Miracle-dz/UniFAR-FTUs huggingface dataset:
Worms, Chameleons, Phantoms and Doves: New Additions to the Biometric Menagerie

Goat, sheep, and lamb are labels for problem users of a biometric system. These user types are defined in terms of their verification performance when matched against themselves (goats) or when matched against others (lambs and wolves). Four new members of the biometric menagerie are proposed based on a user's relationship between their genuine and imposter match scores. Experiments using a variety of biometrics, algorithms, and data sets have indicated that this relationship can exist with real-world data (face, voice, and fingerprint). An analysis of the results indicates that the presence of these user types tends to reflect a weakness of the underlying system. A new framework for the evaluation of biometric systems based on user groups from the biometric menagerie (as opposed to collective statistics) is proposed.
Compiling Fortran D for MIMD distributed-memory machines

for MIMD Distributed-Memory Machines :llel computing resents the only :usible way to continue to increase the computational power available to scientists and engineers. Parallel computers , however, are not likely to be widely successful until they are easy to program. A major component in the success of vector supercomputers is the ability of scientists to write Fortran programs in a "'vec-torizable'" style and expect vectorizing compilers to automatically produce efficient code [8, 35]. The resulting programs are easily maintained , debugged, and ported across different vector machines. Compare this with the current situation for programming parallel machines. Scientists wishing to use such machines must rewrite their programs in an extension of For-tran that explicitly reflects the architecture of the underlying machine. Multiple-instruction, multiple data (MIMD) shared-memory machines such as the Cray Research Y-MP C90 are programmed with explicit synchronization and parallel loops found in Parallel Computing Forum (PCF) Fortran [24]. (SIMD) machines such as the Thinking Machines CM-2 are programmed using parallel array constructs found in Fortran 90 [3]. MIMD distributed-memory machines such as the Intel Paragon provide the most difficult programming model. Users must write message passing Fortran 77 programs that deal with separate address spaces, synchronizing processors, and communicating data using messages. The process is time-consuming, tedious, and error-prone. Significant increases in source code size are not only common but expected. Because parallel programs are extremely machine-specific, scientists are discouraged from utilizing parallel machines because they risk losing their investment whenever the program changes or a new architecture arrives. We propose to solve this problem by developing the compiler technology needed to establish a machine-independent programming model. It must be easy to use, yet perform with acceptable efficiency on different parallel architectures, at least for data-parallel scientific codes. The question is whether any existing Fortran dialect suffices. Parallel Computing Fortran is undesirable because it is easy to inadvertently write programs with data races that produce indeterminate results. Message-passing For-tran 77 is portable, but difficult to use. Fortran 90 is promising, but may not be sufficiently flexible. What all these languages lack is a way to specify the decomposition and placement of data in the program. We find that selecting a data decomposition is one of the most important intellectual steps in developing data-parallel scientific codes. Though many techniques have been developed for automatic data decomposition, we feel that the compiler will not be able to choose an efficient …
The Role of Lupus Nephritis in Development of Adverse Maternal and Fetal Outcomes During Pregnancy

Background: We aimed to investigate the relationship of lupus nephritis (LN) with fetal and maternal outcomes of pregnant patients with systemic lupus erythematosus (SLE). Methods: In a retrospective study, profiles of pregnant women with SLE were selected. Before pregnancy and at the end of first, second and third trimesters, SLE disease activity index-2K was assessed. Clinical and laboratory evaluations were carried out regularly. Maternal and fetal outcomes were recorded. Assessments of the crude effects of statistically significant variables on pregnancy outcomes were performed through multivariate regression analysis. Results: 72 pregnancies in 65 patients were recorded. The mean age of LN patients was 28.7 years, whereas the mean age of patients with clinical nephritis was 26.1 years. No woman with LN experienced pre-term labor or stillbirth. 16 pregnancies either ended in abortion or experienced preeclampsia of which seven had LN. Multivariate logistic regression analyses showed that LN and positive antinuclear antibody were related to preeclampsia, whereas age of SLE development was associated with pre-term labor. Combined maternal and fetal outcomes were associated with the past history of abortion and LN. LN was associated with preeclampsia and SLE flare. Conclusions: Absence of LN was in favor of prevention of SLE flare and preeclampsia.
Two Proximal Skin Electrodes — A Body Sensor for Respiration Rate

