cssi/SciSciGPT-SciSciNet huggingface dataset:
Machine learning pipeline for quantum state estimation with incomplete measurements

Two-qubit systems typically employ 36 projective measurements for high-fidelity tomographic estimation. The overcomplete nature of the 36 measurements suggests possible robustness of the estimation procedure to missing measurements. In this paper, we explore the resilience of machine-learning-based quantum state estimation techniques to missing measurements by creating a pipeline of stacked machine learning models for imputation, denoising, and state estimation. When applied to simulated noiseless and noisy projective measurement data for both pure and mixed states, we demonstrate quantum state estimation from partial measurement results that outperforms previously developed machine-learning-based methods in reconstruction fidelity and several conventional methods in terms of resource scaling. Notably, our developed model does not require training a separate model for each missing measurement, making it potentially applicable to quantum state estimation of large quantum systems where preprocessing is computationally infeasible due to the exponential scaling of quantum system dimension.
Family comes first: Reproductive health and the gender gap in entrepreneurship

Abstract Better access to reproductive healthcare increases women’s propensity to become entrepreneurs. Access correlates positively with female entrepreneurial activity and negatively with female entrepreneurial age. Examining firm size and personal income suggests it also improves the success of female-led businesses. None of these results hold when tested on men, women above 40, or other placebo professions. To establish causality, I exploit Roe v. Wade, state laws restricting abortion providers, and an index tracking state-level regulation of reproductive care. All three analyses suggest that policies securing better reproductive care enable more women to become entrepreneurs. I conclude by discussing various possible channels and mechanisms.
Learning Mixtures of Low-Rank Models

We study the problem of learning mixtures of low-rank models, i.e. reconstructing multiple low-rank matrices from unlabelled linear measurements of each. This problem enriches two widely studied settings — low-rank matrix sensing and mixed linear regression — by bringing latent variables (i.e. unknown labels) and structural priors (i.e. low-rank structures) into consideration. To cope with the non-convexity issues arising from unlabelled heterogeneous data and low-complexity structure, we develop a three-stage meta-algorithm that is guaranteed to recover the unknown matrices with near-optimal sample and computational complexities under Gaussian designs. In addition, the proposed algorithm is provably stable against random noise. We complement the theoretical studies with empirical evidence that confirms the efficacy of our algorithm.
Nonlinear Fokker-Planck Acceleration for Forward-Peaked Transport Problems in Slab Geometry

This paper introduces a new acceleration technique for the convergence of the solution of transport problems with highly forward-peaked scattering. The technique is similar to a conventional high-o...
Leveraging Two-Stage Adaptive Robust Optimization for Power Flexibility Aggregation

Adaptive robust optimization (ARO) is a well-known technique to deal with the parameter uncertainty in optimization problems. While the ARO framework can actually be borrowed to solve some special problems without uncertain parameters, such as the power flexibility aggregation problem studied in this paper. To effectively harness the significant flexibility from massive distributed energy resources (DERs), power flexibility aggregation is performed for a distribution system to compute the feasible region of the exchanged power at the substation over time. Based on two-stage ARO, this paper proposes a novel method to aggregate system-level multi-period power flexibility, considering heterogeneous DER facilities, network operational constraints, and an unbalanced power flow model. This method is applicable to aggregate only the active (or reactive) power, and the joint active-reactive power domain. Accordingly, two power aggregation models with two-stage optimization are developed: one focuses on aggregating active power and computes its optimal feasible intervals over multiple periods, and the other solves the optimal elliptical feasible regions for the aggregate active-reactive power. By leveraging the ARO technique, the disaggregation feasibility of the obtained feasible regions is guaranteed with optimality. Numerical simulations on a real-world distribution feeder with 126 multi-phase nodes demonstrate the effectiveness of the proposed method.
METAL: The Metal Evolution, Transport, and Abundance in the Large Magellanic Cloud Hubble Program. II. Variations of Interstellar Depletions and Dust-to-gas Ratio within the LMC

A key component of the baryon cycle in galaxies is the depletion of metals from the gas to the dust phase in the neutral ISM. The METAL (Metal Evolution, Transport and Abundance in the Large Magellanic Cloud) program on the Hubble Space Telescope acquired UV spectra toward 32 sightlines in the half-solar metallicity LMC, from which we derive interstellar depletions (gas-phase fractions) of Mg, Si, Fe, Ni, S, Zn, Cr, and Cu. The depletions of different elements are tightly correlated, indicating a common origin. Hydrogen column density is the main driver for depletion variations. Correlations are weaker with volume density, probed by CI fine structure lines, and distance to the LMC center. The latter correlation results from an East-West variation of the gas-phase metallicity. Gas in the East, compressed side of the LMC encompassing 30 Doradus and the Southeast HI over-density is enriched by up to +0.3dex, while gas in the West side is metal-deficient by up to -0.5dex. Within the parameter space probed by METAL, no correlation with molecular fraction or radiation field intensity are found. We confirm the factor 3-4 increase in dust-to-metal and dust-to-gas ratios between the diffuse (logN(H)~20 cm-2) and molecular (logN(H)~22 cm-2) ISM observed from far-infrared, 21 cm, and CO observations. The variations of dust-to-metal and dust-to-gas ratios with column density have important implications for the sub-grid physics of chemical evolution, gas and dust mass estimates throughout cosmic times, and for the chemical enrichment of the Universe measured via spectroscopy of damped Lyman-alpha systems.
Event-Based Analysis of Solar Power Distribution Feeder Using Micro-PMU Measurements

