cssi/SciSciGPT-SciSciNet huggingface dataset:
Neural Knowledge Extraction From Cloud Service Incidents

The move from boxed products to services and the widespread adoption of cloud computing has had a huge impact on the software development life cycle and DevOps processes. Particularly, incident management has become critical for developing and operating large-scale services. Prior work on incident management has heavily focused on the challenges with incident triaging and de-duplication. In this work, we address the fundamental problem of structured knowledge extraction from service incidents. We have built SoftNER, a framework for unsupervised knowledge extraction from service incidents. We frame the knowledge extraction problem as a Named-Entity Recognition task for extracting factual information. SoftNER leverages structural patterns like key value pairs and tables for bootstrapping the training data. Further, we build a novel multitask learning based BiLSTM-CRF model which leverages not just the semantic context but also the data-types for named-entity extraction. We have deployed SoftNER at Microsoft, a major cloud service provider and have evaluated it on more than 2 months of cloud incidents. We show that the unsupervised machine learning pipeline has a high precision of 0.96. Our multi-task learning based deep learning model also outperforms the state of the art NER models. Lastly, using the knowledge extracted by SoftNER we are able to build significantly more accurate models for important downstream tasks like incident triaging.
Domino tilings of the Aztec diamond with doubly periodic weightings

In this paper we consider domino tilings of the Aztec diamond with doubly periodic weightings. In particular, a family of models which, for any k∈N, includes models with k smooth regions is analyzed as the size of the Aztec diamond tends to infinity. We use a nonintersecting paths formulation and give a double integral formula for the correlation kernel of the Aztec diamond of finite size. By a classical steepest descent analysis of the correlation kernel, we obtain the local behavior in the smooth and rough regions, as the size of the Aztec diamond tends to infinity. From the mentioned limit the macroscopic picture, such as the arctic curves and, in particular, the number of smooth regions, is deduced. Moreover, we compute the limit of the height function, and, as a consequence, we confirm in the setting of this paper that the limit in the rough region fulfills the complex Burgers’ equation, as stated by Kenyon and Okounkov.
An enlarging neck mass with dyspnea and left finger pain in a 63‐year‐old woman

We described a rare presentation of papillary thyroid cancer metastasized to left finger. This case highlights the importance of an early and effective engagement of multidisciplinary team approach to optimize patient care and that papillary thyroid cancer can occasionally pursue an aggressive, lethal course.
Immigration and Employment: Substitute Versus Complementary Labor in Selected African Countries

This paper uses census and household survey data on Cameroon, Ghana, and South Africa toexamine immigration's impact in the context of a segmented labor market in Sub-SaharanAfrica. We find that immigration affects (i) employment (ii) employment allocation betweeninformal and formal sectors, and (iii) the type of employment within each sector. The directionof the impact depends on the degree of complementarity between immigrants and nativeworkers' skills. Immigration is found to be productivity-enhancing in the short to near term incountries where, the degree of complementarity between immigrants and native workers'skill sets is the highest.
From Basel I to Basel III: Sequencing Implementation in Developing Economies

Developing economies can strengthen their financial systems by implementing the main elements of global regulatory reform. But to build an effective prudential framework, they may need to adapt international standards taking into account the sophistication and size of their financial institutions, the relevance of different financial operations in their market, the granularity of information available and the capacity of their supervisors. Under a proportionate application of the Basel standards, smaller institutions with less complex business models would be subject to a simpler regulatory framework that enhances the resilience of the financial sector without generating disproportionate compliance costs. This paper provides guidance on how non-Basel Committee member countries could incorporate banks' capital and liquidity standards into their framework. It builds on the experience gained by the authors in the course of their work in providing technical assistance on-and assessing compliance with-international standards in banking supervision.
Dual-Cycle Constrained Bijective Vae-Gan For Tagged-To-Cine Magnetic Resonance Image Synthesis

Tagged magnetic resonance imaging (MRI) is a widely used imaging technique for measuring tissue deformation in moving organs. Due to tagged MRI’s intrinsic low anatomical resolution, another matching set of cine MRI with higher resolution is sometimes acquired in the same scanning session to facilitate tissue segmentation, thus adding extra time and cost. To mitigate this, in this work, we propose a novel dual-cycle constrained bijective VAE-GAN approach to carry out tagged-to-cine MR image synthesis. Our method is based on a variational autoencoder backbone with cycle reconstruction constrained adversarial training to yield accurate and realistic cine MR images given tagged MR images. Our framework has been trained, validated, and tested using 1,768, 416, and 1,560 subject-independent paired slices of tagged and cine MRI from twenty healthy subjects, respectively, demonstrating superior performance over the comparison methods. Our method can potentially be used to reduce the extra acquisition time and cost, while maintaining the same workflow for further motion analyses.
HAN-based monopropellant assessment for spacecraft

