cssi/SciSciGPT-SciSciNet huggingface dataset:
ESG as Waving Banners and as Pulling Plows

Environmental, social, and governance (ESG) investors who do no more than exclude stocks of fossil fuel producers from their portfolios are banner-minded; they want to do well and expect to earn market returns, if not higher. Banner-minded investors might want the expressive and emotional benefits of staying true to their values, but they are unwilling to sacrifice any portion of their utilitarian returns for these benefits. More importantly, they do no good, doing nothing to enhance the utilitarian, expressive, and emotional benefits of others. ESG investors who invest in housing for the homeless, however, are plow-minded; they want to do good and are willing to accept lower than market returns. Plow-minded investors want the expressive and emotional benefits of staying true to their values, and they are willing to sacrifice portions of their utilitarian returns for these benefits. More importantly, they do much good, enhancing the utilitarian, expressive, and emotional benefits of others. In the last few decades, the ESG movement lost its way as it transitioned from doing good to doing well and from plow-minded investors to banner-minded and pseudo-ESG investors. That transition expanded the ESG movement by adding investors but degraded the movement from doing good to doing well. It is time for the movement to regain its way, transitioning back from doing well to doing good, from banner-minded and pseudo-ESG investors to plow-minded investors, and from wants for utilitarian returns for oneself to wants for utilitarian, expressive, and emotional benefits for others. TOPICS:Portfolio theory, ESG investing Key Findings • ESG investors wave banners when they do well for themselves, unwilling to sacrifice any investment returns for benefits to others. • ESG investors pull plows when they do good for others, willing to sacrifice some investment returns for benefits to others. • In recent decades the ESG movement lost its way as it turned from plow-minded investors to banner-minded ones. It is time for the movement to regain its way.
Growth rates of permutation classes: Categorization up to the uncountability threshold

In the antecedent paper to this it was established that there is an algebraic number ξ ≈ 2.30522 such that while there are uncountably many growth rates of permutation classes arbitrarily close to ξ, there are only countably many less than ξ. Here we provide a complete characterization of the growth rates less than ξ. In particular, this classification establishes that ξ is the least accumulation point from above of growth rates and that all growth rates less than or equal to ξ are achieved by finitely based classes. A significant part of this classification is achieved via a reconstruction result for sum indecomposable permutations. We conclude by refuting a suggestion of Klazar, showing that ξ is an accumulation point from above of growth rates of finitely based permutation classes.
Why Did the Shape of Your Network Change? (On Detecting Network Anomalies via Non-local Curvatures)

Anomaly detection problems (also called change-point detection problems) have been studied in data mining, statistics and computer science over the last several decades (mostly in non-network context) in applications such as medical condition monitoring, weather change detection and speech recognition. In recent days, however, anomaly detection problems have become increasing more relevant in the context of network science since useful insights for many complex systems in biology, finance and social science are often obtained by representing them via networks. Notions of local and non-local curvatures of higher-dimensional geometric shapes and topological spaces play a fundamental role in physics and mathematics in characterizing anomalous behaviours of these higher dimensional entities. However, using curvature measures to detect anomalies in networks is not yet very common. To this end, a main goal in this paper to formulate and analyze curvature analysis methods to provide the foundations of systematic approaches to find critical components and detect anomalies in networks. For this purpose, we use two measures of network curvatures which depend on non-trivial global properties, such as distributions of geodesics and higher-order correlations among nodes, of the given network. Based on these measures, we precisely formulate several computational problems related to anomaly detection in static or dynamic networks, and provide non-trivial computational complexity results for these problems. This paper must not be viewed as delivering the final word on appropriateness and suitability of specific curvature measures. Instead, it is our hope that this paper will stimulate and motivate further theoretical or empirical research concerning the exciting interplay between notions of curvatures from network and non-network domains, a much desired goal in our opinion.
Closed Loop Application of Electroadhesion for Increased Precision in Texture Rendering

Tactile displays based on friction modulation offer wide-bandwidth forces rendered directly on the fingertip. However, due to a number of touch conditions (e.g., normal force, skin hydration) that result in variations in the friction force and the strength of modulation effect, the precision of the force rendering remains limited. In this paper we demonstrate a closed-loop electroadhesion method for precise playback of friction force profiles on a human finger and we apply this method to the tactile rendering of several textiles encountered in everyday life.
Securing Mesh with Delayed Absorbable Suture Does Not Increase Risk of Prolapse Recurrence After Robotic Sacral Colpopexy

Introduction and Objective: Nonabsorbable monofilament suture is traditionally used to secure vaginal mesh for robot-assisted sacral colpopexy (RASC) but can become exposed postoperatively requirin...
Three-dimensional traction microscopy accounting for cell-induced matrix degradation

