cssi/SciSciGPT-SciSciNet huggingface dataset:
Influenza-associated mortality for different causes of death during the 2010-2011 through the 2014-2015 influenza seasons in Russia

Background: There is limited information on the volume of influenza-associated mortality in Russia. Methods: Using previously developed methodology (Goldstein et al., Epidemiology 2012), we regressed the monthly rates of mortality for respiratory causes, circulatory causes, and for certain infectious and parasitic diseases (available from the Russian Federal State Statistics Service (Rosstat)) linearly against the monthly proxies for the incidence of influenza A/H3N2, A/H1N1 and B (obtained using data from the Smorodintsev Research Institute of Influenza (RII) on levels of ILI/ARI consultations and the percent of respiratory specimens testing positive for influenza A/H3N2, A/H1N1 and B), adjusting for the baseline rates of mortality not associated with influenza circulation and temporal trends. Results: For the 2010/11 through the 2014/15 seasons, influenza circulation was associated with an average annual 11106 (95% CI (4321,17892)) deaths for circulatory causes, 4552 (3744,5360) deaths for respiratory causes, and 343 (63,624) deaths for certain infectious and parasitic diseases, with influenza A/H3N2 being responsible for the majority of influenza-associated respiratory deaths and influenza B making a substantial contribution to influenza-associated mortality for circulatory causes. Conclusions: Influenza circulation is associated with a substantial mortality burden in Russia, particularly for circulatory deaths. Those results support the potential utility of influenza vaccination (with the role played by influenza B in circulatory mortality pointing to the benefit of quadrivalent influenza vaccines), as well as of administration of antiviral drugs for high-risk individuals during periods of active influenza circulation.
Development of a CsI:Tl calorimeter subsystem for the All-Sky Medium-Energy Gamma-Ray Observatory (AMEGO)

We report on the development of the thallium-doped cesium iodide (CsI:Tl) calorimeter subsystem for the All-Sky Medium-Energy Gamma-ray Observatory (AMEGO) [1]. The CsI:Tl calorimeter is one of the three main subsystems that comprise the AMEGO instrument suite; the others include the double-sided silicon strip detector (DSSD) tracker/converter and a cadmium zinc telluride (CZT) calorimeter. Similar to the LAT instrument on Fermi Gamma-ray Space Telescope, the hodoscopic calorimeter consists of orthogonally layered CsI:Tl bars. Unlike the LAT, which uses PIN photodiodes, the scintillation light from each end of the CsI:Tl bar is read out with recently developed large-area silicon photomultiplier (SiPM) arrays. Development of the calorimeter technology for a large space-based γ-ray observatory is being done via funding from the NASA APRA program. Under this program, we have designed, built and are currently testing a prototype consisting of 24 CsI:Tl bars (each with dimensions of 16.7 mm x 16.7 mm x 100 mm) hodoscopically arranged in four layers with six bars per layer. The ends of each bar are read out with SensL ArrayJ quad SiPMs. Signal readout and processing is done with the IDEAS SIPHRA (IDE3380) ASIC. Performance testing of this prototype was done with laboratory sources and at an initial beam test; future testing and characterization will be done during a second beam test (2019) and a balloon flight (2020) in conjunction with the other subsystems led by NASA-GSFC. We will discuss the initial calibration tests, construction of the prototypes and the most up-to-date results from laboratory and beam tests with analog/digital electronics and the SIPHRA DAQ.
Increasing cerebral blood flow improves cognition into late stages in Alzheimer's disease mice

Alzheimer's disease is associated with a 20-30% reduction in cerebral blood flow. In the APP/PS1 mouse model of Alzheimer's disease, inhibiting neutrophil adhesion using an antibody against the neutrophil specific protein Ly6G was recently shown to drive rapid improvements in cerebral blood flow that was accompanied by an improvement in performance on short-term memory tasks. Here, in a longitudinal aging study, we assessed how far into disease development a single injection of anti-Ly6G treatment can acutely improve short-term memory function. We found that APP/PS1 mice as old as 15-16 months had improved performance on the object replacement and Y-maze tests of spatial and working short-term memory, measured at one day after anti-Ly6G treatment. APP/PS1 mice at 17-18 months of age or older did not show acute improvements in cognitive performance, although we did find that capillary stalls were still reduced and cerebral blood flow was still increased by 17% in 21-22-months-old APP/PS1 mice given anti-Ly6G antibody. These data add to the growing body of evidence suggesting that cerebral blood flow reductions are an important contributing factor to the cognitive dysfunction associated with neurodegenerative disease. Thus, interfering with neutrophil adhesion could be a new therapeutic approach for Alzheimer's disease.
Ride-Sharing Networks with Mixed Autonomy

