Miracle-dz/UniFAR-FTUs huggingface dataset:
A Flexible Scan-in Power Control Method in Logic BIST and Its Evaluation with TEG Chips

High power dissipation in scan-based logic built-in self-test (LBIST) is a crucial issue that can cause over-testing, reliability degradation, chip damage, and so on. While many sophisticated approaches to low-power testing have been proposed in the past, it remains a serious problem to control the test power of LBIST to a predetermined appropriate level that matches the power requirements of the circuit-under-test. This paper proposes a novel power-control method for LBIST that can control the scan-shift power to an arbitrary level. The proposed method modifies pseudo-random patterns generated by an embedded test pattern generator (TPG) so that the modified patterns have the specific toggle rate without sacrificing fault coverage and test time. In order to evaluate the effectiveness of the proposed method, this paper shows not only simulation-based experimental results but also measurement results on test element group (TEG) chips.
Translating Denotative Meaning in the Holy Quran : Problems and Solutions

This article reports the findings of a research that aimed basically at examining the loss in denotative meaning in the translation of the Holy Quran, and how these losses can be reduced. It also identified the causes of the identified losses. Five ayahs were purposefully selected to address the research questions. The research results showed that loss in denotative meaning occurred due to many factors such as lack of equivalence and the translation strategies employed. This research also suggests solutions for the identified problems.
Automatic Detection and Classification of Argument Components using Multi-task Deep Neural Network

In this article we propose a novel method for automatically extracting and classifying argu-ment components from raw texts. We introduce a multi-task deep learning framework exploiting weight parameters trained on auxiliary simple tasks, such as Part-Of-Speech tagging or chunking, in order to solve more complex tasks that require a ﬁne-grained understanding of natural language. Interestingly, our results show that the use of advanced deep learning techniques framed in a multi-task setting enables competing with state-of-the-art systems that depend on handcrafted features.
The Effect of Negative Publicity on Consumer Loyalty A Theoretical Model

Negative publicity can harm a organisation in many ways. It can influence the consumers perception towards a firm or brand. This article examines how negative publicity deriving from a corporate crisis influences consumer loyalty. Throughout a literature study the effects of negative publicity and consumer loyalty were examined resulting in a theoretical model showing how negative publicity potentially influences consumer loyalty. The model proposed exists of two parts. The first part dedicated to negative publicity and the second part to consumer loyalty. Consumer loyalty exists of two important constructs. That is, behaviour loyalty and attitudinal loyalty. Both are important for true loyalty to exist. This means that a consumer must have a positive attitude towards a brand or firm in order to be loyal. Most studies on the effects on negative publicity on consumers found out that negative publicity influences consumer attitude. This effect is moderated by both consumer related factors as firm related factors. Most of these factors have in their presence a protective effect against negative publicity. If absent, negative publicity has more room to influence consumer attitude. On theoretical grounds both parts of the model are linked together, forming an overall model which shows how negative publicity influences consumer loyalty. The model proposes that throughout the change in the consumers overall attitude, as a result of negative publicity, attitudinal loyalty is affected and therefore consumer loyalty is affected. Date: 14-03-2013 Course code: MCB-80912 (HBO upgrade) Wageningen University and Research Centre Department of Marketing and Consumer Behaviour Peter Storm ID: 870812809100 Supervisor: Dr. Paul Ingenbleek
Prediction of life under creep-fatigue condition for P 91 steel using energy-based damage model

In this study, creep-fatigue life prediction using energy-based damage model is conducted. This damage model involves energy-based ductile damage, creep damage and fatigue damage. The incremental ductile and creep damages are calculated as the ratio of inelastic energy increment to inelastic fracture energy [1]. In previous fatigue damage model, the incremental fatigue damage is calculated as reciprocal of fatigue life Nf [1], but in the proposed fatigue damage model, the ratio of inelastic energy increment to inelastic fracture energy, same as ductile damage and creep damage. The advantage of the proposed energy-based fatigue damage model is that multiaxial effect on ductility [2] can be considered. The inelastic fracture energy used for calculating creep damage depends on strain rate [1]. Likewise, the inelastic fracture energy used for calculating fatigue damage depends on strain range [3]. Using the proposed damage model, ductile, creep and fatigue damages are calculated for uniaxial creep-fatigue tests [4] using commercial FE software, Abaqus with user-subroutines. Calculated fatigue damage using proposed energy-based damage model agrees with the fatigue damage calculated using previous damage model. We will apply proposed energy-based damage model for cracked specimens under creep-fatigue condition [5]
Strategic Positioning and Asymmetric Cost Behavior in Nepalese Enterprises

