cssi/SciSciGPT-SciSciNet huggingface dataset:
A Distance Measure for the Analysis of Polar Opinion Dynamics in Social Networks

Analysis of opinion dynamics in social networks plays an important role in today’s life. For predicting users’ political preference, it is particularly important to be able to analyze the dynamics of competing polar opinions, such as pro-Democrat vs. pro-Republican. While observing the evolution of polar opinions in a social network over time, can we tell when the network evolved abnormally? Furthermore, can we predict how the opinions of the users will change in the future? To answer such questions, it is insufficient to study individual user behavior, since opinions can spread beyond users’ ego-networks. Instead, we need to consider the opinion dynamics of all users simultaneously and capture the connection between the individuals’ behavior and the global evolution pattern of the social network. In this work, we introduce the Social Network Distance (SND)—a distance measure that quantifies the likelihood of evolution of one snapshot of a social network into another snapshot under a chosen model of polar opinion dynamics. SND has a rich semantics of a transportation problem, yet, is computable in time linear in the number of users and, as such, is applicable to large-scale online social networks. In our experiments with synthetic and Twitter data, we demonstrate the utility of our distance measure for anomalous event detection. It achieves a true positive rate of 0.83, twice as high as that of alternatives. The same predictions presented in precision-recall space show that SND retains perfect precision for recall up to 0.2. Its precision then decreases while maintaining more than 2-fold improvement over alternatives for recall up to 0.95. When used for opinion prediction in Twitter data, SND’s accuracy is 75.6%, which is 7.5% higher than that of the next best method.
Laser-Ion Lens and Accelerator.

Generation of highly collimated monoenergetic relativistic ion beams is one of the most challenging and promising areas in ultraintense laser-matter interactions because of the numerous scientific and technological applications that require such beams. We address this challenge by introducing the concept of laser-ion lensing and acceleration. Using a simple analogy with a gradient-index lens, we demonstrate that simultaneous focusing and acceleration of ions is accomplished by illuminating a shaped solid-density target by an intense laser pulse at $\ensuremath{\sim}{10}^{22}\text{ }\text{ }\mathrm{W}/{\mathrm{cm}}^{2}$ intensity, and using the radiation pressure of the laser to deform or focus the target into a cubic micron spot. We show that the laser-ion lensing and acceleration process can be approximated using a simple deformable mirror model and then validate it using three-dimensional particle-in-cell simulations of a two-species plasma target composed of electrons and ions. Extensive scans of the laser and target parameters identify the stable propagation regime where the Rayleigh-Taylor-like instability is suppressed. Stable focusing is found at different laser powers (from a few to multiple petawatts). Focused ion beams with the focused density of order ${10}^{23}\text{ }\text{ }{\mathrm{cm}}^{\ensuremath{-}3}$, energies in access of 750 MeV, and energy density up to $2\ifmmode\times\else\texttimes\fi{}{10}^{13}\text{ }\text{ }\mathrm{J}/{\mathrm{cm}}^{3}$ at the focal point are predicted for future multipetawatt laser systems.
Statistical Inference on Panel Data Models: A Kernel Ridge Regression Method

We propose statistical inferential procedures for nonparametric panel data models with interactive fixed effects in a kernel ridge regression framework. Compared with the traditional sieve methods,...
One-Bit ExpanderSketch for One-Bit Compressed Sensing

Is it possible to obliviously construct a set of hyperplanes $\mathcal{H}$, such that you can approximate a unit vector x when you are given the side on which the vector lies with respect to every ${\text{h}} \in \mathcal{H}$ƒ In the sparse recovery literature, where x is approximately k-sparse, this problem is called one-bit compressed sensing and has received a fair amount of attention the last decade. In this paper we obtain the first scheme that achieves nearly optimal measurements and sublinear decoding time for one-bit compressed sensing in the non-uniform case. For a large range of parameters, we improve the state of the art in both the number of measurements and the decoding time.
A Novel Optimization-Based Tool to Automate Infeasible Cycle-Free Gapfilling of Genome-Scale Metabolic Models

Stoichiometric metabolic modeling, particularly Genome-Scale Models (GSMs), is now an indispensable tool for systems biology. The model reconstruction process typically involves collecting information from public databases; however, incomplete systems knowledge leaves gaps in any reconstruction. Current tools for addressing gaps use databases of biochemical functionalities to address gaps on a per-metabolite basis and can provide multiple solutions, but cannot avoid Thermodynamically Infeasible Cycles (TICs), invariably requiring lengthy manual curation. To address these limitations, this work introduces an optimization-based multi-step method named OptFill which performs TIC-avoiding whole-model gapfilling. We applied OptFill to three fictional prokaryotic models of increasing sizes and to a published GSM of Escherichia coli, iJR904. This application resulted in holistic and infeasible cycle free gapfilling solutions. Part of OptFill can, in addition, be adapted to automate inherent TICs identification in any GSM, such as iJR904. Overall, OptFill can address critical issues in automated development of high-quality GSMs.nnIn BriefStoichiometric models of metabolism are useful in studying metabolic interactions in biological systems, but are labor-intensive to create, particularly when addressing gaps or cycles in metabolic reconstruction process. Introduced here is a novel tool, OptFill, which can be used to address both gaps and cycles in model reconstruction, increasing automation.nnHighlightsO_LIThis work presents an alternative to state-of-the-art methods for gapfilling.nC_LIO_LIUnlike current methods, this method is holistic and infeasible cycle free.nC_LIO_LIThis method is applied to three test and one published model.nC_LIO_LIThis method might also be used to address infeasible cycling.nC_LI
Multi-party HD Conferencing System for Wired and Wireless Networks

