cssi/SciSciGPT-SciSciNet huggingface dataset:
SOCIAL ANIMAL HOUSE: THE ECONOMIC AND ACADEMIC CONSEQUENCES OF FRATERNITY MEMBERSHIP

We exploit changes in the residential and social environment on campus to identify the economic and academic consequences of fraternity membership at a small Northeastern college. Our estimates suggest that these consequences are large, with fraternity membership lowering student grade point average by approximately 0.25 points on the traditional 4-point scale, but raising future income by approximately 36%, for those students whose decision about membership is affected by changes in the environment. These results suggest that fraternity membership causally produces large gains in social capital, which more than outweigh its negative effects on human capital for potential members. Alcohol-related behavior does not explain much of the effects of fraternity membership on either the human capital or social capital effects. These findings suggest that college administrators face significant trade-offs when crafting policies related to Greek life on campus. (JEL I23, J24, I12)
Carbon Capture and Utilization in the Industrial Sector

The fabrication and manufacturing processes of industrial commodities such as iron, glass, and cement are carbon-intensive, accounting for 23% of global CO2 emissions. As a climate mitigation strategy, CO2 capture from flue gases of industrial processes—much like that of the power sector—has not experienced wide adoption given its high associated costs. However, some industrial processes with relatively high CO2 flue concentration may be viable candidates to cost-competitively supply CO2 for utilization purposes (e.g., polymer manufacturing, etc.). This work develops a methodology that determines the levelized cost ($/tCO2) of separating, compressing, and transporting carbon dioxide. A top-down model determines the cost of separating and compressing CO2 across 18 industrial processes. Further, the study calculates the cost of transporting CO2 via pipeline and tanker truck to appropriately paired sinks using a bottom-up cost model and geo-referencing approach. The results show that truck transportation is ...
Spin flipping at sputtered Co/Ag interfaces

We measured at 4.2 K the current-perpendicular-to-plane magnetoresistances of sputtered ferromagnetically coupled [Co(3 nm)/Ag(1.8 or 2.0 nm)]${}_{n}$Co(3 nm) multilayers with $n$ = 0--8 that were imbedded in the middle of symmetric, Py-based, double exchange-bias spin valves. The measurements yielded the parameter for spin flipping at the Co/Ag interface, ${\ensuremath{\delta}}_{\mathrm{Co}/\mathrm{Ag}}$ = 0.33\ifmmode\pm\else\textpm\fi{}0.1. Despite the expected larger lattice-mismatch-induced disorder at the Co/Ag interface, this value is similar to that reported earlier for the Co/Cu interface, suggesting that such disorder does not play a major role in \ensuremath{\delta} for these metal pairs.
Strategic leadership through performance management: FORESIGHT as PerformanceStat.

Unlike the private sector, no single overarching metric exists to evaluate public sector performance. Without concepts such as profit, it can be difficult for government agencies to be accountable ...
Research: The good, the bad, and the anxious

I n research and in life, human beings are prone to uncertainty and susceptible to the circular thinking of worry. According to the DSM-5, anxiety is anticipation of future threat. Sometimes that threat comes in the form of an overlooked article or a feeble bibliography, darkening the sky of a promising idea for a scholarly endeavor. Anxiety is characterized by excessive concern and distress which can manifest itself in symptoms such as checking and obsessing. The obstacles that are met along the research trail can send up red flags in the brain that lead researchers to check and recheck the same sources and obsess over the unknown. The processes of finding answers are, of course, tied to unknowing, and in that unknowing is a hotbed for apprehension. Some of the remedies for anxiety may be applied to research, probably not Xanax, but certainly a heavy dose of acceptance.
Comments on cosmic censorship in AdS/CFT

Recently Hertog, Horowitz, and Maeda (HHM) (hep-th/0310054) have proposed that cosmic censorship can be violated in the AdS/CFT context. They argue that for certain initial data there is insufficient energy available to make a black hole whose horizon is big enough to cloak the singularity that forms. We have investigated this proposal in the models HHM discuss and have thus far been unable to find initial data that provably satisfy this criterion, despite our development of an improved lower bound on the size of the singular region. This is consistent with recent numerical results (hep-th/0402109). For certain initial data, the energies of our configurations are not far above the lower bound on the requisite black hole mass, and so it is possible that in the exact time development naked singularities do form. We go on to argue that the finite radius cut-off AdS_5 situation discussed by HHM displays instabilities when the full 10D theory is considered. We propose an AdS_3 example that may well be free of this instability.
Microglia from offspring of dams with allergic asthma exhibit epigenomic alterations in genes dysregulated in autism

