importance test statistics. We consider scenarios that include main-effect, interaction, exponential and second-order models while assuming the true model structures are unknown. We apply our algorithm for tumor purity estimation and tumor classification using Cancer Genome Atlas (TCGA) gene expression data. Our results show improved discrimination between difficult-to-discriminate cancer types. Availability and implementation The proposed algorithm is included in the KOBT package, which is available at https://cran.r-project.org/web/packages/KOBT/index.html. Supplementary information Supplementary data are available at Bioinformatics online.
Building Digital Archive through Collaborative UX Research: Relationship-Building with the Community or Knowledge-Building about the Community?

When digital archives are increasingly becoming a space for knowledge production, construction, consumption, maintenance, dissemination, and amplification, the necessary questions to be interrogated are: Who has access to write, participate, and construct pasts, memories, and collective public imaginations through these digital domains? How can the ones who have access to engage in epistemological performances through digital archives make these domains a space to deconstruct essentialized identity categories forced upon the Other by the (hetero)patriarchal and/or imperial-colonial Self? And how can participatory design frameworks contribute to that? Triggered by these questions, my critical digital archival research project attempts at finding theories-praxes to make dialogic rooms in digital archives to build a relationship with historically, structurally, and/or systematically minoritized communities and their distinctive, transitory, plural, and conjunctural narratives. This paper introduces my ongoing doctoral project of the critical digital archive where I am documenting and theorizing a digital archive of my street photography in Nepal that I am building through participatory design frameworks with South Asian communities. In this paper, I offer a look at how the complexities and obstacles faced during UX research can enable technical communicators to rupture the essentialist foundation of identity constructions of Non-Western communities. My paper aims to communicate the necessity of conducting UX research through participatory-design frameworks not only to find answers to design technology but to see a way of completely subverting epistemic violence of colonial discourses through the “failure” to find answers.
Examining the utility of continuously quantified Darcy fluxes through the use of periodic temperature time series

Abstract Fluxes across the groundwater-surface water interface are spatially and temporally variable, difficult to observe and measure, and play a major role in regulating ecological habitat distributions, water quality, and water quantity. The long-term quantification of these fluxes is difficult as field conditions dictate when data can be collected. Ultimately there is a pressing necessity to quantify these long-term flow regimes, as impacts from a changing climate are altering the timing and extent of key groundwater-surface water interactions. The use of periodic temperature time series data is one method that can be utilized to capture these fluxes over long periods of time. Long term deployment of temperature sensors, continuously logging temperature time series data, can be leveraged into time-varying Darcy fluxes via time series analysis and the advection–dispersion equation. However, as hydrologic boundary conditions change, fluxes transition in both magnitude and direction and these temperature time series-based methods are less capable of accurately quantifying fluxes. Real world data taken from five existing United States Geological Survey paired stream gages and riparian groundwater wells sites were used as boundary conditions to inform a one-dimensional heat and mass transport model, with the simulated temperatures used to quantify Darcy fluxes through time. Comparing the Darcy fluxes found using Darcy's Law and the estimated Darcy fluxes from the use of heat as an environmental tracer, periodic temperature time series derived fluxes accurately matched those derived from Darcy's law at three of the sites. For the remaining two sites, hydrologic conditions resulted in erroneous flux estimates, allowing for the identification of specific conditions where temperature time series methods relying on a periodic signal cannot be applied.
Semantic Complexity in End-to-End Spoken Language Understanding.

End-to-end spoken language understanding (SLU) models are a class of model architectures that predict semantics directly from speech. Because of their input and output types, we refer to them as speech-to-interpretation (STI) models. Previous works have successfully applied STI models to targeted use cases, such as recognizing home automation commands, however no study has yet addressed how these models generalize to broader use cases. In this work, we analyze the relationship between the performance of STI models and the difficulty of the use case to which they are applied. We introduce empirical measures of dataset semantic complexity to quantify the difficulty of the SLU tasks. We show that near-perfect performance metrics for STI models reported in the literature were obtained with datasets that have low semantic complexity values. We perform experiments where we vary the semantic complexity of a large, proprietary dataset and show that STI model performance correlates with our semantic complexity measures, such that performance increases as complexity values decrease. Our results show that it is important to contextualize an STI model's performance with the complexity values of its training dataset to reveal the scope of its applicability.
Bayesian Computing in the Undergraduate Statistics Curriculum.

