cssi/SciSciGPT-SciSciNet huggingface dataset:
Revisiting "In a Different Voice".

In 1982 Harvard University Press published Carol Gilligan’s landmark work, In a Different Voice, a book on psychological theory and women’s development, which sparked a heated discussion in the world of psychology. After listening to women speaking about themselves and about morality, Gilligan noticed that psychologists would study men and generalize to humans, and decided to explore the different voice she had heard. That became the basis of her book. In this interview she explains the origins of the book, how the book was received by the psychological community at the time, and why it had the impact that it did.
W-TALC: Weakly-Supervised Temporal Activity Localization and Classification

Most activity localization methods in the literature suffer from the burden of frame-wise annotation requirement. Learning from weak labels may be a potential solution towards reducing such manual labeling effort. Recent years have witnessed a substantial influx of tagged videos on the Internet, which can serve as a rich source of weakly-supervised training data. Specifically, the correlations between videos with similar tags can be utilized to temporally localize the activities. Towards this goal, we present W-TALC, a Weakly-supervised Temporal Activity Localization and Classification framework using only video-level labels. The proposed network can be divided into two sub-networks, namely the Two-Stream based feature extractor network and a weakly-supervised module, which we learn by optimizing two complimentary loss functions. Qualitative and quantitative results on two challenging datasets - Thumos14 and ActivityNet1.2, demonstrate that the proposed method is able to detect activities at a fine granularity and achieve better performance than current state-of-the-art methods.
The Emperor Has No Clothes? Searching for Dysregulation in Sepsis.

The core conception of sepsis—that it is a dysregulated state—is a powerful and durable idea that has inspired decades of research. But is it true that the body’s response to sepsis is dysregulated? To answer that question, this review surveyed the history of trials of experimental sepsis treatments targeting the host response. Sepsis survival is not improved by blocking one or many immune pathways. Similarly, sepsis is resistant to treatment by normalizing one or many physiologic parameters simultaneously. The vast majority of interventions are either ineffective or harmful. With this track record of failure, it is time to consider the alternative hypothesis—regulation instead of dysregulation—and the possibility that sepsis traits are often functional, and that some physiologic alterations in sepsis do more good than harm, while others are neutral. This review discusses the implications of this perspective for the future of sepsis research.
Inequality, Business Cycles, and Monetary-Fiscal Policy

We study optimal monetary and fiscal policy in a model with heterogeneous agents, incomplete markets, and nominal rigidities. We develop numerical techniques to approximate Ramsey plans and apply them to a calibrated economy to compute optimal responses of nominal interest rates and labor tax rates to aggregate shocks. Responses differ qualitatively from those in a representative agent economy and are an order of magnitude larger. Taylor rules poorly approximate the Ramsey optimal nominal interest rate. Conventional price stabilization motives are swamped by an across person insurance motive that arises from heterogeneity and incomplete markets.
Continuity and changes in classroom age composition and achievement in Head Start

Abstract Using data from the Family and Child Experiences Survey 2009 Cohort (n = 1073), this study considered the implications of mixed-age education for young children's academic achievement when they experienced continuity and/or changes in classroom age composition across two years in Head Start (at age 3 and age 4). Results from these analyses revealed that children in classrooms with a greater number of younger children during their second year in Head Start exhibited fewer gains in mathematics and language and literacy. Additionally, children who transitioned from being in classrooms with largely older classmates during year one to classrooms with largely same-age peers during year two exhibited greater gains in academics than children who experienced two years of mixed-age classrooms. Stability in children's teachers, one of the hallmarks of mixed-age programming, was not associated with children's academic achievement nor did it attenuate the negative consequences of mixed-age classrooms.
Local immunodeficiency: Minimal networks and stability

Abstract Some basic aspects of the recently discovered phenomenon of local immunodeficiency (Skums et al. [1]) generated by antigenic cooperation in cross-immunoreactivity (CR) networks are investigated. We prove that local immunodeficiency (LI) that is stable under perturbations already occurs in very small networks and under general conditions on their parameters. Therefore our results are applicable not only to Hepatitis C where CR networks are known to be large (Skums et al. [1]), but also to other diseases with CR. A major necessary feature of such networks is the non-homogeneity of their topology. It is also shown that one can construct larger CR networks with stable LI by using small networks with stable LI as their building blocks. Our results imply that stable LI occurs in networks with quite general topology. In particular, the scale-free property of a CR network, assumed in Skums et al. [1], is not required.
HNF1A is a novel oncogene that regulates human pancreatic cancer stem cell properties.

