cssi/SciSciGPT-SciSciNet huggingface dataset:
Width and mean curvature flow

Given a Riemannian metric on a homotopy n-sphere, sweep it out by a continuous one-parameter family of closed curves starting and ending at point curves. Pull the sweepout tight by, in a continuous way, pulling each curve as tight as possible yet preserving the sweepout. We show: Each curve in the tightened sweepout whose length is close to the length of the longest curve in the sweepout must itself be close to a closed geodesic. In particular, there are curves in the sweepout that are close to closed geodesics. As an application, we bound from above, by a negative constant, the rate of change of the width for a one-parameter family of convex hypersurfaces that flows by mean curvature. The width is loosely speaking up to a constant the square of the length of the shortest closed curve needed to “pull over” M. This estimate is sharp and leads to a sharp estimate for the extinction time; cf our papers [?, ?] where a similar bound for the rate of change for the two dimensional width is shown for homotopy 3–spheres evolving by the Ricci flow (see also Perelman [?]).
Strong bonds and far-from-equilibrium conditions minimize errors in lattice-gas growth

We use computer simulation to study the layer-by-layer growth of particle structures in a lattice gas, taking the number of incorporated vacancies as a measure of the quality of the grown structure. A dynamic scaling relation describes the quality of structures in and out of equilibrium and reveals that the highest-quality structures are obtained, for fixed observation time, using strong interactions and far-from-equilibrium growth conditions. This result contrasts with the usual assumption that weak interactions and mild nonequilibrium conditions are the best way to minimize errors during assembly.
Driving in the Matrix: Can virtual worlds replace human-generated annotations for real world tasks?

Deep learning has rapidly transformed the state of the art algorithms used to address a variety of problems in computer vision and robotics. These breakthroughs have relied upon massive amounts of human annotated training data. This time consuming process has begun impeding the progress of these deep learning efforts. This paper describes a method to incorporate photo-realistic computer images from a simulation engine to rapidly generate annotated data that can be used for the training of machine learning algorithms. We demonstrate that a state of the art architecture, which is trained only using these synthetic annotations, performs better than the identical architecture trained on human annotated real-world data, when tested on the KITTI data set for vehicle detection. By training machine learning algorithms on a rich virtual world, real objects in real scenes can be learned and classified using synthetic data. This approach offers the possibility of accelerating deep learning's application to sensor-based classification problems like those that appear in self-driving cars. The source code and data to train and validate the networks described in this paper are made available for researchers.
Unmixing the Mixed Volume Computation

Computing mixed volume of convex polytopes is an important problem in computational algebraic geometry. This paper establishes sufficient conditions under which the mixed volume of several convex polytopes exactly equals the normalized volume of the convex hull of their union. Under these conditions the problem of computing mixed volume of several polytopes can be transformed into a volume computation problem for a single polytope in the same dimension. We demonstrate through problems from real world applications that substantial reduction in computational costs can be achieved via this transformation in situations where the convex hull of the union of the polytopes has less complex geometry than the original polytopes. We also discuss the important implications of this result in the polyhedral homotopy method for solving polynomial systems.
Matroidal Schur algebras

Motivated by a geometric description of the Schur algebra due to the second author, we define for any matroid M and principal ideal domain k, a quasi-hereditary algebra R(M) defined over k which we call a matroidal Schur algebra. We show that the Ringel dual of R(M) is the matroidal Schur algebra \(R(M^*)\) associated with the dual matroid \(M^*\). When k is a field of characteristic zero, the algebra R(M) can be seen as a categorification of a formula due to Kook–Reiner–Stanton and related work of Denham. More generally, the behavior of R(M) in positive characteristic is closely related to determinant formulas of Schechtman–Varchenko and Brylawski–Varchenko.
Uplink User-Assisted Relaying in Cellular Networks

We use stochastic geometry to analyze the performance of a partial decode-and-forward (PDF) relaying scheme applied in a user-assisted relaying setting, where an active user relays data through another idle user in uplink cellular communication. We present the geometric model of a network deploying user-assisted relaying and propose two geometric cooperation policies for fast and slow fading channels. We analytically derive the cooperation probability for both policies. This cooperation probability is further used in the analytical derivation of the moments of intercell interference power caused by system-wide deployment of this user-assisted PDF relaying. We then model the intercell interference power statistics using the Gamma distribution by matching the first two moments analytically derived. This cooperation and interference analysis provides the theoretical basis for quantitatively evaluating the performance impact of user-assisted relaying in cellular networks. We then numerically evaluate the average transmission rate performance and show that user-assisted relaying can significantly improve per-user transmission rate despite of increased intercell interference. This transmission rate gain is significant for active users near the cell edge and further increases with higher idle user density, supporting user-assisted relaying as a viable solution to crowded population areas.
Estimating volume and surface area of a convex body via its projections or sections

