Miracle-dz/UniFAR-FTUs huggingface dataset:
Energy based clutter filtering for vector flow imaging

To obtain accurate blood flow velocity estimates it is important to remove the clutter signal originating from tissue. Conventionally, the clutter signal has been separated from the blood signal based on the difference of their spectral frequencies. However, this approach is not enough for obtaining vector flow measurements, since the spectra overlaps at high beam-to-flow angles. In this work a distinct approach is proposed, where the energy of the velocity spectrum is used to differentiate among the two signals. The energy based method is applied by limiting the amplitude of the velocity spectrum function to a predetermined threshold. The effect of the clutter filtering is evaluated on a plane wave (PW) scan sequence in combination with transverse oscillation (TO) and directional beamforming (DB) for velocity estimation. The performance of the filter is assessed by comparison of the velocity estimates of the proposed filter against a conventional moving average clutter filter. The effect of tissue motion is investigated using a Field II simulation of a straight vessel with moving wall, while the direct effect of the filter on the velocity estimates is evaluated on a CFD model of a carotid bifurcation with a fixed vessel wall. The results show that the proposed filter outperformed the moving average during moving vessel wall conditions, where standard deviations from the velocity magnitudes and angles were kept consistently below 6% and 6° compared to 63% and 48° on the moving average filter. The results on the CFD showed that on non-moving conditions the velocity estimates had minor statistical differences with errors on the magnitude of −7.95±10.1% and angles of 0.15±6.65° for the proposed filter compared to −5.83±9.08% and −0.12±4.48°.
Synthesizing speech from speech recognition parameters

The merits of different signal preprocessing schemes for speech recognizers are usually assessed purely on the basis of the resulting recognition accuracy. Such benchmarks give a good indication as to whether one preprocessing is better than another, but little knowledge is acquired about why it is better or how it could be further improved. In order to gain more insight in the preprocessing, we seek to re-synthesize speech from speech recognition features. This way, we are able to pin-point some deficiencies in our current preprocessing scheme. Additional analysis of successful new preprocessing schemes may allow us one day to identify precisely those properties that are desirable in a feature set. Next to these purely scientific aims, the re-synthesis of speech from recognition features is of interest to thin-client speech applications, and as an alternative to the classical LPC source-filter model for speech manipulation.
“Making Pies“ – a way of exploring children’s views on curriculum innovation

This article examines the use of focus groups (FGs) as a way of gathering pupils’ views on the implementation of new curricula. We provide illustrations of creative activities which were especially designed for a case-study research project exploring the views of 11-year-old pupils in English schools adopting an innovative approach to the Year 7 curriculum. We argue that combining FGs with creative methods encourages pupils to take an active role in interviews, enriches the quality of the data being collected, and enhances the place of FGs as important tools in eliciting pupils’ voices for both research and curriculum planning purposes.
Defect Density Estimation Through Verification and Validation

In industry, information on defect density of a product tends to become available too late in the software development process to affordably guide corrective actions. Our research objective is to build a parametric model which utilizes a persistent record of the validation and verification (V&V) practices used with a program to estimate the defect density of that program. The persistent record of the V&V practices are recorded as certificates which are automatically recorded and maintained with the code. To date, we have created a parametric modeling process with the help of the Center for Software Engineering at the University of Southern California and have created the second version of an Eclipse plug-in for recording V&V certificates.
The role of actigraphy in sleep medicine.

During the last decade actigraphy (activity-based monitoring) has become an essential tool in sleep research and sleep medicine. The validity, reliability and limitations of actigraphy for documenting sleep-wake patterns have been addressed. Normative data on sleep-wake patterns across development have been collected. Multiple studies have documented the adequacy of actigraphy to distinguish between clinical groups and to identify certain sleep-wake disorders. Actigraphy has also been shown to be effective in documenting the effects of various behavioral and medical interventions on sleep-wake patterns. Actigraphy is less useful for documenting sleep-wake in individuals who have long motionless periods of wakefulness (e.g. insomnia patients) or who have disorders that involve altered motility patterns (e.g. sleep apnea). Potential users should be aware of a number of pitfalls of actigraphy: (1) validity has not been established for all scoring algorithms or devices, or for all clinical groups; (2) actigraphy is not sufficient for diagnosis of sleep disorders in individuals with motor disorders or high motility during sleep; (3) the use of computer scoring algorithms without controlling for potential artifacts can lead to inaccurate and misleading results.
Modelling Multi-object Activity by Gaussian Processes