Dysregulation in immune responses during pregnancy increases the risk of a having a child with an autism spectrum disorder (ASD). Asthma is one of the most common chronic diseases among pregnant women, and symptoms often worsen during pregnancy. We recently developed a mouse model of maternal allergic asthma (MAA) that induces changes in sociability, repetitive, and perseverative behaviors in the offspring. Since epigenetic changes help a static genome adapt to the maternal environment, activation of the immune system may epigenetically alter fetal microglia, the brain's resident immune cells. We therefore tested the hypothesis that epigenomic alterations to microglia may be involved in behavioral abnormalities observed in MAA offspring. We used the genome-wide approaches of whole genome bisulfite sequencing to examine DNA methylation and RNA sequencing to examine gene expression in microglia from juvenile MAA offspring. Differentially methylated regions were enriched for immune signaling pathways and important microglial developmental transcription factor binding motifs. Differential expression analysis identified genes involved in controlling microglial sensitivity to the environment and shaping neuronal connections in the developing brain. Differentially expressed genes significantly overlapped genes with altered expression in human ASD cortex, supporting a role for microglia in the pathogenesis of ASD.
Submodular Risk Allocation

We analyze the optimal allocation of trades to portfolios when the cost associated with an allocation is proportional to each portfolio’s risk. Our investigation is motivated by changes in the over...
Oncologic Mechanical Emergencies

Prevalence of cancer and its various related complications continues to rise. Increasingly these life-threatening complications are initially managed in the emergency department, making a prompt and accurate diagnosis crucial to effectively institute the proper treatment and establish goals of care. The following oncologic emergencies are reviewed in this article: pericardial tamponade, superior vena cava syndrome, brain metastasis, malignant spinal cord compression, and hyperviscosity syndrome.
Langlands Parameters of Quivers in the Sato Grassmannian

Motivated by quantum field theoretic partition functions that can be expressed as products of tau functions of the KP hierarchy we attach several types of local geometric Langlands parameters to quivers in the Sato Grassmannian. We study related questions of Virasoro constraints, of moduli spaces of relevant quivers, and of classical limits of the Langlands parameters.
The multi-cavity free-electron laser☆

Abstract Analysis is made of a free-electron laser with many optical cavities where the cavities communicate with each other, not optically, but through the electron beam. Analytic analysis is made in a one-dimensional, linear approximation. A general expression is given for the growth rate in the exponential (high current) regime. In the regime where lethargy is important expressions are given in the two opposite limits of small and large numbers of cavities and bunches. Numerical simulation results, still in the one-dimensional approximation, but including nonlinearities, are presented. Three examples of the multi-cavity free-electron laser (MC/FEL) are presented.
MaxLength Considered Harmful to the RPKI

User convenience and strong security are often at odds, and most security applications need to find some sort of balance between these two (often opposing) goals. The Resource Public Key Infrastructure (RPKI), a security infrastructure built on top of interdomain routing, is not immune to this issue. The RPKI uses the maxLength attribute to reduce the amount of information that must be explicitly recorded in its cryptographic objects. MaxLength also allows operators to easily reconfigure their networks without modifying their RPKI objects. Our network measurements, however, suggest that the maxLength attribute strikes the wrong balance between security and user convenience. We therefore believe that operators should avoid using maxLength. We give operational recommendations and develop software that allow operators to reap many of the benefits of maxLength without its security costs.
Explaining anomalies in groups with characterizing subspace rules

Anomaly detection has numerous applications and has been studied vastly. We consider a complementary problem that has a much sparser literature: anomaly description. Interpretation of anomalies is crucial for practitioners for sense-making, troubleshooting, and planning actions. To this end, we present a new approach called x-PACS (for eXplaining Patterns of Anomalies with Characterizing Subspaces), which “reverse-engineers” the known anomalies by identifying (1) the groups (or patterns) that they form, and (2) the characterizing subspace and feature rules that separate each anomalous pattern from normal instances. Explaining anomalies in groups not only saves analyst time and gives insight into various types of anomalies, but also draws attention to potentially critical, repeating anomalies. In developing x-PACS, we first construct a desiderata for the anomaly description problem. From a descriptive data mining perspective, our method exhibits five desired properties in our desiderata. Namely, it can unearth anomalous patterns (i) of multiple different types, (ii) hidden in arbitrary subspaces of a high dimensional space, (iii) interpretable by human analysts, (iv) different from normal patterns of the data, and finally (v) succinct, providing a short data description. No existing work on anomaly description satisfies all of these properties simultaneously. Furthermore, x-PACS is highly parallelizable; it is linear on the number of data points and exponential on the (typically small) largest characterizing subspace size. The anomalous patterns that x-PACS finds constitute interpretable “signatures”, and while it is not our primary goal, they can be used for anomaly detection. Through extensive experiments on real-world datasets, we show the effectiveness and superiority of x-PACS in anomaly explanation over various baselines, and demonstrate its competitive detection performance as compared to the state-of-the-art.
A lattice formulation of chiral gauge theories

