cssi/SciSciGPT-SciSciNet huggingface dataset:
Learning controls for blend shape based realistic facial animation

Blend shape animation is the method of choice for keyframe facial animation: a set of blend shapes (key facial expressions) are used to define a linear space of facial expressions. However, in order to capture a significant range of complexity of human expressions, blend shapes need to be segmented into smaller regions where key idiosyncracies of the face being animated are present. Performing this segmentation by hand requires skill and a lot of time. In this paper, we propose an automatic, physically-motivated segmentation that learns the controls and parameters directly from the set of blend shapes. We show the usefulness and efficiency of this technique for both, motion-capture animation and keyframing. We also provide a rendering algorithm to enhance the visual realism of a blend shape model.
Differential hemispheric and visual stream contributions to ensemble coding of crowd emotion

In crowds, where scrutinizing individual facial expressions is inefficient, humans can make snap judgments about the prevailing mood by reading "crowd emotion". We investigated how the brain accomplishes this feat in a set of behavioral and fMRI studies. Participants were asked to either avoid or approach one of two crowds of faces presented in the left and right visual hemifields. Perception of crowd emotion was improved when crowd stimuli contained goal-congruent cues and was highly lateralized to the right hemisphere. The dorsal visual stream was preferentially activated in crowd emotion processing, with activity in the intraparietal sulcus and superior frontal gyrus predicting perceptual accuracy for crowd emotion perception, whereas activity in the fusiform cortex in the ventral stream predicted better perception of individual facial expressions. Our findings thus reveal significant behavioral differences and differential involvement of the hemispheres and the major visual streams in reading crowd versus individual face expressions.
The Association between SFAS No. 157 Fair Value Hierarchy Information and Conditional Accounting Conservatism

ABSTRACT Investors demand conditional conservatism to restrict managers' ability to opportunistically exploit unverifiable accounting estimates. The fair value estimation process is subject to verifiability concerns when market prices are unavailable and, thus, susceptible to managerial discretion. We explore whether banks' exposure to less-verifiable fair value estimates is associated with conditional conservatism. General and bank-specific conservatism measures indicate that banks with greater proportions of less-verifiable fair value assets exhibit more conditional conservatism. Cross-sectional analyses provide evidence that this relation varies predictably with investor-demand and manager-supply proxies. Further analyses indicate that monitoring institutional investors drive the demand for conservatism. We identify high-quality auditors and board independence as two mechanisms used to invoke conservatism. Findings are robust to the exclusion of fair value earnings components, suggesting that the effec...
Cryo-EM reveals the structural basis of microtubule depolymerization by kinesin-13s

Kinesin-13s constitute a distinct group within the kinesin superfamily of motor proteins that promote microtubule depolymerization and lack motile activity. The molecular mechanism by which kinesin-13s depolymerize microtubules and are adapted to perform a seemingly very different activity from other kinesins is still unclear. To address this issue, here we report the near atomic resolution cryo-electron microscopy (cryo-EM) structures of Drosophila melanogaster kinesin-13 KLP10A protein constructs bound to curved or straight tubulin in different nucleotide states. These structures show how nucleotide induced conformational changes near the catalytic site are coupled with movement of the kinesin-13-specific loop-2 to induce tubulin curvature leading to microtubule depolymerization. The data highlight a modular structure that allows similar kinesin core motor-domains to be used for different functions, such as motility or microtubule depolymerization.
Rent Seeking by Low-Latency Traders: Evidence from Trading on Macroeconomic Announcements

Prices of the highly liquid S&P 500 exchange-traded fund (SPY) and the E-mini future (ES) respond to macroeconomic announcement surprises within five milliseconds, with trading intensity increasing over 100-fold following the news release. However, profits from trading quickly are relatively small, roughly $\$$ 19,000 (⁠ $\$$ 50,000) per event for SPY (ES). Although the speed of information incorporation has increased in recent years, profits have not. Order flow has become less informative, consistent with prices responding directly to news rather than indirectly through trading. Our evidence indicates that low-latency liquidity demanders do not benefit materially from short-term monopolistic access to information.
Unsupervised Learning of Semantic Audio Representations

Even in the absence of any explicit semantic annotation, vast collections of audio recordings provide valuable information for learning the categorical structure of sounds. We consider several class-agnostic semantic constraints that apply to unlabeled nonspeech audio: (i) noise and translations in time do not change the underlying sound category, (ii) a mixture of two sound events inherits the categories of the constituents, and (iii) the categories of events in close temporal proximity are likely to be the same or related. Without labels to ground them, these constraints are incompatible with classification loss functions. However, they may still be leveraged to identify geometric inequalities needed for triplet loss-based training of convolutional neural networks. The result is low-dimensional embeddings of the input spectrograms that recover 41% and 84% of the performance of their fully-supervised counterparts when applied to downstream query-by-example sound retrieval and sound event classification tasks, respectively. Moreover, in limited-supervision settings, our unsupervised embeddings double the state-of-the-art classification performance.
Inverting the Turán problem

