cssi/SciSciGPT-SciSciNet huggingface dataset:
The restricted isometry property for random block diagonal matrices

Abstract In Compressive Sensing, the Restricted Isometry Property (RIP) ensures that robust recovery of sparse vectors is possible from noisy, undersampled measurements via computationally tractable algorithms. It is by now well-known that Gaussian (or, more generally, sub-Gaussian) random matrices satisfy the RIP under certain conditions on the number of measurements. Their use can be limited in practice, however, due to storage limitations, computational considerations, or the mismatch of such matrices with certain measurement architectures. These issues have recently motivated considerable effort towards studying the RIP for structured random matrices. In this paper, we study the RIP for block diagonal measurement matrices where each block on the main diagonal is itself a sub-Gaussian random matrix. Our main result states that such matrices can indeed satisfy the RIP but that the requisite number of measurements depends on certain properties of the basis in which the signals are sparse. In the best case, these matrices perform nearly as well as dense Gaussian random matrices, despite having many fewer nonzero entries.
The eta invariant on two-step nilmanifolds

The eta invariant appears regularly in index theorems but is known to be directly computable from the spectrum only in certain examples of locally symmetric spaces of compact type. In this work, we derive some general formulas useful for calculating the eta invariant on closed manifolds. Specifically, we study the eta invariant on nilmanifolds by decomposing the spin Dirac operator using Kirillov theory. In particular, for general Heisenberg three-manifolds, the spectrum of the Dirac operator and the eta invariant are computed in terms of the metric, lattice, and spin structure data. There are continuous families of geometrically, spectrally different Heisenberg three-manifolds whose Dirac operators have constant eta invariant. In the appendix, some needed results of L. Richardson and C. C. Moore are extended from spaces of functions to spaces of spinors.
On the permanent of random Bernoulli matrices

Abstract We show that the permanent of an n × n matrix with iid Bernoulli entries ±1 is of magnitude n ( 1 2 + o ( 1 ) ) n with probability 1 − o ( 1 ) . In particular, it is almost surely non-zero.
Verification and Calibration of Antenna Cross-Polarization Discrimination and Penetration Loss for Millimeter Wave Communications

This article presents measurement guidelines and verification procedures for antenna cross-polarization discrimination (XPD) and penetration loss measurements for millimeter wave (mmWave) channel sounder systems. These techniques are needed to ensure accurate and consistent measurements by different researchers at different frequencies and bandwidths. Measurements at 73 GHz are used to demonstrate and verify the guidelines, and show the consistency of the antenna XPD factor and the penetration loss at different transmitter-receiver (T-R) separation distances, thus providing a systematic method that may be used at any frequency for reliable field measurements.
Hybrid asymptotic/numerical methods for the evaluation of layer heat potentials in two dimensions

We present a hybrid asymptotic/numerical method for the accurate computation of single- and double-layer heat potentials in two dimensions. It has been shown in previous work that simple quadrature schemes suffer from a phenomenon called “geometrically induced stiffness,” meaning that formally high-order accurate methods require excessively small time steps before the rapid convergence rate is observed. This can be overcome by analytic integration in time, requiring the evaluation of a collection of spatial boundary integral operators with non-physical, weakly singular kernels. In our hybrid scheme, we combine a local asymptotic approximation with the evaluation of a few boundary integral operators involving only Gaussian kernels, which are easily accelerated by a new version of the fast Gauss transform. This new scheme is robust, avoids geometrically induced stiffness, and is easy to use in the presence of moving geometries. Its extension to three dimensions is natural and straightforward, and should permit layer heat potentials to become flexible and powerful tools for modeling diffusion processes.
Infinite-dimensional Ellentuck spaces and Ramsey-classification theorems

We extend the hierarchy of finite-dimensional Ellentuck spaces to infinite dimensions. Using uniform barriers B on ω as the prototype structures, we construct a class of continuum many topological Ramsey spaces ℰB which are Ellentuck-like in nature, and form a linearly ordered hierarchy under projections. We prove new Ramsey-classification theorems for equivalence relations on fronts, and hence also on barriers, on the spaces ℰB, extending the Pudlak–Rodl theorem for barriers on the Ellentuck space. The inspiration for these spaces comes from continuing the iterative construction of the forcings &#x1d4ab;([ω]k)/Fin⊗k to the countable transfinite. The σ-closed partial order (ℰB,⊆FinB) is forcing equivalent to &#x1d4ab;(B)/FinB, which forces a non-p-point ultrafilter &#x1d4a2;B. This work forms the basis for further work classifying the Rudin–Keisler and Tukey structures for the hierarchy of the generic ultrafilters &#x1d4a2;B.
Improvisation through Physical Understanding: Using Novel Objects As Tools with Visual Foresight

