cssi/SciSciGPT-SciSciNet huggingface dataset:
The countable admissible ordinal equivalence relation

Let F_(ω1) be the countable admissible ordinal equivalence relation defined on ^ω2 by x F_(ω1) y if and only if ω_1^x=ω_1^y. Some invariant descriptive set theoretic properties of F_(ω1) will be explored using infinitary logic in countable admissible fragments as the main tool. Marker showed F_(ω1) is not the orbit equivalence relation of a continuous action of a Polish group on ^ω2. Becker stengthened this to show F_(ω1) is not even the orbit equivalence relation of a Δ_1^1 action of a Polish group. However, Montalban has shown that F_(ω1) is Δ_1^1 reducible to an orbit equivalence relation of a Polish group action, in fact, F_(ω1) is classifiable by countable structures. It will be shown here that F_(ω1) must be classified by structures of high Scott rank. Let E_(ω1) denote the equivalence of order types of reals coding well-orderings. If E and F are two equivalence relations on Polish spaces X and Y, respectively, E ≤ aΔ_1^1 F denotes the existence of a Δ_1^1 function f:X→Y which is a reduction of E to F, except possibly on countably many classes of E. Using a result of Zapletal, the existence of a measurable cardinal implies E_(ω1) ≤ aΔ_1^1 F_(ω1). However, it will be shown that in Godel's constructible universe L (and set generic extensions of L), E_(ω1) ≤ aΔ_1^1 F_(ω1) is false. Lastly, the techniques of the previous result will be used to show that in L (and set generic extensions of L), the isomorphism relation induced by a counterexample to Vaught's conjecture cannot be Δ_1^1 reducible to F_(ω1). This shows the consistency of a negative answer to a question of Sy-David Friedman.
A Fast Integrated Planning and Control Framework for Autonomous Driving via Imitation Learning

For safe and efficient planning and control in autonomous driving, we need a driving policy which can achieve desirable driving quality in long-term horizon with guaranteed safety and feasibility. Optimization-based approaches, such as Model Predictive Control (MPC), can provide such optimal policies, but their computational complexity is generally unacceptable for real-time implementation. To address this problem, we propose a fast integrated planning and control framework that combines learning- and optimization-based approaches in a two-layer hierarchical structure. The first layer, defined as the "policy layer", is established by a neural network which learns the long-term optimal driving policy generated by MPC. The second layer, called the "execution layer", is a short-term optimization-based controller that tracks the reference trajecotries given by the "policy layer" with guaranteed short-term safety and feasibility. Moreover, with efficient and highly-representative features, a small-size neural network is sufficient in the "policy layer" to handle many complicated driving scenarios. This renders online imitation learning with Dataset Aggregation (DAgger) so that the performance of the "policy layer" can be improved rapidly and continuously online. Several exampled driving scenarios are demonstrated to verify the effectiveness and efficiency of the proposed framework.
Two emission mechanisms in the Fermi Bubbles: A possible signal of annihilating dark matter

Abstract We study the variation of the spectrum of the Fermi Bubbles with Galactic latitude. Far from the Galactic plane ( | b | ≳ 30°), the observed gamma-ray emission is nearly invariant with latitude, and is consistent with arising from inverse Compton scattering of the interstellar radiation field by cosmic-ray electrons with an approximately power-law spectrum. The same electrons in the presence of microgauss-scale magnetic fields can also generate the the observed microwave “haze”. At lower latitudes ( | b | ≲ 20°), in contrast, the spectrum of the emission correlated with the Bubbles possesses a pronounced spectral feature peaking at ∼1–4 GeV (in E2dN/dE) which cannot be generated by any realistic spectrum of electrons. Instead, we conclude that a second (non-inverse-Compton) emission mechanism must be responsible for the bulk of the low-energy, low-latitude emission. This second component is spectrally similar to the excess GeV emission previously reported from the Galactic Center (GC), and also appears spatially consistent with a luminosity per volume falling approximately as r−2.4, where r is the distance from the GC. Consequently, we argue that the spectral feature visible in the low-latitude Bubbles is most likely the extended counterpart of the GC excess, now detected out to at least ∼2–3 kpc from the GC. The spectrum and angular distribution of the signal is broadly consistent with that predicted from ∼10 GeV dark matter particles annihilating to leptons, or from ∼50 GeV dark matter particles annihilating to quarks, following a distribution similar to, but slightly steeper than, the canonical Navarro–Frenk–White (NFW) profile. We also consider millisecond pulsars as a possible astrophysical explanation for the signal, as observed millisecond pulsars possess a spectral cutoff at approximately the required energy. Any such scenario would require a large population of unresolved millisecond pulsars extending at least 2–3 kpc from the GC.
Coverage Analysis of a Vehicular Network Modeled as Cox Process Driven by Poisson Line Process