We present a method for implementing gauge theories of chiral fermions on the lattice. Discussed topics include: the lattice as a UV regulator, a chiral QED model, modification of the fermion determinant, large gauge-field momenta, and a non-perturbative problem.
PIVOT: platform for interactive analysis and visualization of transcriptomics data

Many R packages have been developed for transcriptome analysis but their use often requires familiarity with R and integrating results of different packages requires scripts to wrangle the datatypes. Furthermore, exploratory data analyses often generate multiple derived datasets such as data subsets or data transformations, which can be difficult to track. Here we present PIVOT, an R-based platform that wraps open source transcriptome analysis packages with a uniform user interface and graphical data management that allows non-programmers to interactively explore transcriptomics data. PIVOT supports more than 40 popular open source packages for transcriptome analysis and provides an extensive set of tools for statistical data manipulations. A graph-based visual interface is used to represent the links between derived datasets, allowing easy tracking of data versions. PIVOT further supports automatic report generation, publication-quality plots, and program/data state saving, such that all analysis can be saved, shared and reproduced. PIVOT will allow researchers with broad background to easily access sophisticated transcriptome analysis tools and interactively explore transcriptome datasets.
Sparse Functional Identification of Complex Cells from Spike Times and the Decoding of Visual Stimuli

We investigate the sparse functional identification of complex cells and the decoding of spatio-temporal visual stimuli encoded by an ensemble of complex cells. The reconstruction algorithm is formulated as a rank minimization problem that significantly reduces the number of sampling measurements (spikes) required for decoding. We also establish the duality between sparse decoding and functional identification and provide algorithms for identification of low-rank dendritic stimulus processors. The duality enables us to efficiently evaluate our functional identification algorithms by reconstructing novel stimuli in the input space. Finally, we demonstrate that our identification algorithms substantially outperform the generalized quadratic model, the nonlinear input model, and the widely used spike-triggered covariance algorithm.
Models and Information-Theoretic Bounds for Nanopore Sequencing

Nanopore sequencing is an emerging new technology for sequencing Deoxyribonucleic acid (DNA), which can read long fragments of DNA (~50 000 bases), in contrast to most current short-read sequencing technologies which can only read hundreds of bases. While nanopore sequencers can acquire long reads, the high error rates (20%–30%) pose a technical challenge. In a nanopore sequencer, a DNA is migrated through a nanopore, and current variations are measured. The DNA sequence is inferred from this observed current pattern using an algorithm called a base-caller . In this paper, we propose a mathematical model for the “channel” from the input DNA sequence to the observed current, and calculate bounds on the information extraction capacity of the nanopore sequencer. This model incorporates impairments, such as (non-linear) inter-symbol interference, deletions, and random response. These information bounds have two-fold application: 1) The decoding rate with a uniform input distribution can be used to calculate the average size of the plausible list of DNA sequences given an observed current trace. This bound can be used to benchmark existing base-calling algorithms, as well as serving a performance objective to design better nanopores. 2) When the nanopore sequencer is used as a reader in a DNA storage system, the storage capacity is quantified by our bounds.
Friendsourcing Peer Support for Alzheimer’s Caregivers Using Facebook Social Media

This research piloted an e-health intervention that used social media to friendsource peer support for Alzheimer’s disease (AD) caregivers. Friendsourcing is a variant of crowdsourcing. Crowdsourci...
Thoracic, Lumbar, and Sacral Pedicle Screw Placement Using Stryker-Ziehm Virtual Screw Technology and Navigated Stryker Cordless Driver 3: Technical Note

Object. Utilization of pedicle screws (PS) for spine stabilization is common in spinal surgery. With reliance on visual inspection of anatomical landmarks prior to screw placement, the free-hand technique requires a high level of surgeon skill and precision. Three-dimensional (3D), computer-assisted virtual neuronavigation improves the precision of PS placement and minimization steps. Methods. Twenty-three patients with degenerative, traumatic, or neoplastic pathologies received treatment via a novel three-step PS technique that utilizes a navigated power driver in combination with virtual screw technology. (1) Following visualization of neuroanatomy using intraoperative CT, a navigated 3-mm match stick drill bit was inserted at an anatomical entry point with a screen projection showing a virtual screw. (2) A Navigated Stryker Cordless Driver with an appropriate tap was used to access the vertebral body through a pedicle with a screen projection again showing a virtual screw. (3) A Navigated Stryker Cordless Driver with an actual screw was used with a screen projection showing the same virtual screw. One hundred and forty-four consecutive screws were inserted using this three-step, navigated driver, virtual screw technique. Results. Only 1 screw needed intraoperative revision after insertion using the three-step, navigated driver, virtual PS technique. This amounts to a 0.69% revision rate. One hundred percent of patients had intraoperative CT reconstructed images taken to confirm hardware placement. Conclusions. Pedicle screw placement utilizing the Stryker-Ziehm neuronavigation virtual screw technology with a three step, navigated power drill technique is safe and effective.
MR fingerprinting Deep RecOnstruction NEtwork (DRONE)

