Miracle-dz/UniFAR-FTUs huggingface dataset:
Operational Pro les for the Performability Evaluation of Multiuser Software

E ects of shared use on the performability of modular software can be evaluated by considering an operational environment which distinguishes the users. Speci cally, we examine a total system consisting of a community of n users who share a software system withmmodules, referred to simply as an n m system. The computational demands of these users, as inherited by the modules, is represented by a continuoustime, nite-state Markov process X called the operational pro le (see [1] for details concerning its justi cation). The pro le's construction is based on the isolated pro les of individual users which, in the case of heterogeneous use, are pairwise-distinct Markov processes. Moreover, in the presence of other users, an individual pro le can di er from its isolated version due to slowdowns caused by sharing. Further, due to design faults in the modules, failures may be experienced during execution. In this regard, shared use of a module can a ect the rate at which a user experiences failures. Accordingly, we permit such rates to depend on both the state of the operational pro le and the user in question. The extent to which a failure rate is altered by multiple use is called a stress factor, de ned to be the ratio of that rate to the value experienced in isolation.
Image‐Based Crack Detection for Real Concrete Surfaces

In this paper, we introduce a novel image‐based approach to detect cracks in concrete surfaces. Crack detection is important for the inspection, diagnosis, and maintenance of concrete structures. However, conventional image‐based approaches cannot achieve precise detection since the image of the concrete surface contains various types of noise due to different causes such as concrete blebs, stain, insufficient contrast, and shading. In order to detect the cracks with high fidelity, we assume that they are composed of thin interconnected textures and propose an image‐based percolation model that extracts a continuous texture by referring to the connectivity of brightness and the shape of the percolated region, depending on the length criterion of the scalable local image processing techniques. Additionally, noise reduction based on the percolation model is proposed. We evaluated the validity of the proposed technique by using precision recall and receiver operating characteristic (ROC) analysis by means of some experiments with actual concrete surface images. Copyright © 2007 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
Study on the Application of Ant Colony Algorithm in the Route of Internet of Things

In the routing processing of Internet of things, network traffic distribution changes constantly, network links and nodes will also fail randomly or some new nodes will be added; the autocatalytic and positive feedback mechanism of the ant colony algorithm conforms to the characteristics of the route searching. This paper uses ant colony algorithm to search route; uses the broadcast signaling, which is featured with the random multi-sending and the short life cycle, to overcome the problem of more network nodes and more variable network structure. The simulation results show that searching route by ant colony algorithm in Internet of things can reduce the broadcast storm effectively. With the increasing number of nodes in the route searching, the time of route setup was significantly shortened.
Color-emotion associations: Past experience and personal preference

This study examined color-emotion associations and the reasons for emotional reactions given to colors. Ten fully saturated chromatic colors were chosen from the Munsell color system: red, yellow, green, blue, purple, yellow-red, green-yellow, blue-green, purple-blue, and redpurple. Apart from these ten hue groups, three achromatic colors (white, black and gray) were also used. The sample consisted of 98 volunteered college students at a public institution in the southeast region of the US. Results revealed that the principle hues comprised the highest number of positive emotional responses, followed by the intermediate hues and the achromatic colors. Color symbolism seems to be apparent in how individuals associate colors with things, objects or physical space. Red-purple, for instance, was associated with the color of red wine, plum, bridesmaid dress, or the color of a bedroom. Overall, a color-related emotion was highly dependent on personal preference and one’s past experience with that particular color.
Effectual Upshots on Firm Performance : A determinative perspective of Business Model Innovation

This paper analyzes the relationship between business model innovation and firm performance in terms of both cost and profit efficiency. Unlike previous studies, the present analysis is carried out within a stochastic frontier framework, while we use a suitable econometric model to solve for possible endogeneity issues. The empirical framework is applied to an industry-wide sample of UK firms during the period 2002-2009. The paper, firstly, documents a positive correlation between business innovation and cost and profit efficiency. Secondly, we investigate the effect of individual, organizational and country level characteristics on business model innovation. Using dynamic frameworks, the empirical analysis reveals that business model innovation is affected by the size of the company and the human capital. The board structure plays a crucial role as well as the presence of foreign owners, by contrast to increased ownership concentration which seems to exert a negative influence. Moreover, we find that GDP enhance business innovation. Finally, we identify a positive impact of certain industries on innovation, based on individual investments on R&D.
Towards highly parallel event processing through reconfigurable hardware

