cssi/SciSciGPT-SciSciNet huggingface dataset:
Fields of rationality of automorphic representations: the case of unitary groups

Abstract This paper examines fields of rationality in families of cuspidal automorphic representations of unitary groups. Specifically, for a fixed A and a sufficiently large family F , a small proportion of representations π ∈ F will satisfy [ Q ( π ) : Q ] ≤ A . Like earlier work of Shin and Templier, the result depends on a Plancherel equidistribution result for the local components of representations in families. An innovation of our work is an upper bound on the number of discrete series GL n ( L ) representations with small field of rationality, counted with appropriate multiplicity, which in turn depends upon an asymptotic character expansion of Murnaghan and formal degree computations of Aubert and Plymen.
The moduli space of totally marked degree two rational maps

A rational map $\phi: \mathbb{P}^1 \to \mathbb{P}^1$ along with an ordered list of fixed and critical points is called a totally marked rational map. The space of totally marked degree two rational maps, $Rat^{tm}_2$ can be parametrized by an affine open subset of $(\mathbb{P}^1)^5$. We consider the natural action of $SL_2$ on $Rat^{tm}_2$ induced from the action of $SL_2$ on $(\mathbb{P}^1)^5$ and prove that the quotient space $Rat^{tm}_2/SL_2$ exists as a scheme. The quotient is isomorphic to a Del Pezzo surface with the isomorphism being defined over $\mathbb{Z}[1/2]$.
Ramsey theory on generalized Baire space

Abstract We show that although the Galvin–Prikry Theorem does not hold on generalized Baire space with the standard topology, there are similar theorems which do hold on generalized Baire space with certain coarser topologies.
New Symmetries of QED

The soft photon theorem in U(1) gauge theories with only massless charged particles has recently been shown to be the Ward identity of an infinite-dimensional asymptotic symmetry group. This symmetry group is comprised of gauge transformations which approach angle-dependent constants at null infinity. In this paper, we extend the analysis to all U(1) theories, including those with massive charged particles such as QED.
Weighted mining of massive collections of P-values by convex optimization

Researchers in data-rich disciplines-think of computational genomics and observational cosmology-often wish to mine large bodies of [Formula: see text]-values looking for significant effects, while controlling the false discovery rate or family-wise error rate. Increasingly, researchers also wish to prioritize certain hypotheses, for example, those thought to have larger effect sizes, by upweighting, and to impose constraints on the underlying mining, such as monotonicity along a certain sequence. We introduce Princessp, a principled method for performing weighted multiple testing by constrained convex optimization. Our method elegantly allows one to prioritize certain hypotheses through upweighting and to discount others through downweighting, while constraining the underlying weights involved in the mining process. When the [Formula: see text]-values derive from monotone likelihood ratio families such as the Gaussian means model, the new method allows exact solution of an important optimal weighting problem previously thought to be non-convex and computationally infeasible. Our method scales to massive data set sizes. We illustrate the applications of Princessp on a series of standard genomics data sets and offer comparisons with several previous 'standard' methods. Princessp offers both ease of operation and the ability to scale to extremely large problem sizes. The method is available as open-source software from github.com/dobriban/pvalue_weighting_matlab (accessed 11 October 2017).
Categorification of quantum Kac-Moody superalgebras

We introduce a non-degenerate bilinear form and use it to provide a new characterization of quantum Kac-Moody superalgebras with no isotropic odd simple roots. We show that the spin quiver Hecke algebras introduced by Kang-Kashiwara-Tsuchioka provide a categorification of half the quantum Kac-Moody superalgebras, using the recent work of Ellis-Khovanov-Lauda. A new idea here is that a super sign is categorified as spin (i.e., the parity-shift functor).
A Submodular Approach for Electricity Distribution Network Reconfiguration

Distribution network reconfiguration (DNR) is a tool used by operators to balance line load flows and mitigate losses. As distributed generation and flexible load adoption increases, the impact of DNR on the security, efficiency, and reliability of the grid will increase as well. Today, heuristic-based actions like branch exchange are routinely taken, with no theoretical guarantee of their optimality. This paper considers loss minimization via DNR, which changes the on/off status of switches in the network. The goal is to ensure a radial final configuration (called a spanning tree in the algorithms literature) that spans all network buses and connects them to the substation (called the root of the tree) through a single path. We prove that the associated combinatorial optimization problem is strongly NP-hard and thus likely cannot be solved efficiently. We formulate the loss minimization problem as a supermodular function minimization under a single matroid basis constraint, and use existing algorithms to propose a polynomial time local search algorithm for the DNR problem at hand and derive performance bounds. We show that our algorithm is equivalent to the extensively used branch exchange algorithm, for which, to the best of our knowledge, we pioneer in proposing a theoretical performance bound. Finally, we use a 33-bus network to compare our algorithm's performance to several algorithms published in the literature.
Bounded gaps between primes and the length spectra of arithmetic hyperbolic 3-orbifolds

