Miracle-dz/UniFAR-FTUs huggingface dataset:
FINITE ELEMENT MESHING FOR CARDIAC ANALYSIS ∗

This application paper presents details of the technique we developed to produce an adaptive and quality tetrahedral finite element mesh model of a human heart. Beginning from a polygonal surface model consisting of twenty-two components, we first edit and convert it to volumetric gridded data. A component index for each cell edge and grid point is computed for assisting the boundary and material layer detection. Next we extract adaptive and quality tetrahedral meshes from the volumetric gridded data using our Level Set Boundary and Interior-Exterior (LBIE) Mesher. The mesh adaptivity is controlled using a feature sensitive error function. Multiple layers with different materials were identified and meshed. Furthermore, one of the heart valves in the input multi-component surface model was replaced. The extracted final tetrahedral mesh is being utilized in the analysis of cardiac fluid dynamics via finite element simulations.
The Computational Meaning of Probabilistic Coherence Spaces

We study the probabilistic coherent spaces -- a denotational semantics interpreting programs by power series with non negative real coefficients. We prove that this semantics is adequate for a probabilistic extension of the untyped $\lambda$-calculus: the probability that a term reduces to ahead normal form is equal to its denotation computed on a suitable set of values. The result gives, in a probabilistic setting, a quantitative refinement to the adequacy of Scott's model for untyped $\lambda$-calculus.
High‐Alumina Boron‐Containing Refractory Castables

This work addresses the optimization of alumina-based castables designed for petrochemical applications by adding an additive able to speed up the samples' densification at lower temperatures. Hot elastic modulus, thermal shock, hot modulus of rupture, and erosion resistance measurements were carried out to evaluate these castables. The E profiles confirmed the transient feature of the generated liquid phase, as it was crystallized throughout subsequent heating cycles. Thermal fatigue effect was also observed for the compositions with higher boron content. Nevertheless, the boron-containing castables showed outstanding properties, which could be attained only with using suitable amounts of this engineered additive.
Threshold based Approach for Image Blind Deconvolution

attractiveness in digital cameras, the digital image processing is getting more imperative nowadays. One of the most common problems facing with digital photography is noise and blurring that needs restoration. In this paper, we present a new method for image blind deconvolution (2). The Proposed Method employs threshold based image restoration technique in blind image deconvolution. The goal of this work is to restore the image from a noisy and blurred image where the blurring function is not known. The blur process can be formulated as the image takes convolution operation with the Gaussian noise. One of the basic blind deconvolution method is an iterative blind deconvolution method. (5), (31). Although Iterative Blind Deconvolution method can recover the image from blurred image, it is sensitive to initial estimation and computation time required is more. In order to decrease this computation time and better visual results than Iterative blind Deconvolution, we proposed a threshold based Blind image deconvolution algorithm.
Outage probability for soliton transmission

We study the interplay between amplifier noise and birefringent disorder in the case of strongly nonlinear (soliton) type of transmission in optical fibers. Assuming both noise and disorder to be weak, we evaluate the probability distribution function (PDF) of the Bit-Error-Rate (BER) for the values of BER that are much larger than the typical (average) value. The PDF tail that describes probability of the system outage shows log-normal shape, strongly dependent on the fiber length. We also discuss a simple timing shift technique capable of the outage compensation.
Higher-Order Local Autocorrelation Feature Extraction Methodology for Hand Gestures Recognition

A novel feature extraction method for hand gesture recognition from sequences of image frames is described and tested. The proposed method employs higher order local autocorrelation (HLAC) features for feature extraction. The features are extracted using different masks from Grey-scale images for characterising hands image texture with respect to the possible position, and the product of the pixels marked in white. Then features with the most useful information are selected based on mutual information quotient (MIQ). Multiple linear discriminant analysis (LDA) classifier is adopted to classify different hand gestures. Experiments on the NUS dataset illustrate that the HLAC is efficient for hand gesture recognition compared with other feature extraction methods.
Relational views as a model for automatic distributed implementation of multi-user applications