Abstract Classical questions in extremal graph theory concern the asymptotics of ex ( G , H ) where H is a fixed family of graphs and G = G n is taken from a “standard” increasing sequence of host graphs ( G 1 , G 2 , … ) , most often K n or K n , n . Inverting the question, we can instead ask how large e ( G ) can be with respect to ex ( G , H ) . We show that the standard sequences indeed maximize e ( G ) for some choices of H , but not for others. Many interesting questions and previous results arise very naturally in this context, which also, unusually, gives rise to sensible extremal questions concerning multigraphs and non-uniform hypergraphs.
Imaging and quantifying ganglion cells and other transparent neurons in the living human retina

Ganglion cells (GCs) are fundamental to retinal neural circuitry, processing photoreceptor signals for transmission to the brain via their axons. However, much remains unknown about their role in vision and their vulnerability to disease leading to blindness. A major bottleneck has been our inability to observe GCs and their degeneration in the living human eye. Despite two decades of development of optical technologies to image cells in the living human retina, GCs remain elusive due to their high optical translucency. Failure of conventional imaging-using predominately singly scattered light-to reveal GCs has led to a focus on multiply-scattered, fluorescence, two-photon, and phase imaging techniques to enhance GC contrast. Here, we show that singly scattered light actually carries substantial information that reveals GC somas, axons, and other retinal neurons and permits their quantitative analysis. We perform morphometry on GC layer somas, including projection of GCs onto photoreceptors and identification of the primary GC subtypes, even beneath nerve fibers. We obtained singly scattered images by: (i) marrying adaptive optics to optical coherence tomography to avoid optical blurring of the eye; (ii) performing 3D subcellular image registration to avoid motion blur; and (iii) using organelle motility inside somas as an intrinsic contrast agent. Moreover, through-focus imaging offers the potential to spatially map individual GCs to underlying amacrine, bipolar, horizontal, photoreceptor, and retinal pigment epithelium cells, thus exposing the anatomical substrate for neural processing of visual information. This imaging modality is also a tool for improving clinical diagnosis and assessing treatment of retinal disease.
The risk of being ranked: Investor response to marginal inclusion on the 100 Best Corporate Citizens list

Despite the proliferation of lists and rankings that recognize firms for superior performance, empirical studies have been limited in their ability to causally evaluate how inclusion for the marginal firm influences shareholder value. Using a regression discontinuity design, we address this limitation by examining how investors responded to firms that were just barely included or excluded from the 100 Best Corporate Citizens list. Contrary to prevailing theoretical expectations, our findings indicate that marginal firms that were included in the ranking experienced negative abnormal returns compared to marginal firms that were excluded. We discuss how these findings inspire future research on rankings and status and highlight implications for managers considering strategic decisions related to pursuing rankings.
Import competition and market power: Canadian evidence

Abstract This study estimates price– marginal cost mark-ups for Canadian manufacturing industries during the 1970s in order to assess the impact of import competition on domestic market power. The results are mixed. Based on the analysis, there is no consistent evidence that imports reduced the mark-ups of Canadian firms during that period.
Relaxation Behavior by Time-Salt and Time-Temperature Superpositions of Polyelectrolyte Complexes from Coacervate to Precipitate

Complexation between anionic and cationic polyelectrolytes results in solid-like precipitates or liquid-like coacervate depending on the added salt in the aqueous medium. However, the boundary between these polymer-rich phases is quite broad and the associated changes in the polymer relaxation in the complexes across the transition regime are poorly understood. In this work, the relaxation dynamics of complexes across this transition is probed over a wide timescale by measuring viscoelastic spectra and zero-shear viscosities at varying temperatures and salt concentrations for two different salt types. We find that the complexes exhibit time-temperature superposition (TTS) at all salt concentrations, while the range of overlapped-frequencies for time-temperature-salt superposition (TTSS) strongly depends on the salt concentration (Cs) and gradually shifts to higher frequencies as Cs is decreased. The sticky-Rouse model describes the relaxation behavior at all Cs. However, collective relaxation of polyelectrolyte complexes gradually approaches a rubbery regime and eventually exhibits a gel-like response as Cs is decreased and limits the validity of TTSS.
Crowd-assisted polyp annotation of virtual colonoscopy videos

