cssi/SciSciGPT-SciSciNet huggingface dataset:
Recurrent Neural Network Attention Mechanisms for Interpretable System Log Anomaly Detection

Deep learning has recently demonstrated state-of-the art performance on key tasks related to the maintenance of computer systems, such as intrusion detection, denial of service attack detection, hardware and software system failures, and malware detection. In these contexts, model interpretability is vital for administrator and analyst to trust and act on the automated analysis of machine learning models. Deep learning methods have been criticized as black box oracles which allow limited insight into decision factors. In this work we seek to bridge the gap between the impressive performance of deep learning models and the need for interpretable model introspection. To this end we present recurrent neural network (RNN) language models augmented with attention for anomaly detection in system logs. Our methods are generally applicable to any computer system and logging source. By incorporating attention variants into our RNN language models we create opportunities for model introspection and analysis without sacrificing state-of-the art performance. We demonstrate model performance and illustrate model interpretability on an intrusion detection task using the Los Alamos National Laboratory (LANL) cyber security dataset, reporting upward of 0.99 area under the receiver operator characteristic curve despite being trained only on a single day's worth of data.
Deep Supervised and Convolutional Generative Stochastic Network for Protein Secondary Structure Prediction

Predicting protein secondary structure is a fundamental problem in protein structure prediction. Here we present a new supervised generative stochastic network (GSN) based method to predict local secondary structure with deep hierarchical representations. GSN is a recently proposed deep learning technique (Bengio & Thibodeau-Laufer, 2013) to globally train deep generative model. We present the supervised extension of GSN, which learns a Markov chain to sample from a conditional distribution, and applied it to protein structure prediction. To scale the model to full-sized, high-dimensional data, like protein sequences with hundreds of aminoacids, we introduce a convolutional architecture, which allows efficient learning across multiple layers of hierarchical representations. Our architecture uniquely focuses on predicting structured low-level labels informed with both low and high-level representations learned by the model. In our application this corresponds to labeling the secondary structure state of each amino-acid residue. We trained and tested the model on separate sets of non-homologous proteins sharing less than 30% sequence identity. Our model achieves 66:4% Q8 accuracy on the CB513 dataset, better than the previously reported best performance 64:9% (Wang et al., 2011) for this challenging secondary structure prediction problem.
On the Connectivity of Graphs in Association Schemes

Let $(X,\mathcal{R})$ be a commutative association scheme and let $\Gamma=(X,R\cup R^\top)$ be a connected undirected graph where $R\in \mathcal{R}$. Godsil (resp., Brouwer) conjectured that the edge connectivity (resp., vertex connectivity) of $\Gamma$ is equal to its valency. In this paper, we prove that the deletion of the neighborhood of any vertex leaves behind at most one non-singleton component. Two vertices $a,b\in X$ are called "twins" in $\Gamma$ if they have identical neighborhoods: $\Gamma(a)=\Gamma(b)$. We characterize twins in polynomial association schemes and show that, in the absence of twins, the deletion of any vertex and its neighbors in $\Gamma$ results in a connected graph. Using this and other tools, we find lower bounds on the connectivity of $\Gamma$, especially in the case where $\Gamma$ has diameter two.
The small GTPase RAB1B promotes antiviral innate immunity by interacting with TNF receptor–associated factor 3 (TRAF3)

Innate immune detection of viral nucleic acids during viral infection activates a signaling cascade that induces type I and type III IFNs as well as other cytokines, to generate an antiviral response. This signaling is initiated by pattern recognition receptors, such as the RNA helicase retinoic acid-inducible gene I (RIG-I), that sense viral RNA. These sensors then interact with the adaptor protein mitochondrial antiviral signaling protein (MAVS), which recruits additional signaling proteins, including TNF receptor–associated factor 3 (TRAF3) and TANK-binding kinase 1 (TBK1), to form a signaling complex that activates IFN regulatory factor 3 (IRF3) for transcriptional induction of type I IFNs. Here, using several immunological and biochemical approaches in multiple human cell types, we show that the GTPase-trafficking protein RAB1B up-regulates RIG-I pathway signaling and thereby promotes IFN-β induction and the antiviral response. We observed that RAB1B overexpression increases RIG-I–mediated signaling to IFN-β and that RAB1B deletion reduces signaling of this pathway. Additionally, loss of RAB1B dampened the antiviral response, indicated by enhanced Zika virus infection of cells depleted of RAB1B. Importantly, we identified the mechanism of RAB1B action in the antiviral response, finding that it forms a protein complex with TRAF3 to facilitate the interaction of TRAF3 with mitochondrial antiviral signaling protein. We conclude that RAB1B regulates TRAF3 and promotes the formation of innate immune signaling complexes in response to nucleic acid sensing during RNA virus infection.
Literal or Pedagogic Human? Analyzing Human Model Misspecification in Objective Learning