We present fpga-ToPSS (Toronto Publish/Subscribe System), an efficient event processing platform to support high-frequency and low-latency event matching. fpga-ToPSS is built over reconfigurable hardware---FPGAs---to achieve line-rate processing by exploring various degrees of parallelism. Furthermore, each of our proposed FPGA-based designs is geared towards a unique application requirement, such as flexibility, adaptability, scalability, or pure performance, such that each solution is specifically optimized to attain a high level of parallelism. Therefore, each solution is formulated as a design trade-off between the degree of parallelism versus the desired application requirement. Moreover, our event processing engine supports Boolean expression matching with an expressive predicate language applicable to a wide range of applications including real-time data analysis, algorithmic trading, targeted advertisement, and (complex) event processing.
Robust head pose estimation via Convex Regularized Sparse Regression

This paper studies the problem of learning robust regression for real world head pose estimation. The performance and applicability of traditional regression methods in real world head pose estimation are limited by a lack of robustness to outlying or corrupted observations. By introducing low-rank and sparse regularizations, we propose a novel regression method, named Convex Regularized Sparse Regression (CRSR), for simultaneously removing the noise and outliers from the training data and learning the regression between image features and pose angles. We verify the efficiency of the proposed robust regression method with extensive experiments on real data, demonstrating lower error rates and efficiency than existing methods.
Reduction of Continuous Neuronal Model to Discrete Binary Automata

This article presents the reduction of neuronal models from the classic four-dimensional differential model of Hodgkin and Huxley [7] to discrete binary automata which keep the main properties of more complex models. A reduction of Fitzhugh and Nagumo (FHN) model is performed using a numerical strategy introduced in [3] completed by a linearization in the spirit of McKean model [14]. The resultant discrete binary model keeps the properties of the complete FHN model. The numerical simulations of networks composed by these discrete binary automata demonstrate changes in the system dynamics dependent on the coupling strength. Moreover, for large coupling strength, phase-locking is observed.
The Relationship between Recast and Noticing Sensed by Basic Level EFL Learners

The role of corrective feedback, especially recast, has become a highly controversial issue in language learning. In this study, the researcher intended to see which kind of recast resulted in better noticing on the part of learners. In doing so, 32 EFL learners at the elementary level participated in the current research. They were divided in two different groups, one included 18 and the other 14 learners. The study aims to help teachers to better understand the effectiveness of full recast and partial recast. Chi Square and Paired-Samples t-test were run to analyze the data. The hypothesis was retained, thereby showing there is no difference between full and partial recast in improving the learners’ noticing ability.
Eecient Mpi for Virtual Interface (vi) Architecture

E cient Message Passing Interface implementations for emerging cluster interconnects are an important requirement for useful parallel processing on cost-e ective clusters of NT workstations. This paper reports on a new implementation of MPI for VI Architecture networks. Support for high bandwidth, low latency, and low overhead are considered, as is the match of the MPI speci cation to the VI Architecture features. The ability to o er thread safety, computation and communication overlapping, optimized protocols, and full MPI progress are highlighted.
Data mining and Costas arrays

Using all available Costas arrays up to and including order 26, and data mining techniques, we investigate how the Costas property and the balance of signs in the difference triangle of a permutation are related. Our conclusion is that there is sufficient evidence to believe that the mechanism responsible for the formation of Costas arrays changes from low to high orders.
The MaoP/maoS Site-Specific System Organizes the Ori Region of the E. coli Chromosome into a Macrodomain

The Ori region of bacterial genomes is segregated early in the replication cycle of bacterial chromosomes. Consequently, Ori region positioning plays a pivotal role in chromosome dynamics. The Ori region of the E. coli chromosome is organized as a macrodomain with specific properties concerning DNA mobility, segregation of loci and long distance DNA interactions. Here, by using strains with chromosome rearrangements and DNA mobility as a read-out, we have identified the MaoP/maoS system responsible for constraining DNA mobility in the Ori region and limiting long distance DNA interactions with other regions of the chromosome. MaoP belongs to a group of proteins conserved in the Enterobacteria that coevolved with Dam methylase including SeqA, MukBEF and MatP that are all involved in the control of chromosome conformation and segregation. Analysis of DNA rings excised from the chromosome demonstrated that the single maoS site is required in cis on the chromosome to exert its effect while MaoP can act both in cis and in trans. The position of markers in the Ori region was affected by inactivating maoP. However, the MaoP/maoS system was not sufficient for positioning the Ori region at the ¼–¾ regions of the cell. We also demonstrate that the replication and the resulting expansion of bulk DNA are localized centrally in the cell. Implications of these results for chromosome positioning and segregation in E. coli are discussed.
Representation and structure in the re-use of design rationale by novice analysts

