cssi/SciSciGPT-SciSciNet huggingface dataset:
Financial openness and capital inflows to emerging markets: In search of robust evidence

Abstract We reassess the connection between capital account openness and capital flows in an empirical framework that is grounded in theory and makes use of previously unexplored variation in the data. We demonstrate how our theory-consistent regressions can overcome some ubiquitous measurement problems in the literature. Within our proposed framework, we ask: what can be said robustly about the effect of capital account restrictions on capital flows? Our results warrant against over-interpreting the existing cross-country evidence as we find very few robust relationships between capital account restrictiveness and various types of capital inflows. Countries with a higher degree of financial openness are more susceptible to some, but by no means all, push and pull factors. Overall, the results show that policymakers should carefully weigh the ability to shield the domestic economy from some sources of capital flow volatility against the potentially negative long run growth effects.
A coarse-grid projection method for accelerating incompressible MHD flow simulations

Coarse grid projection (CGP) is a multiresolution technique for accelerating numerical calculations associated with a set of nonlinear evolutionary equations along with stiff Poisson’s equations. In this article, we use CGP for the first time to speed up incompressible magnetohydrodynamics (MHD) flow simulations. Accordingly, we solve the nonlinear advection–diffusion equation on a fine mesh, while we execute the electric potential Poisson equation on the corresponding coarsened mesh. Mapping operators connect two grids together. A pressure correction scheme is used to enforce the incompressibility constrain. The study of incompressible flow past a circular cylinder in the presence of Lorentz force is selected as a benchmark problem with a fixed Reynolds number but various Stuart numbers. We consider two different situations. First, we only apply CGP to the electric potential Poisson equation. Second, we apply CGP to the pressure Poisson equation as well. The maximum speed-up factors achieved here are approximately 3 and 23, respectively, for the first and second situations. For the both situations, we examine the accuracy of velocity and vorticity fields as well as the lift and drag coefficients. In general, the results obtained by CGP are in an excellent to reasonable range of accuracy. The CGP results are significantly more accurate compared to the numerical simulations of the advection–diffusion and electric potential Poisson equations on pure coarse scale grids.
List Decodable Mean Estimation in Nearly Linear Time

Learning from data in the presence of outliers is a fundamental problem in statistics. Until recently, no computationally efficient algorithms were known to compute the mean of a high dimensional distribution under natural assumptions in the presence of even a small fraction of outliers. In this paper, we consider robust statistics in the presence of overwhelming outliers where the majority of the dataset is introduced adversarially. With only an $\alpha fraction of “in-liers” (clean data) the mean of a distribution is unidentifiable. However, in their influential work, [1] introduces a polynomial time algorithm recovering the mean of distributions with bounded covariance by outputting a succinct list of $O(1/\alpha)$ candidate solutions, one of which is guaranteed to be close to the true distributional mean; a direct analog of ‘List Decoding’ in the theory of error correcting codes. In this work, we develop an algorithm for list decodable mean estimation in the same setting achieving up to constants the information theoretically optimal recovery, optimal sample complexity, and in nearly linear time up to polylogarithmic factors in dimension. Our conceptual innovation is to design a descent style algorithm on a nonconvex landscape, iteratively removing minima to generate a succinct list of solutions. Our runtime bottleneck is a saddle-point optimization for which we design custom primal dual solvers for generalized packing and covering SDP's under Ky-Fan norms, which may be of independent interest. We refer the reader to [2] for the full version of this paper.
Kernel Ridge Regression Using Importance Sampling with Application to Seismic Response Prediction

Scalable kernel methods, including kernel ridge regression, often rely on low-rank matrix approximations using the Nystrom method, which involves selecting landmark points from large data sets. The existing approaches to selecting landmarks are typically computationally demanding as they require manipulating and performing computations with large matrices in the input or feature space. In this paper, our contribution is twofold. The first contribution is to propose a novel landmark selection method that promotes diversity using an efficient twostep approach. Our landmark selection technique follows a coarse to fine strategy, where the first step computes importance scores with a single pass over the whole data. The second step performs K-means clustering on the constructed coreset to use the obtained centroids as landmarks. Hence, the introduced method provides tunable trade-offs between accuracy and efficiency. Our second contribution is to investigate the performance of several landmark selection techniques using a novel application of kernel methods for predicting structural responses due to earthquake load and material uncertainties. Our experiments exhibit the merits of our proposed landmark selection scheme against baselines.
Selenium-dependent metabolic reprogramming during inflammation and resolution.

