Miracle-dz/UniFAR-FTUs huggingface dataset:
Automated Texture Extraction from Multiple Images to Support Site Model Refinement and Visualization

Texture mapping has wide and important applications in visualization and virtual reality. Surface texture extraction from a single image su ers from perspective distortion, data de ciency, and corruption caused by shadows and occlusions. In this paper, a system is developed for automated acquisition of complete and consistent texture maps from multiple images in order to support subsequent detailed surface analysis and scene rendering. Given camera and light source parameters for each image, and a geometric model of the scene, the textures of object surfaces are systematically collected into an organized orthographic library. Occlusions and shadows caused by objects in the scene are computed and associated with each retrieved surface. A \Best Piece Representation" algorithm is designed to combine intensities from multiple views, resulting in a unique surface intensity representation. Detailed surface structures, such as windows and doors, are extracted from the uniquely represented surface images to rene the geometric model. Experiments show successful applications of this approach to model re nement and scene visualization.
More Pathogenicity or Just More Pathogens?—On the Interpretation Problem of Multiple Pathogen Detections with Diagnostic Multiplex Assays

Modern molecular diagnostic approaches in the diagnostic microbiological laboratory like real-time quantitative polymerase chain reaction (qPCR) have led to a considerable increase of diagnostic sensitivity. They usually outperform the diagnostic sensitivity of culture-based approaches. Culture-based diagnostics were found to be insufficiently sensitive for the assessment of the composition of biofilms in chronic wounds and poorly standardized for screenings for enteric colonization with multi-drug resistant bacteria. However, the increased sensitivity of qPCR causes interpretative challenges regarding the attribution of etiological relevance to individual pathogen species in case of multiple detections of facultative pathogenic microorganisms in primarily non-sterile sample materials. This is particularly the case in high-endemicity settings, where continuous exposition to respective microorganisms leads to immunological adaptation and semi-resistance while considerable disease would result in case of exposition of a non-adapted population. While biofilms in chronic wounds show higher pathogenic potential in case of multi-species composition, detection of multiple pathogens in respiratory samples is much more difficult to interpret and asymptomatic enteric colonization with facultative pathogenic microorganisms is frequently observed in high endemicity settings. For respiratory samples and stool samples, cycle-threshold-value-based semi-quantitative interpretation of qPCR results has been suggested. Etiological relevance is assumed if cycle-threshold values are low, suggesting high pathogen loads. Although the procedure is challenged by lacking standardization and methodical issues, first evaluations have led to promising results. Future studies should aim at generally acceptable quantitative cut-off values to allow discrimination of asymptomatic colonization from clinically relevant infection.
The Analysis about Harmful Emissions of Micro Combustor Based on Porous Medium

According to the changes of the mixed gas flux ,porosity and burner structure,we measured the contents of CO, HC and Nitrogen Oxides in the harmful emissions of the methane burned in the micro combustor.In the micro scale, because the structure size of the burner is small, the combustion space and time is limited, CO and HC emissions are the products of incomplete combustion, nitrogen oxide are the products in local high temperature and oxygen enriched conditions.
Automating the Large-Scale Collection and Analysis of Performance

Many factors contribute to overall application performance in today’s highperformance cluster computing environments. These factors include the memory subsystem, network hardware and software stack, compilers and libraries, and I/O subsystem. The large variability in hardware and software configurations present in clusters can cause application performance to also exhibit large variability on different platforms or on the same platform over time. Compute-intensive applications may perform well on an architecture with efficient utilization of CPU and single-processor memory, such as the Intel Xeon, while memory-intensive applications may perform well on an architecture with good scalability of the memory subsystem, such as the AMD Opteron node [6]. Even with a fixed hardware configuration, software factors can cause large variations in performance. Compilers that produce acceptable code on some platform configurations may produce suboptimal code on other platform variants. Some math libraries require hand tuning of various compiled-in parameters, and a library that is hand-tuned for one platform may perform poorly on a different variant of the same platform. Some libraries (e.g., BLAS, LAPACK) have standardized APIs that are shared across different implementations that can have considerable variations in performance. It can be difficult to predict which library variant will perform best on a particular platform without testing each variant on that platform. If an application is updated and/or ported to a platform originally not supported, the optimization flags in the application Makefile may be anachronistic or otherwise inappropriate and may need to be altered to achieve acceptable performance on new target platforms and platform variants.
Risk Analysis For Invasive Species And Emerging Infectious Diseases: Concepts And Applications