With High Definition (HD) consumer cameras and dis- plays increasingly affordable and large network bandwidth available, high quality video streaming has become feasi- ble and novel, innovative applications possible. Here we present our work on the HYDRA project (High-performance Data Recording Architecture) which aims to create an over- all architecture for the acquisition, transmission, storage and rendering of high resolution media such as HD quality video and audio. Our demonstration showcases HYDRA's live streaming capabilities that enables media streaming across both wired and wireless IP based network with com- modity equipment.
Recent advances in (optical) network survivability

Recent advances in (optical) network survivability is discussed such as disaster survivability, data protection for cloud services, the notion of degraded service/partial protection, exploiting multipath provisioning and excess capacity for improved robustness.
Exploiting Smartphone Peripherals for Precise Time Synchronization

Achieving precise time synchronization across a collection of smartphones poses unique challenges due to their limited hardware support, exclusively wireless networking interface, and restricted timing stack control. Given the ubiquity and popularity of smartphones in modern distributed applications, clock discrepancies often lead to degraded application performance. In this paper, we present and evaluate alternative approaches to attain precise time synchronization by leveraging the various peripherals available on modern smartphone devices. Our evaluation across Android smartphones typically attains synchronization accuracy within (i) 200μs using audio, (ii) 3000μs using Bluetooth Low Energy, and (iii) 1000μs using Wi-Fi. Under certain conditions, we show that smartphones synchronized using one peripheral can accurately timestamp and generate synchronous events over other peripherals. The provided guide and accompanying open-source implementations offer developers a means to select the appropriate time synchronization technique when building distributed applications.
The Cohomological Brauer Group of a Torsion ${\mathbb{G}}_{m}$-Gerbe

Let $S$ be a scheme and let $\pi : \mathcal{G} \to S$ be a $\mathbb{G}_{m,S}$-gerbe corresponding to a torsion class $[\mathcal{G}]$ in the cohomological Brauer group $\mathrm{Br}'(S)$ of $S$. We show that the cohomological Brauer group $\mathrm{Br}'(\mathcal{G})$ of $\mathcal{G}$ is isomorphic to the quotient of $\mathrm{Br}'(S)$ by the subgroup generated by the class $[\mathcal{G}]$. This is analogous to a theorem proved by Gabber for Brauer-Severi schemes.
The Actin-Capping Protein Alpha-Adducin Is Required for T-Cell Costimulation

Alpha-adducin (Add1) is a critical component of the actin-spectrin network in erythrocytes, acting to cap the fast-growing, barbed ends of actin filaments, and recruiting spectrin to these junctions. Add1 is highly expressed in T cells, but its role in T-cell activation has not been examined. Using a conditional knockout model, we show that Add1 is necessary for complete activation of CD4+ T cells in response to low levels of antigen but is dispensable for CD8+ T cell activation and response to infection. Surprisingly, costimulatory signals through CD28 were completely abrogated in the absence of Add1. This study is the first to examine the role of actin-capping in T cells, and it reveals a previously unappreciated role for the actin cytoskeleton in regulating costimulation.
Engagement and usability of a cognitive behavioral therapy mobile app compared with web-based cognitive behavioral therapy among college students: Randomized heuristic trial

Background: Recent evidence in mobile health has demonstrated that, in some cases, apps are an effective way to improve health care delivery. Health care interventions delivered via mobile technology have demonstrated both practicality and affordability. Lately, cognitive behavioral therapy (CBT) interventions delivered over the internet have also shown a meaningful impact on patients with anxiety and depression. Objective: Given the growing proliferation of smartphones and the trust in apps to support improved health behaviors and outcomes, we were interested in comparing a mobile app with Web-based methods for the delivery of CBT. This study aimed to compare the usability of a CBT mobile app called MoodTrainer with an evidence-based website called MoodGYM. Methods: We used convenience sampling to recruit 30 students from a large Midwestern university and randomly assigned them to either the MoodGYM or MoodTrainer user group. The trial period ran for 2 weeks, after which the students completed a self-assessment survey based on Nielsen heuristics. Statistical analysis was performed to compare the survey results from the 2 groups. We also compared the number of modules attempted or completed and the time spent on CBT strategies. Results: The results indicate that the MoodTrainer app received a higher usability score when compared with MoodGYM. Overall, 87% (13/15) of the participants felt that it was easy to navigate through the MoodTrainer app compared with 80% (12/15) of the MoodGYM participants. All MoodTrainer participants agreed that the app was easy to use and did not require any external assistance, whereas only 67% (10/15) had the same opinion for MoodGYM. Furthermore, 67% (10/15) of the MoodTrainer participants found that the navigation controls were easy to locate compared with 80% (12/15) of the MoodGYM participants. MoodTrainer users, on average, completed 2.5 modules compared with 1 module completed by MoodGYM users. Conclusions: As among the first studies to directly compare the usability of a mobile app–based CBT with smartphone-specific features against a Web-based CBT, there is an opportunity for app-based CBT as, at least in our limited trial, it was more usable and engaging. The study was limited to evaluate usability only and not the clinical effectiveness of the app.
Who Asked Us? How the Theory of Computing Answers Questions about Analysis