Demonstrate a novel fast method for reconstruction of multi-dimensional MR fingerprinting (MRF) data using deep learning methods.A neural network (NN) is defined using the TensorFlow framework and trained on simulated MRF data computed with the extended phase graph formalism. The NN reconstruction accuracy for noiseless and noisy data is compared to conventional MRF template matching as a function of training data size and is quantified in simulated numerical brain phantom data and International Society for Magnetic Resonance in Medicine/National Institute of Standards and Technology phantom data measured on 1.5T and 3T scanners with an optimized MRF EPI and MRF fast imaging with steady state precession (FISP) sequences with spiral readout. The utility of the method is demonstrated in a healthy subject in vivo at 1.5T.Network training required 10 to 74 minutes; once trained, data reconstruction required approximately 10 ms for the MRF EPI and 76 ms for the MRF FISP sequence. Reconstruction of simulated, noiseless brain data using the NN resulted in a RMS error (RMSE) of 2.6 ms for T1 and 1.9 ms for T2 . The reconstruction error in the presence of noise was less than 10% for both T1 and T2 for SNR greater than 25 dB. Phantom measurements yielded good agreement (R2 = 0.99/0.99 for MRF EPI T1 /T2 and 0.94/0.98 for MRF FISP T1 /T2 ) between the T1 and T2 estimated by the NN and reference values from the International Society for Magnetic Resonance in Medicine/National Institute of Standards and Technology phantom.Reconstruction of MRF data with a NN is accurate, 300- to 5000-fold faster, and more robust to noise and dictionary undersampling than conventional MRF dictionary-matching.
Enhancing the performance of a safe controller via supervised learning for truck lateral control

Correct-by-construction techniques, such as control barrier functions (CBFs), can be used to guarantee closed-loop safety by acting as a supervisor of an existing legacy controller. However, supervisory-control intervention typically compromises the performance of the closed-loop system. On the other hand, machine learning has been used to synthesize controllers that inherit good properties from a training dataset, though safety is typically not guaranteed due to the difficulty of analyzing the associated learning structure. In this paper, supervised learning is combined with CBFs to synthesize controllers that enjoy good performance with provable safety. A training set is generated by trajectory optimization that incorporates the CBF constraint for an interesting range of initial conditions of the truck model. A control policy is obtained via supervised learning that maps a feature representing the initial conditions to a parameterized desired trajectory. The learning-based controller is used as the performance controller and a CBF-based supervisory controller guarantees safety. A case study of lane keeping (LK) for articulated trucks shows that the controller trained by supervised learning inherits the good performance of the training set and rarely requires intervention by the CBF supervisor.
Differences in sleep functioning between individuals with seasonal affective disorder and major depressive disorder in Finland.

Abstract Background Sleep problems are commonly reported in seasonal affective disorder (SAD) and major depressive disorder (MDD). However, the specific characteristics of sleep difficulties differ. Frequent sleep problems in MDD are insomnia and night awakenings, whereas SAD patients complain of hypersomnia and daytime sleepiness. No previous studies have reported differences in sleep functioning between these two disorders. Methods We interviewed 4554 subjects from the Health 2011 survey and included 4153 individuals in this study. We selected participants who fulfilled the criteria for SAD (n = 223), nonseasonal-MDD (n = 238), SAD + MDD (n = 65), and controls (n = 3627). They completed the World Health Organization Composite International Diagnostic Interview, Munich version (M-CIDI), the Seasonal Pattern Assessment Questionnaire (SPAQ), The Beck Depression Inventory (BDI), the EuroQoL (EQ-5), the Alcohol Use Disorders Identification Test (AUDIT) and several questions about sleeping, based on the Basic Nordic Sleep Questionnaire (BNSQ). Results We found Significant differences between groups for “enough sleep”, “breathing interruptions during sleep”, “tiredness during the day”, and “sleeping difficulties”. Controls reported better functioning in all sleep variables. SAD + MDD individuals showed more problems in “enough sleep” than SAD, more “breathing interruptions during sleep” than SAD and nonseasonal-MDD, felt more “tired during the day” than SAD and nonseasonal-MDD, and reported more “sleeping difficulties” than SAD and nonseasonal-MDD. Finally, nonseasonal-MDD individuals felt more “tired during the day” than SAD. Conclusion Individuals with SAD + MDD show generalized sleeping problems. However, when SAD and nonseasonal-MDD appear separately, similar sleep functioning is observed. Nonseasonal-MDD subjects report to be more tired during the day than SAD.
Sea Level Rise Impacts on Wastewater Treatment Systems Along the U.S. Coasts