It is incredibly easy for a system designer to misspecify the objective for an autonomous system ("robot''), thus motivating the desire to have the robot learn the objective from human behavior instead. Recent work has suggested that people have an interest in the robot performing well, and will thus behave pedagogically, choosing actions that are informative to the robot. In turn, robots benefit from interpreting the behavior by accounting for this pedagogy. In this work, we focus on misspecification: we argue that robots might not know whether people are being pedagogic or literal and that it is important to ask which assumption is safer to make. We cast objective learning into the more general form of a common-payoff game between the robot and human, and prove that in any such game literal interpretation is more robust to misspecification. Experiments with human data support our theoretical results and point to the sensitivity of the pedagogic assumption.
Discontinuity-Sensitive Optimal Control Learning by Mixture of Experts

This paper proposes a machine learning method to predict the solutions of related nonlinear optimal control problems given some parametric input, such as the initial state. The map between problem parameters to optimal solutions is called the problem-optimum map, and is often discontinuous due to nonconvexity, discrete homotopy classes, and control switching. This causes difficulties for traditional function approximators such as neural networks, which assume continuity of the underlying function. This paper proposes a mixture of experts (MoE) model composed of a classifier and several regressors, where each regressor is tuned to a particular continuous region. A novel training approach is proposed that trains classifier and regressors independently. MoE greatly outperforms standard neural networks, and achieves highly reliable trajectory prediction (over 99.5% accuracy) in several dynamic vehicle control problems.
Molecular dependencies of dynamic stiffening and strengthening through high strain rate microparticle impact of polyurethane and polyurea elastomers

This study investigates the molecular dependencies of dynamic stiffening and strengthening through comparison of high strain rate impact responses of various polyurethanes and polyureas. We use an in-house designed tabletop microimpact experimental platform—the laser-induced particle impact test—to perform high strain rate impacts and measure the corresponding material response. Dynamic mechanical analysis and differential scanning calorimetry are used to show that glass transition temperature is a useful predictor of the impact response at ambient temperatures. Meanwhile, solid-state nuclear magnetic resonance spectroscopy identifies segmental dynamics as an important determinant of the variation in both dynamic stiffening and strengthening. The impact responses of polyurethanes and polyureas both show clear dependencies on the molecular weight of the soft segment. This comparison suggests the state of intermolecular hydrogen bonding plays a key role in dynamic stiffening and strengthening. This study aims to identify the molecular dependencies of the impact response and establish a foundation for further design and testing of optimal high strain rate characteristics in synthetic elastomers.
Algorithmic Guarantees for Inverse Imaging with Untrained Network Priors

Deep neural networks as image priors have been recently introduced for problems such as denoising, super-resolution and inpainting with promising performance gains over hand-crafted image priors such as sparsity. Unlike learned generative priors they do not require any training over large datasets. However, few theoretical guarantees exist in the scope of using untrained network priors for inverse imaging problems. We explore new applications and theory for untrained neural network priors. Specifically, we consider the problem of solving linear inverse problems, such as compressive sensing, as well as non-linear problems, such as compressive phase retrieval. We model images to lie in the range of an untrained deep generative network with a fixed seed. We further present a projected gradient descent scheme that can be used for both compressive sensing and phase retrieval and provide rigorous theoretical guarantees for its convergence. We also show both theoretically as well as empirically that with deep neural network priors, one can achieve better compression rates for the same image quality as compared to when hand crafted priors are used.
Rotate King to get Queen: Word Relationships as Orthogonal Transformations in Embedding Space.

A notable property of word embeddings is that word relationships can exist as linear substructures in the embedding space. For example, ‘gender’ corresponds to v_woman - v_man and v_queen - v_king. This, in turn, allows word analogies to be solved arithmetically: v_king - v_man + v_woman = v_queen. This property is notable because it suggests that models trained on word embeddings can easily learn such relationships as geometric translations. However, there is no evidence that models exclusively represent relationships in this manner. We document an alternative way in which downstream models might learn these relationships: orthogonal and linear transformations. For example, given a translation vector for ‘gender’, we can find an orthogonal matrix R, representing a rotation and reflection, such that R(v_king) = v_queen and R(v_man) = v_woman. Analogical reasoning using orthogonal transformations is almost as accurate as using vector arithmetic; using linear transformations is more accurate than both. Our findings suggest that these transformations can be as good a representation of word relationships as translation vectors.
A Distributed Algorithm for Throughput Optimal Routing in Overlay Networks

We address the problem of optimal routing in overlay networks. An overlay network is constructed by adding new overlay nodes on top of a legacy network. The overlay nodes are capable of implementing any dynamic routing policy, however, the legacy underlay has a fixed, single path routing scheme and uses a simple work-conserving forwarding policy. Moreover, the underlay routes are pre-determined and unknown to the overlay network. The overlay network can increase the achievable throughput of the legacy network by using multiple routes, which consist of direct routes and indirect routes through other overlay nodes. We develop an optimal dynamic routing algorithm for such overlay networks called the Optimal Overlay Routing Policy (OORP). OORP is derived using the classical dual subgradient descent method, and it can be implemented in a distributed manner. We show that the queue-lengths can be used as a substitute for the dual variables in the algorithm. However, the underlay queue-lengths are unknown to the overlay, so we propose two regression based schemes that learn simplified models of the backlog in the underlay using historical data and use them to estimate the queue-lengths in real time. Simulation results show that near-optimal performance can be achieved without any knowledge of the underlay.
Sparse additive machine