The purpose of this paper is to examine the association between Nepalese enterprises’ choice of strategic position and their cost behavior. The research design adopted in this study consists of descriptive and causal-comparative research designs to deal with the various issues raised in this study. Secondary data has been used for this study which was collected from annual audit report of concerned organization of manufacturing and hotel industrities from fiscal year 2000/01 to 2014/15. Descriptive statistics, correlation analysis, and regression analysis are different statistical tool that has been used for this study. Cost stickiness of the enterprises pursuing a differentiation strategy is higher than that of following a cost leadership strategy. This paper contributes to the literature on cost management by explaining how strategic positioning affects firms’ cost behavior using the framework of asymmetric cost behavior.
Is Virtualization Killing Single System Image Research

Nowadays, use of clusters in research centers or industries is undeniable. Cluster usage is typically based on two different models: (i) batch schedulers and (ii) single system image (SSI). In the first case, applications are scheduled by a ``supervisor'', the batch scheduler, according to cluster resources availability. In the second case, an SSI operating system (OS) gives the illusion that a distributed system is a standard SMP machine, allowing users to use standard UNIX tools to manage their applications. Even if SSI solutions are usually more complete in terms of functionality, batch schedulers are usually preferred because of their simplicity in term of both configuration and usage. Moreover, since few years, combining virtual machines and batch systems offer more advanced resource management capabilities, using features such as virtual machine live migration. Because of the latest contributions in the domain, some may argue that SSI technologies are now deprecated. In this paper, we analyze whether virtualization technologies will surpass the SSI approach, or if these two models are not contradictory but complementary. In fact, after evaluating different configurations, we show that by combining both approaches, we can improve several aspects associated to application computation such as flexibility of administration, simplicity of use, security and portability.
Data-Parallel implementation of Monte Carlo Based Financial Risk Calculations

Exploiting the increasing amounts of computational power, that is available on modern parallel hardware, is an ongoing research topic. The issues is to make parallel programming more accessible, maintainable and efficient for use in in the real world. Many solutions have been proposed, ranging from library support for existing mainstream programming languages [14] [16], while outer solutions involve entirely new programming languages. In this thesis the Futhark programming language [12] [9] is examined in the context of writing financial applications, that contain many computeintensive problems that are inherently parallel, and can therefore benefit enormously in terms of performance, when executed in parallel [1]. One such compute-intensive task is simulation of the financial market, in order to price products and calculate risk factors, in particular Monte Carlo simulation. This thesis describes how a small Value-at-Risk (VaR) engine is creating, using Futhark for Monte Carlo simulation, for pricing of financial products. Benchmarks for the resulting VaR-engine shows significant performance improvements when executed on parallel hardware, compared to sequential execution. Furthermore this thesis argues that while Futhark shows promising results in terms of the complier technology, there are still some limitation in terms of expressiveness, and the programming model associated with the language and parallelism, that requires the user to have certain expertise and knowledge about parallel algorithms.
Assessment of Microstructural Changes in Grade 91 Power Station Steel through Magnetic Measurements

The use of Grade 91 steel for high-temperature power plant applications can result in substantial reductions in component thickness compared to using alloys with lower strength, resulting in reduced thermal stresses and impr oved service life. Although Grade 91 offers superior resistance to thermal fatigue and creep to other grades used in the industry, this is dependent on the creation and mai tenance of a specific microstructure, which can be altered through routine component f abrication, installation and maintenance and through thermo-mechanical exposure in service. Electromagnetic inspection has the potential to assess the level of degradation in steel components through in-situ measurements. In this paper, co rrelati ns are drawn between microstructural changes in heat treated Grade 91 samples an d electromagnetic properties including incremental permeability, magnetic Barkhausen noise and distortion analysis of magnetic excitation voltage. R esults are reported from tests carried out on 5mm diameter rod samples machined fr om a section of Grade 91 tube and after heat treatment to produce the following microstructures; service entry, simulated degeneration due to prolonged service exposure and simulated mis-heat treatment. Test results from a rod sample m achined from a section of ex-service Grade 91 tube are also reported. Clear relationship s are established between quantifiable magnetic properties such as coercive force and mechanical properties such as Vickers hardness. Finally, an analysis of the magnetic mechanisms linking the microstructural changes that occur with differe nt h at treatments to MBN and incremental permeability signal features is undertaken. Th e results demonstrate the potential viability of electromagnetic methods for the assessment of microstructure, including changes due to thermal aging in Gr ade 91 power station tubes and pipes.
Measuring Actual Economic Integration

