Miracle-dz/UniFAR-FTUs huggingface dataset:
Feature refactoring a multi-representation program into a product line

Feature refactoring is the process of decomposing a program into aset of modules, called features, that encapsulate increments in program functionality. Different compositions of features yield different programs. As programs are defined using multiple representations, such as code, makefiles, and documentation, feature refactoring requires all representations to be factored. Thus, composing features produces consistent representations of code, make files, documentation, etc. for a target program. We present acase study of feature refactoring a substantial tool suite that usesmultiple representations. We describe the key technical problems encountered, and sketch the tool support needed for simplifying such refactorings in the future.
Contributions of Arylacetamide Deacetylase and Carboxylesterase 2 to Flutamide Hydrolysis in Human Liver

Flutamide, an antiandrogen drug, is widely used for the treatment of prostate cancer. The major metabolic pathways of flutamide are hydroxylation and hydrolysis. The hydrolyzed metabolite, 5-amino-2-nitrobenzotrifluoride (FLU-1), is further metabolized to N-hydroxy FLU-1, an assumed hepatotoxicant. Our previous study demonstrated that arylacetamide deacetylase (AADAC), one of the major serine esterases expressed in the human liver and gastrointestinal tract, catalyzes the flutamide hydrolysis. However, the enzyme kinetics in human tissue microsomes were not consistent with the kinetics by recombinant human AADAC. Thus, it seemed that AADAC is not the sole enzyme responsible for flutamide hydrolysis in human. In the present study, we found that recombinant carboxylesterase (CES) 2 could hydrolyze flutamide at low concentrations of flutamide. In the inhibition assay, the flutamide hydrolase activities at a flutamide concentration of 5 μM in human liver and jejunum microsomes were strongly inhibited by a selective CES2 inhibitor, 10 μM loperamide, with the residual activities of 22.9 ± 3.5 and 18.6 ± 0.7%, respectively. These results suggest that CES2 is also involved in the flutamide hydrolysis in human tissues. Using six individual human livers, the contributions of AADAC and CES2 to flutamide hydrolysis were estimated by using the relative activity factor. The relative contribution of CES2 was approximately 75 to 99% at the concentration of 5 μM flutamide. In contrast, the relative contribution of AADAC increased in parallel with the concentration of flutamide. Thus, CES2, rather than AADAC, largely contributed to the flutamide hydrolysis in clinical therapeutics.
Immunization with Escherichia coli Outer Membrane Vesicles Protects Bacteria-Induced Lethality via Th1 and Th17 Cell Responses

Outer membrane vesicles (OMVs), secreted from Gram-negative bacteria, are spherical nanometer-sized proteolipids enriched with outer membrane proteins. OMVs, also known as extracellular vesicles, have gained interests for use as nonliving complex vaccines and have been examined for immune-stimulating effects. However, the detailed mechanism on how OMVs elicit the vaccination effect has not been studied extensively. In this study, we investigated the immunological mechanism governing the protective immune response of OMV vaccines. Immunization with Escherichia coli–derived OMVs prevented bacteria-induced lethality and OMV-induced systemic inflammatory response syndrome. As verified by adoptive transfer and gene-knockout studies, the protective effect of OMV immunization was found to be primarily by the stimulation of T cell immunity rather than B cell immunity, especially by the OMV-Ag–specific production of IFN-γ and IL-17 from T cells. By testing the bacteria-killing ability of macrophages, we also demonstrated that IFN-γ and IL-17 production is the main factor promoting bacterial clearances. Our findings reveal that E. coli–derived OMV immunization effectively protects bacteria-induced lethality and OMV-induced systemic inflammatory response syndrome primarily via Th1 and Th17 cell responses. This study therefore provides a new perspective on the immunological detail regarding OMV vaccination.
Non-Linear Modelling Time Series from ARIMA Fitting

A procedure for designing non-linear models for predicting time series is proposed. It is based on a set of rules emerging from a previously fitted ARIMA model. These rules are extracted from the set of coefficients in the ARIMA model, so they consider the autocorrelation structure of the time series, but a nonlinear approach is adopted. The proposed procedure is intended to help the user in the task of specifying as simple models as possible, providing an unambiguous methodology to construct machine learning models for time series forecasting. A generalization to time series with interventions is also proposed. The performance of these procedures is empirically studied by means of a comparative analysis involving time series from several domains and the multilayer perceptron is employed to approximate the non-linear models
Numerical study of the mechanical deterioration of insulated rail joints