Algorithmic fractal dimensions—constructs of computability theory—have recently been used to answer open questions in classical geometric measure theory, questions of mathematical analysis whose statements do not involve computability theory or logic. We survey these developments and the prospects for future such results.
Autonomous Embedded System Enabled 3-D Object Detector: (with Point Cloud and Camera)

An Autonomous vehicle or present day smart vehicle is equipped with several ADAS safety features such as Blind Spot Detection, Forward Collision Warning, Lane Departure and Parking Assistance, Surround View System, Vehicular communication System. Recent research utilize deep learning algorithms as a counterfeit for these traditional methods, using optimal sensors. This paper discusses the perception tasks related to autonomous vehicle, specifically the computer-vision approach of 3D object detection and thus proposes a model compatible with embedded system using the RTMaps framework. The proposed model is based on the sensors: camera and Lidar connected to an autonomous embedded system, providing the sensed inputs to the deep learning classifier which on the basis of theses inputs estimates the position and predicts a 3-d bounding box on the physical objects. The Frustum PointNet a contemporary architecture for 3-D object detection is used as base model and is implemented with extended functionality. The architecture is trained and tested on the KITTI dataset and is discussed with the competitive validation precision and accuracy. The Presented model is deployed on the Bluebox 2.0 platform with the RTMaps Embedded framework.
Back to the Future: Predicting Traffic Shockwave Formation and Propagation Using a Convolutional Encoder-Decoder Network

This study proposes a deep learning methodology to predict the propagation of traffic shockwaves. The input to the deep neural network is time-space diagram of the study segment, and the output of the network is the predicted (future) propagation of the shockwave on the study segment in the form of time-space diagram. The main feature of the proposed methodology is the ability to extract the features embedded in the time-space diagram to predict the propagation of traffic shockwaves.
The energy budgets of giant impacts

Giant impacts dominate the final stages of terrestrial planet formation and set the configuration and compositions of the final system of planets. A giant impact is believed to be responsible for the formation of Earth's Moon, but the specific impact parameters are under debate. Because the canonical Moon‐forming impact is the most intensely studied scenario, it is often considered the archetypal giant impact. However, a wide range of impacts with different outcomes are possible. Here we examine the total energy budgets of giant impacts that form Earth‐mass bodies and find that they differ substantially across the wide range of possible Moon‐forming events. We show that gravitational potential energy exchange is important, and we determine the regime in which potential energy has a significant effect on the collision outcome. Energy is deposited heterogeneously within the colliding planets, increasing their internal energies, and portions of each body attain sufficient entropy for vaporization. After gravitational re‐equilibration, post‐impact bodies are strongly thermally stratified, with varying amounts of vaporized and supercritical mantle. The canonical Moon‐forming impact is a relatively low‐energy event and should not be considered the archetype of accretionary giant impacts that form Earth‐mass planets. After a giant impact, bodies are significantly inflated in size compared to condensed planets of the same mass, and there are substantial differences in the magnitudes of their potential, kinetic, and internal energy components. As a result, the conditions for metal‐silicate equilibration and the subsequent evolution of the planet may vary widely between different impact scenarios.
Feeding Outcomes for Infants with Bronchopulmonary Dysplasia Discharged on Nasogastric Feeds.

Objective While previous studies regarding neonatal home feeding regimens have demonstrated a more favorable prognosis for nasogastric (NG) versus gastrostomy tubes (GT), institutional practices of discharge on NG feeds are varied, particularly for infants with bronchopulmonary dysplasia (BPD). Little is known regarding the risk factors for patients treated with GT postdischarge in premature infants with BPD. Our objective was to identify frequency and risk factors for treating premature infants with BPD discharged on NG feeds with GT. Study Design In this retrospective study, we included infants born at 30 weeks’ gestational age with BPD transitioning from a tertiary care center to home on NG feeds from 2010 to 2016. Results Of the 86 infants included in this study, 25 (29%) underwent GT placement at a median age of 264 days postdischarge. Fourteen (56%) were able to remove the GT at a median age of 979.5 days. Infants not requiring GT placement postdischarge were found to have a significantly higher ccipitofrontal circumference (p = 0.0089) and length (p = 0.0166) at discharge compared with infants with GT. Conclusion NG feeding for infants with BPD appears to be a viable treatment with fewer patients (29%) requiring GT placement. Gestational age and abnormal magnetic resonance imaging results were found to have a significant association with GT placement postdischarge.
A maximal energy pointset configuration problem

