cssi/SciSciGPT-SciSciNet huggingface dataset:
Semi-implicit iterative methods for low Mach number turbulent reacting flows

Two formally second-order accurate, semi-implicit, iterative methods for the solution of scalar transportreaction equations are developed for Direct Numerical Simulation (DNS) of low Mach number turbulent reacting flows. The first is a monolithic scheme based on a linearly implicit midpoint method utilizing an approximately factorized exact Jacobian of the transport and reaction operators. The second is an operator splitting scheme based on the Strang splitting approach. The accuracy properties of these schemes, as well as their stability, cost, and the effect of chemical mechanism size on relative performance, are assessed in two one-dimensional test configurations comprising an unsteady premixed flame and an unsteady nonpremixed ignition, which have substantially different Damkhler numbers and relative stiffness of transport to chemistry. All schemes demonstrate their formal order of accuracy in the fully-coupled convergence tests. Compared to a (non-)factorized scheme with a diagonal approximation to the chemical Jacobian, the monolithic, factorized scheme using the exact chemical Jacobian is shown to be both more stable and more economical. This is due to an improved convergence rate of the iterative procedure, and the difference between the two schemes in convergence rate grows as the time step increases. The stability properties of the Strang splitting scheme are demonstrated to outpace those of Lie splitting and monolithic schemes in simulations at high Damkhler number; however, in this regime, the monolithic scheme using the approximately factorized exact Jacobian is found to be the most economical at practical CFL numbers. The performance of the schemes is further evaluated in a simulation of a three-dimensional, spatially evolving, turbulent nonpremixed planar jet flame.
UNDERSTANDING THE PATHOLOGIES

Hip joint preservation remains a preferred treatment option for hips with mechanically correctable pathologies prior to the development of significant secondary arthrosis. The pathologies most amenable to joint preservation are hip dysplasia and femoroacetabular impingement. These pathologies sometimes overlap. Untreated acetabular dysplasia of modest severity, if left uncorrected, always leads to arthrosis. Acetabular dysplasia is best treated by periacetabular osteotomy, usually combined with arthrotomy for management of labral pathology and associated cam-impingement if present. Pre-operative variables associated with the best long-term outcomes include less secondary arthrosis, younger age, and concentric articular surfaces. Femoroacetabular impingement has become progressively recognised as perhaps the most common cause of secondary arthrosis. The etiology of impingement is multifactorial and includes both genetic factors and stresses experienced by the hip prior to cessation of growth. Cam impingement can be quantified by the alpha angle as measured on plain radiographs and radial MR sequences. Further, significant cam impingement is clearly associated with the development of osteoarthrosis. Treatment can be performed either by arthroscopic or open femoral head-neck osteochondroplasty. As with hip dysplasia, prognosis following treatment is correlated with the severity of preoperative secondary arthrosis but unfortunately impinging hips more commonly have some degree of arthrosis preop whereas dysplastic hips can become symptomatic with instability in the absence of arthrosis. The scientific basis for the treatment of pincer impingement is less strong. Unlike cam impingement and hip dysplasia, pincer impingement pathology in the absence of coxa profunda has not been correlated with arthrosis and so rim trimming with labral refixation is probably performed more often than is clinically indicated. Similarly, caution should be exercised when considering rim-trimming for protrusion since high central contact pressures due to an enlarged acetabular notch are not corrected by rim trimming. Overall, joint preserving surgery remains the preferred treatment for hips with mechanically correctable problems prior to the development of significant secondary arthrosis.
Evaluating Different Implementations of Online Junction Temperature Sensing for Switching Power Semiconductors

Switching power semiconductor online junction temperature $T_{j}$ sensing is essential for device switching performance evaluation, device switching control, and device lifetime optimization. The contribution of this paper is a detailed evaluation of the implementation issues (including circuit invasiveness, hardware integration, signal processing, and so forth) of different online $T_{j}$ sensing methods. This paper includes $T_{j}$ sensing methods based on device power dissipation, $T_{j}$ sensing methods based on the “sensor-on-die technology,” $T_{j}$ sensing methods based on device on-state analysis, and $T_{j}$ sensing methods based on device switching transients. Advantages and limits of these methods are also provided.
A 9.8b-ENOB 5.5fJ/step fully-passive compressive sensing SAR ADC for WSN applications

The emerging compressive sensing (CS) theory states that the sparsity of a signal can be exploited to reduce the ADC conversion rate. However, most previous CS frameworks still require dedicated analog CS encoders in front-of low-rate ADCs. Differently, this work proposes a fully-passive CS framework that directly embeds CS into a conventional SAR ADC, reducing the ADC power by 4 times. A prototype chip is fabricated in a 0.13µm CMOS process. Discrete-tone signals are converted and reconstructed with a peak SNDR of 61dB and a maximum signal sparsity of 8.2%. A 1-second long speech signal is also used to demonstrate the capability of the chip to compressively sense real-world signals. At 0.8V and 1MS/s, the CS-SAR ADC consumes 19.2µW in the Nyquist mode and 5µW in the CS mode. The peak FoM in the CS mode is 5.5fJ/step.
Do we need demographic data to forecast plant population dynamics