Abstract Numerical simulations are performed to study plastic deformation and fatigue impact of an insulated joint. The simulations feature a sophisticated constitutive model capable of capturing ratcheting under multi-axial loading conditions. Calculation results are presented in the form of effective strain magnitudes, plastic zone sizes, and multi-axial low cycle fatigue parameters. The simulation results indicate that the main damage mechanism at insulated joints is ratcheting and not low cycle fatigue. A parametric study quantifies effects of increased vertical and longitudinal load magnitudes, as well as the effect of an increased insulating gap. In particular, longitudinal loading is indicated as being severely deteriorating for the rail in the vicinity of the joint. Finally, the effect of rail edge bevelling is assessed and found to be small for the case studied.
An adaptive approach to mammographic image denoising and enhancement

This paper describes a new method for mammographic image noise suppression and enhancement based on the wavelet transform. At each resolution, coefficients associated with noise are modelled by Gaussian random variables; coefficients associated with edges are modelled by generalized Gaussian random variables, and a shrinkage function is assembled based on posterior probabilities. The shrinkage functions at consecutive scales are combined, and then applied to the wavelet coefficients. Given a resolution of analysis, the image denoising process is adaptive (i.e. does not require further parameter adjustments), and the selection of a gain factor provides the desired detail enhancement. The enhancement function was designed to avoid introducing artifacts in the enhancement process, which is essential in mammographic image analysis. Our preliminary results indicate that our method allows us to detect microcalcifications and other suspicious structures in situations where their detection would be difficult otherwise. Compared to other approaches, our method requires less parameter adjustments by the user.
Electron Beam and Soft X-ray Lithography with a Monomolecular Resist

This contribution reviews current state of electron beam and soft X-ray lithography with a monomolecular resist. Self-assembled monolayers (SAMs) represent versatile resist materials for such a lithography. Depending on the architecture of the SAM constituents, they can serve as both positive and negative resists within framework of conventional lithography, but also be used as multi-purpose templates for Chemical Lithography. The latter technique exploits either selective modification of specific tail groups at the SAM-ambient interface (in the case of aromatic backbone) or irradiation-promoted exchange reaction between the primary SAM and potential molecular substituents (in the case of aliphatic backbone). Due to the monolayer thickness of SAM resists and molecular size of their structural building blocks, patterning down to few nanometers is in principle possible. Currently, the lateral resolution is limited by the grain character of the SAM substrates and parameters of the lithographic setup, with the latter being especially important in the case of soft X-ray lithography.
Aggresome–Autophagy Involvement in a Sarcopenic Patient with Rigid Spine Syndrome and a p.C150R Mutation in FHL1 Gene

The four-and-half LIM domain protein 1 (FHL1) is highly expressed in skeletal and cardiac muscle. Mutations of the FHL1 gene have been associated with diverse chronic myopathies including reducing body myopathy, rigid spine syndrome (RSS), and Emery–Dreifuss muscular dystrophy. We investigated a family with a mutation (p.C150R) in the second LIM domain of FHL1. In this family, a brother and a sister were affected by RSS, and their mother had mild lower limbs weakness. The 34-year-old female had an early and progressive rigidity of the cervical spine and severe respiratory insufficiency. Muscle mass evaluated by DXA was markedly reduced, while fat mass was increased to 40%. CT scan showed an almost complete substitution of muscle by fibro-adipose tissue. Muscle biopsy showed accumulation of FHL1 throughout the cytoplasm and around myonuclei into multiprotein aggregates with aggresome/autophagy features as indicated by ubiquitin, p62, and LC3 labeling. DNA deposits, not associated with nuclear lamina components and histones, were also detected in the aggregates, suggesting nuclear degradation. Ultrastructural analysis showed the presence of dysmorphic nuclei, accumulation of tubulofilamentous and granular material, and perinuclear accumulation of autophagic vacuoles. These data point to involvement of the aggresome–autophagy pathway in the pathophysiological mechanism underlying the muscle pathology of FHL1 C150R mutation.
An examination of the nutritional intake and anthropometric status of individuals with intellectual disabilities: Results from the SOPHIE study

The prevalence of obesity appears greater in people with intellectual disabilities than those in the general population. This study aimed to examine the nutritional intake and anthropometric status of individuals with intellectual disabilities. Participants aged 16–64 years were recruited from intellectual disability service provider organizations (n = 131). Data were collected using questionnaires; 4-day food dairies and weight, height and waist circumference measurements. Participants’ mean body mass index (BMI) was 29.4 kg/m2 ± 6.1, 2.4% were underweight, 22.6% were normal weight, 28.2% were overweight and 46.8% were obese. Having a diagnosis of Down syndrome (p = 0.03) was associated with increasing BMI. Increasing waist circumference was associated with increasing severity of ID (p = 0.04). The mean-reported energy intake was 1890 kcal/day. Mean energy intakes from sugar, fat and saturated fat were above recommendations and few participants met micronutrient recommended daily amounts. This study highlights the alarming prevalence of overweight and obesity and poor diet quality of individuals with intellectual disabilities.
A 1.25-Gb/s digitally-controlled dual-loop clock and data recovery circuit with enhanced phase resolution

