cssi/SciSciGPT-SciSciNet huggingface dataset:
A Cyber-Security Investment Game for Networked Control Systems

We formulate a resource-planning game between an attacker and a defender of a Network Control System (NCS). We consider the network to be operating in closed-loop with a linear quadratic regulator (LQR). We construct a general-sum, two-player, mixed strategy (MS) game, where the attacker attempts to destroy communication equipment of some nodes, and thereby render the LQR feedback gain matrix to be sparse, leading to degradation of closed-loop performance. The defender, on the other hand, aims to prevent this loss. Both players trade their control performance objectives for the cost of their actions. A Mixed Strategy Nash Equilibrium (MSNE) of the game represents the allocation of the players' respective resources for attacking or protecting the important network nodes. Numerical results for the New England power system model demonstrate that reliable defense is feasible unless the cost of attack is much smaller than the cost of protection per generator.
Non-uniformity of projection distributions attenuates resolution in Cryo-EM

Virtually all single-particle cryo-EM experiments currently suffer from specimen adherence to the air-water interface, leading to a non-uniform distribution in the set of projection views. Whereas it is well accepted that uniform projection distributions can lead to high-resolution reconstructions, non-uniform (anisotropic) distributions can negatively affect map quality, elongate structural features, and in some cases, prohibit interpretation altogether. Although some consequences of non-uniform sampling have been described qualitatively, we know little about how sampling quantitatively affects resolution in cryo-EM. Here, we show how inhomogeneity in any projection distribution scheme attenuates the global Fourier Shell Correlation (FSC) in relation to the number of particles and a single geometrical parameter, which we term the sampling compensation factor (SCF). The reciprocal of the SCF is defined as the average over Fourier shells of the reciprocal of the per-particle sampling and normalized to unity for uniform distributions. The SCF therefore ranges from one to zero, with values close to the latter implying large regions of poorly sampled or completely missing data in Fourier space. Using two synthetic test cases, influenza hemagglutinin and human apoferritin, we demonstrate how any amount of sampling inhomogeneity always attenuates the FSC compared to a uniform distribution. We advocate quantitative evaluation of the SCF criterion to approximate the effect of non-uniform sampling on resolution within experimental single-particle cryo-EM reconstructions.
A spectral deferred correction strategy for low Mach number reacting flows subject to electric fields

We propose an algorithm for low Mach number reacting flows subjected to electric field that includes the chemical production and transport of charged species. This work is an extension of a multi-i...
Massive computational acceleration by using neural networks to emulate mechanism-based biological models

For many biological applications, exploration of the massive parametric space of a mechanism-based model can impose a prohibitive computational demand. To overcome this limitation, we present a framework to improve computational efficiency by orders of magnitude. The key concept is to train a neural network using a limited number of simulations generated by a mechanistic model. This number is small enough such that the simulations can be completed in a short time frame but large enough to enable reliable training. The trained neural network can then be used to explore a much larger parametric space. We demonstrate this notion by training neural networks to predict pattern formation and stochastic gene expression. We further demonstrate that using an ensemble of neural networks enables the self-contained evaluation of the quality of each prediction. Our work can be a platform for fast parametric space screening of biological models with user defined objectives.
Profile-based Privacy for Locally Private Computations

Differential privacy has emerged as a gold standard in privacy-preserving data analysis. A popular variant is local differential privacy, where the data holder is the trusted curator. A major barrier, however, towards a wider adoption of this model is that it offers a poor privacy-utility trade-off.In this work, we address this problem by introducing a new variant of local privacy called profile-based privacy. The central idea is that the problem setting comes with a graph G of data generating distributions, whose edges encode sensitive pairs of distributions that should be made indistinguishable. This provides higher utility because unlike local differential privacy, we no longer need to make every pair of private values in the domain indistinguishable, and instead only protect the identity of the underlying distribution. We establish privacy properties of the profile-based privacy definition, such as post-processing invariance and graceful composition. Finally, we provide mechanisms that are private in this framework, and show via simulations that they achieve higher utility than the corresponding local differential privacy mechanisms.
New Approaches to Defender and Outsider Roles in School Bullying.