The growing cost of space missions, the need for increased mission performance, and concerns associated with environmental issues are changing rocket design and propellant selection criteria. Whereas a propellant's performance was once defined solely in terms of specific impulse and density, now environmental safety, operability, and cost are considered key drivers. Present emphasis on these considerations has heightened government and commercial launch sector interest in Hydroxylammonium Nitrate (HAN)-based liquid propellants as options to provide simple, safe, reliable, low cost, and high performance monopropellant systems.
Jet-Surface Interaction Noise from High Aspect Ratio Nozzles: Test Summary

Noise and flow data have been acquired for a 16:1 aspect ratio rectangular nozzle exhausting near a simple surface at the NASA Glenn Research Center as part of an ongoing effort to understand, model, and predict the noise produced by current and future concept aircraft employing a tightly integrated engine airframe designs. The particular concept under consideration in this experiment is a blended-wing-body airframe powered by a series of electric fans exhausting through slot nozzle over an aft deck. The exhaust Mach number and surface length were parametrically varied during the test. Far-field noise data were acquired for all nozzle surface geometries and exhaust flow conditions. Phased-array noise source localization data and in-flow pressure data were also acquired for a subset of the isolated (no surface) and surface configurations; these measurements provide data that have proven useful for modeling the jet-surface interaction noise source and the surface effect on the jet-mixing noise in round jets. A summary of the nozzle surface geometry, flow conditions tested, and data collected are presented.
More on zeros and approximation of the Ising partition function

We consider the problem of computing the partition function , where is a quadratic or cubic polynomial on the Boolean cube . In the case of a quadratic polynomial f, we show that the partition function can be approximated within relative error in quasi-polynomial time if the Lipschitz constant of the non-linear part of f with respect to the metric on the Boolean cube does not exceed , for any , fixed in advance. For a cubic polynomial f, we get the same result under a somewhat stronger condition. We apply the method of polynomial interpolation, for which we prove that for complex-valued polynomials in a neighborhood of a real-valued f satisfying the above mentioned conditions. The bounds are asymptotically optimal. Results on the zero-free region are interpreted as the absence of a phase transition in the Lee–Yang sense in the corresponding Ising model. The novel feature of the bounds is that they control the total interaction of each vertex but not every single interaction of sets of vertices.
OntoGUM: Evaluating Contextualized SOTA Coreference Resolution on 12 More Genres

SOTA coreference resolution produces increasingly impressive scores on the OntoNotes benchmark. However lack of comparable data following the same scheme for more genres makes it difficult to evaluate generalizability to open domain data. This paper provides a dataset and comprehensive evaluation showing that the latest neural LM based end-to-end systems degrade very substantially out of domain. We make an OntoNotes-like coreference dataset called OntoGUM publicly available, converted from GUM, an English corpus covering 12 genres, using deterministic rules, which we evaluate. Thanks to the rich syntactic and discourse annotations in GUM, we are able to create the largest human-annotated coreference corpus following the OntoNotes guidelines, and the first to be evaluated for consistency with the OntoNotes scheme. Out-of-domain evaluation across 12 genres shows nearly 15-20% degradation for both deterministic and deep learning systems, indicating a lack of generalizability or covert overfitting in existing coreference resolution models.
Towards A Model-based Prognostics Methodology for Electrolytic Capacitors: A Case Study Based on Electrical Overstress Accelerated Aging

This paper presents a model-driven methodology for predicting the remaining useful life of electrolytic capacitors. This methodology adopts a Kalman filter approach in conjunction with an empirical state-based degradation model to predict the degradation of capacitor parameters through the life of the capacitor. Electrolytic capacitors are important components of systems that range from power supplies on critical avionics equipment to power drivers for electro-mechanical actuators. These devices are known for their comparatively low reliability and given their critical role in the system, they are good candidates for component level prognostics and health management. Prognostics provides a way to assess remaining useful life of a capacitor based on its current state of health and its anticipated future usage and operational conditions. This paper proposes and empirical degradation model and discusses experimental results for an accelerated aging test performed on a set of identical capacitors subjected to electrical stress. The data forms the basis for developing the Kalman-filter based remaining life prediction algorithm.
Targeting Tat–TAR RNA Interaction for HIV-1 Inhibition