We consider ride-sharing networks served by human-driven vehicles and autonomous vehicles. First, we propose a novel model for ride-sharing in this mixed autonomy setting for a multi-location network in which the platform sets prices for riders, compensation for drivers, and operates autonomous vehicles for a fixed price. Then we study the possible benefits, in the form of increased profits, to the ride-sharing platform that are possible by introducing autonomous vehicles. We first establish a nonconvex optimization problem characterizing the optimal profits for a network operating at a steady-state equilibrium and then propose a convex problem with the same optimal profits that allows for efficient computation. Next, we study the relative mix of autonomous and human-driven vehicles that results at equilibrium for various costs of operation for autonomous vehicles. In particular, we show that there is a regime for which the platform will choose to mix autonomous and human-driven vehicles in order to optimize profits. Our results provide insights into how such ride-sharing platforms might choose to integrate autonomous vehicles into their fleet.
Patents for Sharing

Spurred by the Internet, emerging technologies have changed the way commercial firms innovate and have made it possible for individuals to play an important role in that innovation. Producers in the Information Communication Technologies (ICT), and other sectors dealing with complex technologies with many separately patentable components, find it increasingly difficult to make products without infringing on patents held by others. Numerous overlapping patents often cover such products. Producers have developed a new way to use patents: as inclusive rights for sharing their technologies with others through cross-licensing and other private ordering arrangements in order to ensure the freedom to operate and innovate. Individual innovators, and open source software ("OSS") programmers in particular, have also developed a new use of copyrights: using them to share their technologies through OSS licenses. Producers of complex technologies use patents for sharing their technologies with OSS programmers and for protecting themselves from patent assertion. In light of these recent uses, this article proposes a new utilitarian theory for patents: patent as the incentive to share, with the reward of increasing the freedom to operate and innovate. It argues that both the ex ante and ex post incentive to invent theories are outdated because they fail to take into account the patent owners’ lack of control over their products in complex technology sectors. This article urges Congress to reevaluate U.S. patent rights in light of the new patent use. It reviews U.S. patents as property rights from the comparative law perspective and proposes the revitalization of the inclusive side of U.S. patents by introducing a compulsory license for blocking patents. It also proposes that the exclusive side of patent rights should be limited to private and experimental use exceptions to ensure the freedom to operate and innovate by sharing.
Capacity Results for the Noisy Shuffling Channel

Motivated by DNA-based storage, we study the noisy shuffling channel, which can be seen as the concatenation of a standard noisy channel (such as the BSC) and a shuffling channel, which breaks the data block into small pieces and shuffles them. This channel models a DNA storage system, by capturing two of its key aspects: (1) the data is written onto many short DNA molecules that are stored in an unordered way and (2) the molecules are corrupted by noise at synthesis, sequencing, and during storage. For the BSC-shuffling channel we characterize the capacity exactly (for a large set of parameters), and show that a simple index-based coding scheme is optimal.
Horseshoes, hand grenades, and regulatory enforcement: Close experience with potential sanctions and fraud deterrence

Abstract We investigate the deterrence effect of experience with regulatory enforcement on fraud in a unique natural setting. Using ride-level data on New York City taxicab drivers, we identify drivers who fraudulently overcharge customers and pair them with the outcomes of drivers’ experience with regulatory enforcement (taxi court). We examine whether drivers’ experience with the taxi court, specifically whether the taxi court found them guilty or not guilty of fraud, affects their subsequent fraud. Interestingly, we find that the effect of experience with the regulatory enforcement on the likelihood of future fraud depends on the verdict received. Consistent with an impact bias in drivers’ affective forecasting, drivers who are found guilty (not guilty) are more (less) likely to commit fraud than similar drivers without recent taxi court experience. Our results have implications for academics and policymakers by showing that sometimes the threat of enforcement can be more effective at deterring future fraud than the actual enforcement itself.
Handheld mobile photography in very low light

Taking photographs in low light using a mobile phone is challenging and rarely produces pleasing results. Aside from the physical limits imposed by read noise and photon shot noise, these cameras are typically handheld, have small apertures and sensors, use mass-produced analog electronics that cannot easily be cooled, and are commonly used to photograph subjects that move, like children and pets. In this paper we describe a system for capturing clean, sharp, colorful photographs in light as low as 0.3 lux, where human vision becomes monochromatic and indistinct. To permit handheld photography without flash illumination, we capture, align, and combine multiple frames. Our system employs "motion metering", which uses an estimate of motion magnitudes (whether due to handshake or moving objects) to identify the number of frames and the per-frame exposure times that together minimize both noise and motion blur in a captured burst. We combine these frames using robust alignment and merging techniques that are specialized for high-noise imagery. To ensure accurate colors in such low light, we employ a learning-based auto white balancing algorithm. To prevent the photographs from looking like they were shot in daylight, we use tone mapping techniques inspired by illusionistic painting: increasing contrast, crushing shadows to black, and surrounding the scene with darkness. All of these processes are performed using the limited computational resources of a mobile device. Our system can be used by novice photographers to produce shareable pictures in a few seconds based on a single shutter press, even in environments so dim that humans cannot see clearly.
Robust H/sub 2//H/sub /spl infin// state estimation for systems with real parameter uncertainties