Breakdowns have variantly been described as symptoms or causes of difficulties in the design process [4], instances that lead to the reframing of problems [3], or in a more general sense, as occurrences of object/idea emergence [7], to mention a few. They seem to play a large role in design behaviour. They directly affect the decision making process and subsequently the course of action, and vice versa. A common piece of ground in all types of breakdowns is the lack of readily available knowledge of strategies that correspond to the particular problem class.
Factores asociados al proceso de recaída en adolescentes consumidores de alcohol

Teenagers can be involved in alcohol consumption because of different causes: alcohol and drugs availability, search for new sensations, absence of risk perception, curiosity, peers imitation, etc. Peer group has particular influence as a model on the beginning of alcohol and drugs consumption, nevertheless teenagers can learn self control strategies that allow them to consume less or not consume and to avoid relapses after receiving an intervention program. The objective of this work is to identify risk situations and the use of coping strategies on those in two groups: those �� ��� ��� ��� ��� ��� ��� ��� ��� ��� ���� ������������ ��� ���������� ������ ���������� ������ ���������� ������ ���������� ������ . � �� �� � �� �� � �� �� �� ������ �������� �������� �������� ������� ������ �������� �������� �������� �������
Instruction scheduling for clustered VLIW architectures

Clustered VLIW organizations are nowadays a common trend in the design of embedded/DSP processors. In this work we propose a novel modulo scheduling approach for such architectures. The proposed technique performs the cluster assignment and the instruction scheduling in a single pass, which is more effective than doing first the assignment and latter the scheduling. We also show that loop unrolling significantly enhances the performance of the proposed scheduler, especially when the communication channel among clusters is the main performance bottleneck. By selectively unrolling some loops, we can obtain the best performance with the minimum increase in code size. Performance evaluation for the SPECfp95 shows that the clustered architecture achieves about the same IPC (Instructions Per Cycle) as a unified architecture with the same resources. Moreover, when the cycle time is taken into account, a 4-cluster configuration is 3.6 times faster than the unified architecture.
Algorithm for Communication Synchronization on Reconfigurable Processor Arrays with Faults

Efficient fault tolerant techniques for reconfigurable multiprocessor array have been extensively studied to construct maximum target array from host array with faulty processors. Existing work focused on the reconfiguration algorithm without considering the communication synchronization of the target array. This paper proposes an algorithm to rearrange the long interconnects of the target array, in order to improve the communication performance in synchronization. In addition, divide and conquer strategy is utilized for deleting logical rows to form a high performance target array with given size. Experimental results show that the proposed algorithm achieves considerable improvement on communication performance in synchronization for the case of small fault rate which is often occurred in real applications.
Macrophage-tumor cell interactions regulate the function of nitric oxide

Tumor cell-macrophage interactions change as the tumor progresses, and the generation of nitric oxide (NO) by the inducible nitric oxide synthase (iNOS) plays a major role in this interplay. In early stages, macrophages employ their killing mechanisms, particularly the generation of high concentrations of NO and its derivative reactive nitrogen species (RNS) to initiate tumor cell apoptosis and destroy emerging transformed cells. If the tumor escapes the immune system and grows, macrophages that infiltrate it are reprogramed in situ by the tumor microenvironment. Low oxygen tensions (hypoxia) and immunosuppressive cytokines inhibit iNOS activity and lead to production of low amounts of NO/RNS, which are pro-angiogenic and support tumor growth and metastasis by inducing growth factors (e.g., VEGF) and matrix metalloproteinases (MMPs). We review here the different roles of NO/RNS in tumor progression and inhibition, and the mechanisms that regulate iNOS expression and NO production, highlighting the role of different subtypes of macrophages and the microenvironment. We finally claim that some tumor cells may become resistant to macrophage-induced death by increasing their expression of microRNA-146a (miR-146a), which leads to inhibition of iNOS translation. This implies that some cooperation between tumor cells and macrophages is required to induce tumor cell death, and that tumor cells may control their fate. Thus, in order to induce susceptibility of tumors cells to macrophage-induced death, we suggest a new therapeutic approach that couples manipulation of miR-146a levels in tumors with macrophage therapy, which relies on ex vivo stimulation of macrophages and their re-introduction to tumors.
Numerical modeling of CIGS and CdTe solar cells: setting the baseline

Numerical modeling of polycrystalline thin-film solar cells is an important strategy to test the viability of proposed physical explanations and to predict the effect of physical changes on cell performance. In general, this must be done with only partial knowledge of input parameters. Nevertheless, for consistent comparisons between laboratories, it is extremely useful to have a common starting point, or baseline. We will discuss guidelines that should be considered assigning input parameters for numerical modeling. Consequently specific baseline parameters for CIGS and CdTe are proposed. The modeling results for these baseline cases are presented and it is discussed how the baseline cases serve to describe some of the most important complications that are often found in experimental ClGS and CdTe solar cells.
Investigation of Variable -Diffusion Turbulence Model Correction for Round Jets