Abstract Tractions exerted by cells on the extracellular matrix (ECM) are critical in many important physiological and pathological processes such as embryonic morphogenesis, wound healing, and cancer metastasis. Three-dimensional Traction Microscopy (3DTM) is a tool to quantify cellular tractions by first measuring the displacement field in the ECM in response to these tractions, and then using this measurement to infer tractions. Most applications of 3DTM have assumed that the ECM has spatially-uniform mechanical properties, but cells secrete enzymes that can locally degrade the ECM. In this work, a novel computational method is developed to quantify both cellular tractions and ECM degradation. In particular, the ECM is modeled as a hyperelastic, Neo-Hookean solid, whose material parameters are corrupted by a single degradation parameter. The feasibility of determining both the traction and the degradation parameter is first demonstrated by showing the existence and uniqueness of the solution. An inverse problem is then formulated to determine the nodal values of the traction vector and the degradation parameter, with the objective of minimizing the difference between a predicted and measured displacement field, under the constraint that the predicted displacement field satisfies the equation of equilibrium. The inverse problem is solved by means of a gradient-based optimization approach, and the gradient is computed efficiently using appropriately derived adjoint fields. The computational method is validated in-silico using a geometrically realistic neuronal cell model and synthetic traction and degradation fields. It is found that the method accurately recovers both the traction and degradation fields. Moreover, it is found that neglecting ECM degradation can yield significant errors in traction measurements. Our method can extend the range of context where tractions can be appropriately measured.
Covariance of the classical Brink–Schwarz superparticle

We show that the classical Brink-Schwarz superparticle is a generalized AKSZ field theory. We work in the Batalin-Vilkovisky formalism: the main technical tool is the vanishing of Batalin--Vilkovisky cohomology below degree -1.
SOMOSPIE: A Modular SOil MOisture SPatial Inference Engine Based on Data-Driven Decisions

The current availability of soil moisture data over large areas comes from satellite remote sensing technologies (i.e., radar-based systems), but these data have coarse resolution and often exhibit large spatial information gaps. Where data are too coarse or sparse for a given need (e.g., precision farming), one can leverage machine-learning techniques coupled with other sources of environmental information (e.g., topography) to generate gap-free information at a finer spatial resolution (i.e., increased granularity). To this end, we develop a spatial inference engine consisting of modular stages for processing spatial environmental data, generating predictions with machine-learning techniques, and analyzing these predictions. We demonstrate the functionality of this approach and the effects of data processing choices via multiple prediction maps over a United States ecological region with a highly diverse soil moisture profile (i.e., the Middle Atlantic Coastal Plains). The relevance of our work derives from a pressing need to improve the spatial representation of soil moisture for applications in environmental sciences (e.g., ecological niche modeling, carbon monitoring systems, and other Earth system models) and precision farming (e.g., optimizing irrigation practices and other land management decisions).
FRED-QD: A Quarterly Database for Macroeconomic Research

In this paper we present and describe a large quarterly frequency, macroeconomic database. The data provided are closely modeled to that used in Stock and Watson (2012a). As in our previous work on FRED-MD, our goal is simply to provide a publicly available source of macroeconomic “big data” that is updated in real time using the FRED database. We show that factors extracted from this data set exhibit similar behavior to those extracted from the original Stock and Watson data set. The dominant factors are shown to be insensitive to outliers, but outliers do affect the relative influence of the series as indicated by leverage scores. We then investigate the role unit root tests play in the choice of transformation codes with an emphasis on identifying instances in which the unit root-based codes differ from those already used in the literature. Finally, we show that factors extracted from our data set are useful for forecasting a range of macroeconomic series and that the choice of transformation codes can contribute substantially to the accuracy of these forecasts.
Distinct features of nucleolus-associated domains in mouse embryonic stem cells

Heterochromatin in eukaryotic interphase cells frequently localizes to the nucleolar periphery (nucleolus-associated domains (NADs)) and the nuclear lamina (lamina-associated domains (LADs)). Gene expression in somatic cell NADs is generally low, but NADs have not been characterized in mammalian stem cells. Here, we generated the first genome-wide map of NADs in mouse embryonic stem cells (mESCs) via deep sequencing of chromatin associated with biochemically purified nucleoli. As we had observed in mouse embryonic fibroblasts (MEFs), the large type I subset of NADs overlaps with constitutive LADs and is enriched for features of constitutive heterochromatin, including late replication timing and low gene density and expression levels. Conversely, the type II NAD subset overlaps with loci that are not lamina-associated, but in mESCs, type II NADs are much less abundant than in MEFs. mESC NADs are also much less enriched in H3K27me3 modified regions than are NADs in MEFs. Additionally, comparision of MEF and mESC NADs revealed enrichment of developmentally regulated genes in cell-type-specific NADs. Together, these data indicate that NADs are a developmentally dynamic component of heterochromatin. These studies implicate association with the nucleolar periphery as a mechanism for developmentally regulated gene expression and will facilitate future studies of NADs during mESC differentiation.
Off-Grid Aware Spatial Covariance Estimation in mmWave Communications