In this paper, we consider a vehicular network in which the wireless nodes are located on a system of roads. We model the roadways, which are predominantly straight and randomly oriented, by a Poisson line process (PLP) and the locations of nodes on each road as a homogeneous 1D Poisson point process. Assuming that each node transmits independently, the locations of transmitting and receiving nodes are given by two Cox processes driven by the same PLP. For this setup, we derive the coverage probability of a typical receiver, which is an arbitrarily chosen receiving node, assuming independent Nakagami- $m$ fading over all wireless channels. Assuming that the typical receiver connects to its closest transmitting node in the network, we first derive the distribution of the distance between the typical receiver and the serving node to characterize the desired signal power. We then characterize coverage probability for this setup, which involves two key technical challenges. First, we need to handle several cases as the serving node can possibly be located on any line in the network and the corresponding interference experienced at the typical receiver is different in each case. Second, conditioning on the serving node imposes constraints on the spatial configuration of lines, which requires careful analysis of the conditional distribution of the lines. We address these challenges in order to characterize the interference experienced at the typical receiver. We then derive an exact expression for coverage probability in terms of the derivative of Laplace transform of interference power distribution. We analyze the trends in coverage probability as a function of the network parameters: line density and node density. We also provide some theoretical insights by studying the asymptotic characteristics of coverage probability.
The moduli space of cubic fourfolds

We describe the GIT compactification of the moduli space of cubic fourfolds, with a special emphasis on the role played by singularities. Our main result is that a cubic fourfold with only isolated simple (A-D-E) singularities is GIT stable. Conversely, with some minor exceptions, the stability for cubic fourfolds is characterized by this condition.
Probably Approximately Correct MDP Learning and Control With Temporal Logic Constraints

We consider synthesis of control policies that maximize the probability of satisfying given temporal logic specifications in unknown, stochastic environments. We model the interaction between the system and its environment as a Markov decision process (MDP) with initially unknown transition probabilities. The solution we develop builds on the so-called model-based probably approximately correct Markov decision process (PACMDP) methodology. The algorithm attains an e-approximately optimal policy with probability 1− δ using samples (i.e. observations), time and space that grow polynomially with the size of the MDP, the size of the automaton expressing the temporal logic specification, 1 e , 1 δ and a finite time horizon. In this approach, the system maintains a model of the initially unknown MDP, and constructs a product MDP based on its learned model and the specification automaton that expresses the temporal logic constraints. During execution, the policy is iteratively updated using observation of the transitions taken by the system. The iteration terminates in finitely many steps. With high probability, the resulting policy is such that, for any state, the difference between the probability of satisfying the specification under this policy and the optimal one is within a predefined bound.
OPTIMAL HUMAN NAVIGATION IN STEEP TERRAIN: A HAMILTON-JACOBI-BELLMAN APPROACH

Author(s): Parkinson, Christian; Arnold, David; Bertozzi, Andrea L; Chow, Yat Tin; Osher, Stanley | Abstract: We present a method for determining optimal walking paths in steep terrain using the level set method and an optimal control formulation. By viewing the walking direction as a control variable, we can determine the optimal control by solving a Hamilton-Jacobi-Bellman equation. We then calculate the optimal walking path by solving an ordinary differential equation. We demonstrate the effectiveness of our method by computing optimal paths which travel throughout mountainous regions of Yosemite National Park. We include details regarding the numerical implementation of our model and address a specific application of a law enforcement agency patrolling a nationally protected area.
Adversarial Imitation via Variational Inverse Reinforcement Learning

We consider a problem of learning the reward and policy from expert examples under unknown dynamics. Our proposed method builds on the framework of generative adversarial networks and introduces the empowerment-regularized maximum-entropy inverse reinforcement learning to learn near-optimal rewards and policies. Empowerment-based regularization prevents the policy from overfitting to expert demonstrations, which advantageously leads to more generalized behaviors that result in learning near-optimal rewards. Our method simultaneously learns empowerment through variational information maximization along with the reward and policy under the adversarial learning formulation. We evaluate our approach on various high-dimensional complex control tasks. We also test our learned rewards in challenging transfer learning problems where training and testing environments are made to be different from each other in terms of dynamics or structure. The results show that our proposed method not only learns near-optimal rewards and policies that are matching expert behavior but also performs significantly better than state-of-the-art inverse reinforcement learning algorithms.
Modular curvature for toric noncommutative manifolds

A general question behind this paper is to explore a good notion for intrinsic curvature in the framework of noncommutative geometry started by Alain Connes in the 80s. It has only recently begun (2014) to be comprehended via the intensive study of modular geometry on the noncommutative two tori. In this paper, we extend recent results on the modular geometry on noncommutative two tori to a larger class of noncommutative manifolds: toric noncommutative manifolds. The first contribution of this work is a pseudo differential calculus which is suitable for spectral geometry on toric noncommutative manifolds. As the main application, we derive a general expression for the modular curvature with respect to a conformal change of metric on toric noncommutative manifolds. By specializing our results to the noncommutative two and four tori, we recovered the modular curvature functions found in the previous works. An important technical aspect of the computation is that it is free of computer assistance.
Estimating restricted mean treatment effects with stacked survival models

