cssi/SciSciGPT-SciSciNet huggingface dataset:
multiUQ: An intrusive uncertainty quantification tool for gas-liquid multiphase flows

Abstract Uncertainty quantification (UQ) of fluid flows offers the ability to understand the impact of variation in fluid properties, boundary conditions, and initial conditions on simulation results. In this work, an open-source program called multiUQ is developed which performs UQ using an intrusive approach applied to gas-liquid multiphase flows. Intrusive methods require modifying the governing equations by incorporating stochastic (uncertain) variables. This adds complexity but reduces computational cost compared to non-intrusive methods (e.g. Monte Carlo). To date, much of the work on intrusive UQ has focused on single phase flows. We extend this work by adding capabilities for gas-liquid flows which include a stochastic conservative level set method to capture the location of the phase interface, computing a stochastic curvature, and development of a stochastic surface tension force. Several test cases are presented which illustrate the strength of the framework. Both deterministic and stochastic channel flow cases converge to analytic results and demonstrate the accuracy of the level set transport. Zalesak's disk and the deformation test cases further highlight the abilities of the transport method as well as the robustness of the reinitialization equation, which maintains the level set profile. Deterministic and stochastic oscillating droplet test cases paired with analytic results, solve a true multiphase flow problem, and highlight the abilities of the UQ framework. Finally, results from a stochastic atomizing jet show droplet breakup and merging for cases of uncertainty about the surface tension coefficient and incoming velocity.
Semi-Supervised Voice Conversion with Amortized Variational Inference.

In this work we introduce a semi-supervised approach to the voice conversion problem, in which speech from a source speaker is converted into speech of a target speaker. The proposed method makes use of both parallel and non-parallel utterances from the source and target simultaneously during training. This approach can be used to extend existing parallel data voice conversion systems such that they can be trained with semi-supervision. We show that incorporating semi-supervision improves the voice conversion performance compared to fully supervised training when the number of parallel utterances is limited as in many practical applications. Additionally, we find that increasing the number non-parallel utterances used in training continues to improve performance when the amount of parallel training data is held constant.
Selective Influence and Sequential Operations: A Research Strategy for Visual Search.

We discuss the problem of elucidating mechanisms of visual search. We begin by considering the history, logic, and methods of relating behavioural or cognitive processes with neural processes. We t...
Risk Factors for Maternal Readmission with Sepsis

Objective Our primary objective was to identify risk factors for maternal readmission with sepsis. Our secondary objectives were to (1) assess diagnoses and infecting organisms at readmission and (2) compare early ( Study Design We identified our cohort using linked hospital discharge data and birth certificates for California deliveries from 2008 to 2011. Consistent with the 2016 sepsis classification, we defined sepsis as septicemia plus acute organ dysfunction. We compared women with early or late readmission with sepsis to women without readmission with sepsis. Results Among 1,880,264 women, 494 (0.03%) were readmitted with sepsis, 61% after 6 weeks. Risk factors for readmission with sepsis included preterm birth, hemorrhage, obesity, government-provided insurance, and primary cesarean. For both early and late sepsis readmissions, the most common diagnoses were urinary tract infection and pyelonephritis, and the most frequently identified infecting organism was gram-negative bacteria. Women with early compared with late readmission with sepsis shared similar obstetric characteristics. Conclusion Maternal risk factors for both early and late readmission with sepsis included demographic characteristics, cesarean, hemorrhage, and preterm birth. Risks for sepsis after delivery persist beyond the traditional postpartum period of 6 weeks.
High-Q microresonators integrated with microheaters on a 3C-SiC-on-insulator platform.

We demonstrate, to the best of our knowledge, the first thermally reconfigurable high-Q silicon carbide (SiC) microring resonators with integrated microheaters on a 3C-SiC-on-insulator platform. We extract a thermo-optic coefficient of around 2.67×10−5/K for 3C-SiC from wavelength shift of a resonator heated by a hot plate. Finally, we fabricate a 40-μm-radius microring resonator with intrinsic Q of 139,000 at infrared wavelengths (∼1550 nm) after integration with a NiCr microheater. By applying current through the microheater, a resonance shift of 30 pm/mW is achieved in the microring, corresponding to ∼50 mW per π phase shift. This platform offers an easy and reliable way for integration with electronic devices as well as great potential for diverse integrated optics applications.
Firm Compliance and Public Disclosure in Vietnam

Global consumers, international brands, and governments in producing and outsourcing countries aim to improve working conditions in global value chains, but uncertainty exists about what is the best approach. This research uses firm-level data from the International Labour Organization?International Finance Corporation Better Work Vietnam program to assess the relationship between transparency on working conditions and firm compliance in the apparel sector in Vietnam between 2010 and 2018. It exploits a change in the policies of Better Work Vietnam when, in 2015, the program announced the launch of a new public disclosure program that would see factories' names made publicly available along with their compliance (or lack thereof) with certain "critical issues." The paper first examines which firm characteristics correlate with reductions in noncompliance rates over time, and then examines the impact of the public disclosure policy on compliance rates and firm dropout using different empirical techniques. It finds that while continued participation in the Better Work Vietnam program has the strongest effect on changes in firm compliance with labor standards over time, public disclosure is also associated with increased compliance, with stronger effects in some compliance points, including occupational health and safety, work time, and child labor. There is some evidence of increased dropout, but no evidence of firms only making progress on the critical issues is found. The research findings suggest that public disclosure within global value chains matters for firm behavior.
A Constant Approximation for Colorful k-Center.