Abstract In 1992, Reid asked whether hyperbolic 3-manifolds with the same geodesic length spectra are necessarily commensurable. While this is known to be true for arithmetic hyperbolic 3-manifolds, the non-arithmetic case is still open. Building towards a negative answer to this question, Futer and Millichap recently constructed infinitely many pairs of non-commensurable, non-arithmetic hyperbolic 3-manifolds which have the same volume and whose length spectra begin with the same first m geodesic lengths. In the present paper, we show that this phenomenon is surprisingly common in the arithmetic setting. In particular, given any arithmetic hyperbolic 3-orbifold derived from a quaternion algebra, any finite subset S of its geodesic length spectrum, and any k ≥ 2 , we produce infinitely many k -tuples of arithmetic hyperbolic 3-orbifolds which are pairwise non-commensurable, have geodesic length spectra containing S , and have volumes lying in an interval of (universally) bounded length. The main technical ingredient in our proof is a bounded gaps result for prime ideals in number fields lying in Chebotarev sets which extends recent work of Thorner.
The Curious Robot: Learning Visual Representations via Physical Interactions

What is the right supervisory signal to train visual representations? Current approaches in computer vision use category labels from datasets such as ImageNet to train ConvNets. However, in case of biological agents, visual representation learning does not require millions of semantic labels. We argue that biological agents use physical interactions with the world to learn visual representations unlike current vision systems which just use passive observations (images and videos downloaded from web). For example, babies push objects, poke them, put them in their mouth and throw them to learn representations. Towards this goal, we build one of the first systems on a Baxter platform that pushes, pokes, grasps and observes objects in a tabletop environment. It uses four different types of physical interactions to collect more than 130K datapoints, with each datapoint providing supervision to a shared ConvNet architecture allowing us to learn visual representations. We show the quality of learned representations by observing neuron activations and performing nearest neighbor retrieval on this learned representation. Quantitatively, we evaluate our learned ConvNet on image classification tasks and show improvements compared to learning without external data. Finally, on the task of instance retrieval, our network outperforms the ImageNet network on recall@1 by 3 %.
Multi-Toeplitz operators and free pluriharmonic functions

Abstract We initiate the study of weighted multi-Toeplitz operators associated with noncommutative regular domains D q m ( H ) ⊂ B ( H ) n , m , n ≥ 1 , where B ( H ) is the algebra of all bounded linear operators on a Hilbert space H . These operators are acting on the full Fock space with n generators and have as symbols free pluriharmonic functions on the interior of the domain D q m ( H ) . We prove that the set of all weighted multi-Toeplitz operators coincides with A ( D q m ) ⁎ + A ( D q m ) ‾ W O T , where the domain algebra A ( D q m ) is the norm-closed unital non-selfadjoint algebra generated by the universal model ( W 1 , … , W n ) of the noncommutative domain D q m ( H ) . These results are used to study the class of free pluriharmonic functions on D q m ( H ) ∘ . Several classical results from complex analysis concerning harmonic functions have analogues in our noncommutative setting. In particular, we show that the bounded free pluriharmonic functions are precisely those which are noncommutative Berezin transforms of weighted multi-Toeplitz operators, and solve the Dirichlet extension problem in this setting. Using noncommutative Cauchy transforms, we provide a free analytic functional calculus for n-tuples of operators, which extends to free pluriharmonic functions. Our study of weighted multi-Toeplitz operators on Fock spaces is a blend of multi-variable operator theory, noncommutative function theory, operator spaces, and harmonic analysis.
Dynamic Games With Asymmetric Information: Common Information Based Perfect Bayesian Equilibria and Sequential Decomposition

We formulate and analyze a general class of stochastic dynamic games with asymmetric information arising in dynamic systems. In such games, multiple strategic agents control the system dynamics and have different information about the system over time. Because of the presence of asymmetric information, each agent needs to form beliefs about other agents' private information. Therefore, the specification of the agents' beliefs along with their strategies is necessary to study the dynamic game. We use Perfect Bayesian equilibrium (PBE) as our solution concept. A PBE consists of a pair of strategy profile and belief system. In a PBE, every agent's strategy should be a best response under the belief system, and the belief system depends on agents' strategy profile when there is signaling among agents. Therefore, the circular dependence between strategy profile and belief system makes it difficult to compute PBE. Using the common information among agents, we introduce a subclass of PBE called common information based perfect Bayesian equilibria (CIB-PBE), and provide a sequential decomposition of the dynamic game. Such decomposition leads to a backward induction algorithm to compute CIB-PBE. We illustrate the sequential decomposition with an example of a multiple access broadcast game. We prove the existence of CIB-PBE for a subclass of dynamic games.
Anomalous Localized Resonance Phenomena in the Nonmagnetic, Finite-Frequency Regime

