cssi/SciSciGPT-SciSciNet huggingface dataset:
Bayesian Unidimensional Scaling for visualizing uncertainty in high dimensional datasets with latent ordering of observations

Detecting patterns in high-dimensional multivariate datasets is non-trivial. Clustering and dimensionality reduction techniques often help in discerning inherent structures. In biological datasets such as microbial community composition or gene expression data, observations can be generated from a continuous process, often unknown. Estimating data points’ ‘natural ordering’ and their corresponding uncertainties can help researchers draw insights about the mechanisms involved. We introduce a Bayesian Unidimensional Scaling (BUDS) technique which extracts dominant sources of variation in high dimensional datasets and produces their visual data summaries, facilitating the exploration of a hidden continuum. The method maps multivariate data points to latent one-dimensional coordinates along their underlying trajectory, and provides estimated uncertainty bounds. By statistically modeling dissimilarities and applying a DiSTATIS registration method to their posterior samples, we are able to incorporate visualizations of uncertainties in the estimated data trajectory across different regions using confidence contours for individual data points. We also illustrate the estimated overall data density across different areas by including density clouds. One-dimensional coordinates recovered by BUDS help researchers discover sample attributes or covariates that are factors driving the main variability in a dataset. We demonstrated usefulness and accuracy of BUDS on a set of published microbiome 16S and RNA-seq and roll call data. Our method effectively recovers and visualizes natural orderings present in datasets. Automatic visualization tools for data exploration and analysis are available at: https://nlhuong.shinyapps.io/visTrajectory/ .
Analyzing complex survey data: the Kauffman Firm Survey

The Kauffman Firm Survey (KFS) was a panel study of new businesses that employed a complex sample design to collect key data about the dynamics of high-technology, medium-technology, and female-owned business entities. Complex sample designs of the type employed in the KFS typically have multi-frame sampling, stratification, non-response adjustment, and over-sampling components. Each of these design elements has been proven to enhance the efficiency with which researchers analyze and draw inferences from the available data. However, there is also a risk that a complex sample design approach can make data analysis more complicated due to non-independent selections and selection with varying probabilities. In this technical overview of the KFS, we describe the sampling method that was utilized in the panel survey. We examine how failing to take into account the probability-based weights impact the parameter estimates and the resulting standard errors. Through adopting an empirical approach, we show why it is important to take account of stratification and weighting. This paper demonstrates the importance of taking the features of a complex survey design into account during the data analysis process.
Stable leader election in population protocols requires linear time

A population protocol stably elects a leader if, for all n, starting from an initial configuration with n agents each in an identical state, with probability 1 it reaches a configuration $$\mathbf {y}$$ that is correct (exactly one agent is in a special leader state $$\ell $$ ) and stable (every configuration reachable from $$\mathbf {y}$$ also has a single agent in state $$\ell $$ ). We show that any population protocol that stably elects a leader requires $$\varOmega (n)$$ expected “parallel time”— $$\varOmega (n^2)$$ expected total pairwise interactions—to reach such a stable configuration. Our result also informs the understanding of the time complexity of chemical self-organization by showing an essential difficulty in generating exact quantities of molecular species quickly.
Production and spatial distribution of switchgrass and miscanthus in the United States under uncertainty and sunk cost

Abstract The U.S. cellulosic biofuel mandate has not been enforced in recent years. Uncertainty surrounding the enforcement of the mandate in addition to high production and harvest cost have contributed to a delay in the widespread planting of bioenergy crops such as switchgrass and miscanthus. Previous literature has shown that under uncertainty and sunk cost, an investment threshold is further increased due to the value associated from holding the investment option. In this paper, we extend the previous literature by applying a real option switching model to bioenergy crop production. First, we calculate the county-level break-even price which triggers a switching away from traditional field crops (corn, soybeans, and wheat) to bioenergy crops under various scenarios differing by commodity prices, production cost and biomass price expectations. We show that the resulting break-even prices at the county-level can be substantially higher than previously estimated due to the inclusion of the option value. In a second step, we identify counties that are most likely to grow switchgrass or miscanthus by simulating a stochastic biomass price over time. Our results highlight two issues: First, switchgrass or miscanthus are not grown in the Midwest under any scenario. Under low agricultural residue removal rates, biomass crops are mostly grown in the Southeast. Second, under the assumption of a high removal rates, bioenergy crops are not grown anywhere in the U.S. since the cellulosic biofuel mandate can be covered by agricultural residues.
The (Q,S,s) pricing rule: a quantitative analysis

Abstract Are nominal prices sticky because menu costs prevent sellers from continuously adjusting their prices to keep up with inflation or because search frictions make sellers indifferent to any real price over some non-degenerate interval? The paper answers the question by developing and calibrating a model in which both search frictions and menu costs may generate price stickiness and sellers are subject to idiosyncratic shocks. The equilibrium of the calibrated model is such that sellers follow a (Q,S,s) pricing rule: each seller lets inflation erode the effective real value of the nominal prices until it reaches some point s and then pays the menu cost and sets a new nominal price with an effective real value drawn from a distribution with support [S, Q], with s
Acute and long-term effects of Δ9-tetrahydrocannabinol on object recognition and anxiety-like activity are age- and strain-dependent in mice.