Bayesian statistics has gained great momentum since the computational developments of the 1990s. Gradually, advances in Bayesian methodology and software have made Bayesian techniques much more acc...
The first heat: production of entanglement entropy in the early universe

Entanglement entropy (EE) of a spatial region quantifies correlations between the region and its surroundings. For a free scalar in the adiabatic vacuum in de Sitter space the EE is known to remain low, scaling as the surface area of the region. Here, we study the evolution of entanglement after the universe transitions from de Sitter to flat space. We concentrate on the case of a massless minimally coupled scalar. We find numerically that, after the de Sitter stage ends, the EE and the Ŕenyi entropy rapidly grow and saturate at values obeying the volume law. The final state of the subsystem (region) is a partially thermalized state reminiscent of a generalized Gibbs ensemble. We comment on application of our results to the question of when and how cosmological perturbations decohere.
One-Port Direct/Reverse Method for Characterizing VNA Calibration Standards

This paper introduces a one-port method for estimating model parameters of vector network analyzer calibration standards. The method involves measuring the standards through an asymmetrical passive network connected in direct mode and then in reverse mode, and using these measurements to compute the S-parameters of the network. The free parameters of the calibration standards are estimated by minimizing a figure of merit based on the expected equality of the S-parameters of the network when used in direct and reverse modes. The capabilities of the method are demonstrated through simulations, and real measurements are used to estimate the actual offset delay of a 50- ${\Omega }$ calibration load that is assigned zero delay by the manufacturer. The estimated delay is 38.8 ps with a $1\sigma $ uncertainty of 2.1 ps for this particular load. This result is verified through measurements of a terminated airline. The measurements agree better with theoretical models of the airline when the reference plane is calibrated using the new estimate for the load delay.
Characterization of Thin Film Materials using SCAN meta-GGA, an Accurate Nonempirical Density Functional

We discuss self-consistently obtained ground-state electronic properties of monolayers of graphene and a number of 'beyond graphene' compounds, including films of transition-metal dichalcogenides (TMDs), using the recently proposed strongly constrained and appropriately normed (SCAN) meta-generalized gradient approximation (meta-GGA) to the density functional theory. The SCAN meta-GGA results are compared with those based on the local density approximation (LDA) as well as the generalized gradient approximation (GGA). As expected, the GGA yields expanded lattices and softened bonds in relation to the LDA, but the SCAN meta-GGA systematically improves the agreement with experiment. Our study suggests the efficacy of the SCAN functional for accurate modeling of electronic structures of layered materials in high-throughput calculations more generally.
Collective quartics and dangerous singlets in little Higgs

Any extension of the standard model that aims to describe TeV-scale physics without fine-tuning must have a radiatively-stable Higgs potential. In little Higgs theories, radiative stability is achieved through so-called collective symmetry breaking. In this letter, we focus on the necessary conditions for a little Higgs to have a collective Higgs quartic coupling. In one-Higgs doublet models, a collective quartic requires an electroweak triplet scalar. In two-Higgs doublet models, a collective quartic requires a triplet or singlet scalar. As a corollary of this study, we show that some little Higgs theories have dangerous singlets, a pathology where collective symmetry breaking does not suppress quadratically-divergent corrections to the Higgs mass.
Surfactant and gravity dependent instability of two-layer Couette flows and its nonlinear saturation