Machine learning techniques have enabled robots to learn narrow, yet complex tasks and also perform broad, yet simple skills with a wide variety of objects. However, learning a model that can both perform complex tasks and generalize to previously unseen objects and goals remains a significant challenge. We study this challenge in the context of "improvisational" tool use: a robot is presented with novel objects and a user-specified goal (e.g., sweep some clutter into the dustpan), and must figure out, using only raw image observations, how to accomplish the goal using the available objects as tools. We approach this problem by training a model with both a visual and physical understanding of multi-object interactions, and develop a sampling-based optimizer that can leverage these interactions to accomplish tasks. We do so by combining diverse demonstration data with self-supervised interaction data, aiming to leverage the interaction data to build generalizable models and the demonstration data to guide the model-based RL planner to solve complex tasks. Our experiments show that our approach can solve a variety of complex tool use tasks from raw pixel inputs, outperforming both imitation learning and self-supervised learning individually. Furthermore, we show that the robot can perceive and use novel objects as tools, including objects that are not conventional tools, while also choosing dynamically to use or not use tools depending on whether or not they are required.
Topological complexity of spatial polygon spaces

Let ` = (`1, . . . , `n) be an n-tuple of positive real numbers, and let N(`) denote the space of equivalence classes of oriented n-gons in R with consecutive sides of lengths `1, . . . , `n, identified under translation and rotation of R. Using known results about the integral cohomology ring, we prove that its topological complexity satisfies TC(N(`)) = 2n−5, provided that N(`) is nonempty and contains no straight-line polygons. 1. Main result The topological complexity, TC(X), of a topological space X is, roughly, the number of rules required to specify how to move between any two points of X. A “rule” must be such that the choice of path varies continuously with the choice of endpoints. (See [2, §4].) We study TC(X), where X = N(`) is the space of equivalence classes of oriented n-gons in R with consecutive sides of length `1, . . . , `n, identified under translation and rotation of R. (See, e.g., [5] or [4].) Here ` = (`1, . . . , `n) is an n-tuple of positive real numbers. Thus N(`) = {(z1, . . . , zn) ∈ (S) : `1z1 + · · ·+ `nzn = 0}/SO(3). We can think of the sides of the polygon as linked arms of a robot, and then TC(N(`)) is the number of rules required to program the robot to move from any configuration to any other. We say that ` is generic if there is no subset S ⊂ [n] such that ∑
Large-Scale Sparse Inverse Covariance Estimation via Thresholding and Max-Det Matrix Completion

The sparse inverse covariance estimation problem is commonly solved using an $\ell_{1}$-regularized Gaussian maximum likelihood estimator known as "graphical lasso", but its computational cost becomes prohibitive for large data sets. A recent line of results showed--under mild assumptions--that the graphical lasso estimator can be retrieved by soft-thresholding the sample covariance matrix and solving a maximum determinant matrix completion (MDMC) problem. This paper proves an extension of this result, and describes a Newton-CG algorithm to efficiently solve the MDMC problem. Assuming that the thresholded sample covariance matrix is sparse with a sparse Cholesky factorization, we prove that the algorithm converges to an $\epsilon$-accurate solution in $O(n\log(1/\epsilon))$ time and $O(n)$ memory. The algorithm is highly efficient in practice: we solve the associated MDMC problems with as many as 200,000 variables to 7-9 digits of accuracy in less than an hour on a standard laptop computer running MATLAB.
FlowIntent: Detecting Privacy Leakage from User Intention to Network Traffic Mapping

The exponential growth of mobile devices has raised concerns about sensitive data leakage. In this paper, we make the first attempt to identify suspicious location-related HTTP transmission flows from the user's perspective, by answering the question: Is the transmission user-intended? In contrast to previous network-level detection schemes that mainly rely on a given set of suspicious hostnames, our approach can better adapt to the fast growth of app market and the constantly evolving leakage patterns. On the other hand, compared to existing system-level detection schemes built upon program taint analysis, where all sensitive transmissions as treated as illegal, our approach better meets the user needs and is easier to deploy. In particular, our proof-of- concept implementation (FlowIntent) captures sensitive transmissions missed by TaintDroid, the state-of-the-art dynamic taint analysis system on Android platforms. Evaluation using 1002 location sharing instances collected from more than 20,000 apps shows that our approach achieves about 91% accuracy in detecting illegitimate location transmissions.
Regularity results for pluriclosed flow

In [8] the authors introduced a parabolic flow of pluriclosed metrics. New advancements in this flow are given, including improved regularity results, a gradient property and expanding entropy functional, and a conjectural picture of optimal existence results and their consequences. Let (M2n, J) be a complex manifold, and let ω denote a Hermitian metric on M . The metric ω is pluriclosed if ∂∂ω = 0. Consider the initial value problem ∂ ∂t ω = ∂∂∗ω + ∂∂∗ω + √−1 2 ∂∂ log det g ω(0) = ω0. (1) This equation was introduced in [8] as a tool for understanding complex, non-Kahler manifolds. Equation (1) falls into a general class of flows of Hermitian metrics, and as shown in [7], solutions to (1) exist as long as the Chern curvature, torsion, and covariant derivative of torsion are bounded. In this note we will describe many results which refine the existence and singularity formation of solutions to (1), which appear in [9]. The starting point is to interpret equation (1) using the Bismut connection. This is a small but crucial observation which clarifies many aspects of (1). Let us recall the Bismut connection. Let (M2n, ω, J) be a complex manifold with pluriclosed metric, and let D denote the Levi Civita connection. Then the Bismut connection ∇ is defined by 〈∇XY, Z〉 = 〈DXY, Z〉+ 12d ω(X, Y, Z) where dcω(X,Y, Z) := dω(JX, JY, JZ). Let Ω denote the curvature of this connection, and let P denote the Chern form of this connection, i.e. in complex coordinates Pij = Ω k ijk Finally, let PC denote the Ricci form associated to the Chern connection. One can calculate that P = P − dd∗ω. In particular, this implies that a solution to (1) may be expressed as ∂ ∂t ω = − P 1,1 (2) where P 1,1 denotes the projection of P onto (1, 1)-forms. There is another geometric evolution equation naturally associated to a connection with torsion given by a closed Date: August 16, 2010. 1 2 JEFFREY STREETS AND GANG TIAN three-form, namely the B-field renormalization group flow of string theory. A remarkable fact is that (2) is equivalent to this B-field flow, after a change by diffeomorphism. Thus Perelman-type monotone quantities which had previously been discovered for the B-field flow, specifically an energy functional [4] and expanding entropy functional [6], carry over to solutions to (1). To describe the energy functional, first generalize the notation slightly and let (Mn, g) be a Riemannian manifold, and let T denote a three-form on M . Let
Gaussian process Random fields