This paper considers the robust H/sub 2//H/sub /spl infin// estimation problem for time invariant systems with time varying structured parameter uncertainties in the state space model. The uncertain system which has parameter uncertainties in the state and observation matrix is considered. We consider the uncertainty matrices which can represent most structured parameter uncertainties in the state space model. We present a simple sufficient condition in terms of Riccati equations to design a robust state estimator that minimizes the H/sub 2/ error variance bound with an H/sub /spl infin// error bound constraint.
Small-Scale Density Fluctuations in the Adiabatic Toroidal Compressor

A new class of density fluctuations has been observed in the adiabatic toroidal compressor (ATC) tokamak by spectral analysis of scattered microwaves. The observed frequency spectrum is consistent with that of drift waves with amplitudes that are maximum in the wavelength range 0.5-1.0 cm where the transverse inertia of ions is important for plasma stability. The total density fluctuation is ${\stackrel{\ifmmode \tilde{}\else \~{}\fi{}}{n}}_{e}\ensuremath{\gtrsim}5\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}3}{\overline{n}}_{e}$. We estimate that these fluctuations could account for a large fraction of the electron energy losses of the ATC discharge.
Trading Off Computation with Transmission in Status Update Systems

This paper is motivated by emerging edge computing applications in which generated data are preprocessed at the source and then transmitted to an edge server. In such a scenario, there is typically a tradeoff between the amount of pre-processing and the amount of data to be transmitted. We model such a system by considering two non-preemptive queues in tandem whose service times are independent over time but the transmission service time is dependent on the computation service time in mean value. The first queue is in M/GI/1/1 form with a single server, memoryless exponential arrivals, general independent service and no extra buffer to save incoming status update packets. The second queue is in GI/M/1/2* form with a single server receiving packets from the first queue, memoryless service and a single data buffer to save incoming packets. Additionally, mean service times of the first and second queues are dependent through a deterministic monotonic function. We perform stationary distribution analysis in this system and obtain closed form expressions for average age of information (AoI) and average peak AoI. Our numerical results illustrate the analytical findings and highlight the tradeoff between average AoI and average peak AoI generated by the tandem nature of the queueing system with dependent service times.
National secular trends in ambient air volatile organic compound levels and biomarkers of exposure in the United States

Abstract Background Exposure to air pollution is a leading cause of global mortality. Volatile organic compounds (VOCs) are constituents of ambient air that could exert adverse health effects. Objective To examine the relationship between VOC levels in ambient air and individual-level exposure to VOCs, as assessed by urinary VOC metabolites. Methods Secular trends in 11 ambient air VOCs (2005–2013) and individual-level metabolites of 14 VOCs (2005–2014) were assessed using National Monitoring Programs (NMP) and National Health and Nutrition Examination Survey (NHANES) data, respectively. To isolate environmental exposure, individuals reporting exposure to tobacco smoke were excluded. Quantile regression models were used to assess secular trends in VOC exposure, and survey-weighted regression models were built to identify factors associated with VOC exposure. Results All annual levels of ambient VOCs decreased from 2005 to 2013 (Range: 12.5%–77.2%). However, 11 of the corresponding VOC metabolites increased during the same time (Range: 0.3%–53.6%). There was a proportional change in patterns of VOC exposure across NHANES waves, with the middle quantiles of exposure showing the largest increase. VOC exposures were significantly associated with age, sex, race, education, and physical inactivity, but not with secular VOC trends. Discussion In the United States, individual-level exposure to several VOCs increased between 2005 and 2014 despite a decline in ambient air VOC levels. This inverse relationship suggests that ambient VOCs are not the primary source of VOC exposure, therefore, decreasing ambient VOCs alone may not be sufficient to protect against the adverse health effects associated with VOC exposure.
Optimizing Inter-Datacenter Tail Flow Completion Times using Best Worst-case Routing

Flow routing over inter-datacenter networks is a well-known problem where the network assigns a path to a newly arriving flow potentially according to the network conditions and the properties of the new flow. An essential system-wide performance metric for a routing algorithm is the flow completion times, which affect the performance of applications running across multiple datacenters. Current static and dynamic routing approaches do not take advantage of flow size information in routing, which is practical in a controlled environment such as inter-datacenter networks that are managed by the datacenter operators. In this paper, we discuss Best Worst-case Routing (BWR), which aims at optimizing the tail completion times of long-running flows over inter-datacenter networks with non-uniform link capacities. Since finding the path with the best worst-case completion time for a new flow is NP-Hard, we investigate two heuristics, BWRH and BWRHF, which use two different upper bounds on the worst-case completion times for routing. We evaluate BWRH and BWRHF against several real WAN topologies and multiple traffic patterns. Although BWRH better models the BWR problem, BWRH and BWRHF show negligible difference across various system-wide performance metrics, while BWRHF being significantly faster. Furthermore, we show that compared to other popular routing heuristics, BWRHF can reduce the mean and tail flow completion times by over 1.5× and 2×, respectively.
BIBCO Training and Its Influence on PCC Partnerships

BIBCO is one of four programs in the PCC. This article summarizes the development of BIBCO and how its cataloging training program was strikingly different from the other three programs: NACO, CONS...
EMMA: An Emotion-Aware Wellbeing Chatbot