The main goal of this paper is to present a series of inequalities connecting the surface area measure of a convex body and surface area measure of its projections and sections. We present a solution of a question from S. Campi, P. Gritzmann and P. Gronchi regarding the asymptotic behavior of the best constant in a recently proposed reverse Loomis-Whitney inequality. Next we give a new sufficient condition for the slicing problem to have an affirmative answer, in terms of the least "outer volume ratio distance" from the class of intersection bodies of projections of at least proportional dimension of convex bodies. Finally, we show that certain geometric quantities such as the volume ratio and minimal surface area (after a suitable normalization) are not necessarily close to each other.
Mixed Membership Word Embeddings for Computational Social Science

Word embeddings improve the performance of NLP systems by revealing the hidden structural relationships between words. Despite their success in many applications, word embeddings have seen very little use in computational social science NLP tasks, presumably due to their reliance on big data, and to a lack of interpretability. I propose a probabilistic model-based word embedding method which can recover interpretable embeddings, without big data. The key insight is to leverage mixed membership modeling, in which global representations are shared, but individual entities (i.e. dictionary words) are free to use these representations to uniquely differing degrees. I show how to train the model using a combination of state-of-the-art training techniques for word embeddings and topic models. The experimental results show an improvement in predictive language modeling of up to 63% in MRR over the skip-gram, and demonstrate that the representations are beneficial for supervised learning. I illustrate the interpretability of the models with computational social science case studies on State of the Union addresses and NIPS articles.
A higher dimensional foliated Donaldson theory, I

We introduce the foliated anti-self dual equation for higher dimensional smooth manifolds with codimension-4 Riemannian foliations. Several fundamental results are established, towards the defining of a Donaldson type invariant for such foliations.
Hammocks and fractions in relative $$\infty $$-categories

We study the homotopy theory of $$\infty $$ -categories enriched in the $$\infty $$ -category of simplicial spaces. That is, we consider -enriched $$\infty $$ -categories as presentations of ordinary $$\infty $$ -categories by means of a “local” geometric realization functor , and we prove that their homotopy theory presents the $$\infty $$ -category of $$\infty $$ -categories, i.e. that this functor induces an equivalence from a localization of the $$\infty $$ -category of -enriched $$\infty $$ -categories. Following Dwyer–Kan, we define a hammock localization functor from relative $$\infty $$ -categories to -enriched $$\infty $$ -categories, thus providing a rich source of examples of -enriched $$\infty $$ -categories. Simultaneously unpacking and generalizing one of their key results, we prove that given a relative $$\infty $$ -category admitting a homotopical three-arrow calculus, one can explicitly describe the hom-spaces in the $$\infty $$ -category presented by its hammock localization in a much more explicit and accessible way. As an application of this framework, we give sufficient conditions for the Rezk nerve of a relative $$\infty $$ -category to be a (complete) Segal space, generalizing joint work with Low.
Structural Redundancy in Supracellular Actomyosin Networks Enables Robust Tissue Folding

Summary Tissue morphogenesis is strikingly robust. Yet, how tissues are sculpted under challenging conditions is unknown. Here, we combined network analysis, experimental perturbations, and computational modeling to determine how network connectivity between hundreds of contractile cells on the ventral side of the Drosophila embryo ensures robust tissue folding. We identified two network properties that mechanically promote robustness. First, redundant supracellular cytoskeletal network paths ensure global connectivity, even with network degradation. By forming many more connections than are required, morphogenesis is not disrupted by local network damage, analogous to the way redundancy guarantees the large-scale function of vasculature and transportation networks. Second, directional stiffening of edges oriented orthogonal to the folding axis promotes furrow formation at lower contractility levels. Structural redundancy and directional network stiffening ensure robust tissue folding with proper orientation.
On the construction of knockoffs in case–control studies

Consider a case-control study in which we have a random sample, constructed in such a way that the proportion of cases in our sample is different from that in the general population---for instance, the sample is constructed to achieve a fixed ratio of cases to controls. Imagine that we wish to determine which of the potentially many covariates under study truly influence the response by applying the new model-X knockoffs approach. This paper demonstrates that it suffices to design knockoff variables using data that may have a different ratio of cases to controls. For example, the knockoff variables can be constructed using the distribution of the original variables under any of the following scenarios: (1) a population of controls only; (2) a population of cases only; (3) a population of cases and controls mixed in an arbitrary proportion (irrespective of the fraction of cases in the sample at hand). The consequence is that knockoff variables may be constructed using unlabeled data, which is often available more easily than labeled data, while maintaining Type-I error guarantees.
New concavity and convexity results for symmetric polynomials and their ratios

We prove ‘power’ generalizations of Marcus–Lopes style (including McLeod and Bullen) concavity inequalities for elementary symmetric polynomials, and similar generalizations to convexity inequaliti...
Why Size Matters: Feature Coding as Nystrom Sampling