Gaussian processes have been successful in both supervised and unsupervised machine learning tasks, but their computational complexity has constrained practical applications. We introduce a new approximation for large-scale Gaussian processes, the Gaussian Process Random Field (GPRF), in which local GPs are coupled via pairwise potentials. The GPRF likelihood is a simple, tractable, and parallelizeable approximation to the full GP marginal likelihood, enabling latent variable modeling and hyperparameter selection on large datasets. We demonstrate its effectiveness on synthetic spatial data as well as a real-world application to seismic event location.
Scalable kernel k-means clustering with Nyström approximation: relative-error bounds

Kernel k-means clustering can correctly identify and extract a far more varied collection of cluster structures than the linear k-means clustering algorithm. However, kernel k- means clustering is computationally expensive when the non-linear feature map is high-dimensional and there are many input points. Kernel approximation, e.g., the Nystrom method, has been applied in previous works to approximately solve kernel learning problems when both of the above conditions are present. This work analyzes the application of this paradigm to kernel k-means clustering, and shows that applying the linear k-means clustering algorithm to k/e (1 + o(1)) features constructed using a so-called rank-restricted Nystrom approximation results in cluster assignments that satisfy a 1+e approximation ratio in terms of the kernel k-means cost function, relative to the guarantee provided by the same algorithm without the use of the Nystrom method. As part of the analysis, this work establishes a novel 1+e relative-error trace norm guarantee for low-rank approximation using the rank-restricted Nystrom approximation. Empirical evaluations on the 8:1 million instance MNIST8M dataset demonstrate the scalability and usefulness of kernel k-means clustering with Nystrom approximation. This work argues that spectral clustering using Nystrom approximation--a popular and computationally efficient, but theoretically unsound approach to non-linear clustering-- should be replaced with the efficient and theoretically sound combination of kernel k-means clustering with Nystrom approximation. The superior performance of the latter approach is empirically verified.
3GPP-Inspired HetNet Model Using Poisson Cluster Process: Sum-Product Functionals and Downlink Coverage

The growing complexity of heterogeneous cellular networks (HetNets) has necessitated a variety of user and base station (BS) configurations to be considered for realistic performance evaluation and system design. This is directly reflected in the HetNet simulation models proposed by standardization bodies, such as the 3rd Generation Partnership Project (3GPP). Complementary to these simulation models, stochastic geometry-based approach, modeling the locations of the users, and the $K$ tiers of BSs as independent and homogeneous Poisson point processes (PPPs), has gained prominence in the past few years. Despite its success in revealing useful insights, this PPP-based $K$ -tier HetNet model is not rich enough to capture spatial coupling between user and BS locations that exists in real-world HetNet deployments and is included in 3GPP simulation models. In this paper, we demonstrate that modeling a fraction of users and arbitrary number of BS tiers alternatively with a Poisson cluster process (PCP) captures the aforementioned coupling, thus bridging the gap between the 3GPP simulation models and the PPP-based analytic model for HetNets. We further show that the downlink coverage probability of a typical user under maximum signal-to-interference-ratio ( $ \mathtt {SIR}$ ) association can be expressed in terms of the sum-product functionals over PPP, PCP, and its associated offspring point process, which are all characterized as a part of our analysis. We also show that the proposed model converges to the PPP-based HetNet model as the cluster size of the PCPs tends to infinity. Finally, we specialize our analysis based on general PCPs for Thomas and Matern cluster processes. Special instances of the proposed model closely resemble the different configurations for BS and user locations considered in 3GPP simulations.
Mean curvature flows and isotopy problems

In this note, we discuss the mean curvature flow of graphs of maps between Riemannian manifolds. Special emphasis will be placed on estimates of the flow as a non-linear parabolic system of differential equations. Several global existence theorems and applications to isotopy problems in geometry and topology will be presented. The results are based on joint works of the author with his collaborators I. Medoys, K. Smoczyk, and M.-P. Tsui.
Fast Structured Matrix Computations: Tensor Rank and Cohn---Umans Method