Despite the academic and policy interest and in contrast with other aspects of institutional economics (like governance), a systematic and standard index of regional integration is still lacking. An index that gives a quantitative measure of the level of regional integration is deemed useful, because it would allow to determine the trends in the world economy more precisely (e.g. the link between globalization and regionalization), to monitor integration policy initiatives more accurately and to assess the effectiveness of current or past policy initiatives (e.g. aiming at indicating good practices). Yet, in their review, De Lombaerde, Dorrucci, Genna, and Mongelli (2008, p.2) note that “only a few attempts have been undertaken to design composite indices of regional integration and no proposal has been systematically and continuously used as a policy tool. ” The most plausible explanations for this dearth are data availability and methodological issues. Regional integration is a complex and multidimensional process and therefore difficult to capture by a single or a few indicators. Consequently, a larger set of data is used, usually of very different quality in which scoring by the analyst is not uncommon. Interpretation and analysis of the data demands a summary indicator that integrates the information of all the available data, which immediately brings up the problem of how to
An Open Source TrajAnalytics Software for Modeling, Transformation and Visualization of Urban Trajectory Data

We present an open source software, named as TrajAnalytics, which models, transforms, and then visualizes urban trajectory data for transportation and urban study. It allows researchers, administrations, and practitioners to understand the population mobility data and to discover knowledge intuitively. A conceptual data model is presented which integrates trajectory data with geo-structures through a variety of data access queries. The model guides us to develop data processing and management capability and support various visual queries through a Web-based visualization interface. A set of visualization widgets and interaction functions promote easy user engagement. A primary user study shows the usability of the software.
Analysis and prediction of altimetric sea level variations during El Niño , La Niña , and normal conditions

Recent developments in satellite altimetry are due to precise tracking and determining the satellite orbits. This is attained by such techniques as: Satellite Laser Ranging (SLR), Doppler Orbitograhy and Radiopositioning Integrated on Satellite (DORIS), and Global Positioning System (GPS). These techniques were adopted by American-French altimetric missions TOPEX/Poseidon (T/P), and Jason-1 (J-1). Satellite altimetry provides the accurate sea surface height (SSH) measurements. The SSH estimates and the long-term mean sea level are used to compute sea level anomaly (SLA) data. The SLA time series describe the dynamic sea level change. The SLA data provide the knowledge about the large-scale ocean circulation, including El Nino/Southern Oscillation (ENSO). The altimetric data obtained from T/P and J-1 measurements are used to compute the spectra of the SLA data as well as amplitudes of the most energetic oscillations in sea level variation as a function of latitude and longitude. These spectra and amplitudes were computed by the Fourier Transform Band Pass Filter (FTBPF). The SLA time series are often used to determine the trend of sea level change. These estimates may be derived both globally and regionally (for the dissimilar ocean areas). It is also possible to predict SLA data. The SLA predictions are computed using the combination of polynomial-harmonic least-squares and autoregressive models. The SLA forecasts can be used to predict ENSO events, because El Nino and La Nina signals are very well visible in SLA time series.
Static Code Analysis Tools: Effects on Development of Open Source Software

Nowadays, many different tools to perform static analysis on software (ASATs) are available. These can be used as standalone tools, but also integrated into code reviews, build processes, or continuous integration. ASATs can be configured by their user and report a list of warnings for each rule that has been violated by the analyzed code. While some research has been performed regarding ASATs, little is currently known about the correlations between use of ASATs and other properties of projects or their communities, or about the extent to which developers solve violations reported by ASATs. In this thesis, we attempt to answer these questions by obtaining information about a large number of relevant open source projects hosted on GitHub. We found that the usage rate for ASATs is relatively low, while ASAT usage can be associated with several positive changes in other properties; in general, popular and successful projects are more likely to use ASATs. Furthermore, projects that use ASATs typically have a more active community, and receive more contributions. The amount of warnings generated varies between projects, but projects with large code bases tend to have fewer warnings. When looking at the types of warnings reported, not all categories are equally represented; violations of Style Conventions are most common. We also found that warnings of different categories are solved at different rates; warnings with more impact on maintainability were solved faster, while warnings with little impact on correctness or maintainability were left unattended for longer.
Modeling Data With Semicompeting Risks : An ApModeling Data With Semicompeting Risks : An Aplication to Chronic Kidney Disease in Colombia

In this paper, the structure of semicompeting risks data, de ned by Fine, Jiang & Chappell (2001), is studied. Two events are of interest: a nonterminal and a terminal event, the last one, can censor the non-terminal event, but not vice versa. Due to the possible dependence between the times until the occurrence of such events, two approaches are evaluated: modelling the bivariate survival function through Archimedean copulas and a shared frailty model. A simulation is conducted to examine its performance and both approaches are applied to a real data set of patients with chronic kidney disease (CKD).
Ef fi cacy of Sterile Fecal Filtrate Transfer for Treating Patients With Clostridium difficile Infection

