cssi/SciSciGPT-SciSciNet huggingface dataset:
Are Labor Market Indicators Telling the Truth? Role of Measurement Error in the U.S. Current Population Survey

Labor market indicators are critical for policymakers, but measurement error in labor force survey data is known to be substantial. In this paper, I quantify the implications of classification errors in the U.S. Current Population Survey (CPS), in which respondents misreport their true labor force status. Once I correct for measurement error using a latent variable approach, the unemployment rate is on average 0.8 percentage points (ppts) higher than the official unemployment rate, with a maximum of 2.0 ppts higher between 1996 and 2018. This paper further quantifies the contributions to business-cycle fluctuations in the unemployment rate from job separation, job finding, and participation. Correcting for misclassification changes previous studies' results about the contributions of these transition probabilities: job separation accounts for more of the unemployment fluctuations, while participation accounts for fewer. The methodology I propose can be applied to any other labor force survey in which labor force status is observed for three periods.
ROBUST ESTIMATION OF LOSS MODELS FOR LOGNORMAL INSURANCE PAYMENT SEVERITY DATA

The primary objective of this scholarly work is to develop two estimation procedures – maximum likelihood estimator (MLE) and method of trimmed moments (MTM) – for the mean and variance of lognormal insurance payment severity data sets affected by different loss control mechanism, for example, truncation (due to deductibles), censoring (due to policy limits), and scaling (due to coinsurance proportions), in insurance and financial industries. Maximum likelihood estimating equations for both payment-per-payment and payment-per-loss data sets are derived which can be solved readily by any existing iterative numerical methods. The asymptotic distributions of those estimators are established via Fisher information matrices. Further, with a goal of balancing efficiency and robustness and to remove point masses at certain data points, we develop a dynamic MTM estimation procedures for lognormal claim severity models for the above-mentioned transformed data scenarios. The asymptotic distributional properties and the comparison with the corresponding MLEs of those MTM estimators are established along with extensive simulation studies. Purely for illustrative purpose, numerical examples for 1500 US indemnity losses are provided which illustrate the practical performance of the established results in this paper.
Krylov Subspace Methods in Dynamical Sampling

Let B be an unknown linear evolution process on ℂd ≃ l2 (ℤd) driving an unknown initial state x and producing the states {Blx, l = 0,1,…} at different time levels. The problem under consideration in this paper is to find as much information as possible about B and x from the measurements Y = {x(i), Bx(i), …, Blix(i) : i ∈ Ω ⊂ ℤd}. If B is a “low-pass” convolution operator, we show that we can recover both B and x, almost surely, as long as a sufficient number of temporal samples is used. For a general operator B, we can recover parts or even all of its spectrum from Y. As a special case of our method, we derive the centuries old Prony method which recovers a vector with an s-sparse Fourier transform from 2s of its consecutive components.
GAF is essential for zygotic genome activation and chromatin accessibility in the early Drosophila embryo.

Most cells in an organism share the exact same genetic information, yet they still adopt distinct identities. This diversity emerges because only a selection of genes is switched on at any given time in a cell. Proteins that latch onto DNA control this specificity by activating certain genes at the right time. However, to perform this role they first need to physically access DNA: this can be difficult as the genetic information is tightly compacted so it can fit in a cell. A group of proteins can help to unpack the genome to uncover the genes that can then be accessed and activated. While these ‘pioneer factors’ can therefore shape the identity of a cell, much remains unknown about how they can work together to do so. For instance, the pioneer factor Zelda is essential in early fruit fly development, as it enables the genetic information of the egg and sperm to undergo dramatic reprogramming and generate a new organism. Yet, it was unclear whether additional helpers were required for this transition. Using this animal system, Gaskill, Gibson et al. identified GAGA Factor as a protein which works with Zelda to open up and reprogram hundreds of different sections along the genome of fruit fly embryos. This tag-team effort started with Zelda being important initially to activate genes; regulation was then handed over for GAGA Factor to continue the process. Without either protein, the embryo died. Getting a glimpse into early genetic events during fly development provides insights that are often applicable to other animals such as fish and mammals. Ultimately, this research may help scientists to understand how things can go wrong in human embryos.
Prefrontal Deep Projection Neurons Enable Cognitive Flexibility Via Persistent Feedback Monitoring

Cognitive flexibility, the ability to alter one’s strategy in light of changing stimulus-response-reward relationships, is critical for acquiring and updating learned behavior by trial and error. Successful performance of attentional set-shifting, a test of cognitive flexibility, has been shown to critically involve the prefrontal cortex (PFC). It is unclear, however, whether PFC neurons support set-shifting behavior by guiding action selection or by monitoring feedback from completed actions. Using optogenetics and 2-photon calcium imaging, we demonstrate that PFC activity critically encodes trial feedback information, rather than guiding action selection, to enable set-shifting. Moreover, we find that, in two distinct PFC projection populations, representations of trial feedback form a topological gradient, with cells more strongly selective for feedback information located further from the pial surface, regardless of their projection targets. Together, these findings indicate that deep PFC projection neurons enable set-shifting through monitoring of behavioral feedback.
Efficient Subspace Approximation Algorithms