We develop a high dimensional nonparametric classification method named sparse additive machine (SAM), which can be viewed as a functional version of support vector machine (SVM) combined with sparse additive modeling. the SAM is related to multiple kernel learning (MKL), but is computationally more efficient and amenable to theoretical analysis. In terms of computation, we develop an efficient accelerated proximal gradient descent algorithm which is also scalable to large datasets with a provable O(1/k2) convergence rate, where k is the number of iterations. In terms of theory, we provide the oracle properties of the SAM under asymptotic frameworks. Empirical results on both synthetic and real data are reported to back up our theory.
Reconstitution of IgG Subclasses following Immunoglobulin Therapy in Adult Primary Hypogammaglobulinemia.

Background Immunoglobulin (Ig) therapy is highly effective in reducing the frequency and severity of infections. However, a subset of patients does not respond adequately. Objective To determine in adult patients with primary hypogammaglobulinemia (a) if failure to reconstitute IgG subclass(es) is associated with inadequate clinical response, (b) whether reconstitution of IgG subclasses differs between routes of Ig administration, (c) which subclasses contribute to low total IgG, and (d) what are the most commonly impaired Streptococcus pneumoniae serotypes. Methods A retrospective review of the records of patients with primary hypogammaglobulinemia followed up at the Immunology Clinic between 2010 and 2018 was conducted. Demographic, clinical, and laboratory data were collected. Results Seventy-one patients with primary hypogammaglobulinemia were included. All subclasses were reconstituted in 85% of the patients. IgG3 and IgG4 were most commonly not reconstituted. Reconstitution occurred in 85% of the patients on intravenous Ig (IVIG), 81% of the patients on conventional subcutaneous Ig (SCIG), and 100% of the patients on enzyme-facilitated subcutaneous Ig (fSCIG). The annual infection rate was 0.87 with IVIG, 0.88 with conventional SCIG, and 0.6 with fSCIG. IgG subclasses contributing to low total IgG included IgG1 (61%), IgG2 (49%), IgG3 (23%), and IgG4 (28%). In patients with concomitant specific antibody deficiency (n = 47), the most commonly impaired antibody responses were against pneumococcal serotypes 3, 4, 6b, 12f, and 23f. Conclusions Failure to reconstitute subclasses does not correlate with an inadequate clinical response to immunoglobulin therapy in primary hypogammaglobulinemia. Full reconstitution of IgG subclasses was observed with fSCIG. A smaller panel of pneumococcal antibody responses may be used to define specific antibody deficiency.
A tough nut to crack

A study of the microstructure and mechanical properties of macadamia nutshells reveals these to behave like an “isotropic wood.” Their dry density is ∼ 1.3×103kgm−3, their Vickers hardness is 180±30 MPa, their fracture strength is 25 to 80 MPa and their fracture toughness is 1 to 2MPam1/2. Their Young's modulus probably lies nearer the highest (6GPa) than the lowest (2GPa) of the present measurements and their work of fracture lies in the range 100 to 1000 Jm−2. None of the mechanical properties is obviously dependent on the water content of the shell. The results demonstrate quantitatively why these shells have a reputation for being difficult to crack.
Discussion of: A statistical analysis of multiple temperature proxies: Are reconstructions of surface temperatures over the last 1000 years reliable?

This article (MW) has stimulated much valuable discussion and helped to focus attention on an important area for the application of statistics. Given the short amount of space, however, we reluctantly comment only on the second and last sections. Excursions in the history of science. Although Section 2 of this paper is lively reading, we feel that the viewpoint is not balanced and emphasizes statistical correctness over the broader issues of scientific understanding. Recounting a controversy that has both a political dimension and involves scientific issues from several disciplines is perhaps better left to a historian of science. Wegman’s quote on page 9 of the article is actually from a later written response to Representative Stupak, not from the original testimony [see Questions surrounding the hockey stick (2006)]. We encourage readers to also read the transcript of the congressional hearings and the contemporaneous report by the National Academies, NRC (2006) to follow this debate. Paleoclimate reconstructions. The Wegman committee’s original report stopped short of redoing the temperature reconstruction with Mann’s data and with the correct centering of the principal components. Although this exercise was beyond the report’s charge, it is sound statistical practice to evaluate changes in intermediate methodology by their influence on the final statistical inference. The string of references that are cited by MW on page
Nested quantization and Slepian-Wolf coding: a Wyner-Ziv coding paradigm for i.i.d. sources