The difference in restricted mean survival times between two groups is a clinically relevant summary measure. With observational data, there may be imbalances in confounding variables between the two groups. One approach to account for such imbalances is estimating a covariate-adjusted restricted mean difference by modeling the covariate-adjusted survival distribution and then marginalizing over the covariate distribution. Because the estimator for the restricted mean difference is defined by the estimator for the covariate-adjusted survival distribution, it is natural to expect that a better estimator of the covariate-adjusted survival distribution is associated with a better estimator of the restricted mean difference. We therefore propose estimating restricted mean differences with stacked survival models. Stacked survival models estimate a weighted average of several survival models by minimizing predicted error. By including a range of parametric, semi-parametric, and non-parametric models, stacked survival models can robustly estimate a covariate-adjusted survival distribution and, therefore, the restricted mean treatment effect in a wide range of scenarios. We demonstrate through a simulation study that better performance of the covariate-adjusted survival distribution often leads to better mean squared error of the restricted mean difference although there are notable exceptions. In addition, we demonstrate that the proposed estimator can perform nearly as well as Cox regression when the proportional hazards assumption is satisfied and significantly better when proportional hazards is violated. Finally, the proposed estimator is illustrated with data from the United Network for Organ Sharing to evaluate post-lung transplant survival between large-volume and small-volume centers. Copyright © 2016 John Wiley & Sons, Ltd.
Multi-Modal Data Augmentation for End-to-end ASR.

We present a new end-to-end architecture for automatic speech recognition (ASR) that can be trained using \emph{symbolic} input in addition to the traditional acoustic input. This architecture utilizes two separate encoders: one for acoustic input and another for symbolic input, both sharing the attention and decoder parameters. We call this architecture a multi-modal data augmentation network (MMDA), as it can support multi-modal (acoustic and symbolic) input and enables seamless mixing of large text datasets with significantly smaller transcribed speech corpora during training. We study different ways of transforming large text corpora into a symbolic form suitable for training our MMDA network. Our best MMDA setup obtains small improvements on character error rate (CER), and as much as 7-10\% relative word error rate (WER) improvement over a baseline both with and without an external language model.
Quantification of Conflicts of Interest in an Online Point-of-Care Clinical Support Website

Online medical reference websites are utilized by health care providers to enhance their education and decision making. However, these resources may not adequately reveal pharmaceutical-author interactions and their potential conflicts of interest (CoIs). This investigation: (1) evaluates the correspondence of two well-utilized CoI databases: the Centers for Medicare and Medicaid Services Open Payments (CMSOP) and ProPublica’s Dollars for Docs (PDD) and (2) quantifies CoIs among authors of a publicly available point of care clinical support website which is used to inform evidence-based medicine decisions. Two data sources were used: the hundred most common drugs and the top fifty causes of death. These topics were entered into a freely available database. The authors (N = 139) were then input into CMSOP and PDD and compensation and number of payments were determined for 2013–2015. The subset of highly compensated authors that also reported “Nothing to disclose” were further examined. There was a high degree of similarity between CMSOP and PDD for compensation (R2 ≥ 0.998) and payment number (R2 ≥ 0.992). The amount received was 1.4% higher in CMSOP ($4,059,194) than in PDD ($4,002,891). The articles where the authors had received the greatest compensation were in neurology (Parkinson’s Disease = $1,810,032), oncology (Acute Lymphoblastic Leukemia = $616,727), and endocrinology (Type I Diabetes = $377,388). Two authors reporting “Nothing to disclose” received appreciable and potentially relevant compensation. CMSOP and PDD produced almost identical results. CoIs were common among authors but self-reporting may be an inadequate reporting mechanism. Recommendations are offered for improving the CoI transparency of pharmaceutical-author interactions in point-of-care electronic resources.
Jost Asymptotics for Matrix Orthogonal Polynomials on the Real Line

We obtain matrix-valued Jost asymptotics for block Jacobi matrices under an L1-type condition on Jacobi coefficients, and give a necessary and sufficient condition for an analytic matrix-valued function to be the Jost function of a block Jacobi matrix with exponentially converging parameters. This establishes the matrix-valued analogue of Damanik and Simon (Int. Math. Res. Not. 32:19396, 2006).
Structure of attractors for boundary maps associated to Fuchsian groups