We consider the problem of fitting a subspace of a specified dimension k to a set P of n points in ℝ d . The fit of a subspace F is measured by the L τ norm, that is, it is defined as the τ-root of the sum of the τth powers of the Euclidean distances of the points in P from F, for some τ≥1. Our main result is a randomized algorithm that takes as input P, k, and a parameter 0<e<1; runs in $nd \cdot2^{O(\frac{\tau k^{2}}{\varepsilon} \log^{2} \frac {k}{\varepsilon})}$ time, and returns a k-subspace that with probability at least 1/2 has a fit that is at most (1+e) times that of the optimal k-subspace.
Selection of Multiple Donor Gauges via Graphical Lasso for Estimation of Daily Streamflow Time Series

A fundamental challenge in estimations of daily streamflow time series at sites with incomplete records is how to effectively and efficiently select reference or donor gauges from an existing gauge network to infer the missing data. While research on estimating missing streamflow time series is not new, the existing approaches either use a single reference streamflow gauge or employ a set of "ad-hoc" reference gauges, leaving a systematic selection of reference gauges as a long-standing open question. In this work, a novel method is introduced that facilitates systematical selection of multiple reference gauges from any given streamflow network. The idea is to mathematically characterize the network-wise correlation structure of a streamflow network via graphical Markov modeling, and further transforms a dense network into a sparsely connected one. The resulted underlying sparse graph from the graphical model encodes conditional independence conditions among all reference gauges from the streamflow network, allowing determination of an optimum subset of the donor gauges. The sparsity is discovered by using the Graphical Lasso algorithm with an L1-norm regularization parameter and a thresholding parameter. These two parameters are determined by a multi-objective optimization process. Furthermore, the graphical modeling approach is employed to solve another open problem in gauge removal planning decision (e.g., due to operation budget constraints): which gauges to remove would statistically guarantee the least loss of information by estimations from the remaining gauges? Our graphical model-based method is demonstrated with daily streamflow data from a network of 34 gauges over the Ohio River basin.
Asymptotic Theory of Quantum Channel Estimation

A fundamental limit in quantum metrology is obtained: The achievable sensitivity enhancement allowed by quantum strategies is determined, and protocols to reach such levels are proposed.
Review of CFTR modulators 2020

Cystic fibrosis transmembrane conductance regulator (CFTR) modulators are small molecules that directly impact the CFTR protein, improving the function of the CFTR chloride and bicarbonate channel. Beginning in 2012 with the Food and Drug Administration approval of the first CFTR modulator, ivacaftor, this class of medications has had largely positive effects on many outcomes in people with cystic fibrosis (PwCF), including lung function, growth, and other clinical parameters. There have been continued exciting developments in the current research on CFTR modulators, expanding beyond original studies. This first part of a three-part cystic fibrosis (CF) year in review 2020 will focus on research on CFTR modulators. In addition to reviewing new clinical insights, we describe work done on novel outcomes, adverse effects, issues related to cost, and next steps for clinical trials. The review focuses on articles from Pediatric Pulmonology published in 2020, but it includes articles from other journals that are of particular interest to clinicians. New developments in CF research continue to be brought forth to the CF community, deepening the understanding of this disease and improving clinical care.
Habeas Corpus and the American Revolution

Modern debates concerning the protections afforded by the Suspension Clause of the U.S. Constitution have taken place within the Supreme Court’s chosen methodological approach in this context, which openly calls for careful attention to the historical backdrop against which the Clause was drafted. This approach is hardly surprising given that long ago Chief Justice John Marshall declared that when the Founding generation constitutionalized “this great writ,” they invoked “[t]he term . . . in the [C]onstitution, as one which was well understood.” No matter how well the Founding generation understood the content, reach, and application of the “privilege of the writ of habeas corpus,” however, significant portions of the relevant historical backdrop to the ratification of the Suspension Clause remain lost to the annals of history. In particular, the details surrounding one of the most consequential periods in the history leading up to the adoption of the Suspension Clause—namely, the treatment and legal classification of the American colonists by the British during the American Revolutionary War—remain largely unexplored in legal scholarship.
TxSim: Modeling Training of Deep Neural Networks on Resistive Crossbar Systems