Pancreatic ductal adenocarcinoma is the most common form of pancreatic cancer. It is also one of the deadliest types of cancer: fewer than one in ten patients live for five years after being diagnosed with the disease. Several reasons can explain this poor outcome including that the cancer is often diagnosed late, when tumor cells have already spread, and that there are not many effective treatments for it. Pancreatic tumors contain different types of cancer cells with different properties. Among these are the so-called pancreatic cancer stem cells. These aggressive cells produce copies of themselves, contributing to tumor growth and spread. They can also help tumors to resist chemotherapy and radiotherapy. New treatments that specifically target cancer stem cells could therefore prove important for treating pancreatic cancer. It is still not clear what makes pancreatic cancer stem cells so aggressive, or how they differ from the rest of the cells in a tumor. Abel et al. therefore looked for proteins that were more abundant in human pancreatic cancer stem cells than in other, less aggressive cancer cells with the idea that these proteins are likely to be important for the behavior of the pancreatic cancer stem cells. Abel et al. found that a protein called HNF1A is enriched in pancreatic cancer stem cells. Experimentally reducing the levels of HNF1A in cells taken from human pancreatic cancers caused the cells to grow less well and form smaller tumors when injected into the pancreases of mice. These tumors contained few cancer stem cells, suggesting that HNF1A is important for maintaining the stem cell state. Further experiments showed that HNF1A increases the amount of many other proteins inside cells, including one that controls the activity of normal stem cells. Given the importance of HNF1A to pancreatic cancer stem cells, finding ways to prevent this protein from working could lead to new treatments for pancreatic cancer. At the moment there are no drugs that target HNF1A. Further research is therefore needed to develop new drugs that work against HNF1A or one of the other proteins that it affects.
Detecting Outliers in Data with Correlated Measures

Advances in sensor technology have enabled the collection of large-scale datasets. Such datasets can be extremely noisy and often contain a significant amount of outliers that result from sensor malfunction or human operation faults. In order to utilize such data for real-world applications, it is critical to detect outliers so that models built from these datasets will not be skewed by outliers. In this paper, we propose a new outlier detection method that utilizes the correlations in the data (e.g., taxi trip distance vs. trip time). Different from existing outlier detection methods, we build a robust regression model that explicitly models the outliers and detects outliers simultaneously with the model fitting. We validate our approach on real-world datasets against methods specifically designed for each dataset as well as the state of the art outlier detectors. Our outlier detection method achieves better performances, demonstrating the robustness and generality of our method. Last, we report interesting case studies on some outliers that result from atypical events.
Bimolecular Reaction Dynamics in the Phenyl-Silane System: Exploring the Prototype of a Radical Substitution Mechanism

We present a combined experimental and theoretical investigation of the bimolecular gas-phase reaction of the phenyl radical (C6H5) with silane (SiH4) under single collision conditions to investigate the chemical dynamics of forming phenylsilane (C6H5SiH3) via a bimolecular radical substitution mechanism at a tetracoordinated silicon atom. Verified by electronic structure and quasiclassical trajectory calculations, the replacement of a single carbon atom in methane by silicon lowers the barrier to substitution, thus defying conventional wisdom that tetracoordinated hydrides undergo preferentially hydrogen abstraction. This reaction mechanism provides fundamental insights into the hitherto unexplored gas-phase chemical dynamics of radical substitution reactions of mononuclear main group hydrides under single collision conditions and highlights the distinct reactivity of silicon compared to its isovalent carbon. This mechanism might be also involved in the synthesis of cyanosilane (SiH3CN) and methylsilane (CH3SiH3) probed in the circumstellar envelope of the carbon star IRC+10216.
Syntactic Scaffolds for Semantic Structures.

We introduce the syntactic scaffold, an approach to incorporating syntactic information into semantic tasks. Syntactic scaffolds avoid expensive syntactic processing at runtime, only making use of a treebank during training, through a multitask objective. We improve over strong baselines on PropBank semantics, frame semantics, and coreference resolution, achieving competitive performance on all three tasks.
Pinning and depinning: From periodic to chaotic and random media

We study the propagation of dissipative structures in inhomogeneous media with a focus on pinning and depinning transitions. We model spatial complexity in the medium as generated by dynamical systems. We are thus able to capture transitions from periodic to quasiperiodic, to homoclinic and heteroclinic, and to chaotic media. Depinning bifurcations exhibit universal laws depending on extreme value statistics that are encoded in the dimension of ergodic measures, only. A key condition limiting this approach bounds spatial Lyapunov exponents in terms of interface localization, and we explore the breakdown of smoothness and universality when this condition is violated and fluctuations in the medium occur on length scales shorter than a typical interface width.
Sexual Orientation and Gender Identity Discrimination