We study dynamical properties of generalized Bowen–Series boundary maps associated to cocompact torsion-free Fuchsian groups. These maps are defined on the unit circle (the boundary of the Poincare disk) by the generators of the group and have a finite set of discontinuities. We study the two forward orbits of each discontinuity point and show that for a family of such maps the cycle property holds: the orbits coincide after finitely many steps. We also show that for an open set of discontinuity points the associated two-dimensional natural extension maps possess global attractors with finite rectangular structure. These two properties belong to the list of “good” reduction algorithms, equivalence or implications between which were suggested by Zagier.
A Change-Detection Based Framework for Piecewise-Stationary Multi-Armed Bandit Problem

The multi-armed bandit problem has been extensively studied under the stationary assumption. However in reality, this assumption often does not hold because the distributions of rewards themselves may change over time. In this paper, we propose a change-detection (CD) based framework for multi-armed bandit problems under the piecewise-stationary setting, and study a class of change-detection based UCB (Upper Confidence Bound) policies, CD-UCB, that actively detects change points and restarts the UCB indices. We then develop CUSUM-UCB and PHT-UCB, that belong to the CD-UCB class and use cumulative sum (CUSUM) and Page-Hinkley Test (PHT) to detect changes. We show that CUSUM-UCB obtains the best known regret upper bound under mild assumptions. We also demonstrate the regret reduction of the CD-UCB policies over arbitrary Bernoulli rewards and Yahoo! datasets of webpage click-through rates.
On Lebesgue constants for interpolation points on a quasiconformal arc

Abstract Using the theory of quasiconformal mappings, we simplify the proof of the recent result by Taylor and Totik (2010) on the behavior of the Lebesgue constants for interpolation points on a compact set in the complex plane.
Tropicalization, symmetric polynomials, and complexity

Abstract D. Grigoriev-G. Koshevoy recently proved that tropical Schur polynomials have (at worst) polynomial tropical semiring complexity. They also conjectured tropical skew Schur polynomials have at least exponential complexity; we establish a polynomial complexity upper bound. Our proof uses results about (stable) Schubert polynomials, due to R. P. Stanley and S. Billey-W. Jockusch-R. P. Stanley, together with a sufficient condition for polynomial complexity that is connected to the saturated Newton polytope property.
Quasisymmetric maps on the boundary of a negatively curved solvable Lie group

We describe all the self quasisymmetric maps on the ideal boundary of a particular negatively curved solvable Lie group. As applications, we derive some rigidity properties for quasiisometries of the solvable Lie group.
Shuffle-compatible permutation statistics

Abstract Since the early work of Richard Stanley, it has been observed that several permutation statistics have a remarkable property with respect to shuffles of permutations. We formalize this notion of a shuffle-compatible permutation statistic and introduce the shuffle algebra of a shuffle-compatible permutation statistic, which encodes the distribution of the statistic over shuffles of permutations. This paper develops a theory of shuffle-compatibility for descent statistics—statistics that depend only on the descent set and length—which has close connections to the theory of P-partitions, quasisymmetric functions, and noncommutative symmetric functions. We use our framework to prove that many descent statistics are shuffle-compatible and to give explicit descriptions of their shuffle algebras, thus unifying past results of Stanley, Gessel, Stembridge, Aguiar–Bergeron–Nyman, and Petersen.
Nearly Minimax-Optimal Regret for Linearly Parameterized Bandits

We study the linear contextual bandit problem with finite action sets. When the problem dimension is $d$, the time horizon is $T$, and there are $n \leq 2^{d/2}$ candidate actions per time period, we (1) show that the minimax expected regret is $\Omega(\sqrt{dT (\log T) (\log n)})$ for every algorithm, and (2) introduce a Variable-Confidence-Level (VCL) SupLinUCB algorithm whose regret matches the lower bound up to iterated logarithmic factors. Our algorithmic result saves two $\sqrt{\log T}$ factors from previous analysis, and our information-theoretical lower bound also improves previous results by one $\sqrt{\log T}$ factor, revealing a regret scaling quite different from classical multi-armed bandits in which no logarithmic $T$ term is present in minimax regret. Our proof techniques include variable confidence levels and a careful analysis of layer sizes of SupLinUCB on the upper bound side, and delicately constructed adversarial sequences showing the tightness of elliptical potential lemmas on the lower bound side.
Attractors for Damped Semilinear Wave Equations with a Robin--Acoustic Boundary Perturbation

Abstract Under consideration is the damped semilinear wave equation u t t + u t − Δ u + u + f ( u ) = 0 on a bounded domain Ω in R 3 with a perturbation parameter e > 0 occurring in an acoustic boundary condition, limiting ( e = 0 ) to a Robin boundary condition. With minimal assumptions on the nonlinear term f , the existence and uniqueness of global weak solutions is shown for each e ∈ [ 0 , 1 ] . Also, the existence of a family of global attractors is shown to exist. After proving a general result concerning the upper-semicontinuity of a one-parameter family of sets, the result is applied to the family of global attractors. Because of the complicated boundary conditions for the perturbed problem, fractional powers of the Laplacian are not well-defined; moreover, because of the restrictive growth assumptions on f , the family of global attractors is obtained from the asymptotic compactness method developed by J. Ball for generalized semiflows. With more relaxed assumptions on the nonlinear term f , we are able to show the global attractors possess optimal regularity and prove the existence of an exponential attractor, for each e ∈ [ 0 , 1 ] . This result insures that the corresponding global attractor inherits finite (fractal) dimension, however, the dimension is not necessarily uniform in e .
Stochastic Gradient Descent Learns State Equations with Nonlinear Activations

