cssi/SciSciGPT-SciSciNet huggingface dataset:
Algorithmic Jim Crow

This Article contends that current immigration- and security-related vetting protocols risk promulgating an algorithmically driven form of Jim Crow. Under the “separate but equal” discrimination of a historic Jim Crow regime, state laws required mandatory separation and discrimination on the front end, while purportedly establishing equality on the back end. In contrast, an Algorithmic Jim Crow regime allows for “equal but separate” discrimination. Under Algorithmic Jim Crow, equal vetting and database screening of all citizens and noncitizens will make it appear that fairness and equality principles are preserved on the front end. Algorithmic Jim Crow, however, will enable discrimination on the back end in the form of designing, interpreting, and acting upon vetting and screening systems in ways that result in a disparate impact. Currently, security-related vetting protocols often begin with an algorithm-anchored technique of biometric identification — for example, the collection and database screening of scanned fingerprints and irises, digital photographs for facial recognition technology, and DNA. Immigration reform efforts, however, call for the biometric data collection of the entire citizenry in the United States to enhance border security efforts and to increase the accuracy of the algorithmic screening process. Newly developed big data vetting tools fuse biometric data with biographic data and internet and social media profiling to algorithmically assess risk. This Article concludes that those individuals and groups disparately impacted by mandatory vetting and screening protocols will largely fall within traditional classifications — race, color, ethnicity, national origin, gender, and religion. Disparate-impact consequences may survive judicial review if based upon threat risk assessments, terroristic classifications, data-screening results deemed suspect, and characteristics establishing anomalous data and perceived foreignness or dangerousness data — nonprotected categories that fall outside of the current equal protection framework. Thus, Algorithmic Jim Crow will require an evolution of equality law.
Gender and youth employment in Sub-Saharan Africa : a review of constraints and effective interventions

Although the ratio of female to male labor force participation rates is higher in Sub-Saharan Africa than in any other region, these high rates of female labor force participation mask underlying challenges for women. A large majority of employed women work in vulnerable employment. In addition, youth unemployment rates in Sub-Saharan Africa are double those of adult unemployment, and unemployment rates for women are higher than rates faced by men. This paper discusses the specific barriers that youth face in accessing employment in Sub-Saharan Africa, and the ways in which young women's employment is constrained above and beyond the constraints faced by male youth. The paper synthesizes the emerging lessons from a growing evidence base on interventions that aim to support young women's employment, and identifies knowledge gaps and priority research questions for the future. The objective is to develop a gender-informed policy and research agenda on youth employment that can guide practitioners, development partners, and researchers who seek to advance young women's empowerment and employment in the context of youth employment programming and policy making.
Novel gene expression patterns associated with progression and adverse outcome in high-grade gliomas

900 Background: The objective of this study was to identify genes and pathways deregulated within high grade gliomas, specifically with regards to survival, histological grade and pathological features. Although previous reports have provided a list of individual genes associated with progression and outcome, the present study extends these findings by identifying larger molecular pathways associated with adverse outcome and glioma progression. Methods: Thirty-five prospectively-collected high-grade glioma specimens [WHO grade III/IV (AA)and IV/IV (GBM), respectively] were “snap frozen” at time of surgical resection under an IRB-approved protocol. Specimens included in this study were found to represent at least 95% tumor. Isolated mRNA was converted to cDNA and subsequently hybridized to human U133A arrays (Affymetrix). Classification, prediction, differential expression and co-expression were analyzed using Gene Pattern . Pathway information was analyzed using Gene Set Enrichment Analysis (GSEA). Results: We found that GBMs were enriched with pathways related to immune and inflammatory response, as compared to AA. Interestingly, many of these pathways consist of immune cell surface proteins. In comparison, AA was enriched with pathways associated with cell growth and survival. The comparison between de novo and progressive GBM resembled that of the GBM versus AA comparison. De novo GBMs were enriched with pathways involved in antigen-specific immune response. The AKT pathway was the highest ranked pathway enriched in the progressive GBM. Further, we have identified specific molecular pathways that are involved in adverse clinical outcome in high-grade gliomas. As many of these patients have been treated by radiation as the only adjuvant therapy, we also have identified specific molecular pathways that may play a vital role in radiation resistance. Conclusions: Overall, it seems as though GBM, especially those which are de novo , display characteristics of stress, immune and inflammatory response. The progressive GBMs and AAs seem to be typified by deregulation of growth and survival pathways, such as AKT and RAS. This may be a result of a general progression or due to different molecular etiology. We will further describe molecular pathways associated with adverse outcome and radiation resistance in high-grade gliomas.
Does State Community Benefits Regulation Influence Charity Care and Operational Efficiency in U.S. Non-profit Hospitals?