Summary Rapid environmental change has generated growing interest in forecasts of future population trajectories. Traditional population models built with detailed demographic observations from one study site can address the impacts of environmental change at particular locations, but are difficult to scale up to the landscape and regional scales relevant to management decisions. An alternative is to build models using population-level data that are much easier to collect over broad spatial scales than individual-level data. However, it is unknown whether models built using population-level data adequately capture the effects of density-dependence and environmental forcing that are necessary to generate skillful forecasts. Here, we test the consequences of aggregating individual responses when forecasting the population states (percent cover) and trajectories of four perennial grass species in a semi-arid grassland in Montana, USA. We parameterized two population models for each species, one based on individual-level data (survival, growth and recruitment) and one on population-level data (percent cover), and compared their forecasting accuracy and forecast horizons with and without the inclusion of climate covariates. For both models, we used Bayesian ridge regression to weight the influence of climate covariates for optimal prediction. In the absence of climate effects, we found no significant difference between the forecast accuracy of models based on individual-level data and models based on population-level data. Climate effects were weak, but increased forecast accuracy for two species. Increases in accuracy with climate covariates were similar between model types. In our case study, percent cover models generated forecasts as accurate as those from a demographic model. For the goal of forecasting, models based on aggregated individual-level data may offer a practical alternative to data-intensive demographic models. Long time series of percent cover data already exist for many plant species. Modelers should exploit these data to predict the impacts of environmental change.
Optimization for Large-Scale Machine Learning with Distributed Features and Observations

As the size of modern data sets exceeds the disk and memory capacities of a single computer, machine learning practitioners have resorted to parallel and distributed computing. Given that optimization is one of the pillars of machine learning and predictive modeling, distributed optimization methods have recently garnered ample attention in the literature. Although previous research has mostly focused on settings where either the observations, or features of the problem at hand are stored in distributed fashion, the situation where both are partitioned across the nodes of a computer cluster (doubly distributed) has barely been studied. In this work we propose two doubly distributed optimization algorithms. The first one falls under the umbrella of distributed dual coordinate ascent methods, while the second one belongs to the class of stochastic gradient/coordinate descent hybrid methods. We conduct numerical experiments in Spark using real-world and simulated data sets and study the scaling properties of our methods. Our empirical evaluation of the proposed algorithms demonstrates the out-performance of a block distributed ADMM method, which, to the best of our knowledge is the only other existing doubly distributed optimization algorithm.
Active Learning from Imperfect Labelers

We study active learning where the labeler can not only return incorrect labels but also abstain from labeling. We consider different noise and abstention conditions of the labeler. We propose an algorithm which utilizes abstention responses, and analyze its statistical consistency and query complexity under fairly natural assumptions on the noise and abstention rate of the labeler. This algorithm is adaptive in a sense that it can automatically request less queries with a more informed or less noisy labeler. We couple our algorithm with lower bounds to show that under some technical conditions, it achieves nearly optimal query complexity.
A hierarchical electricity market structure for the smart grid paradigm

This paper proposed a hierarchical structure for the electricity market to facilitate the coordination of energy markets in distribution and transmission networks. The proposed market structure enables the integration of microgrids, which provide energy and ancillary services in distribution networks. In the proposed hierarchical structure, microgrids participate in the energy market at the distribution networks settled by the distribution network operator (DNO), and load aggregators (LAs) interact with microgrids and generation companies (GENCOs) to import/export energy to/from the distribution network electricity markets from/to the wholesale electricity market. The proposed approach addressed the synergy of energy markets by introducing dynamic game with complete information for GENCOs, microgrids, and LAs. The proposed hierarchical competition is composed of bi-level optimization problems in which the respective upper-level problems maximize the individual market participants' payoff, and the lower-level problems represent the market settlement accomplished by the DNO or the independent system operator. The bi-level problems are solved by developing sensitivity functions for market participants' payoff with respect to their bidding strategies. A case study is employed to illustrate the effectiveness of the proposed approach.
Biological Weapons—A Primer for Microbiologists

Biological weapons are not new. Biological agents have been used as instruments of warfare and terror for thousands of years to produce fear and harm in humans, animals, and plants. Because they ar...
A dielectrically isolated saturating circuit