This work investigates the problem of spatial covariance matrix estimation in a millimeter Wave (mmWave) hybrid multiple-input multiple-output (MIMO) system with an emphasis on the basis-mismatch effect. The basis mismatch is prevalent in the compressed sensing (CS) schemes which adopt discretization procedure. In such an approach, the algorithm yields a finite discrete point which is an approximation to the continuous parametric space. The quality of this approximation depends on the number of discretized points in the dictionary. Instead of increasing the number of discretized points to combat this off-grid effect, we propose an efficient parameter perturbed framework which uses a controlled perturbation mechanism in conjunction with the orthogonal matching pursuit (OMP) algorithm. Numerical results verify the performance improvement through our proposed algorithm in terms of relative efficiency metric , which is basically due to taking care of the off-grid effect carefully that is ignored in the conventional CS algorithms.
The wisdom of ecosystems: A transactive memory theory of knowledge management in entrepreneurial ecosystems

The contextual turn in entrepreneurship research has shifted scholars’ attention to the place-based forces that shape entrepreneurship in vibrant entrepreneurial ecosystems, such as Silicon Valley, Stockholm, and Bangalore. Studies find that an important component of ecosystems is the knowledge they contain about the entrepreneurship process. However, the critical questions of how entrepreneurs overcome challenges associated with acquiring tacit knowledge, how entrepreneurs leverage their ecosystems to organize knowledge, and what factors influence an ecosystem’s ability to serve as a knowledge repository remain unexamined. To formulate a theory explaining how entrepreneurs use their ecosystems to facilitate knowledge management and organizational learning, insights from group cognition are adapted to introduce the concept of entrepreneurial ecosystem transactive memory. It is theorized that ecosystem-level characteristics, including diversity, coherence, connectivity, prosociality, and interdependence, influence an ecosystem’s transactive memory structure and processes. By examining the linkages between ecosystem characteristics and transactive memory, a more nuanced understanding of the cognitive dynamics of entrepreneurial ecosystems is provided. The theoretical model charts a path for interdisciplinary research on knowledge management in entrepreneurial ecosystems and generates implications for practitioners.
Pulmonary Hypertension in Heart Failure Patients

The development of pulmonary hypertension (PH) in patients with heart failure is associated with increased morbidity and mortality. In this article, the authors examine recent changes to the definition of PH in the setting of left heart disease (PH-LHD), and discuss its epidemiology, pathophysiology and prognosis. They also explore the complexities of diagnosing PH-LHD and the current evidence for the use of medical therapies, promising clinical trials and the role of left ventricular assist device and transplantation.
Vector Field Plot

Longitudinal data are often analyzed using a series of dynamic simultaneous equations. A slope field plots the expected change in one variable with respect to another variable for ranges of each of these variables. A vector field plots the expected changes in variables with respect to each other and with respect to time. A statistical slope field or a statistical vector field offers a similar display, but are exploratory graphs generated from raw data and include information about the variations due to sampling. Keywords: slope field; graphical methods; longitudinal data
Type I, Type II and Type III Sums of Squares†

Whenever factorial designs have equal cell frequencies, the factors are orthogonal. Although orthogonal factors are not required, orthogonal factors permit a unique decomposition of the variance in the dependent variable. If cell frequencies vary, or there are empty cells, the factors are nonorthogonal: The variance decomposition of the dependent variable differs depending on which of Type I, II, III, or IV sums of squares is used for the analysis. Keywords: orthogonal; balanced; sums of squares; missing data; confounded; fixed effects; random effects
Root-Finding Methods for Assessing SRAM Stability in the Presence of Random Dopant Fluctuations

In this paper, we propose a closed-form method to evaluate the read stability of an SRAM cell via quartic root finding. By utilizing a simplified MOSFET device model, we model SRAM cell stability by a system of quartic equations. The algebraic nature of the equations along with simplified region boundaries provide the insight that only a few combinations of device operating regions correspond to the stability of the cell, instead of 729 combinations in the brute force approach. Such an insight not only makes it possible to have a quick "litmus test" to determine cell stability under variability but also significantly speeds up the analysis, compared to a traditional SPICE approach. Experimental results using industrial bulk CMOS models show that the results are in excellent agreement with SPICE results and 65X faster.
Reforms in Eastern Europe and the Former Soviet Union in Light of the East Asian Experiences