Abstract We consider the extremal pointset configuration problem of maximizing a kernel-based energy subject to the geometric constraints that the points are contained in a fixed set, the pairwise distances are bounded below, and that every closed ball of fixed radius contains at least one point. We also formulate an extremal density problem, whose solution provides an upper bound for the pointset configuration problem in the limit as the number of points tends to infinity. Existence of solutions to both problems is established and the relationship between the parameters in the two problems is studied. Several examples are studied in detail, including the density problem for the d-dimensional ball and sphere, where the solution can be computed exactly using rearrangement inequalities. We develop a computational method for the density problem that is very similar to the Merriman-Bence-Osher (MBO) diffusion-generated method. The method is proven to be increasing for all non-stationary iterations and is applied to study more examples.
Bayesian Function-on-Scalars Regression for High-Dimensional Data

We develop a fully Bayesian framework for function-on-scalars regression with many predictors. The functional data response is modeled nonparametrically using unknown basis functions, which produce...
Noncommutative cyclic isolated singularities

The question of whether a noncommutative graded quotient singularity $A^G$ is isolated depends on a subtle invariant of the $G$-action on $A$, called the pertinency. We prove a partial dichotomy theorem for isolatedness, which applies to a family of noncommutative quotient singularities arising from a graded cyclic action on the $(-1)$-skew polynomial ring. Our results generalize and extend some results of Bao, He and the third-named author and results of Gaddis, Kirkman, Moore and Won.
scBatch: batch-effect correction of RNA-seq data through sample distance matrix adjustment.

Motivation Batch effect is a frequent challenge in deep sequencing data analysis that can lead to misleading conclusions. Existing methods do not correct batch effects satisfactorily, especially with single-cell RNA sequencing (RNA-seq) data. Results We present scBatch, a numerical algorithm for batch-effect correction on bulk and single-cell RNA-seq data with emphasis on improving both clustering and gene differential expression analysis. scBatch is not restricted by assumptions on the mechanism of batch-effect generation. As shown in simulations and real data analyses, scBatch outperforms benchmark batch-effect correction methods. Availability and implementation The R package is available at github.com/tengfei-emory/scBatch. The code to generate results and figures in this article is available at github.com/tengfei-emory/scBatch-paper-scripts. Supplementary information Supplementary data are available at Bioinformatics online.
Introduction to Computational Thinking: A New High School Curriculum using CodeWorld

The Louisiana Department of Education partnered with the Gordon A. Cain Center at LSU to pilot a Computing High School Graduation Pathway. The first course in the pathway, Introduction to Computational Thinking (ICT), is designed to teach programming and reinforce mathematical practice skills of nine-grade students, with an emphasis on promoting higher order thinking. In 2017-18, about 200 students and five teachers participated in the pilot, in 2018-2019 the participation increased to 400 students, and in the current 2019-2020 year about 800 students in 11 schools are involved. After describing the course content and the teacher training, we briefly discuss the data we have collected in the last two years. The overall student reception of the course has been positive, but the course was categorized by most students as hard. The pre-post test content assessments show that students have learned not only the language, but also general principles of programming. Lessons learned during the pilot phase have motivated changes, such as emphasizing during Professional Development the need to provide timely feedback to students, provide detailed rubrics for the projects and reorganize the lessons to increase the initial engagement with the material. After two years of running pilots, the course is becoming student-centered, where most of the code and image samples provided in the lessons are based on code created by previous students.
ARL13B regulates Sonic Hedgehog signaling from outside primary cilia

ARL13B is a regulatory GTPase highly enriched in cilia. Complete loss of Arl13b disrupts cilia architecture, protein trafficking and Sonic hedgehog signaling. To determine whether ARL13B is required within cilia, we knocked in a cilia-excluded variant of ARL13B (V358A) and showed it retains all known biochemical function. We found that ARL13BV358A protein was expressed but could not be detected in cilia, even when retrograde ciliary transport was blocked. We showed Arl13bV358A/V358A mice are viable and fertile with normal Shh signal transduction. However, in contrast to wild type cilia, Arl13bV358A/V358A cells displayed short cilia and lacked ciliary ARL3 and INPP5E. These data indicate that ARL13B's role within cilia can be uncoupled from its function outside of cilia. Furthermore, these data imply that the cilia defects upon complete absence of ARL13B do not underlie the alterations in Shh transduction, which is unexpected given the requirement of cilia for Shh transduction.
Labor protection laws and the drain on productivity: Evidence from India