We present a new approach for activity modelling and anomaly detection based on non-parametric Gaussian Process (GP) models. Specifically, GP regression models are formulated to learn non-linear relationships between multi-object activity patterns observed from semantically decomposed regions in complex scenes. Predictive distributions are inferred from the regression models to compare with the actual observations for real-time anomaly detection. The use of a flexible, non-parametric model alleviates the difficult problem of selecting appropriate model complexity encountered in parametric models such as Dynamic Bayesian Networks (DBNs). Crucially, our GP models need fewer parameters; they are thus less likely to overfit given sparse data. In addition, our approach is robust to the inevitable noise in activity representation as noise is modelled explicitly in the GP models. Experimental results on a public traffic scene show that our models outperform DBNs in terms of anomaly sensitivity, noise robustness, and flexibility in modelling complex activity.
Kernel-Based Implicit Regularization of Structured Objects

Weighted Graph regularization provides a rich framework that allows to regularize functions defined over the vertices of a weighted graph. Until now, such a framework has been only defined for real or multivalued functions hereby restricting the regularization framework to numerical data. On the other hand, several kernels have been defined on structured objects such as strings or graphs. Using definite positive kernels, each original object is associated by the ``kernel trick'' to one element of an Hilbert space. As a consequence, this paper proposes to extend the weighted graph regularization framework to objects implicitly defined by their kernel hereby performing the regularization within the Hilbert space associated to the kernel. This work opens the door to the regularization of structured objects.
EFFECT OF A SHORT-TERM PHYSICAL EDUCATION-BASED FLEXIBILITY PROGRAM ON HAMSTRING AND LUMBAR EXTENSIBILITY AND ITS POSTERIOR REDUCTION IN PRIMARY SCHOOLCHILDREN

UDC: 796.015:615.825:616.7-373.3-053.5(460) Abstract: The purpose of this study was to examine the effects of a short-term flexibility program on hamstring and lumbar extensibility and its posterior reduction among primary schoolchildren in a physical education (PE) setting. Forty-five 10-to-11-year-old schoolchildren from two classes were clustered randomly to an experimental group (EG) (n=22) or a control group (CG) (n=23). During the PE classes, the students in EG performed a six-minute flexibility program twice a week for eight weeks. Subsequently, these students underwent a five-week detraining period. The results of the two-way ANOVA showed that the intervention program significantly increased the students' hamstring and lumbar extensibility (pretest=15.7±7.0 cm; posttest=18.2±7.7 cm; p<.001). Although after the detraining period flexibility levels decreased statistically significantly (retest=17.1±7.9 cm; p<.001), the students from EG presented statistically higher values than in the baseline flexibility level (p=.006). For the CG no significant differences were found (pretest=13.4±8.5 cm; posttest=13.1±8.5 cm; retest=13.2±8.4 cm; p=1.000). Although children lose a significant part of the obtained flexibility gains over a five-week detraining period, they do not revert to their baseline flexibility level. Hence, the students might continue working on their flexibility within the next five weeks in order to maintain the gains obtained previously. These findings could help teachers to design programs that guarantee feasible improvement and maintenance of children's flexibility in a physical education setting.
Comparison of Appearance-Based Methods for Generic Object Recognition 1,2

In this paper, we present a large-scale examination of different appearance-based, segmentation- free classification methods for their usability in generic object recognition. Generic object recognition is a method to handle the objects never seen before in classification by a hierarchical approach with a coarse-to-fine graduation. Unknown objects are only classified into coarse categories and rejected to be assigned to classes that are too specific. Comparison of PPCA, NN, and KPCA approaches is made on the basis of their recognition rate. The global generic recognition rate is computed for the best method, and its robustness according to dif- ferent types of noise is examined. Our experiments show that the PCA-based method with nearest neighbor classification provides, in general, the best recognition rates, whereas the models based on principal component analysis outperform the other methods in computation time and model size. Gaussian KPCA models can be used for generic object recognition by varying the variance of the Gaussian kernel.
A Computational Analysis of Neural Mechanisms Underlying the Maturation of Multisensory Speech Integration in Neurotypical Children and Those on the Autism Spectrum

Failure to appropriately develop multisensory integration (MSI) of audiovisual speech may affect a child's ability to attain optimal communication. Studies have shown protracted development of MSI into late-childhood and identified deficits in MSI in children with an autism spectrum disorder (ASD). Currently, the neural basis of acquisition of this ability is not well understood. Here, we developed a computational model informed by neurophysiology to analyze possible mechanisms underlying MSI maturation, and its delayed development in ASD. The model posits that strengthening of feedforward and cross-sensory connections, responsible for the alignment of auditory and visual speech sound representations in posterior superior temporal gyrus/sulcus, can explain behavioral data on the acquisition of MSI. This was simulated by a training phase during which the network was exposed to unisensory and multisensory stimuli, and projections were crafted by Hebbian rules of potentiation and depression. In its mature architecture, the network also reproduced the well-known multisensory McGurk speech effect. Deficits in audiovisual speech perception in ASD were well accounted for by fewer multisensory exposures, compatible with a lack of attention, but not by reduced synaptic connectivity or synaptic plasticity.
An Improved Consensus Linkage Map of Barley Based on Flow‐Sorted Chromosomes and Single Nucleotide Polymorphism Markers