We discuss a generalization of the Cohn---Umans method, a potent technique developed for studying the bilinear complexity of matrix multiplication by embedding matrices into an appropriate group algebra. We investigate how the Cohn---Umans method may be used for bilinear operations other than matrix multiplication, with algebras other than group algebras, and we relate it to Strassen's tensor rank approach, the traditional framework for investigating bilinear complexity. To demonstrate the utility of the generalized method, we apply it to find the fastest algorithms for forming structured matrix---vector product, the basic operation underlying iterative algorithms for structured matrices. The structures we study include Toeplitz, Hankel, circulant, symmetric, skew-symmetric, f-circulant, block Toeplitz---Toeplitz block, triangular Toeplitz matrices, Toeplitz-plus-Hankel, sparse/banded/triangular. Except for the case of skew-symmetric matrices, for which we have only upper bounds, the algorithms derived using the generalized Cohn---Umans method in all other instances are the fastest possible in the sense of having minimum bilinear complexity. We also apply this framework to a few other bilinear operations including matrix---matrix, commutator, simultaneous matrix products, and briefly discuss the relation between tensor nuclear norm and numerical stability.
Transposable elements contribute to dynamic genome content in maize.

Transposable elements (TEs) are ubiquitous components of eukaryotic genomes and can create variation in genome organization and content. Most maize genomes are composed of TEs. We developed an approach to define shared and variable TE insertions across genome assemblies and applied this method to four maize genomes (B73, W22, Mo17 and PH207) with uniform structural annotations of TEs. Among these genomes we identified approximately 400 000 TEs that are polymorphic, encompassing 1.6 Gb of variable TE sequence. These polymorphic TEs include a combination of recent transposition events as well as deletions of older TEs. There are examples of polymorphic TEs within each of the superfamilies of TEs and they are found distributed across the genome, including in regions of recent shared ancestry among individuals. There are many examples of polymorphic TEs within or near maize genes. In addition, there are 2380 gene annotations in the B73 genome that are located within variable TEs, providing evidence for the role of TEs in contributing to the substantial differences in annotated gene content among these genotypes. TEs are highly variable in our survey of four temperate maize genomes, highlighting the major contribution of TEs in driving variation in genome organization and gene content. OPEN RESEARCH BADGES: This article has earned an Open Data Badge for making publicly available the digitally-shareable data necessary to reproduce the reported results. The data is available at https://github.com/SNAnderson/maizeTE_variation; https://mcstitzer.github.io/maize_TEs.
Hidden Treasures in the Comoros : The Impact of Inter-Island Connectivity Improvement on Agricultural Production

The paper revisits the traditional transport-agricultural growth nexus. Connectivity is particularly challenging for small island developing states, such as the Comoros, where domestic markets are limited and transport and transaction costs tend to be high. Using household survey data from Comoros, the paper shows that significant untapped economic potential exists in the domestic market. The analysis shows that better transport connectivity increases crop production and market sales. Accessibility to Moroni, the primary market in the country, and inter-island connectivity are of particular importance. Not only transport infrastructure, but also services are important. Access to intermediary services, such as cooperatives and collectors, is also found to have a positive impact on crop production and market sales.
Gender, Race, and Diversity Values Among Local Government Leaders:

Despite the increased emphasis placed on diversity and inclusion, there is relatively little research that focuses on diversity values in small and medium-sized cities. This research uses data from...
Multi-Agent Coordination under Uncertain Communication.

Multi-agent coordination is not a simple problem. While significant research has gone into computing plans efficiently and managing competing preferences, the execution of multiagent plans can still fail even when the plan space is small and agent goals are universally aligned. The reason for this difficulty is that in order to guarantee successful execution of a plan, effective multi-agent coordination requires communication to ensure that all actors have accurate beliefs about the state of the world. My thesis will focus on the problem of characterizing, modeling, and providing efficient algorithms for addressing planning and execution when there agents cannot maintain perfect communication.
A computational survey of semiconductors for power electronics

Power electronics (PE) are used to control and convert electrical energy in a wide range of applications from consumer products to large-scale industrial equipment. While Si-based power devices account for the vast majority of the market, wide band gap semiconductors such as SiC, GaN, and Ga2O3 are starting to gain ground. However, these emerging materials face challenges due to either non-negligible defect densities, high synthesis and processing costs, or poor thermal properties. Here, we report on a broad computational survey aimed to identify promising materials for future power electronic devices beyond SiC, GaN, and Ga2O3. We consider 863 oxides, sulfides, nitrides, carbides, silicides, and borides that exhibit finite calculated band gaps. We utilize ab initio methods in conjunction with models for intrinsic carrier mobility, and critical breakdown field to compute the widely used Baliga figure of merit. We also compute the lattice thermal conductivity and use it as an additional screening parameter. In addition to correctly identifying known PE materials, our survey has revealed a number of promising candidates exhibiting the desirable combination of high figure of merit and high lattice thermal conductivity, which we propose for further experimental investigations.
Comparing the power of cops to zombies in pursuit-evasion games

Abstract We compare two kinds of pursuit-evasion games played on graphs. In Cops and Robbers, the cops can move strategically to adjacent vertices as they please, while in a new variant, called deterministic Zombies and Survivors, the zombies (the counterpart of the cops) are required to always move towards the survivor (the counterpart of the robber). The cop number of a graph is the minimum number of cops required to catch the robber on that graph; the zombie number of a graph is the minimum number of zombies required to catch the survivor on that graph. We answer two questions from the 2016 paper of Fitzpatrick, Howell, Messinger, and Pike. We show that for any integers m and k such that m ≥ k ≥ 1 , there is a graph with zombie number m and cop number k . We also show that the zombie number of the n -dimensional hypercube is ⌈ 2 n ∕ 3 ⌉ .
RhoB plays an essential role in CXCR2 sorting decisions

