cssi/SciSciGPT-SciSciNet huggingface dataset:
Generalized Foster's identities

Foster's network theorems and their extensions to higher orders involve resistance values and conductances. We establish identities concerning voltage values and conductances. Our identities are analogous to the extended Foster's identities. © 2010 Wiley Periodicals, Inc. Int J Quantum Chem, 2011
Cohomology of line bundles on compactified Jacobians

Let C be an integral projective curve with surficial singularities. We prove that topologically trivial line bundles on the compactified Jacobian of C are in one-to-one correspondence with line bundles on C (the autoduality conjecture), and compute the cohomology of the line bundles. We also show that the natural Fourier-Mukai functor between the derived categories of quasi-coherent sheaves on the Jacobian and on the compactified Jacobian is fully faithful.
Hyperdiscriminant polytopes, Chow polytopes, and Mabuchi energy asymptotics

LetX n ! P N be a smooth, linearly normal algebraic variety. It is shown that the Mabuchi energy of (X;!FSjX) restricted to the Bergman metrics is completely determined by the X-hyperdiscriminant of format (n 1) and the Chow form of X. As a corollary it is shown that the Mabuchi energy is bounded from below for all degenerations in G if and only if the hyperdiscriminant polytope dominates the Chow polytope for all maximal algebraic tori H of G.
Sparse Fisher's linear discriminant analysis for partially labeled data

Classification is an important tool with many useful applications. Fisher's linear discriminant analysis (LDA) is a traditional model-based classification method which makes use of the Gaussian distributional information. However, in the high-dimensional, low-sample-size setting, LDA cannot be directly deployed because the sample covariance is not invertible. While there are modern methods for high-dimensional data, they may not fully use the information as LDA does. Hence in some situations, it is still desirable to use a model-based method for classification. This paper exploits the potential of LDA in a more complicated data setting. In many real applications, it is costly to manually place labels on observations; consequently, often only a small portion of labeled data is available while a large number of observations are left without labels. It is a great challenge to obtain good classification performance through the labeled data alone, especially in the high-dimensional setting. In order to overcome this issue, we propose a semisupervised sparse LDA classifier to take advantage of the seemingly useless unlabeled data, which helps to boost the classification performance in some situations. A direct estimation method is used to reconstruct LDA and achieve sparsity; meanwhile we employ the difference-convex algorithm to handle the nonconvex loss function associated with the unlabeled data. Theoretical properties of the proposed classifier are studied. Our simulated examples help understand when and how the information extracted from the unlabeled data can be useful. A real data example further illustrates the usefulness of the proposed method.
Benefits of Coded Placement for Networks with Heterogeneous Cache Sizes

In this work, we study coded placement in caching systems where the users have unequal cache sizes and demonstrate its performance advantage. In particular, we propose a caching scheme with coded placement for three-user systems that outperforms the best caching scheme with uncoded placement. In our proposed scheme, users cache both uncoded and coded pieces of the files, and the coded pieces at the users with large memories are decoded using the unicast/multicast signals intended to serve users with smaller memories. Furthermore, we extend the proposed scheme to larger systems and show the reduction in delivery load with coded placement compared to uncoded placement.
Application of the Wasserstein metric to seismic signals

Seismic signals are typically compared using travel time difference or $L_2$ difference. We propose the Wasserstein metric as an alternative measure of fidelity or misfit in seismology. It exhibits properties from both of the traditional measures mentioned above. The numerical computation is based on the recent development of fast numerical methods for the Monge-Ampere equation and optimal transport. Applications to waveform inversion and registration are discussed and simple numerical examples are presented.
Global Existence for the Vlasov-Poisson System with Steady Spatial Asymptotics

ABSTRACT A collisionless plasma is modelled by the Vlasov-Poisson system in three space dimensions. A fixed background of positive charge—dependant upon only velocity—is assumed. The situation in which mobile negative ions balance the positive charge as | x | → ∞ is considered. Thus, the total positive charge and the total negative charge are both infinite. Smooth solutions with appropriate asymptotic behavior for large | x |, which were previously shown to exist locally in time, are continued globally. This is done by showing that the charge density decays at least as fast as | x |−6. This article also establishes decay estimates for the electrostatic field and its derivatives.
Intertwining operators among twisted modules associated to not-necessarily-commuting automorphisms

Abstract We introduce intertwining operators among twisted modules or twisted intertwining operators associated to not-necessarily-commuting automorphisms of a vertex operator algebra. Let V be a vertex operator algebra and let g 1 , g 2 and g 3 be automorphisms of V. We prove that for g 1 -, g 2 - and g 3 -twisted V-modules W 1 , W 2 and W 3 , respectively, such that the vertex operator map for W 3 is injective, if there exists a twisted intertwining operator of type ( W 3 W 1 W 2 ) such that the images of its component operators span W 3 , then g 3 = g 1 g 2 . We also construct what we call the skew-symmetry and contragredient isomorphisms between spaces of twisted intertwining operators among twisted modules of suitable types. The proofs of these results involve careful analysis of the analytic extensions corresponding to the actions of the not-necessarily-commuting automorphisms of the vertex operator algebra.
Endomorphisms of power series fields and residue fields of Fargues-Fontaine curves