The phenomenon of anomalous localized resonance (ALR) is observed at the interface between materials with positive and negative material parameters and is characterized by the fact that when a given source is placed near the interface, the electric and magnetic fields start to have very fast and large oscillations around the interface as the absorption in the materials becomes very small while they remain smooth and regular away from the interface. In this paper, we discuss the phenomenon of anomalous localized resonance (ALR) in the context of an infinite slab of homogeneous, nonmagnetic material ( ) with permittivity for some small loss surrounded by positive, nonmagnetic, homogeneous media. We explicitly characterize the limit value of the product between frequency and the width of slab beyond which the ALR phenomenon does not occur and analyze the situation when the phenomenon is observed. In addition, we also construct sources for which the ALR phenomenon never appears.
Small deviations of sums of independent random variables

Abstract A well-known discovery of Feige's is the following [3] : Let X 1 , … , X n be nonnegative independent random variables, with E [ X i ] ≤ 1 ∀ i , and let X = ∑ i = 1 n X i . Then for any n, Pr ⁡ [ X E [ X ] + 1 ] ≥ α > 0 , for some α ≥ 1 / 13 . This bound was later improved to 1/8 by He, Zhang, and Zhang [5] . By a finer consideration of the first four moments, we further improve the bound to approximately .14. The conjectured true bound is 1 / e ≃ . 368 .
Haplotype assembly: An information theoretic view

This paper studies the haplotype assembly problem from an information-theoretic perspective. A haplotype is a sequence of nucleotide bases on a chromosome, often conve- niently represented by a binary string, that differ from the bases in the corresponding positions on the other chromosome in a homologous pair. Information about the order of bases in a genome is readily inferred using short reads provided by high-throughput DNA sequencing technologies. Associating reads that cover variant positions with specific chromosomes in a homologous pairs, which enables haplotype assembly, is challenging due to limited lengths of the reads and presence of sequencing errors. In this paper, the recovery of the target pair of haplotype sequences using short reads is rephrased as a joint source-channel coding problem. Two messages, representing haplotypes and chromosome memberships of reads, are encoded and transmitted over a channel with erasures and errors, where the channel model reflects salient features of high-throughput sequencing. The focus of this paper is on determining the required number of reads for reliable haplotype reconstruction, and both the necessary and sufficient conditions are presented with order- wise optimal bounds.
Generalized Linear Model Regression under Distance-to-set Penalties

Estimation in generalized linear models (GLM) is complicated by the presence of constraints. One can handle constraints by maximizing a penalized log-likelihood. Penalties such as the lasso are effective in high dimensions but often lead to severe shrinkage. This paper explores instead penalizing the squared distance to constraint sets. Distance penalties are more flexible than algebraic and regularization penalties, and avoid the drawback of shrinkage. To optimize distance penalized objectives, we make use of the majorization-minimization principle. Resulting algorithms constructed within this framework are amenable to acceleration and come with global convergence guarantees. Applications to shape constraints, sparse regression, and rank-restricted matrix regression on synthetic and real data showcase the strong empirical performance of distance penalization, even under non-convex constraints.
Improving the Burgess bound via Pólya-Vinogradov

We show that even mild improvements of the Polya-Vinogradov inequality would imply significant improvements of Burgess' bound on character sums. Our main ingredients are a lower bound on certain types of character sums (coming from works of the second author joint with J. Bober and Y. Lamzouri) and a quantitative relationship between the mean and the logarithmic mean of a completely multiplicative function.
GAL: A global-attributes assisted labeling system for outdoor scenes

Abstract An approach that extracts global attributes from outdoor images to facilitate geometric layout labeling is investigated in this work. The proposed Global-attributes Assisted Labeling (GAL) system exploits both local features and global attributes. First, by following a classical method, we use local features to provide initial labels for all super-pixels. Then, we develop a set of techniques to extract global attributes from 2D outdoor images. They include sky lines, ground lines, vanishing lines, etc. Finally, we propose the GAL system that integrates global attributes in the conditional random field (CRF) framework to improve initial labels so as to offer a more robust labeling result. The performance of the proposed GAL system is demonstrated and benchmarked with several state-of-the-art algorithms against a popular outdoor scene layout dataset.
Efficient Rare Event Simulation for Failure Problems in Random Media

In this paper we study rare events associated to the solutions of an elliptic partial differential equation with a spatially varying random coefficient. The random coefficient follows the lognormal distribution, which is determined by a Gaussian process. This model is employed to study the failure problem of elastic materials in random media in which the failure is characterized by the criterion that the strain field exceeds a high threshold. We propose an efficient importance sampling scheme to compute the small failure probability in the high threshold limit. The change of measure in our scheme is parametrized by two density functions. The efficiency of the importance sampling scheme is validated by numerical examples.
An alon-boppana type bound for weighted graphs and lowerbounds for spectral sparsification