In an effort to see how much improvement could be made in a silicon saturating-logic circuit with good noise immunity, using developmental technology, a six-input T2L switching circuit was designed, incorporating dielectric isolation and very small-area devices. A high concentration of gold mast be introduced into the base and collector areas to provide the low-lifetimes necessary for ultra-fast circuits. Since gold will not diffuse through the silicon dioxide dielectric isolation layer, it must be diffused through the openings in the top surface of the wafer into a relatively small volume of silicon. An overabundance of gold can compensate donor impurities in the collector region, resulting in very high resistivity. Thus, control of the amount of gold introduced into each island is highly critical, and was accomplished by separately limiting the amount of gold available to each different-sized island, and by careful control of the goldplating solution and gold diffusion temperature.
The Predominant CD4+ Th1 Cytokine Elicited to Chlamydia trachomatis Infection in Women Is Tumor Necrosis Factor Alpha and Not Interferon Gamma

Chlamydia trachomatis infection is the most prevalent bacterial sexually transmitted infection and can cause significant reproductive morbidity in women. There is insufficient knowledge of C. trachomatis -specific immune responses in humans, which could be important in guiding vaccine development efforts. In contrast, murine models have clearly demonstrated the essential role of Type 1 T helper cells (Th1), especially interferon-gamma (IFN-γ) producing CD4 + T-cells, in protective immunity to chlamydia. To determine the frequency and magnitude of Th1 cytokine responses elicited to C. trachomatis infection in humans, we stimulated peripheral blood mononuclear cells from 90 chlamydia-infected women with C. trachomatis elementary bodies, Pgp3, and major outer membrane protein and measured IFN-γ-, tumor necrosis factor-alpha (TNF-α)-, and interleukin 2 (IL-2)-producing CD4 + and CD8 + T-cell responses using intracellular cytokine staining. The majority of chlamydia-infected women elicited CD4 + TNF-α responses, with frequency and magnitude varying significantly depending on the C. trachomatis antigen used. CD4 + IFN-γ and IL-2 responses occurred infrequently, as did production of any of the three cytokines by CD8 + T-cells. About one-third of TNF-α-producing CD4 + T-cells co-produced IFN-γ or IL-2. In summary, the predominant Th1 cytokine response elicited to C. trachomatis infection in women was a CD4 + TNF-α response, not CD4 + IFN-γ, and a subset of the CD4 + TNF-α-positive cells produced a second Th1 cytokine.
Instantaneous Decentralized Poker

We present efficient protocols for amortized secure multiparty computation with penalties and secure cash distribution, of which poker is a prime example. Our protocols have an initial phase where the parties interact with a cryptocurrency network, that then enables them to interact only among themselves over the course of playing many poker games in which money changes hands.
Impact of height heterogeneity on canopy turbulence

The flow development above and within homogeneous and heterogeneous canopies was experimentally studied using particle image velocimetry in a refractive-index-matching channel. The experiments were designed to gain insight into the effect of height heterogeneity on the structure and spatial distribution of the turbulence. The homogeneous model (base case) is constituted of elements of height $h$ arranged in a staggered configuration; whereas the heterogeneous canopy resembled a row canopy and consisted of elements of two heights $h_{1}=h+(1/3)h$ and $h_{2}=h-(1/3)h$ alternated every two rows. Both canopies had the same density, element geometry and mean height. The flow was studied under three submergences $H/h=2$ , 3 and 4, where $H$ denotes the flow depth. The experiments were performed at Reynolds number $Re_{H}\simeq 6500$ , 11 300 and 12 300 and nearly constant Froude number $Fr\simeq 0.1$ . Turbulence statistics complemented with quadrant analysis and proper orthogonal decomposition reveal richer flow dynamics induced by height heterogeneity. Topography-induced spatially periodic mean flows are observed for the heterogeneous canopy. Furthermore, and in contrast to the homogeneous case, non-vanishing vertical velocity is maintained across the entire length of the heterogeneous canopy with increased levels at lower submergence depths. Further alternations were induced in the magnitude and distribution of the turbulent kinetic energy, Reynolds shear stress and characteristics of the canopy mixing layer, evidencing enhanced mixing and turbulent transport for the heterogeneous canopy especially at lower submergence depths. Overall, the results indicate that heterogeneous canopies exhibit greater vertical turbulent exchange at the canopy interface, suggesting a potential for greater scalar exchange and a greater impact on channel hydraulic resistance than a homogeneous canopy of similar roughness density.
JSForce: A Forced Execution Engine for Malicious JavaScript Detection

The drastic increase of JavaScript exploitation attacks has led to a strong interest in developing techniques to analyze malicious JavaScript. Existing analysis techniques fall into two general categories: static analysis and dynamic analysis. Static analysis tends to produce inaccurate results (both false positive and false negative) and is vulnerable to a wide series of obfuscation techniques. Thus, dynamic analysis is constantly gaining popularity for exposing the typical features of malicious JavaScript. However, existing dynamic analysis techniques possess limitations such as limited code coverage and incomplete environment setup, leaving a broad attack surface for evading the detection. To overcome these limitations, we present the design and implementation of a novel JavaScript forced execution engine named JSForce which drives an arbitrary JavaScript snippet to execute along different paths without any input or environment setup. We evaluate JSForce using 220,587 HTML and 23,509 PDF real-world samples. Experimental results show that by adopting our forced execution engine, the malicious JavaScript detection rate can be substantially boosted by 206.29% using same detection policy without any noticeable false positive increase.
Nash social welfare for indivisible items under separable, piecewise-linear concave utilities