Abstract Management and control of biological invasions and emerging infectious diseases are leading topics of research in theoretical and applied ecology. When the initial number of potentially invasive or infectious individuals is small, demographic stochasticity can lead to rapid extinction, suggesting that the transient dynamics of establishment for invasive species and epidemics should be modeled as a stochastic process. Quantitative risk assessment can exploit this conceptualization to calculate risk metrics such as the chance of invasion or epidemic and to study the potential effectiveness of rapid response interventions. Here I review some simple establishment models and demonstrate how understanding the underlying stochastic processes can lead to more effective policies for risk management. As examples, I study the dynamics of feral nutria (Myocastor coypus) in East Anglia, UK, and infection by bovine tuberculosis (Mycobacterium bovis) in various species. Nutria are fur-bearing aquatic mammals that have been introduced around the world intentionally and through escape from farms and cause severe damage to marsh vegetation. Bovine tuberculosis is a chronic wasting disease of diverse mammal species and results in long term emaciation and decline in animal fitness. I find that both nutria and bovine tuberculosis exhibit high intrinsic rates of increase as measured by their increase in abundance when the population or epidemic is small. While Allee effects may affect the chance of establishment for nutria, analysis of additional data will be required to reject the hypothesis that nutria dynamics follow the simple Ricker growth model. The distinction is important, however, as the estimated chance of establishment under the Allee effect model is considerably less than under the Ricker model for introduced populations less than about 850 individuals. The chance of bovine tuberculosis epidemic for introductions of small numbers of individuals infectious with bovine tuberculosis in populations of brushtail possums (Trichosurus vulpecula) or badgers (Meles meles) is high. Thus, if surveillance programs are not almost completely effective at preventing introductions of infectious individuals, periodic outbreaks are likely to occur. Epidemics in these species might be controlled by culling.
The Research on Combination Forecasting Model of the Automobile Sales Forecasting System

Automobile sells system plays an important role in automobile sales area, through the whole produce and management. Some forecast models have had unilateralism in some side nowadays, such as ARMA model. For example, the data of non-linearity has some error by ARMA model. This paper, assembles curve –regression model, Time Series Decomposition Model and RBF neural networks according to the weight distribution. Putting the Data Mining, math statistics and neural networks technique into automobile sales forecast system, It can improve the problem of unilateralism, The Combination forecasting model improves the veracity and utility range in automobile sales forecast. This paper, can also be used in which some other economic data that take on the obvious time character and trend in car-making and selling.
Characterization of cytolytic effector cells in peripheral blood of healthy individuals and cancer patients. I. Surface markers and K cell activity after separation of B cells and lymphocytes and Fc-receptors by column fractionation.

To characterize the effector cells involved in ADCC in a model system (lysis of 51 Cr-labeled chicken erythrocytes induced by rabbit IgG antibodies), in “spontaneous”, and in tumor-associated cytotoxicity, we prepared subpopulations of peripheral blood lymphocytes from healthy individuals and from cancer patients by fractionation on columns according to three different principles. Columns with anti-immunoglobulin activity but lacking Fc-structures (F(ab′) 2 /anti-F(ab′) 2 columns) removed ∼99% of the SIg + cells (B cells), but only ∼20% of the Fc-receptor-bearing cells, as characterized by rosette formation with IgG-coated bovine erythrocytes (EA + cells), and ∼25% of the complement receptor-bearing cells (EAC + cells). Cells with receptor for sheep erythrocytes (E + cells) were enriched. The K cell activity of this fraction was unchanged. Columns charged with immune complexes (OA/anti-OA columns) which display no anti-immunoglobulin activity retained ∼70% of the EA + cells but only ∼20% of the SIg + cells. About 50% of the EAC + cells and ∼10% of the E + cells were also removed. The K cell activity of this fraction was strongly reduced. IgG/anti-IgG columns very efficiently remove SIg + cells, EA + cells, and EAC + cells whereas E + cells are enriched. Again K cell activity is strongly reduced. The results indicate that B cells with high density SIg are inactive in the K cell assay and that the majority of mature B cells either have Fc-receptors of low avidity or have receptors for immunoglobulin which are qualitatively different from the Fc-receptors of SIg - cells. Furthermore, a significant fraction of the lymphocytes with complement receptors is different from these B cells. It can also be inferred that a significant subpopulation of the cells with receptors for sheep erythrocytes also have Fc-receptors for IgG. The cytotoxicity of these different lymphocyte fractions against a variety of human tumor cells will be reported in the following paper.
Lead selection for SSVEP-based brain-computer interface

SSVEP-based brain-computer interface (BCI) has potential advantage of high information transfer rate. However, individual difference greatly affects its practical applications. This paper presents a method of lead selection to improve the applicability of SSVEP-based BCI system. Independent component analysis (ICA) is employed to decompose EEGs over visual cortex into SSVEP signal and background noise. Optimal bipolar lead is selected by comparing signal correlation and noise correlation between different channels. The system with one optimal bipolar lead has reached an average transfer rate about 42bits/min for normal subjects. It has also been successfully applied to an environmental controller for the motion-disabled.
Phonetic unit selection for cross-lingual query-by-example spoken term detection