Multi-user applications support multiple users performing a related task in a distributed context. This paper describes Weasel, a system for implementing multi-user applications. Weasel is based on the relational view model, in which user interfaces are specified as relations between program data structures and views on a display. These relations are specified in RVL, a high-level, declarative language. Under this model, an application program and a set of RVL specifications are used to generate a multi-user application in which all issues of network communication, concurrency, synchronization, and view customization are handled automatically. These programs have a scalable distribution property, where adding new participants to a session does not greatly degrade over-all system performance. Weasel has been implemented, and was used to generate all examples in this paper. CR Categories: D.2. 2 (Software Tools and Techniques for User Interfaces); D.1. 1 (Applicative Programming); D.3. 4 (Compilers and Runtime Environments) General Terms: Human Factors, Languages Additional
A STRATEGY FOR ASSESSING THE STABILITY OF SEPIC CONVERTER

A strategy is coined with a view to assess the stability margin of a SEPIC Converter. The small signal model of the SEPIC Converter is developed, which allows selection of control coefficients, analysis of parameter variation effects and characterization of the closed-loop behavior in terms of the ratio of its output to duty cycle transfer function. The eigen values of the characteristics equation provide the small-signal stability properties of the system. It is envisaged to incorporate an appropriate compensator in order to aid in the process of accomplishing a stable operation of the Converter. The performance is evaluated through MATLAB based simulation and includes both Root locus and Bode plot analysis to demonstrate the suitability of the use of compensator to encompass the SEPICs stable operation over a desired load range.
Oriented Discriminant Analysis (ODA)

Linear discriminant analysis (LDA) has been an active topic of research during the last century. However, the existing algorithms have several limitations when applied to visual data. LDA is only optimal for gaussian distributed classes with equal covariance matrices and just classes-1 features can be extracted. On the other hand, LDA does not scale well to high dimensional data (over-fitting) and it does not necessarily minimize the classification error. In this paper, we introduce Oriented Discriminant Analysis (ODA), a LDA extension which can overcome these drawbacks. Three main novelties are proposed: • An optimal dimensionality reduction which maximizes the KullbackLiebler divergencebetweenclasses is proposed. This allowsus tomodel class covariances and to extract more than classes-1 features. • Several covariance approximations are introduced to improve classification in the small sample case. • A linear time iterative majorization method is introduced in order to find a local optimal solution. Several synthetic and real experiments on face recognition are reported 1 .
Ultra-Wide Range Wavelength Conversion Using FWM in a Raman DFB Fiber Laser

We demonstrate for the first time to our knowledge, four-wave-mixing (FWM) in a 30cm-long center π phase-shifted Raman distributed-feedback (DFB) fiber laser. The FWM-to-signal conversion efficiency is -24dB and the wavelength conversion range is 94.1nm.
Into the deep: the functionality of mesopelagic excursions by an oceanic apex predator