Deep neural networks (DNNs) have gained tremendous popularity in recent years due to their ability to achieve superhuman accuracy in a wide variety of machine learning tasks. However, the compute and memory requirements of DNNs have grown rapidly, creating a need for energy-efficient hardware. Resistive crossbars have attracted significant interest in the design of the next generation of DNN accelerators due to their ability to natively execute massively parallel vector-matrix multiplications within dense memory arrays. However, crossbar-based computations face a major challenge due to device and circuit-level nonidealities, which manifest as errors in the vector-matrix multiplications and eventually degrade DNN accuracy. To address this challenge, there is a need for tools that can model the functional impact of nonidealities on DNN training and inference. Existing efforts toward this goal are either limited to inference or are too slow to be used for large-scale DNN training. We propose TxSim, a fast and customizable modeling framework to functionally evaluate DNN training on crossbar-based hardware considering the impact of nonidealities. The key features of TxSim that differentiate it from prior efforts are: 1) it comprehensively models nonidealities during all training operations (forward propagation, backward propagation, and weight update) and 2) it achieves computational efficiency by mapping crossbar evaluations to well-optimized Basic Linear Algebra Subprograms (BLAS) routines and incorporates speedup techniques to further reduce simulation time with minimal impact on accuracy. TxSim achieves $6\times $ – $108\times $ improvement in simulation speed over prior works, and thereby makes it feasible to evaluate the training of large-scale DNNs on crossbars. Our experiments using TxSim reveal that the accuracy degradation in DNN training due to nonidealities can be substantial (3%–36.4%) for large-scale DNNs and data sets, underscoring the need for further research in mitigation techniques. We also analyze the impact of various device and circuit-level parameters and the associated nonidealities to provide key insights that can guide the design of crossbar-based DNN training accelerators.
Activation of VTA GABA neurons disrupts reward seeking by altering temporal processing

Abstract The role of ventral tegmental area (VTA) dopamine in reward, cue processing, and interval timing is well characterized. Using a combinatorial viral approach to target activating DREADDs ( D esigner R eceptors E xclusively A ctivated by D esigner D rugs, hM3D) to GABAergic neurons in the VTA of male rats, we previously showed that activation disrupts responding to reward-predictive cues. Here we explored how VTA GABA neurons influence the perception of time in two fixed interval (FI) tasks, one where the reward or interval is not paired with predictive cues (Non-Cued FI), and another where the start of the FI is signaled by a constant tone that continues until the rewarded response is emitted (Cued FI). Under vehicle conditions in both tasks, responding was characterized by “scalloping” over the 30 s FI, in which responding increased towards the end of the FI. However, when VTA GABA neurons were activated in the Non-Cued FI, the time between the end of the 30 s interval and when the rats made a reinforced response increased. Additionally, post-reinforcement pauses and overall session length increased. In the Cued FI task, VTA GABA activation produced erratic responding, with a decrease in earned rewards. Thus, while both tasks were disrupted by VTA GABA activation, responding that is constrained by a cue was more sensitive to this manipulation, possibly due to convergent effects on timing and cue processing. Together these results demonstrate that VTA GABA activity disrupts the perception of interval timing, particularly when the timing is set by cues.
Acute Rheumatic Fever in a COVID-19-Positive Pediatric Patient

Bacterial coinfection and COVID-19 have been reported in pediatric populations We describe a case of Sydenham's chorea, which is exceedingly rare in developed countries, with concurrent COVID-19 Discussed here is the clinical course of an 8-year-old COVID-positive female with pure Sydenham's chorea and subclinical carditis from acute rheumatic fever To our knowledge, there are no documented reports of acute rheumatic fever in a pediatric patient with coexisting COVID-19 infection [ABSTRACT FROM AUTHOR] Copyright of Case Reports in Pediatrics is the property of Hindawi Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission However, users may print, download, or email articles for individual use This abstract may be abridged No warranty is given about the accuracy of the copy Users should refer to the original published version of the material for the full abstract (Copyright applies to all Abstracts )
Connections Between Relational Event Model and Inverse Reinforcement Learning for Characterizing Group Interaction Sequences

In this paper we explore previously unidentified connections between relational event model (REM) from the field of network science and inverse reinforcement learning (IRL) from the field of machine learning with respect to their ability to characterize sequences of directed social interaction events in group settings. REM is a conventional approach to tackle such a problem whereas the application of IRL is a largely unbeaten path. We begin by examining the mathematical components of both REM and IRL and find straightforward analogies between the two methods as well as unique characteristics of the IRL approach. We demonstrate the special utility of IRL in characterizing group social interactions with an empirical experiment, in which we use IRL to infer individual behavioral preferences based on a sequence of directed communication events from a group of virtual-reality game players interacting and cooperating to accomplish a shared goal. Our comparison and experiment introduce fresh perspectives for social behavior analytics and help inspire new research opportunities at the nexus of social network analysis and machine learning.
Determinants of ASP choice: an integrated perspective