Abstract Use of exogenous cannabinoids disrupts the fine-tuned endocannabinoid receptor system, possibly leading to alterations in cognition, memory, and emotional processes that endure long after cannabinoid use has stopped. Long-term adolescent use may uniquely disrupt these behaviors when compared to adult use. The current study explored the acute and long-term behavioral effects of six 10 mg/kg Δ9-tetrahydrocannabinol (THC) injections across the adolescent or early adult period in male inbred C57Bl/6J and DBA/2J mice. The acute and prolonged effects of THC on object memory using the novel object recognition task, unconditioned anxiety in the elevated plus maze and open field, and sedative effects in the open field were examined. Acute THC treatment resulted in anxiogenic activity in both strains, but only caused sedation in B6 mice. Repeated THC treatment resulted in a protracted effect on object recognition, but not unconditioned anxiety, assessed 4 weeks later. In both strains, an adolescent history of THC treatment disrupted later object recognition. Interestingly, in B6 mice an adult history of THC exposure appeared to rescue a deficit in object recognition observed in vehicle-treated adults. Repeated THC administration also produced a protracted effected on CB1R protein expression. Animals treated with THC in adolescence maintained increased levels of CB1R protein expression compared to their adult THC-treated counterparts at five weeks following the last injection. These results indicate that THC use may have long-lasting effects with adolescence being a unique period of susceptibility.
InfoTrim: A DNA read quality trimmer using entropy

Biological DNA reads are often trimmed before mapping, genome assembly, and other tasks to improve the quality of the results. Biological sequence complexity relates to alignment quality as low complexity regions can align poorly. There are many read trimmers, but many do not use sequence complexity for trimming. Alignment of reads generated from whole genome bisulfite sequencing is especially challenging since bisulfite treated reads tend to reduce sequence complexity. InfoTrim, a new read trimmer, was created to explore these issues. It is evaluated against five other trimmers using four read mappers on real and simulated bisulfite treated DNA data. InfoTrim has reasonable results.
Locomotor analysis identifies early compensatory changes during disease progression and subgroup classification in a mouse model of amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis is a motoneuron degenerative disease that is challenging to diagnose and presents with considerable variability in survival. Early identification and enhanced understanding of symptomatic patterns could aid in diagnosis and provide an avenue for monitoring disease progression. Use of the mSOD1G93A mouse model provides control of the confounding environmental factors and genetic heterogeneity seen in amyotrophic lateral sclerosis patients, while investigating underlying disease-induced changes. In the present study, we performed a longitudinal behavioral assessment paradigm and identified an early hindlimb symptom, resembling the common gait abnormality foot drop, along with an accompanying forelimb compensatory mechanism in the mSOD1G93A mouse. Following these initial changes, mSOD1 mice displayed a temporary hindlimb compensatory mechanism resembling an exaggerated steppage gait. As the disease progressed, these compensatory mechanisms were not sufficient to sustain fundamental locomotor parameters and more severe deficits appeared. We next applied these initial findings to investigate the inherent variability in B6SJL mSOD1G93A survival. We identified four behavioral variables that, when combined in a cluster analysis, identified two subpopulations with different disease progression rates: a fast progression group and a slow progression group. This behavioral assessment paradigm, with its analytical approaches, provides a method for monitoring disease progression and detecting mSOD1 subgroups with different disease severities. This affords researchers an opportunity to search for genetic modifiers or other factors that likely enhance or slow disease progression. Such factors are possible therapeutic targets with the potential to slow disease progression and provide insight into the underlying pathology and disease mechanisms.
Financial Spillovers and Macroprudential Policies

We estimate the impact of the extensity of macroprudential policies on the correlation of the policy interest rates between the center economies (CEs, i.e., the U.S., Japan, and the Euro area), and the peripheral economies (PHs). We find a more extensive implementation of macroprudential policies would lead PHs to (re)gain monetary independence from the CEs when the CEs implement expansionary monetary policy; when PHs run current account deficit; when they hold lower levels of international reserves; when their financial markets are relatively closed; when they are experiencing an increase in net portfolio flows; and when they are experiencing credit expansion.
Reduced dimension equalizer and interference canceller for MIMO-OFDM