The delivery of mental health interventions via ubiquitous devices has shown much promise. A conversational chatbot is a promising oracle for delivering appropriate just-in-time interventions. However, designing emotionally-aware agents, specially in this context, is under-explored. Furthermore, the feasibility of automating the delivery of just-in-time mHealth interventions via such an agent has not been fully studied. In this paper, we present the design and evaluation of EMMA (EMotion-Aware mHealth Agent) through a two-week long human-subject experiment with N=39 participants. EMMA provides emotionally appropriate micro-activities in an empathetic manner. We show that the system can be extended to detect a user's mood purely from smartphone sensor data. Our results show that our personalized machine learning model was perceived as likable via self-reports of emotion from users. Finally, we provide a set of guidelines for the design of emotion-aware bots for mHealth.
Can Re-Enrollment Campaigns Help Dropouts Return to College? Evidence From Florida Community Colleges:

In this study, we develop and implement a text messaging re-enrollment campaign to encourage former community college students to return to college. Former students are randomly assigned to one of ...
Farming in the Face of Uncertainty: How Colombian Coffee Farmers Conceptualize and Communicate Their Experiences With Climate Change

Climate change is impacting agricultural systems around the globe, but little research has focused on how agricultural producers communicate their firsthand experiences with climate change impacts. Coffee, Colombia’s largest agricultural export (indirectly responsible for the livelihood of 2 million Colombians), is uniquely vulnerable to climate change. This study lays the groundwork for future adaptation communication efforts by analyzing 45 in-person, in-depth interviews of coffee farmers in Risaralda, Colombia. Dimensionalization, a grounded theory approach, is used to offer a theoretical data matrix to capture the major factors involved in Colombian farmers’ experiences with climate change from the farmers’ own perspective. The findings illustrate the conditions underlying Colombian coffee farmers’ belief that climate change impacts threaten their livelihoods and put farmers in a constant state of uncertainty.
Algorithms for optimal replica placement under correlated failure in hierarchical failure domains

Abstract In data centers, data replication is the primary method used to ensure availability of customer data. To avoid correlated failure, cloud storage infrastructure providers model hierarchical failure domains using a tree, and avoid placing a large number of data replicas within the same failure domain (i.e. on the same branch of the tree). Typical best practices ensure that replicas are distributed across failure domains, but relatively little is known concerning optimization algorithms for distributing data replicas. Using a hierarchical model, we answer how to distribute replicas across failure domains optimally. We formulate a novel optimization problem for replica placement in data centers. As part of our problem, we formalize and present a new criterion for optimizing a replica placement. Our overall goal is to choose placements in which correlated failures disable as few replicas as possible. In this work, we provide two optimization algorithms for dependency models represented by trees. We first present an O ( n + ρ log ⁡ ρ ) time dynamic programming algorithm for optimally placing ρ replicas of a single block on the leaves (representing servers) of a tree with n vertices. We next consider the problem of optimally placing replicas of multiple blocks of data, where every block may have a different replication factor. For this problem, we give a dynamic programming algorithm that runs in O ( n ρ max 3 δ 2 m poly ( δ ) ) , where m denotes the number of blocks, ρ max denotes the maximum replication factor of a block, and δ denotes the maximum difference in the replication factors of any two blocks. The running time of the algorithm is polynomial when the δ, which we refer to as the skew, is a constant.
Exact Distribution Optimal Power Flow (D-OPF) Model using Convex Iteration Technique

The distribution optimal power flow (D-OPF) models have gained attention in recent years to optimally operate a centrally-managed distribution grid. On account of nonconvex formulation that is difficult to solve, several relaxation methods have been proposed; the exactness of the solutions obtained from the relaxed models, however, remain a concern. In this paper, we identify one such problem related to radial distribution feeder where second-order cone program (SOCP) relaxation does not yield a solution that is feasible with respect to the original nonlinear OPF model. Specifically, we formulate an OPF model for PV hosting capacity problem to obtain maximum PV capacity that a feeder can integrate without violating the operating constraints. The SOCP relaxation for this problem yields infeasible solutions. To address this concern, we propose a convex iteration technique to simultaneously achieve optimal and feasible OPF solution for the PV hosting (maximization) problem. The proposed approach minimizes the feasibility gap with respect to original nonlinear constraints at each SOCP iteration. The effectiveness of the proposed approach is validated using IEEE-13 node and IEEE-123 node test systems.
Bulk and single-cell transcriptomics identify tobacco-use disparity in lung gene expression of ACE2, the receptor of 2019-nCov

Abstract In current severe global emergency situation of 2019-nCov outbreak, it is imperative to identify vulnerable and susceptible groups for effective protection and care. Recently, studies found that 2019-nCov and SARS-nCov share the same receptor, ACE2. In this study, we analyzed four large-scale datasets of normal lung tissue to investigate the disparities related to race, age, gender and smoking status in ACE2 gene expression. No significant disparities in ACE2 gene expression were found between racial groups (Asian vs Caucasian), age groups (>60 vs
Basal Ganglia Role in Learning Rewarded Actions and Executing Previously Learned Choices: Healthy and Diseased States