Recent advances in high‐throughput genotyping have made it easier to combine information from different mapping populations into consensus genetic maps, which provide increased marker density and genome coverage compared to individual maps. Previously, a single nucleotide polymorphism (SNP)‐based genotyping platform was developed and used to genotype 373 individuals in four barley (Hordeum vulgare L.) mapping populations. This led to a 2943 SNP consensus genetic map with 975 unique positions. In this work, we add data from six additional populations and more individuals from one of the original populations to develop an improved consensus map from 1133 individuals. A stringent and systematic analysis of each of the 10 populations was performed to achieve uniformity. This involved reexamination of the four populations included in the previous map. As a consequence, we present a robust consensus genetic map that contains 2994 SNP loci mapped to 1163 unique positions. The map spans 1137.3 cM with an average density of one marker bin per 0.99 cM. A novel application of the genotyping platform for gene detection allowed the assignment of 2930 genes to flow‐sorted chromosomes or arms, confirmed the position of 2545 SNP‐mapped loci, added chromosome or arm allocations to an additional 370 SNP loci, and delineated pericentromeric regions for chromosomes 2H to 7H. Marker order has been improved and map resolution has been increased by almost 20%. These increased precision outcomes enable more optimized SNP selection for marker‐assisted breeding and support association genetic analysis and map‐based cloning. It will also improve the anchoring of DNA sequence scaffolds and the barley physical map to the genetic map.
Modeling multi-stream flow in collisionless matter: approximations for large-scale structure beyond shell-crossing

The generally held view that a model of large-scale structure, formed by collisionless matter in the Universe, can be based on the matter model "dust" fails in the presence of multi- stream flow, i.e., velocity dispersion. We argue that models for large-scale structure should rather be constructed for a flow which describes the average motion of a multi-stream system. We present a clearcut reasoning how to approach the problem and derive an evolution equation for the mean peculiar-velocity relative to background solutions of Friedmann-Lemaˆ itre type. We consider restrictions of the nonlinear problem and show that the effect of velocity dispersion gives rise to an effective viscos- ity of non-dissipative gravitational origin. We discuss subcases which arise naturally from this approach: the "sticky particle model" and the "adhesion approximation". We also construct a novel approximation that features adhesive action in the multi- stream regime while conserving momentum, which was consid- ered a drawback of the standard approximation based on Burg- ers' equation. We finally argue that the assumptions made to obtain these models should be relaxed and we discuss how this can be achieved.
Magnesium deficiency after gastro‐intestinal surgery and loss of secretions

In the case reported here the fibromyomatous fundus of the uterus probably entered the sac of the umbilical hernia I 5 months previously. This caused amenorrhea, as in the case of Pullmann (1956), although precisely how is not clear. The severe attack of pain before admission to hospital was due to the uterine torsion and the resulting ischaemia-possibly precipitated by some external force applied to the abdomen, or a sudden muscular effort.
A rigorous derivation of the chemical master equation

We often find it convenient to define the "state" of a well stirred chemically reacting system as "the current number of molecules of each component species". But if we do that, then we must accept the fact that this state cannot evolve with time as purely determinis t ic process. The reason is simply that a specification of the molecular populations does no t constitute a specification of the positions and momenta of all the molecules in the system, and hence does not suffice to uniquely determine the future behavior of the system. Early efforts to mathematically accommodate the intrinsically stochastic nature of chemically reacting systems were reviewed in the 1967 article by McQuarrie [1]. That article introduced many scientists to the principle mathematical features of what is nowadays called the chemica l mas ter equat ion . McQuarrie did not claim in that article to give a rigorous microphysical derivation of the chemical master equation. He simply posited some "transition probabilities" in loose analogy to the conventional chemical reaction rates, and then invoked a "detailed balance" heuristic to obtain master equations for some specific chemical systems; his main concern was to develop solutions to those master equations. But if one cannot demonstrate a rigorous microphysical basis for the chemical master equation, then one cannot accord its solutions any a priori validity. In fact, the prevailing view today seems to be that the chemical master equation does no t have a priori validity, and that its physical fidelity can be assessed only through a posteriori comparisons of its predictions with the results of experiments either real experiments or molecular dynamics experiments.
Can Portability Improve Performance?: An Empirical Study of Parallel Graph Analytics