A new paradigm for Wyner-Ziv coding of i.i.d. sources is proposed that consists of nested quantization and Slepian-Wolf coding. The former plays the role of quantization with side information (at the decoder) and the latter lossless coding with side information. The proposed Slepian-Wolf coded nested quantization (SWC-NQ) framework generalizes the classic source coding approach of quantization and lossless/entropy coding. The main thrust is to treat Wyner-Ziv coding as a source-channel coding problem in which the side information is taken into account in the channel coding component via binning. For Gaussian sources with MSE measure, assuming nested lattice quantization with ideal Slepian-Wolf coding and high rate, we establish system performance bounds of SWC-NQ similar to those in classic source coding, showing that 1-D/2-D nested lattice quantization performs 1.53/1.36 dB worse than the Wyner-Ziv distortion-rate function D/sub WZ/{R). Using nested lattices in higher dimensions or nested trellis-coded quantization (TCQ) could possibly approach D/sub WZ/(R) even further. We implement 1-D and 2-D nested lattice quantization, together with irregular low-density parity-check (LDPC) codes for Slepian-Wolf coding, obtaining performance close to the corresponding theoretical limits.
Conceptualizing Legal Childhood in the Twenty-First Century

The law governing children is complex, sometimes appearing almost incoherent. The relatively simple framework established in the Progressive era, in which parents had primary authority over children, subject to limited state oversight, has broken down over the past few decades. Lawmakers started granting children some adult rights and privileges, raising questions about their traditional status as vulnerable, dependent, and legally incompetent beings. As children emerged as legal persons, children’s rights advocates challenged the rationale for parental authority, contending that robust parental rights often harm children. And a wave of punitive reforms in response to juvenile crime in the 1990s undermined the state’s longstanding role as the protector of children. We address this seeming incoherence by identifying a deep structure and logic in the regulation of children that is becoming clear in the twenty-first century. In our conceptual framework, the law’s central goal, across multiple legal domains, is to promote child wellbeing. This unifying purpose has roots in the Progressive era, but three distinct characteristics distinguish the modern approach. Today, lawmakers advance child wellbeing with greater confidence and success by drawing on a wide body of research on child and adolescent development and the efficacy of related policies. This is bolstered by the clear understanding that promoting child wellbeing generally furthers social welfare, leading to a broader base of support for state policies and legal doctrines. Finally, there is a growing recognition that the regulation of children and families has long been tainted by racial and class bias and that a new commitment to minimizing these pernicious influences is essential to both the legitimacy and fairness of the regime. In combination, these features make the contemporary regulatory framework superior to earlier approaches. Rather than pitting the state, parents, and child in competition for control over children’s lives—the conception of family regulation since the 1960s—our Child Wellbeing framework offers a surprisingly integrated regulatory approach. Properly understood, parental rights and children’s rights, as well as the direct role of the state in children’s lives, are increasingly defined and unified by a researchdriven, social-welfare-regarding effort to promote child wellbeing. This normatively attractive conceptualization of legal childhood does not define every area of legal regulation, but it is a strong throughline and should be elevated and embraced more broadly. In short, our framework brings coherence to the complex legal developments of the past half century and provides guidance moving forward for this critical area of the law.
A virtually ample field that is not ample

A field K is called ample if for every geometrically integral K-variety V with a smooth K-point, V (K) is Zariski-dense in V. A field K is virtually ample if some finite extension of K is ample. We prove that there exists a virtually ample field that is not ample.
Theory predicts plants grow roots to compete with only their closest neighbours.

The combination of individual-based selection with shared access to resources drives individuals to invest more than necessary in taking up their share of resources due to the threat of other indiv...
Compositional and Abstraction-Based Approach for Synthesis of Edit Functions for Opacity Enforcement

This article develops a novel compositional and abstraction-based approach to synthesize edit functions for opacity enforcement in modular discrete event systems. Edit functions alter the output of the system by erasing or inserting events in order to obfuscate the outside intruder, whose goal is to infer the secrets of the system from its observation. We synthesize edit functions to solve the opacity enforcement problem in a modular setting, which significantly reduces the computational complexity compared with the monolithic approach. Two abstraction methods called opaque observation equivalence and opaque bisimulation are first employed to abstract the individual components of the modular system and their observers. Subsequently, we propose a method to transform the synthesis of edit functions to the calculation of modular supremal nonblocking supervisors. We show that the edit functions synthesized in this manner correctly solve the opacity enforcement problem.
The Proof Is in the Process: Self-Reporting Under International Human Rights Treaties

Recent research has shown that state reporting to human rights monitoring bodies is associated with improvements in rights practices, calling into question earlier claims that self-reporting is inconsequential. Yet little work has been done to explore the theoretical mechanisms that plausibly account for this association. This Article systematically documents—across treaties, countries, and years—four mechanisms through which reporting can contribute to human rights improvements: elite socialization, learning and capacity building, domestic mobilization, and law development. These mechanisms have implications for the future of human rights treaty monitoring.
Making Study Populations Visible Through Knowledge Graphs