Virtual colonoscopy (VC) allows a radiologist to navigate through a 3D colon model reconstructed from a computed tomography scan of the abdomen, looking for polyps, the precursors of colon cancer. Polyps are seen as protrusions on the colon wall and haustral folds, visible in the VC y-through videos. A complete review of the colon surface requires full navigation from the rectum to the cecum in antegrade and retrograde directions, which is a tedious task that takes an average of 30 minutes. Crowdsourcing is a technique for non-expert users to perform certain tasks, such as image or video annotation. In this work, we use crowdsourcing for the examination of complete VC y-through videos for polyp annotation by non-experts. The motivation for this is to potentially help the radiologist reach a diagnosis in a shorter period of time, and provide a stronger confirmation of the eventual diagnosis. The crowdsourcing interface includes an interactive tool for the crowd to annotate suspected polyps in the video with an enclosing box. Using our work flow, we achieve an overall polyps-per-patient sensitivity of 87.88p (95.65p for polyps ≥5mm and 70p for polyps <5mm). We also demonstrate the efficacy and effectiveness of a non-expert user in detecting and annotating polyps and discuss their possibility in aiding radiologists in VC examinations.
Non-Uniform Bounds in the Random-Permutation, Ideal-Cipher, and Generic-Group Models

The random-permutation model (RPM) and the ideal-cipher model (ICM) are idealized models that offer a simple and intuitive way to assess the conjectured standard-model security of many important symmetric-key and hash-function constructions. Similarly, the generic-group model (GGM) captures generic algorithms against assumptions in cyclic groups by modeling encodings of group elements as random injections and allows to derive simple bounds on the advantage of such algorithms.
Generative spatiotemporal modeling of neutrophil behavior

Cell motion and appearance have a strong correlation with cell cycle and disease progression. Many contemporary efforts in machine learning utilize spatio-temporal models to predict a cell's physical state and, consequently, the advancement of disease. Alternatively, generative models learn the underlying distribution of the data, creating holistic representations that can be used in learning. In this work, we propose an aggregate model that combine Generative Adversarial Networks (GANs) and Autoregressive (AR) models to predict cell motion and appearance in human neutrophils imaged by differential interference contrast (DIC) microscopy. We bifurcate the task of learning cell statistics by leveraging GANs for the spatial component and AR models for the temporal component. The aggregate model learned results offer a promising computational environment for studying changes in organellar shape, quantity, and spatial distribution over large sequences.
In Vivo Microscopy in Neurosurgical Oncology.

Intraoperative neurosurgical histopathologic diagnoses rely on evaluation of rapid tissue preparations such as frozen sections and smears with conventional light microscopy. Although useful, these techniques are time consuming and therefore cannot provide real-time intraoperative feedback. In vivo molecular imaging techniques are emerging as novel methods for generating real-time diagnostic histopathologic images of tumors and their surrounding tissues. These imaging techniques rely on contrast generated by exogenous fluorescent dyes, autofluorescence of endogenous molecules, fluorescence decay of excited molecules, or light scattering. Large molecular imaging instruments are being miniaturized for clinical in vivo use. This review discusses pertinent imaging systems that have been developed for neurosurgical use and imaging techniques currently under development for neurosurgical molecular imaging.
Parallel, self-organizing hierarchical neural networks

A neural network architecture called the parallel self-organizing hierarchical neural network (PSHNN) is discussed. The PSHNN involves a number of stages in which each stage can be a particular neural network (SNN). At the end of each SNN, error detection is carried out, and a number of input vectors are rejected. Between two SNNs there is a nonlinear first SNN. The PSHNN has an optimized system complexity in the sense of minimized self-organizing number of stages, high classification accuracy, minimized learning and recall times, and parallel architectures in which all SNNs operate simultaneously without waiting for data from each other during testing. In classification experiments with aircraft and satellite remote-sensing data the PSHNN is compared to multilayer networks using backpropagation training. >
Prophylactic cholecystectomy in short bowel syndrome: Is it being utilized?

Abstract Introduction Cholelithiasis is common in patients with short bowel syndrome (SBS). Prophylactic cholecystectomy (PC) of the non-diseased gallbladder has been recommended in SBS patients when laparotomy is being undertaken for other reasons. Our aim was to determine if PC is being utilized. Methods 500 adults with SBS were seen over a 25 year period. 215 undergoing cholecystectomy prior to SBS were excluded, leaving 285 patients for evaluation. Results 151 (53%) SBS patients underwent a subsequent laparotomy. 77 underwent cholecystectomy for cholelithiasis at the 1st opportunity. 27 patients underwent a PC at the 1st opportunity. 47 patients did not undergo PC at the 1st opportunity. 17 (36%) of these 47 patients subsequently developed cholelithiasis with 7 undergoing cholecystectomy. Age, gender, diagnosis and initial BMI and need for longterm parenteral nutrition were similar in patients who had PC or did not. PC patients were more likely to have intestinal remnant length Conclusions Overall 10% of SBS patients underwent PC. However, only 36% of eligible patients undergoing laparotomy had a PC.
Incremental correlation of multiple well logs following geologically optimal neighbors

AbstractWell-log correlation is a crucial step to construct cross sections in estimating structures between wells and building subsurface models. Manually correlating multiple logs can be highly su...
Robust endogenous growth

Abstract This paper studies a generalization of the Schumpeterian models with endogenous market structure that allows the overall production structure to be more than linear in the growth-driving factor and yet generates endogenous growth, defined as steady-state, constant, exponential growth of income per capita. This version of modern growth theory, therefore, is robust in the sense that its key result obtains for a thick set of parameter values instead of, as often claimed, for a set of measure zero. The paper, moreover, pays close attention to transitional dynamics, showing not only the existence but also the global stability of the endogenous-growth steady state.
Changes in computer extracted features of vessel tortuosity on CT scans post-treatment in responders compared to non-responders for non-small cell lung cancer on immunotherapy.