Trace element selenium (Se) is incorporated as the 21st amino acid, selenocysteine, into selenoproteins through tRNA[Ser]Sec. Selenoproteins act as gatekeepers of redox homeostasis and modulate immune function to effect anti-inflammation and resolution. However, mechanistic underpinnings involving metabolic reprogramming during inflammation and resolution remain poorly understood. Bacterial endotoxin lipopolysaccharide (LPS) activation of murine bone marrow–derived macrophages cultured in the presence or absence of Se (as selenite) was used to examine temporal changes in the proteome and metabolome by multiplexed tandem mass tag–quantitative proteomics, metabolomics, and machine-learning approaches. Kinetic deltagram and clustering analysis indicated that addition of Se led to extensive reprogramming of cellular metabolism upon stimulation with LPS enhancing the pentose phosphate pathway, tricarboxylic acid cycle, and oxidative phosphorylation, to aid in the phenotypic transition toward alternatively activated macrophages, synonymous with resolution of inflammation. Remodeling of metabolic pathways and consequent metabolic adaptation toward proresolving phenotypes began with Se treatment at 0 h and became most prominent around 8 h after LPS stimulation that included succinate dehydrogenase complex, pyruvate kinase, and sedoheptulokinase. Se-dependent modulation of these pathways predisposed bone marrow–derived macrophages to preferentially increase oxidative phosphorylation to efficiently regulate inflammation and its timely resolution. The use of macrophages lacking selenoproteins indicated that all three metabolic nodes were sensitive to selenoproteome expression. Furthermore, inhibition of succinate dehydrogenase complex with dimethylmalonate affected the proresolving effects of Se by increasing the resolution interval in a murine peritonitis model. In summary, our studies provide novel insights into the role of cellular Se via metabolic reprograming to facilitate anti-inflammation and proresolution.
Detecting conjugacy stability of subgroups in certain classes of groups

In this paper we consider the {\em conjugacy stability} property of subgroups and provide effective procedures to solve the problem in several classes of groups. In particular, we start with free groups, that is, we give an effective procedure to find out if a finitely generated subgroup of a free group is conjugacy stable. Then we further generalize this result to quasi-convex subgroups of torsion-free hyperbolic groups and finitely generated subgroups of limit groups.
Deep Keypoint-Based Camera Pose Estimation with Geometric Constraints

Estimating relative camera poses from consecutive frames is a fundamental problem in visual odometry (VO) and simultaneous localization and mapping (SLAM), where classic methods consisting of hand-crafted features and sampling-based outlier rejection have been a dominant choice for over a decade. Although multiple works propose to replace these modules with learning-based counterparts, most have not yet been as accurate, robust and generalizable as conventional methods. In this paper, we design an end-to-end trainable framework consisting of learnable modules for detection, feature extraction, matching and outlier rejection, while directly optimizing for the geometric pose objective. We show both quantitatively and qualitatively that pose estimation performance may be achieved on par with the classic pipeline. Moreover, we are able to show by end-to-end training, the key components of the pipeline could be significantly improved, which leads to better generalizability to unseen datasets compared to existing learning-based methods.
TSV Extrusion Morphology Classification Using Deep Convolutional Neural Networks

In this paper, we utilize deep convolutional neural networks (CNNs) to classify the morphology of through-silicon via (TSV) extrusion in three dimensional (3D) integrated circuits (ICs). TSV extrusion is a crucial reliability concern which can deform and crack interconnect layers in 3D ICs and cause device failures. Herein, the white light interferometry (WLI) technique is used to obtain the surface profile of the extruded TSVs. We have developed a program that uses raw data obtained from WLI to create a TSV extrusion morphology dataset, including TSV images with 54 × 54 pixels that are labeled and categorized into three morphology classes. Four CNN architectures with different network complexities are implemented and trained for TSV extrusion morphology classification application. Data augmentation and dropout approaches are utilized to realize a balance between overfitting and underfitting in the CNN models. Results obtained show that the CNN model with optimized complexity, dropout, and data augmentation can achieve a classification accuracy comparable to that of a human expert.
Application of deep learning to large scale riverine flow velocity estimation

Fast and reliable prediction of riverine flow velocities plays an important role in many applications, including flood risk management. The shallow water equations (SWEs) are commonly used for prediction of the riverine flow velocities. However, accurate and fast prediction with standard SWE solvers remains challenging in many cases. Traditional approaches are computationally expensive and require high-resolution measurement of riverbed profile (i.e., bathymetry) for accurate predictions. As a result, they are a poor fit in situations where they need to be evaluated repetitively due, for example, to varying boundary condition (BC) scenarios, or when the bathymetry is not known with certainty. In this work, we propose a two-stage process that tackles these issues. First, using the principal component geostatistical approach we estimate the probability density function of the bathymetry from flow velocity measurements, and then we use multiple machine learning algorithms in order to obtain a fast solver of the SWEs, given augmented realizations from the posterior bathymetry distribution and the prescribed range of potential BCs. The first step of the proposed approach allows us to predict flow velocities without any direct measurement of the bathymetry. Furthermore, the augmentation of the distribution in the second stage allows incorporation of the additional bathymetry information into the flow velocity prediction for improved accuracy and generalization, even if the bathymetry changes over time. Here, we use three different forward solvers, referred to as principal component analysis-deep neural network, supervised encoder, and supervised variational encoder, and validate them on a reach of the Savannah river near Augusta, GA. Our results show that the fast solvers are capable of predicting flow velocities with variable bathymetry and BCs with good accuracy, at a computational cost that is significantly lower than the cost of solving the full boundary value problem with traditional methods.
Representations of twisted toroidal Lie algebras from twisted modules over vertex algebras