We show that for k a perfect field of characteristic p, there exist endomorphisms of the completed algebraic closure of k((t)) which are not bijective. As a corollary, we resolve a question of Fargues and Fontaine by showing that for p a prime and C_p a completed algebraic closure of Q_p, there exist closed points of the Fargues-Fontaine curve associated to C_p whose residue fields are not (even abstractly) isomorphic to C_p as topological fields.
On the bandwidth of the Kneser graph

Abstract Let G = ( V , E ) be a graph on n vertices and f : V → [ 1 , n ] a one to one map of V onto the integers 1 through n . Let d i l a t i o n ( f ) = max { | f ( v ) − f ( w ) | : v w ∈ E } . Define the bandwidth B ( G ) of G to be the minimum possible value of d i l a t i o n ( f ) over all such one to one maps f . Next define the Kneser Graph K ( n , r ) to be the graph with vertex set [ n ] r , the collection of r -subsets of an n element set, and edge set E = { A B : A , B ∈ [ n ] r , A ∩ B = ∅ } . For fixed r ≥ 4 and n growing we show that B ( K ( n , r ) ) = n − 1 r + 1 2 n − 4 r − 1 − 1 2 n − 1 r − 2 + O ( n r − 4 ) .
Prolongation-Collocation Variational Integrators

We introduce a novel technique for constructing higher-order variational integrators for Hamiltonian systems of ODEs. In particular, we are concerned with generating globally smooth approximations to solutions of a Hamiltonian system. Our construction of the discrete Lagrangian adopts Hermite interpolation polynomials and the Euler-Maclaurin quadrature formula, and involves applying collocation to the Euler-Lagrange equation and its prolongation. Considerable attention is devoted to the order analysis of the resulting variational integrators in terms of approximation properties of the Hermite polynomials and quadrature errors. A performance comparison is presented on a selection of these integrators.
The Hadronic Spectrum and Confined Phase in (1+1)-Dimensional Massive Yang-Mills Theory

Massive Yang-Mills theory is known to be renormalizable in 1+1 dimensions. The gluon mass is introduced by coupling the gauge field to an SU(N) principal chiral nonlinear sigma model. The proof of renormalizability relies on the asymptotic freedom of the sigma model. However, renormalization forces the gluon mass to infinity. The continuum theory is in a confined phase rather than a Higgs phase. The physical excitations of the system are hadron-like bound states of sigma model particles. We calculate the massive spectrum of meson-like bound states analytically, using the exact S-matrix of the sigma model. The baryon-like spectrum can be found in principle by solving a quantum mechanical N-body problem. We remark on the evidence for the confined phase found for SU(2) in recent lattice simulations by Gongyo and Zwanziger. Their simulations show evidence for a Higgs-like phase which seems to disappear with increasing volume, finding agreement with our analysis in the continuum.
On the sections of universal hyperelliptic curves

Abstract In this paper, we will give an algebraic proof for determining the sections for the universal pointed hyperelliptic curves C H g , n / k → H g , n / k , when g ≥ 3 and the image of the l-adic cyclotomic character G k → Z × is infinite. Furthermore, we will study the nonabelian phenomena associated to the universal hyperelliptic curves. For example, we will show that the section conjecture holds for C H g / k → H g / k and the unipotent analogue of the conjecture holds for the pointed cases. This work is an extension of Hain's original work on the rational points of universal curves to the hyperelliptic case.
Methods for quantized compressed sensing

In this paper, we compare and catalog the performance of various greedy quantized compressed sensing algorithms that reconstruct sparse signals from quantized compressed measurements. We also introduce two new greedy approaches for reconstruction: Quantized Compressed Sampling Matching Pursuit (QCoSaMP) and Adaptive Outlier Pursuit for Quantized Iterative Hard Thresholding (AOP-QIHT). We compare the performance of greedy quantized compressed sensing algorithms for a given bit-depth, sparsity, and noise level.
A direct verification argument for the Hamilton–Jacobi equation continuum limit of nondominated sorting ☆

Abstract Nondominated sorting is a combinatorial algorithm that sorts points in Euclidean space into layers according to a partial order. It was recently shown that nondominated sorting of random points has a Hamilton–Jacobi equation continuum limit. The original proof, given in Calder et al. (2014), relies on a continuum variational problem. In this paper, we give a new proof using a direct verification argument that completely avoids the variational interpretation. We believe this may be generalized to apply to other stochastic homogenization problems for which there is no obvious underlying variational principle.
Distal and non‐distal behavior in pairs

The aim of this work is an analysis of distal and non-distal behavior in dense pairs of o-minimal structures. A characterization of distal types is given through orthogonality to a generic type in $M^{\operatorname{eq}}$, non-distality is geometrically analyzed through Keisler measures, and a distal expansion for the case of pairs of ordered vector spaces is computed.
Improved Approximation for Node-Disjoint Paths in Grids with Sources on the Boundary