In this paper, we consider the colorful $k$-center problem, which is a generalization of the well-known $k$-center problem. Here, we are given red and blue points in a metric space, and a coverage requirement for each color. The goal is to find the smallest radius $\rho$, such that with $k$ balls of radius $\rho$, the desired number of points of each color can be covered. We obtain a constant approximation for this problem in the Euclidean plane. We obtain this result by combining a "pseudo-approximation" algorithm that works in any metric space, and an approximation algorithm that works for a special class of instances in the plane. The latter algorithm uses a novel connection to a certain matching problem in graphs.
Combating Coloniality: the cultural policy of post-colonialism

The distinguishing characteristic of cultural policy in countries characterized by a legacy of coloniality is the importance of the identity formation and the politics that are involved in formulating its definition. At root, coloniality is an experience involving dominating influence by a stronger power over a subject state. However, this is not just a matter of external governance or economic dependency, but of a cultural dominance that creates an asymmetrical relationship between the ‘center’ and the ‘periphery,’ between the ruling ‘hegemon’ and the marginalized ‘other.’ In these circumstances, what constitutes an “authentic” culture, and how this informs national identity, is a central political and social concern.
Existence and regularity of multivalued solutions to elliptic equations and systems

We extend the work of Simon and Wickramasekera, who constructed a large class of $C^{1,\mu}$ multivalued solutions to the minimal surface equation, to produce $C^{1,\mu}$ multivalued solutions to more general classes of elliptic equations and systems, including the minimal surface system with small boundary data and the Laplace equation. We use methods for differential equations, which are more general than the specific minimal submanifold approach adopted by Simon and Wickramasekera. We also prove the branch set of the graphs of the solutions constructed by Simon and Wickramasekera are real analytic submanifolds by inductively using Schauder estimates.
Do Type II Subsequent Events Impair Financial Reporting Quality

This study examines whether material corporate events that occur during the year-end closing process constrain management's and the auditor's resources and inhibit them from providing high quality ...
Propagate and Pair: A Single-Pass Approach to Critical Point Pairing in Reeb Graphs

With the popularization of Topological Data Analysis, the Reeb graph has found new applications as a summarization technique in the analysis and visualization of large and complex data, whose usefulness extends beyond just the graph itself. Pairing critical points enables forming topological fingerprints, known as persistence diagrams, that provides insights into the structure and noise in data. Although the body of work addressing the efficient calculation of Reeb graphs is large, the literature on pairing is limited. In this paper, we discuss two algorithmic approaches for pairing critical points in Reeb graphs, first a multipass approach, followed by a new single-pass algorithm, called Propagate and Pair.
ASU at TextGraphs 2019 Shared Task: Explanation ReGeneration using Language Models and Iterative Re-Ranking.

In this work we describe the system from Natural Language Processing group at Arizona State University for the TextGraphs 2019 Shared Task. The task focuses on Explanation Regeneration, an intermediate step towards general multi-hop inference on large graphs. Our approach consists of modeling the explanation regeneration task as a learning to rank problem, for which we use state-of-the-art language models and explore dataset preparation techniques. We utilize an iterative reranking based approach to further improve the rankings. Our system secured 2nd rank in the task with a mean average precision (MAP) of 41.3% on the test set.
Unlocking the black box of outpatient rehabilitation for subacute stroke

Date Presented 04/04/19 The evidence to support stroke rehabilitation has grown. There are no studies that have examined the nature of usual and customary OT for outpatient stroke rehabilitation. The findings from this study describe this based on data from a large, multisite Phase III clinical trial. Primary Author and Speaker: Rachel Proffitt Additional Authors and Speakers: Sara Benham, Pamela Roberts
Efficiency through Auto-Sizing: Notre Dame NLP’s Submission to the WNGT 2019 Efficiency Task

This paper describes the Notre Dame Natural Language Processing Group’s (NDNLP) submission to the WNGT 2019 shared task (Hayashi et al., 2019). We investigated the impact of auto-sizing (Murray and Chiang, 2015; Murray et al., 2019) to the Transformer network (Vaswani et al., 2017) with the goal of substantially reducing the number of parameters in the model. Our method was able to eliminate more than 25% of the model’s parameters while suffering a decrease of only 1.1 BLEU.
A metric Sturm–Liouville theory in two dimensions