Given a simple finite-dimensional Lie algebra and an automorphism of finite order, one defines the notion of a twisted toroidal Lie algebra. In this paper, we construct representations of twisted toroidal Lie algebras from twisted modules over affine and lattice vertex algebras.
Executive Network Centrality and Corporate Reporting

This paper investigates the association of corporate reporting and executive network centrality, which measures an executive’s relative position in a massive network consisting of outside corporate...
Multi-spike solutions of a hybrid reaction-transport model

Simulations of classical pattern-forming reactiondiffusion systems indicate that they often operate in the strongly nonlinear regime, with the final steady state consisting of a spatially repeating...
Deep learning to estimate cardiac magnetic resonance–derived left ventricular mass

Background Cardiac magnetic resonance (CMR) is the gold standard for left ventricular hypertrophy (LVH) diagnosis. CMR-derived LV mass can be estimated using proprietary algorithms (eg, InlineVF), but their accuracy and availability may be limited. Objective To develop an open-source deep learning model to estimate CMR-derived LV mass. Methods Within participants of the UK Biobank prospective cohort undergoing CMR, we trained 2 convolutional neural networks to estimate LV mass. The first (ML4Hreg) performed regression informed by manually labeled LV mass (available in 5065 individuals), while the second (ML4Hseg) performed LV segmentation informed by InlineVF (version D13A) contours. We compared ML4Hreg, ML4Hseg, and InlineVF against manually labeled LV mass within an independent holdout set using Pearson correlation and mean absolute error (MAE). We assessed associations between CMR-derived LVH and prevalent cardiovascular disease using logistic regression adjusted for age and sex. Results We generated CMR-derived LV mass estimates within 38,574 individuals. Among 891 individuals in the holdout set, ML4Hseg reproduced manually labeled LV mass more accurately (r = 0.864, 95% confidence interval [CI] 0.847–0.880; MAE 10.41 g, 95% CI 9.82–10.99) than ML4Hreg (r = 0.843, 95% CI 0.823–0.861; MAE 10.51, 95% CI 9.86–11.15, P = .01) and InlineVF (r = 0.795, 95% CI 0.770–0.818; MAE 14.30, 95% CI 13.46–11.01, P Conclusions ML4Hseg is an open-source deep learning model providing automated quantification of CMR-derived LV mass. Deep learning models characterizing cardiac structure may facilitate broad cardiovascular discovery.
Irreducibility of a free group endomorphism is a mapping torus invariant

We prove that the property of a free group endomorphism being irreducible is a group invariant of the ascending HNN extension it defines. This answers a question posed by Dowdall-Kapovich-Leininger. We further prove that being irreducible and atoroidal is a commensurability invariant. The invariance follows from an algebraic characterization of ascending HNN extensions that determines exactly when their defining endomorphisms are irreducible and atoroidal; specifically, we show that the endomorphism is irreducible and atoroidal if and only if the ascending HNN extension has no infinite index subgroups that are ascending HNN extensions.
A quantitative stability theorem for convolution on the Heisenberg group

Although the convolution operators on Euclidean space and the Heisenberg group satisfy the same Lp bounds with the same optimal constants, the former has maximizers while the latter does not. However, as work of Christ has shown, it is still possible to characterize near-maximizers. Specifically, any near-maximizing triple of the trilinear form for convolution on the Heisenberg group must be close to a particular type of triple of ordered Gaussians after adjusting by symmetry. In this paper, we use the expansion method to prove a quantitative version of this characterization.
Products and intersections of prime-power ideals in Leavitt path algebras

We continue a very fruitful line of inquiry into the multiplicative ideal theory of an arbitrary Leavitt path algebra L. Specifically, we show that factorizations of an ideal in L into irredundant ...
Local biasing and the use of nullator-norator pairs in analog circuits designs

Although local biasing of components used in an analog circuit is shown to be a very attractive design methodology, significantly simplifying the design procedure (Hashemian, 2005), it makes the DC supplies distributed and often not in desire locations in the circuit. In response to this problem a new technique is developed that in conjunction with the local biasing it handles the DC supply sources very smoothly and transfers them to the designated locations in the circuit, specified for global biasing. The technique utilizes nullator-norator pairs to do the required source transformation. Overall, the technique totally avoids nonlinearity in the operation due to a novel nullator modeling that is presented for locally biased components. For the lack of space, the method is only applied to MOS transistors here, but it can simply be extended to BJTs as well. As an example, a two stage amplifier with feedback is used to demonstrate the methodology.
Rare regions and avoided quantum criticality in disordered Weyl semimetals and superconductors