We study the classical Node-Disjoint Paths (NDP) problem: given an undirected n-vertex graph G, together with a set {(s_1,t_1),...,(s_k,t_k)} of pairs of its vertices, called source-destination, or demand pairs, find a maximum-cardinality set {P} of mutually node-disjoint paths that connect the demand pairs. The best current approximation for the problem is achieved by a simple greedy O(sqrt{n})-approximation algorithm. Until recently, the best negative result was an Omega(log^{1/2-epsilon}n)-hardness of approximation, for any fixed epsilon, under standard complexity assumptions. A special case of the problem, where the underlying graph is a grid, has been studied extensively. The best current approximation algorithm for this special case achieves an O~(n^{1/4})-approximation factor. On the negative side, a recent result by the authors shows that NDP is hard to approximate to within factor 2^{Omega(sqrt{log n})}, even if the underlying graph is a subgraph of a grid, and all source vertices lie on the grid boundary. In a very recent follow-up work, the authors further show that NDP in grid graphs is hard to approximate to within factor Omega(2^{log^{1-epsilon}n}) for any constant epsilon under standard complexity assumptions, and to within factor n^{Omega(1/(log log n)^2)} under randomized ETH. In this paper we study the NDP problem in grid graphs, where all source vertices {s_1,...,s_k} appear on the grid boundary. Our main result is an efficient randomized 2^{O(sqrt{log n}* log log n)}-approximation algorithm for this problem. Our result in a sense complements the 2^{Omega(sqrt{log n})}-hardness of approximation for sub-graphs of grids with sources lying on the grid boundary, and should be contrasted with the above-mentioned almost polynomial hardness of approximation of NDP in grid graphs (where the sources and the destinations may lie anywhere in the grid). Much of the work on approximation algorithms for NDP relies on the multicommodity flow relaxation of the problem, which is known to have an Omega(sqrt n) integrality gap, even in grid graphs, with all source and destination vertices lying on the grid boundary. Our work departs from this paradigm, and uses a (completely different) linear program only to select the pairs to be routed, while the routing itself is computed by other methods.
MAVNet: An Effective Semantic Segmentation Micro-Network for MAV-Based Tasks

Real-time semantic image segmentation on platforms subject to size, weight, and power constraints is a key area of interest for air surveillance and inspection. In this letter, we propose MAVNet: a small, light-weight, deep neural network for real-time semantic segmentation on micro aerial vehicles (MAVs). MAVNet, inspired by ERFNet [E. Romera, J. M. lvarez, L. M. Bergasa, and R. Arroyo, “ErfNet: Efficient residual factorized convnet for real-time semantic segmentation,” IEEE Trans. Intell. Transp. Syst. , vol. 19, no. 1, pp. 263–272, Jan. 2018.], features 400 times fewer parameters and achieves comparable performance with some reference models in empirical experiments. Additionally, we provide two novel datasets that represent challenges in semantic segmentation for real-time MAV tracking and infrastructure inspection tasks and verify MAVNet on these datasets. Our algorithm and datasets are made publicly available.
A view-dependent adaptive matched filter for LADAR-based vehicle tracking

LADARs mounted on mobile platforms produce a wealth of precise range data on the surrounding objects and vehicles. The challenge we address is to infer from these raw LADAR data the location and orientation of nearby vehicles. We propose a novel view-dependent adaptive matched filter for obtaining fast and precise measurements of target vehicle pose. We derive an analytic expression for the matching function which we optimize to obtain target pose and size. Our algorithm is fast, robust and simple to implement compared to other methods. When used as the measurement component of a tracker on an autonomous ground vehicle, we are able to track in excess of 50 targets at 10 Hz. Once targets are aligned using our matched filter, we use a support vector-based discriminator to distinguish vehicles from other objects. This tracker provides a key sensing component for our autonomous ground vehicles which have accumulated hundreds of miles of on-road and off-road autonomous driving.
On the extinction profile of solutions to fast diffusion

We study the extinction behavior of solutions to the fast diffusion equation $u_t = \Delta u^m$ on $\R^N\times (0,T)$, in the range of exponents $m \in (0, \frac{N-2}{N})$, $N > 2$. We show that if the initial data $u_0$ is trapped in between two Barenblatt solutions vanishing at time $T$, then the vanishing behaviour of $u$ at $T$ is given by a Barenblatt solution. We also give an example showing that for such a behavior the bound from above by a Barenblatt solution $B$ (vanishing at $T$) is crucial: we construct a class of solutions $u$ with initial data $u_0 = B (1 + o(1))$, near $ |x| >> 1$, which live longer than $B$ and change behaviour at $T$. The behavior of such solutions is governed by $B(\cdot,t)$ up to $T$, while for $t >T$ the solutions become integrable and exhibit a different vanishing profile. For the Yamabe flow ($m = \frac{N-2}{N+2}$) the above means that these solutions $u$ develop a singularity at time $T$, when the Barenblatt solution disappears, and at $t >T$ they immediately smoothen up and exhibit the vanishing profile of a sphere. In the appendix we show how to remove the assumption on the bound on $u_0$ from below by a Barenblatt.
Handling Large and Complex Data in a Photovoltaic Research Institution Using a Custom Laboratory Information Management System