2032 Chemokines largely contribute to the process of tumorigenesis through their roles in inflammation, angiogenesis, and tumor growth and metastasis. The elucidation of the mechanisms that regulate the cellular responses mediated by chemokines is crucial for the identification of novel therapeutic targets. An important aspect of chemokine receptor function is the intracellular trafficking of these receptors. The CXCR2 chemokine receptor is a G protein-coupled receptor that undergoes clathrin-mediated endocytosis upon ligand binding. The trafficking of CXCR2 is critical for cells to maintain a proper chemotactic response. The mechanisms that regulate the recycling/degradation sorting decision are largely unknown. The small GTPases RhoB regulates the trafficking of the EGF receptor. We sought to investigate whether RhoB also regulates CXCR2 trafficking. In our studies, we utilized dominant negative (T19N) and GTPase-deficient activated (Q63L) RhoB mutants, as well as RhoB siRNA to investigate the role of RhoB in CXCR2 trafficking. Expression of both RhoB mutants and RhoB siRNA severely impaired CXCR2-mediated chemotaxis. Expression of RhoB T19N and RhoB siRNA impaired sorting of CXCR2 to the lysosome after 3h CXCL8 stimulation and RhoB T19N impaired CXCL8-induced CXCR2 degradation. In cells expressing the RhoB Q63L mutant, CXCL8 stimulated CXCR2 recycling through the Rab11a perinuclear recycling compartment was impaired but CXCR2 degradation was not induced after 30min of CXCL8 stimulation. CXCR2 co-localized with Rab4, a marker for the rapid recycling pathway, and with mannose-6-phosphate receptor, which traffics between the trans-Golgi network and endosomes, in RhoB Q63L expressing cells, suggesting that CXCR2 is recycled through alternative pathways. These data suggest that RhoB plays a key role in CXCR2 recycling/degradation sorting decisions. [This work was supported by NCI CA34590 and Multidisciplinary Basic Research Training in Cancer T32CA09592]
The Attack and Defense Games

The attack-and-defense game is a game in which an attacker (a group of attackers) has an incentive to revise the status quo and a defender (a group of defenders) wants to protect it. The asymmetry in objectives creates incompatible interests and results in a mixed-strategy Nash equilibrium. However, this equilibrium could be heavily impacted by behavioral considerations.
A vanishing result for higher smooth duals

In this paper we prove a general vanishing result for Kohlhaase’s higher smooth duality functors Si. If G is any unramified connected reductive p-adic group, K is a hyperspecial subgroup, and V is a Serre weight, we show that Si(indKGV)=0 for i> dim(G∕B), where B is a Borel subgroup and the dimension is over ℚp. This is due to Kohlhaase for GL2(ℚp), in which case it has applications to the calculation of Si for supersingular representations. Our proof avoids explicit matrix computations by making use of Lazard theory, and we deduce our result from an analogous statement for graded algebras via a spectral sequence argument. The graded case essentially follows from Koszul duality between symmetric and exterior algebras.
Functional interpretation of single cell similarity maps

We present Vision, a tool for annotating the sources of variation in single cell RNA-seq data in an automated and scalable manner. Vision operates directly on the manifold of cell-cell similarity and employs a flexible annotation approach that can operate either with or without preconceived stratification of the cells into groups or along a continuum. We demonstrate the utility of Vision in several case studies and show that it can derive important sources of cellular variation and link them to experimental meta-data even with relatively homogeneous sets of cells. Vision produces an interactive, low latency and feature rich web-based report that can be easily shared among researchers, thus facilitating data dissemination and collaboration.
Deep learning analysis of breast MRIs for prediction of occult invasive disease in ductal carcinoma in situ.

Abstract Purpose To determine whether deep learning-based algorithms applied to breast MR images can aid in the prediction of occult invasive disease following the diagnosis of ductal carcinoma in situ (DCIS) by core needle biopsy. Materials and methods Our study is retrospective. The data was collected from 2000 to 2014. In this institutional review board-approved study, we analyzed dynamic contrast-enhanced fat-saturated T1-weighted MRI sequences from 131 patients with a core needle biopsy-confirmed diagnosis of DCIS. We explored two different deep learning approaches to predict whether there was an occult invasive component in the analyzed tumors that was ultimately identified at surgical excision. In the first approach, we adopted the transfer learning strategy. Specifically, we used the pre-trained GoogleNet. In the second approach, we used a pre-trained network to extract deep features, and a support vector machine (SVM) that utilizes these features to predict the upstaging of DCIS. We used nested 10-fold cross validation and the area under the ROC curve (AUC) to estimate the performance of the predictive models. Results The best classification performance was obtained using the deep features approach with GoogleNet model pre-trained on ImageNet as the feature extractor and a polynomial kernel SVM used as the classifier (AUC = 0.70, 95% CI: 0.58–0.79). For the transfer learning based approach, the highest AUC obtained was 0.68 (95% CI: 0.57–0.77). Conclusions Convolutional neural networks might be used to identify occult invasive disease in patients diagnosed with DCIS by core needle biopsy.
How do capital and labor split economic gains in an age of globalization