Treatment recommendations within Clinical Practice Guidelines (CPGs) are largely based on findings from clinical trials and case studies, referred to here as research studies, that are often based on highly selective clinical populations, referred to here as study cohorts. When medical practitioners apply CPG recommendations, they need to understand how well their patient population matches the characteristics of those in the study cohort, and thus are confronted with the challenges of locating the study cohort information and making an analytic comparison. To address these challenges, we develop an ontology-enabled prototype system, which exposes the population descriptions in research studies in a declarative manner, with the ultimate goal of allowing medical practitioners to better understand the applicability and generalizability of treatment recommendations. We build a Study Cohort Ontology (SCO) to encode the vocabulary of study population descriptions, that are often reported in the first table in the published work, thus they are often referred to as Table 1. We leverage the well-used Semanticscience Integrated Ontology (SIO) for defining property associations between classes. Further, we model the key components of Table 1s, i.e., collections of study subjects, subject characteristics, and statistical measures in RDF knowledge graphs. We design scenarios for medical practitioners to perform population analysis, and generate cohort similarity visualizations to determine the applicability of a study population to the clinical population of interest. Our semantic approach to make study populations visible, by standardized representations of Table 1s, allows users to quickly derive clinically relevant inferences about study populations.
Extended Magic for Negation: Efficient Demand-Driven Evaluation of Stratified Datalog with Precise Complexity Guarantees.

Given a set of Datalog rules, facts, and a query, answers to the query can be inferred bottom-up starting from the facts or top-down starting from the query. For efficiency, top-down evaluation is extended with memoization of inferred facts, and bottom-up evaluation is performed after transformations to make rules driven by the demand from the query. Prior work has shown their precise complexity analysis and relationships. However, when Datalog is extended with even stratified negation, which has a simple and universally accepted semantics, transformations to make rules demand-driven may result in non-stratified negation, which has had many complex semantics and evaluation methods. This paper presents (1) a simple extension to demand transformation, a transformation to make rules demand-driven for Datalog without negation, to support stratified negation, and (2) a simple extension to an optimal bottom-up evaluation method for Datalog with stratified negation, to handle non-stratified negation in the resulting rules. We show that the method provides precise complexity guarantees. It is also optimal in that only facts needed for top-down evaluation of the query are inferred and each firing of a rule to infer such a fact takes worst-case constant time. We extend the precise relationship between top-down evaluation and demand-driven bottom-up evaluation to Datalog with stratified negation. Finally, we show experimental results for performance, as well as applications to previously challenging examples.
Explicit Construction of RIP Matrices Is Ramsey‐Hard

Matrices $\Phi\in\R^{n\times p}$ satisfying the Restricted Isometry Property (RIP) are an important ingredient of the compressive sensing methods. While it is known that random matrices satisfy the RIP with high probability even for $n=\log^{O(1)}p$, the explicit construction of such matrices defied the repeated efforts, and the most known approaches hit the so-called $\sqrt{n}$ sparsity bottleneck. The notable exception is the work by Bourgain et al \cite{bourgain2011explicit} constructing an $n\times p$ RIP matrix with sparsity $s=\Theta(n^{{1\over 2}+\epsilon})$, but in the regime $n=\Omega(p^{1-\delta})$. In this short note we resolve this open question in a sense by showing that an explicit construction of a matrix satisfying the RIP in the regime $n=O(\log^2 p)$ and $s=\Theta(n^{1\over 2})$ implies an explicit construction of a three-colored Ramsey graph on $p$ nodes with clique sizes bounded by $O(\log^2 p)$ -- a question in the extremal combinatorics which has been open for decades.
Environmental association identifies candidates for tolerance to low temperature and drought

Barley (Hordeum vulgare ssp. vulgare) is cultivated from the equator to the Arctic Circle. The wild progenitor species, Hordeum vulgare ssp. spontaneum, occupies a relatively narrow latitudinal range (∼30 - 40° N) primarily at low elevation (< 1,500 m). Adaptation to the range of cultivation has occurred over ∼8,000 years. The genetic basis of adaptation is amenable to study through environmental association. An advantage of environmental association in a well-characterized crop is that many loci that contribute to climatic adaptation and abiotic stress tolerance have already been identified. This provides the opportunity to determine if environmental association approaches effectively identify these loci of large effect. Using published genotyping from 7,864 SNPs in 803 barley landraces, we examined allele frequency differentiation across multiple partitions of the data and mixed model associations relative to bioclimatic variables. Using newly generated resequencing data from a subset of these landraces, we tested for linkage disequilibrium (LD) between SNPs queried in genotyping and SNPs in neighboring loci. Six loci previously reported to contribute to adaptive differences in flowering time and abiotic stress in barley and six loci previously identified in other plant species were identified in our analyses. In many cases, patterns of LD are consistent with the causative variant occurring in the immediate vicinity of the queried SNP. The identification of barley orthologs to well-characterized genes may provide a new understanding of the nature of adaptive variation and could permit a more targeted use of potentially adaptive variants in barley breeding and germplasm improvement.
Kernel Meets Sieve: Post-Regularization Confidence Bands for Sparse Additive Model