We study discrete time dynamical systems governed by the state equation $h_{t+1}=\phi(Ah_t+Bu_t)$. Here $A,B$ are weight matrices, $\phi$ is an activation function, and $u_t$ is the input data. This relation is the backbone of recurrent neural networks (e.g. LSTMs) which have broad applications in sequential learning tasks. We utilize stochastic gradient descent to learn the weight matrices from a finite input/state trajectory $(u_t,h_t)_{t=0}^N$. We prove that SGD estimate linearly converges to the ground truth weights while using near-optimal sample size. Our results apply to increasing activations whose derivatives are bounded away from zero. The analysis is based on i) a novel SGD convergence result with nonlinear activations and ii) careful statistical characterization of the state vector. Numerical experiments verify the fast convergence of SGD on ReLU and leaky ReLU in consistence with our theory.
Estimated impact of hepatitis C-positive lung donor utilization on US donor lung supply.

The availability of highly effective direct-acting antiviral agents (DAAs) for hepatitis C virus (HCV) infection has led to reports of safely transplanting HCV+ donor lungs into HCV- candidates. However, it remains unclear how the ability to use HCV+ donor lungs for lung transplant could affect the number of donor lungs available for transplant. Using Scientific Registry of Transplant Recipient data, we identified all deceased organ donors within the United States from March 1, 2015, to February 28, 2018, and stratified by HCV status. A donor prediction model for lung donation was derived and validated within HCV- donors and applied to HCV+ donors to estimate the number of acceptable HCV+ lung donors. Of 29 481 eligible donors, 2054 (7.0%) were HCV+ donors with 82 HCV+ donors' lungs being used for transplant during the study period. The prediction model for donor lung donation (specificity 92.6%, sensitivity 65.6%) estimated 248 HCV+ donors (75 nonviremic, 173 viremic) were acceptable for lung transplant during the study period, suggesting that 166 acceptable HCV+ lung donors were discarded. The ability to transplant lungs from HCV+ organ donors would lead to an estimated nationwide increase of at least 55 donor lungs per year, including 44 from HCV viremic donors.
Completion of the Vimentin Rod Domain Structure Using Experimental Restraints: A New Tool for Exploring Intermediate Filament Assembly and Mutations

Electron paramagnetic resonance (EPR) spectroscopy of full-length vimentin and X-ray crystallography of vimentin peptides has provided concordant structural data for nearly the entire central rod domain of the protein. In this report, we use a combination of EPR spectroscopy and molecular modeling to determine the structure and dynamics of the missing region and unite the separate elements into a single structure. Validation of the linker 1-2 (L1-2) modeling approach is demonstrated by the close correlation between EPR and X-ray data in the previously solved regions. Importantly, molecular dynamic (MD) simulation of the constructed model agrees with spin label motion as determined by EPR. Furthermore, MD simulation shows L1-2 heterogeneity, with a concerted switching of states among the dimer chains. These data provide the first ever experimentally driven model of a complete intermediate filament rod domain, providing research tools for further modeling and assembly studies.
Sexual Harassment and Cardiothoracic Surgery: #UsToo?

Background Fifty-eight percent of women in science, engineering, and medicine report being affected by sexual harassment (SH). This study sought to determine the extent of SH in cardiothoracic surgery. Methods The study developed a survey that was based on the Sexual Experience Questionnaire-Workplace, physician wellness, and burnout surveys. The survey was open to responses for 45 days and was disseminated through The Society of Thoracic Surgeons, Women in Thoracic Surgery, and Thoracic Surgery Residents Association listservs. A reminder email was issued at 28 days. Student t tests, Fisher exact tests, and χ2 tests were used to compare results. Results Of 790 respondents, 75% were male and 82% were attending surgeons. A total of 81% of female surgeons vs 46% of male attending surgeons experienced SH (P Conclusions SH is present in cardiothoracic surgery among faculty and trainees. Although women surgeons are more commonly affected, male surgeons also are subjected to SH. Despite witnessed events, intervention currently is limited. Policies, safeguards, and bystander training should be instituted to decrease these events.
Exact inference in structured prediction