Author(s): Hummel, Michelle A; Berry, Matthew S; Stacey, Mark T | Abstract: As sea levels rise, coastal communities will experience more frequent and persistent nuisance flooding, and some low‐lying areas may be permanently inundated. Critical components of lifeline infrastructure networks in these areas are also at risk of flooding, which could cause significant service disruptions that extend beyond the flooded zone. Thus, identifying critical infrastructure components that are exposed to sea level rise is an important first step in developing targeted investment in protective actions and enhancing the overall resilience of coastal communities. Wastewater treatment plants are typically located at low elevations near the coastline to minimize the cost of collecting consumed water and discharging treated effluent, which makes them particularly susceptible to coastal flooding. For this analysis, we used geographic information systems to assess the exposure of wastewater infrastructure to various sea level rise projections at the national level. We then estimated the number of people who would lose wastewater services, which could be more than five times as high as previous predictions of the number of people at risk of direct flooding due to sea level rise. We also performed a regional comparison of wastewater exposure to marine and groundwater flooding in the San Francisco Bay Area. Overall, this analysis highlights the widespread exposure of wastewater infrastructure in the United States and demonstrates that local disruptions to infrastructure networks may have far‐ranging impacts on areas that do not experience direct flooding.
Experiments in high-frequency trading: comparing two market institutions

We implement a laboratory financial market where traders can access costly technology that reduces communication latency with a remote exchange. In this environment, we conduct a market design study on high-frequency trading: we contrast the performance of the newly proposed frequent batch auction (FBA) against the continuous double auction (CDA), which organizes trades in most exchanges worldwide. Our evidence suggests that, relative to the CDA, the FBA exhibits (1) less predatory trading behavior, (2) lower investments in low-latency communication technology, (3) lower transaction costs, and (4) lower volatility in market spreads and liquidity. We also find that transitory shocks in the environment have substantially greater impact on market dynamics in the CDA than in the FBA.
Folksonomies to Support Coordination and Coordination of Folksonomies

Members of highly-distributed groups in online production communities face challenges in achieving coordinated action. Existing CSCW research highlights the importance of shared language and artifacts when coordinating actions in such settings. To better understand how such shared language and artifacts are, not only a guide for, but also a result of collaborative work we examine the development of folksonomies (i.e., volunteer-generated classification schemes) to support coordinated action. Drawing on structuration theory, we conceptualize a folksonomy as an interpretive schema forming a structure of signification. Our study is set in the context of an online citizen-science project, Gravity Spy, in which volunteers label “glitches” (noise events recorded by a scientific instrument) to identify and name novel classes of glitches. Through a multi-method study combining virtual and trace ethnography, we analyze folksonomies and the work of labelling as mutually constitutive, giving folksonomies a dual role: an emergent folksonomy supports the volunteers in labelling images at the same time that the individual work of labelling images supports the development of a folksonomy. However, our analysis suggests that the lack of supporting norms and authoritative resources (structures of legitimation and domination) undermines the power of the folksonomy and so the ability of volunteers to coordinate their decisions about naming novel glitch classes. These results have implications for system design. If we hope to support the development of emergent folksonomies online production communities need to facilitate 1) tag gardening, a process of consolidating overlapping terms of artifacts; 2) demarcate a clear home for discourses around folksonomy disagreements; 3) highlight clearly when decisions have been reached; and 4) inform others about those decisions.
Bayesian Regularization for Graphical Models with Unequal Shrinkage

We consider a Bayesian framework for estimating a high-dimensional sparse precision matrix, in which adaptive shrinkage and sparsity are induced by a mixture of Laplace priors. Besides discussing o...
Policy Optimization as Wasserstein Gradient Flows

Policy optimization is a core component of reinforcement learning (RL), and most existing RL methods directly optimize parameters of a policy based on maximizing the expected total reward, or its surrogate. Though often achieving encouraging empirical success, its underlying mathematical principle on {\em policy-distribution} optimization is unclear. We place policy optimization into the space of probability measures, and interpret it as Wasserstein gradient flows. On the probability-measure space, under specified circumstances, policy optimization becomes a convex problem in terms of distribution optimization. To make optimization feasible, we develop efficient algorithms by numerically solving the corresponding discrete gradient flows. Our technique is applicable to several RL settings, and is related to many state-of-the-art policy-optimization algorithms. Empirical results verify the effectiveness of our framework, often obtaining better performance compared to related algorithms.
Dynamic properties of simulated brain network models and empirical resting-state data

Brain network models (BNMs) have become a promising theoretical framework for simulating signals that are representative of whole-brain activity such as resting-state fMRI. However, it has been difficult to compare the complex brain activity obtained from simulations to empirical data. Previous studies have used simple metrics to characterize coordination between regions such as functional connectivity. We extend this by applying various different dynamic analysis tools that are currently used to understand empirical resting-state fMRI (rs-fMRI) to the simulated data. We show that certain properties correspond to the structural connectivity input that is shared between the models, and certain dynamic properties relate more to the mathematical description of the brain network model. We conclude that the dynamic properties that explicitly examine patterns of signal as a function of time rather than spatial coordination between different brain regions in the rs-fMRI signal seem to provide the largest contrasts between different BNMs and the unknown empirical dynamical system. Our results will be useful in constraining and developing more realistic simulations of whole-brain activity.
Complexity Matching and Requisite Variety