Laws concerning sexual orientation and gender identity ( SOGI ) have undergone a sea change. Still, legal protections against SOGI discrimination vary widely around the world. As jurisdictions wrestle with whether and how to protect people against SOGI discrimination, several conceptual questions emerge. This Brill volume reviews and discusses legal developments and scholarly commentary concerning these questions. Specifically, this volume examines the following five questions: (1) Is SOGI discrimination encompassed by existing laws prohibiting discrimination based on sex? (2) Should sexual orientation and gender identity be considered protected categories in and of themselves? (3) Is there a standard sequence of steps for developing legal protections against SOGI discrimination? (4) What are the drawbacks of developing SOGI discrimination protections? (5) To what extent should religious objections justify exemptions from SOGI discrimination bans?
BMP Signaling Determines Body Size via Transcriptional Regulation of Collagen Genes in Caenorhabditis elegans

Body size is a tightly regulated phenotype in metazoans that depends on both intrinsic and extrinsic factors. While signaling pathways are known to control organ and body size, the downstream effectors that mediate their effects remain poorly understood. In the nematode Caenorhabditis elegans, a Bone Morphogenetic Protein (BMP)-related signaling pathway is the major regulator of growth and body size. We investigated the transcriptional network through which the BMP pathway regulates body size and identified cuticle collagen genes as major effectors of growth control. We demonstrate that cuticle collagens can act as positive regulators (col-41), negative regulators (col-141), or dose-sensitive regulators (rol-6) of body size. Moreover, we find a requirement of BMP signaling for stage-specific expression of cuticle collagen genes. We show that the Smad signal transducers directly bind conserved Smad-binding elements in regulatory regions of col-141 and col-142, but not of col-41. Hence, cuticle collagen genes may be directly and indirectly regulated via the BMP pathway. Our work thus connects a conserved signaling pathway with its critical downstream effectors, advancing insight into how body size is specified. Since collagen mutations and misregulation are implicated in numerous human genetic disorders and injury sequelae, understanding how collagen gene expression is regulated has broad implications.
Fluorescence enhancement and quenching in tip-enhanced fluorescence spectroscopy

We study fluorescence enhancement for a dipole emitter placed between a tip and different dielectric substrates. Resonant behavior is observed as a function of tip-substrate distance for a silver tip placed above a glass substrate and displays a strong dependence on the radius of the curvature of the tip. By choosing appropriate sizes for the tip radius and the tip-substrate gap to optimize the competition between field enhancement and fluorescence quenching, we have found fluorescence enhancement exceeding three orders of magnitude can be achieved. The enhancement can be further improved by matching the silver tip with an appropriate dielectric substrate to resonantly excite gap plasmons. This is verified by comparing the matched silver tip-TiO2 substrate pairing with non-matched tip-dielectric substrate pairings. Compared with the large fluorescence enhancement observed in the case of a silver tip above a glass substrate, modest enhancement is obtained for a silicon tip and can be further improved by using a high dielectric material as the substrate. The optimal tip-enhanced fluorescence obtained in a silver tip-TiO2 substrate pairing may be useful in obtaining efficient fluorescence signal in the same setup used for tip-enhanced Raman spectroscopy.
A proteasome-resistant fragment of NIK mediates oncogenic NF-κB signaling in schwannomas

Schwannomas are common, highly morbid and medically untreatable tumors that can arise in patients with germ line as well as somatic mutations in neurofibromatosis type 2 (NF2). These mutations most commonly result in the loss of function of the NF2-encoded protein, Merlin. Little is known about how Merlin functions endogenously as a tumor suppressor and how its loss leads to oncogenic transformation in Schwann cells (SCs). Here, we identify nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB)-inducing kinase (NIK) as a potential drug target driving NF-κB signaling and Merlin-deficient schwannoma genesis. Using a genomic approach to profile aberrant tumor signaling pathways, we describe multiple upregulated NF-κB signaling elements in human and murine schwannomas, leading us to identify a caspase-cleaved, proteasome-resistant NIK kinase domain fragment that amplifies pathogenic NF-κB signaling. Lentiviral-mediated transduction of this NIK fragment into normal SCs promotes proliferation, survival, and adhesion while inducing schwannoma formation in a novel in vivo orthotopic transplant model. Furthermore, we describe an NF-κB-potentiated hepatocyte growth factor (HGF) to MET proto-oncogene receptor tyrosine kinase (c-Met) autocrine feed-forward loop promoting SC proliferation. These innovative studies identify a novel signaling axis underlying schwannoma formation, revealing new and potentially druggable schwannoma vulnerabilities with future therapeutic potential.
Cost-Sensitive Robustness against Adversarial Examples

Several recent works have developed methods for training classifiers that are certifiably robust against norm-bounded adversarial perturbations. These methods assume that all the adversarial transformations are equally important, which is seldom the case in real-world applications. We advocate for cost-sensitive robustness as the criteria for measuring the classifier's performance for tasks where some adversarial transformation are more important than others. We encode the potential harm of each adversarial transformation in a cost matrix, and propose a general objective function to adapt the robust training method of Wong & Kolter (2018) to optimize for cost-sensitive robustness. Our experiments on simple MNIST and CIFAR10 models with a variety of cost matrices show that the proposed approach can produce models with substantially reduced cost-sensitive robust error, while maintaining classification accuracy.
Sectoral Booms and Misallocation of Managerial Talent: Evidence from the Chinese Real Estate Boom