In this paper, an interference cancellation and equalization method is proposed for a multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) system. Standard equalization techniques such as zero-forcing (ZF), and adaptive array processing techniques such as pertone or time-domain equalization, may require large amounts of training and memory for storage of the weight coefficients. In a MIMO-OFDM system with N carriers, ZF considerations help dictate required equalizer lengths, which are characterized by the solution space associated with an underdetermined system of linear equations. In the practical case where the cyclic prefix length, and hence the channel length, is a fraction of the OFDM frame duration, it is shown that the respective equalizers to all tones (across antennas) are characterized by the basis of a data-independent, low-dimensional subspace. A structured per-tone technique is proposed that utilizes the extra degrees of freedom afforded by having multiple antennas at the receiver in order to cancel interfering signals.
Bias correction and out-of-sample forecast accuracy

Abstract We evaluate the usefulness of bias-correction methods for autoregressive (AR) models in enhancing the out-of-sample forecast accuracy. We employ two popular methods, proposed by Hansen (1999) and So and Shin (1999) . Our Monte Carlo simulations show that these methods do not necessarily achieve better forecasting performances than the bias-uncorrected least squares (LS) method, because bias correction increases the variance of the estimator. Both the bias and the relative variance tend to decrease as the sample size ( T ) increases, meaning that larger numbers of observations do not always imply gains from bias-correction. As the degree of persistence increases, the bias becomes greater while the relative variance becomes smaller, which implies a greater gain from correcting for bias for highly persistent data. We also provide real data applications that confirm our major findings overall.
Where is the proton's spin?

A neutron has no electrical charge, yet it has a magnetic moment – a property that is crucial for its exploitation industrially and in scientific research such as in neutron scattering. But how does a neutral neutron possess a magnetic moment? Until very recently any particle physicist would have answered: 'Because it is built from three spinning quarks whose individual charges cancel to zero but whose magnetic moments (charge weighted by spin orientation) do not'.
High-Resolution Epigenomic Atlas of Human Embryonic Craniofacial Development

Summary Defects in patterning during human embryonic development frequently result in craniofacial abnormalities. The gene regulatory programs that build the craniofacial complex are likely controlled by information located between genes and within intronic sequences. However, systematic identification of regulatory sequences important for forming the human face has not been performed. Here, we describe comprehensive epigenomic annotations from human embryonic craniofacial tissues and systematic comparisons with multiple tissues and cell types. We identified thousands of tissue-specific craniofacial regulatory sequences and likely causal regions for rare craniofacial abnormalities. We demonstrate significant enrichment of common variants associated with orofacial clefting in enhancers active early in embryonic development, while those associated with normal facial variation are enriched near the end of the embryonic period. These data are provided in easily accessible formats for both craniofacial researchers and clinicians to aid future experimental design and interpretation of noncoding variation in those affected by craniofacial abnormalities.
A genome-wide cross-trait analysis from UK Biobank highlights the shared genetic architecture of asthma and allergic diseases.

Clinical and epidemiological data suggest that asthma and allergic diseases are associated and may share a common genetic etiology. We analyzed genome-wide SNP data for asthma and allergic diseases in 33,593 cases and 76,768 controls of European ancestry from UK Biobank. Two publicly available independent genome-wide association studies were used for replication. We have found a strong genome-wide genetic correlation between asthma and allergic diseases (rg = 0.75, P = 6.84 × 10−62). Cross-trait analysis identified 38 genome-wide significant loci, including 7 novel shared loci. Computational analysis showed that shared genetic loci are enriched in immune/inflammatory systems and tissues with epithelium cells. Our work identifies common genetic architectures shared between asthma and allergy and will help to advance understanding of the molecular mechanisms underlying co-morbid asthma and allergic diseases. Genome-wide cross-trait analysis shows a strong genetic correlation between asthma and allergic diseases. Shared susceptibility loci are enriched for genes involved in immune and inflammatory responses and genes expressed in epithelial tissues.
Multiple Organization Goals with Feedback from Shared Technological Task Environments

Goals and the performance feedback on those goals are fundamental to organizational learning and adaptation. However, most research has focused on single overall, high-level organizational goals wh...
Automated pulmonary nodule detection using 3D deep convolutional neural networks