Recently Cole and Gkatzelis [10] gave the first constant factor approximation algorithm for the problem of allocating indivisible items to agents, under additive valuations, so as to maximize the Nash social welfare (NSW). We give constant factor algorithms for a substantial generalization of their problem - to the case of separable, piecewise-linear concave utility functions. We give two such algorithms, the first using market equilibria and the second using the theory of real stable polynomials. Both approaches require new algorithmic ideas.
CORRECTING SEVERE VALGUS DEFORMITY: TAKING OUT THE KNOCK

Valgus deformity is less common than varus. There is an associated bone deformity in many cases – dysplasia of the lateral femoral condyle. There are also soft tissue deformities, including tightness of the lateral soft tissues, and stretching of those on the medial side. Unlike varus, where the bone deformity is primarily tibial, in valgus knees it is most often femoral. There is both a distal and a posterior hypoplasia of the lateral femoral condyle. This results in a sloping joint line, and failure to correct this results in valgus malalignment. Posterior lateral bone loss also results in accidental internal rotation of the femoral component, which affects patellar tracking. Using the trans-epicondylar axis and Whiteside9s line helps to position the femoral component in the correct rotation. Soft tissue balancing is more complex in the valgus knee. Releases are performed sequentially, depending on the particular combination of deformities. It is important to note whether the knee is tight in flexion, in extension, or both. Tightness in extension is the most common, and is corrected by release of the iliotibial band. Tightness in flexion as well as extension requires that the lateral collateral ligament +/− the popliteus tendon be released. Cruciate substituting designs are helpful in many cases, and in extreme deformity with medial stretching, a constrained or “total stabilised” design is needed. Patellar maltracking is common, and a lateral retinacular release may be needed. Beware of over-releasing the posterolateral corner, as excessive release may cause marked instability. Use the pie-crust technique of Insall.
Some experiments in discrete utterance recognition

This paper is concerned with the following four aspects of the discrete utterance recognition problem: utterance normalization, dynamic programming (DP) algorithm implementation, boundary error effects, and the importance of the transition states in speech, Performance sensitivity as a function of each aspect of the problem is comparatively studied utilizing several available alternatives, and significant conclusions are drawn regarding each of them. The concept of proportional normalizing is introduced as an effective method of handling the utterance normalization problem. The implications of this method and the conventional mean normalizing method are discussed. In general, normalization by either of these techniques is seen to be about equally effective and leads to substantial improvement in recognition score. Next, several alternative implementations of the dynamic programming algorithm are investigated and their performances are compared. Finally, improved recognition performance is obtained by introducing a smoothing technique which emphasizes the importance of the transition states in speech. A database consisting of the utterances of the alpha-digit vocabulary produced by several male and female speakers is used to conduct all the experiments.
Analysis and Control of Stochastic Systems Using Semidefinite Programming Over Moments

This technical note develops a unified methodology for probabilistic analysis and optimal control design for jump diffusion processes defined by polynomials. The statistical moments of these systems can be described by a system of linear ordinary differential equations. Typically, however, the low-order moments depend on higher order moments, thus requiring an infinite system of equations to compute any moment exactly. Here, we develop a methodology for bounding statistical moments by using the higher order moments as inputs to an auxiliary convex optimal control problem with semidefinite constraints. For steady-state problems, the auxiliary optimal control problem reduces to a static semidefinite program. The method applies to both controlled and uncontrolled stochastic processes. For stochastic optimal control problems, the method gives bounds on achievable performance and can be used to compute approximately optimal solutions. For uncontrolled problems, both upper and lower bounds on desired moments can be computed. While the accuracy of most moment approximations cannot be quantitatively characterized, our method guarantees that the moment of interest is between the computed bounds.
knor: A NUMA-Optimized In-Memory, Distributed and Semi-External-Memory k-means Library

k-means is one of the most influential and utilized machine learning algorithms. Its computation limits the performance and scalability of many statistical analysis and machine learning tasks. We rethink and optimize k-means in terms of modern NUMA architectures to develop a novel parallelization scheme that delays and minimizes synchronization barriers. The k-means NUMA Optimized Routineknor) library has (i) in-memory knori), (ii) distributed memory (knord), and (ii) semi-external memory (\textsf{knors}) modules that radically improve the performance of k-means for varying memory and hardware budgets. knori boosts performance for single machine datasets by an order of magnitude or more. \textsf{knors} improves the scalability of k-means on a memory budget using SSDs. knors scales to billions of points on a single machine, using a fraction of the resources that distributed in-memory systems require. knord retains knori's performance characteristics, while scaling in-memory through distributed computation in the cloud. knor modifies Elkan's triangle inequality pruning algorithm such that we utilize it on billion-point datasets without the significant memory overhead of the original algorithm. We demonstrate knor outperforms distributed commercial products like H2O, Turi (formerly Dato, GraphLab) and Spark's MLlib by more than an order of magnitude for datasets of 107 to 109 points.
Genetic differences in mechanisms involving neuroregulators.