This paper identifies a new mechanism leading to inefficiency in capital reallocation at the extensive margin when an economy experiences a sectoral boom. I argue that imperfections in the financial market and capital barriers to entry in the booming sector create a misallocation of managerial talent. Using comprehensive firm-level data from China, I first provide evidence that more productive firms reallocate capital to the booming real estate sector, and demonstrate that the pattern is likely driven by fewer financial constraints on these firms. I then use a structural estimation to verify the talent misallocation. Finally, I calibrate a dynamic model and find that the without the misallocation, the TFP growth in the manufacturing sector would have improved by 0.5% per year.
A post-processing-free single-photon random number generator with ultra-low latency

The low-latency requirements of a practical loophole-free Bell test preclude time-consuming post-processing steps that are often used to improve the statistical quality of a physical random number generator (RNG). Here we demonstrate a post-processing-free RNG that produces a random bit within 2.4(2) ns of an input trigger. We use weak feedback to eliminate long-term drift, resulting in 24 hour operation with output that is statistically indistinguishable from a Bernoulli process. We quantify the impact of the feedback on the predictability of the output as less than 6.4×10−7 and demonstrate the utility of the Allan variance as a tool for characterizing non-idealities in RNGs.
Single-Cell Membrane Potential Fluctuations Evince Network Scale-Freeness and Quasicriticality.

What information single neurons receive about general neural circuit activity is a fundamental question for neuroscience. Somatic membrane potential fluctuations are driven by the convergence of synaptic inputs from a diverse cross-section of upstream neurons. Furthermore, neural activity is often scale-free implying that some measurements should be the same, whether taken at large or small scales. Together, convergence and scale-freeness support the hypothesis that single membrane potential recordings carry useful information about high-dimensional cortical activity. Conveniently, the theory of “critical branching networks” (one purported explanation for scale-freeness) provides testable predictions about scale-free measurements which are readily applied to membrane potential fluctuations. To investigate, we obtained whole-cell current clamp recordings of pyramidal neurons in visual cortex of turtles with unknown genders. We isolated fluctuations in membrane potential below the firing threshold and analyzed them by adapting the definition of “neuronal avalanches” (spurts of population spiking). The membrane potential fluctuations we analyzed were scale-free and consistent with critical branching. These findings recapitulated results from large-scale cortical population data obtained separately in complementary experiments using microelectrode arrays (previously published (Shew et al., 2015)). Simultaneously recorded single-unit local field potential did not provide a good match, demonstrating the specific utility of membrane potential. Modeling shows that estimation of dynamical network properties from neuronal inputs is most accurate when networks are structured as critical branching networks. In conclusion, these findings extend evidence of critical phenomena while also establishing subthreshold pyramidal neuron membrane potential fluctuations as an informative gauge of high-dimensional cortical population activity. SIGNIFICANCE STATEMENT The relationship between membrane potential dynamics of single neurons and population dynamics is indispensable to understanding cortical circuits. Just as important to the biophysics of computation are emergent properties such as scale-freeness, where critical branching networks offer insight. This report makes progress on both fronts by comparing statistics from single-neuron whole-cell recordings to population statistics obtained with microelectrode arrays. Not only are fluctuations of somatic membrane potential scale-free, they match fluctuations of population activity. Thus, our results demonstrate appropriation of the brain9s own subsampling method (convergence of synaptic inputs), while extending the range of fundamental evidence for critical phenomena in neural systems from the previously observed mesoscale (fMRI, LFP, population spiking) to the microscale, namely, membrane potential fluctuations.
Unsupervised Event-Based Learning of Optical Flow, Depth, and Egomotion

In this work, we propose a novel framework for unsupervised learning for event cameras that learns motion information from only the event stream. In particular, we propose an input representation of the events in the form of a discretized volume that maintains the temporal distribution of the events, which we pass through a neural network to predict the motion of the events. This motion is used to attempt to remove any motion blur in the event image. We then propose a loss function applied to the motion compensated event image that measures the motion blur in this image. We train two networks with this framework, one to predict optical flow, and one to predict egomotion and depths, and evaluate these networks on the Multi Vehicle Stereo Event Camera dataset, along with qualitative results from a variety of different scenes.
On universality of classical probability with contextually labeled random variables: Response to A. Khrennikov