Solar distribution feeders are commonly used in solar farms that are integrated into distribution substations. In this paper, we focus on a real-world solar distribution feeder and conduct an event-based analysis by using micro-PMU measurements. The solar distribution feeder of interest is a behind-the-meter solar farm with a generation capacity of over 4 MW that has about 200 low-voltage distributed photovoltaic (PV) inverters. The event-based analysis in this study seeks to address the following practical matters. First, we conduct event detection by using an unsupervised machine learning approach. For each event, we determine the event's source region by an impedance-based analysis, coupled with a descriptive analytic method. We segregate the events that are caused by the solar farm, i.e., locally-induced events, versus the events that are initiated in the grid, i.e., grid-induced events, which caused a response by the solar farm. Second, for the locally-induced events, we examine the impact of solar production level and other significant parameters to make statistical conclusions. Third, for the grid-induced events, we characterize the response of the solar farm; and make comparisons with the response of an auxiliary neighboring feeder to the same events. Fourth, we scrutinize multiple specific events; such as by revealing the dynamics to the control system of the solar distribution feeder. The results and discoveries in this study are informative to utilities and solar power industry.
Maternal blood metal concentrations and whole blood DNA methylation during pregnancy in the Early Autism Risk Longitudinal Investigation (EARLI).

The maternal epigenome may be responsive to prenatal metals exposures. We tested whether metals are associated with concurrent differential maternal whole blood DNA methylation. In the Early Autism...
Injection of Inner Oort Cloud Objects into the Distant Kuiper Belt by Planet Nine

The outer solar system exhibits an anomalous pattern of orbital clustering, characterized by an approximate alignment of the apsidal lines and angular momentum vectors of distant, long-term stable Kuiper belt objects. One explanation for this dynamical confinement is the existence of a yet-undetected planetary-mass object, "Planet Nine (P9)". Previous work has shown that trans-Neptunian objects, which originate within the scattered disk population of the Kuiper belt, can be corralled into orbital alignment by Planet Nine's gravity over ~Gyr timescales, and characteristic P9 parameters have been derived by matching the properties of a synthetic Kuiper belt generated within numerical simulations to the available observational data. In this work, we show that an additional dynamical process is in play within the framework of the Planet Nine hypothesis, and demonstrate that P9-induced dynamical evolution facilitates orbital variations within the otherwise dynamically frozen inner Oort cloud. As a result of this evolution, inner Oort cloud bodies can acquire orbits characteristic of the distant scattered disk, implying that if Planet Nine exists, the observed census of long-period trans-Neptunian objects is comprised of a mixture of Oort cloud and Kuiper belt objects. Our simulations further show that although inward-injected inner Oort cloud objects exhibit P9-driven orbital confinement, the degree of clustering is weaker than that of objects originating within the Kuiper belt. Cumulatively, our results suggest that a more eccentric Planet Nine is likely necessary to explain the data than previously thought.
Zinc import mediated by AdcABC is critical for colonization of the dental biofilm by Streptococcus mutans in an animal model

Trace metals are essential to all domains of life but toxic when found at high concentrations. Although the importance of iron in host-pathogen interactions is firmly established, contemporary studies indicate that other trace metals, including manganese and zinc, are also critical to the infectious process. In this study, we sought to identify and characterize the zinc uptake system(s) of Streptococcus mutans, a keystone pathogen in dental caries and a causative agent of bacterial endocarditis. Different than other pathogenic bacteria, including several streptococci, that encode multiple zinc import systems, bioinformatic analysis indicated that the S. mutans core genome encodes a single, highly conserved, zinc importer commonly known as AdcABC. Inactivation of the genes coding for the metal-binding AdcA (ΔadcA) or both AdcC ATPase and AdcB permease (ΔadcCB) severely impaired the ability of S. mutans to grow under zinc-depleted conditions. Intracellular metal quantifications revealed that both mutants accumulated less zinc when grown in the presence of a subinhibitory concentration of a zinc-specific chelator. Notably, the ΔadcCB strain displayed a severe colonization defect in a rat oral infection model. Both Δadc strains were hypersensitive to high concentrations of manganese, showed reduced peroxide tolerance, and formed less biofilm in sucrose-containing media when cultivated in the presence of the lowest amount of zinc that support their growth, but not when zinc was supplied in excess. Collectively, this study identifies AdcABC as the major high affinity zinc importer of S. mutans and provides preliminary evidence that zinc is a growth-limiting factor within the dental biofilm.
Analysis of circulating exosomes reveals a peripheral signature of astrocytic pathology in schizophrenia.