Publisher Summary This chapter discusses genetic differences in mechanisms involving neuroregulators. The potential role of genetic factors becomes of great importance in terms of a number of theories of the illness. The possibility of genetic variation in catecholamine synthesizing activity is suggested by several lines of evidence. In animals, strain and subline differences have been reported in the amounts of biogenic amines in brain regions of mice and rats and in the utilization and uptake of cardiac norepinephrine in mice. In humans, a variety of forms of pheochromocytoma have been shown to be associated with familial factors. There are known genetic variations in adrenocortical and adrenomedullary structure. Genetic variations in steroid hormones produced by the adrenal cortex and in thyroid hormone have been associated with behavioral changes, and further e search for genetic variation in the enzymes involved in catecholamine biosynthesis would be fruitful. The use of inbred mouse strains is particularly advantageous in studies of genetic variation and provides a powerful tool for subsequent behavioral and genetic analysis.
Obstacle avoidance system with sonar sensing and fuzzy logic

Automated guided vehicles (AGVs) have many potential applications in manufacturing, medicine, space and defense. The purpose of this paper is to describe exploratory research on the design of an obstacle avoidance system using sonar sensors for a modular autonomous mobile robot controller. The advantages of a modular system are related to portability and the fact that any vehicle can become autonomous with minimal modifications. A mobile robot test-bed has been constructed using a golf cart base. The obstacle avoidance system is based on a micro-controller interfaced with multiple ultrasonic transducers. This micro-controller independently handles all timing and distance calculations and sends a distance measurement back to the computer via the serial line. This design yields a portable independent system. Testing of these systems has been done in the lab as well as on an outside test track with positive results that show that at five mph the vehicle can follow a line and at the same time avoid obstacles. This design, in its modularity, creates a portable autonomous obstacle avoidance controller applicable for any mobile vehicle with only minor adaptations.
Technology shocks and hours revisited: Evidence from household data

Abstract I exploit heterogeneous impulse responses at the household level due to limited stock market participation to provide novel evidence on the degree of nominal rigidities. A number of studies show that positive technology shocks reduce aggregate hours. The finding is often interpreted as evidence in favor of sticky prices. Using the Consumer Expenditure Survey, I show that, while non-stockholders reduce hours in response to a positive technology shock, stockholders increase them. Aggregate hours fall because most households are non-stockholders. This finding is inconsistent with models featuring a high degree of nominal rigidities.
Wi-Fi Hotspot Auto-Discovery: A Practical & Energy-Aware System for Smart Objects using Cellular Signals

The Internet of Things (IoT) paradigm aims to interconnect a variety of heterogeneous Smart Objects (SO) using energy-efficient methodologies and standard communication protocols. A majority of consumer devices sold today come equipped with wireless LAN and cellular technology to connect with the world-wide network. To discover Wi-Fi hot spots, there is a need for constant scanning of Wi-Fi radio in these devices and results in significant battery drain. We present PRiSM, a practical system to automatically locate Wi-Fi hotspots while Wi-Fi radio is turned off, by using the statistical characteristics of cellular signals. Cellular signals are received at zero extra cost in mobile devices and hence PRiSM is highly energy-efficient. It is a lightweight client-side only implementation and needs no prior knowledge on floor plans or wireless infrastructure. We implement PRiSM on Android-based devices and show up to 96% of energy savings in Wi-Fi sensing operations which is equivalent to saving up to 16% of total battery capacity, together with an average prediction accuracy of up to 98%.
On the Fine-Grained Complexity of Empirical Risk Minimization: Kernel Methods and Neural Networks

Empirical risk minimization (ERM) is ubiquitous in machine learning and underlies most supervised learning methods. While there is a large body of work on algorithms for various ERM problems, the exact computational complexity of ERM is still not understood. We address this issue for multiple popular ERM problems including kernel SVMs, kernel ridge regression, and training the final layer of a neural network. In particular, we give conditional hardness results for these problems based on complexity-theoretic assumptions such as the Strong Exponential Time Hypothesis. Under these assumptions, we show that there are no algorithms that solve the aforementioned ERM problems to high accuracy in sub-quadratic time. We also give similar hardness results for computing the gradient of the empirical loss, which is the main computational burden in many non-convex learning tasks.
Evaluation of Classifier Performance for Multiclass Phenotype Discrimination in Untargeted Metabolomics