Abstract During the past five years, there has been an important debate over the differing styles of market reforms in the formerly planned economies in East Asia versus Eastern Europe and the former Soviet Union (EEFSU). This paper puts forward three related propositions. First, the rapid growth of East Asia, compared with economic contraction in EEFSU, reflects differences in economic structure and initial conditions, rather than differences in economic policymaking. Second, East Asian gradualism could not, and did not, work in EEFSU. Third, EEFSU continues to face serious problems with overextended an welfare state inherited from the socialist period. J. Japan. Int. Econ., Dec. 1995, 9 (4), 454–485. Harvard University, Cambridge, Massachusetts 02138.
Chapter 1 – Theoretical Foundations

Publisher Summary This book contains key information on one of the most rapidly developing areas of therapy. It is designed to help play therapists to develop specific treatment goals and develop focused treatment plans. The chapter lays down the theoretical foundations for the play therapy. The book aims to find way through the process of play therapy treatment planning and implementation. It is a map that is based on ecosystemic play therapy theory. This theory addresses intrapsychic, interactional, developmental, and historical processes. In summary, ecosystemic play therapy can be seen as an integrative theory. It incorporates developmental, systemic, contextual, and representational variables from the perspective of the child's functioning in his or her ecosystemic world. Finally, the discussion concludes that when considering the practice of ecosystemic play therapy, it should be remembered that even therapists are human beings. They operate out of a subject-dependent position that is determined by their own history of experiences and learning. Also, they must be aware of the degree to which they impose their own subject-dependent perceptions onto the therapeutic situation. Therapists should adjust their interactions and interventions to take the multiple perspectives into account to make the most of the play therapy.
Negative Energy Densities in Quantum Field Theory

Quantum field theory allows for the suppression of vacuum fluctuations, leading to sub-vacuum phenomena. One of these is the appearance of local negative energy density. Selected aspects of negative energy will be reviewed, including the quantum inequalities which limit its magnitude and duration. However, these inequalities allow the possibility that negative energy and related effects might be observable. Some recent proposals for experiments to search for sub-vacuum phenomena will be discussed. Fluctuations of the energy density around its mean value will also be considered, and some recent results on a probability distribution for the energy density in two dimensional spacetime are summarized.
An Essential Membrane Protein Modulates the Proteolysis of LpxC to Control Lipopolysaccharide Synthesis in Escherichia coli

Gram-negative bacteria are surrounded by a complex cell envelope that includes two membranes. The outer membrane prevents many drugs from entering these cells and is thus a major determinant of their intrinsic antibiotic resistance. This barrier function is imparted by the asymmetric architecture of the membrane with lipopolysaccharide (LPS) in the outer leaflet and phospholipids in the inner leaflet. The LPS and phospholipid synthesis pathways share an intermediate. Proper membrane biogenesis therefore requires that the flux through each pathway be balanced. In Escherichia coli, a major control point in establishing this balance is the committed step of LPS synthesis mediated by LpxC. Levels of this enzyme are controlled through its degradation by the inner membrane protease FtsH and its presumed adapter protein LapB (YciM). How turnover of LpxC is controlled has remained unclear for many years. Here, we demonstrate that the essential protein of unknown function YejM (PbgA) participates in this regulatory pathway. Suppressors of YejM essentiality were identified in lpxC and lapB, and LpxC overproduction was shown to be sufficient to allow survival of ΔyejM mutants. Furthermore, the stability of LpxC was shown to be reduced in cells lacking YejM, and genetic and physical interactions between LapB and YejM were detected. Taken together, our results are consistent with a model in which YejM directly modulates LpxC turnover by FtsH-LapB to regulate LPS synthesis and maintain membrane homeostasis.IMPORTANCE The outer membrane is a major determinant of the intrinsic antibiotic resistance of Gram-negative bacteria. It is composed of both lipopolysaccharide (LPS) and phospholipid, and the synthesis of these lipid species must be balanced for the membrane to maintain its barrier function in blocking drug entry. In this study, we identified an essential protein of unknown function as a key new factor in modulating LPS synthesis in the model bacterium Escherichia coli Our results provide novel insight into how this organism and most likely other Gram-negative bacteria maintain membrane homeostasis and their intrinsic resistance to antibiotics.
Viability of slow-roll inflation in light of the non-zero kmin measured in the CMB power spectrum