Extracellular vesicles, including exosomes, cross the blood brain barrier with their contents intact and can be assayed peripherally. Circulating exosomes have been studied in other neurodegenerati...
Proving Skipping Refinement with ACL2s

We describe three case studies illustrating the use of ACL2s to prove the correctness of optimized reactive systems using skipping refinement. Reasoning about reactive systems using refinement involves defining an abstract, high-level specification system and a concrete, low-level system. Next, one shows that the behaviors of the implementation system are allowed by the specification system. Skipping refinement allows us to reason about implementation systems that can "skip" specification states due to optimizations that allow the implementation system to take several specification steps at once. Skipping refinement also allows implementation systems to, i.e., to take several steps before completing a specification step. We show how ACL2s can be used to prove skipping refinement theorems by modeling and proving the correctness of three systems: a JVM-inspired stack machine, a simple memory controller, and a scalar to vector compiler transformation.
The fiscal–monetary policy mix in the euro area: challenges at the zero lower bound

SUMMARYThis paper explores the reasons for the suboptimal fiscal–monetary policy mix in the euro area in the aftermath of the global financial crisis and ways in which the status quo can be improved. A comparison of fiscal and monetary policy and of economic outcomes in the euro area, the United States and Japan suggests that both fiscal and monetary policies in the euro area have been overly tight. Fiscal policy has been hampered by the institutional framework which constrains individual states and lacks instruments to secure an appropriate aggregate stance. Monetary policy has been hampered by the distributional effects of balance sheet policies which needed to be adopted at the zero lower bound and by discretionary decisions taken before the crisis such as the reliance on credit rating agencies for determining collateral eligibility for monetary operations. The compromising of the ‘safe asset’ status of euro area sovereign debt during the crisis complicated fiscal and monetary policies. Changes in the discretionary decisions governing the implementation of monetary policy in the euro area can potentially reduce the distributional effects of policy and improve the fiscal–policy mix and longer-term prospects for the euro area.
Towards thruster-assisted bipedal locomotion for enhanced efficiency and robustness

Abstract In this paper, we will report our efforts in designing closed-loop feedback for the thruster-assisted walking of bipedal robots. We will assume for well-tuned supervisory controllers and will focus on fine-tuning the joints desired trajectories to satisfy the performance being sought. In doing this, we will devise an intermediary filter based on reference governors that guarantees the satisfaction of performance-related constraints. Since these modifications and impact events lead to deviations from the desired periodic orbits, we will guarantee hybrid invariance in a robust way by applying predictive schemes withing a very short time envelope during the gait cycle. To achieve the hybrid invariance, we will leverage the unique features in our model, that is, the thrusters. The merit of our approach is that unlike existing optimization-based nonlinear control methods, satisfying performance-related constraints during the single support phase does not rely on expensive numeric approaches. In addition, the overall structure of the proposed thruster-assisted gait control allows for exploiting performance and robustness enhancing capabilities during specific parts of the gait cycle, which is unusual and not reported before.
A Two-Level ADMM Algorithm for AC OPF With Global Convergence Guarantees

This paper proposes a two-level distributed algorithmic framework for solving the AC optimal power flow (OPF) problem with convergence guarantees. The presence of highly nonconvex constraints in OPF poses significant challenges to distributed algorithms based on the alternating direction method of multipliers (ADMM). In particular, convergence is not provably guaranteed for nonconvex network optimization problems like AC OPF. In order to overcome this difficulty, we propose a new distributed reformulation for AC OPF and a two-level ADMM algorithm that goes beyond the standard framework of ADMM. We establish the global convergence and iteration complexity of the proposed algorithm under mild assumptions. Extensive numerical experiments over some largest test cases from NESTA and PGLib-OPF (up to 30 000-bus systems) demonstrate advantages of the proposed algorithm over existing ADMM variants in terms of convergence, scalability, and robustness. Moreover, under appropriate parallel implementation, the proposed algorithm exhibits fast convergence comparable to or even better than the state-of-the-art centralized solver.
Adapting Event Extractors to Medical Data: Bridging the Covariate Shift.