The basal ganglia (BG) is a collection of nuclei located deep beneath the cerebral cortex that is involved in learning and selection of rewarded actions. Here, we analyzed BG mechanisms that enable these functions. We implemented a rate model of a BG-thalamo-cortical loop and simulated its performance in a standard action selection task. We have shown that potentiation of corticostriatal synapses enables learning of a rewarded option. However, these synapses became redundant later as direct connections between prefrontal and premotor cortices (PFC-PMC) were potentiated by Hebbian learning. After we switched the reward to the previously unrewarded option (reversal), the BG was again responsible for switching to the new option. Due to the potentiated direct cortical connections, the system was biased to the previously rewarded choice, and establishing the new choice required a greater number of trials. Guided by physiological research, we then modified our model to reproduce pathological states of mild Parkinson’s and Huntington’s diseases. We found that in the Parkinsonian state PMC activity levels become extremely variable, which is caused by oscillations arising in the BG-thalamo-cortical loop. The model reproduced severe impairment of learning and predicted that this is caused by these oscillations as well as a reduced reward prediction signal. In the Huntington state, the potentiation of the PFC-PMC connections produced better learning, but altered BG output disrupted expression of the rewarded choices. This resulted in random switching between rewarded and unrewarded choices resembling an exploratory phase that never ended. Along with other computational studies, our results further reconcile the apparent contradiction between the critical involvement of the BG in execution of previously learned actions and yet no impairment of these actions after BG output is ablated by lesions or deep brain stimulation. We predict that the cortico-BG-thalamo-cortical loop conforms to previously learned choice in healthy conditions, but impedes those choices in disease states.
Detecting Network Soft-failures with the Network Link Outlier Factor (NLOF)

In this paper, we describe and experimentally evaluate the performance of our Network Link Outlier Factor (NLOF) for detecting soft-failures in communication networks. The NLOF is computed using the throughput values derived from NetFlow records. The flow throughput values are clustered in two stages, outlier values are determined within each cluster, and the flow outliers are used to compute the outlier factor or score for each network link. When sampling NetFlow records across the full span of a network, NLOF enables the detection of soft-failures across the span of the network; large NLOF scores correlate well with links experiencing failure.
Simultaneous Surface Reflectance and Fluorescence Spectra Estimation

There is widespread interest in estimating the fluorescence properties of natural materials in an image. However, the separation between reflected and fluoresced components is difficult, because it is impossible to distinguish reflected and fluoresced photons without controlling the illuminant spectrum. We show how to jointly estimate the reflectance and fluorescence from a single set of images acquired under multiple illuminants. We present a framework based on a linear approximation to the physical equations describing image formation in terms of surface spectral reflectance and fluorescence due to multiple fluorophores. We relax the non-convex, inverse estimation problem in order to jointly estimate the reflectance and fluorescence properties in a single optimization step. We provide a software implementation of the solver for our method and prior methods. We evaluate the accuracy and reliability of the method using both simulations and experimental data. To evaluate the methods experimentally we built a custom imaging system using a monochrome camera, a filter wheel with bandpass transmissive filters and a small number of light emitting diodes. We compared the methods based upon our framework with the ground truth as well as with prior methods.
Convergence of glandular trichome morphology and chemistry in two montane monkeyflower (Mimulus, Phrymaceae) species

Abstract Two distantly-related North American montane monkeyflower species, Mimulus lewisii and M. tilingii, possess glandular trichomes. In this study, we characterized the morphological and histochemical features of these glandular trichomes. For each species, we used traditional light microscopy and scanning electron microscopy (SEM) to examine morphological characteristics, and categorized the natural products on the leaf surface using histochemical and thin layer chromatography (TLC) staining techniques. We identified type VI capitate glandular trichomes on leaf surfaces in both species of monkeyflowers. These trichomes exhibited stalk-cell lengths and head-cell counts that varied across adaxial and abaxial leaf surfaces. Both species contained secretory products within the cuticle of the trichome head, which releases the subcuticular metabolites when ruptured. Histochemical tests of M. lewisii and M. tilingii indicated the presence of lipids and polysaccharides on the leaf surface. TLC plate staining further supported their presence, as well as UV active compounds and amines. The common morphology and chemistry of the glandular trichomes suggests an analogous response to similar environmental conditions in these evolutionary distant montane monkeyflowers.
A Control Lyapunov Perspective on Episodic Learning via Projection to State Stability

The goal of this paper is to understand the impact of learning on control synthesis from a Lyapunov function perspective. In particular, rather than consider uncertainties in the full system dynamics, we employ Control Lyapunov Functions (CLFs) as low-dimensional projections. To understand and characterize the uncertainty that these projected dynamics introduce in the system, we introduce a new notion: Projection to State Stability (PSS). PSS can be viewed as a variant of Input to State Stability defined on projected dynamics, and enables characterizing robustness of a CLF with respect to the data used to learn system uncertainties. We use PSS to bound uncertainty in affine control, and demonstrate that a practical episodic learning approach can use PSS to characterize uncertainty in the CLF for robust control synthesis.
Impact of left ventricular assist devices and heart transplants on acute myocardial infarction and heart failure mortality and readmission measures.