The HIV-1 Tat protein interacts with TAR RNA and recruits CDK9/cyclin T1 and other host factors to induce HIV-1 transcription. Thus, Tat-TAR RNA interaction, which is unique for HIV-1, represents an attractive target for anti-HIV-1 therapeutics. To target Tat-TAR RNA interaction, we used a crystal structure of acetylpromazine bound to the bulge of TAR RNA, to dock compounds from the Enamine database containing over two million individual compounds. The docking procedure identified 173 compounds that were further analyzed for the inhibition of HIV-1 infection. The top ten inhibitory compounds with IC50 ≤ 6 µM were selected and the three least toxic compounds, T6780107 (IC50 = 2.97 μM), T0516-4834 (IC50 = 0.2 μM) and T5628834 (IC50 = 3.46 μM), were further tested for HIV-1 transcription inhibition. Only the T0516-4834 compound showed selective inhibition of Tat-induced HIV-1 transcription, whereas the T6780107 compound inhibited equally basal and Tat-induced transcription and the T5628834 compound only inhibited basal HIV-1 transcription. The compounds were tested for the inhibition of translation and showed minimal (<25%) effect. The T0516-4834 compound also showed the strongest inhibition of HIV-1 RNA expression and p24 production in CEM T cells and peripheral blood mononuclear cells infected with HIV-1 IIIB. Of the three compounds, only the T0516-4834 compound significantly disrupted Tat-TAR RNA interaction. Additionally, of the three tested compounds, T5628834 and, to a lesser extent, T0516-4834 disrupted Tat-CDK9/cyclin T1 interaction. None of the three compounds showed significant inhibition of the cellular CDK9 and cyclin T1 levels. In silico modelling showed that the T0516-4834 compound interacted with TAR RNA by binding to the bulge formed by U23, U25, C39, G26,C39 and U40 residues. Taken together, our study identified a novel benzoxazole compound that disrupted Tat-TAR RNA interaction and inhibited Tat-induced transcription and HIV-1 infection, suggesting that this compound might serve as a new lead for anti-HIV-1 therapeutics.
Enhancing Parameter-Free Frank Wolfe with an Extra Subproblem.

Aiming at convex optimization under structural constraints, this work introduces and analyzes a variant of the Frank Wolfe (FW) algorithm termed ExtraFW. The distinct feature of ExtraFW is the pair of gradients leveraged per iteration, thanks to which the decision variable is updated in a prediction-correction (PC) format. Relying on no problem dependent parameters in the step sizes, the convergence rate of ExtraFW for general convex problems is shown to be ${\cal O}(\frac{1}{k})$, which is optimal in the sense of matching the lower bound on the number of solved FW subproblems. However, the merit of ExtraFW is its faster rate ${\cal O}\big(\frac{1}{k^2} \big)$ on a class of machine learning problems. Compared with other parameter-free FW variants that have faster rates on the same problems, ExtraFW has improved rates and fine-grained analysis thanks to its PC update. Numerical tests on binary classification with different sparsity-promoting constraints demonstrate that the empirical performance of ExtraFW is significantly better than FW, and even faster than Nesterov's accelerated gradient on certain datasets. For matrix completion, ExtraFW enjoys smaller optimality gap, and lower rank than FW.
Independent Configurable Architecture for Reliable Operation of Unmanned Systems with Distributed Onboard Services

This paper presents the development of ICAROUS-2 (Independent Configurable Architecture for Reliable Operation of Unmanned Systems with Distributed Onboard Services), the second generation of a software architecture that integrates several algorithms as distributed onboard services to enable robust autonomous UAS applications. In particular, the ICAROUS architecture defines a framework to perform detect and avoid, geofencing, path monitoring, path planning, and autonomous decision making to ensure safety and mission progress. Most of the core algorithms implemented in ICAROUS are formally verified using an interactive theorem prover. These algorithms are composed together using a plan execution engine, whose operational semantics is formally specified. A description of the integrated architecture, services currently available, and flight test results highlighting the capability of ICAROUS are presented.
ExceLint: automatically finding spreadsheet formula errors

Spreadsheets are one of the most widely used programming environments, and are widely deployed in domains like finance where errors can have catastrophic consequences. We present a static analysis specifically designed to find spreadsheet formula errors. Our analysis directly leverages the rectangular character of spreadsheets. It uses an information-theoretic approach to identify formulas that are especially surprising disruptions to nearby rectangular regions. We present ExceLint, an implementation of our static analysis for Microsoft Excel. We demonstrate that ExceLint is fast and effective: across a corpus of 70 spreadsheets, ExceLint takes a median of 5 seconds per spreadsheet, and it significantly outperforms the state of the art analysis.
Boundedness results for 2-adic Galois images associated to hyperelliptic Jacobians

Let $K$ be a number field, and let $C$ be a hyperelliptic curve over $K$ with Jacobian $J$. Suppose that $C$ is defined by an equation of the form $y^{2} = f(x)(x - \lambda)$ for some irreducible monic polynomial $f \in \mathcal{O}_{K}[x]$ of discriminant $\Delta$ and some element $\lambda \in \mathcal{O}_{K}$. Our first main result says that if there is a prime $\mathfrak{p}$ of $K$ dividing $(f(\lambda))$ but not $(2\Delta)$, then the image of the natural $2$-adic Galois representation is open in $\mathrm{GSp}(T_{2}(J))$ and contains a certain congruence subgroup of $\mathrm{Sp}(T_{2}(J))$ depending on the maximal power of $\mathfrak{p}$ dividing $(f(\lambda))$. We also present and prove a variant of this result that applies when $C$ is defined by an equation of the form $y^{2} = f(x)(x - \lambda)(x - \lambda')$ for distinct elements $\lambda, \lambda' \in K$. We then show that the hypothesis in the former statement holds for almost all $\lambda \in \mathcal{O}_{K}$ and prove a quantitative form of a uniform boundedness result of Cadoret and Tamagawa.
Improved algorithms for the bichromatic two-center problem for pairs of points