Recently, the computer vision and machine learning community has been in favor of feature extraction pipelines that rely on a coding step followed by a linear classifier, due to their overall simplicity, well understood properties of linear classifiers, and their computational efficiency. In this paper we propose a novel view of this pipeline based on kernel methods and Nystrom sampling. In particular, we focus on the coding of a data point with a local representation based on a dictionary with fewer elements than the number of data points, and view it as an approximation to the actual function that would compute pair-wise similarity to all data points (often too many to compute in practice), followed by a Nystrom sampling step to select a subset of all data points. Furthermore, since bounds are known on the approximation power of Nystrom sampling as a function of how many samples (i.e. dictionary size) we consider, we can derive bounds on the approximation of the exact (but expensive to compute) kernel matrix, and use it as a proxy to predict accuracy as a function of the dictionary size, which has been observed to increase but also to saturate as we increase its size. This model may help explaining the positive effect of the codebook size and justifying the need to stack more layers (often referred to as deep learning), as flat models empirically saturate as we add more complexity.
Feedback particle filter on matrix lie groups

This paper is concerned with the problem of continuous-time nonlinear filtering of stochastic processes evolving on a compact and connected matrix Lie group without boundary, e.g. SO(n), in the presence of real-valued noisy observations. This problem is important to numerous applications in attitude estimation, visual tracking and robotic localization. The main contribution of this paper is to derive the feedback particle filter (FPF) algorithm as a solution for this problem. In its general form, the FPF provides a coordinate-free description of the filter that satisfies the geometric constraints of the manifold. The particle dynamics are encapsulated in a Stratonovich stochastic differential equation that preserves the feedback structure of the original FPF. Specific examples for SO(2) and SO(3) are provided to help illustrate the filter using the phase and the quaternion coordinates, respectively.
The Robotarium: A remotely accessible swarm robotics research testbed

This paper describes the Robotarium — a remotely accessible, multi-robot research facility. The impetus behind the Robotarium is that multi-robot testbeds constitute an integral and essential part of the multi-robot research cycle, yet they are expensive, complex, and time-consuming to develop, operate, and maintain. These resource constraints, in turn, limit access for large groups of researchers and students, which is what the Robotarium is remedying by providing users with remote access to a state-of-the-art multi-robot test facility. This paper details the design and operation of the Robotarium and discusses the considerations one must take when making complex hardware remotely accessible. In particular, safety must be built into the system already at the design phase without overly constraining what coordinated control programs users can upload and execute, which calls for minimally invasive safety routines with provable performance guarantees.
Robust Stackelberg Equilibria in Extensive-Form Games and Extension to Limited Lookahead.

Stackelberg equilibria have become increasingly important as a solution concept in computational game theory, largely inspired by practical problems such as security settings. In practice, however, there is typically uncertainty regarding the model about the opponent. This paper is, to our knowledge, the first to investigate Stackelberg equilibria under uncertainty in extensive-form games, one of the broadest classes of game. We introduce robust Stackelberg equilibria, where the uncertainty is about the opponent's payoffs, as well as ones where the opponent has limited lookahead and the uncertainty is about the opponent's node evaluation function. We develop a new mixed-integer program for the deterministic limited-lookahead setting. We then extend the program to the robust setting for Stackelberg equilibrium under unlimited and under limited lookahead by the opponent. We show that for the specific case of interval uncertainty about the opponent's payoffs (or about the opponent's node evaluations in the case of limited lookahead), robust Stackelberg equilibria can be computed with a mixed-integer program that is of the same asymptotic size as that for the deterministic setting.
Gnowee: A Hybrid Metaheuristic Optimization Algorithm for Constrained, Black Box, Combinatorial Mixed-Integer Design

This paper introduces Gnowee, a modular, Python-based, open-source hybrid metaheuristic optimization algorithm (available from https://github.com/SlaybaughLab/Gnowee). Gnowee is designed for rapid ...
Implementation of a Volume-of-Fluid method in a finite element code with applications to thermochemical convection in a density stratified fluid in the Earth’s mantle

Abstract We describe the implementation of a second-order accurate Volume-of-Fluid interface tracking algorithm in the open source finite element code ASPECT that is designed to model convection and other processes in the Earth’s mantle. This involves the solution of the incompressible Stokes equations coupled to an advection diffusion equation for the temperature, a Boussinesq approximation that governs the dependence of the density on the temperature, and an advection equation for a marker indicating two initial (constant) density states, that are passively advected in the underlying flow field. The Volume-of-Fluid method in ASPECT is fully parallelized and fully integrated with ASPECT’s adaptive mesh refinement algorithm. We present the results of several interface tracking benchmarks in order to demonstrate the accuracy of the method, as well as the results of several benchmarks commonly used in the computational mantle convection community. Finally, we present the results of computations with and without adaptive mesh refinement of a model problem involving thermochemical convection in a computationally stratified fluid designed to provide insight into how thermal plumes, that eventually reach the Earth’s surface as ocean island basalts, originate at structures near the core-mantle boundary known as Large Low Shear wave Velocity Provinces or “LLSVPs”. LLSVPs are structures in parts of the lowermost portion of the Earth’s mantle, characterized by slow shear wave velocities and higher density than the surrounding mantle, which were discovered by seismic tomography of the deep Earth.
Learning for multi-robot cooperation in partially observable stochastic environments with macro-actions