Employment protection legislation (EPL) is designed to promote securityers by placing restrictions on firing, but it generates unintended consequences. With India as a setting, I argue that EPL shifts jobs from younger to older workers in two ways: by discouraging the hiring of unproven young workers and by preventing the firing of low‐productivity workers. The identification strategy is motivated by Rajan and Zingales (1998): I assume that EPL is more binding in those manufacturing sectors where the involuntary separation rate in other countries is high. The data show that older workers are more likely to have formal jobs, and the effect is strongest in high‐firing sectors, which indicates that EPL shifts jobs from young to old. Additionally, EPL reduces plant‐level total factor productivity, and this effect is seen only in plants which are large enough to fall within the purview of EPL, which provides a useful placebo test.
Dynamic Channel Selection in UAVs through Constellations in the Sky

Wireless communication between an unmanned aerial vehicle (UAV) and the ground base station (BS) is susceptible to adversarial jamming. In such situations, it is important for the UAV to indicate a new channel to the BS. This paper describes a method of creating spatial codes that map the chosen channel to the motion and location of the UAVs in space, wherein the latter physically traverses the space from a given so called ''constellation point'' to another. These points create patterns in the sky, analogous to modulation constellations in classical wireless communications, and are detected at the BS through a millimeter-wave (mmWave) radar sensor. A constellation point represents a distinct n-bit field mapped to a specific channel, allowing simultaneous frequency switching at both ends without any RF transmissions. The main contributions of this paper are: (i) We conduct experimental studies to demonstrate how such constellations may be formed using COTS UAVs and mmWave sensors, given realistic sensing errors and hovering vibrations, (ii) We develop a theoretical framework that maps a desired constellation design to error and band switching time, considering again practical UAV movement limitations, and (iii) We experimentally demonstrate jamming resilient communications and validate system goodput for links formed by UAV-mounted software defined radios.
Fundamentals of Drone Cellular Network Analysis under Random Waypoint Mobility Model

In this paper, we present the first stochastic geometry-based performance analysis of a drone cellular network in which drone base stations (DBSs) are initially distributed based on a Poisson point process (PPP) and move according to a random waypoint (RWP) mobility model. The serving DBS for a typical user equipment (UE) on the ground is selected based on the nearest neighbor association policy. We further assume two service models for the serving DBS: (i) UE independent model (UIM), and (ii) UE dependent model (UDM). All the other DBSs are considered as interfering DBSs for the typical UE. We introduce a simplified RWP (SRWP) mobility model to describe the movement of interfering DBSs and characterize its key distributional properties that are required for our analysis. Building on these results, we analyze the interference field as seen by the typical UE for both the UIM and the UDM using displacement theorem, which forms the basis for characterizing the average rate at the typical UE as a function of time. To the best of our knowledge, this is the first work that analyzes the performance of a mobile drone network in which the drones follow an RWP mobility model on an infinite plane.
Unified Necessary and Sufficient Conditions for the Robust Stability of Interconnected Sector-Bounded Systems

Classical conditions for ensuring the robust stability of a linear system in feedback with a sector-bounded nonlinearity include small gain, circle, passivity, and conicity theorems. In this work, we present a similar stability condition, but expressed in terms of relations defined on a general semi-inner product space. This increased generality leads to a clean result that can be specialized in a variety of ways. First, we show how to recover both sufficient and necessary-and-sufficient versions of the afore-mentioned classical results. Second, we show that suitably choosing the semi-inner product space leads to a new necessary and sufficient condition for weighted stability, which is in turn sufficient for exponential stability.
Signature invariants related to the unknotting number

New lower bounds on the unknotting number of a knot are constructed from the classical knot signature function. These bounds can be twice as strong as previously known signature bounds. They can also be stronger than known bounds arising from Heegaard Floer and Khovanov homology. Results include new bounds on the Gordian distance between knots and information about four-dimensional knot invariants. By considering a related non-balanced signature function, bounds on the unknotting number of slice knots are constructed; these are related to the property of double-sliceness.
Semi-Supervised Learning for Text Classification by Layer Partitioning

Most recent neural semi-supervised learning (SSL) algorithms rely on adding small perturbation to either the input vectors or their representations. These methods have been successful on computer vision tasks as the images form a continuous manifold, but are not appropriate for discrete input such as sentence. To adapt these methods to text input, we propose to decompose a neural network M into two components F and U so that M = U ◦ F . The layers in F are then frozen and only the layers in U will be updated during most time of the training. In this way, F serves as a feature extractor that maps the input to high-level representation and adds systematical noise using dropout. We can then train U using any state-of-the-art SSL algorithms such as Π-model, temporal ensembling, mean teacher, etc. Furthermore, this gradually unfreezing schedule also prevents a pretrained model from catastrophic forgetting. The experimental results demonstrate that our approach provides improvements when compared to state of the art methods especially on short texts.
Adenosine-Related Mechanisms in Non-Adenosine Receptor Drugs