Author(s): Yun, Hye-Young | Advisor(s): Graham, Sandra H. | Abstract: Adolescents’ defender and outsider behaviors in classroom bullying situations, which involve various kinds of participant roles, are likely determined by individual characteristics, social status variables, and classroom/school contextual factors operating simultaneously in the peer ecology. Despite increasing attention in anti-bullying interventions to the group process and participant roles underlying bullying, there is little research on defending behavior that utilizes thisi?½multilevel approach. To comprehend defending behavior, it is necessary to understand both defender and outsider roles. In this dissertation, I refined the traditionally defined defender and outsider roles by modifying measures that have been used for the past 20 years. Then I examined intrapersonal, interpersonal, and contextual factors associated with subtypes of defender and outsider roles simultaneously. In Study 1, using 1373 adolescents (40% girls, Mage: 14 yrs) from 54 classrooms in six middle schools in South Korea, I examined how participant roles are distributed across these classrooms. My dissertation identified six participant roles in Korean middle school classrooms. Moreover, by investigating whether participant roles are associated with social status, I argue that bullying is a group process with a hierarchical structure. In Study 2, I selected 752 defenders and outsiders (54.3% girls) as identified in Study 1. Using latent profile analysis (LPA), I identified subtypes of defender and outsider roles (i.e., assertive defender, comforting defender, sympathetic outsider, indifferent outsider). To better understand the different attributes these role subtypes carried, using multi-method (i.e., peer-ratings, peer nomination, self-ratings) and multilevel multinomial analysis, I investigated how these unique defender and outsider profiles were associated with individual characteristics, social factors, and contextual factors. At the individual level, students with high affective empathy, self-efficacy, and sense of responsibility for intervention were more likely to be assertive defenders, relative to comforting defenders. Furthermore, assertive defenders were perceived as more popular, but less liked by peers, than comforting defenders. On the other hand, students with low perceived responsibility and weaker anti-bullying attitudes were more likely to be both sympathetic and indifferent outsiders. In addition to individual characteristics, the likelihood of being either a sympathetic or indifferent outsider, compared to a comforting defender, was weaker in classrooms where peers’ prosocial norms were high. Results of this dissertation have implications for tailor-made interventions to reduce school bullying and for studying defending behavior in multiple cultural contexts.
Solid-state Mamyshev oscillator

We present the first design and analysis of a solid-state Mamyshev oscillator. We utilize the phase-mismatched cascaded quadratic nonlinear process in a periodically poled lithium niobate waveguide to generate substantial spectral broadening for Mamyshev mode locking. The extensive spectral broadening bridges the two narrowband gain media in the two arms of the same cavity, leading to a broadband mode locking not attainable with either gain medium alone. Two pulses are coupled out of the cavity, and each of the output pulses carries a pulse energy of 25.3 nJ at a repetition rate of 100 MHz. The 10 dB bandwidth of 2.1 THz supports a transform-limited pulse duration of 322 fs, more than 5 times shorter than what can be achieved with either gain medium alone. Finally, effects of group velocity mismatch, group velocity dispersion, and nonlinear saturation on the performance of Mamyshev mode locking are numerically discussed in detail.
The Sound of Motions

Sounds originate from object motions and vibrations of surrounding air. Inspired by the fact that humans is capable of interpreting sound sources from how objects move visually, we propose a novel system that explicitly captures such motion cues for the task of sound localization and separation. Our system is composed of an end-to-end learnable model called Deep Dense Trajectory (DDT), and a curriculum learning scheme. It exploits the inherent coherence of audio-visual signals from a large quantities of unlabeled videos. Quantitative and qualitative evaluations show that comparing to previous models that rely on visual appearance cues, our motion based system improves performance in separating musical instrument sounds. Furthermore, it separates sound components from duets of the same category of instruments, a challenging problem that has not been addressed before.
Variations in brain morphometry among healthy preschoolers born preterm

Abstract Background Preterm birth is associated with an increased risk of neonatal brain injury, which can lead to alterations in brain maturation. Despite being born without the most significant medical consequences of preterm birth, infants born early remain at increased risk for subtle brain injury that affects future neurodevelopment and functioning. Aims To investigate the gray matter morphometry measures of cortical thickness, cortical surface area, and sulcal depth using MRI at 5 years of age in healthy children born preterm. Study design Cohort study. Subjects Participants were 52 children born preterm ( Outcome measures Cortical segmentation and calculation of morphometry measures were completed using FreeSurfer version 5.3.0 and compared between groups using surface-based, voxel-wise analyses. Results The preterm group had a significantly thinner cortex in temporal and parietal regions while cortical thickness was significantly larger within occipital and inferior frontal regions. Surface area was significantly reduced within the fusiform gyrus. Sulcal depth was significantly lower within the posterior parietal and inferior temporal regions but greater in the middle temporal and medial parietal regions. Conclusions Regional differences were found between preschoolers born preterm and full term in cortical thickness, surface area, and sulcal depth. Cortical thickness differences primarily overlapped with regions found in previous studies of older children and adults. Differences in sulcal depth may represent additional areas of maturational differences in preterm children. These findings likely represent a combination of delayed maturation and permanent alterations caused by the perinatal processes associated with preterm birth.
A low-noise latching comparator probe for waveform sampling applications