Abstract In his constructive and well-informed commentary, Andrei Khrennikov acknowledges a privileged status of classical probability theory with respect to statistical analysis. He also sees advantages offered by the Contextuality-by-Default theory, notably, that it “demystifies quantum mechanics by highlighting the role of contextuality,” and that it can detect and measure contextuality in inconsistently connected systems. He argues, however, that classical probability theory may have difficulties in describing empirical phenomena if they are described entirely in terms of observable events. We disagree: contexts in which random variables are recorded are as observable as the variables’ values. Khrennikov also argues that the Contextuality-by-Default theory suffers the problem of non-uniqueness of couplings. We disagree that this is a problem: couplings are all possible ways of imposing counterfactual joint distributions on random variables that de facto are not jointly distributed. The uniqueness of modeling experiments by means of quantum formalisms brought up by Khrennikov is achieved for the price of additional, substantive assumptions. This is consistent with our view of quantum theory as a special-purpose generator of classical probabilities. Khrennikov raises the issue of “mental signaling,” by which he means inconsistent connectedness in behavioral systems. Our position is that it is as inherent to behavioral systems as their stochasticity.
Economics within ABC’s Modern Family

Modern Family debuted in 2009 and has since won 22 Emmys from 75 nominations. The show revolves around patriarch Jay Pritchett and his family. Characters on the show follow a mockumentary style by ...
A Non-Cooperative Game Theory Based Controller Tuning Method for Microgrid DC-DC Converters

PI controllers are widely used in converter control in Micro-grids. The PI controller tuning problem is usually modeled as a multi-objective optimization model because of its conflicting design specifications. This research introduces the idea of a new tuning method for PI controller of DC-DC converter in micro-grids based on non-cooperative game theory. Multi-objective optimization for the tuning problem will be transferred to a non-cooperative game. According to the solved Nash Equilibrium, best tuning parameters for the PI controller may be determined.
Uniformly low serum cobalt levels after modular dual-mobility total hip arthroplasties with ceramic heads: a prospective study in high-risk patients.

Aims Modular dual-mobility constructs reduce the risk of dislocation after revision total hip arthroplasty (THA). However, questions about metal ions from the cobalt-chromium (CoCr) liner persist, ...
Domain insertion permissibility-guided engineering of allostery in ion channels

Allostery is a fundamental principle of protein regulation that remains hard to engineer, particularly in membrane proteins such as ion channels. Here we use human Inward Rectifier K+ Channel Kir2.1 to map site-specific permissibility to the insertion of domains with different biophysical properties. We find that permissibility is best explained by dynamic protein properties, such as conformational flexibility. Several regions in Kir2.1 that are equivalent to those regulated in homologs, such as G-protein-gated inward rectifier K+ channels (GIRK), have differential permissibility; that is, for these sites permissibility depends on the structural properties of the inserted domain. Our data and the well-established link between protein dynamics and allostery led us to propose that differential permissibility is a metric of latent allosteric capacity in Kir2.1. In support of this notion, inserting light-switchable domains into sites with predicted latent allosteric capacity renders Kir2.1 activity sensitive to light. Allostery is a fundamental principle of protein regulation that remains challenging to engineer. Here authors screen human Inward Rectifier K + Channel Kir2.1 for permissibility to domain insertions and propose that differential permissibility is a metric of latent allosteric capacity in Kir2.1.
Regionally refined test bed in E3SM atmosphere model version 1 (EAMv1) and applications for high-resolution modeling

Abstract. Climate simulations with more accurate process-level representation at finer resolutions ( km) are a pressing need in order to provide more detailed actionable information to policy makers regarding extreme events in a changing climate. Computational limitation is a major obstacle for building and running high-resolution (HR, here 0.25 ∘ average grid spacing at the Equator) models (HRMs). A more affordable path to HRMs is to use a global regionally refined model (RRM), which only simulates a portion of the globe at HR while the remaining is at low resolution (LR, 1 ∘ ). In this study, we compare the Energy Exascale Earth System Model (E3SM) atmosphere model version 1 (EAMv1) RRM with the HR mesh over the contiguous United States (CONUS) to its corresponding globally uniform LR and HR configurations as well as to observations and reanalysis data. The RRM has a significantly reduced computational cost (roughly proportional to the HR mesh size) relative to the globally uniform HRM. Over the CONUS, we evaluate the simulation of important dynamical and physical quantities as well as various precipitation measures. Differences between the RRM and HRM over the HR region are predominantly small, demonstrating that the RRM reproduces the precipitation metrics of the HRM over the CONUS. Further analysis based on RRM simulations with the LR vs. HR model parameters reveals that RRM performance is greatly influenced by the different parameter choices used in the LR and HR EAMv1. This is a result of the poor scale-aware behavior of physical parameterizations, especially for variables influencing sub-grid-scale physical processes. RRMs can serve as a useful framework to test physics schemes across a range of scales, leading to improved consistency in future E3SM versions. Applying nudging-to-observations techniques within the RRM framework also demonstrates significant advantages over a free-running configuration for use as a test bed and as such represents an efficient and more robust physics test bed capability. Our results provide additional confirmatory evidence that the RRM is an efficient and effective test bed for HRM development.
Learning Edge Properties in Graphs from Path Aggregations