Many ligands directly target adenosine receptors (ARs). Here we review the effects of noncanonical AR drugs on adenosinergic signaling. Non-AR mechanisms include raising adenosine levels by inhibiting adenosine transport (e.g., ticagrelor, ethanol, and cannabidiol), affecting intracellular metabolic pathways (e.g., methotrexate, nicotinamide riboside, salicylate, and 5-aminoimidazole-4-carboxamide riboside), or undetermined means (e.g., acupuncture). However, other compounds bind ARs in addition to their canonical ‘on-target’ activity (e.g., mefloquine). The strength of experimental support for an adenosine-related role in a drug’s effects varies widely. AR knockout mice are the ‘gold standard’ method for investigating an AR role, but few drugs have been tested on these mice. Given the interest in AR modulation for treatment of cancer, CNS, immune, metabolic, cardiovascular, and musculoskeletal conditions, it is informative to consider AR and non-AR adenosinergic effects of approved drugs and conventional treatments.
Too Red for Crowdfunding: The Legitimation and Adoption of Crowdfunding Across Political Cultures:

Institutional change is typically studied at the organizational field level; we leverage political culture to examine how the context in which these fields are embedded influence processes of insti...
HINT: A hierarchical independent component analysis toolbox for investigating brain functional networks using neuroimaging data.

Abstract Background Independent component analysis (ICA) is a popular tool for investigating brain organization in neuroscience research. In fMRI studies, an important goal is to study how brain networks are modulated by subjects’ clinical and demographic variables. Existing ICA methods and toolboxes don’t incorporate subjects’ covariates effects in ICA estimation of brain networks, which potentially leads to loss in accuracy and statistical power in detecting brain network differences between subjects’ groups. New method We introduce a Matlab toolbox, HINT (Hierarchical INdependent component analysis Toolbox), that provides a hierarchical covariate-adjusted ICA (hc-ICA) for modeling and testing covariate effects and generates model-based estimates of brain networks on both the population- and individual-level. HINT provides a user-friendly Matlab GUI that allows users to easily load images, specify covariate effects, monitor model estimation via an EM algorithm, specify hypothesis tests, and visualize results. HINT also has a command line interface which allows users to conveniently run and reproduce the analysis with a script. Comparison to existing methods HINT implements a new multi-level probabilistic ICA model for group ICA. It provides a statistically principled ICA modeling framework for investigating covariate effects on brain networks. HINT can also generate and visualize model-based network estimates for user-specified subject groups, which greatly facilitates group comparisons. Results We demonstrate the steps and functionality of HINT with an fMRI example data to estimate treatment effects on brain networks while controlling for other covariates. Results demonstrate estimated brain networks and model-based comparisons between the treatment and control groups. In comparisons using synthetic fMRI data, HINT shows desirable statistical power in detecting group differences in networks especially in small sample sizes, while maintaining a low false positive rate. HINT also demonstrates similar or increased accuracy in reconstructing both population- and individual-level source signal maps as compared to some state-of-the-art group ICA methods. Conclusion HINT can provide a useful tool for both statistical and neuroscience researchers to evaluate and test differences in brain networks between subject groups.
FERTILITY: THE ROLE OF CULTURE AND FAMILY EXPERIENCE

This paper attempts to disentangle the direct effects of experience from those of culture in determining fertility. We use the GSS to examine the fertility of women born in the US but from different ethnic backgrounds. We take lagged values of the total fertility rate in the woman's country of ancestry as the cultural proxy and use the woman's number of siblings to capture her direct family experience. We find that both variables are significant determinants of fertility, even after controlling for several individual and family-level characteristics. (JEL: J13, J16, Z10)
Dynamic Model of Planar Sliding

In this paper, we present a principled method to model general planar sliding motion with distributed patch contact between two objects undergoing relative sliding motion. The effect of contact patch can be equivalently modeled as the contact wrench at one point contact. We call this point equivalent contact point (ECP). Our dynamic model embeds ECP within the Newton-Euler equations of slider’s motion and friction model. The discrete-time motion model that we derive consists of a system of quadratic equations relating the contact wrench and slip speed. This discrete-time dynamic model allows us to solve for the two components of tangential friction impulses, the friction moment, and the slip speed. The state of the slider as well as the ECP can be computed by solving a system of linear equations once the contact impulses are computed. In addition, we derive the closed form solutions for the state of slider for quasi-static motion. Furthermore, in pure translation, based on the discrete-time model, we present the closed form expressions for the friction impulses that acts on the slider and the state of the slider at each time step. Our results are dependent on the rigid body assumption and a generalized Coulomb friction model, which assumes that the contact force and moment lies within a quadratic convex cone and the friction force is independent of contact area. The results are not dependent on the exact knowledge of contact geometry or pressure distribution on the contact patch. Simulation examples are shown with both convex and non-convex contact patches to demonstrate the validity of our approach.
Active Nanophotonics