A central result of Sturm–Liouville theory (also called the Sturm–Hurwitz theorem) states that if $$\phi _k$$ is a sequence of eigenfunctions of a second order differential operator on the interval $$I \subset \mathbb {R}$$, then any linear combination satisfies a uniform bound on the roots $$\begin{aligned} \# \left\{ x \in I:\sum _{k \ge n}{ a_k \phi _k(x)} = 0 \right\} \ge n-1. \end{aligned}$$We provide a sharp (up to logarithmic factors) generalization to two dimensions: let (M, g) be a compact two-dimensional manifold (with or without boundary), let $$(\phi _k)$$ denote the sequence of eigenfunctions of a uniformly elliptic operator $$-\text{ div }(a(\cdot ) \nabla )$$ (with Dirichlet or Neumann boundary conditions). Then, for any linear combination of eigenfunctions above a certain index n, $$\begin{aligned} f = \sum _{k \ge n}{a_k \phi _k} ~ \text{ we } \text{ have } \quad \mathcal {H}^1 \left\{ x: f(x) = 0\right\} \gtrsim _{} \frac{\sqrt{n}}{\sqrt{\log {n}}} \log \left( n \frac{\Vert f\Vert _{L^2(M)}}{\Vert f\Vert _{L^1(M)}} \right) ^{-1/2} \frac{\Vert f\Vert _{L^1(M)}}{\Vert f \Vert _{L^{\infty }(M)}} . \end{aligned}$$Examples on $$M=\mathbb {T}^2$$ and $$M=\mathbb {S}^2$$ shows that this is optimal up to the logarithmic factors. The proof is using optimal transport and a new inequality for the Wasserstein metric $$W_p$$: if f(x)dx and g(x)dx are two absolutely continuous measures on a two-dimensional domain M with continuous densities and the same total mass, then, for all $$1 \le p <\infty $$, $$\begin{aligned} W_p(f(x)dx, g(x) dx) \cdot \mathcal {H}^1 \left\{ x \in M: f(x) = g(x) \right\} \gtrsim _{M,p} \frac{\Vert f-g\Vert _{L^1(M)}^{1+1/p}}{\Vert f-g\Vert _{L^{\infty }(M)}}. \end{aligned}$$
Astrovirus Replication Is Inhibited by Nitazoxanide In Vitro and In Vivo.

ABSTRACT Astroviruses (AstV) are a leading cause of diarrhea, especially in the very young, the elderly, and immunocompromised populations. Despite their significant impact on public health, no drug therapies for astrovirus have been identified. In this study, we fill this gap in knowledge and demonstrate that the FDA-approved broad-spectrum anti-infective drug nitazoxanide (NTZ) blocks astrovirus replication in vitro with a 50% effective concentration (EC50) of approximately 1.47 μM. It can be administered up to 8 h postinfection and is effective against multiple human astrovirus serotypes, including clinical isolates. Most importantly, NTZ reduces viral shedding in vivo, exhibiting its potential as a future clinical therapeutic. IMPORTANCE Human astroviruses (HAstV) are thought to cause between 2 and 9% of acute, nonbacterial diarrhea cases in children worldwide. HAstV infection can be especially problematic in immunocompromised people and infants, where the virus has been associated with necrotizing enterocolitis and severe and persistent diarrhea, as well as rare instances of systemic and fatal disease. And yet, no antivirals have been identified to treat astrovirus infection. Our study provides the first evidence that nitazoxanide may be an effective therapeutic strategy against astrovirus disease.
Charting the Complexity of the Marine Microbiome through Single-Cell Genomics.

Summary Marine bacteria and archaea play key roles in global biogeochemistry. To improve our understanding of this complex microbiome, we employed single-cell genomics and a randomized, hypothesis-agnostic cell selection strategy to recover 12,715 partial genomes from the tropical and subtropical euphotic ocean. A substantial fraction of known prokaryoplankton coding potential was recovered from a single, 0.4 mL ocean sample, which indicates that genomic information disperses effectively across the globe. Yet, we found each genome to be unique, implying limited clonality within prokaryoplankton populations. Light harvesting and secondary metabolite biosynthetic pathways were numerous across lineages, highlighting the value of single-cell genomics to advance the identification of ecological roles and biotechnology potential of uncultured microbial groups. This genome collection enabled functional annotation and genus-level taxonomic assignments for >80% of individual metagenome reads from the tropical and subtropical surface ocean, thus offering a model to improve reference genome databases for complex microbiomes.
Photothermalization and Hot Electron Dynamics in the Steady State

Significant recent interest in plasmonic nanomaterials is based on the ability to use the strong resonant absorption to produce large transient populations of photoexcited non-equilibrium “hot” car...
MeCP2 nuclear dynamics in live neurons results from low and high affinity chromatin interactions

Methyl-CpG-binding-Protein 2 (MeCP2) is an abundant nuclear protein highly enriched in neurons. Here we report live-cell single-molecule imaging studies of the kinetic features of mouse MeCP2 at high spatial-temporal resolution. MeCP2 displays dynamic features that are distinct from both highly mobile transcription factors and immobile histones. Stable binding of MeCP2 in living neurons requires its methyl-binding domain and is sensitive to DNA modification levels. Diffusion of unbound MeCP2 is strongly constrained by weak, transient interactions mediated primarily by its AT-hook domains, and varies with the level of chromatin compaction and cell type. These findings extend previous studies of the role of the MeCP2 MBD in high affinity DNA binding to living neurons, and identify a new role for its AT-hooks domains as critical determinants of its kinetic behavior. They suggest that limited nuclear diffusion of MeCP2 in live neurons contributes to its local impact on chromatin structure and gene expression.
Structure and activation mechanism of the BBSome membrane protein trafficking complex.