Slow-roll inflation may simultaneously solve the horizon problem and generate a near scale-free fluctuation spectrum P(k). These two processes are intimately connected via the initiation and duration of the inflationary phase. But a recent study based on the latest Planck release suggests that P(k) has a hard cutoff, k_min > 0, inconsistent with this conventional picture. Here we demonstrate quantitatively that most---perhaps all---slow-roll inflationary models fail to accommodate this minimum cutoff. We show that the small parameter `epsilon' must be > 0.9 throughout the inflationary period to comply with the data, seriously violating the slow-roll approximation. Models with such an epsilon predict extremely red spectral indices, at odds with the measured value. We also consider extensions to the basic picture (suggested by several earlier workers) by adding a kinetic-dominated or radiation-dominated phase preceding the slow-roll expansion. Our approach differs from previously published treatments principally because we require these modifications to---not only fit the measured fluctuation spectrum, but to simultaneously also---fix the horizon problem. We show, however, that even such measures preclude a joint resolution of the horizon problem and the missing correlations at large angles.
Transformers to Learn Hierarchical Contexts in Multiparty Dialogue for Span-based Question Answering.

We introduce a novel approach to transformers that learns hierarchical representations in multiparty dialogue. First, three language modeling tasks are used to pre-train the transformers, token- and utterance-level language modeling and utterance order prediction, that learn both token and utterance embeddings for better understanding in dialogue contexts. Then, multi-task learning between the utterance prediction and the token span prediction is applied to fine-tune for span-based question answering (QA). Our approach is evaluated on the FriendsQA dataset and shows improvements of 3.8% and 1.4% over the two state-of-the-art transformer models, BERT and RoBERTa, respectively.
Noise Modeling, Synthesis and Classification for Generic Object Anti-Spoofing

Using printed photograph and replaying videos of biometric modalities, such as iris, fingerprint and face, are common attacks to fool the recognition systems for granting access as the genuine user. With the growing online person-to-person shopping (e.g., Ebay and Craigslist), such attacks also threaten those services, where the online photo illustration might not be captured from real items but from paper or digital screen. Thus, the study of anti-spoofing should be extended from modality-specific solutions to generic-object-based ones. In this work, we define and tackle the problem of Generic Object Anti-Spoofing (GOAS) for the first time. One significant cue to detect these attacks is the noise patterns introduced by the capture sensors and spoof mediums. Different sensor/medium combinations can result in diverse noise patterns. We propose a GAN-based architecture to synthesize and identify the noise patterns from seen and unseen medium/sensor combinations. We show that the procedure of synthesis and identification are mutually beneficial. We further demonstrate the learned GOAS models can directly contribute to modality-specific anti-spoofing without domain transfer. The code and GOSet dataset are available at cvlab.cse.msu.edu/project-goas.html.
Optimized utilization of COMB3 reactive potentials in LAMMPS

An investigation to optimize the application of the third-generation charge optimized many-body (COMB3) interatomic potential and associated input parameters was carried out through the study of solid-liquid interactions in classical molecular dynamics simulations. The rates of these molecular interactions are understood through the wetting rates of water nano-droplets on a bare copper (111) surface. Implementing the Langevin thermostat, the influence of simulation time step, the number of atoms in the system, the frequency at which charge equilibration is performed, and the temperature relaxation rate are all examined. The results indicate that time steps of 0.4 fs are possible when using longer relaxation times for the system temperature, which is almost double the typical time step used for reactive potentials. The use of the charge equilibration allows for a fewer atomic layers to be used in the Cu slab. In addition, charge equilibrium schemes do not need to be performed every time step to ensure accurate charge transfer. Interestingly, the rate of wetting for the nanodroplets is dominantly dependent on the temperature relaxation time, which is predicted to significantly change the viscosity of the water droplets. This work provides a pathway for optimizing simulations using the COMB3 reactive interatomic potential.
BoXHED: Boosted eXact Hazard Estimator with Dynamic covariates

The proliferation of medical monitoring devices makes it possible to track health vitals at high frequency, enabling the development of dynamic health risk scores that change with the underlying readings. Survival analysis, in particular hazard estimation, is well-suited to analyzing this stream of data to predict disease onset as a function of the time-varying vitals. This paper introduces the software package BoXHED (pronounced 'box-head') for nonparametrically estimating hazard functions via gradient boosting. BoXHED 1.0 is a novel tree-based implementation of the generic estimator proposed in Lee et al. (2017), which was designed for handling time-dependent covariates in a fully nonparametric manner. BoXHED is also the first publicly available software implementation for Lee et al. (2017). Applying it to a cardiovascular disease dataset from the Framingham Heart Study reveals novel interaction effects among known risk factors, potentially resolving an open question in clinical literature.
Using Control Theory to Achieve Service Level Objectives In Performance Management