Recent progress in nanofabrication has led to tremendous technological developments for devices that rely on the interaction of light with nanostructured matter. Nanophotonics has hence experienced a large surge of interest in recent years, from basic research to applied technology. For instance, the increased importance of ultralow-energy data processing at fast speeds has been encouraging the use of light for signal transport and processing. Energy demands and interaction time scales become smaller with the physical size of the nanostructures, hence nanophotonics opens important opportunities for integrating a large number of devices that can generate, control, modulate, sense, and process light signals at ultrafast speeds and below femtojoule/bit energy levels. However, losses and diffraction pose fundamental challenges to the fundamental ability of nanophotonic structures to efficiently confine light in smaller and smaller volumes. In this framework, active nanophotonics, which combines the latest advances in nanotechnology with gain materials, has recently become a vital area of optics research, both from the physics, material science, and engineering standpoint. In this article, we review recent efforts in enabling active nanodevices for lasing and optical sources, loss compensation, and to realize new optical functionalities, like $\mathcal {P}\mathcal {T}$ -symmetry, exceptional points, and nontrivial lasing based on suitably engineered distributions of gain and loss in nanostructures.
Surplus extraction with rich type spaces

Abstract We characterize revenue maximizing mechanisms in auction settings with ‘rich’ type spaces, where bidders obtain information from sources other than their own valuation. By considering a relaxed problem, we provide an upper bound on revenue extraction that explicitly builds on the richness of the information structure. We provide a condition under which this upper bound is achieved and describe an optimal mechanism. Under this condition, we also show that the optimal revenue can be achieved through dominant strategy implementation. The optimal revenue result generalizes the full surplus extraction result of Cremer and McLean (1988) [5] as well as the standard independent private values revenue maximizing auction presented in Myerson (1981) [10] , providing a unified treatment for two sets of results irreconcilable so far.
Pūhāhonu: Earth's biggest and hottest shield volcano

Abstract New bathymetric and gravity mapping, refined volume calculations and petrologic analyses show that the Hawaiian volcano Pūhāhonu is the largest and hottest shield volcano on Earth. This ∼12.5-14.1 Ma volcano in the northwest Hawaiian Ridge (NWHR) is twice the size of Mauna Loa volcano (148 ± 29 vs. 74.0 × 10 3 km 3 ), which was assumed to be not only the largest Hawaiian volcano but also the largest known shield volcano. We considered four testable mechanisms to increase magma production, including 1) thinner lithosphere, 2) slower propagation rate, 3) more fertile source, and 4) hotter mantle. The first three of these have been ruled out. The lithosphere was old (∼88 Myrs) when Pūhāhonu was formed, and thus, too thick and cold to allow for greater extents of partial melting. The propagation rate was relatively fast when it erupted (87 km/Myr), so this is another unlikely reason. Source fertility was Kea-like and no more fertile than for other much smaller NWHR volcanoes. A hotter mantle remains the best mechanism to produce the large magma volumes and is consistent with the high forsteritic olivine phenocryst compositions (up to 91.8%) and the calculated high percent of melting (24%). Thus, the gargantuan size of Pūhāhonu reflects its high melting temperature, the highest reported for any Cenozoic basalt. A solitary wave within the Hawaiian plume is the probable cause of Pūhāhonu's higher melting temperature and the resulting increased volume flux given the absence of a more fertile source for Pūhāhonu basalts, as found for many basalts from the Hawaiian Islands.
Chapter 14 – Unlicensed Products

The distinctions between off-label use and unlicensed use are discussed. The way unlicensed medicines are obtained and used is set out. There are exemptions for production and supply by pharmacists and guidance on how this is achieved is included. Relevant extracts for the regulations are given.
Monetary and Exchange Rate Arrangements for Nafta

Abstract This paper considers the extent to which the North American Free Trade Area (NAFTA) meets the criteria for a common currency area. NAFTA is compared with the EC, a regional grouping for which initial plans for a monetary union are already in place. Most of the anticipated benefits from a monetary union in the EC apply with equal force to NAFTA. However, because the underlying disturbances are more diverse across members of NAFTA, the costs of abandoning the exchange rate instrument are likely to be higher. This is particularly true when NAFTA is compared to the EC's continental core.
Does the Environmental Kuznets Curve Describe How Individual Countries Behave

The environmental Kuznets curve (EKC), an inverted-U relationship between pollution and income, is an influential generalization about the way environmental quality changes as a country makes the transition from poverty to relative affluence. The EKC predicts that pollution will first increase, but subsequently decline if income growth proceeds far enough. We examine within-country time series data on air pollution and income for a sample of individual countries to see if this generalized prediction is commonly borne out. The empirical approach employs robust, nonparametric methods and a recently available data set on SO2, smoke, and particulate air pollution. In most cases examined, the within-country income-pollution patterns we observe do not differ significantly from what would be expected to occur by chance. Where income-pollution relationships are consistent with EKC predictions, the patterns involved are also consistent with a much simpler hypothesis.
TAPAS: A Thresholding Approach for Probability Map Automatic Segmentation in Multiple Sclerosis.

