Miracle-dz/UniFAR-FTUs huggingface dataset:
HRV analysis using wavelet package transform and Least Square Support Vector Machine

In this study we are interested in the feature extraction of HRV which includes Ventricular Fibrillation (VF) and ventricular tachycardia (VT) and its classification by the least square support vector machine (LS-SVM). In the first stage, we are interested in the demonstration of the efficacity of the signals' analysis HRV by WPT compared to the analysis by DWT. Since the DWT analysis of the HRV causes frequency decomposition. In this study we are going to present new solution using WPT to decompose the HRV signal into HF and LF frequency ranges. The obtained frequency bands are too close to LF and HF bands. RMS measure the signal power contained in the specified frequency bands LF and HF. The index of sympathovagal balance (LF/HF) was examined by RMS of wavelet coefficients. The second part is devoted to evaluating the performance of the features extraction method using the outputs towards an LS-SVM classification algorithm. The classifications include exploratory data analysis, optimal input variable selection, parameter estimation, and performance evaluation via Receiver Operating Characteristic (ROC) curve analysis. LS-SVM model with Radial Basis Function (RBF) kernel achieve a best performance.
Making sense of strange sounds : ( mutual ) intelligibility of related language varieties . A tutorial

When we ask how much two language varieties resemble one another, or how different they are (which is basically the same question), it should be clear that the answer cannot be expressed in a single number. Languages differ from each other not in just one dimension but in a great many respects. They may differ in their sound inventories, in the details of the sounds in the inventory, in their stress, tone and intonation systems, in their vocabularies, and in the way they build words from morphemes and sentences from words. Last, but not least, they may differ in the meanings they attach to the forms in the language, in so far as the forms in two languages may be related to each other. In order to express the distance between two languages, we need a weighted average of the component distances along each of the dimensions identified (and probably many more). So, linguistic distance is a multidimensional phenomenon and we have no a priori way of weighing the dimensions. 1
Alumina-based composites reinforced with single-walled carbon nanotubes

Monolithic Al2O3 ceramics and Al2O3 - 3 vol% single-walled carbon nanotubes (SWCNTs) composites were prepared by spark plasma sintering. The influence of SWCNTs and sintering temperature on sintering behavior and mechanical properties were investigated. Nanotubes were relatively homogeneously distributed in the composite powder, although some agglomerates/bundles existed. It was found that SWCNTs addition retards slightly the sinterability of alumina by nanotubes hindering of particle rearrangement. The average microhardness of the composites was lower than that of Al2O3 ceramics, but there were also high microhardness values of composites (20.41 GPa). The reduction in microhardness is explained in this paper. The average values of the fracture toughness (4.94 MPa-m1/2) from the composite sintered at 1500 °C were approximately 6% and 12% higher than those from the Al2O3 ceramics sintered at 1500 °C and 1600 °C, respectively. In addition, the analysis of the phase composition and the parameters of the crystal structure of the samples were made.
Mind the Gap: evaluation report and executive summary October 2014

© Education Endowment Foundation. You may re-use this document/publication (not including logos) free of charge in any format or medium, under the terms of the Open Government Licence v2.0. To view this licence, visit www.nationalarchives. gov.uk/doc/open-government-licence/version/2 or email: psi@nationalarchives.gsi. gov.uk. Where we have identified any third party copyright information you will need to obtain permission from the copyright holders concerned. The views expressed in this report are the authors’ and do not necessarily reflect those of the Department for Education.
Quantitative analysis of neonatal skeletal muscle functional improvement in the mouse

SUMMARY Postnatal skeletal muscle growth is classically attributed to fiber hypertrophy and myogenic differentiation, but these processes do not account for the size-independent increase of muscle mechanical performance that occurs during postnatal growth. There is also little knowledge about the precise time-course of contractile function or the underlying factors that affect it. The present study investigated morphological factors (muscle fiber size and myofibrillar packing), biochemical factors (myosin heavy chain isoform and desmin intermediate filament protein expression), and muscle architecture during postnatal development in mice. Physiological testing of the mouse tibialis anterior revealed that maximum isometric stress increased from 27±3 kPa at postnatal day 1 to 169±10 kPa by postnatal day 28, roughly a sixfold increase. Morphological measurements revealed a robust increase in the size-independent packing of myofibrillar matrix material occurring with the functional improvement, with just 48.1±5.5% of the cross-sectional area filled with myofibrils at postnatal day 1 whereas 92.5±0.9% was filled by day 28. Expression of four myosin heavy chain isoforms (embryonic, neonatal, IIX and IIB), as well as desmin, correlated significantly with muscle mechanical function. Stepwise multiple regression showed that, of the variables measured, percentage content of neonatal myosin heavy chain was the best predictor of mechanical function during the postnatal time-course. These data provide the first specific structural basis for increases in muscle tension development during growth. Therefore, models of muscle growth must be modified to include an intrinsic quality enhancement component.
Models of scientific software development Conference or Workshop Item