Early detection of pulmonary nodules in computed tomography (CT) images is essential for successful outcomes among lung cancer patients. Much attention has been given to deep convolutional neural network (DCNN)-based approaches to this task, but models have relied at least partly on 2D or 2.5D components for inherently 3D data. In this paper, we introduce a novel DCNN approach, consisting of two stages, that is fully three-dimensional end-to-end and utilizes the state-of-the-art in object detection. First, nodule candidates are identified with a U-Net-inspired 3D Faster R-CNN trained using online hard negative mining. Second, false positive reduction is performed by 3D DCNN classifiers trained on difficult examples produced during candidate screening. Finally, we introduce a method to ensemble models from both stages via consensus to give the final predictions. By using this framework, we ranked first of 2887 teams in Season One of Alibaba's 2017 TianChi AI Competition for Healthcare.
mHealth Technology Use and Implications in Historically Underserved and Minority Populations in the United States: Systematic Literature Review.

Background: The proportion of people in the United States who are members of at least two ethnic groups is projected to increase to 10% by the year 2050. This makes addressing health disparities and health inequities in minority populations increasingly more difficult. Minority populations, including those who classify themselves as African American and Hispanic, are using mobile phones to access health information via the internet more frequently than those who classify themselves as white, providing unique opportunities for those in public health and health education to reach these traditionally underserved populations using mobile health (mHealth) interventions. Objective: The objective of this review was to assess studies conducted in the United States that have used mHealth tools and strategies to develop and implement interventions in underserved populations. This review also examines the ways in which mHealth strategies are being employed in public health interventions to these priority population groups, as mobile phone capabilities include text messaging, mobile apps, internet access, emails, video streaming, social media, instant messaging, and more. Methods: A systematic literature review was conducted using key search phrases, the matrix method, and Preferred Reporting Items for Systematic Reviews and Meta-Analyses flowchart diagram to identify key studies conducted between the years of 2009-2016 in the United States. These studies were reviewed for their use of mHealth interventions in historically underserved and minority populations. Results: A total of 16,270 articles were initially identified using key search phrases in three databases. Titles were reviewed and articles not meeting criteria were excluded, leaving 156 articles for further review. After additional review for relevance and inclusion criteria, 16 articles were qualified and analyzed. Conclusions: mHealth is a promising area of development for public health and health education. While successful research has been done using text messaging (short message service, SMS) and other mHealth strategies, there is a need for more research using mobile phones and tablet applications. This literature review demonstrates mHealth technology has the ability to increase prevention and health education in health disparate communities and concludes that more specified research is needed.
Leveraging molecular quantitative trait loci to understand the genetic architecture of diseases and complex traits.

There is increasing evidence that many risk loci found using genome-wide association studies are molecular quantitative trait loci (QTLs). Here we introduce a new set of functional annotations based on causal posterior probabilities of fine-mapped molecular cis-QTLs, using data from the Genotype-Tissue Expression (GTEx) and BLUEPRINT consortia. We show that these annotations are more strongly enriched for heritability (5.84× for eQTLs; P = 1.19 × 10-31) across 41 diseases and complex traits than annotations containing all significant molecular QTLs (1.80× for expression (e)QTLs). eQTL annotations obtained by meta-analyzing all GTEx tissues generally performed best, whereas tissue-specific eQTL annotations produced stronger enrichments for blood- and brain-related diseases and traits. eQTL annotations restricted to loss-of-function intolerant genes were even more enriched for heritability (17.06×; P = 1.20 × 10-35). All molecular QTLs except splicing QTLs remained significantly enriched in joint analysis, indicating that each of these annotations is uniquely informative for disease and complex trait architectures.
Heuristic policies for stochastic knapsack problem with time‐varying random demand

In this paper, we consider the classic stochastic (dynamic) knapsack problem, a fundamental mathematical model in revenue management, with general time-varying random demand. Our main goal is to study the optimal policies, which can be obtained by solving the dynamic programming formulated for the problem, both qualitatively and quantitatively. It is well-known that when the demand size is fixed and the demand distribution is stationary over time, the value function of the dynamic programming exhibits extremely useful first and second order monotonicity properties, which lead to monotonicity properties of the optimal policies. In this paper, we are able to verify that these results still hold even in the case that the price distributions are time-dependent. When we further relax the demand size distribution assumptions and allow them to be arbitrary, for example in random batches, we develop a scheme for using value function of alternative unit demand systems to provide bounds to the value function. These results confirm some of the basic understandings of the stochastic knapsack problem. As a natural consequence, we propose a new class of heuristic policies, two-dimensional switch-over policies, and discuss numerical procedures for optimizing the parameters of the policies. Extensive numerical experiments demonstrate that these switch-over policies can provide performances that are in a close neighborhood of the optimum statistically.
Chip-scale atomic devices