Abstract Disorder in Weyl semimetals and superconductors is surprisingly subtle, attracting attention and competing theories in recent years. In this brief review, we discuss the current theoretical understanding of the effects of short-ranged, quenched disorder on the low energy-properties of three-dimensional, topological Weyl semimetals and superconductors. We focus on the role of non-perturbative rare region effects on destabilizing the semimetal phase and rounding the expected semimetal-to-diffusive metal transition into a cross over. Furthermore, the consequences of disorder on the resulting nature of excitations, transport, and topology are reviewed. New results on a bipartite random hopping model are presented that confirm previous results in a p + i p Weyl superconductor, demonstrating that particle–hole symmetry is insufficient to help stabilize the Weyl semimetal phase in the presence of disorder. The nature of the avoided transition in a model for a single Weyl cone in the continuum is discussed. We close with a discussion of open questions and future directions.
Septate junction proteins are required for egg elongation and border cell migration during oogenesis in Drosophila.

Protein components of the invertebrate occluding junction-known as the septate junction (SJ) - are required for morphogenetic developmental events during embryogenesis in Drosophila melanogaster. In order to determine whether SJ proteins are similarly required for morphogenesis during other developmental stages, we investigated the localization and requirement of four representative SJ proteins during oogenesis: Contactin, Macroglobulin complement-related, Neurexin IV, and Coracle. A number of morphogenetic processes occur during oogenesis, including egg elongation, formation of dorsal appendages, and border cell migration. We found that all four SJ proteins are expressed in egg chambers throughout oogenesis, with the highest and most sustained levels in the follicular epithelium (FE). In the FE, SJ proteins localize along the lateral membrane during early and mid-oogenesis, but become enriched in an apical-lateral domain (the presumptive SJ) by stage 10B. SJ protein relocalization requires the expression of other SJ proteins, as well as Rab5 and Rab11 in a manner similar to SJ biogenesis in the embryo. Knocking down the expression of these SJ proteins in follicle cells throughout oogenesis results in egg elongation defects and abnormal dorsal appendages. Similarly, reducing the expression of SJ genes in the border cell cluster results in border cell migration defects. Together, these results demonstrate an essential requirement for SJ genes in morphogenesis during oogenesis, and suggests that SJ proteins may have conserved functions in epithelial morphogenesis across developmental stages. Article Summary: Septate junction (SJ) proteins are essential for forming an occluding junction in epithelial tissues in Drosophila melanogaster, and also for morphogenetic events that occur prior to the formation of the junction during embryogenesis. Here we show that SJ proteins are expressed in the follicular epithelium of egg chambers during oogenesis and are required for morphogenetic events including egg elongation, dorsal appendages formation, and border cell migration. Additionally, the formation of SJs during oogenesis is similar to that in embryonic epithelia.
Loss of Mafb and Maf distorts myeloid cell ratios and disrupts fetal mouse testis vascularization and organogenesis

Testis differentiation is initiated when Sry in pre-Sertoli cells directs the gonad toward a male-specific fate. Sertoli cells are essential for testis development, but cell types within the interstitial compartment, such as immune and endothelial cells, are also critical for organ formation. Our previous work implicated macrophages in fetal testis morphogenesis, but little is known about genes underlying immune cell development during organogenesis. Here, we examine the role of the immune-associated genes Mafb and Maf in mouse fetal gonad development, and we demonstrate that deletion of these genes leads to aberrant hematopoiesis manifested by supernumerary gonadal monocytes. Mafb; Maf double knockout embryos underwent initial gonadal sex determination normally, but exhibited testicular hypervascularization, testis cord formation defects, Leydig cell deficit, and a reduced number of germ cells. In general, Mafb and Maf alone were dispensable for gonad development; however, when both genes were deleted, we observed significant defects in testicular morphogenesis, indicating that Mafb and Maf work redundantly during testis differentiation. These results demonstrate previously unappreciated roles for Mafb and Maf in immune and vascular development and highlight the importance of interstitial cells in gonadal differentiation. Summary sentence Deletion of Mafb and Maf genes leads to supernumerary monocytes in fetal mouse gonads, resulting in vascular, morphogenetic, and differentiation defects during testicular organogenesis.
Defining, evaluating, preparing for and responding to a cyber Pearl Harbor

Abstract Despite not having a clear meaning, public perception and awareness makes the term cyber Pearl Harbor an important part of the public discourse. This paper considers what the term has meant and proposes its decomposition based on three different aspects of the historical Pearl Harbor attack, allowing the lessons from Pearl Harbor to be applied to threats and subjects that may not align with all aspects of the 1941 attack. Using these three definitions, prior attacks and current threats are assessed and preparation for and response to cyber Pearl Harbor events is discussed.
Learning to Ignore: A Case Study of Organization-Wide Bulk Email Effectiveness