We propose a new body sensor for extracting the respiration rate based on the amplitude changes in the body surface potential differences between two proximal body electrodes. The sensor could be designed as a plaster-like reusable unit that can be easily fixed onto the surface of the body. It could be equipped either with a sufficiently large memory for storing the measured data or with a low-power radio system that can transmit the measured data to a gateway for further processing. We explore the influence of the sensor’s position on the quality of the extracted results using multi-channel ECG measurements and considering all the pairs of two neighboring electrodes as potential respiration-rate sensors. The analysis of the clinical measurements, which also include reference thermistor-based respiration signals, shows that the proposed approach is a viable option for monitoring the respiration frequency and for a rough classification of breathing types. The obtained results were evaluated on a wireless prototype of a respiration body sensor. We indicate the best positions for the respiration body sensor and prove that a single sensor for body surface potential difference on proximal skin electrodes can be used for combined measurements of respiratory and cardiac activities.
Six Sigma - A New Direction to Quality and Productivity Management

The fast changing economic conditions such as global competition, declining profit margin, customer demand for high quality product, product variety and reduced lead- time etc. had a major impact on manufacturing industries. To respond to these needs various industrial engineering and quality management strategies such as ISO 9000, Total Quality Management, Kaizen, Just-in-time manufacturing, Enterprise Resource Planning, Business Process Reengineering, Lean management etc. have been developed. A new paradigm in this area of manufacturing strategies is Six Sigma. The Six Sigma approach has been increasingly adopted world wide in the manufacturing sector in order to enhance productivity and quality performance and to make the process robust to quality variations. This paper discusses the quality and productivity improvement in a manufacturing enterprise through a case study. The paper deals with an application of Six Sigma DMAIC(Define-Measure-Analyze-Improve-Control) methodo -logy in an industry which provides a framework to identify, quantify and eliminate sources of variation in an operational process in question, to optimize the operation variables, improve and sustain performance viz. process yield with well- executed control plans. Six Sigma improves the process performance (process yield) of the critical operational process, leading to better utilization of resources, decreases variations & maintains consistent quality of the process output.
Bounds of the induced norm and model reduction errors for systems with repeated scalar nonlinearities

The class of nonlinear systems described by a discrete-time state equation containing a repeated scalar nonlinearity as in recurrent neural networks is considered. Sufficient conditions are derived for the stability and induced norm of such systems using positive definite diagonally dominant Lyapunov functions or storage functions, satisfying appropriate linear matrix inequalities. Results are also presented for model reduction errors for such systems.
Approach to Detect Air Tightness of Engine Cylinder by Vibration Analysis

Cylinder compression pressure reflects the air tightness of engine. A method for measuring the compression pressure of cylinder indirectly through measuring the vibration signal of cylinder head was studied and then to detect the air tightness. The air pressure signal in cylinder and vibration signals of cylinder head were measured at the same time when the diesel engine was driven by the motor. According to port timing, the vibration signal excited by cylinder pressure was separated using time domain analysis. A RBF neural network model was set up to build the relation between compression pressure and cylinder head vibration. So the air tightness of cylinder can be detected after calculating the compression pressure by use of neural network.
Aromatase (Cyp19) expression is up-regulated by targeted disruption of Dax1

DAX-1 [dosage-sensitive sex reversal, adrenal hypoplasia congenita (AHC) critical region on the X chromosome, gene 1] is an orphan nuclear receptor that represses transcription by steroidogenic factor-1 (SF-1), a factor that regulates expression of multiple steroidogenic enzymes and other genes involved in reproduction. Mutations in the human DAX1 gene (also known as AHC) cause the X-linked syndrome AHC, a disorder that is associated with hypogonadotropic hypogonadism also. Characterization of Dax1-deficient male mice revealed primary testicular defects that included Leydig cell hyperplasia (LCH) and progressive degeneration of the germinal epithelium, leading to infertility. In this study, we investigated the effect of Dax1 disruption on the expression profile of various steroidogenic enzyme genes in Leydig cells isolated from Dax1-deficient male mice. Expression of the aromatase (Cyp19) gene, which encodes the enzyme that converts testosterone to estradiol, was increased significantly in the Leydig cells isolated from mutant mice, whereas the expression of other proteins (e.g., StAR and Cyp11a) was not altered. In in vitro transfection studies, DAX-1 repressed the SF-1-mediated transactivation of the Cyp19 promoter but did not inhibit the StAR or Cyp11a promoters. Elevated Cyp19 expression was accompanied by increased intratesticular levels of estradiol. Administration of tamoxifen, a selective estrogen-receptor modulator, restored fertility to the Dax1-deficient male mice and partially corrected LCH, suggesting that estrogen excess contributes to LCH and infertility. Based on these in vivo and in vitro analyses, aromatase seems to be a physiologic target of Dax-1 in Leydig cells, and increased Cyp19 expression may account, in part, for the infertility and LCH in Dax1-deficient mice.
Sensor faults: Detection methods and prevalence in real-world datasets