Abstract Comprehension of ecological processes in marine animals requires information regarding dynamic vertical habitat use. While many pelagic predators primarily associate with epipelagic waters, some species routinely dive beyond the deep scattering layer. Actuation for exploiting these aphotic habitats remains largely unknown. Recent telemetry data from oceanic whitetip sharks (Carcharhinus longimanus) in the Atlantic show a strong association with warm waters (>20°C) less than 200 m. Yet, individuals regularly exhibit excursions into the meso‐ and bathypelagic zone. In order to examine deep‐diving behavior in oceanic whitetip sharks, we physically recovered 16 pop‐up satellite archival tags and analyzed the high‐resolution depth and temperature data. Diving behavior was evaluated in the context of plausible functional behavior hypotheses including interactive behaviors, energy conservation, thermoregulation, navigation, and foraging. Mesopelagic excursions (n = 610) occurred throughout the entire migratory circuit in all individuals, with no indication of site specificity. Six depth‐versus‐time descent and ascent profiles were identified. Descent profile shapes showed little association with examined environmental variables. Contrastingly, ascent profile shapes were related to environmental factors and appear to represent unique behavioral responses to abiotic conditions present at the dive apex. However, environmental conditions may not be the sole factors influencing ascents, as ascent mode may be linked to intentional behaviors. While dive functionality remains unconfirmed, our study suggests that mesopelagic excursions relate to active foraging behavior or navigation. Dive timing, prey constituents, and dive shape support foraging as the most viable hypothesis for mesopelagic excursions, indicating that the oceanic whitetip shark may regularly survey extreme environments (deep depths, low temperatures) as a foraging strategy. At the apex of these deep‐water excursions, sharks exhibit a variable behavioral response, perhaps, indicating the presence or absence of prey.
Real-Time Implementation and Evaluation of Computed-Torque Scheme

This paper presents the experimental results of the real-time performance of model-based control algorithms. We compare the computed-torque scheme which utilizes the complete dynamics model of the manipulator with the independent joint control scheme which assumes a decoupled and linear model of the manipulator dynamics. The two manipulator control schemes have been implemented on the CMU DD Arm I1 with a sampling period of 2 ms. We discuss the design of controller gains for both the computed-torque and the independent joint control schemes and establish a framework for comparing their trajectory tracking performance Our investigation shows that the computed- torque scheme outperforms the independent joint control scheme as long as there is no torque saturation in the actuators. Based on our experimental results, we conclusively establish the importance of compensating for the nonlinear Coriolis and centrifugal forces even at low speeds of operation. This represents an important result because it serves to resolve the controversy about the need to compensate for these forces at low speeds of operation.
Using Bilingual Parallel Corpora for Cross-Lingual Textual Entailment

This paper explores the use of bilingual parallel corpora as a source of lexical knowledge for cross-lingual textual entailment. We claim that, in spite of the inherent difficulties of the task, phrase tables extracted from parallel data allow to capture both lexical relations between single words, and contextual information useful for inference. We experiment with a phrasal matching method in order to: i) build a system portable across languages, and ii) evaluate the contribution of lexical knowledge in isolation, without interaction with other inference mechanisms. Results achieved on an English-Spanish corpus obtained from the RTE3 dataset support our claim, with an overall accuracy above average scores reported by RTE participants on monolingual data. Finally, we show that using parallel corpora to extract paraphrase tables reveals their potential also in the monolingual setting, improving the results achieved with other sources of lexical knowledge.
Detection of Land-Cover Transitions in Multitemporal Remote Sensing Images With Active-Learning-Based Compound Classification

This paper presents a novel iterative active learning (AL) technique aimed at defining effective multitemporal training sets to be used for the supervised detection of land-cover transitions in a pair of remote sensing images acquired on the same area at different times. The proposed AL technique is developed in the framework of the Bayes' rule for compound classification. At each iteration, it selects the pair of spatially aligned unlabeled pixels in the two images that are classified with the maximum uncertainty. These pixels are then labeled by an external supervisor and included in the training set. The uncertainty of a pair of pixels is assessed by the joint entropy defined by considering two possible different simplifying assumptions: 1) class-conditional independence and 2) temporal independence between multitemporal images. Accordingly, different algorithms are introduced. The proposed joint-entropy-based AL algorithms for compound classification are compared with each other and with a marginal-entropy-based AL technique (in which the entropy is computed separately on single-date images) applied to the postclassification comparison method. The experimental results obtained on two multispectral and multitemporal data sets show the effectiveness of the proposed technique.
A Parallel Radiosity System for Large Data Sets