We prove the following Alon-Boppana type theorem for general (not necessarily regular) weighted graphs: if G is an n-node weighted undirected graph of average combinatorial degree d (that is, G has dn/2 edges) and girth g > 2d1/8 + 1, and if λ1 ≤ λ2 ≤ ⋯ λn are the eigenvalues of the (non-normalized) Laplacian of G, then [EQUATION] (The Alon-Boppana theorem implies that if G is unweighted and d-regular, then [EQUATION] (if the diameter is at least d1.5.) Our result implies a lower bound for spectral sparsifiers. A graph H is a spectral ϵ-sparsifier of a graph G if [EQUATION] where L(G) is the Laplacian matrix of G and L(H) is the Laplacian matrix of H. Batson, Spielman and Srivastava proved that for every G there is an ϵ-sparsifier H of average degree d where [EQUATION] and the edges of H are a (weighted) subset of the edges of G. Batson, Spielman and Srivastava also show that the bound on ϵ cannot be reduced below [EQUATION] when G is a clique; our Alon-Boppana-type result implies that ϵ cannot be reduced below [EQUATION] when G comes from a family of expanders of super-constant degree and super-constant girth. The method of Batson, Spielman and Srivastava proves a more general result, about sparsifying sums of rank-one matrices, and their method applies to an "online" setting. We show that for the online matrix setting the [EQUATION] bound is tight, up to lower order terms.
Efficient Implementation of the Room Simulator for Training Deep Neural Network Acoustic Models

In this paper, we describe how to efficiently implement an acoustic room simulator to generate large-scale simulated data for training deep neural networks. Even though Google Room Simulator in [1] was shown to be quite effective in reducing the Word Error Rates (WERs) for far-field applications by generating simulated far-field training sets, it requires a very large number of Fast Fourier Transforms (FFTs) of large size. Room Simulator in [1] used approximately 80 percent of Central Processing Unit (CPU) usage in our CPU + Graphics Processing Unit (GPU) training architecture [2]. In this work, we implement an efficient OverLap Addition (OLA) based filtering using the open-source FFTW3 library. Further, we investigate the effects of the Room Impulse Response (RIR) lengths. Experimentally, we conclude that we can cut the tail portions of RIRs whose power is less than 20 dB below the maximum power without sacrificing the speech recognition accuracy. However, we observe that cutting RIR tail more than this threshold harms the speech recognition accuracy for rerecorded test sets. Using these approaches, we were able to reduce CPU usage for the room simulator portion down to 9.69 percent in CPU/GPU training architecture. Profiling result shows that we obtain 22.4 times speed-up on a single machine and 37.3 times speed up on Google's distributed training infrastructure.
Entropic CLT and Phase Transition in High-dimensional Wishart Matrices

We consider high dimensional Wishart matrices $\mathbb{X} \mathbb{X}^{\top}$ where the entries of $\mathbb{X} \in {\mathbb{R}^{n \times d}}$ are i.i.d. from a log-concave distribution. We prove an information theoretic phase transition: such matrices are close in total variation distance to the corresponding Gaussian ensemble if and only if $d$ is much larger than $n^3$. Our proof is entropy-based, making use of the chain rule for relative entropy along with the recursive structure in the definition of the Wishart ensemble. The proof crucially relies on the well known relation between Fisher information and entropy, a variational representation for Fisher information, concentration bounds for the spectral norm of a random matrix, and certain small ball probability estimates for log-concave measures.
Stochastic Nonconvex Optimization with Large Minibatches

We study stochastic optimization of nonconvex loss functions, which are typical objectives for training neural networks. We propose stochastic approximation algorithms which optimize a series of regularized, nonlinearized losses on large minibatches of samples, using only first-order gradient information. Our algorithms provably converge to an approximate critical point of the expected objective with faster rates than minibatch stochastic gradient descent, and facilitate better parallelization by allowing larger minibatches.
A hardware-friendly algorithm for scalable training and deployment of dimensionality reduction models on FPGA

With ever-increasing application of machine learning models in various domains such as image classification, speech recognition and synthesis, and health care, designing efficient hardware for these models has gained a lot of popularity. While the majority of researches in this area focus on efficient deployment of machine learning models (a.k.a inference), this work concentrates on challenges of training these models in hardware. In particular, this paper presents a high-performance, scalable, reconfigurable solution for both training and deployment of different dimensionality reduction models in hardware by introducing a hardware-friendly algorithm. Compared to state-of-the-art implementations, our proposed algorithm and its hardware realization decrease resource consumption by 50% without any degradation in accuracy.
SocioSense: Robot navigation amongst pedestrians with social and psychological constraints

We present a real-time algorithm, SocioSense, for socially-aware navigation of a robot amongst pedestrians. Our approach computes time-varying behaviors of each pedestrian using Bayesian learning and Personality Trait theory. These psychological characteristics are used for long-term path prediction and generating proxemic characteristics for each pedestrian. We combine these psychological constraints with social constraints to perform human-aware robot navigation in low- to medium-density crowds. The estimation of time-varying behaviors and pedestrian personalities can improve the performance of long-term path prediction by 21%, as compared to prior interactive path prediction algorithms. We also demonstrate the benefits of our socially-aware navigation in simulated environments with tens of pedestrians.
Moduli of Continuity for Viscosity Solutions