Chip-scale atomic devices combine elements of precision atomic spectroscopy, silicon micromachining, and advanced diode laser technology to create compact, low-power, and manufacturable instruments with high precision and stability. Microfabricated alkali vapor cells are at the heart of most of these technologies, and the fabrication of these cells is discussed in detail. We review the design, fabrication, and performance of chip-scale atomic clocks, magnetometers, and gyroscopes and discuss many applications in which these novel instruments are being used. Finally, we present prospects for future generations of miniaturized devices, such as photonically integrated systems and manufacturable devices, which may enable embedded absolute measurement of a broad range of physical quantities.
Confronting Quasi-Separation in Logistic Mixed Effects for Linguistic Data: A Bayesian Approach

Mixed effects regression models are widely used by language researchers. However, these regressions are implemented with an algorithm which may not converge on a solution. While convergence issues ...
Recommendation Through Mixtures of Heterogeneous Item Relationships

Recommender Systems have proliferated as general-purpose approaches to model a wide variety of consumer interaction data. Specific instances make use of signals ranging from user feedback, item relationships, geographic locality, social influence (etc.). Typically, research proceeds by showing that making use of a specific signal (within a carefully designed model) allows for higher-fidelity recommendations on a particular dataset. Of course, the real situation is more nuanced, in which a combination of many signals may be at play, or favored in different proportion by individual users. Here we seek to develop a framework that is capable of combining such heterogeneous item relationships by simultaneously modeling (a) what modality of recommendation is a user likely to be susceptible to at a particular point in time; and (b) what is the best recommendation from each modality. Our method borrows ideas from mixtures-of-experts approaches as well as knowledge graph embeddings. We find that our approach naturally yields more accurate recommendations than alternatives, while also providing intuitive 'explanations' behind the recommendations it provides.
Exploiting the Shape of CAN Data for In-Vehicle Intrusion Detection

Modern vehicles rely on scores of electronic control units (ECUs) broadcasting messages over a few controller area networks (CANs). Bereft of security features, in-vehicle CANs are exposed to cyber manipulation and multiple researches have proved viable, life-threatening cyber attacks. Complicating the issue, CAN messages lack a common mapping of functions to commands, so packets are observable but not easily decipherable. We present a transformational approach to CAN IDS that exploits the geometric properties of CAN data to inform two novel detectors - one based on distance from a learned, lower dimensional manifold and the other on discontinuities of the manifold over time. Proof-of-concept tests are presented by implementing a potential attack approach on a driving vehicle. The initial results suggest that (1) the first detector requires additional refinement but does hold promise; (2) the second detector gives a clear, strong indicator of the attack; and (3) the algorithms keep pace with high-speed CAN messages. As our approach is data-driven it provides a vehicle-agnostic IDS that eliminates the need to reverse engineer CAN messages and can be ported to an after-market plugin.
Computationally and statistically efficient learning of causal Bayes nets using path queries

Causal discovery from empirical data is a fundamental problem in many scientific domains. Observational data allows for identifiability only up to Markov equivalence class. In this paper we first propose a polynomial time algorithm for learning the exact correctly-oriented structure of the transitive reduction of any causal Bayesian network with high probability, by using interventional path queries. Each path query takes as input an origin node and a target node, and answers whether there is a directed path from the origin to the target. This is done by intervening on the origin node and observing samples from the target node. We theoretically show the logarithmic sample complexity for the size of interventional data per path query, for continuous and discrete networks. We then show how to learn the transitive edges using also logarithmic sample complexity (albeit in time exponential in the maximum number of parents for discrete networks), which allows us to learn the full network. We further extend our work by reducing the number of interventional path queries for learning rooted trees. We also provide an analysis of imperfect interventions.
Machine learning for regularized survey forecast combination: Partially-egalitarian LASSO and its derivatives

Abstract Despite the clear success of forecast combination in many economic environments, several important issues remain incompletely resolved. The issues relate to the selection of the set of forecasts to combine, and whether some form of additional regularization (e.g., shrinkage) is desirable. Against this background, and also considering the frequently-found good performance of simple-average combinations, we propose a LASSO-based procedure that sets some combining weights to zero and shrinks the survivors toward equality (“partially-egalitarian LASSO”). Ex post analysis reveals that the optimal solution has a very simple form: the vast majority of forecasters should be discarded, and the remainder should be averaged. We therefore propose and explore direct subset-averaging procedures that are motivated by the structure of partially-egalitarian LASSO and the lessons learned, which, unlike LASSO, do not require the choice of a tuning parameter. Intriguingly, in an application to the European Central Bank Survey of Professional Forecasters, our procedures outperform simple average and median forecasts; indeed, they perform approximately as well as the ex post best forecaster.
Improved inference of chromosome conformation from images of labeled loci