Recent debates about the 1% vs. 99%, CEO compensation, minimum wage, and income inequality suggest an increasingly unfavorable division of economic gains for labor. Indeed, how capital and labor di...
On the tropical discs counting on elliptic K3 surfaces with general singular fibres

Using Lagrangian Floer theory, we study the tropical geometry of K3 surfaces with general singular fibres. In particular, we give the local models for the type $I_n$, $II$, $III$ and $IV$ singular fibres in the Kodaira's classification and generalize the correspondence theorem between open Gromov-Witten invariants/tropical discs counting to these cases.
Mean action of periodic orbits of area-preserving annulus diffeomorphisms

An area-preserving diffeomorphism of an annulus has an “action function” which measures how the diffeomorphism distorts curves. The average value of the action function over the annulus is known as...
Single-Cell RNA-seq Reveals Profound Alterations in Mechanosensitive Dorsal Root Ganglion Neurons with Vitamin E Deficiency.

Ninety percent of Americans consume less than the estimated average requirements of dietary vitamin E (vitE). Severe vitE deficiency due to genetic mutations in the tocopherol transfer protein (TTPA) in humans results in ataxia with vitE deficiency (AVED), with proprioceptive deficits and somatosensory degeneration arising from dorsal root ganglia neurons (DRGNs). Single-cell RNA-sequencing of DRGNs was performed in Ttpa-/- mice, an established model of AVED. In stark contrast to expected changes in proprioceptive neurons, Ttpa-/- DRGNs showed marked upregulation of voltage-gated Ca2+ and K+ channels in mechanosensitive, tyrosine-hydroxylase positive (TH+) DRGNs. The ensuing significant conductance changes resulted in reduced excitability in mechanosensitive Ttpa-/- DRGNs. A highly supplemented vitE diet (600 mg dl-α-tocopheryl acetate/kg diet) prevented the cellular and molecular alterations and improved mechanosensation. VitE deficiency profoundly alters the molecular signature and functional properties of mechanosensitive TH+ DRGN, representing an intriguing shift of the prevailing paradigm from proprioception to mechanical sensation.
Do Multi-hop Readers Dream of Reasoning Chains?

General Question Answering (QA) systems over texts require the multi-hop reasoning capability, i.e. the ability to reason with information collected from multiple passages to derive the answer. In this paper we conduct a systematic analysis to assess such an ability of various existing models proposed for multi-hop QA tasks. Specifically, our analysis investigates that whether providing the full reasoning chain of multiple passages, instead of just one final passage where the answer appears, could improve the performance of the existing QA models. Surprisingly, when using the additional evidence passages, the improvements of all the existing multi-hop reading approaches are rather limited, with the highest error reduction of 5.8% on F1 (corresponding to 1.3% improvement) from the BERT model. To better understand whether the reasoning chains indeed could help find the correct answers, we further develop a co-matching-based method that leads to 13.1% error reduction with passage chains when applied to two of our base readers (including BERT). Our results demonstrate the existence of the potential improvement using explicit multi-hop reasoning and the necessity to develop models with better reasoning abilities.
Rehaif v. United States: Once Again, a Gun Case Makes Surprising Law

Gun cases often make surprising law, and the U.S. Supreme Court’s June 2019 decision in Rehaif v. United States is no exception. Rehaif involved a defendant convicted of violating 18 U.S.C. § 922(g), which makes it a crime for certain persons to possess a firearm or ammunition in or affecting interstate commerce. For decades, there was judicial consensus that the statute did not require proof that defendants were aware of the status that caused them to fall within the statute’s purview. Nevertheless, in Rehaif, a divided Supreme Court held that the government must prove a defendant’s knowledge of such status to prevail in a Section 922(g) prosecution. Given the number of Section 922(g) cases brought each year, Rehaif is a highly consequential decision, even if limited to its immediate context. But it is also surprising for a variety of reasons. Not only did the Supreme Court hear the case in the absence of a circuit split, but the alignment of the Justices was also unusual. Most importantly, in a first for the Court, the opinion relied on the Model Penal Code’s default rules of statutory interpretation to hold that a statutorily prescribed mens rea term applied to all material elements of the offense. Whether Rehaif signals a stealth mens rea revolution of broader scope remains to be seen, but in any event the opinion is a fascinating study in statutory interpretation that seems destined for law school textbooks.
Mo' States Mo' Problems: Emergency Stop Mechanisms from Observation

In many environments, only a relatively small subset of the complete state space is necessary in order to accomplish a given task. We develop a simple technique using emergency stops (e-stops) to exploit this phenomenon. Using e-stops significantly improves sample complexity by reducing the amount of required exploration, while retaining a performance bound that efficiently trades off the rate of convergence with a small asymptotic sub-optimality gap. We analyze the regret behavior of e-stops and present empirical results in discrete and continuous settings demonstrating that our reset mechanism can provide order-of-magnitude speedups on top of existing reinforcement learning methods.
A destination-based allowance for corporate equity

