Miracle-dz/UniFAR-FTUs huggingface dataset:
Onset, propagation, and evolution of strain localization in undrained plane strain experiments on clay

The conventional triaxial test is the primary laboratory test for determining the shear strength of soils. Geotechnical field conditions such as long earth dams, long embankments, long retaining walls, strip foundations, tunnels, and buried pipelines often experience plane strain states of stress. However, stress strain and load deformation responses in plane strain loading differ considerably from responses observed in the conventional triaxial test. Research has shown that soils loaded in a plane strain state are far more sensitive to imperfections than soils tested in a conventional triaxial device. Plane strain loading leads to material instability manifested as sudden localized failure, resulting in decreased load-carrying capacity of the soil and compromised geotechnical and civil infrastructures. Although previous studies have mostly focused on granular materials, this research investigated the plane strain response of clay. An undrained plane strain compression test program was devised to investigate the effects of past stress history and strain rates on strain localization in kaolin clay. Experiments were carried out in a plane strain (or biaxial) device at Northwestern University, Evanston, Illinois. Because the device was heavily internally instrumented, strain localization progress was closely monitored throughout each biaxial test. Clay response in the biaxial test demonstrated three phases: (1) a homogenous response, (2) the onset and propagation of strain localization, and (3) the evolution of strain localization as a shear band. The duration of each phase was determined for each test, and a Lagrange strain tensor was used to obtain the evolution of volumetric and shear strains at the level of a shear band for three tests. Results revealed the development of large strains in these mesoscale structures. Furthermore, evolution of Mohr-Coulomb effective shear strength parameters was traced throughout the propagation and evolution phases by using two different methods. It showed that in clay samples, unlike in granular materials, the post-peak plateau, which is reached by deviatoric stress, corresponds to friction values that are significantly lower than the critical state values. Other researchers who used scanning electron microscope and anisotropy of magnetic susceptibility detected a significant reorientation of clay particles inside shear bands. Their findings combined with findings in this study lead to the conclusion that the sub-meso scale mechanism responsible for large shear strains and a severe reduction in effective friction is a significant reorientation of clay particles inside shear band.
Active Power Management of Multihybrid Fuel Cell/Supercapacitor Power Conversion System in a Medium Voltage Microgrid

This paper proposes a hierarchical active power management strategy for a medium voltage (MV) islanded microgrid including a multihybrid power conversion system (MHPCS). To guarantee excellent power management, a modular power conversion system is realized by parallel connection of small MHPCS units. The hybrid system includes fuel cells (FC) as main and supercapacitors (SC) as complementary power sources. The SC energy storage compensates the slow transient response of the FC stack and supports the FC to meet the grid power demand. The proposed control strategy of the MHPCS comprises three control loops; dc-link voltage controller, power management controller, and load current sharing controller. Each distributed generation (DG) unit uses an adaptive proportional resonance (PR) controller for regulating the load voltage, and a droop control strategy for average power sharing among the DG units. The performance of the proposed control strategy is verified by using digital time-domain simulation studies in the PSCAD/EMTDC software environment.
Sensor Map Discovery for Developing Robots

Modern mobile robots navigate uncertain environments using complex compositions of camera, laser, and sonar sensor data. Manual calibration of these sensors is a tedious process that involves determining sensor behavior, geometry and location through model specification and system identification. Instead, we seek to automate the construction of sensor model geometry by mining uninterpreted sensor streams for regularities. Manifold learning methods are powerful techniques for deriving sensor structure from streams of sensor data. In recent years, the proliferation of manifold learning algorithms has led to a variety of choices for autonomously generating models of sensor geometry. We present a series of comparisons between different manifold learning methods for discovering sensor geometry for the specific case of a mobile robot with a variety of sensors. We also explore the effect of control laws and sensor boundary size on the efficacy of manifold learning approaches. We find that ”motor babbling” control laws generate better geometric sensor maps than mid-line or wall following control laws and identify a novel method for distinguishing boundary sensor elements. We also present a new learning method, sensorimotor embedding, that takes advantage of the controllable nature of robots to build sensor maps.
Prevalence of Diabetes in the 2009 Influenza A (H1N1) and the Middle East Respiratory Syndrome Coronavirus: A Systematic Review and Meta-Analysis