With the rise in the availability of scalable network technologies and resources, Application Service Providers (ASPs) have become increasingly more feasible sources for obtaining IS services. ASP research represents an opportunity to integrate previous research on outsourcing, applied in the area of ASP choice. Historically, outsourcing research has adopted many theoretical perspectives, including transaction cost, resource-based, resource-dependence, and knowledge-based. We apply these perspectives in the ASP context. When obtaining IS services through an ASP, factors other than cost and those explained by the resource-based and resource-dependent perspectives can become paramount. The knowledge-based perspective enables us to examine knowledge risk and integration requirements. Drawing upon past research and an exploratory study conducted with 35 respondents in 11 firms, an integrated, theoretical model is proposed, which combines the transaction cost, resource-based, resource dependency and knowledge-based perspectives. The model is used to explain the antecedent conditions useful for making decisions on IT sourcing through ASPs.
Power coupling losses for misaligned and mode-mismatched higher-order Hermite–Gauss modes

This paper analytically and numerically investigates misalignment and mode-mismatch-induced power coupling coefficients and losses as a function of Hermite–Gauss (HG) mode order. We show that higher-order HG modes are more susceptible to beam perturbations when, for example, coupling into optical cavities: the misalignment and mode-mismatch-induced power coupling losses scale linearly and quadratically with respect to the mode indices, respectively. As a result, the mode-mismatch tolerance for the ${{\rm HG}_{3,3}}$ mode is reduced to a factor of 0.28 relative to the currently used ${{\rm HG}_{0,0}}$ mode. This is a potential hurdle to using higher-order modes to reduce thermal noise in future gravitational-wave detectors.
Origami edge-paths in the curve graph

Abstract An origami (or flat structure) on a closed oriented surface, S g , of genus g ≥ 2 is obtained from a finite collection of unit Euclidean squares by gluing each right edge to a left one and each top edge to a bottom one. The main objects of study in this note are origami pairs of curves—filling pairs of simple closed curves, ( α , β ) , in S g such that their minimal intersection is equal to their algebraic intersection—they are coherent. An origami pair of curves is naturally associated with an origami on S g . Our main result establishes that for any origami pair of curves there exists an origami edge-path, a sequence of curves, α = α 0 , α 1 , α 2 , ⋯ , α n = β , such that: α i intersects α i + 1 exactly once; any pair ( α i , α j ) is coherent; and thus, any filling pair, ( α i , α j ) , is also an origami. With their existence established, we offer shortest origami edge-paths as an area of investigation.
Restriction of toral eigenfunctions to totally geodesic submanifolds

We estimate the L2 norm of the restriction to a totally geodesic submanifold of the eigenfunctions of the Laplace–Beltrami operator on the standard flat torus 𝕋d, d≥2. We reduce getting correct bounds to counting lattice points in the intersection of some ν-transverse bands on the sphere. Moreover, we prove the correct bounds for rational totally geodesic submanifolds of arbitrary codimension. In particular, we verify the conjecture of Bourgain–Rudnick on L2-restriction estimates for rational hyperplanes. On 𝕋2, we prove the uniform L2 restriction bounds for closed geodesics. On 𝕋3, we obtain explicit L2 restriction estimates for the totally geodesic submanifolds, which improve the corresponding results by Burq–Gerard–Tzvetkov, Hu, and Chen–Sogge.
Revealing the Structure of Deep Neural Networks via Convex Duality

We study regularized deep neural networks (DNNs) and introduce a convex analytic framework to characterize the structure of the hidden layers. We show that a set of optimal hidden layer weights for a norm regularized DNN training problem can be explicitly found as the extreme points of a convex set. For the special case of deep linear networks with K outputs, we prove that each optimal weight matrix is rank-K and aligns with the previous layers via duality. More importantly, we apply the same characterization to deep ReLU networks with whitened data and prove the same weight alignment holds. As a corollary, we prove that norm regularized deep ReLU networks yield spline interpolation for one-dimensional datasets which was previously known only for two-layer networks. Furthermore, we provide closed-form solutions for the optimal layer weights when data is rank-one or whitened. We then verify our theory via numerical experiments.
Visibility of noisy quantum dot-based measurements of Majorana qubits

Measurement schemes of Majorana zero modes (MZMs) based on quantum dots (QDs) are of current interest as they provide a scalable platform for topological quantum computation. In a coupled qubit-QD setup we calculate the dependence of the charge of the QD and its differential capacitance on experimentally tunable parameters for both 2-MZM and 4-MZM measurements. We quantify the effect of noise on the measurement visibility by considering $1/f$ noise in detuning, tunneling amplitudes or phase. We find that on- or close-to-resonance measurements are generally preferable and predict, using conservative noise estimates, that noise coupling to the QDs is not a limitation to high-fidelity measurements of topological qubits.
Characterizing English Variation across Social Media Communities with BERT

Much previous work characterizing language variation across Internet social groups has focused on the types of words used by these groups. We extend this type of study by employing BERT to characterize variation in the senses of words as well, analyzing two months of English comments in 474 Reddit communities. The specificity of different sense clusters to a community, combined with the specificity of a community's unique word types, is used to identify cases where a social group's language deviates from the norm. We validate our metrics using user-created glossaries and draw on sociolinguistic theories to connect language variation with trends in community behavior. We find that communities with highly distinctive language are medium-sized, and their loyal and highly engaged users interact in dense networks.
Understanding Factuality in Abstractive Summarization with FRANK: A Benchmark for Factuality Metrics.