11518Background: Immune-checkpoint blockade treatments demonstrate promising clinical efficacy in patients with non-small cell lung cancer (NSCLC). Nivolumab is a PD-1 inhibitor that is FDA approved for treatment of patients with chemotherapy refractory advanced NSCLC. The current standard clinical approach to evaluating tumor response is sub-optimal in defining clinical benefit from immunotherapy drugs. We sought to evaluate whether computer extracted measurements of vessel tortuosity significantly and differentially change post treatment between NSCLC patients who do and do not respond to immunotherapy. Methods: A total of 50 NSCLC patients including pre- and post- treatment CT scans were included in this study. The patients were either responders or non-responders to Nivolumab. Patients who did not receive Nivolumab after 2 cycles due to lack of response or progression as per RECIST were classified as ‘non-responders’. A total of 35 tortuosity features of the vessels around the lung nodules were invest...
QDDS: A Novel Quantum Swarm Algorithm Inspired by a Double Dirac Delta Potential

In this paper a novel Quantum Double Delta Swarm (QDDS) algorithm modeled after the mechanism of convergence to the center of attractive potential field generated within a single well in a double Dirac delta well setup has been put forward and the preliminaries discussed. Theoretical foundations and experimental illustrations have been incorporated to provide a first basis for further development, specifically in refinement of solutions and applicability to problems in high dimensional spaces. Simulations are carried out over varying dimensionality on four benchmark functions, viz. Rosenbrock, Rastrigrin, Griewank and Sphere as well as the multidimensional Finite Impulse Response (FIR) Filter design problem with different population sizes. Test results illustrate the algorithm yields superior results to some related reports in the literature while reinforcing the need of substantial future work to deliver near-optimal results consistently, especially if dimensionality scales up.
Disjoint Mapping Network for Cross-modal Matching of Voices and Faces

We propose a novel framework, called Disjoint Mapping Network (DIMNet), for cross-modal biometric matching, in particular of voices and faces. Different from the existing methods, DIMNet does not explicitly learn the joint relationship between the modalities. Instead, DIMNet learns a shared representation for different modalities by mapping them individually to their common covariates. These shared representations can then be used to find the correspondences between the modalities. We show empirically that DIMNet is able to achieve better performance than other current methods, with the additional benefits of being conceptually simpler and less data-intensive.
Synthesis cost dictates the commercial viability of lead sulfide and perovskite quantum dot photovoltaics

Any new solar photovoltaic (PV) technology must reach low production costs to compete with today's market-leading crystalline silicon and commercial thin-film PV technologies. Colloidal quantum dots (QDs) could open up new applications by enabling lightweight and flexible PV modules. However, the cost of synthesizing nanocrystals at the large scale needed for PV module production has not previously been investigated. Based on our experience with commercial QD scale-up, we develop a Monte Carlo model to analyze the cost of synthesizing lead sulfide and metal halide perovskite QDs using 8 different reported synthetic methods. We also analyze the cost of solution-phase ligand exchange for preparing deposition-ready PbS QD inks, as well as the manufacturing cost for roll-to-roll solution-processed PV modules using these materials. We find that present QD synthesis costs are prohibitively high for PV applications, with median costs of 11 to 59 $ per g for PbS QDs (0.15 to 0.84 $ per W for a 20% efficient cell) and 73 $ per g for CsPbI3 QDs (0.74 $ per W). QD ink preparation adds 6.3 $ per g (0.09 $ per W). In total, QD materials contribute up to 55% of the total module cost, making even roll-to-roll-processed QDPV modules significantly more expensive than silicon PV modules. These results suggest that the development of new low-cost synthetic methods is critically important for the commercial relevance of QD photovoltaics. Using our cost model, we identify strategies for reducing synthetic cost and propose a cost target of 5 $ per g to move QD solar cells closer to commercial viability.
The Effect of Credit Ratings on Disclosure: Evidence from the Recalibration of Moody's Municipal Ratings

This paper examines how credit rating levels affect municipal debt issuers’ disclosure decisions. Using exogenous upgrades in credit rating levels caused by the recalibration of Moody's municipal ratings scale in 2010, we find that upgraded municipalities significantly reduce their disclosure of required continuing financial information, relative to unaffected municipalities. Consistent with a reduction in debtholders’ demand for information driving these results, the reduction in disclosure is greater when municipal bonds are held by investors who relied more on disclosure ex ante. However, we also find that the reduction in disclosure does not manifest when issuers are monitored by underwriters with greater issuer‐specific expertise and when issuers are subject to direct regulatory enforcement through the receipt of federal funding. Overall, our results suggest that higher credit rating levels lower investor demand for disclosure in the municipal market, and highlight the role of underwriters and direct regulatory enforcement in maintaining disclosure levels when investor demand is low.
Complement C3-Targeted Gene Therapy Restricts Onset and Progression of Neurodegeneration in Chronic Mouse Glaucoma