Cross-lingual query-by-example spoken term detection (QbE STD) has caught the attention of speech researchers, as it makes it possible to develop systems for low-resource languages, in which the available amount of labelled data makes the training of automatic speech recognition approaches prohibitive. The use of phonetic posteriorgrams for speech representation combined with dynamic time warping search is a widely used approach for this task, but little attention has been focused in the suitability of a set of phonetic units to represent speech information spoken in a different language. This paper proposes a technique for estimating the relevance of phonetic units aiming at selecting the most suitable ones for a given target language. Experiments in a Spanish database using phoneme posteriorgrams in Czech, English, Hungarian and Russian proved the validity of the proposed method, as QbE STD performance was enhanced by reducing the set of phonetic units.
Robust Optimization of Test-Access Architectures Under Realistic Scenarios

3-D integration using through-silicon vias offers many benefits, such as high bandwidth, low power, and small footprint. However, test complexity and test cost are major concerns for 3-D ICs. Recent work on the optimization of 3-D test architectures to reduce test cost suffer from the drawback that they ignore potential uncertainties in input parameters; they consider only a single point in the input-parameter space. In realistic scenarios, the assumed values for parameters such as test power and pattern count of logic cores, which are used for optimizing the test architecture for a die, may differ from the actual values that are known only after the design stage. In a 3-D setting, a die can be used in multiple stacks with different properties. As a result, the originally designed test architecture is no longer optimal, which leads to an undesirable increase in the test cost. We propose an optimization approach that takes uncertainties in input parameters into account and provides a solution that is efficient in the presence of input-parameter variations. We formulate a mathematical model for the robust test-architecture optimization problem, and propose an efficient heuristic to solve the problem even for large designs in reasonable time. The proposed optimization framework is evaluated using the ITC'02 SoC benchmarks and we show that robust solutions are superior to single-point solutions in terms of average test time when there are uncertainties in the values of input parameters.
How to Compete Online for News Audience: Modeling Words that Attract Clicks

Headlines are particularly important for online news outlets where there are many similar news stories competing for users' attention. Traditionally, journalists have followed rules-of-thumb and experience to master the art of crafting catchy headlines, but with the valuable resource of large-scale click-through data of online news articles, we can apply quantitative analysis and text mining techniques to acquire an in-depth understanding of headlines. In this paper, we conduct a large-scale analysis and modeling of 150K news articles published over a period of four months on the Yahoo home page. We define a simple method to measure click-value of individual words, and analyze how temporal trends and linguistic attributes affect click-through rate (CTR). We then propose a novel generative model, headline click-based topic model (HCTM), that extends latent Dirichlet allocation (LDA) to reveal the effect of topical context on the click-value of words in headlines. HCTM leverages clicks in aggregate on previously published headlines to identify words for headlines that will generate more clicks in the future. We show that by jointly taking topics and clicks into account we can detect changes in user interests within topics. We evaluate HCTM in two different experimental settings and compare its performance with ALDA (adapted LDA), LDA, and TextRank. The first task, full headline, is to retrieve full headline used for a news article given the body of news article. The second task, good headline, is to specifically identify words in the headline that have high click values for current news audience. For full headline task, our model performs on par with ALDA, a state-of-the art web-page summarization method that utilizes click-through information. For good headline task, which is of more practical importance to both individual journalists and online news outlets, our model significantly outperforms all other comparative methods.
How to manage chronic urticaria ‘beyond’ guidelines: a practical algorithm

Chronic urticaria (CU) is a disease characterized by pruritic weals, angio‐oedema or both occurring for at least 6 weeks. It encompasses spontaneous and inducible urticarias. The national and international guidelines outline the principles of treatment. Omalizumab, an anti‐immunoglobulin E monoclonal antibody, has transformed the management of many severe and treatment‐refractory patients. However, current UK guidance on its use does not address the needs of those with less severe disease, inducible urticarias, idiopathic histaminergic angio‐oedema without weals as a presentation of CU and omalizumab non‐responders. Our algorithm and a summary of the evidence to support its principles offers guidance and a more systematic targeted approach to using a range of ‘off‐label’ agents for specific phenotypes of CU. It will be of use when guideline‐recommended mast cell mediator antagonists fail to control symptoms and/or using omalizumab is ineffective, not practical or unfunded.
Engineering of poly(DL-lactic-co-glycolic acid) nanocomposite particles for dry powder inhalation dosage forms of insulin with the spray-fluidized bed granulating system