A new latching comparator probe is described. The probe is being developed as part of an effort to augment voltage measurement capability in the 10 Hz to 1 MHz frequency range. The probe offers an input voltage range of /spl plusmn/10 V, input impedance of 1 M/spl Omega/ and root mean square noise referred to the input as low as 55 /spl mu/V. The probe's 3-dB bandwidth is approximately 20 MHz. Total harmonic distortion is as low as -93 dB at 50 kHz. Gain flatness is within /spl plusmn/10 /spl mu/V/V from 100 Hz to 100 kHz. Improved step settling performance is achieved using a technique that minimizes circuit thermal errors. The probe's input range can be extended with a frequency-compensated 1-M/spl Omega/ input impedance attenuator allowing measurement of pulses in the microsecond regime up to 100 V. The attenuator can be compensated further with a digital filtering algorithm to achieve gain accuracy better than 100 /spl mu/V/V.
Assessment of surface and bulk-dominated methodologies to measure critical resolved shear stresses in hexagonal materials

Abstract Crystallographic slip in hexagonal metals involves a number of geometrically distinct slip families characterized by their slip direction and slip plane (basal, prismatic, and pyramidal). Owing to the low symmetry of hexagonal lattices, each of these slip families only have a few symmetrically equivalent slip systems (family members). Furthermore, different slip families become active at different resolved shear stress, i.e., they have different critical resolved shear stress values (CRSS). The plastic anisotropy of hexagonal materials makes the numerical prediction of their plastic behavior challenging and depends critically on the knowledge of CRSS values. The present contribution assesses the reliability of three proposed methods (with additional variations) to quantify CRSS values of the different hexagonal slip families. Those methods (a to c) rely on: (a) the statistics of observed surface slip traces in a (slightly) deformed polycrystal; (b) an iterative adjustment of CRSS values until a simulated single crystal indentation matches the corresponding experiment in terms of load–displacement response and residual surface topography of the indent; (c) in-situ high-energy X-ray diffraction to measure the evolution of resolved stress (from lattice strains) in grains for which single-family slip is probable based upon specific lattice reorientation conditions. Virtual experiments are performed on synthetic microstructures such that the (extracted) CRSS values resulting from simulating the different methodologies can be rigorously compared against the (target) CRSS values that are installed in the phenomenological constitutive material description used in the simulations. The resulting CRSS values of methods (a) exhibit a strong dependence on, and deterioration with, decreasing level of slip trace observability, which is an uncertain quantity in experimental measurements. For the inverse indentation method (b), the predicted CRSS values are within 8 % of their reference CRSS values for the two investigated cases. The high-energy X-ray diffraction method (c) most reliably determines CRSS values for basal and prism slip, but lacks a strict grain selection criterion to assess pyramidal slip.
Spatiotemporal calibration of atmospheric nitrogen dioxide concentration estimates from an air quality model for Connecticut

A spatiotemporal calibration and resolution refinement model was fitted to calibrate nitrogen dioxide ($$\hbox {NO}_2$$) concentration estimates from the Community Multiscale Air Quality (CMAQ) model, using two sources of observed data on $$\hbox {NO}_2$$ that differed in their spatial and temporal resolutions. To refine the spatial resolution of the CMAQ model estimates, we leveraged information using additional local covariates including total traffic volume within 2 km, population density, elevation, and land use characteristics. Predictions from this model greatly improved the bias in the CMAQ estimates, as observed by the much lower mean squared error (MSE) at the $$\hbox {NO}_2$$ monitor sites. The final model was used to predict the daily concentration of ambient $$\hbox {NO}_2$$ over the entire state of Connecticut on a grid with pixels of size 300 $$\times $$ 300 m. A comparison of the prediction map with a similar map for the CMAQ estimates showed marked improvement in the spatial resolution. The effect of local covariates was evident in the finer spatial resolution map, where the contribution of traffic on major highways to ambient $$\hbox {NO}_2$$ concentration stands out. An animation was also provided to show the change in the concentration of ambient $$\hbox {NO}_2$$ over space and time for 1994 and 1995.
Toward Undetectable Quantum Key Distribution Over Bosonic Channels

We propose a protocol based on pulse-position modulation and multi-level coding that allows one to bootstrap traditional quantum key distribution protocols while ensuring covertness, in the sense that no statistical test by the adversary can detect the presence of communication over the quantum channel better than a random guess. When run over a bosonic channel, our protocol can leverage existing discrete-modulated continuous-variable protocols. Since existing techniques to bound Eve’s information do not directly apply, we develop a new bound that results in positive, although very low, throughput for a range of channel parameters. The analysis of the protocol performance shows that covert secret key expansion is possible using a public authenticated classical channel and a quantum channel largely but not fully under the control of an adversary, which we precisely define. We also establish a converse result showing that, under the golden standard of quantum key distribution, by which the adversary completely controls the quantum channel, no covert key generation is possible.
Thermodynamic transport properties in dense stars

The thermodynamic transport properties of special relativistic imperfect fluids, as found in dense stars, are investigated. These properties, which include thermal. and electrical conductivities, electrothermal coefficients, and bulk and shear viscosities may be formulated in terms of the momentum distribution functions obtained from the solution of the Boltzmann transport equation. Spherical harmonic solutions of the relaxation form of the relativistic magnetic Boltzmann transport equation have also been obtained which give the non-equilibrium momentum distribution function perturbation f-f(0) = Δf(p) in terms of electromagnetic and thermal fields.
Academic impairment among high school students with ADHD: The role of motivation and goal-directed executive functions.