Statistical classification is a critical component of utilizing metabolomics data for examining the molecular determinants of phenotypes. Despite this, a comprehensive and rigorous evaluation of the accuracy of classification techniques for phenotype discrimination given metabolomics data has not been conducted. We conducted such an evaluation using both simulated and real metabolomics datasets, comparing Partial Least Squares-Discriminant Analysis (PLS-DA), Sparse PLS-DA, Random Forests, Support Vector Machines (SVM), Artificial Neural Network, k-Nearest Neighbors (k-NN), and Naive Bayes classification techniques for discrimination. We evaluated the techniques on simulated data generated to mimic global untargeted metabolomics data by incorporating realistic block-wise correlation and partial correlation structures for mimicking the correlations and metabolite clustering generated by biological processes. Over the simulation studies, covariance structures, means, and effect sizes were stochastically varied to provide consistent estimates of classifier performance over a wide range of possible scenarios. The effects of the presence of non-normal error distributions, the introduction of biological and technical outliers, unbalanced phenotype allocation, missing values due to abundances below a limit of detection, and the effect of prior-significance filtering (dimension reduction) were evaluated via simulation. In each simulation, classifier parameters, such as the number of hidden nodes in a Neural Network, were optimized by cross-validation to minimize the probability of detecting spurious results due to poorly tuned classifiers. Classifier performance was then evaluated using real metabolomics datasets of varying sample medium, sample size, and experimental design. We report that in the most realistic simulation studies that incorporated non-normal error distributions, unbalanced phenotype allocation, outliers, missing values, and dimension reduction, classifier performance (least to greatest error) was ranked as follows: SVM, Random Forest, Naive Bayes, sPLS-DA, Neural Networks, PLS-DA and k-NN classifiers. When non-normal error distributions were introduced, the performance of PLS-DA and k-NN classifiers deteriorated further relative to the remaining techniques. Over the real datasets, a trend of better performance of SVM and Random Forest classifier performance was observed.
Gossip in a Smartphone Peer-to-Peer Network

In this paper, we study the fundamental problem of gossip in the mobile telephone model: a recently introduced variation of the classical telephone model modified to better describe the local peer-to-peer communication services implemented in many popular smartphone operating systems. In more detail, the mobile telephone model differs from the classical telephone model in three ways: (1) each device can participate in at most one connection per round; (2) the network topology can undergo a parameterized rate of change; and (3) devices can advertise a parameterized number of bits about their state to their neighbors in each round before connection attempts are initiated. We begin by describing and analyzing new randomized gossip algorithms in this model under the harsh assumption of a network topology that can change completely in every round. We prove a significant time complexity gap between the case where nodes can advertise 0 bits to their neighbors in each round, and the case where nodes can advertise 1 bit. For the latter assumption, we present two solutions: the first depends on a shared randomness source, while the second eliminates this assumption using a pseudorandomness generator we prove to exist with a novel generalization of a classical result from the study of two-party communication complexity. We then turn our attention to the easier case where the topology graph is stable, and describe and analyze a new gossip algorithm that provides a substantial performance improvement for many parameters. We conclude by studying a relaxed version of gossip in which it is only necessary for nodes to each learn a specified fraction of the messages in the system. We prove that our existing algorithms for dynamic network topologies and a single advertising bit solve this relaxed version up to a polynomial factor faster (in network size) for many parameters. These are the first known gossip results for the mobile telephone model, and they significantly expand our understanding of how to communicate and coordinate in this increasingly relevant setting.
Pricing Variance, Gamma, and Corridor Swaps Using Multinomial Trees

Pricing and hedging real world derivatives requires approximation methods for all but the plainest of plain vanilla cases. The two standard classes of valuation tools are lattices (trees) and Monte Carlo simulation. Lattice methods start at maturity and work backward through the tree to compute the derivative value at the beginning. This works very well as long as the payoff at a given time step depends on the asset price at that date but not on the path the price followed to arrive there. There are many more paths through a tree than there are time steps, so path-dependent problems normally require Monte Carlo and calculations over a very large number of paths to achieve accuracy. This raises major problems for valuing derivatives based on volatility and other higher moments of the returns distribution, because realized values of these statistics do depend on the path and not just the terminal price. In this article, the authors present a new kind of lattice procedure for variance swaps and related contracts that achieves the accuracy of a Monte Carlo approximation in execution time that is very much less—more than two orders of magnitude faster in a basic example. The technique involves a single backward pass through the tree, with efficient calculations of conditional future variance through expiration at each intermediate node being built up along the way. The procedure does not depend on any specific returns process, and examples with several well-known stochastic volatility models show excellent performance on all of them.
Applying Stochastic Control Theory to Robot Sensing, Teaching, and Long Term Control

Abstract Robot applications in industry are essentially repetitious in nature and have a strong stochastic component. Random errors occur because of inaccuracies or wear in jigs, manufacturing tolerances in parts, and imperfect robot behavior. Sensors which provide feedback to the robot also are error-prone. In addition there are systematic errors due to misaligned coordinate frames between the robot and its environment. This paper shows mathematically how stochastic control theory can be used to help robots calibrate and monitor themselves, check for drift, learn the correct spacing between holes or the pitch of palletized parts, and other long term matters of interest to practical intelligent robot behavior. Examples are worked out, computer simulations are presented, and experimental results are described.
Hands-On Labs for Learning Mobile and NoSQL Database Security.