Over the past decade, I have performed several field studies with scientists developing software either on their own or together with software engineers. Based on these field study data, I identify a model of scientific software development as practiced in many scientific laboratories and communities. This model does not fit the standard software engineering models. For example, the tasks of requirement elicitation and software evaluation are not clearly delineated. Nevertheless, it appears to be successful within the context in which it is used. In the context in which scientists collaborate with software engineers, however, I describe problems which arose from the clash of this model with a traditional, phased software engineering model. Given these models, I discuss the issues which have to be addressed in order to determine the software techniques and tools which might best support scientific software development in different contexts. Categor ies and Subject Descr iptors H 1.2 [Human factors]:
A hybrid MAC with prioritization for wireless sensor networks

This paper introduces I-MAC, a new medium access control protocol for wireless sensor networks. I-MAC targets at improving both channel utilisation and energy efficiency while taking into account traffic load for each sensor node according to its role in the network. I-MAC reaches its objectives through prioritized and adaptive access to the channel. I-MAC performances obtained through simulations for different network topologies, scenarios and traffic loads show significant improvements in energy efficiency, channel utilization, loss ratio and delay compared to existing protocols.
Spice it up? Mining Refinements to Online Instructions from User Generated Content

There are a growing number of popular web sites where users submit and review instructions for completing tasks as varied as building a table and baking a pie. In addition to providing their subjective evaluation, reviewers often provide actionable refinements. These refinements clarify, correct, improve, or provide alternatives to the original instructions. However, identifying and reading all relevant reviews is a daunting task for a user. In this paper, we propose a generative model that jointly identifies user-proposed refinements in instruction reviews at multiple granularities, and aligns them to the appropriate steps in the original instructions. Labeled data is not readily available for these tasks, so we focus on the unsupervised setting. In experiments in the recipe domain, our model provides 90.1% F1 for predicting refinements at the review level, and 77.0% F1 for predicting refinement segments within reviews.
Improved Endpoints for Cancer Immunotherapy Trials

Unlike chemotherapy, which acts directly on the tumor, cancer immunotherapies exert their effects on the immune system and demonstrate new kinetics that involve building a cellular immune response, followed by changes in tumor burden or patient survival. Thus, adequate design and evaluation of some immunotherapy clinical trials require a new development paradigm that includes reconsideration of established endpoints. Between 2004 and 2009, several initiatives facilitated by the Cancer Immunotherapy Consortium of the Cancer Research Institute and partner organizations systematically evaluated an immunotherapy-focused clinical development paradigm and created the principles for redefining trial endpoints. On this basis, a body of clinical and laboratory data was generated that supports three novel endpoint recommendations. First, cellular immune response assays generate highly variable results. Assay harmonization in multicenter trials may minimize variability and help to establish cellular immune response as a reproducible biomarker, thus allowing investigation of its relationship with clinical outcomes. Second, immunotherapy may induce novel patterns of antitumor response not captured by Response Evaluation Criteria in Solid Tumors or World Health Organization criteria. New immune-related response criteria were defined to more comprehensively capture all response patterns. Third, delayed separation of Kaplan–Meier curves in randomized immunotherapy trials can affect results. Altered statistical models describing hazard ratios as a function of time and recognizing differences before and after separation of curves may allow improved planning of phase III trials. These recommendations may improve our tools for cancer immunotherapy trials and may offer a more realistic and useful model for clinical investigation.
Receptive Fields in Primate Retina Are Coordinated to Sample Visual Space More Uniformly

In the visual system, large ensembles of neurons collectively sample visual space with receptive fields (RFs). A puzzling problem is how neural ensembles provide a uniform, high-resolution visual representation in spite of irregularities in the RFs of individual cells. This problem was approached by simultaneously mapping the RFs of hundreds of primate retinal ganglion cells. As observed in previous studies, RFs exhibited irregular shapes that deviated from standard Gaussian models. Surprisingly, these irregularities were coordinated at a fine spatial scale: RFs interlocked with their neighbors, filling in gaps and avoiding large variations in overlap. RF shapes were coordinated with high spatial precision: the observed uniformity was degraded by angular perturbations as small as 15°, and the observed populations sampled visual space with more than 50% of the theoretical ideal uniformity. These results show that the primate retina encodes light with an exquisitely coordinated array of RF shapes, illustrating a higher degree of functional precision in the neural circuitry than previously appreciated.
Population prediction of purwanegara village, Indonesia using modified logistic model with migration factor