Twenty-five years ago the desktop computer started becoming ubiquitous in the scientific lab. Researchers were delighted with its ability to both control instrumentation and acquire data on a single system, but they were not completely satisfied. There were often gaps in knowledge that they thought might be gained if they just had more data and they could get the data faster. Computer technology has evolved in keeping with Moore’s Law meeting those desires; however those improvements have of late become both a boon and bane for researchers. Computers are now capable of producing high speed data streams containing terabytes of information; capabilities that evolved faster than envisioned last century. Software to handle large scientific data sets has not kept up. How much information might be lost through accidental mismanagement or how many discoveries are missed through data overload are now vital questions. An important new task in most scientific disciplines involves developing methods to address those issues and to create the software that can handle large data sets with an eye towards scalability. This software must create archived, indexed, and searchable data from heterogeneous instrumentation for the implementation of a strong data-driven materials development strategy. At the National Center for Photovoltaics in the National Renewable Energy Laboratory, we began development a few years ago on a Laboratory Information Management System (LIMS) designed to handle lab-wide scientific data acquisition, management, processing and mining needs for physics and materials science data, and with a specific focus towards future scalability for new equipment or research focuses. We will present the decisions, processes, and problems we went through while building our LIMS system for materials research, its current operational state and our steps for future development.
Polynomial birack modules

Abstract Birack modules are modules over an algebra Z [ X ] associated to a finite birack X. In previous work, birack module structures on Z n were used to enhance the birack counting invariant. In this paper, we use birack modules over Laurent polynomial rings Z n [ q ± 1 ] to enhance the birack counting invariant, defining a customized Alexander polynomial-style signature for each X-labeled diagram; the multiset of these polynomials is an enhancement of the birack counting invariant. We provide examples to demonstrate that the new invariant is stronger than the unenhanced birack counting invariant and is not determined by the generalized Alexander polynomial.
Sharp Restricted Isometry Bounds for the Inexistence of Spurious Local Minima in Nonconvex Matrix Recovery

Nonconvex matrix recovery is known to contain no spurious local minima under a restricted isometry property (RIP) with a sufficiently small RIP constant $\delta$. If $\delta$ is too large, however, then counterexamples containing spurious local minima are known to exist. In this paper, we introduce a proof technique that is capable of establishing sharp thresholds on $\delta$ to guarantee the inexistence of spurious local minima. Using the technique, we prove that in the case of a rank-1 ground truth, an RIP constant of $\delta<1/2$ is both necessary and sufficient for exact recovery from any arbitrary initial point (such as a random point). We also prove a local recovery result: given an initial point $x_{0}$ satisfying $f(x_{0})\le(1-\delta)^{2}f(0)$, any descent algorithm that converges to second-order optimality guarantees exact recovery.
An optimal control approach for the persistent monitoring problem

We propose an optimal control framework for persistent monitoring problems where the objective is to control the movement of mobile agents to minimize an uncertainty metric in a given mission space. For a single agent in a one-dimensional space, we show that the optimal solution is obtained in terms of a sequence of switching locations, thus reducing it to a parametric optimization problem. Using Infinitesimal Perturbation Analysis (IPA) we obtain a complete solution through a gradient-based algorithm. We illustrate our approach with numerical examples.
Program synthesis using conflict-driven learning

We propose a new conflict-driven program synthesis technique that is capable of learning from past mistakes. Given a spurious program that violates the desired specification, our synthesis algorithm identifies the root cause of the conflict and learns new lemmas that can prevent similar mistakes in the future. Specifically, we introduce the notion of equivalence modulo conflict and show how this idea can be used to learn useful lemmas that allow the synthesizer to prune large parts of the search space. We have implemented a general-purpose CDCL-style program synthesizer called Neo and evaluate it in two different application domains, namely data wrangling in R and functional programming over lists. Our experiments demonstrate the substantial benefits of conflict-driven learning and show that Neo outperforms two state-of-the-art synthesis tools, Morpheus and Deepcoder, that target these respective domains.
Polluted bootstrap percolation with threshold two in all dimensions

In the polluted bootstrap percolation model, the vertices of a graph are independently declared initially occupied with probability p or closed with probability q. At subsequent steps, a vertex becomes occupied if it is not closed and it has at least r occupied neighbors. On the cubic lattice $${\mathbb {Z}}^d$$ of dimension $$d\ge 3$$ with threshold $$r=2$$ , we prove that the final density of occupied sites converges to 1 as p and q both approach 0, regardless of their relative scaling. Our result partially resolves a conjecture of Morris, and contrasts with the $$d=2$$ case, where Gravner and McDonald proved that the critical parameter is $$q/{p^2}$$ .
Boda-RTC: Productive generation of portable, efficient code for convolutional neural networks on mobile computing platforms