Abstract Attention-Deficit/Hyperactivity Disorder (ADHD) is associated with academic failure in high school; however the underpinnings of these difficulties are insufficiently understood. This study examined deficits in self-regulated learning in a sample of high school students with ADHD (n = 32) compared to demographically similar classmates without ADHD (n = 18). A multimethod battery of self and parent rating scales and cognitive tasks measured aspects of intrinsic motivation, extrinsic motivation, and goal-directed executive functions. A multiple regression modeled predictors of current Grade Point Average (GPA). Results indicated that high school students with ADHD placed lower value on academics (d = .99), were less likely to use goal-setting strategies (d = .95), possessed lower levels of metacognition (d = 1.86), and showed significant deficits in task-based cognitive flexibility (d = .80). After controlling for covariates, the set of self-regulated learning variables explained 23% of the variance in GPA, with metacognition (6% of variance explained) and cognitive flexibility (7% of variance explained) serving as significant predictors of outcome. Findings suggest that higher-order executive function deficits play a critical role in the academic functioning of high school students and students with ADHD show large deficits in these areas. Thus, interventions that target metacognition and cognitive flexibility (i.e., the ability to think through decisions before acting, inhibit automatic responses, and make effective decisions for a desired goal) may be particularly promising to remediate ADHD-related academic problems in high school.
The cosmic-ray source composition

The cosmic-ray source composition is shown to be related to the observed cosmic-ray abundances by a set of linear equations which are valid for either ad hoc path length distributions or quantitative solutions of galactic propagation models. In the limit of no ionization energy loss, this method is exact for all path length distributions and can replace more detailed numerical integrations; it provides a simple way to calculate directly the source composition implied by a particular set of cross sections, observed abundances, and propagation model. This method is used to determine the source abundances of the galactic cosmic rays in a model-independent manner. Compared to a simple exponential, the path length distribution required by observations is deficient in the contributions from short (not greater than 0.5 g/sq cm) paths at energies of a few GeV per nucleon.
DiffTaichi: Differentiable Programming for Physical Simulation

We present DiffTaichi, a new differentiable programming language tailored for building high-performance differentiable physical simulators. Based on an imperative programming language, DiffTaichi generates gradients of simulation steps using source code transformations that preserve arithmetic intensity and parallelism. A light-weight tape is used to record the whole simulation program structure and replay the gradient kernels in a reversed order, for end-to-end backpropagation. We demonstrate the performance and productivity of our language in gradient-based learning and optimization tasks on 10 different physical simulators. For example, a differentiable elastic object simulator written in our language is 4.2x shorter than the hand-engineered CUDA version yet runs as fast, and is 188x faster than the TensorFlow implementation. Using our differentiable programs, neural network controllers are typically optimized within only tens of iterations.
Long-term antithrombotic therapy after venous stent placement.

ObjectivesTo examine the prescribing patterns and outcomes of antithrombotic regimens after venous stent placement.MethodsA total of 87 patients who received inferior vena cava or iliofemoral venou...
Academic language and listening comprehension—Two sides of the same coin? An empirical examination of their dimensionality, relations to reading comprehension, and assessment modality

Two widely studied language skills in relation to reading comprehension are listening comprehension skill and academic language proficiency. Although their constituent skills and theoretical accounts of how they are related to reading comprehension share a large overlap, they have been studied in separate lines of work. In this study, we investigated the dimensionality of listening comprehension and academic language proficiency tasks, their relations to reading comprehension, and the impact of assessment modality (reading vs. oral language) of academic language proficiency, using data from children in Grade 2 (N = 350). Two cohorts of children from the same schools were assessed on the same set of listening comprehension, word reading, reading comprehension, and academic language tasks. Whereas the first three constructs were assessed in identical manner across the 2 cohorts, academic language tasks were assessed in different modalities (1 cohort in a reading context and the other cohort in an oral language context). Academic language proficiency and listening comprehension skill tasks were best described as having a general oral language construct that captured common variance among all the tasks as well as having specific residual factors. Students’ average performance on academic language tasks was lower in the reading context, wherein students’ reading skill was also captured beyond the academic “language” proficiency. Across assessment modalities, it was the general oral language construct, not the specific factors, that was reliable, and consistently and most dominantly related to reading comprehension after accounting for word reading. (PsycInfo Database Record (c) 2020 APA, all rights reserved)
Asymptotic Series and Precocious Scaling

A heuristic proof is given that the divergent QCD perturbation series is, asymptotic. By treating it as an asymptotic expansion we show that it makes sense to keep only the first few terms. The example of e+e− annihilation is considered. It is shown that by keeping only the first few terms one can get within a per cent (or smaller) of the complete sum of the series even at very low momenta where the coupling is large. More generally, this affords an explanation of the phenomena of precocious scaling and why keeping only leading order corrections generally works so well.
Doubly-adaptive artificial compression methods for incompressible flow

This report presents adaptive artificial compression methods in which the time-step and artificial compression parameter $\varepsilon $ are independently adapted. The resulting algorithms are supported by analysis and numerical tests. The first and second-order methods are embedded. As a result, the computational, cognitive and space complexities of the adaptive $% \varepsilon ,k$ algorithms are negligibly greater than that of the simplest, first-order, constant $\varepsilon ,$ constant $k$ artificial compression method.
Phase Segmentation in Atom-Probe Tomography Using Deep Learning-Based Edge Detection.