Abstract We consider a bichromatic two-center problem for pairs of points. Given a set S of n pairs of points in the plane, for every pair, we want to assign a red color to one point and a blue color to the other, in such a way that the value max ⁡ { r 1 , r 2 } is minimized, where r 1 (resp., r 2 ) is the radius of the smallest enclosing disk of all red (resp., blue) points. Previously, an exact algorithm of O ( n 3 log 2 ⁡ n ) time and a ( 1 + e ) -approximate algorithm of O ( n + ( 1 / e ) 6 log 2 ⁡ ( 1 / e ) ) time were known. In this paper, we propose a new exact algorithm of O ( n 2 log 2 ⁡ n ) time and a new ( 1 + e ) -approximate algorithm of O ( n + ( 1 / e ) 3 log 2 ⁡ ( 1 / e ) ) time.
Fast magnetic reconnection induced by resistivity gradients in 2D magnetohydrodynamics

Using two-dimensional (2D) magnetohydrodynamics simulations, we show that Petschek-type magnetic reconnection can be induced using a simple resistivity gradient in the reconnection outflow direction, revealing the key ingredient of steady fast reconnection in the collisional limit. We find that the diffusion region self-adjusts its half-length to fit the given gradient scale of resistivity. The induced reconnection x-line and flow stagnation point always reside within the resistivity transition region closer to the higher resistivity end. The opening of one exhaust by this resistivity gradient will lead to the opening of the other exhaust located on the other side of the x-line, within the region of uniform resistivity. Potential applications of this setup to reconnection-based thrusters and solar spicules are discussed. In a separate set of numerical experiments, we explore the maximum plausible reconnection rate using a large and spatially localized resistivity right at the x-line. Interestingly, the resulting current density at the x-line drops significantly so that the normalized reconnection rate remains bounded by the value ≃ 0.2, consistent with the theoretical prediction.
Are native plants green? Assessing environmental performances of locally-owned facilities

We study the impact of corporate ownership and community conditions on firm environmental pollution. While the existing literature often thinks of environmental pollution as a unitary construct, we emphasize the distinction between toxic emissions, which have immediate but locally bounded impact, and greenhouse gas (GHG) emissions which have gradual but global impact, producing climate change. Using a facility-level panel of all manufacturing facilities in the US from 2010-2018, and leveraging within-facility changes in ownership status, we show that locally owned firms have lower levels of toxic emissions, but they are also less likely to report GHG emissions, and have higher levels of such emissions when they do report them, with these effects being stronger where the owner is not only headquartered locally, but has operations limited to that state. Our study suggests that while the pressures of local embeddedness may drive firms to be more environmentally responsible towards their local community, they also make firms more indifferent to their global environmental impact.
Saddle Flow Dynamics: Observable Certificates and Separable Regularization

This paper proposes a certificate, rooted in observability, for asymptotic convergence of saddle flow dynamics of convex-concave functions to a saddle point. This observable certificate directly bridges the gap between the invariant set and the equilibrium set in a LaSalle argument, and generalizes conventional conditions such as strict convexity-concavity and proximal regularization. We further build upon this certificate to propose a separable regularization method for saddle flow dynamics that makes minimal requirements on convexity-concavity and yet still guarantees asymptotic convergence to a saddle point. Our results generalize to saddle flow dynamics with projections on the vector field and have an immediate application as a distributed solution to linear programs.
Machine learning for modeling the progression of Alzheimer disease dementia using clinical data: a systematic literature review.

Objective Alzheimer disease (AD) is the most common cause of dementia, a syndrome characterized by cognitive impairment severe enough to interfere with activities of daily life. We aimed to conduct a systematic literature review (SLR) of studies that applied machine learning (ML) methods to clinical data derived from electronic health records in order to model risk for progression of AD dementia. Materials and methods We searched for articles published between January 1, 2010, and May 31, 2020, in PubMed, Scopus, ScienceDirect, IEEE Explore Digital Library, Association for Computing Machinery Digital Library, and arXiv. We used predefined criteria to select relevant articles and summarized them according to key components of ML analysis such as data characteristics, computational algorithms, and research focus. Results There has been a considerable rise over the past 5 years in the number of research papers using ML-based analysis for AD dementia modeling. We reviewed 64 relevant articles in our SLR. The results suggest that majority of existing research has focused on predicting progression of AD dementia using publicly available datasets containing both neuroimaging and clinical data (neurobehavioral status exam scores, patient demographics, neuroimaging data, and laboratory test values). Discussion Identifying individuals at risk for progression of AD dementia could potentially help to personalize disease management to plan future care. Clinical data consisting of both structured data tables and clinical notes can be effectively used in ML-based approaches to model risk for AD dementia progression. Data sharing and reproducibility of results can enhance the impact, adaptation, and generalizability of this research.
Breast cancer survivorship care during the COVID-19 pandemic within an urban New York Hospital System.