Dysregulation of the complement system is implicated in neurodegeneration, including human and animal glaucoma. Optic nerve and retinal damage in glaucoma is preceded by local complement upregulation and activation, but whether targeting this early innate immune response could have therapeutic benefit remains undefined. Because complement signals through three pathways that intersect at complement C3 activation, here we targeted this step to restore complement balance in the glaucomatous retina and to determine its contribution to degeneration onset and/or progression. To achieve this, we combined adeno-associated virus retinal gene therapy with the targeted C3 inhibitor CR2-Crry. We show that intravitreal injection of AAV2.CR2-Crry produced sustained Crry overexpression in the retina and reduced deposition of the activation product complement C3d on retinal ganglion cells and the inner retina of DBA/2J mice. This resulted in neuroprotection of retinal ganglion cell axons and somata despite continued intraocular pressure elevation, suggesting a direct restriction of neurodegeneration onset and progression and significant delay to terminal disease stages. Our study uncovers a damaging effect of complement C3 or downstream complement activation in glaucoma, and it establishes AAV2.CR2-Crry as a viable therapeutic strategy to target pathogenic C3-mediated complement activation in the glaucomatous retina.
Preferred Pharmacy Networks and Drug Costs

Selective contracting is an increasingly popular tool for reducing health care costs, but these savings must be weighed against consumer surplus losses from restricted access. In both public and private prescription drug insurance plans, issuers utilize preferred pharmacy networks to reduce drug prices. We show that, in the Medicare Part D program, drug plans with more restrictive preferred pharmacy networks, and plans with fewer enrollees who are insensitive to preferred pharmacy discounts on copays, pay lower retail drug prices. We then use estimates of plan and pharmacy demand to estimate the first-order costs and benefits of selective contracting in the presence of enrollees with heterogeneous sensitivity to preferred supplier incentives.
Towards Privacy-Preserving Visual Recognition via Adversarial Training: A Pilot Study

This paper aims to improve privacy-preserving visual recognition, an increasingly demanded feature in smart camera applications, by formulating a unique adversarial training framework. The proposed framework explicitly learns a degradation transform for the original video inputs, in order to optimize the trade-off between target task performance and the associated privacy budgets on the degraded video. A notable challenge is that the privacy budget, often defined and measured in task-driven contexts, cannot be reliably indicated using any single model performance, because a strong protection of privacy has to sustain against any possible model that tries to hack privacy information. Such an uncommon situation has motivated us to propose two strategies, i.e., budget model restarting and ensemble, to enhance the generalization of the learned degradation on protecting privacy against unseen hacker models. Novel training strategies, evaluation protocols, and result visualization methods have been designed accordingly. Two experiments on privacy-preserving action recognition, with privacy budgets defined in various ways, manifest the compelling effectiveness of the proposed framework in simultaneously maintaining high target task (action recognition) performance while suppressing the privacy breach risk. The code is available at https://github.com/wuzhenyusjtu/Privacy-AdversarialLearning.
EGBTER: capturing degree distribution, clustering coefficients, and community structure in a single random graph model

Random graph models are important constructs for data analytic applications as well as pure mathematical developments, as they provide capabilities for network synthesis and principled analysis. Several models have been developed with the aim of faithfully preserving important graph metrics and substructures. With the goal of capturing degree distribution, clustering coefficient, and communities in a single random graph model, we propose a new model to address shortcomings in a progression of network modeling capabilities. The Block Two-Level Erdos-Renyi (BTER) model of Seshadhri et aI., designed to allow prescription of expected degree and clustering coefficient distributions, neglects community modeling, while the Generalized BTER (GBTER) model of Bridges et aI., designed to add community modeling capabilities to BTER, struggles to faithfully represent all three characteristics simultaneously. In this work, we fit BTER and two GBTER configurations to several real-world networks and compare the results with that of our new model, the Extended GBTER (EGBTER) model. Our results support that EBGTER adds a community-modeling flexibility to BTER, while retaining a satisfactory level of accuracy in terms of degree and clustering coefficient. Our insights and empirical testing of previous models as well as the new model are novel contributions to the literature.
Mechanisms of Protein Search for Targets on DNA: Theoretical Insights

Protein-DNA interactions are critical for the successful functioning of all natural systems. The key role in these interactions is played by processes of protein search for specific sites on DNA. Although it has been studied for many years, only recently microscopic aspects of these processes became more clear. In this work, we present a review on current theoretical understanding of the molecular mechanisms of the protein target search. A comprehensive discrete-state stochastic method to explain the dynamics of the protein search phenomena is introduced and explained. Our theoretical approach utilizes a first-passage analysis and it takes into account the most relevant physical-chemical processes. It is able to describe many fascinating features of the protein search, including unusually high effective association rates, high selectivity and specificity, and the robustness in the presence of crowders and sequence heterogeneity.
Security Mental Model: Cognitive Map Approach