We tackle the task of adapting event extractors to new domains without labeled data, by aligning the marginal distributions of source and target domains. As a testbed, we create two new event extraction datasets using English texts from two medical domains: (i) clinical notes, and (ii) doctor-patient conversations. We test the efficacy of three marginal alignment techniques: (i) adversarial domain adaptation (ADA), (ii) domain adaptive fine-tuning (DAFT), and (iii) a new instance weighting technique based on language model likelihood scores (LIW). LIW and DAFT improve over a no-transfer BERT baseline on both domains, but ADA only improves on notes. Deeper analysis of performance under different types of shifts (e.g., lexical shift, semantic shift) explains some of the variations among models. Our best-performing models reach F1 scores of 70.0 and 72.9 on notes and conversations respectively, using no labeled target data.
BayesPerf: minimizing performance monitoring errors using Bayesian statistics

Hardware performance counters (HPCs) that measure low-level architectural and microarchitectural events provide dynamic contextual information about the state of the system. However, HPC measurements are error-prone due to non determinism (e.g., undercounting due to event multiplexing, or OS interrupt-handling behaviors). In this paper, we present BayesPerf, a system for quantifying uncertainty in HPC measurements by using a domain-driven Bayesian model that captures microarchitectural relationships between HPCs to jointly infer their values as probability distributions. We provide the design and implementation of an accelerator that allows for low-latency and low-power inference of the BayesPerf model for x86 and ppc64 CPUs. BayesPerf reduces the average error in HPC measurements from 40.1% to 7.6% when events are being multiplexed. The value of BayesPerf in real-time decision-making is illustrated with a simple example of scheduling of PCIe transfers.
Membership Inference Attacks and Defenses in Classification Models

We study the membership inference (MI) attack against classifiers, where the attacker's goal is to determine whether a data instance was used for training the classifier. Through systematic cataloging of existing MI attacks and extensive experimental evaluations of them, we find that a model's vulnerability to MI attacks is tightly related to the generalization gap---the difference between training accuracy and test accuracy. We then propose a defense against MI attacks that aims to close the gap by intentionally reduces the training accuracy. More specifically, the training process attempts to match the training and validation accuracies, by means of a new set regularizer using the Maximum Mean Discrepancy between the softmax output empirical distributions of the training and validation sets. Our experimental results show that combining this approach with another simple defense (mix-up training) significantly improves state-of-the-art defense against MI attacks, with minimal impact on testing accuracy.
Magnetic nulls in interacting dipolar fields

The prominence of nulls in reconnection theory is due to the expected singular current density and the indeterminacy of field lines at a magnetic null. Electron inertia changes the implications of both features. Magnetic field lines are distinguishable only when their distance of closest approach exceeds a distance is a characteristic spatial scale of the magnetic field. The behaviour of the magnetic field lines in the presence of nulls is studied for a dipole embedded in a spatially constant magnetic field. In addition to the implications of distinguishability, a constraint on the current density at a null is obtained, and the time required for thin current sheets to arise is derived.
Point-Based Value Iteration and Approximately Optimal Dynamic Sensor Selection for Linear-Gaussian Processes

The problem of synthesizing an optimal sensor selection policy is pertinent to a variety of engineering applications ranging from event detection to autonomous navigation. We consider such a synthesis problem in the context of linear-Gaussian systems over an infinite time horizon with a discounted cost criterion. We formulate this problem in terms of a value iteration over the continuous space of covariance matrices. To obtain a computationally tractable solution, we subsequently formulate an approximate sensor selection problem, which is solvable through a point-based value iteration over a finite “mesh” of covariance matrices with a user-defined bounded trace. We provide theoretical guarantees bounding the suboptimality of the sensor selection policies synthesized through this method and provide numerical examples comparing them to known results.
A statistical analysis of the COHERENT data and applications to new physics

The observation of coherent elastic neutrino nucleus scattering (CEνNS) by the COHERENT collaboration in 2017 has opened a new window to both test Standard Model predictions at relatively low energies and probe new physics scenarios. Our investigations show, however, that a careful treatment of the statistical methods used to analyze the data is essential to derive correct constraints and bounds on new physics parameters. In this manuscript we perform a detailed analysis of the publicly available COHERENT CsI data making use of all available background data. We point out that Wilks’ theorem is not fulfilled in general and a calculation of the confidence regions via Monte Carlo simulations following a Feldman-Cousins procedure is necessary. As an example for the necessity of this approach to test new physics scenarios we quantify the allowed ranges for several scenarios with neutrino non-standard interactions. Furthermore, we provide accompanying code to enable an easy implementation of other new physics scenarios as well as data files of our results: https://github.com/JuliaGehrlein/7stats .
Mechanical heterogeneity along single cell-cell junctions is driven by lateral clustering of cadherins during vertebrate axis elongation

Morphogenesis is governed by the interplay of molecular signals and mechanical forces across multiple length scales. The last decade has seen tremendous advances in our understanding of the dynamics of protein localization and turnover at subcellular length scales, and at the other end of the spectrum, of mechanics at tissue-level length scales. Integrating the two remains a challenge, however, because we lack a detailed understanding of the subcellular patterns of mechanical properties of cells within tissues. Here, in the context of the elongating body axis of Xenopus embryos, we combine tools from cell biology and physics to demonstrate that individual cell-cell junctions display finely-patterned local mechanical heterogeneity along their length. We show that such local mechanical patterning is essential for the cell movements of convergent extension and is imparted by locally patterned clustering of a classical cadherin. Finally, the patterning of cadherins and thus local mechanics along cell-cell junctions are controlled by Planar Cell Polarity signaling, a key genetic module for CE that is mutated in diverse human birth defects.
Optimal Control of Connected and Automated Vehicles at Multiple Adjacent Intersections