We develop a novel procedure for constructing confidence bands for components of a sparse additive model. Our procedure is based on a new kernel-sieve hybrid estimator that combines two most popula...
The Chvátal–Erdős condition for prism-Hamiltonicity

Abstract The prism over a graph G is the cartesian product G □ K 2 . It is known that the property of having a Hamiltonian prism (prism-Hamiltonicity) is stronger than that of having a 2-walk (spanning closed walk using every vertex at most twice) and weaker than that of having a Hamilton path. For a graph G , it is known that α ( G ) ≤ 2 κ ( G ) , where α ( G ) is the independence number and κ ( G ) is the connectivity, implies existence of a 2-walk in G , and the bound is sharp. West asked for a bound on α ( G ) in terms of κ ( G ) guaranteeing prism-Hamiltonicity. In this paper we answer this question and prove that α ( G ) ≤ 2 κ ( G ) implies the stronger condition, prism-Hamiltonicity of G .
Bold diagrammatic Monte Carlo in the lens of stochastic iterative methods

This work aims at understanding of bold diagrammatic Monte Carlo (BDMC) methods for stochastic summation of Feynman diagrams from the angle of stochastic iterative methods. The convergence enhancement trick of the BDMC is investigated from the analysis of condition number and convergence of the stochastic iterative methods. Numerical experiments are carried out for model systems to compare the BDMC with related stochastic iterative approaches.
Scalable String Reconciliation by Recursive Content-Dependent Shingling

We consider the problem of reconciling similar, but remote, strings with minimum communication complexity. This “string reconciliation” problem is a fundamental building block for a variety of networking applications, including those that maintain large-scale distributed networks and perform remote file synchronization. We present the novel Recursive Content-Dependent Shingling (RCDS) protocol that is computationally practical for large strings and scales linearly with the edit distance between the remote strings. We provide comparisons to the performance of rsync, one of the most popular file synchronization tools in active use. Our experiments show that, with minimal engineering, RCDS outperforms the heavily optimized rsync in reconciling release revisions for about 51% of the 5000 top starred git repositories on GitHub. The improvement is particularly evident for repositories that see frequent, but small, updates.
Online Coloring a Token Graph

We study a combinatorial coloring game between two players, Spoiler and Painter, who alternate turns. First, Spoiler places a new token at a vertex in G, and Painter responds by assigning a color to the new token. Painter must ensure that tokens on the same or adjacent vertices receive distinct colors. Spoiler must ensure that the token graph (in which two tokens are adjacent if and only if their distance in G is at most 1) has chromatic number at most w. Painter wants to minimize the number of colors used, and Spoiler wants to force as many colors as possible. Let $$f(w,G)$$ be the minimum number of colors needed in an optimal Painter strategy. The game is motivated by a natural online coloring problem on the real line which remains open. A graph G is token-perfect if $$f(w,G) = w$$ for each w. We show that a graph is token-perfect if and only if it can be obtained from a bipartite graph by cloning vertices. We also give a forbidden induced subgraph characterization of the class of token-perfect graphs, which may be of independent interest. When G is not token-perfect, determining $$f(w,G)$$ seems challenging; we establish $$f(w,G)$$ asymptotically for some of the minimal graphs that are not token-perfect.
Integral equation characterization of the Feynman–Kac formula for a regime-switching diffusion

Abstract In this paper, we provide an integral equation characterization of the solution to a Cauchy problem associated to the Feynman–Kac formula for a regime-switching diffusion. We give a sufficient condition to guarantee the uniqueness of solutions to the integral equation and provide an example in the context of option pricing under the Ornstein–Uhlenbeck regime-switching model.
Foliations with One-Sided Branching

We generalize the main results from the author's paper in Geom. Topol. 4 (2000), 457–515 and from Thurston's eprint math.GT/9712268 to taut foliations with one-sided branching. First constructed by Meigniez, these foliations occupy an intermediate position between ℝ-covered foliations and arbitrary taut foliations of 3-manifolds. We show that for a taut foliation $$F$$ with one-sided branching of an atoroidal 3-manifold M, one can construct a pair of genuine laminations Λ± of M transverse to $$F$$ with solid torus complementary regions which bind every leaf of $$F$$ in a geodesic lamination. These laminations come from a universal circle, a refinement of the universal circles proposed by Thurston (unpublished), which maps monotonely and π1(M)-equivariantly to each of the circles at infinity of the leaves of $$\tilde F$$ , and is minimal with respect to this property. This circle is intimately bound up with the extrinsic geometry of the leaves of $$\tilde F$$ . In particular, let $$\tilde F$$ denote the pulled-back foliation of the universal cover, and co-orient $$\tilde F$$ so that the leaf space branches in the negative direction. Then for any pair of leaves of $$\tilde F$$ with μλ, the leaf λ is asymptotic to μ in a dense set of directions at infinity. This is a macroscopic version of an infinitesimal result from Thurston and gives much more drastic control over the topology and geometry of $$F$$ , than is achieved by him. The pair of laminations Λ± can be used to produce a pseudo-Anosov flow transverse to $$F$$ which is regulating in the nonbranching direction. Rigidity results for Λ± in the ℝ-covered case are extended to the case of one-sided branching. In particular, an ℝ-covered foliation can only be deformed to a foliation with one-sided branching along one of the two laminations canonically associated to the ℝ-coveredfoliation constructed in Geom. Topol. 4 (2000), 457–515, and these laminations become exactly the laminations Λ± for the new branched foliation. Other corollaries include that the ambient manifold is δ-hyperbolic in the sense of Gromov, and that a self-homeomorphism of this manifold homotopic to the identity is isotopic to the identity.
The Big Market Delusion: Valuation and Investment Implications