We previously published a method that infers chromosome conformation from images of fluorescently-tagged genomic loci, for the case when there are many loci labeled with each distinguishable color. Here we build on our previous work and improve the reconstruction algorithm to address previous limitations. We show that these improvements 1) increase the reconstruction accuracy and 2) allow the method to be used on large-scale problems involving several hundred labeled loci. Simulations indicate that full-chromosome reconstructions at 1/2 Mb resolution are possible using existing labeling and imaging technologies. The updated reconstruction code and the script files used for this paper are available at: https://github.com/heltilda/align3d.
Active Learning for New Domains in Natural Language Understanding

We explore active learning (AL) for improving the accuracy of new domains in a natural language understanding (NLU) system. We propose an algorithm called Majority-CRF that uses an ensemble of classification models to guide the selection of relevant utterances, as well as a sequence labeling model to help prioritize informative examples. Experiments with three domains show that Majority-CRF achieves 6.6%-9% relative error rate reduction compared to random sampling with the same annotation budget, and statistically significant improvements compared to other AL approaches. Additionally, case studies with human-in-the-loop AL on six new domains show 4.6%-9% improvement on an existing NLU system.
Corporate governance and dividend policy: Evidence of tunneling from master limited partnerships

Abstract Using a sample of 85 Delaware master limited partnerships (“MLPs”) from 2004 to 2016, we examine the relation between cash dividend policy and the strength of corporate governance measured by contractual governance provisions, such as fiduciary waiver, mandatory distributions, and voting rights, and by ownership structure. We find support for the tunneling model of dividend determination. Specifically, we document that firms with weaker governance pay out more cash dividends than better governed firms. We also find that, in the presence of low quality governance, these payments reduce firm value as well as the value of the firm's cash holdings, suggesting that they are viewed by the market as a tunneling (extraction) of resources by the general partner at the expense of limited unitholders.
Radiation friction force effects on electron dynamics in ultra-intensity laser pulse

The electron dynamics in the ultra-high intensity laser pulse with radiation friction force in the Landau-Lifshitz form are studied. It is demonstrated that widely used approximation, where only the term dominating the dissipation of electron kinetic energy is retained in the expression for the radiation friction, is incorrect for the case of diverging electron trajectories. As a matter of fact, for large friction force effects, all components of the radiation friction force in the Landau-Lifshitz form have the same order in the equation of motion for electron trajectories, being equally important for both electron trajectory and thus energy gain in the case of diverging electron trajectories (e.g., determined by the superposition of few electromagnetic waves).
Symbolic Types for Lenient Symbolic Execution

We present \lcsym, a typed $\lambda$-calculus for lenient symbolic execution, where some language constructs do not recognize symbolic values. Its type system, however, ensures safe behavior of all symbolic values in a program. Our calculus extends a base occurrence typing system with symbolic types and mutable state, making it a suitable model for both functional and imperative symbolically executed languages. Naively allowing mutation in this mixed setting introduces soundness issues, however, so we further add concreteness polymorphism, which restores soundness without rejecting too many valid programs. To show that our calculus is a useful model for a real language, we implemented Typed Rosette, a typed extension of the solver-aided Rosette language. We evaluate Typed Rosette by porting a large code base, demonstrating that our type system accommodates a wide variety of symbolically executed programs.
Plasmonic parametric absorbers

Exploiting the dynamics of plasmonic parametric resonance (PPR), we introduce the theory of plasmonic parametric absorbers (PPAs). The key insight informing the PPA idea is that in the PPR process a pump field experiences an extinction rate that strongly depends on the intensity of the pump itself, creating two distinct regimes: one of weak absorption under low intensity illumination, and one of strong absorption when the threshold of parametric resonance is met or exceeded. Due to this reverse saturable absorption behavior, PPAs are promising candidates for optical-limiting applications.
Assessment of postoperative outcomes in spinal epidural abscess following surgical decompression.