Abstract Purpose To examine clinicodemographic determinants associated with breast cancer survivorship follow-up during COVID-19. Methods We performed a retrospective, population-based cohort study including early stage (Stage I-II) breast cancer patients who underwent resection between 2006 and 2018 in a New York City hospital system. The primary outcome was oncologic follow-up prior to and during the COVID-19 pandemic. Secondary analyses compared differences in follow-up by COVID-19 case rates stratified by ZIP code. Results A total of 2942 patients with early-stage breast cancer were available for analysis. 1588 (54%) of patients had attended follow-up in the year prior to the COVID-19 period but failed to continue to follow-up during the pandemic, either in-person or via telemedicine. 1242 (42%) patients attended a follow-up appointment during the COVID-19 pandemic. Compared with patients who did not present for follow-up during COVID-19, patients who continued their oncologic follow-up during the pandemic were younger (p = 0.049) more likely to have received adjuvant radiation therapy (p = 0.025), and have lower household income (p = 0.031) on multivariate modeling. When patients who live in Bronx, New York, were stratified by ZIP code, there was a modest negative association (r = −0.56) between COVID-19 cases and proportion of patients who continued to follow-up during the COVID-19 period. Conclusion We observed a dramatic disruption in routine breast cancer follow-up during the COVID-19 pandemic. Providers and health systems should emphasize reintegrating patients who missed appointments during COVID-19 back into regular surveillance programs to avoid significant morbidity and mortality from missed breast cancer recurrences.
Quantile regression with interval data

This paper investigates the identification of quantiles and quantile regression parameters when observations are set valued. We dei¬ne the identification set of quantiles of random sets in a way that extends the dei¬nition of quantiles for regular random variables. We then give sharp characterization of this set by extending concepts from random set theory. For quantile regression parameters, we show that the identification set is characterized by a system of conditional moment inequalities. This characterization extends that of parametric quantile regression for regular random variables. Estimation and inference theories are developed for continuous cases, discrete cases, nonparametric conditional quantiles, and parametric quantile regressions. A fast computational algorithm of set linear programming is proposed. Monte Carlo experiments support our theoretical properties. We apply the proposed method to analyze the ei¬ects of cleanup of hazardous waste sites on local housing values.
Unsteady control of supersonic turbulent cavity flow based on resolvent analysis

We use resolvent analysis to develop a physics-based, open-loop, unsteady control strategy to attenuate pressure fluctuations in turbulent flow over a rectangular cavity with a length-to-depth ratio of along cavity walls relative to the baseline and is approximately twice that achievable by comparable steady blowing. Dynamic mode decomposition on the controlled flows confirms that the optimal actuation input indeed suppresses the formation of the large-scale Rossiter modes. It is expected that the present flow control guideline derived from resolvent analysis will also be applicable at higher Reynolds numbers with the aid of physical insights and further validation.
Dark Matter Detection with Bound Nuclear Targets: The Poisson Phonon Tail.

Dark matter (DM) scattering with nuclei in solid-state systems may produce elastic nuclear recoil at high energies and single-phonon excitation at low energies. When the DM momentum is comparable to the momentum spread of nuclei bound in a lattice, ${q}_{0}=\sqrt{2{m}_{N}{\ensuremath{\omega}}_{0}}$ where ${m}_{N}$ is the mass of the nucleus and ${\ensuremath{\omega}}_{0}$ is the optical phonon energy, an intermediate scattering regime characterized by multiphonon excitations emerges. We study a greatly simplified model of a single nucleus in a harmonic potential and show that, while the mean energy deposited for a given momentum transfer $q$ is equal to the elastic value ${q}^{2}/(2{m}_{N})$, the phonon occupation number follows a Poisson distribution and thus the energy spread is $\mathrm{\ensuremath{\Delta}}E=q\sqrt{{\ensuremath{\omega}}_{0}/(2{m}_{N})}$. This observation suggests that low-threshold calorimetric detectors may have significantly increased sensitivity to sub-GeV DM compared to the expectation from elastic scattering, even when the energy threshold is above the single-phonon energy, by exploiting the tail of the Poisson distribution for phonons above the elastic energy. We use a simple model of electronic excitations to argue that this multiphonon signal will also accompany ionization signals induced from DM-electron scattering or the Migdal effect. In well-motivated models where DM couples to a heavy, kinetically mixed dark photon, we show that these signals can probe experimental milestones for cosmological DM production via thermal freeze-out, including the thermal target for Majorana fermion DM.
Rigidity and stability of submanifolds with entropy close to one

We show that an n-dimensional surface whose entropy is close to that of an n-dimensional plane is close in Hausdorff distance to some n-dimensional plane at every scale. Moreover we show that self-expanders of low entropy converge in the Hausdorff sense to their asymptotic cones.
utr.annotation: a tool for annotating genomic variants that could influence post-transcriptional regulation.