Bardet-Biedl syndrome (BBS) is a currently incurable ciliopathy caused by the failure to correctly establish or maintain cilia-dependent signaling pathways. Eight proteins associated with BBS assemble into the BBSome, a key regulator of the ciliary membrane proteome. We report the electron cryomicroscopy (cryo-EM) structures of the native bovine BBSome in inactive and active states at 3.1 and 3.5 A resolution, respectively. In the active state, the BBSome is bound to an Arf-family GTPase (ARL6/BBS3) that recruits the BBSome to ciliary membranes. ARL6 recognizes a composite binding site formed by BBS1 and BBS7 that is occluded in the inactive state. Activation requires an unexpected swiveling of the β-propeller domain of BBS1, the subunit most frequently implicated in substrate recognition, which widens a central cavity of the BBSome. Structural mapping of disease-causing mutations suggests that pathogenesis results from folding defects and the disruption of autoinhibition and activation.
Interactions between the gut microbiome and host gene regulation in cystic fibrosis

Cystic fibrosis is the most common autosomal recessive genetic disease in Caucasians. It is caused by mutations in the CFTR gene, leading to poor hydration of mucus and impairment of the respiratory, digestive, and reproductive organ functions. Advancements in medical care have led to markedly increased longevity of patients with cystic fibrosis, but new complications have emerged, such as early onset of colorectal cancer. Although the pathogenesis of colorectal cancer in cystic fibrosis remains unclear, altered host-microbe interactions might play a critical role. To investigate this, we characterized changes in the microbiome and host gene expression in the colonic mucosa of cystic fibrosis patients relative to healthy controls, and identified host gene-microbiome interactions in the colon of cystic fibrosis patients. We performed RNA-seq on colonic mucosa samples from cystic fibrosis patients and healthy controls to determine differentially expressed host genes. We also performed 16S rRNA sequencing to characterize the colonic mucosal microbiome and identify gut microbes that are differentially abundant between patients and healthy controls. Lastly, we modeled associations between relative abundances of specific bacterial taxa in the gut mucosa and host gene expression. We find that 1543 genes, including CFTR, show differential expression in the colon of cystic fibrosis patients compared to healthy controls. These genes are enriched with functions related to gastrointestinal and colorectal cancer, such as metastasis of colorectal cancer, tumor suppression, p53, and mTOR signaling pathways. In addition, patients with cystic fibrosis show decreased gut microbial diversity, decreased abundance of butyrate producing bacteria, such as Ruminococcaceae and Butyricimonas, and increased abundance of other taxa, such as Actinobacteria and Clostridium. An integrative analysis identified colorectal cancer-related genes, including LCN2 and DUOX2, for which gene expression is correlated with the abundance of colorectal cancer-associated bacteria, such as Ruminococcaceae and Veillonella. In addition to characterizing host gene expression and mucosal microbiome in cystic fibrosis patients, our study explored the potential role of host-microbe interactions in the etiology of colorectal cancer in cystic fibrosis. Our results provide biomarkers that may potentially serve as targets for stratifying risk of colorectal cancer in patients with cystic fibrosis.
Minimally disruptive optical control of protein tyrosine phosphatase 1B

Protein tyrosine phosphatases regulate a myriad of essential subcellular signaling events, yet they remain difficult to study in their native biophysical context. Here we develop a minimally disruptive optical approach to control protein tyrosine phosphatase 1B (PTP1B)-an important regulator of receptor tyrosine kinases and a therapeutic target for the treatment of diabetes, obesity, and cancer-and we use that approach to probe the intracellular function of this enzyme. Our conservative architecture for photocontrol, which consists of a protein-based light switch fused to an allosteric regulatory element, preserves the native structure, activity, and subcellular localization of PTP1B, affords changes in activity that match those elicited by post-translational modifications inside the cell, and permits experimental analyses of the molecular basis of optical modulation. Findings indicate, most strikingly, that small changes in the activity of PTP1B can cause large shifts in the phosphorylation states of its regulatory targets.
Cellular Heterogeneity and Lineage Restriction during Mouse Digit Tip Regeneration at Single-Cell Resolution