This paper presents a data-driven approach for multi-robot coordination in partially-observable domains based on Decentralized Partially Observable Markov Decision Processes (Dec-POMDPs) and macro-actions (MAs). Dec-POMDPs provide a general framework for cooperative sequential decision making under uncertainty and MAs allow temporally extended and asynchronous action execution. To date, most methods assume the underlying Dec-POMDP model is known a priori or a full simulator is available during planning time. Previous methods which aim to address these issues suffer from local optimality and sensitivity to initial conditions. Additionally, few hardware demonstrations involving a large team of heterogeneous robots and with long planning horizons exist. This work addresses these gaps by proposing an iterative sampling based Expectation-Maximization algorithm (iSEM) to learn polices using only trajectory data containing observations, MAs, and rewards. Our experiments show the algorithm is able to achieve better solution quality than the state-of-the-art learning-based methods. We implement two variants of multi-robot Search and Rescue (SAR) domains (with and without obstacles) on hardware to demonstrate the learned policies can effectively control a team of distributed robots to cooperate in a partially observable stochastic environment.
Floer trajectories and stabilizing divisors

We incorporate pearly Floer trajectories into the tranversality scheme for pseudoholomorphic maps introduced by Cieliebak–Mohnke (J Symplectic Geom 5(3): 281–356, 2007). By choosing generic domain-dependent almost complex structures, we obtain zero- and one-dimensional moduli spaces with the structure of cell complexes with rational fundamental classes. Integrating over these moduli spaces gives a definition of Floer cohomology over Novikov rings via stabilizing divisors for rational Lagrangians that are fixed point sets of anti-symplectic involutions satisfying certain Maslov index conditions as well as Hamiltonian Floer cohomology of compact rational symplectic manifolds.
Near Optimal Coded Data Shuffling for Distributed Learning

Data shuffling between distributed cluster of nodes is one of the critical steps in implementing large-scale learning algorithms. Randomly shuffling the data-set among a cluster of workers allows different nodes to obtain fresh data assignments at each learning epoch. This process has been shown to provide improvements in the learning process (via testing and training error). However, the statistical benefits of distributed data shuffling come at the cost of extra communication overhead from the master node to worker nodes, and can act as one of the major bottlenecks in the overall time for computation. There has been significant recent interest in devising approaches to minimize this communication overhead. One approach is to provision for extra storage at the computing nodes. The other emerging approach is to leverage coded communication to minimize the overall communication overhead. The focus of this work is to understand the fundamental tradeoff between the amount of storage and the communication overhead for distributed data shuffling. In this paper, we first present an information theoretic formulation for the data shuffling problem, accounting for the underlying problem parameters (number of workers, $K$ , number of data points, $N$ , and available storage, and $S$ per node). We then present an information theoretic lower bound on the communication overhead for data shuffling as a function of these parameters. We next present a novel coded communication scheme and show that the resulting communication overhead of the proposed scheme is within a multiplicative factor of at most $\frac {K}{K-1}$ from the lower bound (which is upper bounded by 2 for $K\geq 2$ ). Furthermore, we present new results towards closing this gap through a novel coded communication scheme, which we call the aligned coded shuffling. This scheme is inspired by the ideas of coded shuffling and interference alignment. In particular, we show that the aligned scheme achieves the optimal storage vs communication trade-off for $K , and further reduces the maximum multiplicative gap down to $\frac {K-\frac {1}{3}}{K-1}$ , for $K\geq 5$ .
Using Machine Learning to Discern Eruption in Noisy Environments: A Case Study Using CO2‐Driven Cold‐Water Geyser in Chimayó, New Mexico