Background Concern has been raised about consequences of including patients with left ventricular assist device (LVAD) or heart transplantation in readmission and mortality measures. Methods We calculated unadjusted and hospital-specific 30-day risk-standardized mortality (RSMR) and readmission (RSRR) rates for all Medicare fee-for-service beneficiaries with a primary diagnosis of AMI or HF discharged between July 2010 and June 2013. Hospitals were compared before and after excluding LVAD and heart transplantation patients. LVAD indication was measured. Results In the AMI mortality (n = 506,543) and readmission (n = 526,309) cohorts, 1,166 and 1,016 patients received an LVAD while 3 and 2 had a heart transplantation, respectively. In the HF mortality (n = 1,015,335) and readmission (n = 1,254,124) cohorts, 789 and 931 received an LVAD, while 212 and 202 received a heart transplantation, respectively. Less than 2% of hospitals had either ≥6 patients who received an LVAD or, independently, had ≥1 heart transplantation. The AMI mortality and readmission cohorts used 1.8% and 2.8% of LVADs for semi-permanent/permanent indications, versus 73.8% and 78.0% for HF patients, respectively. The rest were for temporary/external indications. In the AMI cohort, RSMR for hospitals without LVAD patients versus hospitals with ≥6 LVADs was 14.8% and 14.3%, and RSRR was 17.8% and 18.3%, respectively; the HF cohort RSMR was 11.9% and 9.7% and RSRR was 22.6% and 23.4%, respectively. In the AMI cohort, RSMR for hospitals without versus with heart transplantation patients was 14.7% and 13.9% and RSRR was 17.8% and 17.7%, respectively; in the HF cohort, RSMR was 11.9% and 11.0%, and RSRR was 22.6% and 22.6%, respectively. Estimations changed ≤0.1% after excluding LVAD or heart transplantation patients. Conclusion Hospitals caring for ≥6 patients with LVAD or ≥1 heart transplantation typically had a trend toward lower RSMRs but higher RSRRs. Rates were insignificantly changed when these patients were excluded. LVADs were primarily for acute-care in the AMI cohort and chronic support in the HF cohort. LVAD and heart transplantation patients are a distinct group with differential care requirements and outcomes, thus should be considered separately from the rest of the HF cohort.
Good for managers, bad for society? Causal evidence on the association between risk-taking incentives and corporate social responsibility

Using FAS 123R as an exogenous shock to stock options, I provide evidence that equity‐based risk‐taking incentives discourage corporate social responsibility (CSR). This finding suggests that compensation incentives can motivate managers not to pursue CSR strategies because CSR reduces firms’ risk and provides insurance‐like benefits. Firms with a greater demand for CSR's risk reduction are more sensitive to changes in risk‐taking incentives. I triangulate my results by confirming that CSR weaknesses are positively related to subsequent stock return volatility. Overall, using a robust empirical design, I find that risk‐taking incentives are a determinant of firms’ CSR.
Principles of RNA processing from analysis of enhanced CLIP maps for 150 RNA binding proteins

A critical step in uncovering rules of RNA processing is to study the in vivo regulatory networks of RNA binding proteins (RBPs). Crosslinking and immunoprecipitation (CLIP) methods enable mapping RBP targets transcriptome-wide, but methodological differences present challenges to large-scale analysis across datasets. The development of enhanced CLIP (eCLIP) enabled the mapping of targets for 150 RBPs in K562 and HepG2, creating a unique resource of RBP interactomes profiled with a standardized methodology in the same cell types. Our analysis of 223 eCLIP datasets reveals a range of binding modalities, including highly resolved positioning around splicing signals and mRNA untranslated regions that associate with distinct RBP functions. Quantification of enrichment for repetitive and abundant multicopy elements reveals 70% of RBPs have enrichment for non-mRNA element classes, enables identification of novel ribosomal RNA processing factors and sites, and suggests that association with retrotransposable elements reflects multiple RBP mechanisms of action. Analysis of spliceosomal RBPs indicates that eCLIP resolves AQR association after intronic lariat formation, enabling identification of branch points with single-nucleotide resolution, and provides genome-wide validation for a branch point-based scanning model for 3′ splice site recognition. Finally, we show that eCLIP peak co-occurrences across RBPs enable the discovery of novel co-interacting RBPs. This work reveals novel insights into RNA biology by integrated analysis of eCLIP profiling of 150 RBPs with distinct functions. Further, our quantification of both mRNA and other element association will enable further research to identify novel roles of RBPs in regulating RNA processing.
NEARLY OPTIMAL TEST FOR LONG-RUN PREDICTABILITY WITH NEARLY INTEGRATED REGRESSORS