Summary Innate regeneration following digit tip amputation is one of the few examples of epimorphic regeneration in mammals. Digit tip regeneration is mediated by the blastema, the same structure invoked during limb regeneration in some lower vertebrates. By genetic lineage analyses, the digit tip blastema has been defined as a population of heterogeneous, lineage-restricted progenitor cells. These previous studies, however, do not comprehensively evaluate blastema heterogeneity or address lineage restriction of closely related cell types. In this report, we present single-cell RNA sequencing of over 38,000 cells from mouse digit tip blastemas and unamputated control digit tips and generate an atlas of the cell types participating in digit tip regeneration. We computationally define differentiation trajectories of vascular, monocytic, and fibroblastic lineages over regeneration, and while our data confirm broad lineage restriction of progenitors, our analysis reveals 67 genes enriched in blastema fibroblasts including a novel regeneration-specific gene, Mest.
Synthesize Phasor Measurement Unit Data Using Large-Scale Electric Network Models

Bigdata analytic applications using phasor measurements help improve the situation awareness of grid operators to better operate and control the system. Phasor measurement unit (PMU) data from actual grids is viewed as highly confidential and is not publicly available to researchers and educators. This paper develops a methodology to synthesize PMU data that can be accessed and shared freely, with a focus on input data preparation. Time series of demand- and generation-side input data are generated using public data from different utilities and statistical analysis methods. Detailed load dynamics modeling is also performed in this paper to extend the synthetic electric network models.
Genetic drift does not sufficiently explain patterns of electric signal variation among populations of the mormyrid electric fish Paramormyrops kingsleyae

Communication signals serve crucial survival and reproductive functions. In Gabon, the widely distributed mormyrid fish Paramormyrops kingsleyae emits an electric organ discharge (EOD) signal with a dual role in communication and electrolocation that exhibits remarkable variation: populations of P. kingsleyae have either biphasic or triphasic EODs, a feature that characterizes interspecific signal diversity among the Paramormyrops genus. We quantified variation in EODs of 327 P. kingsleyae from nine populations and compared it to genetic variation estimated from microsatellite loci. We found no correlation between electric signal and genetic distances, suggesting that EOD divergence cannot be explained by drift alone. An alternative hypothesis is that EOD differences are used for mate discrimination, which would require P. kingsleyae be capable of differentiating between divergent EOD waveforms. Using a habituation-dishabituation assay, we found that P. kingsleyae can discriminate between biphasic and triphasic EOD types. Nonetheless, patterns of genetic and electric organ morphology divergence provide evidence for hybridization between these signal types. Although reproductive isolation with respect to signal type is incomplete, our results suggest that EOD variation in P. kingsleyae could be a cue for assortative mating.
Koopman Operators for Generalized Persistence of Excitation Conditions for Nonlinear Systems

It is hard to identify nonlinear biological models strictly from data, with results that are often sensitive to experimental conditions. Automated experimental workflows and liquid handling enables unprecedented throughput, as well as the capacity to generate extremely large datasets. We seek to develop generalized identifiability conditions for informing the design of automated experiments to discover predictive nonlinear biological models. For linear systems, identifiability is characterized by persistence of excitation conditions. For nonlinear systems, no such persistence of excitation conditions exist. We use the input-Koopman operator method to model nonlinear systems and derive identifiability conditions for open-loop systems initialized from a single initial condition. We show that nonlinear identifiability is intrinsically tied to the rank of a given dataset’s power spectral density, transformed through the lifted Koopman observable space. We illustrate these identifiability conditions with a simulated synthetic gene circuit model, the repressilator. We illustrate how rank degeneracy in datasets results in overfitted nonlinear models of the repressilator, resulting in poor predictive accuracy. Our findings provide novel experimental design criteria for discovery of globally predictive nonlinear models of biological phenomena.
Forecasting the Success of Television Series using Machine Learning

Television is an ever-evolving multi-billion-dollar industry. The success of a television show in an increasingly technological society is a vast multi-variable formula. The art of success is not just something that happens, but is studied, replicated, and applied. Hollywood can be unpredictable regarding success, as many movies and sitcoms that are hyped up and promise to be a hit end up being box office failures and complete disappointments. In current studies, linguistic exploration is being performed on the relationship between Television series and target community of viewers. Having a decision support system that can display sound and predictable results would be needed to build confidence in the investment of a new TV series. The models presented in this study use data to study and determine what makes a sitcom successful. In this paper, we use descriptive and predictive modeling techniques to assess the continuing success of television comedies: The Office, Big Bang Theory, Arrested Development, Scrubs, and South Park. The factors that are tested for statistical significance on episode ratings are character presence, director, and writer. These statistics show that while characters are indeed crucial to the shows themselves, the creation and direction of the shows pose implication upon the ratings and therefore the success of the shows. We use machine learning based forecasting models such as linear regression, K Nearest Neighbors, Stochastic Gradient Descent, Decision Tree and Forests, Neural Network, and Facebook Prophet, to accurately predict the success of shows. The models represent a baseline to understanding the success of a television show and how producers can increase the success of current television shows or utilize this data in the creation of future shows. Due to the many factors that go into a series, the empirical analysis in this work shows that there is no one-fits-all model to forecast the rating or success of a television show. However, because the variables are statistically significant, they are still able to optimize and affect the rating positively.
Efficient Identification of Linear Evolutions in Nonlinear Vector Fields: Koopman Invariant Subspaces