Atom-probe tomography (APT) facilitates nano- and atomic-scale characterization and analysis of microstructural features. Specifically, APT is well suited to study the interfacial properties of granular or heterophase systems. Traditionally, the identification of the interface between, for precipitate and matrix phases, in APT data has been obtained either by extracting iso-concentration surfaces based on a user-supplied concentration value or by manually perturbing the concentration value until the iso-concentration surface qualitatively matches the interface. These approaches are subjective, not scalable, and may lead to inconsistencies due to local composition inhomogeneities. We introduce a digital image segmentation approach based on deep neural networks that transfer learned knowledge from natural images to automatically segment the data obtained from APT into different phases. This approach not only provides an efficient way to segment the data and extract interfacial properties but does so without the need for expensive interface labeling for training the segmentation model. We consider here a system with a precipitate phase in a matrix and with three different interface modalities—layered, isolated, and interconnected—that are obtained for different relative geometries of the precipitate phase. We demonstrate the accuracy of our segmentation approach through qualitative visualization of the interfaces, as well as through quantitative comparisons with proximity histograms obtained by using more traditional approaches.
Loss of Conformality in Efimov Physics

The loss of conformal invariance in Efimov physics is due to the merger and disappearance of an infrared and an ultraviolet fixed point of a three-body renormalization group flow as the spatial dimension d is varied. In the case of identical bosons at unitarity, it is known that there are two critical dimensions \(d_\mathrm{1}=2.30\) and \(d_\mathrm{2}=3.76\) at which there is loss of conformality. For \(d d_\mathrm{2}\), the beta function of the three-body coupling has real roots which correspond to infrared and ultraviolet fixed points. The fixed points merge and disappear into the complex plane at the critical dimensions \(d_1\) and \(d_2\). For \(d_\mathrm{1}<d<d_\mathrm{2}\), the beta function has complex roots and the renormalization group flow for the three-body coupling constant is a limit cycle.
Graphs in which all maximal bipartite subgraphs have the same order

Motivated by the concept of well-covered graphs, we define a graph to be well-bicovered if every vertex-maximal bipartite subgraph has the same order (which we call the bipartite number). We first give examples of them, compare them with well-covered graphs, and characterize those with small or large bipartite number. We then consider graph operations including the union, join, and lexicographic and cartesian products. Thereafter we consider simplicial vertices and 3-colored graphs where every vertex is in triangle, and conclude by characterizing the maximal outerplanar graphs that are well-bicovered.
Quadrupolar interactions between acceptor pairs in p -doped semiconductors

We consider the interaction between acceptor pairs in doped semiconductors in the limit of large interacceptor separation relevant for low doping densities. Modeling individual acceptors via the spherical model of Baldereschi and Lipari, we calculate matrix elements of the quadrupole tensor between the four degenerate ground states and show that the acceptor has a nonzero quadrupole moment. As a result, the dominant contribution to the large-separation acceptor-acceptor interaction comes from direct (charge-density) terms rather than exchange terms. The quadrupole is the leading nonzero moment, so the electric quadrupole-quadrupole interaction dominates for large separation. We calculate the matrix elements of the quadrupole-quadrupole interaction Hamiltonian in a product-state basis and diagonalize, obtaining a closed-form expression for the energies and degeneracies of the sixteen-state energy spectrum. All dependence on material parameters enters via an overall prefactor, resulting in surprisingly simple and universal results. This simplicity is due, in part, to a mathematical happenstance, the nontrivial vanishing of a particular Wigner 6-$j$ symbol, $\left\{\begin{array}{ccc}2a 2a 2\\ \frac{3}{2}a \frac{3}{2}a \frac{3}{2}\end{array}\right\}=0$. Results are relevant to the control of two-qubit interactions in quantum computing implementations based on acceptor spins, as well as calculations of the thermodynamic properties of insulating $p$-type semiconductors.
Comparison of the long-term impact and clinical outcomes of fewer doses and standard doses of human papillomavirus vaccine in the United States: A database study