Following renewed academic and policy interest in the destination-based principle for taxing profits—particularly through a destination-based cash-flow tax (DBCFT)—this paper studies other forms of efficient destination-based taxes. Specifically, it analyzes the Destination-Based Allowance for Corporate Equity (DBACE) and Allowance for Corporate Capital (DBACC). It describes adjustments that are required to turn an origin into a destination-based version of these taxes. These include adjustments to capital and equity, which are additional to the border adjustments needed under a DBCFT. The paper finds that the DBACC and DBACE reduce profit shifting and tax competition, but cannot fully eliminate them, with the DBACE more sensitive than the DBACC. Overall, given the potential major political cost of switching from an origin to a destination-based tax system, we conclude that advantages of the DBCFT are likely to outweigh the transitional advantages of the DBACE/DBACC.
Light-driven depolymerization of native lignin enabled by proton-coupled electron transfer

Here, we report a catalytic, light-driven method for the redox-neutral depolymerization of native lignin biomass at ambient temperature. This transformation proceeds via a proton-coupled electron-transfer (PCET) activation of an alcohol O–H bond to generate a key alkoxy radical intermediate, which then facilitates the β-scission of a vicinal C–C bond. Notably, this single-step depolymerization is driven solely by visible-light irradiation, requires no stoichiometric chemical reagents, and produces no stoichiometric waste. This method exhibits good efficiency and excellent selectivity for the activation and fragmentation of the β-O-4 linkage in the polymer backbone, even in the presence of numerous other PCET-active functional groups. The feasibility of this protocol in enabling the cleavage of the β-1 linkage in model lignin dimers was also demonstrated. These results provide further evidence that visible-light photocatalysis can serve as a viable method for the direct conversion of lignin biomass into va...
Supports for Youth in Accelerated High School Curricula: An Initial Study of Applicability and Acceptability of a Motivational Interviewing Intervention:

High school students in accelerated classes face heightened school-related stressors but have access to few specialized supports. This article describes the development and initial application of t...
Associations Between Maternal Thyroid Function in Pregnancy and Obstetric and Perinatal Outcomes.

Context The effects of maternal subclinical hypothyroidism on pregnancy outcomes are not clear. Objective We aimed to assess potential associations between maternal thyrotropin (thyroid-stimulating hormone [TSH]) levels in pregnancy and obstetric and perinatal outcomes. Design Retrospective cohort study. Setting Tertiary academic medical center. Patients Women aged ≥18 years with a singleton gestation and no known thyroid disease seen for prenatal care at Boston Medical Center from January 1, 2003 through May 22, 2014, and their fetuses and infants were included. Main outcome measures Risk ratios of adverse obstetric and perinatal outcomes. Results A total of 8,413 pregnant women (mean age 29.1 years, 15% white, 60% black, 13% Hispanic) and their fetuses and infants (mean gestational age at birth 38.5 weeks, 52% male, mean birth weight 3.2 kg) were included in the analyses. The median (interquartile range) TSH level was 1.06(0.62-1.60) mIU/L, and 130 women (1.6%) had TSH > 4 mIU/L. Maternal TSH levels > 4 mIU/L were associated with increased risks of prematurity (risk ratio [RR] 2.17 [95% confidence interval 1.15-4.07] P = .016) and neonatal respiratory distress syndrome (RDS) (RR 2.83 [95% confidence interval 1.02-7.86] P = .046) compared to TSH levels ≤ 4 mIU/L. Although not statistically significant, TSH levels > 4 mIU/L were also associated with increased RRs for fetal loss, preeclampsia/eclampsia, and low birth weight. TSH levels > 4 mIU/L were not associated with preterm labor, placental abruption, cesarean section, gestational hypertension or diabetes, or neonatal intensive care unit admission. Conclusion Maternal serum TSH concentration > 4 mIU/L in pregnancy was associated with approximately 2-fold increased risks of prematurity and RDS in offspring. Elevated TSH was also associated with statistically non-significant increases in the risk of fetal loss, preeclampsia/eclampsia, and low birth weight.
Fast algorithm for computing nonlocal operators with finite interaction distance

Developments of nonlocal operators for modeling processes that traditionally have been described by local differential operators have been increasingly active during the last few years. One example is peridynamics for brittle materials and another is nonstandard diffusion including the use of fractional derivatives. A major obstacle for application of these methods is the high computational cost from the numerical implementation of the nonlocal operators. It is natural to consider fast methods of fast multipole or hierarchical matrix type to overcome this challenge. Unfortunately the relevant kernels do not satisfy the standard necessary conditions. In this work a new class of fast algorithms is developed and analyzed, which is some cases reduces the computational complexity of applying nonlocal operators to essentially the same order of magnitude as the complexity of standard local numerical methods.
From Time Inversion to Nonlinear QED