Security models have been designed to ensure data is accessed and used in proper manner according to the security policies. Unfortunately, human role in designing security models has been ignored. Human behavior relates to many security breaches and plays a significant part in many security situations. In this paper, we study users’ decision-making toward security and usability through the mental model approach. To elicit and depict users’ security and usability mental models, crowd sourcing techniques and a cognitive map method are applied and we have performed an experiment to evaluate our findings using Amazon MTurk.
Three-body interactions drive the transition to polar order in a simple flocking model

A large class of mesoscopic or macroscopic flocking theories are coarse-grained from microscopic models that feature binary interactions as the chief aligning mechanism. However, while such theories seemingly predict the existence of polar order with just binary interactions, actomyosin motility assay experiments show that binary interactions are insufficient to obtain polar order, especially at high densities. To resolve this paradox, here we introduce a solvable one-dimensional flocking model and derive its stochastic hydrodynamics. We show that two-body interactions are insufficient to generate polar order unless the noise is non-Gaussian. We show that noisy three-body interactions in the microscopic theory allow us to capture all essential dynamical features of the flocking transition, in systems that achieve orientational order above a critical density.
Income and Payroll Tax Policy and Labor Supply

Income and payroll taxes account for about 75 percent of federal revenues. The proportion of federal tax revenue raised by these two taxes has gone up markedly in the past decade with the amounts growing faster than the underlying inflation rate. The rise in the income tax collections occurs because of its progressive rate structure and insufficient indexing of tax brackets to account for inflation. The rise in the payroll tax has occurred because of legislative actions to fund social security payments. Both the tax rate of the payroll tax and the maximum earnings limit have increased significantly. In Table 1 we indicate the effects of the income and payroll taxes over the last two decades. Note that the combined percentage of the two taxes has risen from 56% of government revenues in 1960 to 76% of government revenues in 1978. This increasing trend is likely to continue in the future.
Managing Polypharmacy in the 15-Minute Office Visit

Polypharmacy is an underappreciated factor in undesirable patient outcomes. In older adults, polypharmacy is considered a syndrome of harm and presents a challenge to primary care providers. The United States has one of the highest medication use rates per capita in the world. With the aging population, and polypharmacy a significant part of the lives of older adults, management of polypharmacy poses both a growing challenge and an opportunity for all health care providers. This article provides an overview of skills to improve medication use management in older adults living with polypharmacy.
Measuring Information Leakage in Website Fingerprinting Attacks and Defenses

Tor provides low-latency anonymous and uncensored network access against a local or network adversary. Due to the design choice to minimize traffic overhead (and increase the pool of potential users) Tor allows some information about the client's connections to leak. Attacks using (features extracted from) this information to infer the website a user visits are called Website Fingerprinting (WF) attacks. We develop a methodology and tools to measure the amount of leaked information about a website. We apply this tool to a comprehensive set of features extracted from a large set of websites and WF defense mechanisms, allowing us to make more fine-grained observations about WF attacks and defenses.
Dynamic Voltage and Frequency Scaling in NoCs with Supervised and Reinforcement Learning Techniques

Network-on-Chips (NoCs) are the de facto choice for designing the interconnect fabric in multicore chips due to their regularity, efficiency, simplicity, and scalability. However, NoC suffers from excessive static power and dynamic energy due to transistor leakage current and data movement between the cores and caches. Power consumption issues are only exacerbated by ever decreasing technology sizes. Dynamic Voltage and Frequency Scaling (DVFS) is one technique that seeks to reduce dynamic energy; however this often occurs at the expense of performance. In this paper, we propose LEAD Learning-enabled Energy-Aware Dynamic voltage/frequency scaling for multicore architectures using both supervised learning and reinforcement learning approaches. LEAD groups the router and its outgoing links into the same V/F domain and implements proactive DVFS mode management strategies that rely on offline trained machine learning models in order to provide optimal V/F mode selection between different voltage/frequency pairs. We present three supervised learning versions of LEAD that are based on buffer utilization, change in buffer utilization and change in energy/throughput, which allow proactive mode selection based on accurate prediction of future network parameters. We then describe a reinforcement learning approach to LEAD that optimizes the DVFS mode selection directly, obviating the need for label and threshold engineering. Simulation results using PARSEC and Splash-2 benchmarks on a 4 × 4 concentrated mesh architecture show that by using supervised learning LEAD can achieve an average dynamic energy savings of 15.4 percent for a loss in throughput of 0.8 percent with no significant impact on latency. When reinforcement learning is used, LEAD increases average dynamic energy savings to 20.3 percent at the cost of a 1.5 percent decrease in throughput and a 1.7 percent increase in latency. Overall, the more flexible reinforcement learning approach enables learning an optimal behavior for a wider range of load environments under any desired energy versus throughput tradeoff.
Echo-Doppler assessment of ventricular filling pressures in adult Fontan patients