We present an approach based on machine learning (ML) to distinguish eruption and precursory signals of Chimayo geyser (New Mexico, USA) under noisy environments. This geyser can be considered as a natural analog of $\mathrm{CO}_2$ intrusion into shallow water aquifers. By studying this geyser, we can understand upwelling of $\mathrm{CO}_2$-rich fluids from depth, which has relevance to leak monitoring in a $\mathrm{CO}_2$ sequestration project. ML methods such as Random Forests (RF) are known to be robust multi-class classifiers and perform well under unfavorable noisy conditions. However, the extent of the RF method's accuracy is poorly understood for this $\mathrm{CO}_2$-driven geysering application. The current study aims to quantify the performance of RF-classifiers to discern the geyser state. Towards this goal, we first present the data collected from the seismometer that is installed near the Chimayo geyser. The seismic signals collected at this site contain different types of noises such as daily temperature variations, seasonal trends, animal movement near the geyser, and human activity. First, we filter the signals from these noises by combining the Butterworth-Highpass filter and an Autoregressive method in a multi-level fashion. We show that by combining these filtering techniques, in a hierarchical fashion, leads to reduction in the noise in the seismic data without removing the precursors and eruption event signals. We then use RF on the filtered data to classify the state of geyser into three classes -- remnant noise, precursor, and eruption states. We show that the classification accuracy using RF on the filtered data is greater than 90\%.These aspects make the proposed ML framework attractive for event discrimination and signal enhancement under noisy conditions, with strong potential for application to monitoring leaks in $\mathrm{CO}_2$ sequestration.
A probabilistic Harnack inequality and strict positivity of stochastic partial differential equations

Under general conditions we show an a priori probabilistic Harnack inequality for the non-negative solution of a stochastic partial differential equation of the following form $$\begin{aligned} \partial _t u=\mathrm {div}{\; (}{\mathbb {A}}\nabla u)+f(t,x,u;\omega )+g_i(t,x,u;\omega )\dot{w}_t^i. \end{aligned}$$ We also show that the solutions of the above equation are almost surely strictly positive if the initial condition is non-negative and not identically vanishing.
Countdown Regression: Sharp and Calibrated Survival Predictions

Probabilistic survival predictions from models trained with Maximum Likelihood Estimation (MLE) can have high, and sometimes unacceptably high variance. The field of meteorology, where the paradigm of maximizing sharpness subject to calibration is popular, has addressed this problem by using scoring rules beyond MLE, such as the Continuous Ranked Probability Score (CRPS). In this paper we present the \emph{Survival-CRPS}, a generalization of the CRPS to the survival prediction setting, with right-censored and interval-censored variants. We evaluate our ideas on the mortality prediction task using two different Electronic Health Record (EHR) data sets (STARR and MIMIC-III) covering millions of patients, with suitable deep neural network architectures: a Recurrent Neural Network (RNN) for STARR and a Fully Connected Network (FCN) for MIMIC-III. We compare results between the two scoring rules while keeping the network architecture and data fixed, and show that models trained with Survival-CRPS result in sharper predictive distributions compared to those trained by MLE, while still maintaining calibration.
Demographic model for inheritable cardiac disease.

Abstract The cardiac muscle proteins, generating and regulating energy transduction during a heartbeat, assemble in the sarcomere into a cyclical machine repetitively translating actin relative to myosin filaments. Myosin is the motor transducing ATP free energy into actin movement against resisting force. Cardiac myosin binding protein C (mybpc3) regulates shortening velocity probably by transient N-terminus binding to actin while its C-terminus strongly binds the myosin filament. Inheritable heart disease associated mutants frequently modify these proteins involving them in disease mechanisms. Nonsynonymous single nucleotide polymorphisms (SNPs) cause single residue substitutions with independent characteristics (sequence location, residue substitution, human demographic, and allele frequency) hypothesized to decide dependent phenotype and pathogenicity characteristics in a feed-forward neural network model. Trial models train and validate on a dynamic worldwide SNP database for cardiac muscle proteins then predict phenotype and pathogenicity for any single residue substitution in myosin, mybpc3, or actin. A separate Bayesian model formulates conditional probabilities for phenotype or pathogenicity given independent SNP characteristics. Neural/Bayes forecasting tests SNP pathogenicity vs (in)dependent SNP characteristics to assess individualized disease risk and in particular to elucidate gender and human subpopulation bias in disease. Evident subpopulation bias in myosin SNP pathogenicities imply myosin normally engages multiple sarcomere proteins functionally. Consistent with this observation, mybpc3 forms a third actomyosin interaction competing with myosin essential light chain N-terminus suggesting a novel strain-dependent mechanism adapting myosin force-velocity to load dynamics. The working models, and the integral myosin/mybpc3 motor concept, portends the wider considerations involved in understanding heart disease as a systemic maladaptation.
Data Poisoning against Differentially-Private Learners: Attacks and Defenses

Data poisoning attacks aim to manipulate the model produced by a learning algorithm by adversarially modifying the training set. We consider differential privacy as a defensive measure against this type of attack. We show that such learners are resistant to data poisoning attacks when the adversary is only able to poison a small number of items. However, this protection degrades as the adversary poisons more data. To illustrate, we design attack algorithms targeting objective and output perturbation learners, two standard approaches to differentially-private machine learning. Experiments show that our methods are effective when the attacker is allowed to poison sufficiently many training items.
Formality of derived intersections and the orbifold HKR isomorphism