Complexity matching emphasizes the condition necessary to efficiently transport information from one complex system to another and the mechanism can be traced back to the 1957 \textit{Introduction to Cybernetics} by Ross Ashby. Unlike this earlier work we argue that complexity can be expressed in terms of crucial events, which are generated by the processes of spontaneous self-organization. Complex processes, ranging from biological to sociological, must satisfy the homeodynamic condition and host crucial events that have recently been shown to drive the information transport between complex systems. We adopt a phenomenological approach, based on the subordination to periodicity that makes it possible to combine homeodynamics and self-organization induced crucial events. The complexity of crucial events is defined by the waiting- time probability density function (PDF) of the intervals between consecutive crucial events, which have an inverse power law (IPL) PDF $\psi (\tau )\propto 1/(\tau )^{\mu }$ with $1<\mu <3$. We establish the coupling between two temporally complex systems using a phenomenological approach inspired by models of swarm cognition and prove that complexity matching, namely sharing the same IPL index $\mu $, facilitates the transport of information, generating perfect synchronization, reminiscent of, but distinct from chaos synchronization. This advanced form of complexity matching is expected to contribute a significant progress in understanding and improving the bio-feedback therapies.
Exact Combinatorial Inference for Brain Images

The permutation test is known as the exact test procedure in statistics. However, often it is not exact in practice and only an approximate method since only a small fraction of every possible permutation is generated. Even for a small sample size, it often requires to generate tens of thousands permutations, which can be a serious computational bottleneck. In this paper, we propose a novel combinatorial inference procedure that enumerates all possible permutations combinatorially without any resampling. The proposed method is validated against the standard permutation test in simulation studies with the ground truth. The method is further applied in twin DTI study in determining the genetic contribution of the minimum spanning tree of the structural brain connectivity.
An Introduction to Global Navigation Satellite Systems

Products that rely on Global Navigation Satellite Systems (GNSS) have become an essential part of daily life for millions of people around the world. In addition to enabling navigation, these constellations of satellites and the signals they transmit provide a global, precise timing source, used in everything from electrical power grid phasing to synchronization of financial networks. This tutorial introduces the concept of radio navigation, describes the features of GNSS signals that make navigation possible, and explains how these signals are processed by GNSS receivers. The resulting measurements and error sources, such as atmospheric effects and multipath, are discussed. Then methods are presented for combining these measurements into a position, velocity, and time estimate. The tutorial concludes with a brief status report on the GNSS constellations, space applications and recent flight experiences, and active areas of research.
Molecular bases for the constitutive photomorphogenic phenotypes in Arabidopsis .

The transition from skotomorphogenesis to photomorphogenesis is regulated in part by the COP1/SPA complex and phytochrome-interacting factors (PIFs) in Arabidopsis The constitutive photomorphogenic (cop) phenotypes of cop1 and spaQ mutants have been shown to result from a high abundance of positively acting transcription factors. Here, we show that the four major PIF proteins are unstable in cop1 mutants and that overexpression of PIF1, PIF3, PIF4 and PIF5 suppresses cop1 phenotypes in the dark. A comparison of the transcriptome data among cop1, spaQ and pifQ reveals remarkably overlapping gene expression profiles with preferential regulation of PIF direct target genes. Additionally, HFR1 strongly inhibits the in vivo binding and transcriptional activation activity of PIF1 in the dark. Taken together, these data suggest that the cop phenotypes of the cop1 and spaQ mutants are due to a combination of the reduced level of PIFs, increased levels of positively acting transcription factors (e.g. HY5/HFR1) and the HFR1-mediated inhibition of PIF-targeted gene expression in the dark. This article has an associated 'The people behind the papers' interview.
Learning Stabilizable Dynamical Systems via Control Contraction Metrics

We propose a novel framework for learning stabilizable nonlinear dynamical systems for continuous control tasks in robotics. The key idea is to develop a new control-theoretic regularizer for dynamics fitting rooted in the notion of stabilizability, which guarantees that the learned system can be accompanied by a robust controller capable of stabilizing any open-loop trajectory that the system may generate. By leveraging tools from contraction theory, statistical learning, and convex optimization, we provide a general and tractable semi-supervised algorithm to learn stabilizable dynamics, which can be applied to complex underactuated systems. We validated the proposed algorithm on a simulated planar quadrotor system and observed notably improved trajectory generation and tracking performance with the control-theoretic regularized model over models learned using traditional regression techniques, especially when using a small number of demonstration examples. The results presented illustrate the need to infuse standard model-based reinforcement learning algorithms with concepts drawn from nonlinear control theory for improved reliability.
Bioinformatic Analysis of Nucleotide Cyclase Functional Centers and Development of ACPred Webserver