Background Human papillomavirus (HPV)-related disease remains a significant source of morbidity and mortality, and this underscores the need to increase HPV vaccination to reduce the burden of the disease. The objective of this study was to examine the association between the number of HPV vaccine doses and the risk of histologically confirmed preinvasive cervical disease and high-grade cytology. Methods This retrospective matched cohort study used administrative data from Optum's Clinformatics DataMart Database to identify females aged 9 to 26 years who received 1 or more quadrivalent HPV vaccine doses between January 2006 and June 2015. Cases and controls were matched on region, age, sexually transmitted disease history, and pregnancy. All had a Papanicolaou test ≥1 year after the date of the matched case's final dose. Cox proportional hazards models were used to examine the association between the number of HPV vaccine doses and the incidence of preinvasive cervical disease and high-grade cytology. The Kaplan-Meier method was used to estimate the cumulative incidence rate at the 5-year follow-up. Results The study included 133,082 females (66,541 vaccinated and 66,541 unvaccinated) stratified by the number of HPV vaccine doses and the vaccine initiation age. Among those aged 15 to 19 years, the hazard ratio (HR) for high-grade cytology for the 3-dose group was 0.84 (95% confidence interval [CI], 0.73-0.97), whereas the HRs for histologically confirmed preinvasive cervical disease for 1, 2, and 3 doses were 0.64 (95% CI, 0.47-0.88), 0.72 (95% CI, 0.54-0.95), and 0.66 (95% CI, 0.55-0.80), respectively. Conclusions The receipt of 1, 2, or 3 doses of an HPV vaccine by females aged 15 to 19 years was associated with a lower incidence of preinvasive cervical disease in comparison with unvaccinated females, and this supports the use of any HPV vaccination in reducing the burden of the disease.
Actions Speak Louder Than (Pass)words: Passive Authentication of Smartphone * Users via Deep Temporal Features

Prevailing user authentication schemes on smartphones rely on explicit user interaction, where a user types in a passcode or presents a biometric cue such as face, fingerprint, or iris. In addition to being cumbersome and obtrusive to the users, such authentication mechanisms pose security and privacy concerns. Passive authentication systems can tackle these challenges by unobtrusively monitoring the user’s interaction with the device. We propose a Siamese Long Short-Term Memory (LSTM) network architecture for passive authentication, where users can be verified without requiring any explicit authentication step. On a dataset comprising of measurements from 30 smartphone sensor modalities for 37 users, we evaluate our approach on 8 dominant modalities, namely, keystroke dynamics, GPS location, accelerometer, gyroscope, magnetometer, linear accelerometer, gravity, and rotation sensors. Experimental results find that a genuine user can be correctly verified 96.47% a false accept rate of 0.1% within 3 seconds.
An uncertainty principle for neural coding: Conjugate representations of position and velocity are mapped onto firing rates and co-firing rates of neural spike trains.

The hippocampal system contains neural populations that encode an animal's position and velocity as it navigates through space. Here, we show that such populations can embed two codes within their spike trains: a firing rate code ( R) conveyed by within-cell spike intervals, and a co-firing rate code ( R˙ ) conveyed by between-cell spike intervals. These two codes behave as conjugates of one another, obeying an analog of the uncertainty principle from physics: information conveyed in R comes at the expense of information in R˙ , and vice versa. An exception to this trade-off occurs when spike trains encode a pair of conjugate variables, such as position and velocity, which do not compete for capacity across R and R˙ . To illustrate this, we describe two biologically inspired methods for decoding R and R˙ , referred to as sigma and sigma-chi decoding, respectively. Simulations of head direction and grid cells show that if firing rates are tuned for position (but not velocity), then position is recovered by sigma decoding, whereas velocity is recovered by sigma-chi decoding. Conversely, simulations of oscillatory interference among theta-modulated "speed cells" show that if co-firing rates are tuned for position (but not velocity), then position is recovered by sigma-chi decoding, whereas velocity is recovered by sigma decoding. Between these two extremes, information about both variables can be distributed across both channels, and partially recovered by both decoders. These results suggest that populations with different spatial and temporal tuning properties-such as speed versus grid cells-might not encode different information, but rather, distribute similar information about position and velocity in different ways across R and R˙ . Such conjugate coding of position and velocity may influence how hippocampal populations are interconnected to form functional circuits, and how biological neurons integrate their inputs to decode information from firing rates and spike correlations.
Toward Filament Segmentation Using Deep Neural Networks