Using a comprehensive sample of U.S. non-profit hospitals from 2011 to 2015, we examine the effects of state community benefits regulation (CBR) on the amount of charity care provided by and the operational efficiency of U.S. non-profit hospitals. First, we document that, under such regulations, non-profit hospitals provide more charity care and less compensated care as a proportion of net revenue. We infer from these findings that CBR has the potential to increase both non-profit hospitals’ amount of charity care and their efficiency of operations. Second, by examining variation in CBR types, we find no differences between having provision or having reporting requirements on the amount of charity care offered. Moreover, when we consider CBR with both provision and reporting requirements, the combination of these two requirements does not incrementally enhance charity care offerings, suggesting that the requirements may serve as substitutes with comparable effect. Lastly, we show that several state-level characteristics influence the relationship between CBR and charity care: CBRs in states with a higher gross domestic product and percentage of revenue received in taxes have incrementally lower associations with charity care, while hospitals in states where populations have, on average, higher household incomes have greater associations between CBR and charity care. Our findings highlight the effects that state-enacted regulation can have on socially beneficial behavior by non-profits.
Activation State-Dependent Substrate Gating in Ca2+/Calmodulin-Dependent Protein Kinase II.

Calcium/calmodulin-dependent protein kinase II (CaMKII) is highly concentrated in the brain where its activation by the Ca2+ sensor CaM, multivalent structure, and complex autoregulatory features make it an ideal translator of Ca2+ signals created by different patterns of neuronal activity. We provide direct evidence that graded levels of kinase activity and extent of T287 (T286 α isoform) autophosphorylation drive changes in catalytic output and substrate selectivity. The catalytic domains of CaMKII phosphorylate purified PSDs much more effectively when tethered together in the holoenzyme versus individual subunits. Using multisubstrate SPOT arrays, high-affinity substrates are preferentially phosphorylated with limited subunit activity per holoenzyme, whereas multiple subunits or maximal subunit activation is required for intermediate- and low-affinity, weak substrates, respectively. Using a monomeric form of CaMKII to control T287 autophosphorylation, we demonstrate that increased Ca2+/CaM-dependent activity for all substrates tested, with the extent of weak, low-affinity substrate phosphorylation governed by the extent of T287 autophosphorylation. Our data suggest T287 autophosphorylation regulates substrate gating, an intrinsic property of the catalytic domain, which is amplified within the multivalent architecture of the CaMKII holoenzyme.
Effects of El Niño on Summertime Ozone Air Quality in the Eastern United States

We investigate the effect of El Nino on maximum daily 8-hour average surface ozone over the eastern United States in summer during 1980-2016. El Nino can influence the extra-tropical climate through the propagation of stationary waves, leading to (1) reduced transport of moist, clean air into the mid- and southern Atlantic states and greater subsidence, reduced precipitation, and increased surface solar radiation in this region, as well as (2) intensified southerly flow into the south central states, which here enhances flux of moist and clean air. As a result, each standard deviation increase in the Nino 1+2 index is associated with an increase of 1-2 ppbv ozone in the Atlantic states and a decrease of 0.5-2 ppbv ozone in the south central states. These influences can be predicted 4 month in advance. We show that U.S. summertime ozone responds differently to eastern-type El Nino events compared to central-type events.
Volumetric comparison of hippocampal subfields extracted from 4-minute accelerated vs. 8-minute high-resolution T2-weighted 3T MRI scans.

The hippocampus has been widely studied using neuroimaging, as it plays an important role in memory and learning. However, hippocampal subfield information is difficult to capture by standard magnetic resonance imaging (MRI) techniques. To facilitate morphometric study of hippocampal subfields, ADNI introduced a high resolution (0.4 mm in plane) T2-weighted turbo spin-echo sequence that requires 8 min. With acceleration, the protocol can be acquired in 4 min. We performed a comparative study of hippocampal subfield volumes using standard and accelerated protocols on a Siemens Prisma 3T MRI in an independent sample of older adults that included 10 cognitively normal controls, 9 individuals with subjective cognitive decline, 10 with mild cognitive impairment, and 6 with a clinical diagnosis of Alzheimer’s disease (AD). The Automatic Segmentation of Hippocampal Subfields (ASHS) software was used to segment 9 primary labeled regions including hippocampal subfields and neighboring cortical regions. Intraclass correlation coefficients were computed for reliability tests between 4 and 8 min scans within and across the four groups. Pairwise group analyses were performed, covaried for age, sex and total intracranial volume, to determine whether the patterns of group differences were similar using 4 vs. 8 min scans. The 4 and 8 min protocols, analyzed by ASHS segmentation, yielded similar volumetric estimates for hippocampal subfields as well as comparable patterns of differences between study groups. The accelerated protocol can provide reliable imaging data for investigation of hippocampal subfields in AD-related MRI studies and the decreased scan time may result in less vulnerability to motion.
Simultaneous two-photon imaging and two-photon optogenetics of cortical circuits in three dimensions.