Cyclic mononucleotides, in particular 3',5'-cyclic guanosine monophosphate (cGMP) and 3',5'-cyclic adenosine monophosphate (cAMP), are molecular signals that mediate a myriad of biological responses in organisms across the tree of life. In plants, they transduce signals such as hormones and peptides perceived at receptors on the cell surface into the cytoplasm to orchestrate a cascade of biochemical reactions that enable them to grow and develop, and adapt to light, hormones, salt and drought stresses as well as pathogens. However, their generating enzymes (guanylyl cyclases, GCs and adenylyl cyclases, ACs) have just been recently discovered and are still poorly understood. Here, we employed a computational approach to probe the physicochemical properties of the catalytic centers of these enzymes and the knowledge of which, was used to create a web-based tool, ACPred (http://gcpred.com/acpred) for the prediction of AC functional centers from amino acid sequence. Understanding the nature of such catalytic centers have enabled the creation of predictive tools such as ACPred which will in turn, facilitate the discovery of novel cellular components across different systems.
Supply chain network competition among blood service organizations: a Generalized Nash Equilibrium framework

In this paper we present a Generalized Nash Equilibrium model of supply chain network competition among blood service organizations which compete not only for blood donors but also for business from hospitals and medical centers. The model incorporates not only link capacities and associated arc multipliers to capture perishability, but also bounds on the number of donors in regions as well as lower and upper bounds on the demands at the demand points in order to ensure needed amounts for surgeries, treatments, etc., while reducing wastage. The concept of a variational equilibrium is utilized to transform the problem into a variational inequality problem, and alternative formulations are given. A Lagrange analysis yields economic insights. The proposed algorithmic procedure is then applied to a series of numerical examples in order to illustrate the impacts of disruptions in the form of a reduction on the number of donors as well as that of decreases in capacities of critical links such as testing and processing on RBC prices, demands, net revenues of the blood service organizations, and their overall utilities.
Investor tastes: Implications for asset pricing in the public debt market

Abstract Some firms create innovative financial debt and equity products to attract specific investor tastes (i.e., non-monetary preferences unrelated to expected cash flows). Innovations can be costly, but they may also widen a firm's investor base, leading to a reduction in the cost of capital. Prior research is inconclusive about whether tailoring financial products to meet specific investor tastes affects asset pricing, which I posit stems from a lack of consensus on which assets satisfy a particular taste. My research design, however, avoids this ambiguity by testing the relation between asset pricing and an objectively identified investor taste—a taste for Shariah-compliant investments, which is satisfied via Islamic Finance. Using Malaysian data, I estimate price differences between conventional and Shariah-compliant bonds over an eight-year period (2005 to 2013). I find that an investor taste for Shariah-compliant bonds significantly affects bond pricing. These results hold even after controlling for issuer and issuance characteristics, including differences in covenant types, as well as the possibility that price and demand may be simultaneously determined. I also find evidence that investors with a taste for Shariah-compliant bonds have a significantly different demand distribution than investors without this taste, corroborating my assertion that a preference for Shariah-compliant investments is a distinct taste. Overall, my results are consistent with investors taste affecting the cost of capital.
Comparative advantage, complexity, and volatility

Abstract Less developed countries tend to experience higher output volatility, a fact that is in part explained by their specialization in more volatile sectors. This paper proposes theoretical explanations for this pattern of specialization – with the complexity of the goods playing a central role. Specifically, less developed countries with lower institutional ability to enforce contracts, or alternately, with low levels of human capital will specialize in less complex goods which are also characterized by higher levels of output volatility. We provide novel empirical evidence that less complex industries are indeed more volatile.
Optimal Relaying Beamforming in Multiple Access Broadcast Channel (MABC) Bidirectional Cognitive Radio Networks in Presence of Interferers

In this paper, a general cognitive radio system consisting of a set of users with different level of spectrum access including two primary transceivers and several types of secondary users is considered. It is assumed that two secondary users operate based on an underlay model at the same frequency bandwidth and at the same time as the primary users based on a multiple access broadcast channel bidirectional beamforming scheme. Other secondary users provide a relaying service to the primary users in exchange for the opportunity to send their messages towards their own destinations for a fixed portion of the communication cycle. In addition, it is assumed that some interferers are active during the communication cycle and cause interference for the network. Furthermore, it is assumed that only partial channel state information (CSI) between interferers and other nodes in the network is available. We provide a robust optimization method against imperfection on the interferers’ CSI to maximize the joint primary and secondary signal-to-interference-plus-noise-ratio with the assumption of limited available power at the secondary relays. An amplify-and-forward relaying scheme is deployed at the secondary relays and the optimal beamforming is obtained using second order convex programming method. The simulation results show the performance of the proposed beamforming method against the existence of interferers, and demonstrate the effectiveness of our robust method against uncertainty in knowledge of interferers’ CSIs.
Dynamical Computation of the Density of States and Bayes Factors Using Nonequilibrium Importance Sampling