This paper describes a 1.25-Gb/s digitally-controlled dual-loop clock and data recovery circuit with a 256-level phase resolution using only 4-phase reference clock. A novel scheme is proposed to enhance the phase resolution with little additional power consumption and chip area. A digitally-controlled delay buffer having a variable delay tunes output phase finely for a higher resolution. A prototype chip was fabricated with 0.18 mum CMOS technology. In the measurement, the CDR has plusmn400ppm frequency offset tolerance and a flat jitter performance for wide variations of delay buffer. The power consumption of the CDR core is 17.8mW with 1.8V supply and the core occupies 255 mum times 165mum
Unweighted fusion in microphone forensics using a decision tree and linear logistic regression models

For the exemplarily chosen domain of microphone forensics we show that media forensics can strongly benefit from combining statistical pattern recognition (using supervised classification) and unweighted information fusion (on the example of match-, rank- and decision level fusion). The practical results presented show that, by using a carefully selected fusion strategy and two multi-class classifiers (a decision tree and linear logistic regression models), the accuracy achieved in practical testing can be increased to 100%. This result is based on first tests on two sets of four and seven different microphones. For each of those microphones ten reference samples are recorded in ten different locations and are used in the ratio 80% to 20% for supervised training and testing by the two classifiers. The overall positive tendency indicates that microphone forensics might become an important security mechanism for the verification of source authenticity. Recent gunshot classification approaches, which try to determine the gun used in gunshot audio recordings, have the problem that they rely on carefully controlled conditions, amongst them the fact that the microphone used for all evaluations has to remain the same. A microphone classification approach as introduced here would allow for similarity estimation for microphones and thereby would enable exchanging microphones in such a gunshot classification approach without complete loss of confidence. Furthermore microphone forensics could be used in provenance verification of digital audio media to verify the microphone used for recordings to be submitted into secure long term archiving systems.
Dynamic brittle material response based on a continuum damage model

The response of brittle materials to dynamic loads was studied in this investigation based on a continuum damage model. Damage mechanism was selected to be interaction and growth of subscale cracks. Briefly, the cracks are activated by bulk tension and the density of activated cracks are described by a Weibull statistical distribution. The moduli of a cracked solid derived by Budiansky and O`Connell are then used to represent the global material degradation due to subscale cracking. This continuum damage model was originally developed to study rock fragmentation and was modified in the present study to improve on the post-limit structural response. The model was implemented into a transient dynamic explicit finite element code PRONTO 2D and then used for a numerical study involving the sudden stretching of a plate with a centrally located hole. Numerical results characterizing the dynamic responses of the material were presented. The effect of damage on dynamic material behavior was discussed.
Collective Annotation: Perspectives for Information Retrieval Improvement

Nowadays we enter the Web 2.0 era where people's participation is a key principle. In this context, collective annotations enable to share and discuss readers' feedback with regard to digital documents. The results of this activity are going to be used in the Information Retrieval context, which already tends to harness similar collective contributions. In this paper, we propose a collective annotation model supporting feedback exchange through discussion threads. Considering this model, we associate annotations with a measure of the sparked consensus degree (social validation), this allows to provide a synthesized view of associated discussions. Finally, we investigate how Information Retrieval systems may benefit from the proposed model, thus taking advantage of human-contributed highly value-added information, namely collective annotations.
SCI-F: Social-Corporate Data Integration Framework

Opinions are considered a powerful source of market research. Social networks are the most popular mean for people to state their opinions about products and services. Companies invest in analysing their customers' opinions from multiple existing social media platforms. The knowledge extracted from social media contains sentiment data that is not included in corporate database. This extracted data can be used to improve marketing campaigns to better retain customers and meet their needs. The integration between both social media data and corporate data can lead to better insights that would not have been possible to gain without such integration. This paper proposes a framework to integrate sentiment data, extracted from customers' opinions with corporate data of an organization. The process of integrating opinions streams into corporate database is proposed, then a multidimensional data warehouse is built over the integrated database to perform advanced analytical tasks and answer queries that would not have been possible without the integration. The new framework has been experimented with a case study of integrating opinions about digital devices and corporate data to illustrate the usage of the integrated model.
Cyclic Deformation of Powder Metallurgy Stainless Steel/Mg‐PSZ Composite Materials