Modern summarization models generate highly fluent but often factually unreliable outputs. This motivated a surge of metrics attempting to measure the factuality of automatically generated summaries. Due to the lack of common benchmarks, these metrics cannot be compared. Moreover, all these methods treat factuality as a binary concept and fail to provide deeper insights on the kinds of inconsistencies made by different systems. To address these limitations, we devise a typology of factual errors and use it to collect human annotations of generated summaries from state-of-the-art summarization systems for the CNN/DM and XSum datasets. Through these annotations we identify the proportion of different categories of factual errors and benchmark factuality metrics, showing their correlation with human judgement as well as their specific strengths and weaknesses.
Functional Performance of Pyrovalves

Following several flight and ground test failures of spacecraft systems using single-shot, 'normally closed' pyrotechnically actuated valves (pyrovalves), a Government/Industry cooperative program was initiated to assess the functional performance of five qualified designs. The goal of the program was to provide information on functional performance of pyrovalves to allow users the opportunity to improve procurement requirements. Specific objectives included the demonstration of performance test methods, the seating; these gases/particles entered the fluid path of measurement of 'blowby' (the passage of gases from the pyrotechnic energy source around the activating piston into the valve's fluid path), and the quantification of functional margins for each design. Experiments were conducted at NASA's Langley Research Center on several units for each of the five valve designs. The test methods used for this program measured the forces and energies required to actuate the valves, as well as the energies and the pressures (where possible) delivered by the pyrotechnic sources. Functional performance ranged widely among the designs. Blowby cannot be prevented by o-ring seals; metal-to-metal seals were effective. Functional margin was determined by dividing the energy delivered by the pyrotechnic sources in excess to that required to accomplish the function by the energy required for that function. Two of the five designs had inadequate functional margins with the pyrotechnic cartridges evaluated.
Interplay between cracking and delamination in incrementally deposited plasma sprayed coatings

Abstract Cracking and delamination in constrained films and coatings during fabrication and service has been an important subject of scientific inquiry for several decades. Considerable past efforts have focused on developing analytical models based predicting steady state energy release rates to determine the occurrence of either cracking and/or delamination in thin films. Such models have been reconciled into a ‘Design Map’. However, the extent of available experimental validation of such Design Maps is limited. In addition, these analytical Design Maps lack the description of nuances in the processing of layered films such as progressive incremental deposition. In this study, the interplay between cracking and delamination in progressively deposited plasma sprayed coatings is defined based on the aforementioned models. Following the adaptation of the models and presiding assumptions, a carefully designed set of experiments to probe responses from different ceramics and process conditions have been conducted, which allowed the controlled observation of cracking and/or delamination events. These experiments elucidated the underlying conditions that determine the onset of such stress relaxation events. The experimental data is reconciled with an adapted analytical Design Map for single, isolated, rapidly solidified droplets (splats) and for incrementally deposited thick plasma sprayed coatings, thus providing a framework for the robust design and processing of advanced coatings.
Exploring Lexical Irregularities in Hypothesis-Only Models of Natural Language Inference

Natural Language Inference (NLI) or Recognizing Textual Entailment (RTE) is the task of predicting the entailment relation between a pair of sentences (premise and hypothesis). This task has been described as “a valuable testing ground for the development of semantic representations” [1], and is a key component in natural language understanding evaluation benchmarks. Models that understand entailment should encode both, the premise and the hypothesis. However, experiments by Poliak et al. [2] revealed a strong preference of these models towards patterns observed only in the hypothesis, based on a 10 dataset comparison. Their results indicated the existence of statistical irregularities present in the hypothesis that bias the model into performing competitively with the state of the art. While recast datasets provide large scale generation of NLI instances due to minimal human intervention, the papers that generate them do not provide fine-grained analysis of the potential statistical patterns that can bias NLI models. In this work, we analyze hypothesis-only models trained on one of the recast datasets provided in Poliak et al. [2] for word-level patterns. Our results indicate the existence of potential lexical biases that could contribute to inflating the models’ performance.
Rational engineering of low temperature activity in thermoalkalophilic Geobacillus thermocatenulatus lipase