A horizontal channel flow of two immiscible fluid layers with different densities, viscosities and thicknesses, subject to vertical gravitational forces and with an insoluble surfactant monolayer present at the interface, is investigated. The base Couette flow is driven by the uniform horizontal motion of the channel walls. Linear and nonlinear stages of the (inertialess) surfactant and gravity dependent long-wave instability are studied using the lubrication approximation, which leads to a system of coupled nonlinear evolution equations for the interface and surfactant disturbances. The (inertialess) instability is a combined result of the surfactant action characterized by the Marangoni number and the gravitational effect corresponding to the Bond number that ranges from to . The other parameters are the top-to-bottom thickness ratio , which is restricted to by a reference frame choice, the top-to-bottom viscosity ratio and the base shear rate . The linear stability is determined by an eigenvalue problem for the normal modes, where the complex eigenvalues (determining growth rates and phase velocities) and eigenfunctions (the amplitudes of disturbances of the interface, surfactant, velocities and pressures) are found analytically by using the smallness of the wavenumber. For each wavenumber, there are two active normal modes, called the surfactant and the robust modes. The robust mode is unstable when falls below a certain value dependent on and . The surfactant branch has instability for , and any , although the range of unstable wavenumbers decreases as the stabilizing effect of gravity represented by increases. Thus, for certain parametric ranges, even arbitrarily strong gravity cannot completely stabilize the flow. The correlations of vorticity-thickness phase differences with instability, present when gravitational effects are neglected, are found to break down when gravity is important. The physical mechanisms of instability for the two modes are explained with vorticity playing no role in them. This is in marked contrast to the dynamical role of vorticity in the mechanism of the well-known Yih instability due to effects of inertia, and is contrary to some earlier literature. Unlike the semi-infinite case that we previously studied, a small-amplitude saturation of the surfactant instability is possible in the absence of gravity. For certain -ranges, the interface deflection is governed by a decoupled Kuramoto–Sivashinsky equation, which provides a source term for a linear convection–diffusion equation governing the surfactant concentration. When the diffusion term is negligible, this surfactant equation has an analytic solution which is consistent with the full numerics. Just like the interface, the surfactant wave is chaotic, but the ratio of the two waves turns out to be constant.
Isomorphism and classification for countable structures

We introduce a topology on the space of all isomorphism types represented in a given class of countable models, and use this topology as an aid in classifying the isomorphism types. This mixes ideas from effective descriptive set theory and computable structure theory, extending concepts from the latter beyond computable structures to examine the isomorphism problem on arbitrary countable structures. We give examples using specific classes of fields and of trees, illustrating how the new concepts can yield classifications that reveal differences between seemingly similar classes. Finally, we use a computable homeomorphism to define a measure on the space of isomorphism types of algebraic fields, and examine the prevalence of relative computable categoricity under this measure.
Partial Regularity for the Steady Hyperdissipative Fractional Navier–Stokes Equations

We extend the Caffarelli–Kohn–Nirenberg type partial regularity theory for the steady 5-dimensional fractional Navier–Stokes equations with external force to the hyperdissipative setting. In our argument we use the methods of Colombo–De Lellis–Massaccesi to apply a blowup procedure adapted from work of Ladyzhenskaya–Seregin.
A Fourier Transformed Bremsstrahlung Flash Model for the Production of X-Ray Time Lags in Accreting Black Hole Sources

Accreting black hole sources show a wide variety of rapid time variability, including the manifestation of time lags during X-ray transients, in which a delay (phase shift) is observed between the Fourier components of the hard and soft spectra. Despite a large body of observational evidence for time lags, no fundamental physical explanation for the origin of this phenomenon has been presented. We develop a new theoretical model for the production of X-ray time lags based on an exact analytical solution for the Fourier transform describing the diffusion and Comptonization of seed photons propagating through a spherical corona. The resulting Green's function can be convolved with any source distribution to compute the associated Fourier transform and time lags, hence allowing us to explore a wide variety of injection scenarios. We show that thermal Comptonization is able to self-consistently explain both the X-ray time lags and the steady-state (quiescent) X-ray spectrum observed in the low-hard state of Cyg X-1. The reprocessing of bremsstrahlung seed photons produces X-ray time lags that diminish with increasing Fourier frequency, in agreement with the observations for a wide range of sources.
Heterogeneous susceptibilities in social influence models