We use a well-known deep neural network framework, called Mask R-CNN, for identification of solar filaments in full-disk H-$\alpha$ images from Big Bear Solar Observatory (BBSO). The image data, collected from BBSO’s archive, are integrated with the spatiotemporal metadata of filaments retrieved from the Heliophysics Events Knowledgebase (HEK) system. This integrated data is then treated as the ground-truth in the training process of the model. The available spatial metadata are the output of a currently running filament-detection module developed and maintained by the Feature Finding Team; an international consortium selected by NASA. Despite the known challenges in the identification and characterization of filaments by the existing module, which in turn are inherited into any other module that intends to learn from such outputs, Mask R-CNN shows promising results. Trained and validated on two years worth of BBSO data, this model is then tested on the three following years. Our case-by-case and overall analyses show that Mask R-CNN can clearly compete with the existing module and in some cases even perform better. Several cases of false positives and false negatives, that are correctly segmented by this model are also shown. The overall advantages of using the proposed model are two-fold: First, deep neural networks’ performance generally improves as more annotated data, or better annotations are provided. Second, such a model can be scaled up to detect other solar events, as well as a single multi-purpose module. The results presented in this study introduce a proof of concept in benefits of employing deep neural networks for detection of solar events, and in particular, filaments.
False Discovery Variance Reduction in Large Scale Simultaneous Hypothesis Tests

Statistical dependence between hypotheses poses a significant challenge to the stability of large scale multiple hypotheses testing. Ignoring it often results in an unacceptably large spread in the false positive proportion even though the average value is acceptable (Fan et al., J Amer Statist Assoc 107(499): 1019-1035, 2012; Owen J R Stat Soc Ser B 67(3): 411–426, 2005; Qiu et al., Stat Appl Genet Mol Biol 4: 32, 2005 and Schwartzman and Lin Biometrika 98(1): 199–214, 2011). However, the statistical dependence structure of data is often unknown. Using a generic signal-processing model, Bayesian multiple testing, and simulations, we demonstrate that the variance of the false positive proportion can be substantially reduced even under unknown short range dependence. We do this by modeling the data generating process as a stationary ergodic binary signal process embedded in noisy observations. We derive conditional probabilities needed for the Bayesian multiple testing by incorporating nearby observations into a second order Taylor series approximation. Simulations under general conditions are carried out to assess the validity and the variance reduction of the approach. Along the way, we address the problem of sampling a random Markov matrix with specified stationary distribution and lower bounds on the top absolute eigenvalues, which is of interest in its own right.
Detection of latent heteroscedasticity and group-based regression effects in linear models via Bayesian model selection

Standard linear modeling approaches make potentially simplistic assumptions regarding the structure of categorical effects that may obfuscate more complex relationships governing data. For example,...
Convergence of Parameter Estimates for Regularized Mixed Linear Regression Models

We consider Mixed Linear Regression (MLR), where training data have been generated from a mixture of distinct linear models (or clusters) and we seek to identify the corresponding coefficient vectors. We introduce a Mixed Integer Programming (MIP) formulation for MLR subject to regularization constraints on the coefficient vectors. We establish that as the number of training samples grows large, the MIP solution converges to the true coefficient vectors in the absence of noise. Subject to slightly stronger assumptions, we also establish that the MIP identifies the clusters from which the training samples were generated. In the special case where training data come from a single cluster, we establish that the corresponding MIP yields a solution that converges to the true coefficient vector even when training data are perturbed by (martingale difference) noise. We provide a counterexample indicating that in the presence of noise, the MIP may fail to produce the true coefficient vectors for more than one clusters. We also provide numerical results testing the MIP solutions in synthetic examples with noise.
Distributed Sequential Hypothesis Testing with Dependent Sensor Observations

In this paper, we consider the problem of distributed sequential detection using wireless sensor networks (WSNs) in the presence of imperfect communication channels between the sensors and the fusion center (FC). We assume that sensor observations are spatially dependent. We propose a copula-based distributed sequential detection scheme that characterizes the spatial dependence. Specifically, each local sensor collects observations regarding the phenomenon of interest and forwards the information obtained to the FC over noisy channels. The FC fuses the received messages using a copula-based sequential test. Moreover, we show the asymptotic optimality of the proposed copula-based sequential test. Numerical experiments are conducted to demonstrate the effectiveness of our approach.
Cohomology groups of Fermat curves via ray class fields of cyclotomic fields

Abstract The absolute Galois group of the cyclotomic field K = Q ( ζ p ) acts on the etale homology of the Fermat curve X of exponent p. We study a Galois cohomology group which is valuable for measuring an obstruction for K-rational points on X. We analyze a 2-nilpotent extension of K which contains the information needed for measuring this obstruction. We determine a large subquotient of this Galois cohomology group which arises from Heisenberg extensions of K. For p = 3 , we perform a Magma computation with ray class fields, group cohomology, and Galois cohomology which determines it completely.
Endothelial mTOR maintains hematopoiesis during aging