Database systems are at the heart of modern information systems and have become primary targets of cybersecurity attacks. With the advent of mobile and big data computing, new database models and systems, such as mobile and NoSQL databases, have been emergent and gaining popularity. Nevertheless, these new systems not only recapitulate some of the important security issues in traditional database systems but also introduce new security challenges. Few educational resources are readily available for the security education of these new database systems. This paper presents our ongoing work on developing a set of hands-on labs for the security education of mobile and NoSQL databases. We discuss the security issues of mobile and NoSQL databases, present a list of labs that are being developed, and use an example to illustrate the design of the labs.
Renal Allografts: Prospective Analysis of Doppler Sonography

Fifty-six consecutively transplanted renal allografts were prospectively evaluated with serial Doppler sonographic examinations. Thirty-eight episodes of transplant rejection in 32 patients (63% proved pathologically) and 24 episodes of acute tubular necrosis (ATN) in 24 patients were encountered. The Doppler spectral waveform was characterized by means of the pulsatility index (PI), systolic/diastolic ratio (SDR), diastolic/systolic ratio (SDR), diastolic/systolic ratio (DSR), and resistive index (RI). Accuracy was optimized with use of top normal values as follows: PI = 1.8, SDR = 4.0, DSR = 0.25, RI = 0.75. There were no significant differences in the indices for those patients undergoing rejection versus those with ATN. The sensitivity for predicting transplant rejection was adversely affected by the history of either ATN or a previous rejection episode in the same allograft. Comparison with concurrent radionuclide examinations revealed similar sensitivities for rejection with scintigraphy and sonogra...
Effect of laser induced orbital momentum on magnetization switching

Abstract The observed magnetization switching by circularly polarized ultrafast laser pulses has been attributed to the inverse Faraday effect in which the induced non-equilibrium orbital momentum serves as an effective magnetic field via spin-orbit coupling for magnetization rotation and switching. We critically examine this scenario by explicitly calculating the magnitude of the induced orbital momentum for generic itinerant band. We show that the calculated induced angular momentum is not large enough for reversing the magnetization by one laser pulse with the order of 100 femtosecond duration. Instead, we propose that each laser pulse is capable to expand a reverse domain a few nano-meters and it takes multiple pulses to complete the magnetization reversal process via domain wall motion.
Beyond Police Compliance With Electronic Recording of Interrogation Legislation: Toward Error Reduction:

Video recording police interrogations is a widespread reform designed to reduce false confessions. An exploratory survey of Michigan police departments following the adoption of a video recording l...
EXTENSOR MECHANISM DEFICIENCY: A SYNTHETIC MESH APPROACH

Complications involving the knee extensor mechanism occur in 1% to 12% of patients following total knee arthroplasty (TKA), and have negative effects on patient outcomes. While multiple reconstruction options have been described, the results in patients with a prior TKA are inferior to those in patients without a TKA. However, optimistic results have been reported by Browne and Hanssen with the use of a synthetic mesh (knitted monofilament polypropylene)3. In this technique, a synthetic graft is created by folding a 10 × 14 inch sheet of mesh and securing it with nonabsorbable sutures. A burr is then used to create a trough in the anterior aspect of the tibia to accept the mesh graft. The graft is inserted into the trough and secured with cement. After the cement cures, a transfixion screw with a washer is placed. A portal is subsequently created in the lateral soft tissues to allow delivery of the graft from deep to superficial. The patella and quadriceps tendon are mobilised, and the graft is secured with sutures to the lateral retinaculum, vastus lateralis, and quadriceps tendon. The vastus medialis is then mobilised in a pants-over-vest manner over the mesh graft, and secured with sutures. Finally, the distal arthrotomy is closed tightly to completely cover the mesh graft with host tissue. In their series, Browne and Hanssen noted that 9 of 13 patients achieved an extensor lag of > 10 degrees with preserved knee flexion and significant improvements in the mean Knee Society scores for pain and function.
Online Learning in Weakly Coupled Markov Decision Processes: A Convergence Time Study