Modern microscopy provides a window into the brain. The first light microscopes were able to magnify cells only in thin slices of tissue. By contrast, today’s light microscopes can image cells below the surface of the brain of a living animal. Even so, this remains challenging for several reasons. One is that the brain is three-dimensional. Another is that brain tissue scatters light. Trying to view neurons deep within the brain is a little like trying to view them through a glass of milk. Most of the light scatters on its way through the tissue with the result that little of the light reaches the target neurons. Yang et al. have now tackled these challenges using a technique called holography. Holography produces 3D images of objects by splitting a beam of light and then recombining the beams in a specific way. Yang et al. applied this technique to an infrared laser beam, opting for infrared because it scatters much less in brain tissue than visible light. Directing each of the infrared beams to a different neuron can produce 3D images of multiple cells within the brain’s outer layer, the cortex, all at the same time. The holographic infrared microscope can be used alongside two techniques called optogenetics and calcium imaging, in which light-sensitive proteins are inserted into neurons. Depending on the proteins introduced, shining light onto the neurons will either change their activity, or cause them to fluoresce whenever they are active. Just as a computer can both “read” and “write” data, the holographic microscope can thus read out existing neuronal activity or write new patterns of activity. By combining these techniques, Yang et al. were able to stimulate more than 80 neurons at the same time – and meanwhile visualize the activity of the surrounding neurons – at multiple depths within the mouse cortex. This new microscopy technique, while a clear advance over existing methods, still cannot image and control neurons throughout the entire cortex. The next goal is to further extend this method across multiple brain areas and manipulate the activity of any subset of neurons at will. Neuroscientists will greatly benefit from the ability to image and alter the activity of living neural circuits in 3D. In the future, clinicians may be able to use this technique to treat brain disorders by adjusting the activity of abnormal neural circuits.
Choriocapillaris Loss in Advanced Age-Related Macular Degeneration

The purpose of this review is to summarize the current knowledge on choriocapillaris loss in advanced age macular degeneration (AMD). Several histopathological studies in animal models and human eyes had showed that the choriocapillaris density decreases with age. However, the role of choriocapillaris loss is still unclear in AMD and its advanced forms, either choroidal neovascularization (CNV) or geographic atrophy (GA). Some authors have hypothesized that choriocapillaris loss might precede overt retinal pigment epithelium atrophy. Others have hypothesized that deposition of complement complexes on and around the choriocapillaris could be related to the tissue loss observed in early AMD. The development of imaging modalities, such as optical coherence tomography angiography (OCTA), have led to a better understanding of underlying physiopathological mechanisms in AMD. OCTA showed atrophy of choriocapillaris underneath and beyond the region of photoreceptors and RPE loss, in agreement with previous histopathologic studies. The evolution of OCTA technology suggests that CNV seems to originate from regions of severe choriocapillaris alteration. Significant progress has been made in the understanding of development and progression of GA and CNV. In vivo investigation of the choriocapillaris using OCTA may lead to new insights related to underlying disease mechanisms in AMD.
Letter from a Law Teacher

This essay is an actual response to a new teacher asking about my approach in the classroom. More than seven years on the job taught me that quality teaching depends on much more than subject-matter expertise and a good PowerPoint.
Tmem16F forms a Ca2+-Activated Cation Channel Required for Lipid Scrambling in Platelets during Blood Coagulation

Collapse of membrane lipid asymmetry is a hallmark of blood coagulation. TMEM16F of the TMEM16 family that includes TMEM16A/B Ca2+-activated Cl- channels (CaCCs) is linked to Scott Syndrome with deficient Ca2+-dependent lipid scrambling. We generated TMEM16F-knockout mice that exhibit bleeding defects and protection in an arterial thrombosis model associated with platelet deficiency in Ca2+-dependent phosphatidylserine exposure and procoagulant activity, and lack a Ca2+-activated cation current in the platelet precursor megakaryocytes. Heterologous expression of TMEM16F generates a novel Small-conductance Ca2+-Activated Non-selective cation (SCAN) current with sub-picosiemens single channel conductance rather than a CaCC. TMEM16F-SCAN channels permeate both monovalent and divalent cations including Ca2+, and exhibit synergistic gating by Ca2+ and voltage. We further pinpointed a residue in the putative pore region important for the cation versus anion selectivity of TMEM16F-SCAN and TMEM16A-CaCC channels. This study thus identifies a novel Ca2+-activated channel permeable to Ca2+ and critical for Ca2+-dependent scramblase activity.
Crowdsourcing Control: Moving Beyond Multiple Choice.