In entrepreneurs’ minds, big markets offer the promise of easily scalable revenues that, coupled with profitability, can translate into large profits. This article examines how the “big market prom...
Novel Bacterial Diversity and Fragmented eDNA Identified in Hyperbiofilm-Forming Pseudomonas aeruginosa Rugose Small Colony Variant

Pseudomonas aeruginosa biofilms represent a major threat to health care. Rugose small colony variants (RSCV) of P. aeruginosa, isolated from chronic infections, display hyperbiofilm phenotype. RSCV biofilms are highly resistant to antibiotics and host defenses. This work shows that RSCV biofilm aggregates consist of two distinct bacterial subpopulations that are uniquely organized displaying contrasting physiological characteristics. Compared with that of PAO1, the extracellular polymeric substance of RSCV PAO1ΔwspF biofilms presented unique ultrastructural characteristics. Unlike PAO1, PAO1ΔwspF released fragmented extracellular DNA (eDNA) from live cells. Fragmented eDNA, thus released, was responsible for resistance of PAO1ΔwspF biofilm to disruption by DNaseI. When added to PAO1, such fragmented eDNA enhanced biofilm formation. Disruption of PAO1ΔwspF biofilm was achieved by aurine tricarboxylic acid, an inhibitor of DNA-protein interaction. This work provides critical novel insights into the contrasting structural and functional characteristics of a hyperbiofilm-forming clinical bacterial variant relative to its own wild-type strain.
The Jacobian, Reflection Arrangement and Discriminant for Reflection Hopf Algebras

We study finite dimensional semisimple Hopf algebra actions on noetherian connected graded Artin-Schelter regular algebras, and introduce definitions of the Jacobian, the reflection arrangement and the discriminant in a noncommutative setting.
Galaxy External Display Applications: closing a dataflow interoperability loop.

Interoperability of datasets, tools, and resources is essential to modern scientific investigation and analysis. The necessity to gather disparate datasets together, perform analysis with a collection of discrete tools, and visualize the results remains a standard approach for exploring and making sense across scientific research domains. Here, we describe the Galaxy External Display Application (GEDA) framework which provides researchers with the ability to facilitate the interoperability of Galaxy user data and external resources, while promoting findability, accessibility, and reuse. The only requirement on the external resource for GEDA accessibility is that it is able to accept a parameter value that contains a URL pointing to user data.
Eleven-Year Experience Treating Blunt Thoracic Aortic Injury at a Tertiary Referral Center

Background Blunt thoracic aortic injury treatment has evolved over the past decade particularly with respect to endovascular intervention options. We investigated the trends in blunt thoracic aortic injury management and outcomes over an 11-year span at the sole tertiary referral center in our state. Methods We retrospectively reviewed all patients who presented to our institution with blunt traumatic aortic injury between 2007 and 2017. Baseline demographics including aortic injury grade, injury severity score, and abbreviated injury scale were collected. Outcomes were compared by type and timing of treatment, which included either nonoperative management, endovascular repair, or open surgical repair. Bivariate and multivariable analyses were performed to examine treatment group differences and factors associated with 30-day mortality. Results In total, 229 patients were reviewed. The distribution of injury severity was grade 1 (30%), grade 2 (8%), grade 3 (30%), and grade 4 (31%). Overall, 27% of patients underwent endovascular repair, 29% open surgery, and 44% definitive nonoperative management. Over the study period, there was a dramatic decline in open surgery and a corresponding rise in endovascular treatment. Thirty-day mortality for the entire cohort was 22%. Mortality by treatment subgroup was 30% for nonoperative management, 8.2% for endovascular treatment, and 21% for open surgery. Delaying endovascular or open surgical treatment by at least 24 hours after admission was associated with significantly improved 30-day survival. Conclusions Procedural intervention, whether endovascular or surgical, is associated with improved mortality compared with nonoperative treatment. Delayed intervention, particularly in the case of high-grade injuries, may allow for initial patient stabilization and improved outcomes.
Persistence and extinction of nonlocal dispersal evolution equations in moving habitats