A widely used approach to achieving service level objectives for a software system (e.g., an email server) is to add a controller that manipulates the target system’s tuning parameters. We describe a methodology for designing such controllers for software systems that builds on classical control theory. The classical approach proceeds in two steps: system identification and controller design. In system identification, we construct mathematical models of the target system. Traditionally, this has been based on a first-principles approach, using detailed knowledge of the target system. Such models can be complex and difficult to build, validate, use, and maintain. In our methodology, a statistical (ARMA) model is fit to historical measurements of the target being controlled. These models are easier to obtain and use and allow us to apply control-theoretic design techniques to a larger class of systems. When applied to a Lotus Notes groupware server, we obtain model-fits with R^{2} no lower than 75% and as high as 98%. In controller design, an analysis of the models leads to a controller that will achieve the service level objectives. We report on an analysis of a closed-loop system using an integral control law with Lotus Notes as the target. The objective is to maintain a reference queue length. Using root-locus analysis from control theory, we are able to predict the occurrence (or absence) of controller-induced oscillations in the system’s response. Such oscillations are undesirable since they increase variability, thereby resulting in a failure to meet the service level objective. We implement this controller for a real Lotus Notes system, and observe a remarkable correspondence between the behavior of the real system and the predictions of the analysis. This indicates that the control theoretic analysis is sufficient to select controller parameters that meet the desired goals, and the need for simulations is reduced.
Global forensic geolocation with deep neural networks

An important problem in modern forensic analyses is identifying the provenance of materials at a crime scene, such as biological material on a piece of clothing. This procedure, which is known as geolocation, is conventionally guided by expert knowledge of the biological evidence and therefore tends to be application specific, labour intensive and often subjective. Purely data‐driven methods have yet to be fully realized in this domain, because in part of the lack of a sufficiently rich source of data. However, high throughput sequencing technologies can identify tens of thousands of fungi and bacteria taxa by using DNA recovered from a single swab collected from nearly any object or surface. This microbial community, or microbiome, may be highly informative of the provenance of the sample, but data on the spatial variation of microbiomes are sparse and high dimensional and have a complex dependence structure that render them difficult to model with standard statistical tools. Deep learning algorithms have generated a tremendous amount of interest within the machine learning community for their predictive performance in high dimensional problems. We present DeepSpace: a new algorithm for geolocation that aggregates over an ensemble of deep neural network classifiers trained on randomly generated Voronoi partitions of a spatial domain. The DeepSpace algorithm makes remarkably good point predictions; for example, when applied to the microbiomes of over 1300 dust samples collected across continental USA, more than half of geolocation predictions produced by this model fall less than 100 km from their true origin, which is a 60% reduction in error from competing geolocation methods. Moreover, we apply DeepSpace to a novel data set of global dust samples collected from nearly 30 countries, finding that dust‐associated fungi alone predict a sample's country of origin with nearly 90% accuracy.
Convergence rate to equilibrium in Wasserstein distance for reflected jump–diffusions

Abstract Convergence rate to the stationary distribution for continuous-time Markov processes can be studied using Lyapunov functions. Recent work by the author provided explicit rates of convergence in special case of a reflected jump–diffusion on a half-line. These results are proved for total variation distance and its generalizations: measure distances defined by test functions regardless of their continuity. Here we prove similar results for Wasserstein distance, convergence in which is related to convergence for continuous test functions. In some cases, including the reflected Ornstein–Uhlenbeck process, we get faster exponential convergence rates for Wasserstein distance than for total variation distance.
Synergies and competition: Export survival in Africa and Latin America

Using firm‐level export data from six African (Burkina Faso and Senegal) and Latin American (Guatemala, Mexico, Peru, and Uruguay) countries, we examine factors that determine the survival of export flows. We explore the effects on export survival of changes in the number of home‐country exporters serving the same destination, firm‐level export diversification, and country‐level factors. Unlike previous studies, we find that export survival rates decrease with the number of co‐exporters selling the same product to the same country. We also find that the relationship between firm‐level product diversification and export flow survival is hump‐shaped: firms that do not diversify or are highly diversified have lower survival of product‐destination flows. Our findings are robust to various alternative specifications. The main findings hold across both regions and all countries. However, the number of co‐exporters negatively affects survival in Africa more than in Latin America.
The Politics of Aspiration

Aspiration is an essential component of politics. It articulates goals, affirms identities and values, and structures action at all levels of social life. Yet political scientists have spent little time theorizing aspiration - what it is, how it relates to other concepts, and the kinds of effects it creates. In this paper, we develop the concept theoretically and argue that aspiration creates a distinct “aspirational politics” that differs from our international relations (IR) models of both norm-driven social activism and interest-driven rational choice. We identify three core features of aspiration that undergird its theoretical utility: lofty goals, change over time, and transformation through imagination. In the hands of skilled political actors, aspiration does essential work in both facilitating agreement and mobilizing social action that create change in the world. But aspiration also has a dark side and can be manipulated to dodge accountability, postpone action, and to serve private, rather than public, goals.
Inequality and Growth: What Can the Data Say?