Population growth model is a widely been used model to do an estimation and forecasting towards the population of peoples, animals, bac-teria and even in economics growth. Many studies have been carried out on population growth model concerning the factors of birth, death and carrying capacity in order to predict the number of population at certain area. From these studies there is only one study involved the constant value factor of migration as an input in the logistic model. Therefore contradicting with the above modified logistic model, in this study logistic model is modified by adding a migration factor as a function of population. This function takes into account the migration and the interaction between peoples that is limited to the carrying capacity of the environment. This model can be solved qualitatively using the analysis of equilibrium point and quantitatively using the separable variables method. This modified logistic model with migration factor has been applied in the population prediction of Purwanegara village in Central Java Province, Indonesia. Throughout the results, the modified logistic model with migration factor as a function of population gives a better result for population prediction of Purwanegara village in Central Java Province, Indonesia compared with logistic model.
Wandering Among Newton Wonders

In the electromagnetic fields t^2 B = b(r/t), E = r x B/t trajectories of non-relativistic charged particles conserve (r-vt)^2. The transformation r'=r/t t'=1/t maps such trajectories into orbits in the constant magnetic field all of which conserve kinetic energy. The transformation may also be used to transform any fields obeying Curl E = B/dt, div B = 0 into others and relates the particle trajectories in them.
Cerebral Interleukin-6 is Neuroprotective during Permanent Focal Cerebral Ischemia in the Rat

Interleukin-6 (IL-6) is a neurotrophic cytokine expressed in both neurons and glia. The present study shows that cerebral ischemia produced by permanent occlusion of the middle cerebral artery (MCAO) produces a dramatic increase in IL-6 bioactivity in the ischemic hemisphere within 2 hours of MCAO (167 ± 55 IU versus sham: 50 ± 35 IU), with further increases at 8 hours (3,456 ± 1,162 IU) and 24 hours (6,088 ± 1,772 IU). In a separate series of experiments, intracerebroventricular injection of recombinant IL-6 (3,100 or 31,000 IU) significantly reduced ischemic brain damage after MCAO (to 52% and 65% of controls, respectively). The large increase in endogenous IL-6 bioactivity in response to ischemia, together with the marked neuroprotection produced by exogenous IL-6 suggest that this cytokine is an important endogenous inhibitor of neuronal death during cerebral ischemia.
Tree cover mapping for assessing greater sage-grouse habitat in Eastern Oregon

We used a predictive model to map canopy cover of vegetation over seven feet in height (“tall woody vegetation”) at 30-meter resolution over nearly 29 million acres within and adjacent to the range of the greater sage-grouse in Oregon (Figure 1). Texture measures computed at various resolutions from colorinfrared aerial photography provided the main source of predictor data used to produce the map. Canopy cover was treated as a categorical variable using six cover classes: absent (cover class C0), present at less than 4% (C1), 4 – 10% (C2), 10 – 20% (C3), 20 – 50% (C4), and 50% and over (C5). The map is referenced to conditions in the years 2011 and 2012.
Predicting the Behavior of the Supreme Court of the United States: A General Approach

Building upon developments in theoretical and applied machine learning, as well as the efforts of various scholars including Guimera and Sales-Pardo (2011), Ruger et al. (2004), and Martin et al. (2004), we construct a model designed to predict the voting behavior of the Supreme Court of the United States. Using the extremely randomized tree method first proposed in Geurts, et al. (2006), a method similar to the random forest approach developed in Breiman (2001), as well as novel feature engineering, we predict more than sixty years of decisions by the Supreme Court of the United States (1953-2013). Using only data available prior to the date of decision, our model correctly identifies 69.7% of the Court’s overall affirm/reverse decisions and correctly forecasts 70.9% of the votes of individual justices across 7,700 cases and more than 68,000 justice votes. Our performance is consistent with the general level of prediction offered by prior scholars. However, our model is distinctive as it is the first robust, generalized, and fully predictive model of Supreme Court voting behavior offered to date. Our model predicts six decades of behavior of thirty Justices appointed by thirteen Presidents. With a more sound methodological foundation, our results represent a major advance for the science of quantitative legal prediction and portend a range of other potential applications, such as those described in Katz (2013).
LOCI: Local Clustering Service for Large Scale Wireless Sensor Networks