Over the past two decades a number of severe acute respiratory infection outbreaks such as the 2009 influenza A (H1N1) and the Middle East respiratory syndrome coronavirus (MERS-CoV) have emerged and presented a considerable global public health threat. Epidemiologic evidence suggests that diabetic subjects are more susceptible to these conditions. However, the prevalence of diabetes in H1N1 and MERS-CoV has not been systematically described. The aim of this study is to conduct a systematic review and meta-analysis of published reports documenting the prevalence of diabetes in H1N1 and MERS-CoV and compare its frequency in the two viral conditions. Meta-analysis for the proportions of subjects with diabetes was carried out in 29 studies for H1N1 (n=92,948) and 9 for MERS-CoV (n=308). Average age of H1N1 patients (36.2±6.0 years) was significantly younger than that of subjects with MERS-CoV (54.3±7.4 years, P<0.05). Compared to MERS-CoV patients, subjects with H1N1 exhibited 3-fold lower frequency of cardiovascular diseases and 2- and 4-fold higher prevalence of obesity and immunosuppression, respectively. The overall prevalence of diabetes in H1N1 was 14.6% (95% CI: 12.3-17.0%; P<0.001), a 3.6-fold lower than in MERS-CoV (54.4%; 95% CI: 29.4-79.5; P<0.001). The prevalence of diabetes among H1N1 cases from Asia and North America was ~two-fold higher than those from South America and Europe. The prevalence of diabetes in MERS-CoV cases is higher than in H1N1. Regional comparisons suggest that an etiologic role of diabetes in MERS-CoV may exist distinctive from that in H1N1. Significance for public health Outbreaks of the 2009 influenza A (H1N1) and the Middle East respiratory syndrome coronavirus (MERS-CoV) have presented a considerable global public health threat over the past few years. Evidence suggest that infected subjects who are also diabetic are more likely to be susceptible to severe outcome of H1N1 and MERS-CoV. Systematic analysis of ~93,000 H1N1 cases and ~300 MERS-CoV cases indicated an overall prevalence of 14.6% and 54.4% for diabetes in H1N1 and MERS-CoV cases, respectively. This may suggest a possible etiological relationship between diabetes and each of the two infectious conditions. Introduction of effective public health vaccination intervention strategies against severe acute respiratory infections should be developed to target subjects with chronic disorders such as diabetes, obesity and cardiovascular conditions.
The Yo-Yo Intermittent Recovery Test in Junior Basketball Players According to Performance Level and Age Group

Abstract Vernillo, G, Silvestri, A, and La Torre, A. The Yo-Yo intermittent recovery test in junior basketball players according to performance level and age group. J Strength Cond Res 26(9): 2490–2494, 2012—The aim of this study was to evaluate the Yo-Yo Intermittent Recovery Test Level 1 (Yo-Yo IR1) ability to discriminate between elite, subelite junior basketball players, and a group of nonathletic healthy male athletes at 3 different age groups (U-14 to U-17). In a cross-sectional design, 119 age-matched participants spread over 3 groups, elite (n = 46), subelite (n = 42) junior basketball players, and nonathletic healthy male athletes (n = 31), were evaluated over a 5-week period. The participants undertook 2 familiarization trials of the Yo-Yo test performance and 3 test sessions on an indoor basketball court. When controlling for the effect of the participants' body mass, the results showed that elite athletes had a significantly higher Yo-Yo performance compared with the subelite athletes (1,271 ± 385 vs. 861 ± 428 m; p < 0.0017; effect size [ES] 1.0 ± 0.35) and the nonathletic group (1,271 ± 385 vs. 738 ± 345 m; p < 0.0017; ES 1.45 ± 0.38). No statistical differences (p > 0.0017; ES from 0.02 to 0.39) were noted between participants' performance levels across age groups. Typical between-performance levels and -age groups differences in the Yo-Yo IR1 were observed. However, when controlling for the effect of the participants' body mass, this study demonstrates that the Yo-Yo test is accurate only to discriminate elite junior basketball players but cannot be used to differentiate the basketball-specific aerobic performance for age.
ERP evidence for the time course of graphic, phonological, and semantic information in Chinese meaning and pronunciation decisions.

Two words that varied in their relationship were presented sequentially to Chinese readers who made meaning and pronunciation decisions. In the meaning task, they decided whether the words had the same meaning. In the pronunciation task, they decided whether the words had the same pronunciation. In both tasks, the word pairs represented 1 of 4 relationships: graphically similar, homophonic, semantically related, or unrelated. Event related potentials (ERP) recordings made from the onset of the 2nd word suggested a temporal unfolding of graphic, phonological, and semantic effects. Specifically, graphically-related pairs produced a smaller P200 in the pronunciation task and a smaller N400 in the meaning task. Homophones produced reduced N400 component with bilateral sources in the meaning task.
Coordinated Beamforming for Energy Efficient Transmission in Multicell Multiuser Systems