Graph edges, along with their labels, can represent information of fundamental importance, such as links between web pages, friendship between users, the rating given by users to other users or items, and much more. We introduce LEAP, a trainable, general framework for predicting the presence and properties of edges on the basis of the local structure, topology, and labels of the graph. The LEAP framework is based on the exploration and machine-learning aggregation of the paths connecting nodes in a graph. We provide several methods for performing the aggregation phase by training path aggregators, and we demonstrate the flexibility and generality of the framework by applying it to the prediction of links and user ratings in social networks. We validate the LEAP framework on two problems: link prediction, and user rating prediction. On eight large datasets, among which the arXiv collaboration network, the Yeast protein-protein interaction, and the US airlines routes network, we show that the link prediction performance of LEAP is at least as good as the current state of the art methods, such as SEAL and WLNM. Next, we consider the problem of predicting user ratings on other users: this problem is known as the edge-weight prediction problem in weighted signed networks (WSN). On Bitcoin networks, and Wikipedia RfA, we show that LEAP performs consistently better than the Fairness & Goodness based regression models, varying the amount of training edges between 10 to 90%. These examples demonstrate that LEAP, in spite of its generality, can match or best the performance of approaches that have been especially crafted to solve very specific edge prediction problems.
Missing Novelty in Drug Development

This paper provides evidence that risk aversion leads pharmaceutical firms to underinvest in radical innovation. We define a drug candidate as novel if it is molecularly distinct from prior candidates. Using our measure, we show that firms face a risk-reward tradeoff when investing in novel drugs: while novel drug candidates are less likely to be approved by the FDA, they are based on patents with higher indicators of value. We show that–counter to the predictions of frictionless models–firms respond to a plausibly exogenous positive shock to their net worth by developing more of these riskier novel candidates. This pattern suggests that financial market imperfections may lead even large public firms to behave as though they are risk averse, therefore hindering their willingness to invest in potentially valuable novel drugs.
Financial Inclusion, Financial Literacy and Economically Vulnerable Populations in the Middle East and North Africa

Using microdata from the 2014 Global Findex, along with macroeconomic indicators, we investigate financial inclusion for the MENA region. We find that economically vulnerable populations are signif...
X-ray reflectivity with a twist: Quantitative time-resolved X-ray reflectivity using monochromatic synchrotron radiation

We have developed an improved method of time-resolved x-ray reflectivity (XRR) using monochromatic synchrotron radiation. Our method utilizes a polycapillary x-ray optic to create a range of incident angles and an area detector to collect specular reflections. By rotating the sample normal out of the plane of the incident fan, we can separate the surface diffuse scatter from the reflectivity signal, greatly improving the quality of the XRR spectra compared to previous implementations. We demonstrate the time-resolved capabilities of this system, with the temporal resolution as low as 10 ms, by measuring XRR during the annealing of Al/Ni nano-scale multilayers, and use this information to extract the activation energy for interdiffusion in this system.
Identifiability of two‐component skew normal mixtures with one known component

We give sufficient identifiability conditions for estimating mixing proportions in two-component mixtures of skew normal distributions with one known component. We consider the univariate case as well as two multivariate extensions: a multivariate skew normal distribution (MSN) by Azzalini and Dalla Valle (1996) and the canonical fundamental skew normal distribution (CFUSN) by Arellano-Valle and Genton (2005). The characteristic function of the CFUSN distribution is additionally derived.
Correspondence Analysis Using Neural Networks

Correspondence analysis (CA) is a multivariate statistical tool used to visualize and interpret data dependencies. CA has found applications in fields ranging from epidemiology to social sciences. However, current methods used to perform CA do not scale to large, high-dimensional datasets. By re-interpreting the objective in CA using an information-theoretic tool called the principal inertia components, we demonstrate that performing CA is equivalent to solving a functional optimization problem over the space of finite variance functions of two random variable. We show that this optimization problem, in turn, can be efficiently approximated by neural networks. The resulting formulation, called the correspondence analysis neural network (CA-NN), enables CA to be performed at an unprecedented scale. We validate the CA-NN on synthetic data, and demonstrate how it can be used to perform CA on a variety of datasets, including food recipes, wine compositions, and images. Our results outperform traditional methods used in CA, indicating that CA-NN can serve as a new, scalable tool for interpretability and visualization of complex dependencies between random variables.
Lens-Based Millimeter Wave Reconfigurable Antenna NOMA