To ensure quality results from crowdsourced tasks, requesters often aggregate worker responses and use one of a plethora of strategies to infer the correct answer from the set of noisy responses. However, all current models assume prior knowledge of all possible outcomes of the task. While not an unreasonable assumption for tasks that can be posited as multiple-choice questions (e.g. n-ary classification), we observe that many tasks do not naturally fit this paradigm, but instead demand a free-response formulation where the outcome space is of infinite size (e.g. audio transcription). We model such tasks with a novel probabilistic graphical model, and design and implement LAZYSUSAN, a decision-theoretic controller that dynamically requests responses as necessary in order to infer answers to these tasks. We also design an EM algorithm to jointly learn the parameters of our model while inferring the correct answers to multiple tasks at a time. Live experiments on Amazon Mechanical Turk demonstrate the superiority of LAZYSUSAN at solving SAT Math questions, eliminating 83.2% of the error and achieving greater net utility compared to the state-of-the-art strategy, majority-voting. We also show in live experiments that our EM algorithm outperforms majority-voting on a visualization task that we design.
The effectiveness of monetary policy in small open economies

Abstract This paper examines the relative effectiveness of the use of direct and indirect monetary policy instruments in Barbados, Jamaica and Trinidad and Tobago, by estimating a restricted Vector Autoregressive model with Exogenous Variables (VARX). The model captures the dynamic interaction of the key sectors in the economy and it accounts for the fact that the banking system in those countries is characterized by high levels of excess reserves. Also, the study assumes that the central bank conducts monetary policy using a Taylor-type rule, and it evaluates the effects of a reserve requirement policy. The results show that although a positive shock to the policy interest rate has a direct effect on commercial banks’ interest rates, there is a weak transmission to the real variables. Furthermore, an increase in the required reserve ratio is successful in reducing private sector credit and excess reserves, while at the same time alleviating pressures on the exchange rate. The findings therefore indicate that central banks in small open economies should consider using reserve requirements as a complement to interest rate policy, to achieve their macroeconomic objectives.
Credit risk dynamics of infrastructure investment : considerations for financial regulators

Prudential regulation of infrastructure investment plays an important role in creating an enabling environment for mobilizing long-term finance from institutional investors, such as insurance companies, and, thus, gives critical support to sustainable development. Infrastructure projects are asset-intensive and generate predictable and stable cash flows over the long term, with low correlation to other assets; hence they provide a natural match for insurers'liabilities-driven investment strategies. The historical default experience of infrastructure debt suggests a"hump-shaped"credit risk profile, which converges to investment grade quality within a few years after financial close -- supported by a consistently high recovery rate with limited cross-country variation in non-accrual events. However, the resilient credit performance of infrastructure -- also in emerging market and developing economies -- is not reflected in the standardized approaches for credit risk in most regulatory frameworks. Capital charges would decline significantly for a differentiated regulatory treatment of infrastructure debt as a separate asset class. Supplementary analysis suggests that also banks would benefit from greater differentiation, but only over shorter risk horizons, encouraging a more efficient allocation of capital by shifting the supply of long-term funding to insurers.
Understanding HANK: Insights From a PRANK

Using an analytically tractable heterogeneous agent New Keynesian model, we show that whether incomplete markets resolve New Keynesian “paradoxes” depends on the cyclicality of income risk. Incomplete markets reduce the effectiveness of forward guidance and multipliers in a liquidity trap only with procyclical risk. Countercyclical risk amplifies these “puzzles.” Procyclical risk permits determinacy under a peg; countercyclical risk may generate indeterminacy even under the Taylor principle. By affecting the cyclicality of risk, even “passive” fiscal policy influences the effects of monetary policy.
Characterizing MPI matching via trace-based simulation

Abstract With the increased scale expected on future leadership-class systems, detailed information about the resource usage and performance of MPI message matching provides important insights into how to maintain application performance on next-generation systems. However, obtaining MPI message matching performance data is often not possible without significant effort. A common approach is to instrument an MPI implementation to collect relevant statistics. While this approach can provide important data, collecting matching data at runtime perturbs the application’s execution, including its matching performance, and is highly dependent on the MPI library’s matchlist implementation. In this paper, we introduce a trace-based simulation approach to obtain detailed MPI message matching performance data for MPI applications without perturbing their execution. Using a number of key parallel workloads and microbenchmarks, we demonstrate that this simulator approach can rapidly and accurately characterize matching behavior. Specifically, we use our simulator to collect several important statistics about the operation of the MPI posted and unexpected queues. For example, we present data about search lengths and the duration that messages spend in the queues waiting to be matched. Data gathered using this simulation-based approach have significant potential to aid hardware designers in determining resource allocation for MPI matching functions and provide application and middleware developers with insight into the scalability issues associated with MPI message matching.
Metformin does not reduce inflammation in diabetics with abdominal aortic aneurysm or at high risk of abdominal aortic aneurysm formation.