Bulk email is a primary communication channel within organizations, with all-company emails and regular newsletters serving as a mechanism for making employees aware of policies and events. Ineffective communication could result in wasted employee time and a lack of compliance or awareness. Previous studies on organizational emails focused mostly on recipients. However, organizational bulk email system is a multi-stakeholder problem including recipients, communicators, and the organization itself. We studied the effectiveness, practice, and assessments of the organizational bulk email system of a large university from multi-stakeholders' perspectives. We conducted a qualitative study with the university's communicators, recipients, and managers. We delved into the organizational bulk email's distributing mechanisms of the communicators, the reading behaviors of recipients, and the perspectives on emails' values of communicators, managers, and recipients. We found that the organizational bulk email system as a whole was strained, and communicators are caught in the middle of this multi-stakeholder problem. First, though the communicators had an interest in preserving the effectiveness of channels in reaching employees, they had high-level clients whose interests might outweigh judgment about whether a message deserves widespread circulation. Second, though communicators thought they were sending important information, recipients viewed most of the organizational bulk emails as not relevant to them. Third, this disagreement was amplified by the success metric used by communicators. They viewed their bulk emails as successful if they had a high open rate. But recipients often opened and then rapidly discarded emails without reading the details. Last, while the communicators in general understood the challenge, they had a limited set of targeting and feedback tools to support their task.
Map-Based Temporally Consistent Geolocalization through Learning Motion Trajectories

In this paper, we propose a novel trajectory learning method that exploits motion trajectories on topological map using recurrent neural network for temporally consistent ge-olocalization of object. Inspired by human's ability to both be aware of distance and direction of self-motion in navigation, our trajectory learning method learns a pattern representation of trajectories encoded as a sequence of distances and turning angles to assist self-localization. We pose the learning process as a conditional sequence prediction problem in which each output locates the object on a traversable edge in a map. Considering the prediction sequence ought to be topologically connected in the graph-structured map, we adopt two different hypotheses generation and elimination strategies to eliminate disconnected sequence prediction. We demonstrate our approach on the KITTI stereo visual odometry dataset which is a city-scale environment. The key benefits of our approach to geolocalization are that 1) we take advantage of powerful sequence modeling ability of recurrent neural network and its robustness to noisy input, 2) only require a map in the form of a graph and 3) simply use an affordable sensor that generates motion trajectory. The experiments show that the motion trajectories can be learned by training an recurrent neural network, and temporally consistent geolocation can be predicted with both of the proposed strategies.
Mitochondrial ATP Synthase Subunit d, a Component of the Peripheral Stalk, is Essential for Growth and Heat Stress Tolerance in Arabidopsis thaliana

As rapid changes in climate threaten global crop yields, an understanding of plant heat stress tolerance is increasingly relevant. Heat stress tolerance involves the coordinated action of many cellular processes and is particularly energy demanding. We acquired a knockout mutant and generated knockdown lines in Arabidopsis thaliana of the d subunit of mitochondrial ATP synthase (gene name: ATPQ, AT3G52300, referred to hereafter as ATPd), a subunit of the peripheral stalk, and used these to investigate the phenotypic significance of this subunit in normal growth and heat stress tolerance. Homozygous knockout mutants for ATPd could not be obtained due to gametophytic defects, while heterozygotes possess no visible phenotype. Therefore, we used RNA interference to create knockdown plant lines for further studies. Proteomic analysis and blue native gels revealed that ATPd downregulation impairs only subunits of the mitochondrial ATP synthase (complex V). Knockdown plants were more sensitive to heat stress, had abnormal leaf morphology, and were severely slow growing compared to wild type. These results indicate that ATPd plays a crucial role in proper function of the mitochondrial ATP synthase holoenzyme, which, when reduced, leads to wide-ranging defects in energy-demanding cellular processes. In knockdown plants, more hydrogen peroxide accumulated and mitochondrial dysfunction stimulon (MDS) genes were activated. These data establish the essential structural role of ATPd and support the importance of complex V in normal plant growth, and provide new information about its requirement for heat stress tolerance.
Bounded Exhaustive Test-Input Generation on GPUs

Bounded exhaustive testing is an effective methodology for detecting bugs in a wide range of applications. A well-known approach for bounded exhaustive testing is Korat. It generates all test inputs, up to a given small size, based on a formal specification that is written as an executable predicate and characterizes properties of desired inputs. Korat uses the predicate's executions on candidate inputs to implement a backtracking search based on pruning to systematically explore the space of all possible inputs and generate only those that satisfy the specification. This paper presents a novel approach for speeding up test generation for bounded exhaustive testing using Korat. The novelty of our approach is two-fold. One, we introduce a new technique for writing the specification predicate based on an abstract representation of candidate inputs, so that the predicate executes directly on these abstract structures and each execution has a lower cost. Two, we use the abstract representation as the basis to define the first technique for utilizing GPUs for systematic test generation using executable predicates. Moreover, we present a suite of optimizations that enable effective utilization of the computational resources offered by modern GPUs. We use our prototype tool KoratG to experimentally evaluate our approach using a suite of 7 data structures that were used in prior studies on bounded exhaustive testing. Our results show that our abstract representation can speed up test generation by 5.68 times on a standard CPU, while execution on a GPU speeds up the execution, on average, by 17.46 times.
Communication-Efficient Policy Gradient Methods for Distributed Reinforcement Learning