Abstract Network autocorrelation models are widely used to evaluate the impact of social influence on some variable of interest. This is a large class of models that parsimoniously accounts for how one's neighbors influence one's own behaviors or opinions by incorporating the network adjacency matrix into the joint distribution of the data. These models assume homogeneous susceptibility to social influence, however, which may be a strong assumption in many contexts. This paper proposes a hierarchical model that allows the influence parameter to be a function of individual attributes and/or of local network topological features. We derive an approximation of the posterior distribution in a general framework that is applicable to the Durbin, network effects, network disturbances, or network moving average autocorrelation models. The proposed approach can also be applied to investigating determinants of social influence in the context of egocentric network data. We apply our method to a data set collected via mobile phones in which we determine the effect of social influence on physical activity levels, as well as classroom data in which we investigate peer influence on student defiance. With this last data set, we also investigate the performance of the proposed egocentric network model.
Buckling without Bending: A New Paradigm in Morphogenesis

A curious feature of organ and organoid morphogenesis is that in certain cases, spatial oscillations in the thickness of the growing "film" are out of phase with the deformation of the slower-growing "substrate," while in other cases, the oscillations are in phase. The former cannot be explained by elastic bilayer instability, and contradict the notion that there is a universal mechanism by which brains, intestines, teeth, and other organs develop surface wrinkles and folds. Inspired by the microstructure of the embryonic cerebellum, we develop a new model of 2D morphogenesis in which system-spanning elastic fibers endow the organ with a preferred radius, while a separate fiber network resides in the otherwise fluidlike film at the outer edge of the organ and resists thickness gradients thereof. The tendency of the film to uniformly thicken or thin is described via a "growth potential." Several features of cerebellum, +blebbistatin organoid, and retinal fovea morphogenesis, including out-of-phase behavior and a film thickness amplitude that is comparable to the radius amplitude, are readily explained by our simple analytical model, as may be an observed scale invariance in the number of folds in the cerebellum. We also study a nonlinear variant of the model, propose further biological and bioinspired applications, and address how our model is and is not unique to the developing nervous system.
A CHIRAL SCHWINGER MODEL, ITS CONSTRAINT STRUCTURE AND APPLICATIONS TO ITS QUANTIZATION

The Jackiw–Rajaraman version of the chiral Schwinger model is studied as a function of the renormalization parameter. The constraints are obtained and they are used to carry out canonical quantization of the model by means of Dirac brackets. By introducing an additional scalar field, it is shown that the model can be made gauge invariant. The gauge invariant model is quantized by establishing a pair of gauge fixing constraints in order that the method of Dirac can be used.
Fast and Accurate Retrieval of Methane Concentration From Imaging Spectrometer Data Using Sparsity Prior

The strong radiative forcing by atmospheric methane has stimulated interest in identifying natural and anthropogenic sources of this potent greenhouse gas. Point sources are important targets for quantification, and anthropogenic targets have the potential for emissions reduction. Methane point-source plume detection and concentration retrieval have been previously demonstrated using data from the Airborne Visible InfraRed Imaging Spectrometer-Next Generation (AVIRIS-NG). Current quantitative methods have tradeoffs between computational requirements and retrieval accuracy, creating obstacles for processing real-time data or large data sets from flight campaigns. We present a new computationally efficient algorithm that applies sparsity and an albedo correction to matched the filter retrieval of trace gas concentration path length. The new algorithm was tested using the AVIRIS-NG data acquired over several point-source plumes in Ahmedabad, India. The algorithm was validated using the simulated AVIRIS-NG data, including synthetic plumes of known methane concentration. Sparsity and albedo correction together reduced the root-mean-squared error of retrieved methane concentration-path length enhancement by 60.7% compared with a previous robust matched filter method. Background noise was reduced by a factor of 2.64. The new algorithm was able to process the entire 300 flight line 2016 AVIRIS-NG India campaign in just over 8 h on a desktop computer with GPU acceleration.
A Beale–Kato–Majda Criterion with Optimal Frequency and Temporal Localization