Due to increasingly large datasets, graph analytics - traversals, all-pairs shortest path computations, centrality measures, etc. - are becoming the focus of high-performance computing (HPC). Because HPC is currently dominated by many-core architectures (both CPUs and GPUs), new graph processing solutions have to be defined to efficiently use such computing resources. Prior work focuses on platform-specific performance studies and on platform-specific algorithm development, successfully proving that algorithms highly tuned to GPUs or multi-core CPUs can provide high performance graph analytics. However, the portability of such algorithms remains an important concern for many users, especially the many companies without the resources to invest in HPC or concerned about lock-in in single-use parallel techniques. In this work, we investigate the functional portability and performance of graph analytics algorithms. We conduct an empirical study measuring the performance of 3 graph analytics algorithms (a single code implemented in OpenCL and targeted at many-core CPUs and GPUs), on 3 different platforms, using 11 real-world and synthetic datasets. Our results show that the code is functionally portable, that is, applications can run unchanged on both CPUs and GPUs. The large variation in their observed performance indicates that portability is necessary not only for productivity, but, surprisingly, also for performance. We conjecture that the impact of datasets on performance is too high to allow platform-specific algorithms to outperform the portable algorithms by large margins, in all cases. Our conclusion is that portable parallel graph analytics is feasible without significant performance loss, and provides a productive alternative to the expensive trial-and-error selection of one algorithm for each (graph,platform) pair.
Using Word Embeddings for Query Translation for Hindi to English Cross Language Information Retrieval

Cross-Language Information Retrieval (CLIR) has become an important problem to solve in the recent years due to the growth of content in multiple languages in the Web. One of the standard methods is to use query translation from source to target language. In this paper, we propose an approach based on word embeddings, a method that captures contextual clues for a particular word in the source language and gives those words as translations that occur in a similar context in the target language. Once we obtain the word embeddings of the source and target language pairs, we learn a projection from source to target word embeddings, making use of a dictionary with word translation pairs. We then propose various methods of query translation and aggregation. The advantage of this approach is that it does not require the corpora to be aligned (which is difficult to obtain for resource-scarce languages), a dictionary with word translation pairs is enough to train the word vectors for translation. We experiment with Forum for Information Retrieval and Evaluation (FIRE) 2008 and 2012 datasets for Hindi to English CLIR. The proposed word embedding based approach outperforms the basic dictionary based approach by 70% and when the word embeddings are combined with the dictionary, the hybrid approach beats the baseline dictionary based method by 77%. It outperforms the English monolingual baseline by 15%, when combined with the translations obtained from Google Translate and Dictionary.
Methodological Issues in the Analysis of Phonotactic Probability Effects in Nonwords

When an unfamiliar word consists of phoneme sequences that are attested in many of the words that a listener already knows, it should be easier to incorporate into the mental lexicon. Results of many studies with nonword materials support this idea, showing that a nonce word form with such high phonotactic probability sounds more like a possible word, and is repeated and learned more easily. However, the inferential statistical tests that are typically used make it difficult to generalize beyond the particular set of nonword items used in any one study, and there are few replications. This paper addresses these methodological issues. It uses a partial replication of one earlier study in previously unreported data for filler items from a second, to describe how the use of mixed-effects models can resolve problems with prior analyses.
PREDICTION OF CANNABIS AND COCAINE USE IN ADOLESCENCE USING DECISION TREES AND LOGISTIC REGRESSION

Spain is one of the European countries with the highest prevalence of cannabis and cocaine use among young people. The aim of this study was to investigate the factors related to the consumption of cocaine and cannabis among adolescents. A questionnaire was administered to 9,284 students between 14 and 18 years of age in Palma de Mallorca (47.1% boys and 52.9% girls) whose mean age was 15.59 years. Logistic regression and decision trees were carried out in order to model the consumption of cannabis and cocaine. The results show the use of legal substances and committing fraudulence or theft are the main variables that raise the odds of consuming cannabis. In boys, cannabis consumption and a family history of drug use increase the odds of consuming cocaine, whereas in girls the use of alcohol, behaviours of fraudulence or theft and difficulty in some personal skills influence their odds of consuming cocaine. Finally, ease of access to the substance greatly raises the odds of consuming cocaine and cannabis in both genders. Decision trees highlight the role of consuming other substances and committing fraudulence or theft. The results of this study gain importance when it comes to putting into practice effective prevention programmes.