This paper deals with distributed policy optimization in reinforcement learning, which involves a central controller and a group of learners. In particular, two typical settings encountered in several applications are considered: multi-agent reinforcement learning (RL) and parallel RL, where frequent information exchanges between the learners and the controller are required. For many practical distributed systems, however, the overhead caused by these frequent communication exchanges is considerable, and becomes the bottleneck of the overall performance. To address this challenge, a novel policy gradient approach is developed for solving distributed RL. The novel approach adaptively skips the policy gradient communication during iterations, and can reduce the communication overhead without degrading learning performance. It is established analytically that: i) the novel algorithm has convergence rate identical to that of the plain-vanilla policy gradient; while ii) if the distributed learners are heterogeneous in terms of their reward functions, the number of communication rounds needed to achieve a desirable learning accuracy is markedly reduced. Numerical experim
The effect of planetary flybys on singly synchronous binary asteroids

Abstract The evolution of binary asteroid dynamics are not fully understood. The method of formation is widely accepted as spin-up and fission due to the YORP effect, but it is unclear why systems exist at a variety of energy levels. Binary asteroids are observed in states ranging from relaxed, synchronous equilibriums to chaotic dynamics. Previous planetary encounters may have an effect on the dynamic configuration of binary asteroids. We develop a model for the spherical restricted full three-body problem to simulate a binary asteroid perturbed by a planetary flyby, as well as a method of obtaining initial conditions for a relaxed, singly synchronous binary asteroid system. These are used in a numeric analysis employing a modified version of the General Use Binary Asteroid Simulator, where Monte Carlo simulations are leveraged to investigate how a range of Earth encounters affect the dynamic evolution of a generic binary asteroid system in a singly synchronous equilibrium. The results show that planetary flybys can disrupt the synchronous equilibrium, with the effects dropping off at far encounter distances and fast flyby speeds. After the impulsive perturbation of a planetary flyby, the binary asteroid spin-orbit coupling can prevent the system from settling into a new equilibrium, forcing the system to go through the energy dissipation process to re-equilibrate. It is therefore possible that planetary encounters may have a part in creating chaotic binary asteroid systems from previously relaxed equilibrated systems.
A multiscale model of vascular function in chronic thromboembolic pulmonary hypertension

This article presents novel computational framework for predicting pulmonary hemodynamics in chronic thromboembolic pulmonary hypertension. The mathematical model is used to identify the optimal ta...
Contamination effects on Space Station Freedom electric power system performance

One design issue for Space Station Freedom (SSF) is the potential performance decrease of the electric power system (EPS) solar arrays and radiators as a result of contamination on sensitive surfaces. The authors discuss SSF potential contamination sources and contamination effects on the solar array and radiator performance due to these sources. With these contamination consideration, the SSF EPS is designed for the induced contamination environment at an optimal cost. The efforts on contamination protection and control are undergoing continual update because of the changes in the SSF configuration and in the contamination requirements, and observations from recent flight and laboratory test data are continuously being incorporated into the design.
High resolution multispectral camera system for ERTS A & B

A very high resolution multispectral television camera system has been developed for NASA for use on the Earth Resources Technology Satellite (ERTS) program. There are three cameras in the system, each viewing the same area but operating in the blue-green, red, and near-infrared spectral bands. In the laboratory the cameras' limiting resolution is 4500 TV lines over the 25 x 25-mm image format of the Return Beam Vidicon (RBV). Analysis of typical ERTS scenes shows that actual contrast ratios will be much lower than those of laboratory test targets. A model was developed to predict the resolving power performance of the RBV camera under realistic conditions. To verify the model, tests were conducted using the RBV camera, a laser-beam image reproducer and a series of AF tribar test patterns of known values of contrast. As a more graphic demonstration, simulated multispectral images were generated using color-IR photographs from Apollo 9. The measured signal-to-noise, resolution, and spectral characteristics of the ERTS Flight A and Flight B three-camera systems are presented in conclusion.
Lubricating Characteristics of Polyimide Bonded Graphite Fluoride and Polyimide Thin Films