The popularity of neural networks (NNs) spans academia [1], industry [2], and popular culture [3]. In particular, convolutional neural networks (CNNs) have been applied to many image based machine learning tasks and have yielded strong results [4]. The availability of hardware/software systems for efficient training and deployment of large and/or deep CNN models is critical for the continued success of the field [5] [1]. Early systems for NN computation focused on leveraging existing dense linear algebra techniques and libraries [6] [7]. Current approaches use low-level machine specific programming [8] and/or closed-source, purpose-built vendor libraries [9]. In this work, we present an open source system that, compared to existing approaches, achieves competitive computational speed while achieving significantly greater portability. We achieve this by targeting the vendor-neutral OpenCL platform [10] using a code-generation approach. We argue that our approach allows for both: (1) the rapid development of new computational kernels for existing hardware targets, and (2) the rapid tuning of existing computational kernels for new hardware targets. Results are presented for a case study of targeting the Qualcomm Snapdragon 820 mobile computing platform [11] for CNN deployment.
Approximate dynamic programming using halfspace queries and multiscale Monge decomposition

We consider the problem of approximating a signal P with another signal F consisting of a few piecewise constant segments. This problem arises naturally in applications including databases (e.g., histogram construction), speech recognition, computational biology (e.g., denoising aCGH data) and many more. Specifically, let P = (P1, P2,..., Pn), Pi ∈ R for all i, be a signal and let C be a constant. Our goal is to find a function F: [n] → R which optimizes the following objective function:[EQUATION]The above optimization problem reduces to solving the following recurrence, which can be done using dynamic programming in O(n2) time:[EQUATION]This recurrence arises naturally in several applications where one wants to approximate a given signal P with a signal F which ideally consists of few piecewise constant segments. Such applications include histogram construction in databases, determining DNA copy numbers in cancer cells from micro-array data, speech recognition, data mining and many others.In this work we present two new techniques for optimizing dynamic programming that can handle cost functions not treated by other standard methods. The basis of our first algorithm is the definition of a constant-shifted variant of the objective function that can be efficiently approximated using state of the art methods for range searching. Our technique approximates the optimal value of our objective function within additive e error and runs in O(n4/3+δ log (U/e)) time, where δ is an arbitrarily small positive constant and U = max{√C, (|Pi|)i=1,..., n}. The second algorithm we provide solves a similar recurrence that's within a multiplicative factor of (1+e) and runs in O(n log n/e). The new technique introduced by our algorithm is the decomposition of the initial problem into a small (logarithmic) number of Monge optimization subproblems which we can speed up using existing techniques.
Optimal grouping for group minimax hypothesis testing

Bayesian hypothesis testing and minimax hypothesis testing represent extreme instances of detection in which the prior probabilities of the hypotheses are either completely and precisely known, or are completely unknown. Group minimax, also known as Gamma -minimax, is a robust intermediary between Bayesian and minimax hypothesis testing that allows for coarse or partial advance knowledge of the hypothesis priors by using information on sets in which the prior lies. Existing work on group minimax, however, does not consider the question of how to define the sets or groups of priors; it is assumed that the groups are given. In this paper, we propose a novel intermediate detection scheme formulated through the quantization of the space of prior probabilities that optimally determines groups and also representative priors within the groups. We show that when viewed from a quantization perspective, group minimax amounts to determining centroids with a minimax Bayes risk error divergence distortion criterion: the appropriate Bregman divergence for this task. In addition, the optimal partitioning of the space of prior probabilities is a Bregman Voronoi diagram. Together, the optimal grouping and representation points are an epsilon -net with respect to Bayes risk error divergence, and permit a rate-distortion type asymptotic analysis of detection performance with the number of groups. Examples of detecting signals corrupted by additive white Gaussian noise and of distinguishing exponentially-distributed signals are presented.
A robust volume conserving method for character-water interaction

We propose a novel volume conserving framework for character-water interaction, using a novel volume-of-fluid solver on a skinned tetrahedral mesh, enabling the high degree of the spatial adaptivity in order to capture thin films and hair-water interactions. For efficiency, the bulk of the fluid volume is simulated with a standard Eulerian solver which is two way coupled to our skinned arbitrary Lagrangian-Eulerian mesh using a fast, robust, and straight-forward to implement partitioned approach. This allows for a specialized and efficient treatment of the volume-of-fluid solver, since it is only required in a subset of the domain. The combination of conservation of fluid volume and a kinematically deforming skinned mesh allows us to robustly implement interesting effects such as adhesion, and anisotropic porosity. We illustrate the efficacy of our method by simulating various water effects with solid objects and animated characters.
Mixing Coefficients Between Discrete and Real Random Variables: Computation and Properties

