Miracle-dz/UniFAR-FTUs huggingface dataset:
An improved image segmentation approach based on level set and mathematical morphology

Level set methods offer a powerful approach for the medical image segmentation since it can handle any of the cavities, concavities, convolution, splitting or merging. However, this method requires specifying initial curves and can only provide good results if these curves are placed near symmetrically with respect to the object boundary. Another well known segmentation technique - morphological watershed transform can segment unique boundaries from an image, but it is very sensitive to small variations of the image magnitude and consequently the number of generated regions is undesirably large and the segmented boundaries is not smooth enough. In this paper, a hybrid 3D medical image segmentation algorithm, which combines the watershed transform and level set techniques, is proposed. This hybrid algorithm resolves the weaknesses of each method. An initial partitioning of the image into primitive regions is produced by applying the watershed transform on the image gradient magnitude, then this segmentation results is treated as the initial localization of the desired contour, and used in the following level set method, which provides closed, smoothed and accurately localized contours or surfaces. Experimental results are also presented and discussed.
Infarct Size Estimated from Serial Serum Creatine Phosphokinase in Relation to Left Ventricular Hemodynamics

In 50 patients with proven acute myocardial infarction (AMI), left ventricular hemodynamics (pulmonary end-diastolic pressure [PAEDP]; cardiac index [CI]; stroke volume index [SVI]; and SVI/PAEDP were related to the size of the acute infarct. Acute infarct mass was calculated from serial determinations of serum creatine phosphokinase (CPK) every two hours, using a computer program. In 15 cases postmortem measurement of acute infarct size after staining with Nitro-BT was made and correlated with calculated infarct size. Correlation in this limited number of cases was good with a mean difference of 7 g. Acute infarct mass in 38 survivors was 46 ± 5 g and was significantly smaller (P < 0.05) than in the 12 nonsurvivors (76 ± 12 g.) PAEDP in surviving patients was significantly lower (17 ± I mm Hg) and SVI (36 ml/m2) and SVI/PAEDP (2.4 ml/m2/mm Hg) significantly higher than in the nonsurvivors (PAEDP: 24 mm Hg; SVI: 23 ml/m2; SVI/PAEDP: 0.86 ml/m2/mm Hg) (P < 0.001 for all differences). Similar significant differences were observed between patients not in shock and those in cardiogenic shock. Although in 39 patients, in whom the infarction was their first, infarct mass was larger (58 ± 6 g) than in 11 patients with repeat infarctions (37 ± 8 g), left ventricular hemodynamics were slightly more impaired in reinfarctions (PAEDP: 21 ± 3 mm Hg; Cl: 2.60 L/min/m') than in first infarctions (PAEDP: 18 ± I mm Hg; CI: 2.82 L/min/m'). The occurrence of cardiogenic shock was a strong predictor of death; however, the wide scatter of the data for the parameters cardiac index, PAEDP, and acute infarct mass precluded their usefulness, when taken individually, in predicting survival.When a relationship between hemodynamics and infarct size was looked for, four constellations of individual patients were identified. These groups were defined by PAEDPs of above or below 18 mm Hg and infarct sizes above or below 65 g. Class A patients (N = 22) had a small infarct (29 ± 4 g) and good pump function (PAEDP: 13 mm Hg; SVI: 40 ml/m'; SVI/PAEDP: 3.27 ml/m'/mm Hg); prognosis was good for these patients. In class B (N = 13) the infarct was large (96 ± 8 g) and pump function markedly impaired (PAEDP: 26 mm Hg; SVI: 24 ml/mi; SVI/PAEDP: 0.98 ml/m'/mm Hg); 54% of these patients died. Five patients in class C had, in the presence of a large infarct (84 g), only a slightly elevated PAEDP of 17 mm Hg and an almost normal SVI of 37 ml/mi. In contrast, the ten class D patients had an infarct size (34 g) similar to that in class A, but high PAEDP (23 mm Hg) and moderately reduced SVI (31 ml/ml). In this group a high incidence of reinfarctions (six out of ten) occurred.It is concluded that infarct mass calculated from serial CPK analysis, as a single parameter, cannot be used to predict mortality or development of cardiogenic shock in an individual patient. In conjunction with hemodynamic monitoring of left ventricular pump function, however, infarct mass is of considerable importance for assessment of the hemodynamic function of the residual myocardium.
Indigenous Voices on the Web: Folksonomies and Endangered Languages

Examining categories created by Sámi users on Twitter, this article investigates the advantages and limits of global social media for a small localized group. Folksonomies illustrate the empowering potential of Twitter as a site of performance for continuity of cultural practices, vernacular expressions, and “artistic communication in small groups” (Ben-Amos 1971).
Proximity within paragraph: a measure to enhance document retrieval performance

We created a proximity measure, called Proximity Within Paragraph (PWP), which is based on the concept of value assignment to queried words, grouped by associated ideas within paragraphs. Based on the WT10G dataset, a test system comprising three test sets and fifty queries were constructed to evaluate the effectiveness of PWP by comparing it with the existing method: Minimum Distance Between Queried Pairs. A further experiment combines the scores obtained from both methods and the results suggest that the combination can significantly improve the effectiveness.
The Iron-Hydrogenase of Thermotoga maritima Utilizes Ferredoxin and NADH Synergistically: a New Perspective on Anaerobic Hydrogen Production