Abstract Background context A spinal epidural abscess (SEA) is a serious condition that may be managed with antibiotics alone or with decompressive surgery combined with antibiotics. Purpose The objectives of this study were to assess the clinical outcomes of SEA after surgical management and to identify the patient-level factors that are associated with outcomes following surgical decompression and removal of SEA. Study design/setting Retrospective chart review analysis. Patient sample An analysis of 154 consecutive patients who initially presented to a tertiary-care, academic medical center with SEA, and were subsequently treated with surgery between 2010 and 2015 was performed. Outcome measures Postoperative predischarge American Spinal Injury Association Impairment Scale (AIS) scores, 6-month follow-up encounter AIS scores, need for revision surgery, and mortality during SEA surgery were the primary outcomes.Physiological Measures: AIS scores. Method Fisher's exact and Wilcoxon rank-sum tests were used to assess the associations between patient-level factors and surgical outcomes. Moreover, an interactive, predictive model for postoperative predischarge AIS score was developed using a proportional odds regression model. There was no funding secured for this study and there is no conflict of interest-associated biases. Results One hundred fifty-four patients (mean age of 58 years) were treated using surgical decompression in addition to antibiotics. The majority of patients were Caucasian (81%) and male (61%). No intraoperative mortality was reported. A second SEA surgery was performed in 8% of patients. A comparison of the preoperative and postoperative predischarge AIS scores showed that 49% of patients maintained a score of E or improved, while 45% remained at their preoperative status and 6% worsened. Among a subset of patients (n=36; 23%) for whom a 6-month follow-up encounter occurred, 75% maintained an AIS score of E or improved, 19% remained at their preoperative status, and 6% worsened. Both the presence and longer duration of preoperative paresis was associated with an increased risk of remaining at the same AIS score or worsening at the predischarge encounter (both p Conclusions Surgical decompression can contribute to improving or maintaining AIS scores in a high percentage of SEA patients. The presence and duration of preoperative paresis are prognostic for poorer outcomes and suggest that rapid surgical intervention before paresis develops may lead to improved postoperative outcomes. Our modeling tool enables an estimation of probabilities of patients’ predischarge condition.
Encoding of Wind Direction by Central Neurons in Drosophila

Wind is a major navigational cue for insects, but how wind direction is decoded by central neurons in the insect brain is unknown. Here we find that walking flies combine signals from both antennae to orient to wind during olfactory search behavior. Movements of single antennae are ambiguous with respect to wind direction, but the difference between left and right antennal displacements yields a linear code for wind direction in azimuth. Second-order mechanosensory neurons share the ambiguous responses of a single antenna and receive input primarily from the ipsilateral antenna. Finally, we identify novel "wedge projection neurons" that integrate signals across the two antennae and receive input from at least three classes of second-order neurons to produce a more linear representation of wind direction. This study establishes how a feature of the sensory environment-wind direction-is decoded by neurons that compare information across two sensors.
Loss of Nmp4 optimizes osteogenic metabolism and secretion to enhance bone quality.

A goal of osteoporosis therapy is to restore lost bone with structurally sound tissue. Mice lacking the transcription factor nuclear matrix protein 4 (Nmp4, Zfp384, Ciz, ZNF384) respond to several ...
The politics of upgrading in global value chains: The case of Rwanda’s coffee sector

Two parallel tracks of research on economic transformation in developing countries have operated at a distance from each other over the last two decades. A global track – global value chains/global production networks (GVC/GPNs) – has focused on the increasing interconnectedness of global trading networks and has overlooked the role of the state and the explanatory power of domestic political economy. Meanwhile, a domestic track – including literature on developmental states, industrial policy and political settlements – has tended to take a methodologically nationalist perspective to examine economic transformation in developing countries, with limited reflections on external economic and political pressures. This paper contributes to an emerging stream of literature that examines how the domestic and global scales influence how developing country governments and firms tackle the challenge of economic upgrading. By combining insights from the political settlements and GVC/GPNs literature, this paper examines the Rwandan government’s attempt at upgrading its coffee production to enter specialty coffee markets. It shows how the existing GVC/GPNs literature makes an important contribution to describing how multipolar governance influences the pathways for economic upgrading in Rwanda’s coffee sector, but that even where access is granted, benefits are captive to the demands of international buyers, and gains for some have not translated across the sector. Insights from the political settlements literature showcase how domestic politics influences who benefits from insertion to GVC/GPNs and how the unequal provision of opportunities affects political stability.
How effective are community pedestrian safety training workshops? Short-term findings from a program in California