In this paper, we study the problem of estimating the alpha-, beta-, and phi-mixing coefficients between two random variables, that can either assume values in a finite set or the set of real numbers. In either case, explicit closed-form formulas for the beta-mixing coefficient are already known. Therefore for random variables assuming values in a finite set, our contributions are twofold: 1) In the case of the alpha-mixing coefficient, we show that determining whether or not it exceeds a prespecified threshold is NP-complete, and provide efficiently computable upper and lower bounds. 2) We derive an exact closed-form formula for the phi-mixing coefficient. Next, we prove analogs of the data-processing inequality from information theory for each of the three kinds of mixing coefficients. Then we move on to real-valued random variables, and show that by using percentile binning and allowing the number of bins to increase more slowly than the number of samples, we can generate empirical estimates that are consistent, i.e., converge to the true values as the number of samples approaches infinity.
Adaptive Hard Thresholding for Near-optimal Consistent Robust Regression

We study the problem of robust linear regression with response variable corruptions. We consider the oblivious adversary model, where the adversary corrupts a fraction of the responses in complete ignorance of the data. We provide a nearly linear time estimator which consistently estimates the true regression vector, even with $1-o(1)$ fraction of corruptions. Existing results in this setting either don't guarantee consistent estimates or can only handle a small fraction of corruptions. We also extend our estimator to robust sparse linear regression and show that similar guarantees hold in this setting. Finally, we apply our estimator to the problem of linear regression with heavy-tailed noise and show that our estimator consistently estimates the regression vector even when the noise has unbounded variance (e.g., Cauchy distribution), for which most existing results don't even apply. Our estimator is based on a novel variant of outlier removal via hard thresholding in which the threshold is chosen adaptively and crucially relies on randomness to escape bad fixed points of the non-convex hard thresholding operation.
Weakly Labeling the Antarctic: The Penguin Colony Case

Antarctic penguins are important ecological indicators -- especially in the face of climate change. In this work, we present a deep learning based model for semantic segmentation of Adelie penguin colonies in high-resolution satellite imagery. To train our segmentation models, we take advantage of the Penguin Colony Dataset: a unique dataset with 2044 georeferenced cropped images from 193 Adelie penguin colonies in Antarctica. In the face of a scarcity of pixel-level annotation masks, we propose a weakly-supervised framework to effectively learn a segmentation model from weak labels. We use a classification network to filter out data unsuitable for the segmentation network. This segmentation network is trained with a specific loss function, based on the average activation, to effectively learn from the data with the weakly-annotated labels. Our experiments show that adding weakly-annotated training examples significantly improves segmentation performance, increasing the mean Intersection-over-Union from 42.3 to 60.0% on the Penguin Colony Dataset.
Knockout of Babesia bovis rad51 ortholog and its complementation by expression from the BbACc3 artificial chromosome platform

Babesia bovis establishes persistent infections of long duration in cattle, despite the development of effective anti-disease immunity. One mechanism used by the parasite to achieve persistence is rapid antigenic variation of the VESA1 cytoadhesion ligand through segmental gene conversion (SGC), a phenomenon thought to be a form of homologous recombination (HR). To begin investigation of the enzymatic basis for SGC we initially identified and knocked out the Bbrad51 gene encoding the B. bovis Rad51 ortholog. BbRad51 was found to be non-essential for in vitro growth of asexual-stage parasites. However, its loss resulted in hypersensitivity to methylmethane sulfonate (MMS) and an apparent defect in HR. This defect rendered attempts to complement the knockout phenotype by reinsertion of the Bbrad51 gene into the genome unsuccessful. To circumvent this difficulty, we constructed an artificial chromosome, BbACc3, into which the complete Bbrad51 locus was inserted, for expression of BbRad51 under regulation by autologous elements. Maintenance of BbACc3 makes use of centromeric sequences from chromosome 3 and telomeric ends from chromosome 1 of the B. bovis C9.1 line. A selection cassette employing human dihydrofolate reductase enables recovery of transformants by selection with pyrimethamine. We demonstrate that the BbACc3 platform is stably maintained once established, assembles nucleosomes to form native chromatin, and expands in telomere length over time. Significantly, the MMS-sensitivity phenotype observed in the absence of Bbrad51 was successfully complemented at essentially normal levels. We provide cautionary evidence, however, that in HR-competent parasites BbACc3 can recombine with native chromosomes, potentially resulting in crossover. We propose that, under certain circumstances this platform can provide a useful alternative for the genetic manipulation of this group of parasites, particularly when regulated gene expression under the control of autologous elements may be important.
A Coherent Measure

AbstractAccording to many contemporary scholars, an originalist reading of the Fourteenth Amendment is difficult, if not impossible, because the Amendment did not have a coherent original meaning. ...
Electrospun Nanofibers for Label-Free Sensor Applications

Electrospinning is a simple, low-cost and versatile method for fabricating submicron and nano size fibers. Due to their large surface area, high aspect ratio and porous structure, electrospun nanofibers can be employed in wide range of applications. Biomedical, environmental, protective clothing and sensors are just few. The latter has attracted a great deal of attention, because for biosensor application, nanofibers have several advantages over traditional sensors, including a high surface-to-volume ratio and ease of functionalization. This review provides a short overview of several electrospun nanofibers applications, with an emphasis on biosensor applications. With respect to this area, focus is placed on label-free sensors, pertaining to both recent advances and fundamental research. Here, label-free sensor properties of sensitivity, selectivity, and detection are critically evaluated. Current challenges in this area and prospective future work is also discussed.
An Adaptive Empirical Bayesian Method for Sparse Deep Learning