Structured prediction can be thought of as a simultaneous prediction of multiple labels. This is often done by maximizing a score function on the space of labels, which decomposes as a sum of pairwise and unary potentials. The above is naturally modeled with a graph, where edges and vertices are related to pairwise and unary potentials, respectively. We consider the generative process proposed by Globerson et al. (2015) and apply it to general connected graphs. We analyze the structural conditions of the graph that allow for the exact recovery of the labels. Our results show that exact recovery is possible and achievable in polynomial time for a large class of graphs. In particular, we show that graphs that are bad expanders can be exactly recovered by adding small edge perturbations coming from the \Erdos-\Renyi model. Finally, as a byproduct of our analysis, we provide an extension of Cheeger's inequality.
The role of information disclosure in financial intermediation with investment risk

Abstract We study how information disclosure affects financial intermediation when the payoff to the long-term investment is risky. The analysis is based on a business-cycle version of the bank run model wherein a bank provides risk sharing to demand depositors who experience unobservable shocks to their liquidity preferences. The bank pre-commits to the precision of an interim signal regarding the payoff to the long-term investment. We examine the impact of bank disclosure on optimal risk sharing achieved by run-proof, signal-contingent demand-deposit contracts. We show that for utility functions that display non-increasing absolute risk aversion, more informative disclosure improves the ex ante risk sharing provided by financial intermediation.
Universality in Learning from Linear Measurements

We study the problem of recovering a structured signal from independently and identically drawn linear measurements. A convex penalty function $f(\cdot)$ is considered which penalizes deviations from the desired structure, and signal recovery is performed by minimizing $f(\cdot)$ subject to the linear measurement constraints. The main question of interest is to determine the minimum number of measurements that is necessary and sufficient for the perfect recovery of the unknown signal with high probability. Our main result states that, under some mild conditions on $f(\cdot)$ and on the distribution from which the linear measurements are drawn, the minimum number of measurements required for perfect recovery depends only on the first and second order statistics of the measurement vectors. As a result, the required of number of measurements can be determining by studying measurement vectors that are Gaussian (and have the same mean vector and covariance matrix) for which a rich literature and comprehensive theory exists. As an application, we show that the minimum number of random quadratic measurements (also known as rank-one projections) required to recover a low rank positive semi-definite matrix is $3nr$, where $n$ is the dimension of the matrix and $r$ is its rank. As a consequence, we settle the long standing open question of determining the minimum number of measurements required for perfect signal recovery in phase retrieval using the celebrated PhaseLift algorithm, and show it to be $3n$.
Performance enhancement for relational systems through query compilation

In recent years, considerable research has been directed toward the relational model of data first proposed in Reference 5 . The advantages of this approach have been discussed elsewhere, however they can be summarized as follows: (1) the user is presented with a very simple view of his data (i.e., organized as tables of information), (2) the user is freed from explicitly knowing the underlying implementation structure of his data (i.e., data independence) and (3) very powerful non-procedural, set-oriented query languages can be defined for the relational model because of its simple conceptual structure.
Tumor microenvironment and immunology of ovarian cancer: 12th Biennial Rivkin Center Ovarian Cancer Research Symposium

The 12th Biennial Ovarian Cancer Research Symposium organized by the Rivkin Center for Ovarian Cancer and the American Association for Cancer Research held on September 13-15, 2018 covered cutting edge and relevant research topics in ovarian cancer biology and therapy. Sessions included detection and prevention, genomics and molecular mechanisms, tumor microenvironment and immunology, novel therapeutics, and an education session. In this article we provide an overview of the key findings presented in the tumor microenvironment and immunology session.
Nonlocal gradient operators with a nonspherical interaction neighborhood and their applications

Nonlocal gradient operators are prototypical nonlocal differential operators that are very important in the studies of nonlocal models. One of the simplest variational settings for such studies is the nonlocal Dirichlet energies wherein the energy densities are quadratic in the nonlocal gradients. There have been earlier studies to illuminate the link between the coercivity of the Dirichlet energies and the interaction strengths of radially symmetric kernels that constitute nonlocal gradient operators in the form of integral operators. In this work we adopt a different perspective and focus on nonlocal gradient operators with a non-spherical interaction neighborhood. We show that the truncation of the spherical interaction neighborhood to a half sphere helps making nonlocal gradient operators well-defined and the associated nonlocal Dirichlet energies coercive. These become possible, unlike the case with full spherical neighborhoods, without any extra assumption on the strengths of the kernels near the origin. We then present some applications of the nonlocal gradient operators with non-spherical interaction neighborhoods. These include nonlocal linear models in mechanics such as nonlocal isotropic linear elasticity and nonlocal Stokes equations, and a nonlocal extension of the Helmholtz decomposition.
Power Efficient Discontinuous Reception in THz and mmWave Wireless Systems