In this article, we establish a decentralized optimal control framework for connected and automated vehicles (CAVs) crossing multiple adjacent, multilane signal-free intersections to minimize energy consumption and improve traffic throughput. Our framework consists of two layers of planning. In the upper level planning, each CAV computes its optimal arrival time at each intersection recursively along with the optimal lane to improve the traffic throughput. In the low-level planning, we formulate an energy-optimal control problem with interior-point constraints, the solution of which yields the optimal control input (acceleration/deceleration) of each CAV to cross the intersections at the time specified by the upper level planning. Moreover, we extend the results of the proposed bi-level framework to include a bounded steady-state error in tracking the optimal position of the CAVs. Finally, we demonstrate the effectiveness of the proposed framework through simulation for symmetric and asymmetric intersections and comparison with traditional signalized intersections.
Sensor Uncertainty Mitigation and Dynamic Well Clear Volumes in DAIDALUS

This paper presents recent updates to DAIDALUS (Detect & Avoid Alerting Logic for Unmanned Systems), a detect and avoid (DAA) software package for the integration of civil UAS into the airspace. DAIDALUS is the reference implementation of detect and avoid for unmanned aircraft systems chosen by RTCA Special Committee 228 (SC-228), and it is included in its corresponding Minimum Operational Performance Standards (MOPS) document, DO-365. This paper reports on the integration into DAIDALUS of two new capabilities, namely dynamic well clear volumes and sensor uncertainty mitigation.'
A new method to accurately identify single nucleotide variants using small FFPE breast samples.

Most tissue collections of neoplasms are composed of formalin-fixed and paraffin-embedded (FFPE) excised tumor samples used for routine diagnostics. DNA sequencing is becoming increasingly important in cancer research and clinical management; however it is difficult to accurately sequence DNA from FFPE samples. We developed and validated a new bioinformatic pipeline to use existing variant-calling strategies to robustly identify somatic single nucleotide variants (SNVs) from whole exome sequencing using small amounts of DNA extracted from archival FFPE samples of breast cancers. We optimized this strategy using 28 pairs of technical replicates. After optimization, the mean similarity between replicates increased 5-fold, reaching 88% (range 0-100%), with a mean of 21.4 SNVs (range 1-68) per sample, representing a markedly superior performance to existing tools. We found that the SNV-identification accuracy declined when there was less than 40 ng of DNA available and that insertion-deletion variant calls are less reliable than single base substitutions. As the first application of the new algorithm, we compared samples of ductal carcinoma in situ of the breast to their adjacent invasive ductal carcinoma samples. We observed an increased number of mutations (paired-samples sign test, P < 0.05), and a higher genetic divergence in the invasive samples (paired-samples sign test, P < 0.01). Our method provides a significant improvement in detecting SNVs in FFPE samples over previous approaches.
Explicit Alignment Objectives for Multilingual Bidirectional Encoders.

Pre-trained cross-lingual encoders such as mBERT (Devlin et al., 2019) and XLM-R (Conneau et al., 2020) have proven impressively effective at enabling transfer-learning of NLP systems from high-resource languages to low-resource languages. This success comes despite the fact that there is no explicit objective to align the contextual embeddings of words/sentences with similar meanings across languages together in the same space. In this paper, we present a new method for learning multilingual encoders, AMBER (Aligned Multilingual Bidirectional EncodeR). AMBER is trained on additional parallel data using two explicit alignment objectives that align the multilingual representations at different granularities. We conduct experiments on zero-shot cross-lingual transfer learning for different tasks including sequence tagging, sentence retrieval and sentence classification. Experimental results on the tasks in the XTREME benchmark (Hu et al., 2020) show that AMBER obtains gains of up to 1.1 average F1 score on sequence tagging and up to 27.3 average accuracy on retrieval over the XLM-R-large model which has 3.2x the parameters of AMBER. Our code and models are available at http://github.com/junjiehu/amber.
Ground Exposure of Composite Materials for Helicopters

Residual strength results are presented on four composite material systems that were exposed for three years at locations on the North American Continent. The exposure locations are near the areas where Bell Model 206L Helicopters, that are in a NSA/U.S. Army sponsored flight service program, are flying in daily commercial service. The composite systems are: (1) Kevlar-49 fabric/F-185 epoxy; (2) Kevlar-49 fabric/LRF-277 epoxy; (3) Kevlar-49 fabric/CE-306 epoxy; and (4) T-300 Graphite/E-788 epoxy. All material systems exhibited good strength retention in compression and short beam shear. The Kevlar-49/LRF-277 epoxy retained 88 to 93 percent of the baseline strength while the other material systems exceeded 95 percent of baseline strength. Residual tensile strength of all materials did not show a significant reduction. The available moisture absorption data is also presented.
Corpus-Level Evaluation for Event QA: The IndiaPoliceEvents Corpus Covering the 2002 Gujarat Violence