Abstract Background Diastolic dysfunction is felt to be part of the natural history of patients with prior Fontan operation. Despite that, data on noninvasive assessment of diastolic function and ventricular filling pressures in Fontan patients are limited. Methods We included 45 adult Fontan patients who underwent right heart catheterization with pulmonary artery wedge pressure (PAWP) measurement and transthoracic echo-Doppler assessment within 7 days. Offline measurement of systemic atrioventricular valve pulsed-wave (PW) and medial/lateral atrioventricular annular tissue Doppler velocities was performed and correlated to PAWP. Results Median age was 31.4 years (IQR 24.8–37); 51% of patients were females. Median ventricular ejection fraction was 55% (IQR 50.3–57.5) and median PAWP was 10 mm Hg (IQR 8–11). PW Doppler E velocity (r = 0.64, p ≤ 0.0001), deceleration time (r = −0.40, p = 0.008), and E/A ratio (r = 0.33, p = 0.03) correlated with PAWP. Median medial and lateral E/e′ ratios also correlated to PAWP (r = 0.71, p = 0.002 and r = 0.75, p 75 cm/s had 88% sensitivity and 86% specificity [97% negative predictive value (NPV); 58% positive predictive value (PPV)], E/A ratio >1.7 had 100% sensitivity and 61% specificity (100% NPV; 26% PPV), and deceleration time 12 mm Hg. Conclusion Our data suggest that PW Doppler atrioventricular E velocity, E/A ratio, and deceleration time could be used to identify adult Fontan patients with normal filling pressures. However, PPVs for PAWP >12 mm Hg were poor. Further studies, particularly prospective, simultaneous echo-catheterization correlation, are critically needed.
Managerial control benefits and takeover market efficiency

Abstract How and to what extent do managerial control benefits shape the efficiency of the takeover market? We revisit this question by estimating both the dark and bright sides of managerial control benefits in an industry equilibrium model. On the dark side, managers’ private benefits of control distort firms’ takeover incentives and hinder the reallocation role of the takeover market. On the bright side, fear of a takeover induces underperforming managers to exert more effort and enhances the disciplinary role of the takeover market. Our estimates suggest that the bright-side effect increases the value created by an active takeover market by 21%, comparable in magnitude to the dark-side effect. It is also important to account for this bright-side effect in explaining certain features of the takeover market, including a low takeover-performance sensitivity.
Caste and the Indian Economy

Caste plays a role at every stage of an Indian's economic life, in school, university, the labor market, and into old age. The influence of caste extends beyond private economic activity into the public sphere, where caste politics determine access to public resources. The aggregate evidence indicates that there has been convergence in education, occupations, income, and access to public resources across caste groups in the decades after independence. Some of this convergence is likely due to affirmative action, but caste-based networks could also have played an equalizing role by exploiting the opportunities that became available in a globalizing economy. Ethnic networks were once active in many advanced economies but ceased to be salient once markets developed. With economic development, it is possible that caste networks will cease to be salient in India. The affirmative action programs may also be rolled back, and (statistical) discrimination in urban labor markets may come to an end if and when there is convergence across caste groups. In the interim period, however, it is important to understand the positive and negative consequences of caste involvement across a variety of spheres in the Indian economy.
Annuity Markets and Capital Accumulation

We examine how the absence of annuities in financial markets affects capital accumulation in a two-period overlapping generations model. Our findings indicate that the effect on capital is ambiguous in general equilibrium, because there are two competing mechanisms at work. On the one hand, the absence of annuities increases the price of old-age consumption relative to the price of early-life consumption. This induces a substitution effect that reduces saving and capital, and an income effect that has the opposite effect as households want to consume less when young, causing them to save more. On the other hand, accidental bequests originate from the assets of the deceased under missing annuity markets. The bequest received in early life always has a positive income effect on saving, but the bequest received in old age, conditional on survival, is effectively a partial annuity with both substitution and income effects. We find that when the desire to smooth consumption is high, the income effects dominate, so the capital stock always increases when annuity markets are missing. However, when the desire to smooth consumption is low, the substitution effects dominate, and the capital stock decreases with missing annuity markets.
Individual Analysts' Stock Recommendations, Earnings Forecasts, and the Informativeness of Conference Call Question and Answer Sessions

This paper deepens our understanding of the anatomy of an earnings conference call. Prior research indicates that, on average, analysts providing bullish stock recommendations or beatable earnings ...
Why does the zebrafish cloche mutant develop lens cataract