We develop a method for long-run predictability testing in series Y by a persistent series X. We consider a class of tests based on the long-run behavior of these series that are robust to short-run dynamics and attempt to attain the highest possible power. The test is based on the Whittle approximation to the likelihood ratio that is adjusted to remain accurate across a range of persistence in X. We verify the properties of this test in small simulations and compare this test against a group of recently proposed methods.
Growth Cycles and Market Crashes

Abstract Market booms are often followed by dramatic falls. To explain this requires an asymmetry in the underlying shocks. A straightforward model of technological progress generates asymmetries that are also the source of growth cycles. Assuming a representative consumer, we show that the stock market generally rises, punctuated by occasional dramatic falls. With high risk aversion, bad news causes dramatic increases in prices. Bad news does not correspond to a contraction of existing production possibilities, but to a slowdown in its expansion. This economy provides a model of endogenous growth cycles in which recoveries and recessions are dictated by the adoption of innovations. Journal of Economic Literature Classification Numbers: O40, G12, O41, O30.
STyLuS*: A Temporal Logic Optimal Control Synthesis Algorithm for Large-Scale Multi-Robot Systems:

This article proposes a new highly scalable and asymptotically optimal control synthesis algorithm from linear temporal logic specifications, called STyLuS* for large-Scale optimal Temporal Logic S...
The Cost of Labor Adjustment: Inferences from the Gap

Abstract This paper studies the aggregate implications of micro-level labor adjustment costs. Caballero and Engel [Caballero, R., Engel, E., 1993. Microeconomic adjustment hazards and aggregate dynamics. Quarterly Journal of Economics 108, 313–358] find a dependence of aggregate employment growth on the cross sectional distribution of “employment gaps.” This paper uses those results as moments in an indirect inference procedure to infer the underlying labor adjustment costs. We specify a dynamic optimization problem at the plant level, allowing for both convex and non-convex adjustment costs. Consistent with evidence at the micro level, our findings indicate that non-convex adjustment costs are necessary to match these aggregate moments.
Well-posedness for the linearized motion of an incompressible liquid with free surface boundary

We study the problem of the motion of the free surface of a liquid. We prove existence and stability for the linearized equations.
Economic freedom and migration: A metro area-level analysis

We examine the determinants of intra‐U.S. population migration at the metropolitan area level (MSA), with an emphasis on the presence of policies that are consistent with economic freedom. We are the first to produce a multivariate regression analysis of migration and economic freedom at the local level. Combining a 1993–2014 unbalanced panel of MSA‐to‐MSA migration data from the Internal Revenue Service with a new economic freedom index for U.S. metropolitan areas, we find that a 10% increase in economic freedom of a destination MSA, relative to the economic freedom of an origin MSA, was associated with a 27.4% increase in net migration from the origin MSA to the destination MSA. If we use mean net migration flows as a benchmark, we would expect a 10% increase in relative economic freedom to increase net migration to the destination MSA by 22 workers per year from each other MSA.
Programming Substrate-Independent Kinetic Barriers With Thermodynamic Binding Networks

Engineering molecular systems that exhibit complex behavior requires the design of kinetic barriers. For example, an effective catalytic pathway must have a large barrier when the catalyst is absent. While programming such energy barriers seems to require knowledge of the specific molecular substrate, we develop a novel substrate-independent approach. We extend the recently-developed model known as thermodynamic binding networks, demonstrating programmable kinetic barriers that arise solely from the thermodynamic driving forces of bond formation and the configurational entropy of forming separate complexes. Our kinetic model makes relatively weak assumptions, which implies that energy barriers predicted by our model would exist in a wide variety of systems and conditions. We demonstrate that our model is robust by showing that several variations in its definition result in equivalent energy barriers. We apply this model to design catalytic systems with an arbitrarily large energy barrier to uncatalyzed reactions. Our results could yield robust amplifiers using DNA strand displacement, a popular technology for engineering synthetic reaction pathways, and suggest design strategies for preventing undesired kinetic behavior in a variety of molecular systems.
Mariner 6 and 7 television pictures: preliminary analysis.

Before the space era, Mars was thought to be like the earth; after Mariner 4, Mars seemed to be like the moon; Mariners 6 and 7 have shown Mars to have its own distinctive features, unknown elsewhere within the solar system.
Use of nanosphere self-assembly to pattern nanoporous membranes for the study of extracellular vesicles

Nanoscale biocomponents naturally released by cells, such as extracellular vesicles (EVs), have recently gained interest due to their therapeutic and diagnostic potential. Membrane based isolation and co-culture systems have been utilized in an effort to study EVs and their effects. Nevertheless, improved platforms for the study of small EVs are still needed. Suitable membranes, for isolation and co-culture systems, require pore sizes to reach into the nanoscale. These pore sizes cannot be achieved through traditional lithographic techniques and conventional thick nanoporous membranes commonly exhibit low permeability. Here we utilized nanospheres, similar in size and shape to the targeted small EVs, as patterning features for the fabrication of freestanding SiN membranes (120 nm thick) released in minutes through a sacrificial ZnO layer. We evaluated the feasibility of separating subpopulation of EVs based on size using these membranes. The membrane used here showed an effective size cut-off of 300 nm with the majority of the EVs ≤200 nm. This work provides a convenient platform with great potential for studying subpopulations of EVs.
Absolutely robust controllers for chemical reaction networks.