This paper describes the correlations between inequality and the growth rates in cross-country data. Using non-parametric methods, we show that the growth rate is an inverted U-shaped function of net changes in inequality: changes in inequality (in any direction) are associated with reduced growth in the next period. The estimated relationship is robust to variations in control variables and estimation methods. This inverted U-curve is consistent with a simple political economy model but it could also reflect the nature of measurement errors, and, in general, efforts to interpret this evidence causally run into difficult identification problems. We show that this non-linearity is sufficient to explain why previous estimates of the relationship between the level of inequality and growth are so different from one another.
Outage Detection in Partially Observable Distribution Systems Using Smart Meters and Generative Adversarial Networks

In this paper, we present a novel data-driven approach to detect outage events in partially observable distribution systems by capturing the changes in smart meters’ (SMs) data distribution. To achieve this, first, a breadth-first search (BFS)-based mechanism is proposed to decompose the network into a set of zones that maximize outage location information in partially observable systems. Then, using SM data in each zone, a generative adversarial network (GAN) is designed to implicitly extract the temporal-spatial behavior in normal conditions in an unsupervised fashion. After training, an anomaly scoring technique is leveraged to determine if real-time measurements indicate an outage event in the zone. Finally, to infer the location of the outage events in a multi-zone network, a zone coordination process is proposed to take into account the interdependencies of intersecting zones. We have provided analytical guarantees of performance for our algorithm using the concept of entropy , which is leveraged to quantify outage location information in multi-zone grids. The proposed method has been tested and verified on distribution feeder models with real SM data.
Deep Learning-Based Feature Silencing for Accurate Concrete Crack Detection

An autonomous concrete crack inspection system is necessary for preventing hazardous incidents arising from deteriorated concrete surfaces. In this paper, we present a concrete crack detection framework to aid the process of automated inspection. The proposed approach employs a deep convolutional neural network architecture for crack segmentation, while addressing the effect of gradient vanishing problem. A feature silencing module is incorporated in the proposed framework, capable of eliminating non-discriminative feature maps from the network to improve performance. Experimental results support the benefit of incorporating feature silencing within a convolutional neural network architecture for improving the network’s robustness, sensitivity, and specificity. An added benefit of the proposed architecture is its ability to accommodate for the trade-off between specificity (positive class detection accuracy) and sensitivity (negative class detection accuracy) with respect to the target application. Furthermore, the proposed framework achieves a high precision rate and processing time than the state-of-the-art crack detection architectures.
Visually evoked responses are enhanced when engaging in a video game.

While it is well known that vision guides movement, less appreciated is that the motor cortex also provides input to the visual system. Here, we asked whether neural processing of visual stimuli is acutely modulated during motor activity, hypothesizing that visual evoked responses are enhanced when engaged in a motor task that depends on the visual stimulus. To test this, we told participants that their brain activity was controlling a video game that was in fact the playback of a prerecorded game. The deception, which was effective in half of participants, aimed to engage the motor system while avoiding evoked responses related to actual movement or somatosensation. In other trials, subjects actively played the game with keyboard control or passively watched a playback. The strength of visually evoked responses was measured as the temporal correlation between the continuous stimulus and the evoked potentials on the scalp. We found reduced correlation during passive viewing, but no difference between active and sham play. Alpha-band (8-12 Hz) activity was reduced over central electrodes during sham play, indicating recruitment of motor cortex despite the absence of overt movement. To account for the potential increase of attention during gameplay, we conducted a second study with subjects counting screen items during viewing. We again found increased correlation during sham play, but no difference between counting and passive viewing. While we cannot fully rule out the involvement of attention, our findings do demonstrate an enhancement of visual evoked responses during active vision.
Intracellular photoswitchable neuropharmacology driven by luminescence from upconverting nanoparticles

Photoswitchable drugs are small-molecule optical probes that undergo chromatically selective control of drug efficacy using, most often, UV-visible light. Here we report that luminescence produced by near-infrared stimulation of NaYF4:TmYb nanoparticles can be used for “remote control” of an azobenzene-based photochromic ion channel blocker of neurons in living brain slices.
Active Status Update Packet Drop Control in an Energy Harvesting Node