This paper proposes a new multiple access technique based on the millimeter wave lens-based reconfigurable antenna systems. In particular, to support a large number of groups of users with different angles of departures (AoDs), we integrate recently proposed reconfigurable antenna multiple access (RAMA) into non-orthogonal multiple access (NOMA). The proposed technique, named reconfigurable antenna NOMA (RA-NOMA), divides the users with respect to their AoDs and channel gains. Users with different AoDs and comparable channel gains are served via RAMA while users with the same AoDs but different channel gains are served via NOMA. This technique results in the independence of the number of radio frequency chains from the number of NOMA groups. Further, we derive the feasibility conditions and show that the power allocation for RA-NOMA is a convex problem. We then derive the maximum achievable sum-rate of RA-NOMA. Simulation results show that RA-NOMA outperforms conventional orthogonal multiple access (OMA) as well as the combination of RAMA with the OMA techniques.
The Effect of Voluntary Disclosure on Investment Inefficiency

We introduce real decisions (a project choice decision, an investment scale decision, and an information acquisition decision) to the Dye (1985) voluntary disclosure framework and examine how the p...
Control over Contemporary Photography: A Tangle of Copyright, Right of Publicity, and the First Amendment

Professional photographers who make photographs of people negotiate a tense relationship between their own creative freedoms and the right of their subjects to control their images. This negotiation formally takes place over the terrain of copyright, right of publicity, and the First Amendment. Informally, photographers describe implied understandings and practice norms guiding their relationship with subjects, infrequently memorialized in short, boilerplate contractual releases. This short essay explores these formal and informal practices described by contemporary professional photographers. Although the evidence for this essay comes from professional photographic practice culled from interviews with contemporary photographers, the analysis of the evidence speaks to the more general challenge of balancing privacy and freedom of expression in the digital age. At the outset of this essay, I describe the scope of the empirical project and the process of collecting data. Then, in three parts, I describe how photographers simultaneously collaborate with and control the subjects of the photographs they make in order to assert themselves as civic storytellers with broad free speech rights in our digital age. I identify a conflict between photographers and their subjects, which serves to maximize the aesthetic freedom of photographers at the expense of their subjects. This conflict resolves in the photographers’ accounts through their caretaking role over their photographs on behalf of the subjects themselves. I conclude with a brief explanation of why it matters to better understand these professional photographic norms in our Internet age when free speech and privacy are increasingly in conflict.
Partially Redundant Actin Genes in Chlamydomonas Control Transition Zone Organization and Flagellum-Directed Traffic

The unicellular green alga Chlamydomonas reinhardtii is a biflagellated cell with two actin genes: one encoding a conventional actin (IDA5) and the other encoding a divergent novel actin-like protein (NAP1). Here, we probe how actin redundancy contributes to flagellar assembly. Disrupting a single actin allows complete flagellar assembly. However, when disrupting both actins using latrunculin B (LatB) treatment on the nap1 mutant background, we find that actins are necessary for flagellar growth from newly synthesized limiting flagellar proteins. Under total actin disruption, transmission electron microscopy identified an accumulation of Golgi-adjacent vesicles. We also find that there is a mislocalization of a key transition zone gating and ciliopathy protein, NPHP-4. Our experiments demonstrate that each stage of flagellar biogenesis requires redundant actin function to varying degrees, with an absolute requirement for these actins in transport of Golgi-adjacent vesicles and flagellar incorporation of newly synthesized proteins.
An improved synthesis of 2‐amino‐5‐[(4‐chlorophenyl)thio]‐4‐morpholinopyrimidine (BW 394U) — A potential antisenility agent

This paper describes the synthesis of 2-amino-5-[(4-chlorophenyl)thio]-4-morpholinopyrimidine (BW 394U, compound 4), a potential antisenility agent. The key intermediates 3a/3b were obtained from an in situ-generated Vilsmeier-Haack reagent that simultaneously protected the 2-amino group prior to further manipulations. Displacement of the chloro group in 3a gave 4 in 40% yield and 4-dimethylamino analogue 5. However, displacement of 3b with morpholine followed by treatment with aqueous base gave 4 in 74% yield.
Modelling of the homogeneously catalyzed and uncatalyzed pyrolysis of neopentane : thermochemistry of the neopentyl radical

The mechanism for neopentane (NpH) pyrolysis in the absence and presence of additives isobutene, HCl and HBr, in the temperature range 750–800 K, has been reinvestigated with the aid of computer simulation and sensitivity analysis techniques. With best values assigned to all rate constants in the kinetic chain, a basic mechanism comprising 18 reversible reactions involving 19 atomic, radical, and molecular species has been used to simulate pure neopentane pyrolysis data. Predictions of major and minor product yields provided quantitative agreement with experimental data against which the model was tested. The mechanism was supplemented by additional species and reactions in order to simulate experimental neopentane pyrolysis data in the presence of HCl and HBr additives. An apparent discrepancy between a recent direct measurement of k5, the rate constant for thermal decomposition of the neopentyl radical [1], and that reported from studies of neopentane pyrolysis in the presence and absence of HCl [2], has been identified as being due to the use of an incomplete mechanism in the latter determination. Simulations of hydrogen halide catalyzed pyrolyses exhibit a high sensitivity to the thermochemical parameters associated with the neopentyl radical (Np). The influence of uncertainties in ΔH(Np) and S(Np) are evaluated and lead to suggested values ΔH(Np) = 8.7 ± 0.8 kcal mol−1 and S(Np) = 78.8 ± 1.0 cal mol−1 K−1. © 1993 John Wiley & Sons, Inc.
Alterations in neurovascular coupling following acute traumatic brain injury.