Nonequilibrium sampling is potentially much more versatile than its equilibrium counterpart, but it comes with challenges because the invariant distribution is not typically known when the dynamics breaks detailed balance. Here, we derive a generic importance sampling technique that leverages the statistical power of configurations transported by nonequilibrium trajectories and can be used to compute averages with respect to arbitrary target distributions. As a dissipative reweighting scheme, the method can be viewed in relation to the annealed importance sampling (AIS) method and the related Jarzynski equality. Unlike AIS, our approach gives an unbiased estimator, with a provably lower variance than directly estimating the average of an observable. We also establish a direct relation between a dynamical quantity, the dissipation, and the volume of phase space, from which we can compute quantities such as the density of states and Bayes factors. We illustrate the properties of estimators relying on this sampling technique in the context of density of state calculations, showing that it scales favorable with dimensionality-in particular, we show that it can be used to compute the phase diagram of the mean-field Ising model from a single nonequilibrium trajectory. We also demonstrate the robustness and efficiency of the approach with an application to a Bayesian model comparison problem of the type encountered in astrophysics and machine learning.
Women's preferences for permanent contraception method and willingness to be randomized for a hypothetical trial.

Abstract Objective To understand women's preferences for permanent contraception by salpingectomy or tubal occlusion following standardized counseling and evaluate the practicality of a future randomized trial. Study design We invited pregnant and non-pregnant women planning permanent contraception at the University of California, Davis (UCD) and University of Tennessee (UT) Obstetrics and Gynecology clinics to participate. We enrolled women when they received routine counseling and signed procedure consent. Participants received standardized information sheets reviewing permanent contraception options based on pregnancy status then completed an anonymous survey with questions about demographics, method preference, and willingness to participate in a hypothetical randomized trial comparing salpingectomy and tubal occlusion. We evaluated predictors for salpingectomy preference using multivariable analysis. Results From July 2015 to October 2016, we enrolled 75 women at UCD and 63 women at UT. Overall, respondents preferred salpingectomy (63.0%); among the 47 women not currently pregnant at both sites, 40 (85.1%) preferred salpingectomy, most commonly because of higher efficacy. Although population characteristics differed significantly between the sites, only UCD site (aOR 4.2; 95% CI 1.9, 9.4) and non-pregnancy status (aOR 4.2; 95% CI 1.6, 10.8) predicted preference for salpingectomy in the multivariable model. Most participants (n=84, 60.9%) would not be willing to be randomized to a theoretical trial comparing salpingectomy and tubal occlusion procedures. Conclusion Among a diverse group of women from two different areas in the U.S. given a choice of permanent contraception methods, salpingectomy is preferred over tubal occlusion. Most women planning a permanent contraceptive procedure would not agree to a randomized comparison of these methods. Implications statement Salpingectomy, which offers theoretically higher efficacy and potentially greater ovarian cancer protection compared to tubal occlusion, is preferred by the majority of patients and should be offered to all women seeking permanent contraception. Differences in method choices less likely reflect the patient population and more likely the counseling provided.
Full Cost-of-Attendance Scholarships and College Choice: Evidence From NCAA Football

In 2015, the National Collegiate Athletic Association Division I schools were permitted to cover the “full cost of attendance” as a part of athletic scholarships for the first time, which allowed s...
On the riskiness of lower-tier suppliers: Managing sustainability in supply networks

Abstract Although multinational companies (MNCs) have increasingly embraced a sustainability strategy for their own operations, fewer have tried to engage their (tier-one) suppliers in their sustainability initiatives. It is even rarer that MNCs engage their suppliers' suppliers (lower-tier suppliers), despite the latter having a higher incidence of violations with more acute environmental and social impacts that can jeopardize the MNCs’ operations and reputation. We conducted inductive research on three supply networks in the automotive, electronics, and consumer product/pharmaceutical industries. We collected data on three leading sustainable MNCs and a subset of 9 tier-one suppliers and 22 lower-tier suppliers and complemented that information with data on several NGOs and industry organizations. This study (1) reveals that many lower-tier suppliers address their environmental and labor issues passively and constitute the riskiest suppliers in a supply network; (2) provides a grounded theoretical framework for managing a sustainable supply network that accounts for multiple network members as well as three sustainability dimensions (the 3Ps: profit, people, and planet); and (3) shows how processes MNCs use to manage their suppliers differ from processes these suppliers use with their own (lower-tier) suppliers. The study reveals the practices that leading sustainable MNCs use to manage their supply networks and provides important future research directions.