The zebrafish has become a valuable model for examining ocular lens development, physiology and disease. The zebrafish cloche mutant, first described for its loss of hematopoiesis, also shows reduced eye and lens size, interruption in lens cell differentiation and a cataract likely caused by abnormal protein aggregation. To facilitate the use of the cloche mutant for studies on cataract development and prevention we characterized variation in the lens phenotype, quantified changes in gene expression by qRT-PCR and RNA-Seq and compared the ability of two promoters to drive expression of introduced proteins into the cloche lens. We found that the severity of cloche embryo lens cataract varied, while the decrease in lens diameter and retention of nuclei in differentiating lens fiber cells was constant. We found very low expression of both αB-crystallin genes (cryaba and cryabb) at 4 days post fertilization (dpf) by both qRT-PCR and RNA-Seq in cloche, cloche sibling and wildtype embryos and no significant difference in αA-crystallin (cryaa) expression. RNA-Seq analysis of 4 dpf embryos identified transcripts from 25,281 genes, with 1,329 showing statistically significantly different expression between cloche and wildtype samples. Downregulation of eight lens β- and γM-crystallin genes and 22 retinal related genes may reflect a general reduction in eye development and growth. Six stress response genes were upregulated. We did not find misregulation of any known components of lens development gene regulatory networks. These results suggest that the cloche lens cataract is not caused by loss of αA-crystallin or changes to lens gene regulatory networks. Instead, we propose that the cataract results from general physiological stress related to loss of hematopoiesis. Our finding that the zebrafish αA-crystallin promoter drove strong GFP expression in the cloche lens demonstrates its use as a tool for examining the effects of introduced proteins on lens crystallin aggregation and cataract prevention.
Effects of RMP-induced changes of radial electric fields on microturbulence in DIII-D pedestal top

Gyrokinetic simulations of DIII-D tokamak with axisymmetric equilibrium show that the reduction in the radial electric field shear at the top of the pedestal during edge localized mode (ELM) suppression with the n = 2 resonant magnetic perturbations (RMPs) leads to enhanced drift-wave turbulence and extended turbulence spreading to the top of the pedestal relative to ELMing plasmas with similar RMP and pedestal parameters. The simulated turbulent transport at the top of the pedestal in ELM suppressed conditions is consistent with experimental observations of enhanced turbulence at the top of the pedestal during ELM suppression by the RMPs. These results imply that enhanced drift-wave turbulence due to reduced shear at the pedestal top can contribute to the additional transport required to prevent the pedestal growing to a width that is unstable to ELMs.
Parallel complexity analysis with temporal session types

We study the problem of parametric parallel complexity analysis of concurrent, message-passing programs. To make the analysis local and compositional, it is based on a conservative extension of binary session types, which structure the type and direction of communication between processes and stand in a Curry-Howard correspondence with intuitionistic linear logic. The main innovation is to enrich session types with the temporal modalitiesnext (◯ A), always (□ A), and eventually (◇ A), to additionally prescribe the timing of the exchanged messages in a way that is precise yet flexible. The resulting temporal session types uniformly express properties such as the message rate of a stream, the latency of a pipeline, the response time of a concurrent queue, or the span of a fork/join parallel program. The analysis is parametric in the cost model and the presentation focuses on communication cost as a concrete example. The soundness of the analysis is established by proofs of progress and type preservation using a timed multiset rewriting semantics. Representative examples illustrate the scope and usability of the approach.
Discovering Context Effects from Raw Choice Data

Many applications in preference learning assume that decisions come from the maximization of a stable utility function. Yet a large experimental literature shows that individual choices and judgements can be affected by "irrelevant" aspects of the context in which they are made. An important class of such contexts is the composition of the choice set. In this work, our goal is to discover such choice set effects from raw choice data. We introduce an extension of the Multinomial Logit (MNL) model, called the context dependent random utility model (CDM), which allows for a particular class of choice set effects. We show that the CDM can be thought of as a second-order approximation to a general choice system, can be inferred optimally using maximum likelihood and, importantly, is easily interpretable. We apply the CDM to both real and simulated choice data to perform principled exploratory analyses for the presence of choice set effects.
Superconducting Cavity Resonators

A key component of the modern particle accelerator is the device that imparts energy to the charged particles. This is an electromagnetic radio frequency (RF) cavity resonating at microwave frequencies typically between 50MHz and 3000 MHz. Superconducting cavities economically provide high CW operating fields. Another benefit is that superconducting cavities can be designed to have a large beam aperture that reduces the beam cavity interactions, allowing higher beam quality and higher beam current. There are two distinct types of superconducting cavities, depending on the velocity of the particles. The first category is for accelerating charged particles that move at nearly the speed of light, such as electrons in a high-energy linear accelerator [e.g., at TJNAF at Jefferson Lab in Newport News, VA] or a storage ring [e.g., LEP at CERN in Switzerland]. The second type is for particles that move at a small fraction (e.g., 0.01 to 0.3) of the speed of light, such as the heavy ions emerging from a dc highvoltage Van de Graaff accelerator. Keywords: superconducting cavities; continuous wave mode; thermometry; bulk niobium cavities; resonators