In this paper, we investigate the moduli of continuity for viscosity solutions of a wide class of nonsingular quasilinear evolution equations and also for the level set mean curvature flow, which is an example of singular degenerate equations. We prove that the modulus of continuity is a viscosity subsolution of some one dimensional equation. This work extends B. Andrews' recent result on moduli of continuity for smooth spatially periodic solutions.
Multipliers and hereditary subalgebras of operator algebras

We generalize some technical results of Glicksberg to the realm of general operator algebras and use them to give a characterization of open and closed projections in terms of certain multiplier algebras. This generalizes a theorem of J. Wells characterizing an important class of ideals in uniform algebras. The difficult implication in our main theorem is that if a projection is open in an operator algebra, then the multiplier algebra of the associated hereditary subalgebra arises as the closure of the subalgebra with respect to the strict topology of the multiplier algebra of a naturally associated hereditary C*-subalgebra. This immediately implies that the multiplier algebra of an operator algebra A may be obtained as the strict closure of A in the multiplier algebra of the C*-algebra generated by A.
Efficient Low Rank Tensor Ring Completion

Using the matrix product state (MPS) representation of the recently proposed tensor ring (TR) decompositions, in this paper we propose a TR completion algorithm, which is an alternating minimization algorithm that alternates over the factors in the MPS representation. This development is motivated in part by the success of matrix completion algorithms that alternate over the (low-rank) factors. We propose a novel initialization method and analyze the computational complexity of the TR completion algorithm. The numerical comparison between the TR completion algorithm and the existing algorithms that employ a low rank tensor train (TT) approximation for data completion shows that our method outperforms the existing ones for a variety of real computer vision settings, and thus demonstrates the improved expressive power of tensor ring as compared to tensor train.
Numerical schemes and rates of convergence for the Hamilton---Jacobi equation continuum limit of nondominated sorting

Non-dominated sorting arranges a set of points in n-dimensional Euclidean space into layers by repeatedly removing the coordinatewise minimal elements. It was recently shown that nondominated sorting of random points has a Hamilton---Jacobi equation continuum limit. The obvious numerical scheme for this PDE has a slow convergence rate of $$O(h^\frac{1}{n})$$O(h1n). In this paper, we introduce two new numerical schemes that have formal rates of O(h) and we prove the usual $$O(\sqrt{h})$$O(h) theoretical rates. We also present the results of numerical simulations illustrating the difference between the formal and theoretical rates.
Influence of body mass index on outcomes in patients undergoing surgery for diverticular disease.

Abstract Background We hypothesized that increasing body mass index is a risk factor for surgical complications in surgery for diverticulitis. We assessed the relationship of body mass index and surgical complications following surgery for diverticular disease. Methods We used National Surgical Quality Improvement Program database from 2005 to 2015. Patients undergoing surgery for diverticular disease during that period were included and stratified into 9 groups based on their body mass index ( 55). Outcomes of interest were complications of superficial surgical site infection, deep incisional surgical site infection, organ space surgical site infection, wound disruption complications, pneumonia, ventilator dependence > 48 hours, acute renal failure, myocardial infarction, return to operating room, and 30-day mortality. Results Morbidly obese patients had higher rates of diabetes, hypertension, and steroid use. They had higher American Society of Anesthesiologists classification and were more likely to have emergency and open cases. Interestingly, increased body mass index was inversely associated with age. Increasing body mass index was associated with worse outcomes including superficial surgical site infection, deep incisional surgical site infection, organ space surgical site infection, wound disruption complications, ventilator dependence > 48 hours, acute renal failure, and return to operating room. Risk of developing pneumonia didn't have similar correlation with body mass index. Overweight status had protective effect on mortality. No statistically significant differences in increased rates of myocardial infarction were noted. Underweight patients also developed worse outcomes. Conclusion Obesity is associated with a number of complications following surgery for diverticulitis. Elevated body mass index adds significant risk to procedures for diverticulitis and should be accounted for in risk stratification models. Patients should be counseled on weight reduction before undergoing elective surgery for diverticular disease.
ADMM for Efficient Deep Learning with Global Convergence

Alternating Direction Method of Multipliers (ADMM) has been used successfully in many conventional machine learning applications and is considered to be a useful alternative to Stochastic Gradient Descent (SGD) as a deep learning optimizer. However, as an emerging domain, several challenges remain, including 1) The lack of global convergence guarantees, 2) Slow convergence towards solutions, and 3) Cubic time complexity with regard to feature dimensions. In this paper, we propose a novel optimization framework for deep learning via ADMM (dlADMM) to address these challenges simultaneously. The parameters in each layer are updated backward and then forward so that the parameter information in each layer is exchanged efficiently. The time complexity is reduced from cubic to quadratic in (latent) feature dimensions via a dedicated algorithm design for subproblems that enhances them utilizing iterative quadratic approximations and backtracking. Finally, we provide the first proof of global convergence for an ADMM-based method (dlADMM) in a deep neural network problem under mild conditions. Experiments on benchmark datasets demonstrated that our proposed dlADMM algorithm outperforms most of the comparison methods.
Benchmarks in Search Markets