We consider multiple parallel Markov decision processes (MDPs) coupled by global constraints, where the time varying objective and constraint functions can only be observed after the decision is made. Special attention is given to how well the decision maker can perform in T slots, starting from any state, compared to the best feasible randomized stationary policy in hindsight. We develop a new distributed online algorithm where each MDP makes its own decision each slot after observing a multiplier computed from past information. While the scenario is significantly more challenging than the classical online learning context, the algorithm is shown to have a tight O(√T) regret and constraint violations simultaneously. To obtain such a bound, we combine several new ingredients including ergodicity and mixing time bound in weakly coupled MDPs, a new regret analysis for online constrained optimization, a drift analysis for queue processes, and a perturbation analysis based on Farkas' Lemma.
A local limit theorem and loss of rotational symmetry of planar symmetric simple random walk

We derive a local limit theorem for normal, moderate, and large deviations for symmetric simple random walk on the square lattice in dimensions one and two that is an improvement of existing results for points that are particularly distant from the walk's starting point. More specifically, we give explicit asymptotic expressions in terms of $n$ and $x$, where $x$ is thought of as dependent on $n$, in dimensions one and two for $P(S_n=x)$, the probability that symmetric simple random walk $S$ started at the origin is at some point $x$ at time $n$, that are valid for all $x$. We also show that the behavior of planar symmetric simple random walk differs radically from that of planar standard Brownian motion outside of the disk of radius $n^{3/4}$, where the random walk ceases to be approximately rotationally symmetric. Indeed, if $n^{3/4}=o(|S_n|)$, $S_n$ is more likely to be found along the coordinate axes. In this paper, we give a description of how the transition from approximate rotational symmetry to complete concentration of $S$ along the coordinate axes occurs.
Observations of Accreting Pulsars

We discuss recent observations of accreting binary pulsars with the all-sky BATSE instrument on the Compton Gamma Ray Observatory. BATSE has detected and studied nearly half of the known accreting pulsar systems. Continuous timing studies over a two-year period have yielded accurate orbital parameters for 9 of these systems, as well as new insights into long-term accretion torque histories.
Thermal-ignition analysis in boundary-layer flows

The weakly reactive state leading to the ignition of a steady laminar boundary-layer flow of a combustible mixture over a hot, isothermal, non-permeable, non-catalytic flat plate is studied both numerically and using matched asymptotic analysis in the realistic limit of large activation energy. It is shown that the flow consists of a locally similar diffusive-reactive region next to the plate and a non-similar diffusive-convective region outside it; that the analytic solution obtained reproduces the lower half of the S-shaped ignition-extinction response curve such that ignition is expected to occur when a suitably defined Damkohler number, which increases with the streamwise distance, reaches unity; and that at the point of ignition the heat transfer from the wall vanishes identically. An explicit expression for the minimum distance for ignition to occur is also derived.
Hoxa9 and Hoxa10 induce CML myeloid blast crisis development through activation of Myb expression

Mechanisms underlying the progression of Chronic Myeloid Leukemia (CML) from chronic phase to myeloid blast crisis are poorly understood. Our previous studies have suggested that overexpression of SETBP1 can drive this progression by conferring unlimited self-renewal capability to granulocyte macrophage progenitors (GMPs). Here we show that overexpression of Hoxa9 or Hoxa10, both transcriptional targets of Setbp1, is also sufficient to induce self-renewal of primary myeloid progenitors, causing their immortalization in culture. More importantly, both are able to cooperate with BCR/ABL to consistently induce transformation of mouse GMPs and development of aggressive leukemias resembling CML myeloid blast crisis, suggesting that either gene can drive CML progression by promoting the self-renewal of GMPs. We further identify Myb as a common critical target for Hoxa9 and Hoxa10 in inducing self-renewal of myeloid progenitors as Myb knockdown significantly reduced colony-forming potential of myeloid progenitors immortalized by the expression of either gene. Interestingly, Myb is also capable of immortalizing primary myeloid progenitors in culture and cooperating with BCR/ABL to induce leukemic transformation of mouse GMPs. Significantly increased levels of MYB transcript also were detected in all human CML blast crisis samples examined over chronic phase samples, further suggesting the possibility that MYB overexpression may play a prevalent role in driving human CML myeloid blast crisis development. In summary, our results identify overexpression of HOXA9, HOXA10, and MYB as critical drivers of CML progression, and suggest MYB as a key therapeutic target for inhibiting the self-renewal of leukemia-initiating cells in CML myeloid blast crisis patients.
Auditor-client geographic proximity and audit report timeliness

Abstract While investors and regulators value the timely release of audited financial information, recent changes in the regulatory environment have increased the difficulty of providing timely audited financial information. In this paper we examine the association between auditor-client geographic proximity and external audit report delay, since identifying the factors that influence audit delay remains an important issue. We find strong evidence that audit reports are more timely for geographically proximate auditors and clients. Further, we show that the improvement in audit report timeliness is more pronounced for non-accelerated filers relative to accelerated filers. These results are robust to controls for potential self-selection bias.