Abstract This paper is devoted to the study of persistence and extinction of a species modeled by nonlocal dispersal evolution equations in moving habitats with moving speed c . It is shown that the species becomes extinct if the moving speed c is larger than the so called spreading speed c ∗ , where c ∗ is determined by the maximum linearized growth rate function. If the moving speed c is smaller than c ∗ , it is shown that the persistence of the species depends on the patch size of the habitat, namely, the species persists if the patch size is greater than some number L ∗ and in this case, there is a traveling wave solution with speed c , and it becomes extinct if the patch size is smaller than L ∗ .
Direct Measurement of the Impact of Teaching Experimentation in Physics Labs

Traditional physics labs can have a negative impact on student learning, whereas nontraditional inquiry-based labs improve performance and engagement while maintaining exam scores.
Moment preserving constrained resampling with applications to particle-in-cell methods

Abstract The Moment Preserving Constrained Resampling (MPCR) algorithm for particle resampling is introduced and applied to particle-in-cell (PIC) methods to increase simulation accuracy, reduce compute cost, and/or avoid numerical instabilities. The general algorithm partitions the system space into smaller subsets and resamples the distribution within each subset. Further, the algorithm is designed to conserve any number of particle and grid moments with a high degree of accuracy (i.e. machine accuracy). The effectiveness of MPCR is demonstrated with several numerical tests, including a use-case study in gyrokinetic fusion plasma simulations. The computational cost of MPCR is negligible compared to the cost of particle evolution in PIC methods, and the tests demonstrate that periodic particle resampling yields a significant improvement in the accuracy and stability of the results.
Use of the Mitrofanoff Principle in Urinary Reconstruction

The Mitrofanoff principle for continent reconstruction of the lower urinary tract may be summarized as: 1) the use of a narrow catheterizable conduit (appendix or ureter) brought to the skin; 2) anti-refluxing connection of the conduit for catheterization to the reservoir to provide continence; 3) a large low pressure leak-proof urine storage reservoir (bladder, augmented bladder, or colon segment); 4) anti-refluxing connection of the upper urinary tract to the reservoir; 5) effective complete emptying of the reservoir by clean intermittent catheterization. Our experience with eight successful cases is reviewed.
A strong splitting of the Frobenius morphism on the algebra of distributions of $SL_2$

Let $p$ be a prime number, and let $Dist(SL_2)$ be the algebra of distributions, supported at $1$, on the algebraic group $SL_2$ over $\mathbb{F}_p$. The Frobenius map $Fr:SL_2\to SL_2$ induces a map $Fr:Dist(SL_2)\to Dist(SL_2)$ which is in particular a surjective algebra homomorphism. In this note, we construct a section of this map, whenever $p\geq 3$. The main ingredient of this construction is a certain congruence modulo $p^3$, reminiscent of the congruence $\binom{np}{p}\equiv n\mod p^3$.
Navigation of a Quadratic Potential with Ellipsoidal Obstacles

Successful navigation of a convex quadratic potential in a space with ellipsoidal obstacles can be attained with Rimon-Koditschek artificial potentials in spaces where the ellipsoids are not too eccentric (flat). This paper proposes a modification to gradient dynamics that allows successful navigation of an environment with a quadratic cost and ellipsoidal obstacles regardless of their eccentricity. This is accomplished by altering gradient dynamics with the addition of a second order curvature correction that is intended to imitate worlds with spherical obstacles in which Rimon-Koditschek potentials are known to work. Convergence to the goal is proven for all environments with a single obstacle. In worlds with multiple obstacles convergence is guaranteed in cases when the obstacles are not tightly packed around the agent’s target. Results are numerically verified with a discretized version of the proposed flow dynamics.
Control-Lyapunov and Control-Barrier Functions based Quadratic Program for Spatio-temporal Specifications

This paper presents a method for control synthesis under spatio-temporal constraints. First, we consider the problem of reaching a set S in a user-defined or prescribed time T. We define a new class of control Lyapunov functions, called prescribed-time control Lyapunov functions (PT CLF), and present sufficient conditions on the existence of a controller for this problem in terms of PT CLF. Then, we formulate a quadratic program (QP) to compute a control input that satisfies these sufficient conditions. Next, we consider control synthesis under spatio-temporal objectives given as: the closed- loop trajectories remain in a given set S s at all times; and, remain in a specific set S i during the time interval [t i ,t i+1 ) for i = 0,1,⋯, N; and, reach the set S i+1 on or before t = t i+1 . We show that such spatio-temporal specifications can be translated into temporal logic formulas. We present sufficient conditions on the existence of a control input in terms of PT CLF and control barrier functions. Then, we present a QP to compute the control input efficiently, and show its feasibility under the assumptions of existence of a PT CLF. To the best of authors’ knowledge, this is the first paper proposing a QP based method for the aforementioned problem of satisfying spatiotemporal specifications for nonlinear control-affine dynamics with input constraints. We also discuss the limitations of the proposed methods and directions of future work to overcome these limitations. We present numerical examples to corroborate our proposed methods.