This paper presents a data-driven approach to identify finite-dimensional Koopman invariant subspaces and eigenfunctions of the Koopman operator. Given a dictionary of functions and a collection of data snapshots of the dynamical system, we rely on the Extended Dynamic Mode Decomposition (EDMD) method to approximate the Koopman operator. We start by establishing that, if a function in the space generated by the dictionary evolves linearly according to the dynamics, then it must correspond to an eigenvector of the matrix obtained by EDMD. A counterexample shows that this necessary condition is however not sufficient. We then propose a necessary and sufficient condition for the identification of linear evolutions according to the dynamics based on the application of EDMD forward and backward in time. Due to the complexity of checking this condition, we propose an alternative characterization based on the identification of the largest Koopman invariant subspace in the span of the dictionary. This leads us to introduce the Symmetric Subspace Decomposition strategy to identify linear evolutions using efficient linear algebraic methods. Various simulations illustrate our results.
Adversarial Attacks and Defenses in Images, Graphs and Text: A Review

Deep neural networks (DNN) have achieved unprecedented success in numerous machine learning tasks in various domains. However, the existence of adversarial examples raises our concerns in adopting deep learning to safety-critical applications. As a result, we have witnessed increasing interests in studying attack and defense mechanisms for DNN models on different data types, such as images, graphs and text. Thus, it is necessary to provide a systematic and comprehensive overview of the main threats of attacks and the success of corresponding countermeasures. In this survey, we review the state of the art algorithms for generating adversarial examples and the countermeasures against adversarial examples, for three most popular data types, including images, graphs and text.
A log-barrier Newton-CG method for bound constrained optimization with complexity guarantees

We describe an algorithm based on a logarithmic barrier function, Newton's method, and linear conjugate gradients that obtains an approximate minimizer of a smooth function over the nonnegative orthant. We develop a bound on the complexity of the approach, stated in terms of the required accuracy and the cost of a single gradient evaluation of the objective function and/or a matrix-vector multiplication involving the Hessian of the objective. The approach can be implemented without explicit calculation or storage of the Hessian.
Defining Clusters of Related Industries

Clusters are geographic concentrations of industries related by knowledge, skills, inputs, demand and/or other linkages. There is an increasing need for cluster-based data to support research, facilitate comparisons of clusters across regions and support policymakers in defining regional strategies. This article develops a novel clustering algorithm that systematically generates and assesses sets of cluster definitions (i.e., groups of closely related industries). We implement the algorithm using 2009 data for U.S. industries (six-digit NAICS), and propose a new set of benchmark cluster definitions that incorporates measures of inter-industry linkages based on co-location patterns, input–output links, and similarities in labor occupations. We also illustrate the algorithm’s ability to compare alternative sets of cluster definitions by evaluating our new set against existing sets in the literature. We find that our proposed set outperforms other methods in capturing a wide range of inter-industry linkages, including the grouping of industries within the same three-digit NAICS.
Tax Incentives and the Decision to Purchase Health Insurance: Evidence from the Self-Employed

The Tax Reform Act of 1986 introduced a new tax subsidy for health insurance purchases by the self-employed. We analyze the changing patterns of insurance demand before and after tax reform to generate new estimates of how the after-tax price of insurance affects the discrete choice of whether to buy insurance. We employ both traditional regression models and difference-in-difference methods that compare changes in insurance coverage across groups around TRA86. The results from our most carefully controlled comparison suggest that a 1 percent increase in the cost of insurance coverage reduces the probability that a self-employed single person will be insured by 1.8 percentage points.
GLACIAL LANDFORMS, ICE SHEETS | Evidence of Glacier and Ice Sheet Extent

The most direct evidence of the areal extent of former glaciers is the distribution of erratic boulders, till, or ice-contact stratified deposits. Often the distribution of glacial landforms such as moraines, ice-contact deltas and fans, and drumlins can be identified from aerial photographs. Features formed by subglacial water such as tunnel channels, eskers, hummocky or pitted sand and gravel, and ice-marginal channels also provide evidence of former ice extent. Scoured bedrock surfaces with striations or other small-scale and large-scale abrasion and plucking marks may, in some areas, be the only evidence of former ice cover. The vertical extent, or thickness of ice, is more difficult to determine. Isostatic rebound can be used to calculate former ice thickness over broad areas and within broad limits. Commonly, the areal extent of former glacier ice is easily determined, but in some places there appears to be no till, very few erratics, deep weathering, and little sign of glacial erosion. In these areas, especially where there is also dispute over former ice thickness, the amount of over-consolidation of preglacial clay, the distribution of cosmogenic dates on boulders and striated bedrock surfaces, and modeling provide clues to former horizontal and vertical extent of glacier ice.
RESTRUCTURING ENGINEERING EDUCATION: Why, How and When?