Following acute traumatic brain injury (TBI), timely transport to a hospital can significantly improve the prognosis for recovery. There is, however, a dearth of quantitative biomarkers for brain injury that can be rapidly acquired and interpreted in active, field environments in which TBIs are frequently incurred. We explored potential functional indicators for TBI that can be noninvasively obtained through portable detection modalities, namely optical and electrophysiological approaches. By combining diffuse correlation spectroscopy with colocalized electrophysiological measurements in a mouse model of TBI, we observed concomitant alterations in sensory-evoked cerebral blood flow (CBF) and electrical potentials following controlled cortical impact. Injury acutely reduced the peak amplitude of both electrophysiological and CBF responses, which mostly recovered to baseline values within 30 min, and intertrial variability for these parameters was also acutely altered. Notably, the postinjury dynamics of the CBF overshoot and undershoot amplitudes differed significantly; whereas the amplitude of the initial peak of stimulus-evoked CBF recovered relatively rapidly, the ensuing undershoot did not appear to recover within 30 min of injury. Additionally, acute injury induced apparent low-frequency oscillatory behavior in CBF (<1 Hz). Histological assessment indicated that these physiological alterations were not associated with any major, persisting anatomical changes. Several time-domain features of the blood flow and electrophysiological responses showed strong correlations in recovery kinetics. Overall, our results reveal an array of stereotyped, injury-induced alterations in electrophysiological and hemodynamic responses that can be rapidly obtained using a combination of portable detection techniques.
Computational study of ethylene, acetylene, and cyclopropene addition to heterocycles with two heteroatoms in the 1,2 position

The AM1 semiempirical and B3LYP density functional theory computational studies are undertaken with the target being to explore cycloaddition reactions with heterocycles that have two heteroatoms in the ring's 1 and 2 positions as an initial synthetic step in the preparation of allene's acetylene derivatives. Several qualitative computational approaches were used to evaluate the heterocycle reactivity, including two new approaches using electronic and bond order changes in transformation of the reactant pair into corresponding transition state structures. Finally, the reactivity was evaluated by computing activation barriers, and the feasibility of the proposed synthetic transformations was discussed.
An open-source control system for in vivo fluorescence measurements from deep-brain structures.

Abstract Background Intracranial photometry through chronically implanted optical fibers is a widely adopted technique for measuring signals from fluorescent probes in deep-brain structures. The recent proliferation of bright, photo-stable, and specific genetically encoded fluorescent reporters for calcium and for other neuromodulators has greatly increased the utility and popularity of this technique. New method Here we describe an open-source, cost-effective, microcontroller-based solution for controlling optical components in an intracranial photometry system and processing the resulting signal. Results We show proof-of-principle that this system supports high quality intracranial photometry recordings from dorsal striatum in freely moving mice. A single system supports simultaneous fluorescence measurements in two independent color channels, but multiple systems can be integrated together if additional fluorescence channels are required. This system is designed to work in combination with either commercially available or custom-built optical components. Parts can be purchased for less than one tenth the cost of commercially available alternatives and complete assembly takes less than one day for an inexperienced user. Comparison with existing method(s) Currently available hardware draws on a variety of commercial, custom-built, or hybrid elements for both optical and electronic components. Many of these hardware systems are either specialized and inflexible, or over-engineered and expensive. Conclusions This open-source system increases experimental flexibility while reducing cost relative to current commercially available components. All software and firmware are open-source and customizable, affording a degree of experimental flexibility that is not available in current commercial systems.
(Fractional) intersection numbers, tadpoles and anomalies

We use the Witten index in the open string sector to determine tadpole charges of orientifold planes and D-branes. As specific examples we consider type I compactifications on Calabi Yau manifolds and noncompact orbifolds. The tadpole constraints suggest that the standard embedding is not a natural choice for the gauge bundle. Rather there should be a close connection of the gauge bundle and the spin bundle. In the case of a four fold, the standard embedding does not in general fulfill the tadpole conditions. We show that this agrees with the Green-Schwarz mechanism. In the case of noncompact orbifolds we are able to solve the tadpole constraints with a gauge bundle, which is related to the spin bundle. We compare these results to anomaly cancellation on the fixed plane of the orbifold. In the case of branes wrapping noncompact cycles, there are fractional intersection numbers and anomaly coefficients, which we explain in geometric terms.