The objective of the present research is to develop a new particulate preparation process for dry powder inhalation by which peptide-loaded poly(DL-lactic-co-glycolic acid) nanospheres are granulated with sugar alcohol (mannitol, around 4 μm in diameter). A spray-drying fluidized bed granulator was used to form soft matrix composite granules. A nanosphere suspension can be readily obtained by dispersing the composite granules in distilled water. The composite granules showed inhalation performance superior to that of freeze-dried nanosphere powder. More than 50% of composite granules were delivered to the bronchioles and alveoli of a rat. The composite granules of insulin-loaded nanosphere have a strong and prolonged pharmacological effect compared to the inhalation of insulin solution.
Factors affecting the long-term response to tacrolimus in renal transplant patients: Pharmacokinetic and pharmacogenetic approach

Background: The aim of our study was to determine the impact of CYP3A5*1 and CYP3A5*3 on the kinetics of tacrolimus in renal transplant recipients. Material and methods: Forty kidney recipients were selected to participate. Maintenance scheme consisted of tacrolimus, a purine inhibitor and a steroid. CYP3A5 genotyping was performed with PCR and RFLP. Pharmacokinetic model was developed with Linear Regression and General Linear Model repeated measures approach. The impact of sex, CYP3A5*1 allele, age at transplantation, hepatic and renal function on tacrolimus kinetics was examined. Results: The frequency of CYP3A5*3/*3 and CYP3A5*1/*3 genotype was 35/40 and 5/40, respectively. No CYP3A5*1/*1 was detected. CYP3A5*1 variant was associated with significant lower TAC dose adjusted concentration at 3, 6, 12 and 36 months after transplantation. Hepatic and renal function showed a significant effect on tacrolimus dose adjusted concentration 3 months after transplantation (p=0.000 and 0.028, respectively). Sex did not show a significant impact on tacrolimus kinetics. Carriers of CYP3A5*1 allele had lower predicted measures for tacrolimus dose adjusted concentration and higher predicted measures for volume of distribution. Conclusion: We proved that CYP3A5*1 carriers need higher tacrolimus dose than CYP3A5*3 homozygotes to achieve the target blood concentration.
Are School-of-thought Words Characterizable?

School of thought analysis is an important yet not-well-elaborated scientific knowledge discovery task. This paper makes the first attempt at this problem. We focus on one aspect of the problem: do characteristic school-of-thought words exist and whether they are characterizable? To answer these questions, we propose a probabilistic generative School-Of-Thought (SOT) model to simulate the scientific authoring process based on several assumptions. SOT defines a school of thought as a distribution of topics and assumes that authors determine the school of thought for each sentence before choosing words to deliver scientific ideas. SOT distinguishes between two types of school-of-thought words for either the general background of a school of thought or the original ideas each paper contributes to its school of thought. Narrative and quantitative experiments show positive and promising results to the questions raised above
Creating and simulating skeletal muscle from the visible human data set

Simulation of the musculoskeletal system has important applications in biomechanics, biomedical engineering, surgery simulation, and computer graphics. The accuracy of the muscle, bone, and tendon geometry as well as the accuracy of muscle and tendon dynamic deformation are of paramount importance in all these applications. We present a framework for extracting and simulating high resolution musculoskeletal geometry from the segmented visible human data set. We simulate 30 contact/collision coupled muscles in the upper limb and describe a computationally tractable implementation using an embedded mesh framework. Muscle geometry is embedded in a nonmanifold, connectivity preserving simulation mesh molded out of a lower resolution BCC lattice containing identical, well-shaped elements, leading to a relaxed time step restriction for stability and, thus, reduced computational cost. The muscles are endowed with a transversely isotropic, quasiincompressible constitutive model that incorporates muscle fiber fields as well as passive and active components. The simulation takes advantage of a new robust finite element technique that handles both degenerate and inverted tetrahedra.
Predictable programming on a precision timed architecture

In a hard real-time embedded system, the time at which a result is computed is as important as the result itself. Modern processors go to extreme lengths to ensure their function is predictable, but have abandoned predictable timing in favor of average-case performance. Real-time operating systems provide timing-aware scheduling policies, but without precise worst-case execution time bounds they cannot provide guarantees. We describe an alternative in this paper: a SPARC-based processor with predictable timing and instruction-set extensions that provide precise timing control. Its pipeline executes multiple, independent hardware threads to avoid costly, unpredictable bypassing, and its exposed memory hierarchy provides predictable latency. We demonstrate the effectiveness of this precision-timed (PRET) architecture through example applications running in simulation.
A Planning Model for Agents in Dynamic and Uncertain Real-Time Environments*

The management of an agent in a dynamic and uncertain real-time environment is fairly intractable with traditional planning methods. Such methods cannot be computed efficiently, are not capable of replanning, and mostly lack explicit temporal formalisms. The planning model proposed in this paper overcomes these weaknesses by applying iterative repair methods, constraintprogramming techniques, and an explicit timeline approach. For the management of incomplete knowledge, a single-plan approach is proposed in which only one plan possibility is considered. The application of UNKNOWN values, KNOWN values, NOT values, and continuous domain fluents enables to cope with incomplete knowledge.