Automated event extraction in social science applications often requires corpus-level evaluations: for example, aggregating text predictions across metadata and unbiased estimates of recall. We combine corpus-level evaluation requirements with a real-world, social science setting and introduce the IndiaPoliceEvents corpus--all 21,391 sentences from 1,257 English-language Times of India articles about events in the state of Gujarat during March 2002. Our trained annotators read and label every document for mentions of police activity events, allowing for unbiased recall evaluations. In contrast to other datasets with structured event representations, we gather annotations by posing natural questions, and evaluate off-the-shelf models for three different tasks: sentence classification, document ranking, and temporal aggregation of target events. We present baseline results from zero-shot BERT-based models fine-tuned on natural language inference and passage retrieval tasks. Our novel corpus-level evaluations and annotation approach can guide creation of similar social-science-oriented resources in the future.
Correlated Adversarial Joint Discrepancy Adaptation Network

Domain adaptation aims to mitigate the domain shift problem when transferring knowledge from one domain into another similar but different domain. However, most existing works rely on extracting marginal features without considering class labels. Moreover, some methods name their model as so-called unsupervised domain adaptation while tuning the parameters using the target domain label. To address these issues, we propose a novel approach called correlated adversarial joint discrepancy adaptation network (CAJNet), which minimizes the joint discrepancy of two domains and achieves competitive performance with tuning parameters using the correlated label. By training the joint features, we can align the marginal and conditional distributions between the two domains. In addition, we introduce a probability-based top-$\mathcal{K}$ correlated label ($\mathcal{K}$ -label), which is a powerful indicator of the target domain and effective metric to tune parameters to aid predictions. Extensive experiments on benchmark datasets demonstrate significant improvements in classification accuracy over the state of the art.
Computation of Three-Dimensional Turbulent Separated Flows at Supersonic Speeds

Numerical solutions of the time-averaged Navier-Stokes equations employing a simple eddy-viscosity model have been obtained for three dimensional turbulent flow fields at supersonic speeds. The computer results are compared with a series of experimental test flows describing the interaction of a swept shock wave with a turbulent boundary layer for various shock-wave strengths. Very good agreement is obtained between the computed and experimental surface and flow-field results. The computed flow fields are examined in detail to investigate the physics of this type of flow field. Questions concerning the existence of a vortex and the relationship between converging surface oil streaks and the resulting flow field are addressed.
NASA research on general aviation power plants

Research activities within NASA to support general aviation industry in improving propulsion engines are described. Near-term objectives include improvements of gasoline piston engines to achieve fuel savings and reduce emissions well below EPA levels. To meet the longer term goals, advanced combustion research has been considered as essential in obtaining further improvements in BSFC (break specific fuel consumption). Modifications of an aircraft rotary engine were tested and it was found that by increasing the compression ratio and other refinements the BSFC was improved by 15%. The applicability of available large turbofan engine technology to small engines in order to obtain significant reductions in noise and pollutant emissions is being tested. Studies have been conducted at exploring the possibility of achieving high improvements in cost and performance for turboprop engines of less than 1000 horsepower.
A survey on security attacks and defense techniques for connected and autonomous vehicles

Abstract Autonomous Vehicle has been transforming intelligent transportation systems. As telecommunication technology improves, autonomous vehicles are getting connected to each other and to infrastructures, forming Connected and Autonomous Vehicles (CAVs). CAVs will help humans achieve safe, efficient, and autonomous transportation systems. However, CAVs will face significant security challenges because many of their components are vulnerable to attacks, and a successful attack on a CAV may have significant impacts on other CAVs and infrastructures due to their interconnectivity. In this paper, we conduct a survey on 189 papers from 2000 to 2020 to understand state-of-the-art CAV attacks and defense techniques. Of those 189 papers, 131 were directly concerned with attack models or defense strategies for CAVs. This survey first presents a comprehensive overview of security attacks and their corresponding countermeasures on CAVs. We then discuss the details of attack models based on the targeted CAV components of attacks, access requirements, and attack motives. Finally, we identify some current research challenges and trends from the perspectives of both academic research and industrial development. Based on our studies of academic literature and industrial publications, we have not found any strong connection between academic research and industry’s implementation of CAV-related security issues. While efforts from CAV manufacturers to secure CAVs have been reported, there is no evidence to show that CAVs on the market can defend against some novel attack models that the research community has recently found. This survey may give researchers and engineers a better understanding of the current status and trend of CAV security for CAV’s future improvement.
A Method for Calculating the Probability of Successfully Completing a Rocket Propulsion Ground Test