IntroductionThe protective effect of diabetes mellitus on abdominal aortic aneurysm formation and growth has been repeatedly observed in population studies but continues to be poorly understood. Ho...
Stochastic Learning for Sparse Discrete Markov Random Fields with Controlled Gradient Approximation Error.

We study the L1-regularized maximum likelihood estimator/estimation (MLE) problemfor discrete Markov random fields (MRFs), where efficient and scalable learning requires both sparse regularization and approximate inference. To address these challenges, we consider a stochastic learning framework called stochastic proximal gradient (SPG; Honorio 2012a, Atchade etal. 2014, Miasojedow and Rejchel 2016). SPG is an inexact proximal gradient algorithm [Schmidt et al., 2011], whose inexactness stems from the stochastic oracle (Gibbs sampling) for gradient approximation - exact gradient evaluation is infeasible in general due to the NP-hard inference problem for discrete MRFs [Koller and Friedman, 2009]. Theoretically, we provide novel verifiable bounds to inspect and control the quality of gradient approximation. Empirically, we propose the tighten asymptotically (TAY) learning strategy based on the verifiable bounds to boost the performance of SPG.
Tsunami Researchers Outline Steps for Better Data

A tsunami is a long wave. The length of the wave is much longer than its depth. A typical tsunami wave in deep water is several tens to hundreds of kilometers long. Hence even if such a wave propagated over a 4000- m-deep abyssal plain, its evolution would be strongly affected by the bottom bathymetry. This is not the case for storm-generated waves, which are typically less than 500 m long; waves whose length is less than twice their depth are not affected by the presence of the ocean bottom.
Spatiotemporal KSVD Dictionary Learning for Online Multi-target Tracking

In this paper, we present a new spatiotemporal discriminative KSVD dictionary algorithm (STKSVD) for learning target appearance in online multi-target tracking system. Different from other classification/recognition tasks (e.g. face, image recognition), learning target's appearance in online multi-target tracking is impacted by factors such as: posture/articulation changes, partial occlusion by background scene or other targets, background changes (human detection bounding box covers both human parts and part of the scene), etc. However, we observe that these variations occur gradually relative to spatial and temporal dynamics. We characterize the spatial and temporal information between target's samples through a new STKSVD appearance learning algorithm to better discriminate targets. Our STKSVD method is able to learn discriminative sparse code, linear classifier parameters, and minimize reconstruction error in single optimization system. Our appearance learning algorithm and tracking framework employs two different methods of calculating appearance similarity score in each stage of a two-stage association: a linear classifier in the first stage, and minimum residual errors in the second stage. The results tested using 2DMOT2015 dataset and its public Aggregated Channel Features (ACF) human detection for all comparisons show that our method outperforms the existing related learning methods.
On approximating (sparse) covering integer programs

We consider approximation algorithms for covering integer programs of the form min 〈c, x〉 over x ϵ Zn≤o s.t. Ax ≤ b and x ≥ d; where A ϵ Rm X n≤o, b ϵ Rm≤o, and c, d ϵ Rn≤o all have nonnegative entries. We refer to this problem as CIP, and the special case without the multiplicity constraints x ≤ d as CIP∞. These problems generalize the well-studied Set Cover problem. We make two algorithmic contributions.First, we show that a simple algorithm based on randomized rounding with alteration improves or matches the best known approximation algorithms for CIP and CIP∞ in a wide range of parameter settings, and these bounds are essentially optimal. As a byproduct of the simplicity of the alteration algorithm and analysis, we can derandomize the algorithm without any loss in the approximation guarantee or efficiency. Previous work by Chen, Harris and Srinivasan [13] which obtained near-tight bounds is based on a resampling-based randomized algorithm whose analysis is complex.Non-trivial approximation algorithms for CIP are based on solving the natural LP relaxation strengthened with knapsack cover (KC) inequalities [5, 26, 13]. Our second contribution is a fast (essentially near-linear time) approximation scheme for solving the strengthened LP with a factor of n speed up over the previous best running time [5]. To achieve this fast algorithm we combine recent work on accelerating the multiplicative weight update framework with a partially dynamic approach to the knapsack covering problem.Together, our contributions lead to near-optimal (deterministic) approximation bounds with near-linear running times for CIP and CIP∞.
Impure Impact Giving: Theory and Evidence

We present a new model of charitable giving where individuals regard out-of-pocket donations and the matches they induce as different. We show that match-price elasticities combine conventional pri...
Neoadjuvant chemotherapy is associated with a high rate of perioperative blood transfusion at the time of interval cytoreductive surgery.