SUMMARY Whole genome sequencing of patient populations is identifying thousands of new variants in UnTranslated Regions(UTRs). While the consequences of UTR mutations are not as easily predicted from primary sequence as coding mutations are, there are some known features of UTRs that modulate their function. utr.annotation is an R package that can be used to annotate potential deleterious variants in the UTR regions for both human and mouse species. Given a CSV or VCF format variant file, utr.annotation provides information of each variant on whether and how it alters known translational regulators including: upstream Open Reading Frames (uORFs), upstream Kozak sequences, polyA signals, Kozak sequences at the annotated translation start site, start codons, and stop codons, conservation scores in the variant position, and whether and how it changes ribosome loading based on a model derived from empirical data. AVAILABILITY utr.annotation is freely available on Bitbucket (https://bitbucket.org/jdlabteam/utr.annotation/src/master/) and CRAN (https://cran.r-project.org/web/packages/utr.annotation/index.html). SUPPLEMENTARY INFORMATION Supplementary data are available at https://wustl.box.com/s/yye99bryfin89nav45gv91l5k35fxo7z.
Millisecond exoplanet imaging: II. regression equations and technical discussion.

The leading difficulty in achieving the contrast necessary to directly image exoplanets and associated structures (e.g., protoplanetary disks) at wavelengths ranging from the visible to the infrared is quasi-static speckles (QSSs). QSSs are hard to distinguish from planets at the necessary level of precision to achieve high contrast. QSSs are the result of hardware aberrations that are not compensated for by the adaptive optics (AO) system; these aberrations are called non-common path aberrations (NCPAs). In 2013, Frazin showed how simultaneous millisecond telemetry from the wavefront sensor (WFS) and a science camera behind a stellar coronagraph can be used as input into a regression scheme that simultaneously and self-consistently estimates NCPAs and the sought-after image of the planetary system (exoplanet image). When run in a closed-loop configuration, the WFS measures the corrected wavefront, called the AO residual (AOR) wavefront. The physical principle underlying the regression method is rather simple: when an image is formed at the science camera, the AOR modules both the speckles arising from NCPAs as well as the planetary image. Therefore, the AOR can be used as a probe to estimate NCPA and the exoplanet image via regression techniques. The regression approach is made more difficult by the fact that the AOR is not exactly known since it can be estimated only from the WFS telemetry. The simulations in the Part I paper provide results on the joint regression on NCPAs and the exoplanet image from three different methods, called ideal, naive, and bias-corrected estimators. The ideal estimator is not physically realizable (it is useful as a benchmark for simulation studies), but the other two are. The ideal estimator uses true AOR values (available in simulation studies), but it treats the noise in focal plane images via standard linearized regression. Naive regression uses the same regression equations as the ideal estimator, except that it substitutes the estimated values of the AOR for true AOR values in the regression formulas, which can result in problematic biases (however, Part I provides an example in which the naive estimate makes a useful estimate of NCPAs). The bias-corrected estimator treats the errors in AOR estimates, but it requires the probability distribution that governs the errors in AOR estimates. This paper provides the regression equations for ideal, naive, and bias-corrected estimators, as well as a supporting technical discussion.
Some aspects of Ricci flow on the 4-sphere

In this paper, on 4-spheres equipped with Riemannian metrics we study some integral conformal invariants, the sign and size of which under Ricci flow characterize the standard 4-sphere. We obtain a conformal gap theorem, and for Yamabe metrics of positive scalar curvature with L^2 norm of the Weyl tensor of the metric suitably small, we establish the monotonic decay of the L^p norm for certain p>2 of the reduced curvature tensor along the normalized Ricci flow, with the metric converging exponentially to the standard 4-sphere.
Renormalization of the 3D exciton spectrum by the disorder

Abstract Effect of short-range disorder on the excited states of the exciton is studied. Disorder causes an obvious effect of broadening. Microscopically, an exciton, as an entity, is scattered by the large-scale disorder fluctuations. Much less trivial is that short-scale fluctuations, with a period of the order of the Bohr radius, cause a well-defined down-shift of the exciton levels. We demonstrate that this shift exceeds the broadening parametrically and study the dependence of this shift on the orbital number. Difference of the shifts for neighboring levels leads to effective renormalization of the Bohr energy. Most remarkable effect is the disorder-induced splitting of S and P exciton levels. The splitting originates from the fact that disorder lifts the accidental degeneracy of the hydrogen-like levels. We draw an analogy between this splitting and the Lamb shift in quantum electrodynamics.
Stimulation of phospholipase Cβ1 by Gαq promotes the assembly of stress granule proteins.