Aging leads to a decline in hematopoietic stem and progenitor cell (HSPC) function. We recently discovered that aging of bone marrow endothelial cells (BMECs) leads to an altered crosstalk between the BMEC niche and HSPCs, which instructs young HSPCs to behave as aged HSPCs. Here, we demonstrate aging leads to a decrease in mTOR signaling within BMECs that potentially underlies the age-related impairment of their niche activity. Our findings reveal that pharmacological inhibition of mTOR using Rapamycin has deleterious effects on hematopoiesis. To formally determine whether endothelial-specific inhibition of mTOR can influence hematopoietic aging, we conditionally deleted mTOR in ECs (mTOR(ECKO)) of young mice and observed that their HSPCs displayed attributes of an aged hematopoietic system. Transcriptional profiling of HSPCs from mTOR(ECKO) mice revealed that their transcriptome resembled aged HSPCs. Notably, during serial transplantations, exposure of wild-type HSPCs to an mTOR(ECKO) microenvironment was sufficient to recapitulate aging-associated phenotypes, confirming the instructive role of EC-derived signals in governing HSPC aging.
The Predictability of Future Aggregate Earnings Growth and the Relation between Aggregate Analyst Recommendation Changes and Future Returns

We extend prior research examining the relation between aggregate recommendation changes and future returns by documenting that this relation varies over time as a function of the predictability of...
T-cell activation is modulated by the 3D mechanical microenvironment.

T cells recognize mechanical forces through a variety of cellular pathways, including mechanical triggering of both the T-cell receptor (TCR) and integrin LFA-1. Here we show that T cells can recognize forces arising from the mechanical rigidity of the microenvironment. We fabricated 3D scaffold matrices with mechanical stiffness tuned to the range 4-40 kPa and engineered them to be microporous, independently of stiffness. We cultured T cells and antigen presenting cells within the matrices and studied T-cell activation by flow cytometry and live-cell imaging. We found that there was an augmentation of T-cell activation, proliferation, and migration speed in the context of mechanically stiffer 3D matrices as compared to softer materials. These results show that T cells can sense their 3D mechanical environment and alter both their potential for activation and their effector responses in different mechanical environments. A 3D scaffold of tunable stiffness and consistent microporosity offers a biomaterial advancement for both translational applications and reductionist studies on the impact of tissue microenvironmental factors on cellular behavior.
Aftershocks of Monetary Unification: Hysteresis with a Financial Twist

Abstract In the 1990s it was widely agreed that neither Europe nor the United States satisfied the conditions for constituting an optimum currency area, although the U.S. came closer (Bayoumi and Eichengreen 1993). Moderating this concern about Europe was the fact that it was possible to distinguish a regional core and periphery. Using updated data, we confirm that the United States remains closer to an optimum currency area. More intriguingly, the Euro Area shows striking changes in correlations and responses. We interpret these as reflecting hysteresis with a financial twist, in which the financial system causes aggregate supply and demand shocks to reinforce each other. An implication is that the Euro Area needs vigorous, coordinated regulation of its banking and financial systems by a single supervisor—that monetary union without banking union will not work.
The removal of atmospheric gases by particulate matter

The conditions for removal of an atmospheric pollutant by water droplets or on the surface of an aerosol have been investigated. For pollutants which do not react in water droplets or on aerosol surfaces, the removal lifetimes of the pollutant equal the atmospheric turnover lifetimes (~ 106s) of the droplets or aerosol if κƒ < 1 (water droplets) or CvapNθeq < 1 (aerosol); where κ is the Henry's law constant for the pollutant,ƒ is the fraction of air occupied by the water droplets, Cvap is the vapor phase concentration of the pollutant, N is the total number of aerosol sites (occupied plus unoccupied) per particle, and θeq is the fraction of those sites occupied at equilibrium. The quantities ƒ and N are taken to be 1.2 × 10−11 in the lower troposphere and 106 sites/particle, respectively, for typical atmospheric conditions. The equilibrium parameters κ and θeq are estimated from the approximate relationships κ ∼- CvapoCsoluo, (14) θeq ∼- 10(CvapCvapo) (1 + 10CvapCvapo)−1, (26) where Cvapo is the equilibrium vapor pressure of the pure liquid pollutant and Csoluo is the total concentration of pollutant (including any dissociated species) in a saturated water solution. With Equations (14) and (26) and the fact Csoluo < 1022 molecules cm−3, we find that Cvapo must be less than ∼- 1 × 1011 molecules cm−3 (4 ppb) for removal at rates equal to those for atmospheric removal rates of the particulate matter. For pollutants that do undergo reactions in water droplets or on aerosol surfaces, the removal rate can be enhanced over the non-reactive removal rate only if the effective removal rate coefficient for the pollutant in the water droplet or on the aerosol surface exceeds the reciprocal turnover lifetime of the particulate matter (i.e. for removal rate coefficients greater than ∼- 10−6 s−1).
Ask Your Doctor? Direct-to-Consumer Advertising of Pharmaceuticals

We measure the impact of direct-to-consumer television advertising (DTCA) by drug manufacturers. Our identification strategy exploits shocks to local advertising markets generated by the political advertising cycle and a regulatory intervention affecting a single product. We find evidence of significant business stealing effects among branded, advertised drugs. In addition, we show positive spillovers from drug advertisements to non-advertised competitors in the same class. We decompose the effect and show it is primarily due to new customers. Finally, we provide evidence that DTCA is cost-effective from a societal standpoint in our setting.