We propose a novel adaptive empirical Bayesian (AEB) method for sparse deep learning, where the sparsity is ensured via a class of self-adaptive spike-and-slab priors. The proposed method works by alternatively sampling from an adaptive hierarchical posterior distribution using stochastic gradient Markov Chain Monte Carlo (MCMC) and smoothly optimizing the hyperparameters using stochastic approximation (SA). The convergence of the proposed method to the asymptotically correct distribution is established under mild conditions. Empirical applications of the proposed method lead to the state-of-the-art performance on MNIST and Fashion MNIST with shallow convolutional neural networks (CNN) and the state-of-the-art compression performance on CIFAR10 with Residual Networks. The proposed method also improves resistance to adversarial attacks.
A Game-theoretic Taxonomy and Survey of Defensive Deception for Cybersecurity and Privacy

Cyberattacks on both databases and critical infrastructure have threatened public and private sectors. Ubiquitous tracking and wearable computing have infringed upon privacy. Advocates and engineers have recently proposed using defensive deception as a means to leverage the information asymmetry typically enjoyed by attackers as a tool for defenders. The term deception, however, has been employed broadly and with a variety of meanings. In this article, we survey 24 articles from 2008 to 2018 that use game theory to model defensive deception for cybersecurity and privacy. Then, we propose a taxonomy that defines six types of deception: perturbation, moving target defense, obfuscation, mixing, honey-x, and attacker engagement. These types are delineated by their information structures, agents, actions, and duration: precisely concepts captured by game theory. Our aims are to rigorously define types of defensive deception, to capture a snapshot of the state of the literature, to provide a menu of models that can be used for applied research, and to identify promising areas for future work. Our taxonomy provides a systematic foundation for understanding different types of defensive deception commonly encountered in cybersecurity and privacy.
Structure and Functional Binding Epitope of V-domain Ig Suppressor of T Cell Activation.

Summary V-domain immunoglobulin (Ig) suppressor of T cell activation (VISTA) is an immune checkpoint protein that inhibits the T cell response against cancer. Similar to PD-1 and CTLA-4, a blockade of VISTA promotes tumor clearance by the immune system. Here, we report a 1.85 A crystal structure of the elusive human VISTA extracellular domain, whose lack of homology necessitated a combinatorial MR-Rosetta approach for structure determination. We highlight features that make the VISTA immunoglobulin variable (IgV)-like fold unique among B7 family members, including two additional disulfide bonds and an extended loop region with an attached helix that we show forms a contiguous binding epitope for a clinically relevant anti-VISTA antibody. We propose an overlap of this antibody-binding region with the binding epitope for V-set and Ig domain containing 3 (VSIG3), a purported functional binding partner of VISTA. The structure and functional epitope presented here will help guide future drug development efforts against this important checkpoint target.
Performance Monitoring for End-to-End Speech Recognition.

Measuring performance of an automatic speech recognition (ASR) system without ground-truth could be beneficial in many scenarios, especially with data from unseen domains, where performance can be highly inconsistent. In conventional ASR systems, several performance monitoring (PM) techniques have been well-developed to monitor performance by looking at tri-phone posteriors or pre-softmax activations from neural network acoustic modeling. However, strategies for monitoring more recently developed end-to-end ASR systems have not yet been explored, and so that is the focus of this paper. We adapt previous PM measures (Entropy, M-measure and Auto-encoder) and apply our proposed RNN predictor in the end-to-end setting. These measures utilize the decoder output layer and attention probability vectors, and their predictive power is measured with simple linear models. Our findings suggest that decoder-level features are more feasible and informative than attention-level probabilities for PM measures, and that M-measure on the decoder posteriors achieves the best overall predictive performance with an average prediction error 8.8%. Entropy measures and RNN-based prediction also show competitive predictability, especially for unseen conditions.
A Scalable Noisy Speech Dataset and Online Subjective Test Framework.

Background noise is a major source of quality impairments in Voice over Internet Protocol (VoIP) and Public Switched Telephone Network (PSTN) calls. Recent work shows the efficacy of deep learning for noise suppression, but the datasets have been relatively small compared to those used in other domains (e.g., ImageNet) and the associated evaluations have been more focused. In order to better facilitate deep learning research in Speech Enhancement, we present a noisy speech dataset (MS-SNSD) that can scale to arbitrary sizes depending on the number of speakers, noise types, and Speech to Noise Ratio (SNR) levels desired. We show that increasing dataset sizes increases noise suppression performance as expected. In addition, we provide an open-source evaluation methodology to evaluate the results subjectively at scale using crowdsourcing, with a reference algorithm to normalize the results. To demonstrate the dataset and evaluation framework we apply it to several noise suppressors and compare the subjective Mean Opinion Score (MOS) with objective quality measures such as SNR, PESQ, POLQA, and VISQOL and show why MOS is still required. Our subjective MOS evaluation is the first large scale evaluation of Speech Enhancement algorithms that we are aware of.
Reeb posets and tree approximations