Abstract We study when the derived intersection of two smooth subvarieties of a smooth variety is formal. As a consequence we obtain a derived base change theorem for non-transversal intersections. We also obtain applications to the study of the derived fixed locus of a finite group action and argue that for a global quotient orbifold the exponential map is an isomorphism between the Lie algebra of the free loop space and the loop space itself. This allows us to give new proofs of the HKR decomposition of orbifold Hochschild (co)homology into twisted sectors.
Triangulation-based isogeometric analysis of the Cahn–Hilliard phase-field model

Abstract This paper presents triangulation-based Isogeometric Analysis of the Cahn–Hilliard phase-field model. The Cahn–Hilliard phase-field model is governed by a time-dependent fourth-order partial differential equation. The corresponding primal variational form involves second-order operators, making it difficult to be directly analyzed with traditional C 0 finite element analysis. In this paper, we construct C 1 Bernstein–Bezier simplicial elements through macro-element techniques, including various triangle-split based macro-elements in both 2D and 3D space. We extend triangulation-based isogeometric analysis to solving the primal variational form of the Cahn–Hilliard equation. We validate our method by convergence analysis, showing the nodal and degree-of-freedom advantages over C 0 Finite Element Analysis. We then demonstrate detailed system evolution from randomly perturbed initial conditions in periodic two-dimensional squares and three-dimensional cubes. We incorporate an adaptive time-stepping scheme in these numerical experiments. Our numerical study demonstrates that triangulation-based isogeometric analysis offers optimal convergence and time step stability, is applicable to complex geometry and allows local refinement.
Discovery of Novel Thrips Vector Proteins That Bind to the Viral Attachment Protein of the Plant Bunyavirus Tomato Spotted Wilt Virus.

ABSTRACT The plant-pathogenic virus tomato spotted wilt virus (TSWV) encodes a structural glycoprotein (GN) that, like with other bunyavirus/vector interactions, serves a role in viral attachment and possibly in entry into arthropod vector host cells. It is well documented that Frankliniella occidentalis is one of nine competent thrips vectors of TSWV transmission to plant hosts. However, the insect molecules that interact with viral proteins, such as GN, during infection and dissemination in thrips vector tissues are unknown. The goals of this project were to identify TSWV-interacting proteins (TIPs) that interact directly with TSWV GN and to localize the expression of these proteins in relation to virus in thrips tissues of principal importance along the route of dissemination. We report here the identification of six TIPs from first-instar larvae (L1), the most acquisition-efficient developmental stage of the thrips vector. Sequence analyses of these TIPs revealed homology to proteins associated with the infection cycle of other vector-borne viruses. Immunolocalization of the TIPs in L1 revealed robust expression in the midgut and salivary glands of F. occidentalis, the tissues most important during virus infection, replication, and plant inoculation. The TIPs and GN interactions were validated using protein-protein interaction assays. Two of the thrips proteins, endocuticle structural glycoprotein and cyclophilin, were found to be consistent interactors with GN. These newly discovered thrips protein-GN interactions are important for a better understanding of the transmission mechanism of persistent propagative plant viruses by their vectors, as well as for developing new strategies of insect pest management and virus resistance in plants. IMPORTANCE Thrips-transmitted viruses cause devastating losses to numerous food crops worldwide. For negative-sense RNA viruses that infect plants, the arthropod serves as a host as well by supporting virus replication in specific tissues and organs of the vector. The goal of this work was to identify thrips proteins that bind directly to the viral attachment protein and thus may play a role in the infection cycle in the insect. Using the model plant bunyavirus tomato spotted wilt virus (TSWV), and the most efficient thrips vector, we identified and validated six TSWV-interacting proteins from Frankliniella occidentalis first-instar larvae. Two proteins, an endocuticle structural glycoprotein and cyclophilin, were able to interact directly with the TSWV attachment protein, GN, in insect cells. The TSWV GN-interacting proteins provide new targets for disrupting the viral disease cycle in the arthropod vector and could be putative determinants of vector competence.
Heat-Transfer Coefficients for Staggered Arrays of Short Pin Fins

Short pin fins are often used to increase the heat transfer to the coolant in the trailing edge of a turbine blade. Due primarily to limits of casting technology, it is not possible to manufacture pins of optimum length for heat-transfer purposes in the trailing edge region. In many cases the pins are so short that they actually decrease the total heat-transfer surface area compared to a plain wall. A heat-transfer data base for these short pins is not available in the literature. Heat-transfer coefficients on pin and endwall surfaces were measured for several staggered arrays of short pin fins. The measured Nusselt numbers when plotted versus Reynolds numbers were found to fall on a single curve for all surfaces tested. The heat-transfer coefficients for the short pin fins (length to diameter ratios of 1/2 and 2) were found to be about a factor of two lower than data from the literature for longer pin arrays (length to diameter ratios of about 8).
Non-Contact Photoplethysmogram and Instantaneous Heart Rate Estimation from Infrared Face Video