The radiosity method gives realistic results for synthesising images based on diffuse light interaction; however, the method is also very computationally expensive. Here we present a system for parallelising a progressive refinement radiosity method using a ray casting approach. The system decomposes the environment into two distinct entities, the model geometry and the radiosity sample points. These are then distributed amongst the processors and message passing communication protocols and caching methods are introduced to enable the system to cope with very large models while maintaining a high processing rate.
Bypassing Space Explosion in Regular Expression Matching for Network Intrusion Detection and Prevention Systems

Network intrusion detection and prevention systems commonly use regular expression (RE) signatures to represent individual security threats. While the corresponding DFA for any one RE is typically small, the DFA that corresponds to the entire set of REs is usually too large to be constructed or deployed. To address this issue, a variety of alternative automata implementations that compress the size of the final automaton have been proposed such as XFA and DFA. The resulting final automata are typically much smaller than the corresponding DFA. However, the previously proposed automata construction algorithms do suffer from some drawbacks. First, most employ a “Union then Minimize” framework where the automata for each RE are first joined before minimization occurs. This leads to an expensive NFA to DFA subset construction on a relatively large NFA. Second, most construct the corresponding large DFA as an intermediate step. In some cases, this DFA is so large that the final automaton cannot be constructed even though the final automaton is small enough to be deployed. In this paper, we propose a “Minimize then Union” framework for constructing compact alternative automata focusing on the DFA. We show that we can construct an almost optimal final DFA with small intermediate parsers. The key to our approach is a space and time efficient routine for merging two compact DFA into a compact DFA. In our experiments, our algorithm runs up to 302 times faster and uses 1390 times less memory than previous algorithms. For example, we are able to construct a DFA with over 80,000,000 states using only 1GB of main memory in only 77 minutes.
MArray: analysing single, replicated or reversed microarray experiments

UNLABELLED MArray is a Matlab toolbox with a graphical user interface that allows the user to analyse single or paired microarray datasets by direct input of the raw data output file from image analysis packages, such as QuantArray or GenePiX. The application provides simple procedures to manually evaluate the quality of each measurement, multiple approaches to both ratio normalization (simple normalization, intensity dependent normalization) and evaluation of the reproducibility of paired experiments (using the techniques 'simple statistical method' and 'quality control ellipse' and 'significance analysis of microarrays'). Specifically, interactive spot evaluation functions are available in MArray and an online gene information database (NCBI UniGene) is linked. The application may provide a valuable aid in selecting and optimizing experimental procedures, as well as serving as an analytical tool for two-state biological comparisons, such as a study of single-dose activation. It is entirely platform independent, and only requires Matlab installed. AVAILABILITY http://matrise.uio. no/marray/marray.html
The evaluation of music therapy process in the intersubjective perspective: the music therapy rating scale. A pilot study

This study presents a tool (Music Therapy Rating Scale [MTRS]) to evaluate the progression of the relationship between the patient and the therapist during music therapy sessions. The rating scale was developed from an intersubjective framework and from an improvisational music therapy technique. It mainly consists of two behavioral classes: non-verbal communication (NVC) and sonorous-musical communication (SMC). Five levels for each class are given (from 0 to 4). Every 15 seconds, two sets of two music therapists coded the behaviors in seven sessions with different patients. The patients were seven children (four males; three females) aged 3–10 years (mean age = 6.28), diagnosed with Pervasive Developmental Disorder. The reliability indices were computed and there was a substantial agreement in the assessments of the video raters (non-verbal relationship: ρ = 0.70; sonorous-musical relationship: ρ = 0.77). A digital output of the program to use the MTRS is presented and clinical implications are discussed.
Screening system for drug-induced arrhythmogenic risk combining a patch clamp and heart simulator