There is strong interest in broadening engineering education, bringing in more liberal arts content as well as additional subjects such as economics, business and law, with which engineers now have to be familiar. There are also cogent arguments for balancing against what is now the almost exclusively quantitative nature of the curriculum, adding more elements that relate to the actual practice of engineering, and structuring engineering education so as to provide multiple and later entry points, which should enable more informed career choices and make engineering attractive to a more diverse range of the population. Many have also sought a change in the level of the professional engineering degree from the bachelor‟s to the graduate level, which would logically, and probably also necessarily, accompany these changes. However, progress towards such changes in the United States has been marginal, in large part because incentives on the micro- and meso-scales do not match those on the macro-scale. On the other hand, there is much more substantial change in other countries, driven in part by the Bologna process. For the United States to be the last to change would be counter to the goal of retaining higher-functioning engineering jobs in the U. S. What needs to happen in order for the U. S. to change is evaluated.
Diffusion in Random Networks: Impact of Degree Distribution

Motivated by viral marketing on social networks, we study the diffusion process of a new product on a network where each agent is connected to a random subset of others. The number of contacts (i.e., degree) varies across agents, and the firm knows the degree of each agent. Further, the firm can seed a fraction of the population and incurs a fixed cost per contact. Under any bounded degree distribution and for any target adoption proportion, we compute both the cost and the time it takes to reach the target in the limit of network size. Our characterization of the diffusion process in such a general setting is the first of its kind for a problem that is generally deemed intractable and solved using approximation methods such as mean-field. Our solution indicates that the degree distribution impacts the diffusion process even beyond its first and second moments. Using our limit results, we conduct comparative statics on degree distribution and uncover a trade-off between cost-efficiency and fast growth. Fixing the average degree, a minimum-variance degree distribution incurs the minimum cost to reach any adoption proportion. On the other hand, higher variance results in faster growth for low or moderately high target adoption proportions, but it incurs higher cost. This trade-off arises partly because of an endogenous effect of diffusion on the distribution of adopters’ neighbors: as the diffusion progresses, adopters become more likely to be connected to other adopters. This highlights the benefit of our exact analysis compared to mean-field approximation methods that rely on perfect mixing of adopters and non-adopters (i.e., there is no change in the distribution of adopters’ neighbors with the progress of diffusion). Further, we study the impact of the degree distribution on optimal seeding strategies for a given seeding budget. Somewhat surprisingly, we show that to minimize cost, it is optimal to seed low-degree agents. Even if the objective is to minimize time, for certain regimes, the optimal seeding strategy is a mixture of low- and high-degree agents.
Pre-colonial Ethnic Institutions and Contemporary African Development

We investigate the role of deeply rooted pre-colonial ethnic institutions in shaping comparative regional development within African countries. We combine information on the spatial distribution of ethnicities before colonization with regional variation in contemporary economic performance, as proxied by satellite images of light density at night. We document a strong association between pre-colonial ethnic political centralization and regional development. This pattern is not driven by differences in local geographic features or by other observable ethnic-specific cultural and economic variables. The strong positive association between pre-colonial political complexity and contemporary development also holds within pairs of adjacent ethnic homelands with different legacies of pre-colonial political institutions.
Chapter 9 – Foraging

Publisher Summary Foraging is a fundamental behavior that defines animals. There are many ways to forage, but many of them can be placed into the framework of optimality theory, which is not limited to foraging behavior—almost any decision can be optimized—but it has perhaps had the biggest impact there. When considering optimality and animal behavior, or optimality and any product of evolution, it is important to understand that many constraints can prevent optimal outcomes in nature. In the study of foraging, optimality models have classically been applied to two large areas: decisions about where to forage (patch choice) and decisions about what to eat (prey choice). Both of these areas are based on several assumptions, including the fact that animals are likely to have sampled their environment and be familiar with the choices on offer. The ways that animals get food are so diverse as to defy description or categorization. From fungal gardens to central place foraging (and hoarding), from antelope grazing to cheetah decisions, this diversity is both a wonderful showcase for how evolution encourages animals to convert nutrients into offspring and also a schoolhouse, teaching the importance of clear assumptions and careful experiments.
What Works in Fighting Diarrheal Diseases in Developing Countries? A Critical Review

The millennium development goals call for reducing by half the proportion of people without sustainable access to safe drinking water. This goal was adopted in large part because clean water was seen as critical to fighting diarrheal disease, which kills two million children annually. There is compelling evidence that provision of piped water and sanitation can substantially reduce child mortality. However, in dispersed rural settlements, providing complete piped water and sanitation infrastructure to households is expensive. Many poor countries have therefore focused instead on providing community level water infrastructure, such as wells. Various traditional child health interventions have been shown to be effective in fighting diarrhea. Among environmental interventions, hand washing and point-of-use water treatment both reduce diarrhea, although more needs to be learned about ways to encourage households to take up these behavior changes. In contrast, there is little evidence that providing community-level rural water infrastructure substantially reduces diarrheal disease or that this infrastructure can be effectively maintained. Investments in communal water infrastructure short of piped water may serve other needs, and may reduce diarrhea in particular circumstances, but the case for prioritizing communal infrastructure provision needs to be made rather than assumed.
Weakly Supervised Semantic Segmentation in 3D Graph-Structured Point Clouds of Wild Scenes