Various sensor network measurement studies have reported instances of transient faults in sensor readings. In this work, we seek to answer a simple question: How often are such faults observed in real deployments? We focus on three types of transient faults, caused by faulty sensor readings that appear abnormal. To understand the prevalence of such faults, we first explore and characterize four qualitatively different classes of fault detection methods. Rule-based methods leverage domain knowledge to develop heuristic rules for detecting and identifying faults. Estimation methods predict “normal” sensor behavior by leveraging sensor correlations, flagging anomalous sensor readings as faults. Time-series-analysis-based methods start with an a priori model for sensor readings. A sensor measurement is compared against its predicted value computed using time series forecasting to determine if it is faulty. Learning-based methods infer a model for the “normal” sensor readings using training data, and then statistically detect and identify classes of faults. We find that these four classes of methods sit at different points on the accuracy/robustness spectrum. Rule-based methods can be highly accurate, but their accuracy depends critically on the choice of parameters. Learning methods can be cumbersome to train, but can accurately detect and classify faults. Estimation methods are accurate, but cannot classify faults. Time-series-analysis-based methods are more effective for detecting short duration faults than long duration ones, and incur more false positives than the other methods. We apply these techniques to four real-world sensor datasets and find that the prevalence of faults as well as their type varies with datasets. All four methods are qualitatively consistent in identifying sensor faults, lending credence to our observations. Our work is a first step towards automated online fault detection and classification.
In Silico Preclinical Trials: Methodology and Engineering Guide to Closed-Loop Control in Type 1 Diabetes Mellitus

This article sets forth guidelines for in silico (simulation-based) proof-of-concept testing of artificial pancreas control algorithms. The goal was to design a test procedure that can facilitate regulatory approval [e.g., Food and Drug Administration Investigational Device Exemption] for General Clinical Research Center experiments without preliminary testing on animals. The methodology is designed around a software package, based on a recent meal simulation model of the glucose-insulin system. Putting a premium on generality, this document starts by specifying a generic, rather abstract, meta-algorithm for control. The meta-algorithm has two main components: (1) patient assessment and tuning of control parameters, i.e., algorithmic processes for collection and processing patient data prior to closed-loop operation, and (2) controller warm-up and run-time operation, i.e., algorithmic processes for initializing controller states and managing blood glucose. The simulation-based testing methodology is designed to reveal the conceptual/mathematical operation of both main components, as applied to a large population of in silico patients with type 1 diabetes mellitus.
Conservation Biological Control of Pests in the Molecular Era: New Opportunities to Address Old Constraints

Biological control has long been considered a potential alternative to pesticidal strategies for pest management but its impact and level of use globally remain modest and inconsistent. A rapidly expanding range of molecular – particularly DNA-related – techniques is currently revolutionizing many life sciences. This review identifies a series of constraints on the development and uptake of conservation biological control and considers the contemporary and likely future influence of molecular methods on these constraints. Molecular approaches are now often used to complement morphological taxonomic methods for the identification and study of biological control agents including microbes. A succession of molecular techniques has been applied to ‘who eats whom’ questions in food-web ecology. Polymerase chain reaction (PCR) approaches have largely superseded immunological approaches such as enzyme-linked immunosorbent assay (ELISA) and now – in turn – are being overtaken by next generation sequencing (NGS)-based approaches that offer unparalleled power at a rapidly diminishing cost. There is scope also to use molecular techniques to manipulate biological control agents, which will be accelerated with the advent of gene editing tools, the CRISPR/Cas9 system in particular. Gene editing tools also offer unparalleled power to both elucidate and manipulate plant defense mechanisms including those that involve natural enemy attraction to attacked plants. Rapid advances in technology will allow the development of still more novel pest management options for which uptake is likely to be limited chiefly by regulatory hurdles.
Allergen-Induced CD4+ T Cell Cytokine Production within Airway Mucosal Dendritic Cell–T Cell Clusters Drives the Local Recruitment of Myeloid Effector Cells

Allergic asthma develops in the mucosal tissue of small bronchi. At these sites, local cytokine production by Th2/Th17 cells is believed to be critical for the development of tissue eosinophilia/neutrophilia. Using the mouse trachea as a relevant model of human small airways, we performed advanced in vivo dynamic and in situ static imaging to visualize individual cytokine-producing T cells in the airway mucosa and to define their immediate cellular environment. Upon allergen sensitization, newly recruited CD4+ T cells formed discrete Ag-driven clusters with dendritic cells (DCs). Within T cell–DC clusters, a small fraction of CD4+ T cells produced IL-13 or IL-17 following prolonged Ag-specific interactions with DCs. As a result of local Th2 cytokine signaling, eosinophils were recruited into these clusters. Neutrophils also infiltrated these clusters in a T cell–dependent manner, but their mucosal distribution was more diffuse. Our findings reveal the focal nature of allergen-driven responses in the airways and define multiple steps with potential for interference with the progression of asthmatic pathology.
"What Parts of Your Apps are Loved by Users?" (T)