A Reynolds -Averaged Navier -Stokes ( RANS) correction is described to account for a key effect of acoustic interaction near the end of the potential core for axisymmetric jets. Specifically, the Variable -Diffusion (Var -D) co rrection is formulated to mimic the effect of increased shear layer instability/motion near the end of the potential core by adjusting the local diffusion of k and �. The proposed Var -D correction is initially evaluated for an unheated axisymmetric jet wi th a perfectly expanded jet Mach number of 0.5. Results for centerline velocity distribution and turbulent kinetic energy contours using commonly employed turbulence models (i.e. SST and k-�) and k-� with the Var -D correction are compared. Substantial im provement for prediction of potential core length, centerline velocity decay, and k field is demonstrated with the Var -D correction. The sensitivity of the proposed model to the closure coefficients associated with the correction is also examined using th is cold subsonic jet case. Similar turbulence model performance comparisons are made for three other jets to evaluate the sensitivity of the correction to increased jet exhaust compressibility and heated jet flow conditions. The effect of the improved me an and turbulence field predictions obtained using the Var -D correction on farfield noise prediction is also evaluated using the JeNo acoustic analogy code, and shown to be negligible. Although the approach holds promise, the applicability of the Var -D co rrection is currently limited to round jets.
Following Newton direction in Policy Gradient with parameter exploration

This paper investigates the use of second-order methods to solve Markov Decision Processes (MDPs). Despite the popularity of second-order methods in optimization literature, so far little attention has been paid to the extension of such techniques to face sequential decision problems. Here we provide a model-free Reinforcement Learning method that estimates the Newton direction by sampling directly in the parameter space. In order to compute the Newton direction we provide the formulation of the Hessian of the expected return, a technique for variance reduction in the sample-based estimation and a finite sample analysis in the case of Normal distribution. Beside discussing the theoretical properties, we empirically evaluate the method on an instructional linear-quadratic regulator and on a complex dynamical quadrotor system.
Facing the most difficult case of Semantic Role Labeling: A collaboration of word embeddings and co-training

We present a successful collaboration of word embeddings and co-training to tackle in the most difficult test case of semantic role labeling: predicting out-of-domain and unseen semantic frames. Despite the fact that co-training is a successful traditional semi-supervised method, its application in SRL is very limited especially when a huge amount of labeled data is available. In this work, co-training is used together with word embeddings to improve the performance of a system trained on a large training dataset. We also introduce a semantic role labeling system with a simple learning architecture and effective inference that is easily adaptable to semi-supervised settings with new training data and/or new features. On the out-of-domain testing set of the standard benchmark CoNLL 2009 data our simple approach achieves high performance and improves state-of-the-art results.
Curl-Constrained Gradient Estimation for Image Recovery From Highly Incomplete Spectral Data

In this paper, we introduce new gradient-based methods for image recovery from a small collection of spectral coefficients of the Fourier transform, which is of particular interest for several scanning technologies, such as magnetic resonance imaging. Since gradients of a medical image are much more sparse or compressible than the corresponding image, classical $\ell _{1}$ -minimization methods have been used to recover these relative differences. The image values can then be obtained by integration algorithms imposing boundary constraints. Compared with classical gradient recovery methods, we propose two new techniques that improve reconstruction. First, we cast the gradient recovery problem as a compressed sensing problem taking into account that the curl of the gradient field should be zero. Second, inspired by the emerging field of signal processing on graphs, we formulate the gradient recovery problem as an inverse problem on graphs. Iteratively reweighted $\ell _{1}$ recovery methods are proposed to recover these relative differences and the structure of the similarity graph. Once the gradient field is estimated, the image is recovered from the compressed Fourier measurements using least squares estimation. Numerical experiments show that the proposed approach outperforms the state-of-the-art image recovery methods.
Deadline-Constrained Clustered Scheduling for VLIW Architectures using Power-Gated Register Files

Designing energy-efficient Digital Signal Processor (DSP) cores has become a key concern in embedded systems development. This paper proposes an energy-proportional computing scheme for Very Long Instruction Word (VLIW) architectures. To make the processor power scales with adapted parallelism, we propose incorporating distributed Power-Gated Register Files (PGRF) into VLIW to achieve a PGRF-VLIW architecture. For energy efficiency, we also propose an instruction scheduling algorithm called the Deadline-Constrained Clustered Scheduling (DCCS) algorithm. The algorithm clusters the data dependence graph to reduce data transfer energy and makes optimal use of low-powered local registers for tree-structured data dependence graphs. The results of evaluations conducted using the MiBench and DSPstone benchmark suites substantiate the expected power saving and scaling effects.