Abstract While thermophilic enzymes have thermostability desired for broad industrial applications, they can lose activity at ambient temperatures far from their optimal temperature. Engineering cold activity into thermophilic enzymes has the potential to broaden the range of temperatures resulting in significant activity (i.e., decreasing the temperature dependence of kcat). Even though it has been widely suggested that cold activity, which results from active site flexibility, is at odds with thermostability, which results from enzyme rigidity, directed evolution experiments have shown that these properties are not mutually exclusive. In this study, rational protein engineering was used to introduce glycine as flexibility inducing mutations around the active site of Geobacillus thermocatenulatus lipase (GTL). Two mutants were found to have enhanced specific activity compared to wild-type at temperatures between 283 K and 363 K with p-nitrophenol butyrate but not with larger substrates. Kinetics assay revealed both mutations resulted in psychrophilic traits, such as lower activation enthalpy and more negative entropy values compared to wild type in all substrates. Furthermore, the mutants had significantly improved thermostability compared to wild type enzyme, which proves that it is feasible to improve the cold activity without trade-off. Our study provides insight into the enzyme cold adaptation mechanism and design principles for engineering cold activity into thermostable enzymes.
Robust Knowledge Graph Completion with Stacked Convolutions and a Student Re-Ranking Network

Knowledge Graph (KG) completion research usually focuses on densely connected benchmark datasets that are not representative of real KGs. We curate two KG datasets that include biomedical and encyclopedic knowledge and use an existing commonsense KG dataset to explore KG completion in the more realistic setting where dense connectivity is not guaranteed. We develop a deep convolutional network that utilizes textual entity representations and demonstrate that our model outperforms recent KG completion methods in this challenging setting. We find that our model’s performance improvements stem primarily from its robustness to sparsity. We then distill the knowledge from the convolutional network into a student network that re-ranks promising candidate entities. This re-ranking stage leads to further improvements in performance and demonstrates the effectiveness of entity re-ranking for KG completion.
Strong solutions to a modified Michelson-Sivashinsky equation

We prove a global well-posedness and regularity result of strong solutions to a slightly modified Michelson-Sivashinsky equation in any spatial dimension and in the absence of physical boundaries. Local-in-time well-posedness (and regularity) in the space $W^{1,\infty}(\mathbb{R}^d)$ is established and is shown to be global if in addition the initial data is either periodic or vanishes at infinity. The proof of the latter result utilizes ideas previously introduced by Kiselev, Nazarov, Volberg and Shterenberg to handle the critically dissipative surface quasi-geostrophic equation and the critically dissipative fractional Burgers equation. Namely, the global regularity result is achieved by constructing a time-dependent modulus of continuity that must be obeyed by the solution of the initial-value problem for all time, preventing blowup of the gradient of the solution. This work provides an example where regularity is shown to persist even when a-priori bounds are not available.
Trapping and escape in a turbid medium

We investigate the absorption of diffusing molecules in a fluid-filled spherical beaker that contains many small reactive traps. The molecules are absorbed either by hitting a trap or by escaping via the beaker walls. In the physical situation where the number N of traps is large and their radii a are small compared to the beaker radius R, the fraction of molecules E that escape to the beaker wall and the complementary fraction T that eventually are absorbed by the traps depend only on the dimensionless parameter combination λ = Na/R. We compute E and T as a function of λ for a spherical beaker and for beakers of other three-dimensional shapes. The asymptotic behavior is found to be universal: 1 - E ∼ λ for λ → 0 and E ∼ λ-1/2 for λ → ∞.
EasyRL: A Simple and Extensible Reinforcement Learning Framework.

In recent years, Reinforcement Learning (RL), has become a popular field of study as well as a tool for enterprises working on cutting-edge artificial intelligence research. To this end, many researchers have built RL frameworks such as openAI Gym, and KerasRL for ease of use. While these works have made great strides towards bringing down the barrier of entry for those new to RL, we propose a much simpler framework called EasyRL, by providing an interactive graphical user interface for users to train and evaluate RL agents. As it is entirely graphical, EasyRL does not require programming knowledge for training and testing simple built-in RL agents. EasyRL also supports custom RL agents and environments, which can be highly beneficial for RL researchers in evaluating and comparing their RL models.
Interpretation of Liapunov stability regions.

Interpretation of results obtained by Liapunov method, using parallelepiped imbedded in region of asymptotic stability
What Do Mutual Fund Investors Really Care About

Rational investors should account for risk factor exposure when allocating capital to mutual funds. Two recent influential studies use mutual fund flows to test whether investors distinguish between performance driven by managers' skill and systematic risk factors. Both studies found that investors use the Capital Asset Pricing Model (CAPM), and one concluded that the CAPM is the "closest to the true asset pricing model." We re-examine these results and show that, in fact, fund flow data are most consistent with investors relying blindly on fund rankings (specifically, Morningstar ratings) and chasing recent returns. We find no evidence that investors account for any of the common systematic risk factors when allocating capital among mutual funds.
Contingency Analysis Based on Partitioned and Parallel Holomorphic Embedding

In the steady-state contingency analysis, the traditional Newton-Raphson method suffers from non-convergence issues when solving post-outage power flow problems, which hinders the integrity and accuracy of security assessment. In this paper, we propose a novel robust contingency analysis approach based on holomorphic embedding (HE). The HE-based simulator provides theoretical convergence guarantee, which is desirable because it avoids the influence of numerical issues and provides a credible security assessment conclusion. In addition, based on the multi-area characteristics of real-world power systems, a partitioned HE (PHE) method is proposed with an interface-based partitioning of HE formulation. The PHE method does not undermine the numerical robustness of HE and significantly reduces the computation burden in large-scale contingency analysis. The PHE method is further enhanced by parallel or distributed computation to become parallel PHE (P ${}^\mathrm{2}$ HE). Tests on a 458-bus system, a synthetic 419-bus system and a large-scale 21447-bus system demonstrate the advantages of the proposed methods in robustness and efficiency.
Mentoring Experiences of Male Faculty in Nursing Programs.