In Minkowski flat space-time, it is perceived that time inversion is unitary rather than antiunitary, with energy being a time vector changing sign under time inversion. The Dirac equation, in the case of electromagnetic interaction, is not invariant under unitary time inversion, giving rise to a “Klein paradox.” To render unitary time inversion invariance, a nonlinear wave equation is constructed, in which the “Klein paradox” disappears. In the case of Coulomb interaction, the revised nonlinear equation can be linearized to give energy solutions for Hydrogen-like ions without singularity when nuclear number Z>137, showing a reversed energy order pending for experimental tests such as Zeeman effects. In non-relativistic limit, this nonlinear equation reduces to nonlinear Schrodinger equation with soliton-like solutions. Moreover, particle conjugation and electron-proton scattering with a nonsingular current-potential interaction are discussed. Finally the explicit form of gauge function is found, the uniqueness of Lorentz gauge is proven and the Lagrangian density of quantum electrodynamics (QED) is revised as well. The implementation of unitary time inversion leads to the ultimate derivation of nonlinear QED.
Tucker Tensor Decomposition on FPGA

Tensor computation has emerged as a powerful mathematical tool for solving high-dimensional and/or extreme-scale problems in science and engineering. The last decade has witnessed tremendous advancement of tensor computation and its applications in machine learning and big data. However, its hardware optimization on resource-constrained devices remains an (almost) unexplored field. This paper presents an hardware accelerator for a classical tensor computation framework, Tucker decomposition. We study three modules of this architecture: tensor-times-matrix (TTM), matrix singular value decomposition (SVD), and tensor permutation, and implemented them on Xilinx FPGA for prototyping. In order to further reduce the computing time, a warm-start algorithm for the Jacobi iterations in SVD is proposed. A fixed-point simulator is used to evaluate the performance of our design. Some synthetic data sets and a real MRI data set are used to validate the design and evaluate its performance. We compare our work with state-of-the-art software toolboxes running on both CPU and GPU, and our work shows 2.16 – 30.2× speedup on the cardiac MRI data set.
Moderate Nrf2 Activation by Genetic Disruption of Keap1 Has Sex-Specific Effects on Bone Mass in Mice.

Keap1 is a negative controller of the transcription factor Nrf2 for its activity. The Keap1/Nrf2 signaling pathway has been considered as a master regulator of cytoprotective genes, and exists in many cell types including osteoblasts and osteoclasts. Our previous study shows Nrf2 deletion decreases bone formation. Recent studies show hyperactivation of Nrf2 causes osteopenia in Keap1−/− mice, and Keap1−/− osteoblasts have significantly less proliferative potential than Keap1+/− osteoblasts. We aimed to examine if moderate Nrf2 activation by disruption of Keap1 impacts bone metabolism. We examined bone phenotype of Keap1 heterozygotic mice (Ht) in comparison with Keap1 wild type (WT) mice. Deletion or knockdown of Keap1 enhanced the gene expression of Nrf2, ALP and wnt5a in cultured primary osteoblasts compared to WT control. In male mice, compared with their age-matched littermate WT controls, Keap1 Ht mice showed significant increase in bone formation rate (+30.7%, P = 0.0029), but did not change the ultimate force (P < 0.01). The osteoclast cell numbers (−32.45%, P = 0.01) and surface (−32.58%, P = 0.03) were significantly reduced by Keap1 deficiency in male mice. Compared to male WT mice, serum bone resorption marker in male Keap1 Ht mice was significantly decreased. Our data suggest that moderate Nrf2 activation by disruption of Keap1 improved bone mass by regulating bone remodeling in male mice.
Mice with GNAO1 R209H Movement Disorder Variant Display Hyperlocomotion Alleviated by Risperidone.

Neurodevelopmental disorder with involuntary movements (NEDIM, OMIM: 617493) is a severe, early onset neurological condition characterized by a delay in psychomotor development, hypotonia, and hyperkinetic involuntary movements. Heterozygous de novo mutations in the GNAO1 gene cause NEDIM. Gαo, the gene product of GNAO1, is the alpha subunit of Go, a member of the heterotrimeric Gi/o family of G-proteins. Go is found abundantly throughout the brain but the pathophysiological mechanisms linking Gαo functions to clinical manifestations of GNAO1-related disorders are still poorly understood. One of the most common mutant alleles among the GNAO1 encephalopathies is the c.626G>A or p.Arg209His (R209H) mutation. We developed heterozygous knock-in Gnao1+/R209H mutant mice using CRISPR/Cas9 methodology to assess whether a mouse model could replicate aspects of the NEDIM clinical pattern. Mice carrying the R209H mutation exhibited increased locomotor activity and a modest gait abnormality at 8-12 weeks. In contrast to mice carrying other mutations in Gnao1, the Gnao1+/R209H mice did not show enhanced seizure susceptibility. Levels of protein expression in multiple brain regions were unchanged from WT mice but the nucleotide exchange rate of mutant R209H Gαo was 9 times faster than WT. The atypical neuroleptic risperidone has shown efficacy in a patient with the R209H mutation. It also alleviated the hyperlocomotion phenotype observed in our mouse model but suppressed locomotion in WT mice as well. In this study, we show that Gnao1+/R209H mice mirror elements of the patient phenotype and respond to an approved pharmacological agent. SIGNIFICANCE STATEMENT Children with de novo mutations in the GNAO1 gene may present with movement disorders with limited effective therapeutic options. The most common mutant variant seen in children with GNAO1-associated movement disorder is R209H. Here we show, using a novel Gnao1+/R209H mouse, that there is a clear behavioral phenotype that is suppressed by risperidone. However, risperidone also affects WT mouse activity, so its effects are not selective for the GNAO1-associated movement disorder.