We obtain a Beale–Kato–Majda-type criterion with optimal frequency and temporal localization for the 3D Navier–Stokes equations. Compared to previous results our condition only requires the control of Fourier modes below a critical frequency, whose value is explicit in terms of time scales. As applications it yields a strongly frequency-localized condition for regularity in the space $$B^{-1}_{\infty ,\infty }$$ and also a lower bound on the decaying rate of $$L^p$$ norms $$2\le p <3$$ for possible blowup solutions. The proof relies on new estimates for the cutoff dissipation and energy at small time scales which might be of independent interest.
Deep convolutional networks for automated detection of posterior-element fractures on spine CT

Injuries of the spine, and its posterior elements in particular, are a common occurrence in trauma patients, with potentially devastating consequences. Computer-aided detection (CADe) could assist in the detection and classification of spine fractures. Furthermore, CAD could help assess the stability and chronicity of fractures, as well as facilitate research into optimization of treatment paradigms. In this work, we apply deep convolutional networks (ConvNets) for the automated detection of posterior element fractures of the spine. First, the vertebra bodies of the spine with its posterior elements are segmented in spine CT using multi-atlas label fusion. Then, edge maps of the posterior elements are computed. These edge maps serve as candidate regions for predicting a set of probabilities for fractures along the image edges using ConvNets in a 2.5D fashion (three orthogonal patches in axial, coronal and sagittal planes). We explore three different methods for training the ConvNet using 2.5D patches along the edge maps of `positive', i.e. fractured posterior-elements and `negative', i.e. non-fractured elements. An experienced radiologist retrospectively marked the location of 55 displaced posterior-element fractures in 18 trauma patients. We randomly split the data into training and testing cases. In testing, we achieve an area-under-the-curve of 0.857. This corresponds to 71% or 81% sensitivities at 5 or 10 false-positives per patient, respectively. Analysis of our set of trauma patients demonstrates the feasibility of detecting posterior-element fractures in spine CT images using computer vision techniques such as deep convolutional networks.
Symmetry breaking of solitons in one-dimensional parity-time-symmetric optical potentials

Symmetry breaking of solitons in a class of one-dimensional parity-time (PT) symmetric complex potentials with cubic nonlinearity is reported. In generic PT-symmetric potentials, such symmetry breaking is forbidden. However, in a special class of PT-symmetric potentials V(x)=g2(x)+αg(x)+ig′(x), where g(x) is a real and even function and α a real constant, symmetry breaking of solitons can occur. That is, a branch of non-PT-symmetric solitons can bifurcate out from the base branch of PT-symmetric solitons when the base branch’s power reaches a certain threshold. At the bifurcation point, the base branch changes stability, and the bifurcated branch can be stable.
A two-sided estimate for the Gaussian noise stability deficit

The Gaussian noise-stability of a set $$A \subset {\mathbb R}^n$$ is defined by $$ \begin{aligned} {\mathcal {S}}_\rho (A) = {\mathbb P}\left( X \in A ~ \& ~ Y \in A \right) \end{aligned}$$ where $$X,Y$$ are standard jointly Gaussian vectors satisfying $${\mathbb E}[X_i Y_j] = \delta _{ij} \rho $$ . Borell’s inequality states that for all $$0 < \rho < 1$$ , among all sets $$A \subset {\mathbb R}^n$$ with a given Gaussian measure, the quantity $${\mathcal {S}}_\rho (A)$$ is maximized when $$A$$ is a half-space. We give a novel short proof of this fact, based on stochastic calculus. Moreover, we prove an almost tight, two-sided, dimension-free robustness estimate for this inequality: by introducing a new metric to measure the distance between the set $$A$$ and its corresponding half-space $$H$$ (namely the distance between the two centroids), we show that the deficit $${\mathcal {S}}_\rho (H) - {\mathcal {S}}_\rho (A)$$ can be controlled from both below and above by essentially the same function of the distance, up to logarithmic factors. As a consequence, we also establish the conjectured exponent in the robustness estimate proven by Mossel-Neeman, which uses the total-variation distance as a metric. In the limit $$\rho \rightarrow 1$$ , we obtain an improved dimension-free robustness bound for the Gaussian isoperimetric inequality. Our estimates are also valid for a generalized version of stability where more than two correlated vectors are considered.
Revised Estimates of Ocean Surface Drag in Strong Winds