During adverse conditions, mammalian cells suppress protein production by sequestering the translational machinery in membrane-less organelles known as stress granules. Here, we found that activati...
Multi-Class Cell Detection Using Spatial Context Representation

In digital pathology, both detection and classification of cells are important for automatic diagnostic and prognostic tasks. Classifying cells into subtypes, such as tumor cells, lymphocytes or stromal cells is particularly challenging. Existing methods focus on morphological appearance of individual cells, whereas in practice pathologists often infer cell classes through their spatial context. In this paper, we propose a novel method for both detection and classification that explicitly incorporates spatial contextual information. We use the spatial statistical function to describe local density in both a multi-class and a multi-scale manner. Through representation learning and deep clustering techniques, we learn advanced cell representation with both appearance and spatial context. On various benchmarks, our method achieves better performance than state-of-the-arts, especially on the classification task.
Enabling large-scale multidisciplinary design optimization through adjoint sensitivity analysis

This paper is written to honor Raphael T. Haftka’s seminal contributions to multidisciplinary design optimization. We focus on those contributions that directly impacted our research, namely: the adjoint method for computing derivatives, wing aerostructural design optimization, and architectures for multidisciplinary design optimization. For each of these topics, we describe Haftka’s contributions, how they impacted our research, and examples of what they enabled us to do. The overarching theme of the contributions and developments described in this paper is the efficient computation of derivatives, which, together with gradient-based optimizers, enables the optimization with respect to large numbers of design variables, even when using costly high-fidelity models.
Electron Pulse Compression with Optical Beat Note

Compressing electron pulses is important in many applications of electron beam systems. In this study, we propose to use optical beat notes to compress electron pulses. The beat frequency is chosen to match the initial electron pulse duration, which enables the compression of electron pulses with a wide range of durations. This functionality extends the optical control of electron beams, which is important in compact electron beam systems such as dielectric laser accelerators. We also find that the dominant frequency of the electron charge density changes continuously along its drift trajectory, which may open up new opportunities in coherent interaction between free electrons and quantum or classical systems.
Understanding Health and Behavioral Trends of Successful Students Through Machine Learning Models

This study analyzes patterns of physical, mental, lifestyle, and personality factors in college students in different periods over the course of a semester and models their relationships with students’ academic performance. The data analyzed was collected through smartphones and Fitbit. The use of machine learning models derived from the gathered data was employed to observe the extent of students’ behavior associated with their GPA, lifestyle, physical health, mental health, and personality attributes. A mutual agreement method was used in which rather than looking at the accuracy of results, the model parameters and weights of features were used to find common behavioral trends. From the results of the model creation, it was determined that the most significant indicator of academic success defined as a higher GPA, was the places a student spent their time. Lifestyle and personality factors were deemed more significant than mental and physical factors. This study will provide insight into the impact of different factors and the timing of those factors on students’ academic performance .
Linear combinations of polynomials with three-term recurrence

We study the zero distribution of the sum of the first $n$ polynomials satisfying a three-term recurrence whose coefficients are linear polynomials. We also extend this sum to a linear combination, whose coefficients are powers of $az+b$ for $a,b\in\mathbb{R}$, of Chebyshev polynomials. In particular, we find necessary and sufficient conditions on $a$, $b$ such that this linear combination is hyperbolic.
Spatial alignment between faces and voices improves selective attention to audio-visual speech

The ability to see a talker's face improves speech intelligibility in noise, provided that the auditory and visual speech signals are approximately aligned in time. However, the importance of spatial alignment between corresponding faces and voices remains unresolved, particularly in multi-talker environments. In a series of online experiments, we investigated this using a task that required participants to selectively attend a target talker in noise while ignoring a distractor talker. In experiment 1, we found improved task performance when the talkers' faces were visible, but only when corresponding faces and voices were presented in the same hemifield (spatially aligned). In experiment 2, we tested for possible influences of eye position on this result. In auditory-only conditions, directing gaze toward the distractor voice reduced performance, but this effect could not fully explain the cost of audio-visual (AV) spatial misalignment. Lowering the signal-to-noise ratio (SNR) of the speech from +4 to −4 dB increased the magnitude of the AV spatial alignment effect (experiment 3), but accurate closed-set lipreading caused a floor effect that influenced results at lower SNRs (experiment 4). Taken together, these results demonstrate that spatial alignment between faces and voices contributes to the ability to selectively attend AV speech.
Isometric Force Pillow: Using Air Pressure to Quantify Involuntary Finger Flexion in the Presence of Hypertonia

Survivors of central nervous system injury commonly present with spastic hypertonia. The affected muscles are hyperexcitable and can display involuntary static muscle tone and an exaggerated stretch reflex. These symptoms affect posture and disrupt activities of daily living. Symptoms are typically measured using subjective manual tests such as the Modified Ashworth Scale; however, more quantitative measures are necessary to evaluate potential treatments. The hands are one of the most common targets for intervention, but few investigators attempt to quantify symptoms of spastic hypertonia affecting the fingers. We present the isometric force pillow (IFP) to quantify involuntary grip force. This lightweight, computerized tool provides a holistic measure of finger flexion force and can be used in various orientations for clinical testing and to measure the impact of assistive devices.
Do prize-linked incentives promote positive financial behavior? Evidence from a debt reduction intervention

Abstract We conduct the first field experiment examining whether prize-linked incentives can be effective in promoting debt reduction by randomizing access among 6,907 borrowers in a debt management plan. We find strong take-up of the program and that takers were timelier with repayment and paid off more debt. However, average treatment effect estimates for on-time payments, programmatic dropout, and debt levels (46.7 dollars, 95% CI = [−$132, $225], control mean = $11,558) are all precise zeros. Our results suggest that despite strong interest and positive correlations, prize-linked incentives may not modify debt repayment behavior and may simply attract individuals who are ex-ante likely to repay.
Estimating repeat spectra and genome length from low-coverage genome skims with RESPECT.