Abstract A well known result in the analysis of finite metric spaces due to Gromov says that given any metric space ( X , d X ) there exists a tree metric t X on X such that | d X − t X | ∞ is bounded above by twice hyp ( X ) ⋅ log ( 2 | X | ) . Here hyp ( X ) is the hyperbolicity of X , a quantity that measures the treeness of 4-tuples of points in X . This bound is known to be asymptotically tight. We improve this bound by restricting ourselves to metric spaces arising from filtered posets. By doing so we are able to replace the cardinality appearing in Gromov’s bound by a certain poset theoretic invariant which can be much smaller thus significantly improving the approximation bound. The setting of metric spaces arising from posets is rich: For example, every finite metric graph can be induced from a filtered poset. Since every finite metric space can be isometrically embedded into a finite metric graph, our ideas are applicable to finite metric spaces as well. At the core of our results lies the adaptation of the Reeb graph and Reeb tree constructions and the concept of hyperbolicity to the setting of posets, which we use to formulate and prove a tree approximation result for any filtered poset.
The grain-size effect on thermal conductivity of uranium dioxide

We have investigated the grain-boundary scattering effect on the thermal transport behavior of uranium dioxide ( UO 2). The polycrystalline samples having different grain-sizes (0.125, 1.8, and 7.2 μ m) have been prepared by a spark plasma sintering technique and characterized by x-ray powder diffraction, scanning electron microscope, and Raman spectroscopy. The thermal transport properties (the thermal conductivity and thermoelectric power) have been measured in the temperature range of 2–300 K, and the results were analyzed in terms of various physical parameters contributing to thermal conductivity in these materials in relation to grain-size. We show that thermal conductivity decreases systematically with lowering grain-size in the temperatures below 30 K, where the boundary scattering dominates the thermal transport. At higher temperatures, more scattering processes are involved in the heat transport in these materials, making the analysis difficult. We determined the grain-boundary Kapitza resistance that would result in the observed increase in thermal conductivity with grain-size and compared the value with Kapitza resistances calculated for UO 2 using molecular dynamics from the literature.
The influence of daily stress on glycemic control and mortality in adults with diabetes.

There is mixed evidence regarding the relationship between different types of stress and outcomes in adults with diabetes. The aim of this study was to understand the relationship between daily stress and glycemic control (HbA1c), and to examine whether multiple daily stressors is associated with early mortality among individuals with diabetes. This was a cross-sectional analysis of national Midlife Development in the United States (MIDUS) study data. A total of 141 adults with diabetes completed the National Study of Daily Experiences (NSDE) project during the initial phase of the study, which was summarized through a series of measures about daily stress frequency, type and impact. General linear models investigated the relationship between daily stress and HbA1c. Kaplan–Meier curves based on national death index information linked to MIDUS were investigated for individuals reporting no/one stressor per week versus multiple stressors per week. On average, this population of adults with diabetes reported 3.1 days with a stressor and 2.45 stressor types per week. No significant relationships existed between glycemic control and frequency of daily stress. Higher stress from work was associated with higher HbA1c (β = 0.65, 95% CI 0.08, 1.22) and higher perceived risk of stress influencing physical health was associated with higher HbA1c (β = 0.60, 95% CI 0.01, 1.20). In conclusion, while many ways of measuring daily stress were shown not to have a significant influence on glycemic control, daily stress related to work and the perceived risk of stress influencing one’s physical health may influence outcomes for adults with diabetes. Interventions incorporating stress management, and in particular coping with the risk that stress has on health may help adults with diabetes better manage glycemic control over time.
Modeling, Fabrication and Performance Measurements of a Piezoelectric Energy Converter for Power Harvesting in Autonomous Microsystems

This paper deals with the energy conversion via the piezoelectric effect. A mechanoelectrical energy converter based on piezoelectric thick films of lead zirconate titanate (PZT) deposited on a steel cantilever by a low-temperature process is presented. Modeling and measurement results on the performances of the piezoelectric converter are reported, and its potential use in self-powered autonomous sensor devices is proposed
Entity, Relation, and Event Extraction with Contextualized Span Representations

We examine the capabilities of a unified, multi-task framework for three information extraction tasks: named entity recognition, relation extraction, and event extraction. Our framework (called DyGIE++) accomplishes all tasks by enumerating, refining, and scoring text spans designed to capture local (within-sentence) and global (cross-sentence) context. Our framework achieves state-of-the-art results across all tasks, on four datasets from a variety of domains. We perform experiments comparing different techniques to construct span representations. Contextualized embeddings like BERT perform well at capturing relationships among entities in the same or adjacent sentences, while dynamic span graph updates model long-range cross-sentence relationships. For instance, propagating span representations via predicted coreference links can enable the model to disambiguate challenging entity mentions. Our code is publicly available at https://github.com/dwadden/dygiepp and can be easily adapted for new tasks or datasets.