We present a local, scalable, and fault-tolerant distributed clustering service, LOCI, that partitions a multi-hop wireless network into clusters of bounded physical radius [ ] where is a constant greater than or equal to . That is, each cluster has a leader node such that all nodes within distance of the leader belong to the cluster but no node beyond distance from the leader belongs to the cluster. LOCI is local in that each node only needs information about nodes that are at most distance away. It is scalable in that each node maintains only a constant amount of state and completes its role in clustering in time ( for a stronger system model), independent of the network size. It is fault-tolerant in that it is self-stabilizing in the presence of state corruption, node and link fail-stops, and node joins. The network partitions generated by LOCI form a Voronoi tessellation as each non-clusterhead node joins the cluster of the nearest clusterhead and the number of clusters LOCI yields is within a constant factor approximation of the minimum number of clusters theoretically feasible. Our simulations demonstrate that, for , the number of clusters constructed by LOCI exceed the minimum number of clusters theoretically feasible only by a factor of 1.5 for a 1-D network and 2.3 for a 2-D network. As hierarchical clustering is readily achieved by instantiating LOCI at multiple levels, LOCI provides a framework for scalable and fault-tolerant distributed tracking structure for pursuer-evader scenarios that has arisen in our recent work in sensor networks. Furthermore, as part of our efforts towards developing sensor network services in the DARPA Network Embedded Software Technology (NEST) program, we have implemented LOCI in TinyOS on the Mica2 mote platform.
TT Mutant Homozygote of Kruppel-like Factor 5 Is a Key Factor for Increasing Basal Metabolic Rate and Resting Metabolic Rate in Korean Elementary School Children

We investigated the contribution of genetic variations of KLF5 to basal metabolic rate (BMR) and resting metabolic rate (RMR) and the inhibition of obesity in Korean children. A variation of KLF5 (rs3782933) was genotyped in 62 Korean children. Using multiple linear regression analysis, we developed a model to predict BMR in children. We divided them into several groups; normal versus overweight by body mass index (BMI) and low BMR versus high BMR by BMR. There were no differences in the distributions of alleles and genotypes between each group. The genetic variation of KLF5 gene showed a significant correlation with several clinical factors, such as BMR, muscle, low-density lipoprotein cholesterol, and insulin. Children with the TT had significantly higher BMR than those with CC (p = 0.030). The highest muscle was observed in the children with TT compared with CC (p = 0.032). The insulin and C-peptide values were higher in children with TT than those with CC (p= 0.029 vs. p = 0.004, respectively). In linear regression analysis, BMI and muscle mass were correlated with BMR, whereas insulin and C-peptide were not associated with BMR. In the high-BMR group, we observed that higher muscle, fat mass, and C-peptide affect the increase of BMR in children with TT (p < 0.001, p < 0.001, and p = 0.018, respectively), while Rohrer's index could explain the usual decrease in BMR (adjust r2 = 1.000, p < 0.001, respectively). We identified a novel association between TT of KLF5 rs3782933 and BMR in Korean children. We could make better use of the variation within KLF5 in a future clinical intervention study of obesity.
Conformant Planning Heuristics Based on Plan Reuse in Belief States

Conformant planning involves generating plans under the condition that the initial situation and action effects are undeterministic and sensing is unavailable during plan execution. (Bonet & Geffner 2000) has shown that conformant planning can be transformed into a search problem in the space of belief states. Since then, heuristic based conformant planning has become a promising approach. Following the direction, some effective heuristics, such as those introduced in MBP (Bertoli & Cimatti 2002), Conformant-FF (Brafman & Hoffmann 2004) and POND (Bryce, Kambhampati, & Smith 2006), were proposed. An additive heuristic that obtains the heuristic value of a given belief state by summing the heuristic value of each world state is sensitive to the progress of the search procedure and often guides a search algorithm efficiently. However, it suffers from the problem of overestimating the distances-to-goal of belief states and therefore leads to overlong plans generally. One of the reasons is that the additive heuristic does not take into account relationships between different world states in a belief state. For example, an action sequence for one world state may have progressive effects on others. In this work, we use plan reuse information to make the additive heuristic more reasonable. The information is discovered with the aid of a classical planning heuristic function and in a real time learning style (Barto, Bradtke, & Singh 1995). The resulted heuristic is tested on a conformant planning system that is implemented as an extension of the classical planner Fast Downward (Helmert 2006) to the conformant setting. Preliminary results show that our heuristic is promising in getting better plans on tasks where plan reuse possibilities exist.
MULTI-TEMPORAL OBJECT-ORIENTED CLASSIFICATIONS AND ANALYSIS OF QUICKBIRD SCENES AT A METROPOLITAN AREA IN BRAZIL ( BELO HORIZONTE , MINAS GERAIS STATE )

Due to the strong urbanization process occurring in Brazil and of population increase in the cities and to serious impacts on environment, there is a growing need to develop technologies and methodologies which allow to monitor, analyze and take decisions (planning) referring to urban expansion. This paper presents the results of a larger and detailed study done in two quarters of Belo Horizonte, the Minas Gerais State Capital, using high-resolution images from two dates. Automatic land use classification algorithms were applied, using a new paradigm: object-oriented classification. It uses context relations, hierarchy and fuzzy logic that are new and efficient procedures to map urban land cover, specially when analyzing high-resolution images. The analysis of the datasets and ancillary data allowed spatial inferences. Finally an analysis on the quality of the classification is done using Kappa statistics. The class stability was also analyzed.