The oncologic safety of allogeneic blood transfusion in ovarian cancer patients is unknow. We sought to determine the prevalence and oncologic safety of perioperative allogeneic blood transfusion during interval cytoreduction surgery among women receiving neoadjuvant chemotherapy for ovarian cancer. We utilized retrospective chart review to identify a cohort of patients undergoing interval cytoreduction at a large academic tertiary referral center. We compared outcomes in patients who were exposed to perioperative blood transfusion compared with patients who were not exposed. Our primary endpoint was progression free survival; our secondary endpoint was overall survival. Baseline clinical characteristics were collected for patients in each group. Sixty-six women were included in the final cohort of women undergoing interval cytoreductive surgery after NACT. A total of 51 women (77%) were exposed to allogeneic perioperative pRBC transfusion. Fifteen women (23%) were not exposed to transfusion. The baseline characteristics were generally well matched. Women who were not exposed to a perioperative blood transfusion were more likely to have a normalized CA125 prior to undergoing cytoreductive surgery. Preoperative hemoglobin concentration was lower in the transfusion group (10.5 g/dLvs 11.5 g/dL, p < 0.009). Perioperative transfusion was not associated with a significant difference in progression free survival (PFS = 7.6 months for transfused, 9.4 months for not transfused; log-rank test p = 0.4617). Similarly, there was no observed difference between groups for overall survival (OS = 23.6 months for transfused, 22.5 months for not transfused; log-rank test p = 0.1723). Women undergoing neoadjuvant chemotherapy for ovarian cancer are at high risk of exposure to blood transfusion at the time of interval cytoreductive surgery. Future studies will continue to evaluate the safety and impact of transfusion on ovarian cancer survival in this at risk population.
Perturbations of Transcription and Gene Expression-Associated Processes Alter Distribution of Cell Size Values in Saccharomyces cerevisiae.

The question of what determines whether cells are big or small has been the focus of many studies because it is thought that such determinants underpin the coupling of cell growth with cell division. In contrast, what determines the overall pattern of how cell size is distributed within a population of wild type or mutant cells has received little attention. Knowing how cell size varies around a characteristic pattern could shed light on the processes that generate such a pattern and provide a criterion to identify its genetic basis. Here, we show that cell size values of wild type Saccharomyces cerevisiae cells fit a gamma distribution, in haploid and diploid cells, and under different growth conditions. To identify genes that influence this pattern, we analyzed the cell size distributions of all single-gene deletion strains in Saccharomyces cerevisiae. We found that yeast strains which deviate the most from the gamma distribution are enriched for those lacking gene products functioning in gene expression, especially those in transcription or transcription-linked processes. We also show that cell size is increased in mutants carrying altered activity substitutions in Rpo21p/Rpb1, the largest subunit of RNA polymerase II (Pol II). Lastly, the size distribution of cells carrying extreme altered activity Pol II substitutions deviated from the expected gamma distribution. Our results are consistent with the idea that genetic defects in widely acting transcription factors or Pol II itself compromise both cell size homeostasis and how the size of individual cells is distributed in a population.
Faculty perceptions of a tobacco cessation train-the-trainer workshop and experiences with implementation: A qualitative follow-up study.

Abstract Background Between 2003 and 2005, pharmacy faculty members (n = 191) participated in a national train-the-trainer workshop designed to equip faculty with the necessary knowledge and skills to implement a shared curriculum, Rx for Change: Clinician-Assisted Tobacco Cessation, at pharmacy schools across the United States. Objective To conduct a long-term, qualitative follow-up study of faculty participants to describe (a) perceptions of the train-the-trainer workshop, and (b) subsequent experiences with curricular implementation. Results of this investigation will inform a national survey of all train-the-trainer participants. Methods Participants were selected via random sampling from the group of 191 faculty members who participated in the workshop. Semi-structured telephone interviews with participants were audio-recorded and transcribed, and qualitative thematic analysis was conducted. Results Eighteen (62%) of 29 invited individuals participated in the interviews. All participants reported implementing components of Rx for Change at their institution. The analysis yielded eight major themes pertaining to faculty perceptions and experiences with implementation: (1) accessibility to tools for teaching, (2) increased confidence and skills, (3) flexibility delivering the curriculum, (4) factors facilitating implementation and challenges encountered by faculty, (5) enhancement in treating tobacco users in clinical practice, (6) students' confidence and cognizance of the pharmacists' role as a public health advocate, (7) networking and career development opportunities, and (8) useful background for research. Conclusion Participation in the train-the-trainer workshop increased self-reported confidence for teaching tobacco cessation, and faculty valued access to useful, updated tools for teaching. Furthermore, their newly acquired counseling skills were deemed helpful for treating patients' tobacco use and dependence in clinical practice. Participants also perceived improved pharmacy students' confidence and beneficial networking opportunities. Results can help future trainers understand faculty experiences with implementing a shared, national curriculum and inform faculty participants of some of the potential long-term outcomes as a result of participation.
Role of cash and costs of inflation for different income groups in the U.S.