Extracting the instantaneous heart rate (iHR) from face videos has been well studied in recent years. It is well known that changes in skin color due to blood flow can be captured using conventional cameras. One of the main limitations of methods that rely on this principle is the need of an illumination source. Moreover, they have to be able to operate under different light conditions. One way to avoid these constraints is using infrared cameras, allowing the monitoring of iHR under low light conditions. In this work, we present a simple, principled signal extraction method that recovers the iHR from infrared face videos. We tested the procedure on 7 participants, for whom we recorded an electrocardiogram simultaneously with their infrared face video. We checked that the recovered signal matched the ground truth iHR, showing that infrared is a promising alternative to conventional video imaging for heart rate monitoring, especially in low light conditions. Code is available at https://github.com/natalialmg/IR_iHR.
Active data structures

Data structures have traditionally been regarded as passive software objects implemented by procedures and as having only one view, i.e., only a single set of access functions for manipulation. The advent of microprocessor technology suggests that we rethink our previous models of data structures. This leads to a different approach in which data structures are active and provide multiple views to users. Both these ideas imply that data structures will be implemented by processes. This makes it feasible to implement each data structure on its own processor. In this paper, we describe the active data structures/multiple views model and provide two examples of its utility: a file directory system and a graphics application.
Arithmetic models for Shimura varieties

We describe recent work on the construction of well-behaved arithmetic models for large classes of Shimura varieties and report on progress in the study of these models.
Primitive and geometric-progression-free sets without large gaps

We prove the existence of primitive sets (sets of integers in which no element divides another) in which the gap between any two consecutive terms is substantially smaller than the best known upper bound for the gaps in the sequence of prime numbers. The proof uses the probabilistic method. Using the same techniques we improve the bounds obtained by He for gaps in geometric-progression-free sets.
Chapter 1 – Overview of Finite Element Method

The basic idea of the finite element method in finding an approximate solution of any complex real-life engineering problem by replacing it by a simpler one is presented. The possibility of improving the accuracy of the approximate solution by using more computational effort is also indicated through a simple example. The historical background and the broad applicability of the method to solve a wide variety of problems from different areas of engineering are shown using simple applications from several areas. The wide range of areas to which the method is applicable is indicated through a representative list of real-life engineering problems solved in the literature in a variety of areas. The general description of the finite element method is presented in the form of a six-step procedure and the solution procedure is demonstrated using simple examples from different areas such as stress analysis, heat transfer, fluid flow in pipes, and bending of beams. The material is presented such that the reader can get an overall picture of the finite element method; anyone with no time to read the complete book can understand the basic methodology just by reading this chapter.
Counting Chinese People in a Catholic Country: Religious Difference, Racial Discrimination, and the 1930 Mexican Population Census

On May 15, 1930, in the small border town of Naco, Sonora, census enumerator Miguel Robles started to count. When he finished walking his assigned portions of Calle Hidalgo, Avenida Independencia, and Calle Morelos, he had enumerated 143 individuals, each of whom answered the single question regarding religious identity the same way: Catholic. A block over, a carpenter named Ignacio Ledgard left his tools for the day to canvas his own neighborhood, counting 252 individuals, including his own family. They also all identified as Catholic, except for two Chinese men with no religion.
Specification and inference of trace refinement relations

The modern software engineering process is evolutionary, with commits/patches begetting new versions of code, progressing steadily toward improved systems. In recent years, program analysis and verification tools have exploited version-based reasoning, where new code can be seen in terms of how it has changed from the previous version. When considering program versions, refinement seems a natural fit and, in recent decades, researchers have weakened classical notions of concrete refinement and program equivalence to capture similarities as well as differences between programs. For example, Benton, Yang and others have worked on state-based refinement relations. In this paper, we explore a form of weak refinement based on trace relations rather than state relations. The idea begins by partitioning traces of a program C1 into trace classes, each identified via a restriction r1. For each class, we specify similar behavior in the other program C2 via a separate restriction r2 on C2. Still, these two trace classes may not yet be equivalent so we further permit a weakening via a binary relation A on traces, that allows one to, for instance disregard unimportant events, relate analogous atomic events, etc. We address several challenges that arise. First, we explore one way to specify trace refinement relations by instantiating the framework to Kleene Algebra with Tests (KAT) due to Kozen. We use KAT intersection for restriction, KAT hypotheses for A, KAT inclusion for refinement, and have proved compositionality. Next, we present an algorithm for automatically synthesizing refinement relations, based on a mixture of semantic program abstraction, KAT inclusion, a custom edit-distance algorithm on counterexamples, and case-analysis on nondeterministic branching. We have proved our algorithm to be sound. Finally, we implemented our algorithm as a tool called Knotical, on top of Interproc and Symkat. We demonstrate promising first steps in synthesizing trace refinement relations across a hand-crafted collection of 37 benchmarks that include changing fragments of array programs, models of systems code, and examples inspired by the thttpd and Merecat web servers.
An Integrated Permanent-Magnet-Synchronous Generator–Rectifier Architecture for Limited-Speed-Range Applications