Finding the silent skipped beat: Predicting arrhythmia-causing drugs using a high-throughput hybrid heart simulator. To save time and cost for drug discovery, a paradigm shift in cardiotoxicity testing is required. We introduce a novel screening system for drug-induced arrhythmogenic risk that combines in vitro pharmacological assays and a multiscale heart simulator. For 12 drugs reported to have varying cardiotoxicity risks, dose-inhibition curves were determined for six ion channels using automated patch clamp systems. By manipulating the channel models implemented in a heart simulator consisting of more than 20 million myocyte models, we simulated a standard electrocardiogram (ECG) under various doses of drugs. When the drug concentrations were increased from therapeutic levels, each drug induced a concentration-dependent characteristic type of ventricular arrhythmia, whereas no arrhythmias were observed at any dose with drugs known to be safe. We have shown that our system combining in vitro and in silico technologies can predict drug-induced arrhythmogenic risk reliably and efficiently.
G Protein β3 Subunit Gene Variants and Essential Hypertension in the Northern Chinese Han Population

Recently a novel C825T polymorphism in the G protein β3 subunit gene was identified that showed an association with hypertension in a German population; the results of studies in other populations have been inconsistent. To examine the contribution of GNB3 polymorphisms to the development of hypertension in the northern Chinese Han population, we conducted a case‐control study consisting of 501 hypertensive cases and 503 controls using the G(‐350)A, C825T and C1429T polymorphisms. Genotypes of samples were determined by PCR and restriction digestion. Single locus analysis showed a significant association between G(‐350)A and hypertension (P = 0.01) but no association for C825T or C1429T. The three polymorphisms were in tight linkage disequilibrium (D′=−1 for G(‐350)A‐C825T, D′= 0.92 for C825T‐C1429T) and a total of 7 haplotypes were observed in the entire population. Haplotype A‐C‐C was found to be significantly related to hypertension (P = 0.032) and A‐C‐C carriers had a more than two‐fold higher risk of hypertension than non‐carriers, after adjustment for BMI and glucose. In conclusion, our study suggests that G(‐350)A is a potential functional polymorphism that may be related to hypertension, whereas the C825T and C1429T polymorphisms are not associated with hypertension in the northern Chinese Han population.
Characterizing Behavioral and Brain Changes Associated with Practicing Reasoning Skills

We have reported previously that intensive preparation for a standardized test that taxes reasoning leads to changes in structural and functional connectivity within the frontoparietal network. Here, we investigated whether reasoning instruction transfers to improvement on unpracticed tests of reasoning, and whether these improvements are associated with changes in neural recruitment during reasoning task performance. We found behavioral evidence for transfer to a transitive inference task, but no evidence for transfer to a rule generation task. Across both tasks, we observed reduced lateral prefrontal activation in the trained group relative to the control group, consistent with other studies of practice-related changes in brain activation. In the transitive inference task, we observed enhanced suppression of task-negative, or default-mode, regions, consistent with work suggesting that better cognitive skills are associated with more efficient switching between networks. In the rule generation task, we found a pattern consistent with a training-related shift in the balance between phonological and visuospatial processing. Broadly, we discuss general methodological considerations related to the analysis and interpretation of training-related changes in brain activation. In summary, we present preliminary evidence for changes in brain activation associated with practice of high-level cognitive skills.
The Ultrastructure Of Southern Pine Bordered Pit Membranes As Revealed By Specialized Drying Techniques

The study of the mature bordered pit membrane from three species revealed that there were no major differences in ultrastructure. The pit membrane consists of an imperforated torus and a margo with considerable variation in porosity. Springwood pit membranes from the sapwood were rarely incrusted, whereas summerwood pit membranes from both the sapwood and heartwood were heavily incrusted. The use of specialized drying techniques, specifically solvent-exchange, critical-point, and freeze-drying techniques, prevented pit aspiration and permitted a detailed study of the pit membrane. Since the margo structure was not altered to any substantial degree by the different drying systems, it appears that the surface tension of the evaporating liquid is important only with regard to pit aspiration. A study of trees from several age classes showed the decrease of the number of margo microfibrils to be related to the age of the wood rather than to the time of formation. The mechanism of the deterioration process was not determined.