The deficiency of 3D segmentation labels is one of the main obstacles to effective point cloud segmentation, especially for scenes in the wild with varieties of different objects. To alleviate this issue, we propose a novel deep graph convolutional network-based framework for large-scale semantic scene segmentation in point clouds with sole 2D supervision. Different with numerous preceding multi-view supervised approaches focusing on single object point clouds, we argue that 2D supervision is capable of providing sufficient guidance information for training 3D semantic segmentation models of natural scene point clouds while not explicitly capturing their inherent structures, even with only single view per training sample. Specifically, a Graph-based Pyramid Feature Network (GPFN) is designed to implicitly infer both global and local features of point sets and an Observability Network (OBSNet) is introduced to further solve object occlusion problem caused by complicated spatial relations of objects in 3D scenes. During the projection process, perspective rendering and semantic fusion modules are proposed to provide refined 2D supervision signals for training along with a 2D-3D joint optimization strategy. Extensive experimental results demonstrate the effectiveness of our 2D supervised framework, which achieves comparable results with the state-of-the-art approaches trained with full 3D labels, for semantic point cloud segmentation on the popular SUNCG synthetic dataset and S3DIS real-world dataset.
Effect of Granulocyte Colony-Stimulating Factor on Neutropenia and Associated Morbidity Due to Chemotherapy for Transitional-Cell Carcinoma of the Urothelium

We evaluated the ability of human recombinant granulocyte colony-stimulating factor (rhG-CSF) to prevent chemotherapy-induced neutropenia or to accelerate recovery from this complication and thus allow patients to receive full doses of antineoplastic agents on time, according to protocol design. Twenty-seven patients with transitional-cell carcinoma of the urothelium who were undergoing treatment with methotrexate, doxorubicin, vinblastine, and cisplatin were given rhG-CSF (up to 60 micrograms per kilogram of body weight per day) before their first cycle of combination chemotherapy, during the first cycle, or at both points. Treatment with rhG-CSF before chemotherapy resulted in a dose-dependent increase in the absolute neutrophil count. Treatment with rhG-CSF after chemotherapy significantly reduced the number of days (91 percent) per patient on which the absolute neutrophil count was 1000 per microliter or less (P = 0.0039), reduced the number of days (1 vs. 35) on which antibiotics were used to treat fever and neutropenia, and significantly increased the percentage (100 vs. 29 percent) of patients qualified to receive planned chemotherapy on day 14 of the treatment cycle (P = 0.0015). In addition, the incidence of mucositis was significantly decreased (11 vs. 44 percent, P = 0.041), as was its severity. These findings demonstrate that rhG-CSF is a potent stimulus of normal neutrophil proliferation and maturation. In addition, its administration can reduce both the hematopoietic and oral toxicity of chemotherapy.
Contribution of CTCF binding to transcriptional activity at the HOXA locus in NPM1-mutant AML cells

Transcriptional regulation of the HOXA genes is thought to involve CTCF-mediated chromatin loops and the opposing actions of the COMPASS and Polycomb epigenetic complexes. We investigated the role of these mechanisms at the HOXA cluster in AML cells with the common NPM1c mutation, which express both HOXA and HOXB genes. CTCF binding at the HOXA locus is conserved across primary AML samples, regardless of HOXA gene expression, and defines a continuous chromatin domain marked by COMPASS-associated histone H3 trimethylation in NPM1-mutant primary AML samples. Profiling of the three-dimensional chromatin architecture in primary AML samples with the NPM1c mutation identified chromatin loops between the HOXA cluster and loci in the SNX10 and SKAP2 genes, and an intergenic region located 1.4 Mbp upstream of the HOXA locus. Deletion of CTCF binding sites in the NPM1-mutant OCI-AML3 AML cell line reduced multiple long-range interactions, but resulted in CTCF-independent loops with sequences in SKAP2 that were marked by enhancer-associated histone modifications in primary AML samples. HOXA gene expression was maintained in CTCF binding site mutants, indicating that transcriptional activity at the HOXA locus in NPM1-mutant AML cells may be sustained through persistent interactions with SKAP2 enhancers, or by intrinsic factors within the HOXA gene cluster.
Nuclear brightness profiles of the X-ray globular cluster M15 compared to M3, M5, and M13

Densitometer tracings of the globular clusters M3, M5, M13 and M15 indicate that the central peak of the brightness profile for M15 is narrower in the red than in the blue region. It is suggested that the red excess in the nucleus of M15 is due to red giants.