We characterize the role of benchmarks in price transparency of over-the-counter markets. A benchmark can raise social surplus by increasing the volume of beneficial trade, facilitating more efficient matching between dealers and customers, and reducing search costs. Although the market transparency promoted by benchmarks reduces dealers' profit margins, dealers may nonetheless introduce a benchmark to encourage greater market participation by investors. Low-cost dealers may also introduce a benchmark to increase their market share relative to high-cost dealers. We construct a revelation mechanism that maximizes welfare subject to search frictions, and show conditions under which it coincides with announcing the benchmark. This article is protected by copyright. All rights reserved
Learning Quickly to Plan Quickly Using Modular Meta-Learning

Multi-object manipulation problems in continuous state and action spaces can be solved by planners that search over sampled values for the continuous parameters of operators. The efficiency of these planners depends critically on the effectiveness of the samplers used, but effective sampling in turn depends on details of the robot, environment, and task. Our strategy is to learn functions called speciatizers that generate values for continuous operator parameters, given a state description and values for the discrete parameters. Rather than trying to learn a single specializer for each operator from large amounts of data on a single task, we take a modular meta-learning approach. We train on multiple tasks and learn a variety of specializers that, on a new task, can be quickly adapted using relatively little data – thus, our system learns quickly to plan quickly using these specializers. We validate our approach experimentally in simulated 3D pick-and-place tasks with continuous state and action spaces. Visit http://tinyurl.com/chitnis-icra-19 for a supplementary video.
Students First: The Need for Adoption of Education and Incentive-Based Sport Agent Policies by NCAA Division I FBS Member Institutions

The Federal Bureau of Investigation (“FBI”) and U.S. Attorney’s Office’s investigation into National Collegiate Athletic Association (“NCAA”) Division I men’s basketball revealed allegations of NCAA Division I men’s basketball coaches accepting money from sport agents to persuade NCAA Division I men’s basketball players to become clients of said sport agents. This investigation highlights the pervasiveness of violations of preexisting laws governing sport agents, namely, the Uniform Athlete Agents Act (the “UAAA”) and Sport Agent Responsibility and Trust Act (“SPARTA”). Despite the believed routine violation of the UAAA and SPARTA in the recruitment of NCAA student-athletes as clients by sport agents, the laws are rarely used to prosecute sport agents. Thus, the investigation into NCAA Division I men’s basketball highlights the need for new mechanisms to safeguard the rights and interests of NCAA student-athletes related to sport agents. This paper analyzes the efficacy of existing legal and NCAA mechanisms regulating sport agents and presents a thematic analysis of NCAA Division I Football Bowl Subdivision (“FBS”) member institutions’ sport agent policies to highlight the need for and present a model NCAA Division I FBS member institution sport agent policy and education model.
Source Localization and Tracking for Dynamic Radio Cartography using Directional Antennas

Utilization of directional antennas is a promising solution for efficient spectrum sensing and accurate source localization and tracking. Spectrum sensors equipped with directional antennas should constantly scan the space in order to track emitting sources and discover new activities in the area of interest. In this paper, we propose a new formulation that unifies received-signal-strength (RSS) and direction of arrival (DoA) in a compressive sensing (CS) framework. The underlying CS measurement matrix is a function of beamforming vectors of sensors and is referred to as the propagation matrix. Comparing to the omni-directional antenna case, our employed propagation matrix provides more incoherent projections, an essential factor in the compressive sensing theory. Based on the new formulation, we optimize the antenna beams, enhance spectrum sensing efficiency, track active primary users accurately and monitor spectrum activities in an area of interest. In many practical scenarios there is no fusion center to integrate received data from spectrum sensors. We propose the distributed version of our algorithm for such cases. Experimental results show a significant improvement in source localization accuracy, compared with the scenario when sensors are equipped with omni-directional antennas. Applicability of the proposed framework for dynamic radio cartography is shown. Moreover, comparing the estimated dynamic RF map over time with the ground truth demonstrates the effectiveness of our proposed method for accurate signal estimation and recovery.
MicroRNA-mediated control of developmental lymphangiogenesis