In this paper we study energy efficient joint power allocation and beamforming for coordinated multicell multiuser downlink systems. The considered optimization problem is in a non-convex fractional form and hard to tackle. We propose to first transform the original problem into an equivalent optimization problem in a parametric subtractive form, by which we reach its solution through a two-layer optimization scheme. The outer layer only involves one-dimension search for the energy efficiency parameter which can be addressed using the bi-section search, the key issue lies in the inner layer where a non-fractional sub-problem needs to tackle. By exploiting the relationship between the user rate and the mean square error, we then develop an iterative algorithm to solve it. The convergence of this algorithm is proved and the solution is further derived in closed-form. Our analysis also shows that the proposed algorithm can be implemented in parallel with reasonable complexity. Numerical results illustrate that our algorithm has a fast convergence and achieves near-optimal energy efficiency. It is also observed that at the low transmit power region, our solution almost achieves the optimal sum rate and the optimal energy efficiency simultaneously; while at the middle-high transmit power region, a certain sum rate loss is suffered in order to guarantee the energy efficiency.
A New Approach to Extract PD Pulse using Independent Component Analysis

Assumption, ambiguities, and solution of the basic independent component analysis (ICA) model are briefly introduced in this paper. According to the leptokurtic distribution of PD pulse magnitude and other practical conditions, it indicates that there is a feasibility for the basic ICA model used to extract PD pulse signal under strong white noise. Simulation tests for PD pulse extraction using UWB detection technology with two channels have been done. And how to eliminate the ambiguities for the basic ICA model to extract PD pulse signal under DC voltage is also presented. The results show PD pulse can be exactly extracted from the strong white noise with keeping PD pulse amplitude of the relative relationship and the maximum amplitude corresponding to the time point, and other important information of the PD pulse sequence.
Detecting splogs via temporal dynamics using self-similarity analysis

This article addresses the problem of spam blog (splog) detection using temporal and structural regularity of content, post time and links. Splogs are undesirable blogs meant to attract search engine traffic, used solely for promoting affiliate sites. Blogs represent popular online media, and splogs not only degrade the quality of search engine results, but also waste network resources. The splog detection problem is made difficult due to the lack of stable content descriptors. We have developed a new technique for detecting splogs, based on the observation that a blog is a dynamic, growing sequence of entries (or posts) rather than a collection of individual pages. In our approach, splogs are recognized by their temporal characteristics and content. There are three key ideas in our splog detection framework. (a) We represent the blog temporal dynamics using self-similarity matrices defined on the histogram intersection similarity measure of the time, content, and link attributes of posts, to investigate the temporal changes of the post sequence. (b) We study the blog temporal characteristics using a visual representation derived from the self-similarity measures. The visual signature reveals correlation between attributes and posts, depending on the type of blogs (normal blogs and splogs). (c) We propose two types of novel temporal features to capture the splog temporal characteristics. In our splog detector, these novel features are combined with content based features. We extract a content based feature vector from blog home pages as well as from different parts of the blog. The dimensionality of the feature vector is reduced by Fisher linear discriminant analysis. We have tested an SVM-based splog detector using proposed features on real world datasets, with appreciable results (90% accuracy).
Control-theoretic techniques and thermal-RC modeling for accurate and localized dynamic thermal management

This paper proposes the use of formal feedback control theory as a way to implement adaptive techniques in the processor architecture. Dynamic thermal management (DTM) is used as a test vehicle, and variations of a PID controller (Proportional-Integral-Differential) are developed and tested for adaptive control of fetch "toggling. " To accurately test the DTM mechanism being proposed, this paper also develops a thermal model based on lumped thermal resistances and thermal capacitances. This model is computationally efficient and tracks temperature at the granularity of individual functional blocks within the processor. Because localized heating occurs much faster than chip-wide heating, some parts of the processor are more likely, to be "hot spots" than others. Experiments using Wattch and the SPEC2000 benchmarks show that the thermal trigger threshold can be set within 0.2/spl deg/ of the maximum temperature and yet never enter thermal emergency. This cuts the performance loss of DTM by 65% compared to the previously described fetch toggling technique that uses a response of fixed magnitude.
Verification of Programs by Combining Iterated Specialization with Interpolation

We present a verification technique for program safety that combines Iterated Specialization and Interpolating Horn Clause Solving. Our new method composes together these two techniques in a modular way by exploiting the common Horn Clause representation of the verification problem. The Iterated Specialization verifier transforms an initial set of verification conditions by using unfold/fold equivalence preserving transformation rules. During transformation, program invariants are discovered by applying widening operators. Then the output set of specialized verification conditions is analyzed by an Interpolating Horn Clause solver, hence adding the effect of interpolation to the effect of widening. The specialization and interpolation phases can be iterated, and also combined with other transformations that change the direction of propagation of the constraints (forward from the program preconditions or backward from the error conditions). We have implemented our verification technique by integrating the VeriMAP verifier with the FTCLP Horn Clause solver, based on Iterated Specialization and Interpolation, respectively. Our experimental results show that the integrated verifier improves the precision of each of the individual components run separately.
The evolution of cooperation