Conventional high-power ac--dc conversion architectures rely heavily on active rectifiers, which consist of fully controlled power-electronic switches. These make the system bulky, lossy, and less reliable. This article presents an alternative approach—integrating a multiport permanent-magnet synchronous generator (PMSG) with series-stacked power converters. An active rectifier processes only a fraction of the total converted power while regulating the dc bus. The remaining power is processed by diode bridges, which allows a substantial increase in overall efficiency, power density, and reliability. Theoretical analysis shows that for wind-power generation applications, the active rectifier processes a maximum of 39% of the rated power while the generator operates in a speed range similar to the conventional doubly fed induction machine. The conversion loss is reduced by 47%. Results from a laboratory-scale experimental setup corroborate the proposed architecture. This approach potentially increases integration of medium-voltage dc distribution to the megawatt-class mechanical-to-electrical energy conversion systems to achieve higher efficiency, higher power density, and lower cost compared to the conventional solutions based on a single-port PMSG with a full-power-rated converter.
Scribble-based Hierarchical Weakly Supervised Learning for Brain Tumor Segmentation

The recent state-of-the-art deep learning methods have significantly improved brain tumor segmentation. However, fully supervised training requires a large amount of manually labeled masks, which is highly time-consuming and needs domain expertise. Weakly supervised learning with scribbles provides a good trade-off between model accuracy and the effort of manual labeling. However, for segmenting the hierarchical brain tumor structures, manually labeling scribbles for each substructure could still be demanding. In this paper, we use only two kinds of weak labels, i.e., scribbles on whole tumor and healthy brain tissue, and global labels for the presence of each substructure, to train a deep learning model to segment all the sub-regions. Specifically, we train two networks in two phases: first, we only use whole tumor scribbles to train a whole tumor (WT) segmentation network, which roughly recovers the WT mask of training data; then we cluster the WT region with the guide of global labels. The rough substructure segmentation from clustering is used as weak labels to train the second network. The dense CRF loss is used to refine the weakly supervised segmentation. We evaluate our approach on the BraTS2017 dataset and achieve competitive WT dice score as well as comparable scores on substructure segmentation compared to an upper bound when trained with fully annotated masks.
Data money: The socio-technical infrastructure of cryptocurrency blockchains

Drawing on an empirical study of cryptocurrency white papers, this paper proposes an actor-based taxonomy of cryptocurrency blockchains. First, it describes the evolution of blockchain architecture...
Monotone Flows with Dense Periodic Orbits

The main result is Theorem 1: A flow on a connected open set X ⊂ R d is globally periodic provided (i) periodic points are dense in X, and (ii) at all positive times the flow preserves the partial order defined by a closed convex cone that has nonempty interior and contains no straight line. The proof uses the analog for homeomorphisms due to B. Lemmens et al. [27], a classical theorem of D. Montgomery [31, 32], and a sufficient condition for the nonstationary periodic points in a closed order interval to have rationally related periods (Theorem 2).
Theoretical Justification and Error Analysis for Slender Body Theory

Slender body theory facilitates computational simulations of thin fibers immersed in a viscous fluid by approximating each fiber using only the geometry of the fiber centerline curve and the line force density along it. However, it has been unclear how well slender body theory actually approximates Stokes flow about a thin but truly three-dimensional fiber, in part due to the fact that simply prescribing data along a one-dimensional curve does not result in a well-posed boundary value problem for the Stokes equations in $\mathbb{R}^3$. Here, we introduce a PDE problem to which slender body theory (SBT) provides an approximation, thereby placing SBT on firm theoretical footing. The slender body PDE is a new type of boundary value problem for Stokes flow where partial Dirichlet and partial Neumann conditions are specified everywhere along the fiber surface. Given only a 1D force density along a closed fiber, we show that the flow field exterior to the thin fiber is uniquely determined by imposing a fiber integrity condition: the surface velocity field on the fiber must be constant along cross sections orthogonal to the fiber centerline. Furthermore, a careful estimation of the residual, together with stability estimates provided by the PDE well-posedness framework, allow us to establish error estimates between the slender body approximation and the exact solution to the above problem. The error is bounded by an expression proportional to the fiber radius (up to logarithmic corrections) under mild regularity assumptions on the 1D force density and fiber centerline geometry.