The cost of sequencing the genome is dropping at a much faster rate compared to assembling and finishing the genome. The use of lightly sampled genomes (genome-skims) could be transformative for genomic ecology, and results using k-mers have shown the advantage of this approach in identification and phylogenetic placement of eukaryotic species. Here, we revisit the basic question of estimating genomic parameters such as genome length, coverage, and repeat structure, focusing specifically on estimating the k-mer repeat spectrum. We show using a mix of theoretical and empirical analysis that there are fundamental limitations to estimating the k-mer spectra due to ill-conditioned systems, and that has implications for other genomic parameters. We get around this problem using a novel constrained optimization approach (Spline Linear Programming), where the constraints are learned empirically. On reads simulated at 1X coverage from 66 genomes, our method, REPeat SPECTra Estimation (RESPECT), had < 1.5% error in length estimation compared to 34% error previously achieved. In shotgun sequenced read samples with contaminants, RESPECT length estimates had median error 4%, in contrast to other methods that had median error 80%. Together, the results suggest that low-pass genomic sequencing can yield reliable estimates of the length and repeat content of the genome. The RESPECT software will be publicly available at https://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_shahab-2Dsarmashghi_RESPECT.git&d=DwIGAw&c=-35OiAkTchMrZOngvJPOeA&r=ZozViWvD1E8PorCkfwYKYQMVKFoEcqLFm4Tg49XnPcA&m=f-xS8GMHKckknkc7Xpp8FJYw_ltUwz5frOw1a5pJ81EpdTOK8xhbYmrN4ZxniM96&s=717o8hLR1JmHFpRPSWG6xdUQTikyUjicjkipjFsKG4w&e=.
Leveraging Spatial and Temporal Correlations in Sparsified Mean Estimation

We study the problem of estimating at a central server the mean of a set of vectors distributed across several nodes (one vector per node). When the vectors are high-dimensional, the communication cost of sending entire vectors may be prohibitive, and it may be imperative for them to use sparsification techniques. While most existing work on sparsified mean estimation is agnostic to the characteristics of the data vectors, in many practical applications such as federated learning, there may be spatial correlations (similarities in the vectors sent by different nodes) or temporal correlations (similarities in the data sent by a single node over different iterations of the algorithm) in the data vectors. We leverage these correlations by simply modifying the decoding method used by the server to estimate the mean. We provide an analysis of the resulting estimation error as well as experiments for PCA, K-Means and Logistic Regression, which show that our estimators consistently outperform more sophisticated and expensive sparsification methods.
MORPHOLOGICAL VARIATION IN MULGA. II. COVARIATION AMONG MORPHOLOGY, PHENOLOGY, AND SPATIAL PATTERNS IN ACACIA-DOMINATED VEGETATION

ABSTRACT Morphological variability is high among individuals within Acacia aneura populations and varies in extent among populations inversely with the average annual precipitation of the site. Within sites, reproductive phenology is correlated with individual morphology in A. aneura, with a similar form of the correlation extending across sites. A similar correspondence between reproductive phenology and morphology is evident among species in mixed-species Acacia vegetation. Shrub combined size vs. distance relationships in near-neighbors are of positive slope and tighter where moisture is more limited. Different ecomorphs are not randomly distributed within the vegetation. Residuals from size-distance relationships are positively correlated with the degree of morphological difference in neighboring shrubs. Evidence suggests that variation is neither selectively neutral, nor primarily associated with herbivore defense via apostatic selection, but overall it favors the hypothesis that morphological differ...
Optimum Simulation of Meshes by Small Hypercubes

We consider optimum simulations of large mesh networks by hypercubes. For any arbitrary mesh M, let "M's optimum hypercube" be the smallest hypercube that has at least as many processors as M and, for any k>0, let Q(M/2k) be "M's 1/2k-size hypercube", which has 1/2k as many processors as M's optimum hypercube. The ratio MI/Q(M/2k) is called M's 1/2 k - density. We show that (a) for every 2-D mesh M, if M's 1/2-density≤1.828, then M can be embedded into its 1/2-size hypercube with dilation 1 and load factor 2, (b) for every 2-D mesh M, if M's 1/4-density≤3.809, then M can be embedded into its 1/4-size hypercube with dilation 1 and load factor 4, and if M's 1/4-density≤2.8125, then M can be embedded into its 1/4-size hypercube with dilation 1 and load factor 3, (c) If every 2-D mesh M with 1/2k1-density≤a can be embedded into its 1/2k1-size hypercube with dilation 1 and load factor l1, and every 2-D mesh M with 1/2k2-density ≤b can be embedded into its 1/2k2-size hypercube with dilation 1 and load factor l2, then we can obtain the densities for load factor l1+l2 and load factor l1×l2 based on a, b.