Abstract Due to the adverse effects of the Great Recession, economic inequality has received much attention in the macroeconomic literature. While U.S. monetary policy set by the Federal Reserve does not seek to directly influence economic inequality by operating under the dual mandate, it has indirect consequences on economic inequality as well as important redistributional effects as transaction patterns and cash constraints are not evenly distributed for different income groups. We build a monetary growth model where money is introduced via a Cash-In-Advance (CIA) constraint. Our model successfully generates patterns of income, wealth, and earnings inequality in the U.S. and we use this framework to analyze the redistributional impact of long-run inflation. We observe significant redistributional effects as inflation acts as a regressive tax on consumption; it adversely affects households at the bottom of the income partition and benefits those at the top. This paper contributes to the recent and growing literature on the redistributive effects of monetary policy.
Cut polytope has vertices on a line

Abstract The cut polytope CUT(n) is the convex hull of the cut vectors in a complete graph with vertex set {1,…, n}. It is well known in the area of combinatorial optimization and recently has also been studied in a direct relation with admissible correlations of symmetric Bernoulli random variables. That probabilistic interpretation is a starting point of this work in conjunction with a natural binary encoding of the CUT(n). We show that for any n, with appropriate scaling, all encoded vertices of the polytope 1-CUT(n) are approximately on the line y = x − 1 / 2 .
Deformations of pairs (X, L) when X is singular

We give an elementary construction of the tangent-obstruction theory of the deformations of the pair $(X,L)$ with $X$ a reduced local complete intersection scheme and $L$ a line bundle on $X$. This generalizes the classical deformation theory of pairs in case $X$ is smooth. A criteria for sections of $L$ to extend is also given.
A simple genetic architecture and low constraint allow rapid floral evolution in a diverse and recently radiating plant genus.

Genetic correlations among different components of phenotypes, especially those resulting from pleiotropy, can constrain or facilitate trait evolution. These factors could especially influence the evolution of traits that are functionally integrated, such as those comprising the flower. Indeed, pleiotropy is proposed as a main driver of repeated convergent trait transitions, including the evolution of phenotypically similar pollinator syndromes. We assessed the role of pleiotropy in the differentiation of floral and other reproductive traits between two species - Jaltomata sinuosa and J. umbellata (Solanaceae) - that have divergent suites of floral traits consistent with bee and hummingbird pollination, respectively. To do so, we generated a hybrid population and examined the genetic architecture (trait segregation and quantitative trait locus (QTL) distribution) underlying 25 floral and fertility traits. We found that most floral traits had a relatively simple genetic basis (few, predominantly additive, QTLs of moderate to large effect), as well as little evidence of antagonistic pleiotropy (few trait correlations and QTL colocalization, particularly between traits of different classes). However, we did detect a potential case of adaptive pleiotropy among floral size and nectar traits. These mechanisms may have facilitated the rapid floral trait evolution observed within Jaltomata, and may be a common component of rapid phenotypic change more broadly.
Reducing Information Frictions in Venture Capital: The Role of New Venture Competitions

Abstract Venture capital, an important source of financing for potentially high-growth new businesses, is believed to suffer from information frictions. This paper quantifies the magnitude of these frictions among participants in new venture competitions. In a regression discontinuity design with data from 87 competitions, winning a round increases the chances of external financing by about 35%. Winning is most impactful for ventures that are ranked just above the cutoff but receive no cash prize, and judge ranks strongly predict venture success. The results indicate that these information problems in new venture finance are large, and competitions can help resolve them through certification.
Ten Years after the Financial Crisis: What Have We Learned from the Renaissance in Fiscal Research?

Author(s): Ramey, Valerie A | Abstract: This paper takes stock of what we have learned from the “Renaissance” in fiscal research in the ten years since the financial crisis. I first discuss the new innovations in methodology and various strengths and weaknesses of the main approaches to estimating fiscal multipliers. Reviewing the estimates, I come to the surprising conclusion that the bulk of the estimates for average spending and tax change multipliers lie in a fairly narrow range, 0.6 to 1 for spending multipliers and -2 to -3 for tax change multipliers. However, I identify economic circumstances in which multipliers lie outside those ranges. Finally, I review the debate on whether multipliers were higher for the 2009 Obama stimulus spending in the United States or for fiscal consolidations in Europe.
Classifying Treatment Responders Under Causal Effect Monotonicity

In the context of individual-level causal inference, we study the problem of predicting whether someone will respond or not to a treatment based on their features and past examples of features, treatment indicator (e.g., drug/no drug), and a binary outcome (e.g., recovery from disease). As a classification task, the problem is made difficult by not knowing the example outcomes under the opposite treatment indicators. We assume the effect is monotonic, as in advertising's effect on a purchase or bail-setting's effect on reappearance in court: either it would have happened regardless of treatment, not happened regardless, or happened only depending on exposure to treatment. Predicting whether the latter is latently the case is our focus. While previous work focuses on conditional average treatment effect estimation, formulating the problem as a classification task rather than an estimation task allows us to develop new tools more suited to this problem. By leveraging monotonicity, we develop new discriminative and generative algorithms for the responder-classification problem. We explore and discuss connections to corrupted data and policy learning. We provide an empirical study with both synthetic and real datasets to compare these specialized algorithms to standard benchmarks.
Employment inequality: Why do the low-skilled work less now?