The post-transcriptional mechanisms contributing to molecular regulation of developmental lymphangiogenesis and lymphatic network assembly are not well understood. MicroRNAs are important post-transcriptional regulators during development. Here, we use high throughput small RNA sequencing to identify miR-204, a highly conserved microRNA dramatically enriched in lymphatic vs. blood endothelial cells in human and zebrafish. Suppressing miR-204 leads to loss of lymphatic vessels while endothelial overproduction of miR-204 accelerates lymphatic vessel formation, suggesting a critical positive role for this microRNA during developmental lymphangiogenesis. We also identify the NFATC1 transcription factor as a key miR-204 target in human and zebrafish, and show that NFATC1 suppression leads to lymphatic hyperplasia. The loss of lymphatics caused by miR-204 deficiency can be largely rescued by either endothelial autonomous expression of miR-204 or by suppression of NFATC1. Together, our results highlight a miR-204/NFATC1 molecular regulatory axis required for proper lymphatic development.
Family planning training at Catholic and other religious hospitals: a national survey

Background Catholic and other faith-based hospitals often restrict family planning service provision based on institutional doctrine. Approximately 11% of U.S. accredited obstetrics and gynecology residency programs occur at such hospitals, creating a challenge to educational leaders who must ensure comprehensive family planning training. Objective To evaluate and summarize family planning training at obstetrics and gynecology residency programs that are affiliated with Catholic and other faith-based hospitals that restrict reproductive services. Study Design Using an online database search and survey screening questions, we identified 30 of 278 accredited 2017-2018 programs in which at least 70% of resident time is spent in faith-based hospitals that restrict family planning services; Jewish programs were excluded. We queried program leaders between March 2017 and April 2018 about education and training using an online or paper survey and asked them to report on training settings, provision of family planning services in such settings, and to rate aspects of training as “poor,” “adequate,” or “strong.” We compared responses at Catholic versus other faith-based programs using Fisher’s exact tests, chi-square analyses and median tests. Results Among 30 programs, 25 responded (83%); majority of respondents were program directors (88%) and represented Catholic hospitals (76%). All reported adequate contraceptive training with 47% of Catholic programs relying on off-site locations. The majority of Catholic sites (84%) relied on off-site sterilization training sites. Survey respondents from Catholic programs most commonly endorsed concerns for inadequate training in postpartum tubal ligations (53% of Catholic respondents vs. 0% of other faith-based respondents, p=0.05). Approximately half (56%) offered abortion training as part of the curriculum (“routine”), 32% offered residents the opportunity to arrange training (“elective”), and 12% did not offer; most (84%) relied on off-site collaborations. Catholic sites were more likely than other religious programs to report poor abortion training (47% vs. 0%, p=0.04). Five Catholic programs (26% of Catholic programs) reported their residents did not meet the graduate training requirement for completion of 20 D&Cs. One-third reported a prior Residency Review Committee family planning citation(s) and many commented that these citations helped provide leverage for improved training. Conclusions While Catholic and other restrictive, faith-based obstetrics and gynecology residency training programs have developed strategies in response to institutional restrictions, many report ongoing deficiencies and almost half reported they were noncompliant with abortion training requirements. Programs with deficient trainings may benefit from strategic approaches including enhanced onsite education and collaborations with off-site facilities.
Comparison of two in-water optical profilers in a dynamic coastal marine ecosystem.

We compared the mean normalized water-leaving radiances ([LW¯(λ)]N) of two in-water optical profilers, a compact optical profiling system (C-OPS) and a HyperPro II Optical Profiler (HP2), with modeled [LW¯(λ)]N at five stations in Monterey Bay, California. Although C-OPS and HP2 [LW¯(λ)]Ns were mostly within one standard deviation, C-OPS and modeled [LW¯(λ)]N showed the lowest absolute percent differences (≤25% for most wavelengths) at four of the stations. We attribute this to C-OPS’s high vertical resolution (∼1 cm), which is important for detecting changes in optical layers and for measuring the upper 0–0.5 m of the water column. HP2’s low vertical resolution (∼50 cm), low signal-to-noise ratio, and inability to measure the upper 0–0.5 m are problematic in Monterey Bay. Although a significant component of the [LW¯(λ)]N differences between C-OPS and HP2 likely stems from fluctuating water mass’s inherent optical properties, the 25% error in [LW(λ)]Ns processed from HP2 casts, compared to <1% for C-OPS, is also important.
Soft Error Resilient System Design through Error Correction

This paper presents an overview of the Built-In Soft Error Resilience (BISER) technique for correcting soft errors in latches, flip-flops and combinational logic. The BISER technique enables more than an order of magnitude reduction in chip-level error soft rate with minimal area impact, 6-10% chip-level power impact, and 1-5% performance impact (depending on whether combinational logic error correction is implemented or not). In comparison, several classical error-detection techniques introduce 40-100% power, performance and area overheads, and require significant efforts for designing and validating corresponding recovery mechanisms. Design trade-offs associated with the BISER technique and other existing soft error protection techniques are also analyzed.
A Genetically Engineered Primary Human Natural Killer Cell Platform for Cancer Immunotherapy