A friction and wear life study was conducted on the use of a polyimide (PI) varnish as a binder for the solid lubricant, graphite fluoride (CF1.1)n. Also studied were the lubricating properties of PI thin films with no solid lubricant additives. For comparison, similar experiments on PI-bonded MoS2 and burnished films of MoS2 or (CF1.1)n were conducted. The best results were obtained with the PI-bonded (CF1.1)n films. The polyimide thin films (with no solid lubricant additives) did not lubricate well at 25 C; however, when heated to 100 C, low friction and long wear lives were obtained. Presented as an American Society of Lubrication Engineers paper ot the ASME/ASLE International Lubrication Conference held in New York City, October 9–12, 1972
Energy-Based Reranking: Improving Neural Machine Translation Using Energy-Based Models

The discrepancy between maximum likelihood estimation (MLE) and task measures such as BLEU score has been studied before for autoregressive neural machine translation (NMT) and resulted in alternative training algorithms (Ranzato et al., 2016; Norouzi et al., 2016; Shen et al., 2016; Wu et al., 2018). However, MLE training remains the de facto approach for autoregressive NMT because of its computational efficiency and stability. Despite this mismatch between the training objective and task measure, we notice that the samples drawn from an MLE-based trained NMT support the desired distribution – there are samples with much higher BLEU score comparing to the beam decoding output. To benefit from this observation, we train an energy-based model to mimic the behavior of the task measure (i.e., the energy-based model assigns lower energy to samples with higher BLEU score), which is resulted in a re-ranking algorithm based on the samples drawn from NMT: energy-based re-ranking (EBR). We use both marginal energy models (over target sentence) and joint energy models (over both source and target sentences). Our EBR with the joint energy model consistently improves the performance of the Transformer-based NMT: +3.7 BLEU points on IWSLT’14 German-English, +3.37 BELU points on Sinhala-English, +1.4 BLEU points on WMT’16 English-German tasks.
Learning by Fixing: Solving Math Word Problems with Weak Supervision.

Previous neural solvers of math word problems (MWPs) are learned with full supervision and fail to generate diverse solutions. In this paper, we address this issue by introducing a weakly-supervised paradigm for learning MWPs. Our method only requires the annotations of the final answers and can generate various solutions for a single problem. To boost weakly-supervised learning, we propose a novel learning-by-fixing (LBF) framework, which corrects the misperceptions of the neural network via symbolic reasoning. Specifically, for an incorrect solution tree generated by the neural network, the fixing mechanism propagates the error from the root node to the leaf nodes and infers the most probable fix that can be executed to get the desired answer. To generate more diverse solutions, tree regularization is applied to guide the efficient shrinkage and exploration of the solution space, and a memory buffer is designed to track and save the discovered various fixes for each problem. Experimental results on the Math23K dataset show the proposed LBF framework significantly outperforms reinforcement learning baselines in weakly-supervised learning. Furthermore, it achieves comparable top-1 and much better top-3/5 answer accuracies than fully-supervised methods, demonstrating its strength in producing diverse solutions.
Effect of crosslinking density on biaxial relaxation of SBR by using reduced variables

The use of reduced variables to account for the effect of crosslinking density in a styrene-butadiene rubber (SBR) system is demonstrated for general biaxial stress states. Recently published results from stress relaxation tests on five SBR vulcanizates crosslinked to different degrees by tetramethylthiuram disulfide were superposed by using the crosslinking density as a reduction variable. The equilibrium shear modulus calculated from the master relaxation curve at long reduced times was in satisfactory agreement with other results for SBR. The time-axis shifts were related in a linear logarithmic manner to the crosslinking density but had a slope slightly less than values previously reported for elastomer systems.
A Practical Assessment of the Power Grid Inertia Constant Using PMUs

Installation of phasor measurement units (PMUs) in a number of substations in the power grid can help assess a set of its values and parameters, in particular those related to the dynamics when disturbances occur in the system. Inertia constant of the power grid is one of the system stability related parameters that is essential for planning of the system spinning reserve. Estimates of the grid inertia constant require precise information of the system frequency at the time of disturbance. In this paper, an improved method for such estimate is presented, which is based on the derived data of frequency variations obtained from PMUs. In addition, a way to obtain the appropriate interval for the lowest error estimate is discussed. The method has been applied to assess the inertia constant of the Iranian power grid, by obtaining the rate of change of frequency after a disturbance in the defined interval, and the rate of specified power imbalance between electrical power input and output.
The Holdout Randomization Test for Feature Selection in Black Box Models

We propose the holdout randomization test (HRT), an approach to feature selection using black box predictive models. The HRT is a specialized version of the conditional randomization test (CRT) tha...
VAL genes regulate vegetative phase change via miR156-dependent and independent mechanisms