Department of Internal Medicine I, University Hospital Schleswig-Holstein, Kiel, Germany; CONARIS Research Institute AG, Kiel, Germany; Institute of Clinical Molecular Biology, University of Kiel, Kiel, Germany; Max Planck Institute for Evolutionary Biology, Plön, Germany; Department of Biology, San Diego State University, San Diego, California; Institute of Experimental Medicine, University of Kiel, Kiel, Germany; Institute for Infection Medicine, University of Kiel and University Hospital Schleswig-Holstein, Kiel, Germany
Crystal structure of human IRAK 1

Interleukin 1 (IL-1) receptor-associated kinases (IRAKs) are serine/ threonine kinases that play critical roles in initiating innate immune responses against foreign pathogens and other types of dangers through their role in Toll-like receptor (TLR) and interleukin 1 receptor (IL-1R) mediated signaling pathways. Upon ligand binding, TLRs and IL-1Rs recruit adaptor proteins, such as myeloid differentiation primary response gene 88 (MyD88), to the membrane, which in turn recruit IRAKs via the death domains in these proteins to form the Myddosome complex, leading to IRAK kinase activation. Despite their biological and clinical significance, only the IRAK4 kinase domain structure has been determined among the four IRAK family members. Here, we report the crystal structure of the human IRAK1 kinase domain in complex with a small molecule inhibitor. The structure reveals both similarities and differences between IRAK1 and IRAK4 and is suggestive of approaches to develop IRAK1or IRAK4-specific inhibitors for potential therapeutic applications. While the IRAK4 kinase domain is capable of homodimerization in the unphosphorylated state, we found that the IRAK1 kinase domain is constitutively monomeric regardless of its phosphorylation state. Additionally, the IRAK1 kinase domain forms heterodimers with the phosphorylated, but not unphosphorylated, IRAK4 kinase domain. Collectively, these data indicate a two-step kinase activation process in which the IRAK4 kinase domain first homodimerizes in the Myddosome, leading to its trans-autophosphorylation and activation. The phosphorylated IRAK4 kinase domain then forms heterodimers with the IRAK1 kinase domain within the Myddosome, leading to its subsequent phosphorylation and activation.
Predicting Software Flaws with Low Complexity Models based on Static Analysis Data

Due to the constant evolution of technology, each day brings new programming languages, development paradigms, and ways of evaluating processes. This is no different with source code metrics, where there is always new metric classes. To use a software metric to support decisions, it is necessary to understand how to perform the metric collection, calculation, interpretation, and analysis. The tasks of collecting and calculating source code metrics are most often automated, but how should we monitor them during the software development cycle? Our research aims to assist the software engineer to monitor metrics of vulnerability threats present in the source code through a reference prediction model, considering that real world software have non-functional security requirements, which implies the need to know how to monitor these requirements during the software development cycle. As a first result, this paper presents an empirical study on the evolution of the Linux project. Based on static analysis data, we propose low complexity models to study flaws in the Linux source code. About 391 versions of the project were analyzed by mining the official Linux repository using an approach that can be reproduced to perform similar studies. Our results show that it is possible to predict the number of warnings triggered by a static analyzer for a given software project revision as long as the software is continuously monitored.
Data-Driven Visual Forecasting

Understanding the temporal dimension of images is a fundamental part of computer vision. Humans are able to interpret how the entities in an image will change over time. However, it has only been relatively recently that researchers have focused on visual forecasting— getting machines to anticipate events in the visual world before they actually happen. This aspect of vision has many practical implications for tasks ranging from human-computer interaction to anomaly detection. In addition, temporal prediction can serve as a task for representation learning, useful for various other recognition problems. In this thesis, we focus on visual forecasting that is data-driven, self-supervised, and relies on little to no explicit semantic information. Towards this goal, we explore prediction at different timeframes. We first consider predicting instantaneous pixel motion—optical flow. We apply convolutional neural networks to predict optical flow in static images. We then extend this idea to a longer timeframe, generalizing to pixel trajectory prediction in spacetime. We incorporate models such as variational autoencoders to generate future possible motions in the scene. After this, we consider a mid-level element approach to forecasting. By combining a Markovian reasoning framework with an intermediate representation, we are able to forecast events over longer timescales. This dissertation then builds upon these ideas towards structured representations for visual forecasting. Specifically, we aim to reason about the future of images in a structured state space. Instead of directly predicting events in a low-level feature space such as pixels or motion, we forecast events in a higher level representation that is still visually meaningful. This approach confers a number of advantages. It is not restricted by explicit timescales like motion-based approaches, and, unlike direct pixel-based approaches, predictions are less likely to “fall off” the manifold of the true visual world.