AIM The aim of this study was to examine the mentoring experiences of male faculty in nursing programs. BACKGROUND Recruiting and retaining male nursing faculty is important to achieve a workforce that reflects the diversity of society. Mentoring is one way to help recruit and retain male nursing faculty. METHOD Ten male nursing faculty in academia comprised the sample for this qualitative, descriptive study. RESULTS Reflection on the mentoring male nursing faculty received for career and psychosocial development revealed the following themes: early supporters, figuring it out, making connections, paying it forward, and the role of gender. Facilitators and barriers to mentoring experiences are described. CONCLUSION Results of this study may be used to support investment in mentoring programs to recruit and retain male nursing faculty in academia and promote a diverse nursing workforce.
Coarse-grained and Emergent Distributed Parameter Systems from Data

We explore the derivation of distributed parameter system evolution laws (and in particular, partial differential operators and associated partial differential equations, PDEs) from spatiotemporal data. This is, of course, a classical identification problem; our focus here is on the use of manifold learning techniques (and, in particular, variations of Diffusion Maps) in conjunction with neural network learning algorithms that allow us to attempt this task when the dependent variables, and even the independent variables of the PDE are not known a priori and must be themselves derived from the data. The similarity measure used in Diffusion Maps for dependent coarse variable detection involves distances between local particle distribution observations; for independent variable detection we use distances between local short-time dynamics. We demonstrate each approach through an illustrative established PDE example. Such variable-free, emergent space identification algorithms connect naturally with equation-free multiscale computation tools.
TGIF: Tree-Graph Integrated-Format Parser for Enhanced UD with Two-Stage Generic- to Individual-Language Finetuning

We present our contribution to the IWPT 2021 shared task on parsing into enhanced Universal Dependencies. Our main system component is a hybrid tree-graph parser that integrates (a) predictions of spanning trees for the enhanced graphs with (b) additional graph edges not present in the spanning trees. We also adopt a finetuning strategy where we first train a language-generic parser on the concatenation of data from all available languages, and then, in a second step, finetune on each individual language separately. Additionally, we develop our own complete set of pre-processing modules relevant to the shared task, including tokenization, sentence segmentation, and multiword token expansion, based on pre-trained XLM-R models and our own pre-training of character-level language models. Our submission reaches a macro-average ELAS of 89.24 on the test set. It ranks top among all teams, with a margin of more than 2 absolute ELAS over the next best-performing submission, and best score on 16 out of 17 languages.
Catalase Activity is Critical for Proteus mirabilis Biofilm Development, EPS Composition, and Dissemination During Catheter-Associated Urinary Tract Infection

Proteus mirabilis is a leading uropathogen of catheter-associated urinary tract infections (CAUTIs), which are among the most common healthcare-associated infections worldwide. A key factor that contributes to P. mirabilis pathogenesis and persistence during CAUTI is the formation of catheter biofilms, which provide increased resistance to antibiotic treatment and host defense mechanisms. Another factor that is important for bacterial persistence during CAUTI is the ability to resist reactive oxygen species (ROS), such as through the action of the catalase enzyme. Potent catalase activity is one of the defining biochemical characteristics of P. mirabilis, and the single catalase gene (katA) encoded in strain HI4320 was recently identified as a candidate fitness factor for UTI, CAUTI, and bacteremia. Here we show that disruption of katA results in increased ROS levels, increased sensitivity to peroxide, and decreased biofilm biomass. The biomass defect was due to a decrease in extracellular polymeric substances (EPS) production by the ΔkatA mutant, and specifically due to reduced carbohydrate content. Importantly, the biofilm defect resulted in decreased antibiotic resistance in vitro and a colonization defect during experimental CAUTI. The ΔkatA mutant also exhibited decreased fitness in a bacteremia model, supporting a dual role for catalase in P. mirabilis biofilm development and immune evasion.
Triangle-Free Subgraphs of Hypergraphs