Enhancing natural killer (NK) cell cytotoxicity by blocking inhibitory signaling could lead to improved NK-based cancer immunotherapy. Thus, we have developed a highly efficient method for editing the genome of human NK cells using CRISPR/Cas9 to knock out inhibitory signaling molecules. Our method efficiently edits up to 90% of primary peripheral blood NK cells. As a proof-of-principle we demonstrate highly efficient knockout of ADAM17 and PDCD1, genes that have a functional impact on NK cells, and demonstrate that these gene-edited NK cells have significantly improved activity, cytokine production, and cancer cell cytotoxicity. Furthermore, we were able to expand cells to clinically relevant numbers, without loss of activity. We also demonstrate that our CRISPR/Cas9 method can be used for efficient knockin of genes by delivering homologous recombination template DNA using recombinant adeno-associated virus serotype 6 (rAAV6). Our platform represents a feasible method for generating engineered primary NK cells as a universal therapeutic for cancer immunotherapy.
Learning Shape Templates With Structured Implicit Functions

Template 3D shapes are useful for many tasks in graphics and vision, including fitting observation data, analyzing shape collections, and transferring shape attributes. Because of the variety of geometry and topology of real-world shapes, previous methods generally use a library of hand-made templates. In this paper, we investigate learning a general shape template from data. To allow for widely varying geometry and topology, we choose an implicit surface representation based on composition of local shape elements. While long known to computer graphics, this representation has not yet been explored in the context of machine learning for vision. We show that structured implicit functions are suitable for learning and allow a network to smoothly and simultaneously fit multiple classes of shapes. The learned shape template supports applications such as shape exploration, correspondence, abstraction, interpolation, and semantic segmentation from an RGB image.
Thermal expansion in insulating solids from first principles

In this Tutorial, we describe the use of the quasiharmonic approximation and first-principles density functional theory (DFT) to calculate and analyze the thermal expansion of insulating solids. We discuss the theory underlying the quasiharmonic approximation and demonstrate its practical use within two common frameworks for calculating thermal expansion: the Helmholtz free energy framework and the Gruneisen theory. Using the example of silicon, we provide a guide for predicting how the lattice parameter changes as a function of temperature using DFT, including the calculation of phonon modes and phonon density of states, elastic constants, and specific heat. We also describe the calculation and interpretation of Gruneisen parameters, as well as how they relate to coefficients of thermal expansion. The limitations of the quasiharmonic approximation are briefly touched on, as well as the comparison of theoretical results with experimental data. Finally, we use the example of ferroelectric PbTiO 3 to illustrate how the methods used can be adapted to study anisotropic systems.
Efficient evaluation of AGP reduced density matrices

We propose and implement an algorithm to calculate the norm and reduced density matrices (RDMs) of the antisymmetrized geminal power of any rank with polynomial cost. Our method scales quadratically per element of the RDMs. Numerical tests indicate that our method is very fast and capable of treating systems with a few thousand orbitals and hundreds of electrons reliably in double-precision. In addition, we present reconstruction formulas that allow one to decompose higher order RDMs in terms of linear combinations of lower order ones and geminal coefficients, thereby reducing the computational cost significantly.
On the unramified cohomology of certain quotient varieties

In this note, we consider unramified cohomology with $${{\mathbb {Z}}}/2$$ coefficients for some (degree two) quotient varieties and describe a method that allows one to prove the non-vanishing of these groups under certain conditions. We apply this method to prove a non-vanishing statement in the case of Kummer varieties. Combining this with work of Colliot-Thelene and Voisin, we obtain a new type of three-dimensional counterexample to the integral Hodge conjecture.
Transportation and health in the antebellum United States 1820-1847.

I study the impact of transportation on health in the rural United States, 1820–1847. Measuring health by average stature, I find that greater transportation linkage, as measured by market access, in a cohort’s county-year of birth had an adverse impact on its health. A one-standard-deviation increase in market access reduced average stature by 0.14 inches, and rising market access over the study period can explain 37 percent of the contemporaneous decline in average stature, known as the Antebellum Puzzle. I find evidence that transportation affected health by increasing population density, leading to a worse epidemiological environment.
Hot Shots: An Analysis of the “Hot Hand” in NBA Field Goal and Free Throw Shooting:

We investigate the hot hand using detailed data on free throws and field goals attempts for 12 NBA seasons. We find a small hot hand for free throws, which more than doubles for longer streaks of m...
On Estimating Conditional Conservatism

ABSTRACT: The concept of conditional conservatism (asymmetric earnings timeliness) has provided new insight into financial reporting and stimulated considerable research since Basu (1997). Patatoukas and Thomas (2011) report bias in firm-level cross-sectional asymmetry estimates that they attribute to scale effects. We do not agree with their advice that researchers should avoid conditional conservatism estimates and inferences from research based on such estimates. Our theoretical and empirical analyses suggest the explanation is a correlated omitted variables problem that can be addressed in a straightforward fashion, including fixed-effects regression. Correlation between the expected components of earnings and returns biases estimates of how earnings incorporate the information contained in returns. Further, the correlation varies with returns, biasing asymmetric timeliness estimates. When firm-specific effects are taken into account, estimates do not exhibit the bias, are statistically and economical...