Abstract Low-skilled prime-age men are less likely to be employed than high-skilled prime-age men, and the differential has increased since the 1970s. I build a search model encompassing three explanations: (1) automation and trade reduced the demand for low-skilled workers; (2) health, welfare, and recreational gaming/computer technology reduced the supply of low-skilled workers; and (3) factors affecting job search, such as online job boards, reduced frictions for high-skilled workers. I find a shift in demand away from low-skilled workers was the leading cause, a shift in supply had little effect, and search frictions actually reduced employment inequality.
Transnational legal methodology and domestic markets for food

Transnational legal scholars often describe how today transnational private ordering may evade, even weaken, state power. This article, by contrast, explores how transnational private ordering and ...
ARREST: A RSSI Based Approach for Mobile Sensing and Tracking of a Moving Object

We present Autonomous Rssi based RElative poSitioning and Tracking (ARREST), a new system for dynamic tracking of moving, RF-emitting object, referred to as the Leader, by a tracking robot that solely employs radio strength measurements. Our proposed tracking agent, referred to as the TrackBot, uses a single rotating, off-the-shelf, directional antenna, novel angle and relative speed estimation algorithms, and Kalman filtering to continually estimate the Leader's relative position with decimeter level accuracy (which is comparable to a multiple access point based RF-localization system) and the relative speed of the Leader with accuracy on the order of 1 m/s. The TrackBot feeds the relative position and speed estimates into a Linear Quadratic Gaussian controller (LQG) to generate a set of control outputs to control the orientation and the movement of the TrackBot. We perform an extensive set of real world experiments with a full-fledged prototype to demonstrate that the TrackBot is able to stay within 5m of the Leader with: (1) more than 99 percent probability in line of sight scenarios, and (2) more than 70 percent probability in no line of sight scenarios, when it moves 1.8X faster than the Leader.
NELEC at SemEval-2019 Task 3: Think Twice Before Going Deep.

Existing Machine Learning techniques yield close to human performance on text-based classification tasks. However, the presence of multi-modal noise in chat data such as emoticons, slang, spelling mistakes, code-mixed data, etc. makes existing deep-learning solutions perform poorly. The inability of deep-learning systems to robustly capture these covariates puts a cap on their performance. We propose NELEC: Neural and Lexical Combiner, a system which elegantly combines textual and deep-learning based methods for sentiment classification. We evaluate our system as part of the third task of ‘Contextual Emotion Detection in Text’ as part of SemEval-2019. Our system performs significantly better than the baseline, as well as our deep-learning model benchmarks. It achieved a micro-averaged F1 score of 0.7765, ranking 3rd on the test-set leader-board. Our code is available at https://github.com/iamgroot42/nelec
Comparison of the Standardized Video Interview and Interview Assessments of Professionalism and Interpersonal Communication Skills in Emergency Medicine

Objectives The Association of American Medical Colleges Standardized Video Interview (SVI) was recently added as a component of emergency medicine (EM) residency applications to provide additional information about interpersonal communication skills (ICS) and knowledge of professionalism (PROF) behaviors. Our objective was to ascertain the correlation between the SVI and residency interviewer assessments of PROF and ICS. Secondary objectives included examination of 1) inter- and intrainstitutional assessments of ICS and PROF, 2) correlation of SVI scores with rank order list (ROL) positions, and 3) the potential influence of gender on interview day assessments. Methods We conducted an observational study using prospectively collected data from seven EM residency programs during 2017 and 2018 using a standardized instrument. Correlations between interview day PROF/ICS scores and the SVI were tested. A one-way analysis of variance was used to analyze the association of SVI and ROL position. Gender differences were assessed with independent-groups t-tests. Results A total of 1,264 interview-day encounters from 773 unique applicants resulted in 4,854 interviews conducted by 151 interviewers. Both PROF and ICS demonstrated a small positive correlation with the SVI score (r = 0.16 and r = 0.17, respectively). ROL position was associated with SVI score (p < 0.001), with mean SVI scores for top-, middle-, and bottom-third applicants being 20.9, 20.5, and 19.8, respectively. No group differences with gender were identified on assessments of PROF or ICS. Conclusions Interview assessments of PROF and ICS have a small, positive correlation with SVI scores. These residency selection tools may be measuring related, but not redundant, applicant characteristics. We did not identify gender differences in interview assessments.