Abstract Introduction Pedestrians and bicyclists make up a disproportionate share of road deaths and injuries, and low-income, majority person-of-color communities tend to face the greatest danger. Comprehensive pedestrian safety programs targeted toward such communities have the potential to build communities’ capacity to address safety issues, but there is a lack of systematic research and evaluation on how effective these programs are. Methods We use a mixed-methods approach of surveys, participant observation, and follow-up interviews to evaluate a community-based pedestrian and bicycle safety program in 13 California communities for its short-term effectiveness in meeting five goals: providing relevant safety information to participants, building community partnerships, increasing walking and cycling in host communities, improving perceptions of pedestrian and bicyclist safety, and planning for additional safety countermeasures. Results Workshops have beneficial effects on identifying community needs, developing partnerships between stakeholders, and changing perceptions of safety in historically disadvantaged communities. The program improves participants’ perceptions of the role that community organizations and community events play in pedestrian safety efforts. The program also provides a critical space for community stakeholders to meet, learn a common language about safety, and develop partnerships around pedestrian and bicycle safety. Host communities have used the workshops as support for grant applications, and several have obtained funding for pedestrian and bicycle improvements. Conclusion The workshops intervene in the short-term on multiple levels to improve pedestrian safety as described in program goals. This study provides a model for evaluating a program for its short-term effects, providing a baseline set of conditions for longer-term evaluation.
Does it get better? LGBTQ social work students and experiences with harmful discourse

Although the field of social work is grounded in social justice, the social work educational experience, including classrooms, may not live up to this value, especially for lesbian, gay, bisexual, ...
Macroeconomic Drivers of Chinese ADRs: Home Country vs. US Effects*

The potential connections between macroeconomic variables and the stock market becomes less straightforward for shares issued in one country but traded in another. Whereas past work has suggested t...
Speech production and disorganization in schizotypy: Investigating the role of cognitive and affective systems.

Abstract Diminished productivity and elevated disorganization have been detected in the speech of individuals with schizotypy. However, the underlying mechanisms for these disruptions are not well understood. Separate lines of research suggest potential contributions from cognitive and affective systems. In this study, disorganized speech and speech production were examined in speech samples generated by schizotypy (n = 47) and non-schizotypy (n = 51) groups by assessing “reactivity” (i.e., a change in experimental compared with baseline conditions) across baseline, affective, and dual-task (i.e., cognitive) conditions. Relationships with social functioning were also examined within each group. Three key findings emerged: 1) compared to the non-schizotypy group, those with schizotypy exhibited diminished speech production in the affective condition and affective reactivity was observed; 2) the schizotypy group displayed greater levels of disorganized speech in dual-task conditions and cognitive reactivity was observed; and 3) affective reactivity for disorganized speech was linked to worse social functioning within the schizotypy group. This study provides evidence that cognitive and affective systems are uniquely involved in separate characteristics of speech in schizotypy. At this stage, cognitive systems appear to have a specific role in the organization of speech, whereas affective systems are more heavily involved in speech production. Regarding the association between affective reactivity and social functioning, previous research has demonstrated individuals highly reactive to emotional stimuli carry additional risk for conversion to psychosis. Future research identifying a subset with schizotypy who demonstrate affective reactivity could lead to a better understanding of links between schizotypy and future psychosis symptoms.
TAFE-Net: Task-Aware Feature Embeddings for Low Shot Learning

Learning good feature embeddings for images often requires substantial training data. As a consequence, in settings where training data is limited (e.g., few-shot and zero-shot learning), we are typically forced to use a general feature embedding across prediction tasks. Ideally, we would like to construct feature embeddings that are tuned for the given task and even input image. In this work, we propose Task Aware Feature Embedding Networks (TAFE-Nets) to learn how to adapt the image representation to a new task in a meta learning fashion. Our network is composed of a meta learner and a prediction network, where the meta learner generates parameters for the feature layers in the prediction network based on a task input so that the feature embedding can be accurately adjusted for that task. We show that TAFE-Net is highly effective in generalizing to new tasks or concepts and evaluate the TAFE-Net on a range of benchmarks in zero-shot and few-shot learning. Our model matches or exceeds the state-of-the-art on all tasks. In particular, our approach improves the prediction accuracy of unseen attribute-object pairs by 4 to 15 points on the challenging visual attribute-object composition task.
A Modified Risk Parity Method for Asset Allocation

We propose a return based modification of the portfolio variance matrix for asset allocation using risk parity. The modification is based upon a single scalar parameter which can be tuned to tailor the allocation for desired expected risk and/or return. The present work contributes a new twist on risk parity. While classical risk parity methods are based exclusively on volatility, the new solution (Modified Risk Parity) considers both historical returns and their variance in the construction of an optimal, diversified investment portfolio. We present two examples for periods including the recent financial market crises. The results suggest that the modification may lead to significantly improved risk adjusted returns over those realized by the conventional risk parity method.
Cooperation without communication

Intelligent agents must be able to interact even without the benefit of communication. In this paper we examine various constraints on the actions of agents in such situations and discuss the effects of these constraints on their derived utility. In particular, we define and analyze basic rationality; we consider various assumptions about independence; and we demonstrate the advantages of extending the definition of rationality from individual actions to decision procedures.