In this paper, we consider an analog of the well-studied extremal problem for triangle-free subgraphs of graphs for uniform hypergraphs. A loose triangle is a hypergraph T consisting of three edges e, f and g such that $$|e \cap f| = |f \cap g| = |g \cap e| = 1$$ and $$e \cap f \cap g = \emptyset $$ . We prove that if H is an n-vertex r-uniform hypergraph with maximum degree $$\triangle $$ , then as $$\triangle \rightarrow \infty $$ , the number of edges in a densest T-free subhypergraph of H is at least $$\begin{aligned} \frac{e(H)}{\triangle ^{\frac{r-2}{r-1} + o(1)}}. \end{aligned}$$ For $$r = 3$$ , this is tight up to the o (1) term in the exponent. We also show that if H is a random n-vertex triple system with edge-probability p such that $$pn^3\rightarrow \infty $$ as $$n\rightarrow \infty $$ , then with high probability as $$n \rightarrow \infty $$ , the number of edges in a densest T-free subhypergraph is $$\begin{aligned} {{\mathrm{min}}}\left\{ (1-o(1))p\left( {\begin{array}{c}n\\ 3\end{array}}\right) ,p^{\frac{1}{3}}n^{2-o(1)}\right\} . \end{aligned}$$ We use the method of containers together with probabilistic methods and a connection to the extremal problem for arithmetic progressions of length three due to Ruzsa and Szemeredi.
Quantum Spectrum Testing

In this work, we study the problem of testing properties of the spectrum of a mixed quantum state. Here one is given n copies of a mixed state $$\rho \in \mathbb {C}^{d\times d}$$ and the goal is to distinguish (with high probability) whether $$\rho $$ ’s spectrum satisfies some property $${\mathcal {P}}$$ or whether it is at least $$\epsilon $$ -far in $$\ell _1$$ -distance from satisfying $${\mathcal {P}}$$ . This problem was promoted in the survey of Montanaro and de Wolf (A survey of quantum property testing. Technical report, arXiv:1310.2035 , 2013) under the name of testing unitarily invariant properties of mixed states. It is the natural quantum analogue of the classical problem of testing symmetric properties of probability distributions. Unlike property testing probability distributions—where one generally hopes for algorithms with sample complexity that is sublinear in the domain size—here the hope is for algorithms with subquadratic copy complexity in the dimension d. This is because the (frequently rediscovered) “empirical Young diagram (EYD) algorithm” (Alicki et al. in J Math Phys 29(5):1158–1162, 1988; Keyl and Werner in Phys Rev A 64(5):052311, 2011; Hayashi and Matsumoto in Phys Rev A 66(2):022311, 2002; Christandl and Mitchison in Commun. Math. Phys. 261(3):789–797, 2006) can estimate the spectrum of any mixed state up to $$\epsilon $$ -accuracy using only $${\widetilde{O}}(d^2/\epsilon ^2)$$ copies. In this work, we show that given a mixed state $$\rho \in \mathbb {C}^{d \times d}$$ : Our techniques involve the asymptotic representation theory of the symmetric group; in particular Kerov’s algebra of polynomial functions on Young diagrams.
A projector-based approach to quantifying total and excess uncertainties for sketched linear regression

Linear regression is a classic method of data analysis. In recent years, sketching -- a method of dimension reduction using random sampling, random projections, or both -- has gained popularity as an effective computational approximation when the number of observations greatly exceeds the number of variables. In this paper, we address the following question: How does sketching affect the statistical properties of the solution and key quantities derived from it? To answer this question, we present a projector-based approach to sketched linear regression that is exact and that requires minimal assumptions on the sketching matrix. Therefore, downstream analyses hold exactly and generally for all sketching schemes. Additionally, a projector-based approach enables derivation of key quantities from classic linear regression that account for the combined model- and algorithm-induced uncertainties. We demonstrate the usefulness of a projector-based approach in quantifying and enabling insight on excess uncertainties and bias-variance decompositions for sketched linear regression. Finally, we demonstrate how the insights from our projector-based analyses can be used to produce practical sketching diagnostics to aid the design of judicious sketching schemes.
A structure theorem on doubling measures with different bases

Abstract In this paper, we prove a structure theorem for the infinite union of n-adic doubling measures via techniques which involve far numbers. Our approach extends the results of Wu in 1998, and as a byproduct, we also prove a classification result related to normal numbers.
Explanatory Pluralism in Explainable AI.

The increasingly widespread application of AI models motivates increased demand for explanations from a variety of stakeholders. However, this demand is ambiguous because there are many types of ‘explanation’ with different evaluative criteria. In the spirit of pluralism, I chart a taxonomy of types of explanation and the associated XAI methods that can address them. When we look to expose the inner mechanisms of AI models, we develop Diagnostic-explanations. When we seek to render model output understandable, we produce Explication-explanations. When we wish to form stable generalizations of our models, we produce Expectation-explanations. Finally, when we want to justify the usage of a model, we produce Role-explanations that situate models within their social context. The motivation for such a pluralistic view stems from a consideration of causes as manipulable relationships and the different types of explanations as identifying the relevant points in AI systems we can intervene upon to affect our desired changes. This paper reduces the ambiguity in use of the word ‘explanation’ in the field of XAI, allowing practitioners and stakeholders a useful template for avoiding equivocation and evaluating XAI methods and putative explanations.