In this work, we design a type of controller that consists of adding a specific set of reactions to an existing mass-action chemical reaction network in order to control a target species. This set ...
Age-related Changes in the Neural Dynamics of Bottom-Up and Top-Down Processing During Visual Object Recognition: An Electrophysiological Investigation

When recognizing objects in our environments, we rely on both what we see and what we know. While older adults often display increased sensitivity to top-down influences of contextual information during object recognition, the locus of this increased sensitivity remains unresolved. To examine the effects of aging on the neural dynamics of bottom-up and top-down visual processing during rapid object recognition, we probed the differential effects of object perceptual ambiguity and scene context congruity on specific EEG event-related potential components indexing dissociable processes along the visual processing stream. Older adults displayed larger behavioral scene congruity effects than young adults. Older adults' larger visual P2 amplitudes to object perceptual ambiguity (as opposed to the scene congruity P2 effects in young adults) suggest continued resolution of perceptual ambiguity that interfered with scene congruity processing, while post-perceptual semantic integration (as indexed by N400) remained largely intact. These findings suggest that compromised bottom-up perceptual processing in healthy aging leads to an increased involvement of top-down processes to resolve greater perceptual ambiguity during object recognition.
Tobramycin Serum Concentrations in Tracheostomy-Dependent Children Receiving Inhaled Tobramycin

Introduction: In recent years, administration of inhaled aminoglycosides has gained popularity in tracheostomy-dependent pediatric patients because of medication delivery to the target site of acti...
Accurate measurement of in-plane thermal conductivity of layered materials without metal film transducer using frequency domain thermoreflectance

Measuring anisotropic thermal conductivity has always been a challenging task in thermal metrology. Although recent developments of pump-probe thermoreflectance techniques such as variable spot sizes, offset pump-probe beams, and elliptical beams have enabled the measurement of anisotropic thermal conductivity, a metal film transducer enabled for the absorption of the modulated pump laser beam and the detection of the thermoreflectance signal. However, the existence of the transducer would cause in-plane heat spreading, suppressing the measurement sensitivity to the in-plane thermal conductivity. In addition, the transducer film also adds complexity to data processing, since it requires careful calibration or fitting to determine extra parameters such as the film thickness and conductivity, and interface conductance between the transducer and the sample. In this work, we discussed the methodology for measuring in-plane thermal conductivity of layered semiconductors and semimetals without any transducer layer. We show that the removal of transducer results in the dominantly large sensitivity to in-plane thermal conductivity compared with other parameters, such as cross-plane thermal conductivity and the absorption depth of the laser beams. Transducerless frequency-domain thermoreflectance (FDTR) measurements are performed on three reference layered-materials, highly ordered pyrolytic graphite, molybdenum disulfide (MoS2), and bismuth selenide (Bi2Se3) and demonstrated using the analytical thermal model that the measured in-plane thermal conductivity showed much-improved accuracy compared with conventional FDTR measurement with a transducer.
Faster parallel algorithm for approximate shortest path

We present the first m polylog(n) work, polylog(n) time algorithm in the PRAM model that computes (1+є)-approximate single-source shortest paths on weighted, undirected graphs. This improves upon the breakthrough result of Cohen [JACM’00] that achieves O(m 1+є0 ) work and polylog(n) time. While most previous approaches, including Cohen’s, leveraged the power of hopsets, our algorithm builds upon the recent developments in continuous optimization, studying the shortest path problem from the lens of the closely-related minimum transshipment problem. To obtain our algorithm, we demonstrate a series of near-linear work, polylogarithmic-time reductions between the problems of approximate shortest path, approximate transshipment, and l1-embeddings, and establish a recursive algorithm that cycles through the three problems and reduces the graph size on each cycle. As a consequence, we also obtain faster parallel algorithms for approximate transshipment and l1-embeddings with polylogarithmic distortion. The minimum transshipment algorithm in particular improves upon the previous best m 1+o(1) work sequential algorithm of Sherman [SODA’17]. To improve readability, the paper is almost entirely self-contained, save for several staple theorems in algorithms and combinatorics.
Familywise Error Rate Control by Interactive Unmasking

We propose a method for multiple hypothesis testing with familywise error rate (FWER) control, called the i-FWER test. Most testing methods are predefined algorithms that do not allow modifications after observing the data. However, in practice, analysts tend to choose a promising algorithm after observing the data; unfortunately, this violates the validity of the conclusion. The i-FWER test allows much flexibility: a human (or a computer program acting on the human's behalf) may adaptively guide the algorithm in a data-dependent manner. We prove that our test controls FWER if the analysts adhere to a particular protocol of "masking" and "unmasking". We demonstrate via numerical experiments the power of our test under structured non-nulls, and then explore new forms of masking.