Discontinuous reception (DRX), where a user equipment (UE) temporarily disables its receiver, is a critical power saving feature in modern cellular systems. DRX is likely to be particularly aggressively used in the mmWave and THz frequencies due to the high front end power consumption. A key challenge of DRX in these frequencies is that individual links are directional and highly susceptible to blockage. MmWave and THz UEs will therefore likely need to monitor multiple cells in multiple directions to ensure continuous reliable connectivity. This work proposes a novel, heuristic algorithm to dynamically select the cells to monitor to attempt to optimally trade-off link reliability and power consumption. The paper provides preliminary estimates of connected mode DRX mode consumption using detailed and realistic statistical models of blockers at both 28 and 140 GHz. It is found that although blockage dynamics are faster at 140 GHz, reliable connectivity at low power can be maintained with sufficient macro-diversity and link prediction.
Critical Factor Graph Situation Clusters for Accelerated Automotive Safety Validation

Modern validation approaches of advanced automotive safety systems involve simulations of human driving behavior in safety-critical traffic events. Critical situations are often painstakingly enumerated and modeled, and it is difficult to establish confidence that the space of critical traffic events is adequately covered. This work presents an automated method for identifying and clustering critical situations that capture severity and frequency of occurrence, thereby allowing for risk-based safety validation. We demonstrate the ability of the new approach to accelerate the safety validation of an automotive safety system using importance sampling and efficiently optimize its parameters.
Latin American Racial Equality Law as Criminal Law

After years of struggle for visibility, recognition, and equality in Latin America, Afro-descendants have garnered a number of anti-discrimination law enactments. These legal gains range from multi...
Algebraic theories and commutativity in a sheaf topos

For any site of definition $$\mathcal {C}$$ of a Grothendieck topos $$\mathcal {E}$$, we define a notion of a $$\mathcal {C}$$-ary Lawvere theory $$\tau : \mathscr {C} \rightarrow \mathscr {T}$$ whose category of models is a stack over $$\mathcal {E}$$. Our definitions coincide with Lawvere’s finitary theories when $$\mathcal {C}=\aleph _0$$ and $$\mathcal {E} = {{\,\mathrm{\mathbf {Set}}\,}}$$. We construct a fibered category $${{\,\mathrm{\mathbf {Mod}}\,}}^{\mathscr {T}}$$ of models as a stack over $$\mathcal {E}$$ and prove that it is $$\mathcal {E}$$-complete and $$\mathcal {E}$$-cocomplete. We show that there is a free-forget adjunction $$F \dashv U: {{\,\mathrm{\mathbf {Mod}}\,}}^{\mathscr {T}} \leftrightarrows \mathscr {E}$$. If $$\tau $$ is a commutative theory in a certain sense, then we obtain a “locally monoidal closed” structure on the category of models, which enhances the free-forget adjunction to an adjunction of symmetric monoidal $$\mathcal {E}$$-categories. Our results give a general recipe for constructing a monoidal $$\mathcal {E}$$-cosmos in which one can do enriched $$\mathcal {E}$$-category theory. As an application, we describe a convenient category of linear spaces generated by the theory of Lebesgue integration.
Prioritization of Cognitive Assessments in Alzheimer's Disease via Learning to Rank using Brain Morphometric Data

We propose an innovative machine learning paradigm enabling precision medicine for prioritizing cognitive assessments according to their relevance to Alzheimer's disease at the individual patient level. The paradigm tailors the cognitive biomarker discovery and cognitive assessment selection process to the brain morphometric characteristics of each individual patient. We implement this paradigm using a newly developed learning-to-rank method PLTR. Our empirical study on the ADNI data yields promising results to identify and prioritize individual-specific cognitive biomarkers as well as cognitive assessment tasks based on the individual's structural MRI data. The resulting top ranked cognitive biomarkers and assessment tasks have the potential to aid personalized diagnosis and disease subtyping.
Analysis of the Rigorous Coupled Wave Approach for p-polarized light in gratings

Abstract We study the convergence properties of the two-dimensional Rigorous Coupled Wave Approach (RCWA) for s -polarized monochromatic incident light. The RCWA is widely used to solve electromagnetic boundary-value problems where the relative permittivity varies periodically in one direction, i.e., scattering by a grating. This semi-analytical approach expands all the electromagnetic field phasors as well as the relative permittivity as Fourier series in the spatial variable along the direction of periodicity, and also replaces the relative permittivity with a stairstep approximation along the direction normal to the direction of periodicity. Thus, there is error due to Fourier truncation and also due to the approximation of grating permittivity. We prove that the RCWA is a Galerkin scheme, which allows us to employ techniques borrowed from the Finite Element Method to analyze the error. An essential tool is a Rellich identity that shows that certain continuous problems have unique solutions that depend continuously on the data with a continuity constant having explicit dependence on the relative permittivity. We prove that the RCWA converges with an increasing number of retained Fourier modes and with a finer approximation of the grating interfaces. Numerical results show that our convergence results for increasing the number of retained Fourier modes are seen in practice, while our estimates of convergence in slice thickness are pessimistic.
Learning 3D Human Dynamics From Video