Propulsion ground test facilities face the daily challenge of scheduling multiple customers into limited facility space and successfully completing their propulsion test projects. Over the last decade NASA s propulsion test facilities have performed hundreds of tests, collected thousands of seconds of test data, and exceeded the capabilities of numerous test facility and test article components. A logistic regression mathematical modeling technique has been developed to predict the probability of successfully completing a rocket propulsion test. A logistic regression model is a mathematical modeling approach that can be used to describe the relationship of several independent predictor variables X(sub 1), X(sub 2),.., X(sub k) to a binary or dichotomous dependent variable Y, where Y can only be one of two possible outcomes, in this case Success or Failure of accomplishing a full duration test. The use of logistic regression modeling is not new; however, modeling propulsion ground test facilities using logistic regression is both a new and unique application of the statistical technique. Results from this type of model provide project managers with insight and confidence into the effectiveness of rocket propulsion ground testing.
Scribbles, scrawls, and scratches: Graphic play as subtext in the picture books of Ezra Jack Keats

Although Keats works in a two-dimensional medium, his illustrations are fully three-dimensional in both geometric perspective and narrative intentionality. Using collage technique that floats planes of color on a sea of textural richness, Keats is able to maximize his voice, or “voices,” as he speaks to us through multiple images—personalities—embedded within his illustrative frame. Keats's technique, paradoxically, runs counter to the historical development of collage as a form of antiaesthetic criticism used by early surrealist painters. Whereas such painters as Duchamp, Arp, Schwitters, and Ernst strike out against traditional aesthetic categories, Keats embraces them, focusing upon the achievement of a certain “rhythmic unity” in his work. In this sense, he achieves the height of picture book art by dissolving the thin line between text and illustration, and he does so primarily by “playing” with various graphic images as subtexts that both clarify and extend his main narrative intentions.
Down for failure: Active power status monitoring

Abstract Despite society’s strong dependence on electricity, power outages remain prevalent. Standard methods for directly measuring power availability are complex, often inaccurate, and prone to attack. This paper explores an alternative approach to identifying power outages through intelligent monitoring of IP address availability. In finding these outages, we explore the trade-off between the accuracy of detection and false alarms. We begin by experimentally demonstrating that static, residential Internet connections serve as good indicators of power, as they are mostly active unless power fails and rarely have battery backups. We construct metrics that dynamically score the reliability of each residential IP, where a higher score indicates a higher correlation between the IP’s availability and its regional power. We then select and monitor subsets of residential IPs and evaluate the exactitude with which they indicate current county power status. Using data gathered during the power outages caused by Hurricane Florence, we demonstrate that we can track power outages at different granularities (state and county), in both sparse and dense regions. By comparing our detections with the reports gathered from power utility companies, we achieve an average detection accuracy of 90%, where we also show some of our false alarms and missed outage events could be due to imperfect ground truth data. Therefore, our method can be used as a complementary technique for power outage detection.
Racial Disparities in Pre-K Quality: Evidence From New York City’s Universal Pre-K Program:

New York City’s universal prekindergarten (pre-K) program, which increased full-day enrollment from 19,000 to almost 70,000 children, is ambitious in both scale and implementation speed. We provide...
Traveling wave solutions for two species competitive chemotaxis systems

Abstract In this paper, we consider two species chemotaxis systems with Lotka–Volterra competition reaction terms. Under appropriate conditions on the parameters in such a system, we establish the existence of traveling wave solutions of the system connecting two spatially homogeneous equilibrium solutions with wave speed greater than some critical number c ∗ . We also show the non-existence of such traveling waves with speed less than some critical number c 0 ∗ , which is independent of the chemotaxis. Moreover, under suitable hypotheses on the coefficients of the reaction terms, we obtain explicit range for the chemotaxis sensitivity coefficients ensuring c ∗ = c 0 ∗ , which implies that the minimum wave speed exists and is not affected by the chemoattractant.
Unmanned aerial vehicle path planning for traffic estimation and detection of non-recurrent congestion

Unmanned aerial vehicles (UAVs) provide a novel means of extracting road and traffic information from video data. In particular, by analyzing objects in a video frame, UAVs can detect traffic chara...
LD-CNV: rapid and simple discovery of chromosomal translocations using linkage disequilibrium between copy number variable loci.