Abstract Total brain white matter lesion (WML) volume is the most widely established magnetic resonance imaging (MRI) outcome measure in studies of multiple sclerosis (MS). To estimate WML volume, there are a number of automatic segmentation methods available, yet manual delineation remains the gold standard approach. Automatic approaches often yield a probability map to which a threshold is applied to create lesion segmentation masks. Unfortunately, few approaches systematically determine the threshold employed; many methods use a manually selected threshold, thus introducing human error and bias into the automated procedure. In this study, we propose and validate an automatic thresholding algorithm, Thresholding Approach for Probability Map Automatic Segmentation in Multiple Sclerosis (TAPAS), to obtain subject-specific threshold estimates for probability map automatic segmentation of T2-weighted (T2) hyperintense WMLs. Using multimodal MRI, the proposed method applies an automatic segmentation algorithm to obtain probability maps. We obtain the true subject-specific threshold that maximizes the Sorensen-Dice similarity coefficient (DSC). Then the subject-specific thresholds are modeled on a naive estimate of volume using a generalized additive model. Applying this model, we predict a subject-specific threshold in data not used for training. We ran a Monte Carlo-resampled split-sample cross-validation (100 validation sets) using two data sets: the first obtained from the Johns Hopkins Hospital (JHH) on a Philips 3 Tesla (3T) scanner (n = 94) and a second collected at the Brigham and Women's Hospital (BWH) using a Siemens 3T scanner (n = 40). By means of the proposed automated technique, in the JHH data we found an average reduction in subject-level absolute error of 0.1 mL per one mL increase in manual volume. Using Bland-Altman analysis, we found that volumetric bias associated with group-level thresholding was mitigated when applying TAPAS. The BWH data showed similar absolute error estimates using group-level thresholding or TAPAS likely since Bland-Altman analyses indicated no systematic biases associated with group or TAPAS volume estimates. The current study presents the first validated fully automated method for subject-specific threshold prediction to segment brain lesions.
QuickRNASeq: Guide for Pipeline Implementation and for Interactive Results Visualization

Sequencing of transcribed RNA molecules (RNA-Seq) has been used wildly for studying cell transcriptomes in bulk or at the single-cell level (Wang et al., Nat Rev Genet, 10:57-63, 2009; Ozsolak and Milos, Nat Rev Genet, 12:87-98, 2011; Sandberg, Nat Methods, 11:22-24, 2014) and is becoming the de facto technology for investigating gene expression level changes in various biological conditions, on the time course, and under drug treatments. Furthermore, RNA-Seq data helped identify fusion genes that are related to certain cancers (Maher et al., Nature, 458:97-101, 2009). Differential gene expression before and after drug treatments provides insights to mechanism of action, pharmacodynamics of the drugs, and safety concerns (Dixit et al., Genomics, 107:178-188, 2016). Because each RNA-Seq run generates tens to hundreds of millions of short reads with size ranging from 50 to 200 bp, a tool that deciphers these short reads to an integrated and digestible analysis report is in high demand. QuickRNASeq (Zhao et al., BMC Genomics, 17:39-53, 2016) is an application for large-scale RNA-Seq data analysis and real-time interactive visualization of complex data sets. This application automates the use of several of the best open-source tools to efficiently generate user friendly, easy to share, and ready to publish report. Figures in this protocol illustrate some of the interactive plots produced by QuickRNASeq. The visualization features of the application have been further improved since its first publication in early 2016. The original QuickRNASeq publication (Zhao et al., BMC Genomics, 17:39-53, 2016) provided details of background, software selection, and implementation. Here, we outline the steps required to implement QuickRNASeq in user's own environment, as well as demonstrate some basic yet powerful utilities of the advanced interactive visualization modules in the report.
Discovery and design of fatigue-resistant high-entropy alloys

Abstract Fatigue resistance is a crucial requirement to the novel high-entropy alloys when they come to engineering applications, as many metal structures used in practice are failed by cyclic loading. Here, a thorough analysis of the information on the low-cycle fatigue, high-cycle fatigue, crack-growth rates, and fatigue mechanisms in the high-entropy alloy literature unveils a guideline through which the discovery and design of fatigue-resistant high-entropy alloys can be facilitated. Overall, multi-phase alloys, particularly the metastable ones, are favorable to fatigue resistance over single-phase alloys. Suggestions are proposed in the end to accelerate the discovery and design of candidates for fatigue-resistant applications.
Source Separation with Deep Generative Priors

Despite substantial progress in signal source separation, results for richly structured data continue to contain perceptible artifacts. In contrast, recent deep generative models can produce authentic samples in a variety of domains that are indistinguishable from samples of the data distribution. This paper introduces a Bayesian approach to source separation that uses generative models as priors over the components of a mixture of sources, and noise-annealed Langevin dynamics to sample from the posterior distribution of sources given a mixture. This decouples the source separation problem from generative modeling, enabling us to directly use cutting-edge generative models as priors. The method achieves state-of-the-art performance for MNIST digit separation. We introduce new methodology for evaluating separation quality on richer datasets, providing quantitative evaluation of separation results on CIFAR-10. We also provide qualitative results on LSUN.