From an image of a person in action, we can easily guess the 3D motion of the person in the immediate past and future. This is because we have a mental model of 3D human dynamics that we have acquired from observing visual sequences of humans in motion. We present a framework that can similarly learn a representation of 3D dynamics of humans from video via a simple but effective temporal encoding of image features. At test time, from video, the learned temporal representation give rise to smooth 3D mesh predictions. From a single image, our model can recover the current 3D mesh as well as its 3D past and future motion. Our approach is designed so it can learn from videos with 2D pose annotations in a semi-supervised manner. Though annotated data is always limited, there are millions of videos uploaded daily on the Internet. In this work, we harvest this Internet-scale source of unlabeled data by training our model on unlabeled video with pseudo-ground truth 2D pose obtained from an off-the-shelf 2D pose detector. Our experiments show that adding more videos with pseudo-ground truth 2D pose monotonically improves 3D prediction performance. We evaluate our model on the recent challenging dataset of 3D Poses in the Wild and obtain state-of-the-art performance on the 3D prediction task without any fine-tuning. The project website with video can be found at https://akanazawa.github.io/human_dynamics/.
Accelerated insulation aging due to fast, repetitive voltages: A review identifying challenges and future research needs

Although the adverse effects of using power electronic conversion on the insulation systems used in different apparatuses have been investigated, they are limited to low slew rates and repetitions. These results cannot be used for next-generation wide bandgap (WBG)-based conversion systems targeted to be fast (with a dv/dt up to 100 kV/ws) and operate at a high switching frequency up to 500 kHz. Frequency and slew rate are two of the most important factors of a voltage pulse, influencing the level of degradation of the insulation systems that are exposed to such voltage pulses. The paper reviews challenges concerning insulation degradation when benefitting from WBG-based conversion systems with the mentioned dv/dt and switching frequency values and identifies technical gaps and future research needs. The paper provides a framework for future research in dielectrics and electrical insulation design for systems under fast, repetitive voltage pluses originated by WBG-based conversion systems.
Key Elements of Robust Vocabulary Instruction for Emergent Bilingual Adolescents

Purpose In this clinical focus article, the authors argue for robust vocabulary instruction with emergent bilingual learners both in inclusive classroom settings and in clinical settings for emergent bilinguals with language and literacy disorders. Robust vocabulary instruction focuses on high-utility academic words that carry abstract meanings and appear in texts across content areas (e.g., diminish, ambiguous). For emergent bilinguals, vocabulary instruction should be infused with morphological analysis emphasizing Latin roots to support students to problem-solve meanings of new, unfamiliar words and make connections between semantic clusters of related words in English. An innovative and critical component of this instructional approach is to support emergent bilinguals to leverage their linguistic resources by making connections to their home languages. Five design principles for teaching emergent bilinguals to engage in morphological analysis with Latin roots are presented. These design principles are illustrated with examples of evidence-based practices from intervention materials for instruction. Examples are drawn from varied instructional contexts. We present a synthesis of findings from implementation trials of our instructional program. Finally, application of the approach to clinical settings for speech-language pathologists are addressed. Conclusions Clinical practice with emergent bilingual learners at intermediate and advanced stages of proficiency should incorporate robust vocabulary instruction for emergent bilinguals from a variety of cultural and linguistic backgrounds. Clinicians should focus on high-utility academic words, and they should teach morphological problem-solving skills for generative word learning. Clinicians should leverage emergent bilingual learners' home language resources for developing morphological problem-solving skill. Supplemental Material https://doi.org/10.23641/asha.9745169.
Intervention as a research strategy

JOM has made Design Science central to its development strategy, creating a department to incubate papers using this methodology. An inaugural editorial published in 2016 called for papers with a generic design supported by a design proposition that provides pragmatic guidelines to bridge the gap between the case specific and the universal. In this article, I propose ways in which the scope of interventions can be expanded beyond the editorial’s proposal to foster theoretical developments and better align with JOM’s mission. I propose that, rather than focus on the design propositions, we explore the role that interventions (the ultimate manifestation of design science) can play in testing and developing theory (the ultimate goal of an academic endeavor). Taking as a point of departure the principles of action research and the explanatory framework of process theories, I propose two modes of research for leveraging interventions as a mechanism for testing existing theory and develop theories about organizational and system transformation. I illustrate the application of these two frameworks in the context of a previously published article.
Asymptotic expansion of Warlimont functions on Wright semigroups

We calculate full asymptotic expansions of prime-independent multiplicative functions on additive arithmetic semigroups that satisfy a strong form of Knopfmacher’s axioms. When applied to the semigroup of unlabeled graphs, our method yields detailed asymptotic information on how graphs decompose into connected components. As a second class of examples, we discuss polynomials in several variables over a finite field.