How organisms control when to transition between different stages of development is a key question in biology. In plants, epigenetic silencing by Polycomb repressive complex 1 (PRC1) and PRC2 plays a crucial role in promoting developmental transitions, including from juvenile-to-adult phases of vegetative growth. PRC1/2 are known to repress the master regulator of vegetative phase change, miR156, leading to the transition to adult growth, but how this process is regulated temporally is unknown. Here we investigate whether transcription factors in the VIVIPAROUS/ABI3-LIKE (VAL) gene family provide the temporal signal for the epigenetic repression of miR156. Exploiting a novel val1 allele, we found that VAL1 and VAL2 redundantly regulate vegetative phase change by controlling the overall level, rather than temporal dynamics, of miR156 expression. Furthermore, we discovered that VAL1 and VAL2 also act independently of miR156 to control this important developmental transition. In combination, our results highlight the complexity of temporal regulation in plants.
Association of Maternal-Neonatal Steroids With Early Pregnancy Endocrine Disrupting Chemicals and Pregnancy Outcomes

Context Steroids play an important role in fetal development and parturition. Gestational exposures to endocrine-disrupting chemicals (EDCs) affect steroidal milieu and pregnancy outcomes, raising the possibility of steroids serving as biomarkers. Most studies have not addressed the impact of EDC mixtures, which are reflective of real life scenarios. Objective Assess the association of maternal and neonatal steroids with pregnancy outcomes and early pregnancy EDC levels. Design Prospective analysis of mother-infant dyads. Setting University hospital. Participants 121 mother-infant dyads. Main outcome measures The associations of maternal and neonatal steroidal hormones from 121 dyads with pregnancy outcomes, the associations of first trimester EDCs individually and as mixtures with maternal and neonatal steroids in a subset of 56 dyads and the influence of body mass index (BMI), age, and offspring sex in modulating the EDC associations with steroids were determined. Results Steroid-specific positive or negative associations with pregnancy measures were evident; many maternal first trimester EDCs were negatively associated with estrogens and positively with androgen/estrogen ratios; EDC-steroid associations were influenced by maternal age, pre-pregnancy BMI, and fetal sex; and EDCs individually and as mixtures showed direct and inverse fetal sex-dependent associations with maternal and neonatal steroids. Conclusions This proof-of-concept study indicates association of steroids with pregnancy outcomes depending on maternal age, prepregnancy BMI, and fetal sex, with the effects of EDCs differing when considered individually or as mixtures. These findings suggest that steroidal hormonal measures have potential to serve as biomarkers of impact of EDC exposures and pregnancy outcome.
Task-agnostic Continual Learning with Hybrid Probabilistic Models

Learning new tasks continuously without forgetting on a constantly changing data distribution is essential for real-world problems but extremely challenging for modern deep learning. In this work we propose HCL, a Hybrid generative-discriminative approach to Continual Learning for classification. We model the distribution of each task and each class with a normalizing flow. The flow is used to learn the data distribution, perform classification, identify task changes, and avoid forgetting, all leveraging the invertibility and exact likelihood which are uniquely enabled by the normalizing flow model. We use the generative capabilities of the flow to avoid catastrophic forgetting through generative replay and a novel functional regularization technique. For task identification, we use state-of-the-art anomaly detection techniques based on measuring the typicality of the model's statistics. We demonstrate the strong performance of HCL on a range of continual learning benchmarks such as split-MNIST, split-CIFAR, and SVHN-MNIST.
Virtues of Patience in Strategic Queuing Systems

We consider the problem of selfish agents in discrete-time queuing systems, where competitive queues try to get their packets served. In this model, a queue gets to send a packet each step to one of the servers, which will attempt to serve the oldest arriving packet, and unprocessed packets are returned to each queue. We model this as a repeated game where queues compete for the capacity of the servers, but where the state of the game evolves as the length of each queue varies, resulting in a highly dependent random process. In classical work for learning in repeated games, the learners evaluate the outcome of their strategy in each step---in our context, this means that queues estimate their success probability at each server. Earlier work by the authors [in EC'20] shows that with no-regret learners, the system needs twice the capacity as would be required in the coordinated setting to ensure queue lengths remain stable despite the selfish behavior of the queues. In this paper, we demonstrate that this myopic way of evaluating outcomes is suboptimal: if more patient queues choose strategies that selfishly maximize their long-run success rate, stability can be ensured with just e/e-1 ~1.58 times extra capacity, strictly better than what is possible assuming the no-regret property. As these systems induce highly dependent random processes, our analysis draws heavily on techniques from the theory of stochastic processes to establish various game-theoretic properties of these systems. Though these systems are random even under fixed stationary policies by the queues, we show using careful probabilistic arguments that surprisingly, under such fixed policies, these systems have essentially deterministic and explicit asymptotic behavior. We show that the growth rate of a set can be written as the ratio of a submodular and modular function, and use the resulting explicit description to show that the subsets of queues with largest growth rate are closed under union and non-disjoint intersections, which we use in turn to prove the claimed sharp bicriteria result for the equilibria of the resulting system. Our equilibrium analysis relies on a novel deformation argument towards a more analyzable solution that is quite different from classical price of anarchy bounds. While the intermediate points in this deformation will not be Nash, the structure will ensure the relevant constraints and incentives similarly hold to establish monotonicity along this continuous path.