Cooperation in organisms, whether bacteria or primates, has been a difficulty for evolutionary theory since Darwin. On the assumption that interactions between pairs of individuals occur on a probabilistic basis, a model is developed based on the concept of an evolutionarily stable strategy in the context of the Prisoner's Dilemma game. Deductions from the model, and the results of a computer tournament show how cooperation based on reciprocity can get started in an asocial world, can thrive while interacting with a wide range of other strategies, and can resist invasion once fully established. Potential applications include specific aspects of territoriality, mating, and disease.
Numerical solution of multivariate polynomial systems by homotopy continuation methods

pn(xi,.,xn) = 0 for x = (x\,. ,xn). This problem is very common in many fields of science and engineering, such as formula construction, geometric intersection problems, inverse kinematics, power flow problems with PQ-specified bases, computation of equilibrium states, etc. Elimination theory-based methods, most notably the Buchberger algorithm (Buchberger 1985) for constructing Grobner bases, are the classical approach to solving (1.1), but their reliance on symbolic manipulation makes those methods seem somewhat unsuitable for all but small problems.
Link Adaptation Scheme for Uplink MIMO Transmission with Turbo Receivers

In this paper, the turbo minimum mean square error- parallel interference cancelation (Turbo MMSE-PIC) receiver is incorporated with link adaptation schemes to achieve promising performance gains for uplink MIMO transmission, in parallel, adapt the link in the actual channel conditions with assist from performance prediction technology. We first predict the turbo receiver's performance as a basic metric for link adaptation scheme. Then, a new strategy of selecting the precoding matrices, the number of spatially multiplexed layers as well as modulation coding schemes are proposed, resulting in the proposed three-steps selection. In this method, the number of layers and modulation coding schemes to be feedback is initialized and used to set a shortened range of them. Then the number of layers is updated to take full advantage of iterative processing, instead of the initial value in the conventional strategy. In so doing, the benefit of turbo receivers is efficiently claimed while low computational complexity can be achieved.
Guided Perturbations: Self-Corrective Behavior in Convolutional Neural Networks

Convolutional Neural Networks have been a subject of great importance over the past decade and great strides have been made in their utility for producing state of the art performance in many computer vision problems. However, the behavior of deep networks is yet to be fully understood and is still an active area of research. In this work, we present an intriguing behavior: pre-trained CNNs can be made to improve their predictions by structurally perturbing the input. We observe that these perturbations - referred as Guided Perturbations - enable a trained network to improve its prediction performance without any learning or change in network weights. We perform various ablative experiments to understand how these perturbations affect the local context and feature representations. Furthermore, we demonstrate that this idea can improve performance of several existing approaches on semantic segmentation and scene labeling tasks on the PASCAL VOC dataset and supervised classification tasks on MNIST and CIFAR10 datasets.
Coordinated Voltage Control With Online Energy Margin Constraints

Based on data available from phase measurement units (PMUs), this paper proposes an energy margin constrained online coordinated voltage control (CVC) algorithm. First, the energy function model is established, which is based on detailed device models that include the effects of shunt capacitor banks, reactive power output of generators and under-load-tap-changers (ULTCs). In order to improve the computational efficiency, a Lie derivative-based method is used to map the energy change into the system trajectory along the energy manifold. Then, the change of energy margin based on the Lie derivative is used as the objective function and the reactive power margin is used as a constraint to formulate a linear programming problem. A coordinated strategy is proposed to help achieve the optimal solution when the linear programming problem is solved. In comparison with the other control modes, the proposed method improves the stability margin and the control effectiveness of voltage profile. Numerical results are provided for a three-machine system, the IEEE-30 bus system and the New England-39 bus benchmark system.
Genetic Annealing Optimization: Design and Real World Applications

Both simulated annealing (SA) and the genetic algorithms (GA) are stochastic and derivative-free optimization technique. SA operates on one solution at a time, while the GA maintains a large population of solutions, which are optimized simultaneously. Thus, the genetic algorithm takes advantage of the experience gained in the past exploration of the solution space. Since SA operates on one solution at a time, it has very little history to use in learning from past trials. SA has the ability to escape from any local point; even it is a global optimization technique. On the other hand, there is no guarantee that the GA algorithm will succeeded in escaping from any local minima, thus it makes sense to hybridize the genetic algorithm and the simulated annealing technique. In this paper, a novel genetically annealed algorithm is proposed and is tested against multidimensional and highly nonlinear cases; fed-batch fermentor for Penicillin production, and isothermal continuous stirred tank reactor CSTR. It is evident from the results that the proposed algorithm gives good performance.