Large-scale structural variations, such as chromosomal translocations, can have profound effects on fitness and phenotype, but are difficult to identify and characterize. Here, we describe a simple and effective method aimed at identifying translocations using only the dosage of sequence reads mapped on the reference genome. We binned reads on genomic segments sized according to sequencing coverage and identified instances when copy number segregated in populations. For each dosage-polymorphic 1 Mb bin, we tested independence, effectively an apparent linkage disequilibrium (LD), with other variable bins. In nine potato (Solanum tuberosum) dihaploid families translocations affecting pericentromeric regions were common and in two cases were due to genomic misassembly. In two populations, we found evidence for translocation affecting euchromatic arms. In cv. PI 310467, a nonreciprocal translocation between chromosomes (chr.) 7 and 8 resulted in a 5-3 copy number change affecting several Mb at the respective chromosome tips. In cv. "Alca Tarma," the terminal arm of chr. 4 translocated to the tip of chr. 1. Using oligonucleotide-based fluorescent in situ hybridization painting probes (oligo-FISH), we tested and confirmed the predicted arrangement in PI 310467. In 192 natural accessions of Arabidopsis thaliana, dosage haplotypes tended to vary continuously and resulted in higher noise, while apparent LD between pericentromeric regions suggested the effect of repeats. This method, LD-CNV, should be useful in species where translocations are suspected because it tests linkage without the need for genotyping.
Multi-armed bandit with sub-exponential rewards

Abstract We consider a general class of multi-armed bandits (MAB) problems with sub-exponential rewards. This is primarily motivated by service systems with exponential inter-arrival and service distributions. It is well-known that the celebrated Upper Confidence Bound (UCB) algorithm can achieve tight regret bound for MAB under sub-Gaussian rewards. There has been subsequent work by Bubeck et al. (2013) [4] extending this tightness result to any reward distributions with finite variance by leveraging robust mean estimators. In this paper, we present three alternative UCB based algorithms, termed UCB-Rad, UCB-Warm, and UCB-Hybrid, specifically for MAB with sub-exponential rewards. While not being the first to achieve tight regret bounds, these algorithms are conceptually simpler and provide a more explicit analysis for this problem. Moreover, we present a rental bike revenue management application and conduct numerical experiments. We find that UCB-Warm and UCB-Hybrid outperform UCB-Rad in our computational experiments.
Universality and least singular values of random matrix products: A simplified approach

In this note, we show how to provide sharp control on the least singular value of a certain translated linearization matrix arising in the study of the local universality of products of independent random matrices. This problem was first considered in a recent work of Kopel, O’Rourke, and Vu, and compared to their work, our proof is substantially simpler and established in much greater generality. In particular, we only assume that the entries of the ensemble are centered, and have second and fourth moments uniformly bounded away from 0 and infinity, whereas previous work assumed a uniform subgaussian decay condition and that the entries within each factor of the product are identically distributed. A consequence of our least singular value bound is that the four moment matching universality results for the products of independent random matrices, recently obtained by Kopel, O’Rourke, and Vu, hold under much weaker hypotheses. Our proof technique is also of independent interest in the study of structured sparse matrices.
A systematic analysis of phase stability in refractory high entropy alloys utilizing linear and non-linear cluster expansion models

Abstract The phase segregation behavior of three key refractory high entropy alloys (NbTiVZr, HfNbTaTiZr, and AlHfNbTaTiZr) is studied using first-principles calculations. Several linear and non-linear methods are utilized to generate surrogate models for three key refractory high entropy alloys (NbTiVZr, HfNbTaTiZr, and AlHfNbTaTiZr) via the cluster expansion formalism. The characteristics of each of the generated models is explored and the regression methods are compared. Finally, these surrogate models are utilized to generate Monte Carlo trajectories in order to explore the link between phase segregation and previously documented mechanical degradation in these materials. Phase segregation and intermetallic phases documented in the experimental literature are reproduced in all three high entropy alloys. NbTiVZr forms vanadium and zirconium clusters at lower temperatures (250 K) which disperse into the single-phase matrix by 1000 K. HfNbTaTiZr forms HfZr, NbTa, and possibly TiZr intermetallic phases at lower temperatures (250 K). Unlike the other HEAs studied here, HfNbTaTiZr does not lose short-range ordering in the solid state until around 3500 K, which is above its melting temperature. AlHfNbTaTiZr forms NbTa and AlHfTiZr phases at lower temperatures (250 K), which are not observed at higher temperatures (1000 K).
Private and Resource-Bounded Locally Decodable Codes for Insertions and Deletions

We construct locally decodable codes (LDCs) to correct insertion-deletion errors in the setting where the sender and receiver share a secret key or where the channel is resource-bounded. Our constructions rely on a so-called “Hamming-to-InsDel” compiler (Ostrovsky and Paskin-Cherniavsky, ITS ‘15 & Block et al., FSTTCS '20), which compiles any locally decodable Hamming code into a locally decodable code resilient to insertion-deletion (InsDel) errors. While the compilers were designed for the classical coding setting, we show that the compilers still work in a secret key or resource-bounded setting. Applying our results to the private key Hamming LDC of Ostrovsky, Pandey, and Sahai (ICALP '07), we obtain a private key InsDel LDC with constant rate and polylogarithmic locality. Applying our results to the construction of Blocki, Kulkarni, and Zhou (ITC '20), we obtain similar results for resource-bounded channels; i.e., a channel where computation is constrained by resources such as space or time.