This paper considers an energy harvesting sensor node with battery size B max that recharges its battery through an incremental energy harvesting process and receives updates from a single information source in slotted time. The node actively decides to power down (OFF) or up (ON) the communication circuitry for a portion of its operation time in order to maintain energy efficiency. Update packets arriving in ON (OFF) periods are received (discarded). A deterministic energy cost per time is paid during ON periods. The power down decision can be in partial or full nature, yielding various options for deciding ONOFF intervals. We develop age-threshold based power ON-OFF schemes to minimize age of information at the node subject to energy harvesting constraints with partial and full power down options for B max = 1 and B max = TO cases.
A Local Limit Theorem for Linear Random Fields

In this paper, we establish a local limit theorem for linear fields of random variables constructed from independent and identically distributed innovations each with finite second moment. When the coe_cients are absolutely summable we do not restrict the region of summation. However, when the coeffcients are only square‐summable we add the variables on unions of rectangle and we impose regularity conditions on the coeffcients depending on the number of rectangles considered. Our results are new also for the dimension 1, i.e. for linear sequences of random variables. The examples include the fractionally integrated processes for which the results of a simulation study is also included.
FauxWard: a graph neural network approach to fauxtography detection using social media comments

Online social media has been a popular source for people to consume and share news content. More recently, the spread of misinformation online has caused widespread concerns. In this work, we focus on a critical task of detecting fauxtography on social media where the image and associated text together convey misleading information. Many efforts have been made to mitigate misinformation online, but we found that the fauxtography problem has not been fully addressed by existing work. Solutions focusing on detecting fake images or misinformed texts alone on social media often fail to identify the misinformation delivered together by the image and the associated text of a fauxtography post. In this paper, we develop FauxWard, a novel graph convolutional neural network framework that explicitly explores the complex information extracted from a user comment network of a social media post to effectively identify fauxtography. FauxWard is content-free in the sense that it does not analyze the visual or textual contents of the post itself, which makes it robust against sophisticated fauxtography uploaders who intentionally craft image-centric posts by editing either the text or image content. We evaluate FauxWard on two real-world datasets collected from mainstream social media platforms (i.e., Reddit and Twitter). The results show that FauxWard is both effective and efficient in identifying fauxtography posts on social media.
Prevalence of delay embeddings with a fixed observation function

Abstract The Whitney embedding theorem is a basic result of differential topology and it is natural to ask for versions of the embedding theorem for time series data. Embedding theorems for dynamical time series data were claimed by Takens (1981) and proved in a different setting by Sauer, Yorke, and Casdagli (1991). Those results were stated assuming the observation function to be parametrized. A possibly more pertinent setting is to assume the dynamical system to be parametrized, with the observation function fixed, for example, as a projection to a certain coordinate. We prove an embedding theorem in such a setting. Our proof introduces a technique that relies on the notion of Lebesgue points.
When 'Me' Trumps 'We': Narcissistic Leaders and the Cultures They Create

Research has shown that a leader’s personality can affect organizational culture. We focus on leader narcissism and examine how it affects two specific organizational culture dimensions—collaborati...
Vision and Inertial Sensing Fusion for Human Action Recognition: A Review

Human action recognition is used in many applications such as video surveillance, human–computer interaction, assistive living, and gaming. Many papers have appeared in the literature showing that the fusion of vision and inertial sensing improves recognition accuracies compared to the situations when each sensing modality is used individually. This article provides a survey of the papers in which vision and inertial sensing are used simultaneously within a fusion framework in order to perform human action recognition. The surveyed papers are categorized in terms of fusion approaches, features, classifiers, as well as multimodality datasets considered. Challenges as well as possible future directions are also stated for deploying the fusion of these two sensing modalities under realistic conditions.
Identifiability of linear compartmental models: the effect of moving inputs, outputs, and leaks

A mathematical model is identifiable if its parameters can be recovered from data. Here we investigate, for linear compartmental models, whether (local, generic) identifiability is preserved when p...
Open Chemistry, JupyterLab, REST, and quantum chemistry

Author(s): Hanwell, MD; Harris, C; Genova, A; Haghighatlari, M; El Khatib, M; Avery, P; Hachmann, J; de Jong, WA | Abstract: Quantum chemistry must evolve if it wants to fully leverage the benefits of the internet age, where the worldwide web offers a vast tapestry of tools that enable users to communicate and interact with complex data at the speed and convenience of a button press. The Open Chemistry project has developed an open-source framework that offers an end-to-end solution for producing, sharing, and visualizing quantum chemical data interactively on the web using an array of modern tools and approaches. These tools build on some of the best open-source community projects such as Jupyter for interactive online notebooks, coupled with 3D accelerated visualization, state-of-the-art computational chemistry codes including NWChem and Psi4, and emerging machine learning and data mining tools such as ChemML and ANI. They offer flexible formats to import and export data, along with approaches to compare computational and experimental data.