Recently, Begel et al. found that one of the most important questions software developers ask is "what parts of software are used/loved by users. " User reviews provide an effective channel to address this question. However, most existing review summarization tools treat reviews as bags-of-words (i.e., mixed review categories) and are limited to extract software aspects and user preferences. We present a novel review summarization framework, SUR-Miner. Instead of a bags-of-words assumption, it classifies reviews into five categories and extracts aspects for sentences which include aspect evaluation using a pattern-based parser. Then, SUR-Miner visualizes the summaries using two interactive diagrams. Our evaluation on seventeen popular apps shows that SUR-Miner summarizes more accurate and clearer aspects than state-of-the-art techniques, with an F1-score of 0.81, significantly greater than that of ReviewSpotlight (0.56) and Guzmans' method (0.55). Feedback from developers shows that 88% developers agreed with the usefulness of the summaries from SUR-Miner.
Combined application of alginate dressing and human granulocyte-macrophage colony stimulating factor promotes healing in refractory chronic skin ulcers

The aim of the present study was to evaluate the clinical therapeutic effect of the combined application of alginate and recombinant human granulocyte-macrophage colony-stimulating factor (rhGM-CSF) on the healing of refractory chronic skin ulcers. A single center, three arm, randomized study was performed at Jinan Central Hospital (Jinan, Shandong, China). A total of 60 patients with refractory chronic skin ulcers, which persisted for >1 month, were enrolled and randomly assigned into one of the following three groups: alginate dressing/rhGM-CSF group (group A), rhGM-CSF only group (group B) and conventional (vaseline dressing) group (group C). The wound area rate was measured, granulation and color were observed and pain was evaluated. The data were summarized and statistical analysis was performed. The results demonstrated that group A exhibited a significantly faster wound healing rate and lower pain score compared with the other groups (P<0.01). In conclusion, the combined application of alginate dressing and rhGM-CSF for the treatment of refractory chronic skin ulcers demonstrated significant advantages. It promoted the growth of granulation tissue, accelerated re-epithelialization and also effectively reduced wound pain, and thus improved the quality of life for the patient. This suggests that the combined application of alginate and rhGM-CSF may be an effective therapeutic strategy for the clinical treatment of refractory chronic skin ulcers.
Development of intelligent mould for online detection of defects in steel billets

AbstractThis study, which is based on six instrumented mould trials conducted at four Canadian minimills, has focused on the development of strategies for the detection of process upsets and defect deformation in the continuous casting of steel billets, using thermocouples installed in the mould wall. Laps and transverse depressions can be detected using two thermocouples installed in the mould wall at the midface location, with the first one located at least 100 mm below the meniscus. With respect to bleeds, it is preferable to monitor metal level fluctuations and mould wall temperatures, maintaining values within a desired range where bleed formation is minimal. In the case of rhomboidity, the average difference between maximum and minimum temperature for the four walls computed for one or more locations situated 25–50 mm below the meniscus (in the vicinity of the peak hot face temperature), for a time period corresponding to the entire billet length, correlates well with the severity of rhomboidity. Lo.
Bridge Over Troubled Water? Migration and Social Capital

The problem of integrating non-Western immigrants into Western welfare states is the focus of this paper. To address this issue, we suggest a social capital approach in which we apply the conceptual pair of bridging social capital (BR), which connects an individual to the broader social structure, and bonding social capital (BO), which closely binds an individual to his or her narrow social group. By this we hope to grasp both the sunny and the more shadowy side of network cooperation and trust in relation to the integration of immigrants. Our data, on non-Western immigrants in Denmark, show a positive relationship between the levels of bridging and bonding capital, suggesting that bonding social capital in the immigrant group does not seem to impede the establishment of the bridging social capital needed for integration.
Preferred Operators and Deferred Evaluation in Satisficing Planning

Heuristic forward search is the dominant approach to satisficing planning to date. Most successful planning systems, however, go beyond plain heuristic search by employing various search-enhancement techniques. One example is the use of helpful actions or preferred operators, providing information which may complement heuristic values. A second example is deferred heuristic evaluation, a search variant which can reduce the number of costly node evaluations. Despite the wide-spread use of these search-enhancement techniques however, we note that few results have been published examining their usefulness. In particular, while various ways of using, and possibly combining, these techniques are conceivable, no work to date has studied the performance of such variations. In this paper, we address this gap by examining the use of preferred operators and deferred evaluation in a variety of settings within best-first search. In particular, our findings are consistent with and help explain the good performance of the winners of the satisficing tracks at IPC 2004 and 2008.