Air-sea drag governs the momentum transfer between the atmosphere and the ocean, and remains largely unknown in hurricane winds. We revisit the momentum budget and eddy-covariance methods to estimate the surface drag coefficient in the laboratory. Our drag estimates agree with field measurements in low-to-moderate winds, and previous laboratory measurements in hurricane-force winds. The drag coefficient saturates at $2.6 \times 10^{-3}$ and $U_{10} \approx 25\ m\ s^{-1}$, in agreement with previous laboratory results by Takagaki et al. (2012). During our analysis, we discovered an error in the original source code used by Donelan et al. (2004). We present the corrected data and describe the correction procedure. Although the correction to the data does not change the key finding of drag saturation in strong winds, its magnitude and wind speed threshold are significantly changed. Our findings emphasize the need for an updated and unified drag parameterization based on field and laboratory data.
THE NO-HAIR THEOREM FOR THE ABELIAN HIGGS MODEL

We consider the general procedure for proving no-hair theorems for static, spherically symmetric black holes. We apply this method to the Abelian Higgs model and find a proof of the no-hair conjecture that circumvents the objections raised against the original proof due to Adler and Pearson.
A mechanism for reversible mesoscopic aggregation in liquid solutions

Solutions of proteins and other molecules exhibit puzzling, mesoscopically sized inclusions of a solute-rich liquid, well outside the region of stability of the solute-rich phase. This mesoscopic size is in conflict with existing views on heterophase fluctuations. Here we systematically work out a microscopic mechanism by which a metastable solute-rich phase can readily nucleate in a liquid solution. A requisite component of the mechanism is that the solute form long-lived complexes with itself or other molecules. After nucleated in this non-classical fashion, individual droplets grow until becoming mechanically unstable because of a concomitant drop in the internal pressure, the drop caused by the metastability of the solute-rich phase. The ensemble of the droplets is steady-state. In a freshly prepared solution, the ensemble is predicted to evolve in a way similar to the conventional Ostwald ripening, during which larger droplets grow at the expense of smaller droplets. Solutions of proteins and other molecules can host puzzling, solute-rich inclusions of mesoscopic dimensions. Here the authors report a mechanism by which mesoscopic clusters can nucleate and ripen, requiring that the solute form long-lived complexes, with implications for biologically and industrially relevant systems.
Layered material GeSe and vertical GeSe/MoS2 p-n heterojunctions

Group-IV monochalcogenides are emerging as a new class of layered materials beyond graphene, transition metal dichalcogenides (TMDCs), and black phosphorus (BP). In this paper, we report experimental and theoretical investigations of the band structure and transport properties of GeSe and its heterostructures. We find that GeSe exhibits a markedly anisotropic electronic transport, with maximum conductance along the armchair direction. Density functional theory calculations reveal that the effective mass is 2.7 times larger along the zigzag direction than the armchair direction; this mass anisotropy explains the observed anisotropic conductance. The crystallographic orientation of GeSe is confirmed by angleresolved polarized Raman measurements, which are further supported by calculated Raman tensors for the orthorhombic structure. Novel GeSe/MoS2 p-n heterojunctions are fabricated, combining the natural p-type doping in GeSe and n-type doping in MoS2. The temperature dependence of the measured junction current reveals that GeSe and MoS2 have a type-II band alignment with a conduction band offset of ∼0.234 eV. The anisotropic conductance of GeSe may enable the development of new electronic and optoelectronic devices, such as high-efficiency thermoelectric devices and plasmonic devices with resonance frequency continuously tunable through light polarization direction. The unique GeSe/MoS2 p-n junctions with type-II alignment may become essential building blocks of vertical tunneling field-effect transistors for low-power applications. The novel p-type layered material GeSe can also be combined with n-type TMDCs to form heterogeneous complementary metal oxide semiconductor (CMOS) circuits.