In this study, the low‐cycle fatigue (LCF) behavior of powder metallurgy stainless steel/MgO partially stabilized zirconia (Mg‐PSZ) composite materials is presented. The steel matrix based on conventional AISI 304 steel (1.4301) is reinforced with Mg‐PSZ. The investigated composite materials were manufactured using the spark plasma sintering (SPS) technique. Total strain‐controlled LCF tests were performed on materials containing 0, 5, and 10 vol% Mg‐PSZ, respectively, in order to evaluate the influence of the ceramic reinforcement. Electron backscatter diffraction (EBSD) measurements were applied to identify the locations where the martensitic phase transformations in the steel matrix and stress‐assisted as well as athermal martensitic phase transformations of the Mg‐PSZ ceramic reinforcement take place. The resulting cyclic deformation behavior is correlated with the microstructural features of the composite material.
Cellular Potts Modeling of Tumor Growth, Tumor Invasion, and Tumor Evolution

Despite a growing wealth of available molecular data, the growth of tumors, invasion of tumors into healthy tissue, and response of tumors to therapies are still poorly understood. Although genetic mutations are in general the first step in the development of a cancer, for the mutated cell to persist in a tissue, it must compete against the other, healthy or diseased cells, for example by becoming more motile, adhesive, or multiplying faster. Thus, the cellular phenotype determines the success of a cancer cell in competition with its neighbors, irrespective of the genetic mutations or physiological alterations that gave rise to the altered phenotype. What phenotypes can make a cell “successful” in an environment of healthy and cancerous cells, and how? A widely used tool for getting more insight into that question is cell-based modeling. Cell-based models constitute a class of computational, agent-based models that mimic biophysical and molecular interactions between cells. One of the most widely used cell-based modeling formalisms is the cellular Potts model (CPM), a lattice-based, multi particle cell-based modeling approach. The CPM has become a popular and accessible method for modeling mechanisms of multicellular processes including cell sorting, gastrulation, or angiogenesis. The CPM accounts for biophysical cellular properties, including cell proliferation, cell motility, and cell adhesion, which play a key role in cancer. Multiscale models are constructed by extending the agents with intracellular processes including metabolism, growth, and signaling. Here we review the use of the CPM for modeling tumor growth, tumor invasion, and tumor progression. We argue that the accessibility and flexibility of the CPM, and its accurate, yet coarse-grained and computationally efficient representation of cell and tissue biophysics, make the CPM the method of choice for modeling cellular processes in tumor development.
NetRec : Product Recommendation With Heterogeneous Networks

Picking a good recommendation strategy is extremely important because a bad strategy can cause users to lose faith in application. Accordingly, the bad recommendations make it impossible to deliver a nice personal experience to the end user. [1] Collaborative recommender systems can be used to present similarities between users as well as products in order to create a generalized relationship between entities based on basket analysis. There currently exists a multitude of recommendation strategies utilizing networks. A large focus is placed on link prediction. In this paper we examine, our own alogrithm to predict consumer purchases and rank products.
Efficiently evaluating graph constraints in content-based publish/subscribe

We introduce the problem of evaluating graph constraints in content-based publish/subscribe (pub/sub) systems. This problem formulation extends traditional content-based pub/sub systems in the following manner: publishers and subscribers are connected via a (logical) directed graph G with node and edge constraints, which limits the set of valid paths between them. Such graph constraints can be used to model a Web advertising exchange (where there may be restrictions on how advertising networks can connect advertisers and publishers) and content delivery problems in social networks (where there may be restrictions on how information can be shared via the social graph). In this context, we develop efficient algorithms for evaluating graph constraints over arbitrary directed graphs G. We also present experimental results that demonstrate the effectiveness and scalability of the proposed algorithms using a realistic dataset from Yahoo! 's Web advertising exchange.
The Omega test: A fast and practical integer programming algorithm for dependence analysis

The Omega test is an integer programming algorithm that can determine whether a dependence exists between two array references, and if so, under what conditions. Conventional wisdom holds that integer programming techniques are far too expensive to be used for dependence analysis, except as a method of last resort for situations that cannot be decided by simpler methods. We present evidence that suggests this wisdom is wrong, and that the Omega test is competitive with approximate algorithms used in practice and suitable for use in production compilers. The Omega test is based on an extension of FourierMotzkin variable elimination to integer programming, and has worst-case exponential time complexity. However, we show that for many situations in which other (polynomial) methods are accurate, the Omega test has low order polynomial time complexity. The Omega test can be used to simplify integer programming problems, rather than just deciding them. This has many applications, including accurately and efficiently computing dependence direction and distance vectors.