ABSTRACT The hyperthermophilic and anaerobic bacterium Thermotoga maritima ferments a wide variety of carbohydrates, producing acetate, CO2, and H2. Glucose is degraded through a classical Embden-Meyerhof pathway, and both NADH and reduced ferredoxin are generated. The oxidation of these electron carriers must be coupled to H2 production, but the mechanism by which this occurs is unknown. The trimeric [FeFe]-type hydrogenase that was previously purified from T. maritima does not use either reduced ferredoxin or NADH as a sole electron donor. This problem has now been resolved by the demonstration that this hydrogenase requires the presence of both electron carriers for catalysis of H2 production. The enzyme oxidizes NADH and ferredoxin simultaneously in an approximately 1:1 ratio and in a synergistic fashion to produce H2. It is proposed that the enzyme represents a new class of bifurcating [FeFe] hydrogenase in which the exergonic oxidation of ferredoxin (midpoint potential, −453 mV) is used to drive the unfavorable oxidation of NADH (E 0′ = −320 mV) to produce H2 (E 0′ = −420 mV). From genome sequence analysis, it is now clear that there are two major types of [FeFe] hydrogenases: the trimeric bifurcating enzyme and the more well-studied monomeric ferredoxin-dependent [FeFe] hydrogenase. Almost one-third of the known H2-producing anaerobes appear to contain homologs of the trimeric bifurcating enzyme, although many of them also harbor one or more homologs of the simpler ferredoxin-dependent hydrogenase. The discovery of the bifurcating hydrogenase gives a new perspective on our understanding of the bioenergetics and mechanism of H2 production and of anaerobic metabolism in general.
Efficient Simulation of Population Overflow in Parallel Queues

In this paper we propose a state-dependent importance sampling heuristic to estimate the probability of population overflow in networks of parallel queues. This heuristic approximates the "optimal" state-dependent change of measure without the need for difficult mathematical analysis or costly optimization involved in adaptive methodologies. Comprehensive simulations of networks with an arbitrary number of parallel queues and different traffic intensities yield asymptotically efficient estimates (with relative error increasing sub-linearly in the overflow level) where no other state-independent importance sampling techniques are known to be efficient. The efficiency of the proposed heuristic surpasses those based on adaptive importance sampling algorithms, yet it is easier to determine and implement and scales better for large networks
Light Activates Output from Evening Neurons and Inhibits Output from Morning Neurons in the Drosophila Circadian Clock

Animal circadian clocks are based on multiple oscillators whose interactions allow the daily control of complex behaviors. The Drosophila brain contains a circadian clock that controls rest–activity rhythms and relies upon different groups of PERIOD (PER)–expressing neurons. Two distinct oscillators have been functionally characterized under light-dark cycles. Lateral neurons (LNs) that express the pigment-dispersing factor (PDF) drive morning activity, whereas PDF-negative LNs are required for the evening activity. In constant darkness, several lines of evidence indicate that the LN morning oscillator (LN-MO) drives the activity rhythms, whereas the LN evening oscillator (LN-EO) does not. Since mutants devoid of functional CRYPTOCHROME (CRY), as opposed to wild-type flies, are rhythmic in constant light, we analyzed transgenic flies expressing PER or CRY in the LN-MO or LN-EO. We show that, under constant light conditions and reduced CRY function, the LN evening oscillator drives robust activity rhythms, whereas the LN morning oscillator does not. Remarkably, light acts by inhibiting the LN-MO behavioral output and activating the LN-EO behavioral output. Finally, we show that PDF signaling is not required for robust activity rhythms in constant light as opposed to its requirement in constant darkness, further supporting the minor contribution of the morning cells to the behavior in the presence of light. We therefore propose that day–night cycles alternatively activate behavioral outputs of the Drosophila evening and morning lateral neurons.
The understanding of Indigenous peoples towards professional relationships: The case of Taiwan

Based on a theme that emerged from a study conducted with 25 Indigenous stakeholders between 2009 and 2010, this article argues for the inadequacy of Western models of the practitioner–client relationship, and a need to consider rural and cultural characteristics of Indigenous social work in relationship building and maintaining. The findings suggest that historical and affective contexts, life contextualized scenarios and the collective interest which affect professional boundaries and the dyadic relationship are important in terms of addressing the relationship in a tribal community. The article ends by highlighting implications for Indigenous social work.
Supernova and Hypernova Misbehavior Detection Scheme for Opportunistic Networks

The design of routing protocols for opportunistic networks (OppNets) generally assume that some cooperation prevail between the nodes. But, in the presence of non-collaborative attacks such as supernova and hypernova, the routing operations become a challenge. This paper proposes a defense mechanism against the misbehavior of supernova and hypernova nodes in an OppNet running Epidemic and ProPHet as underlying routing protocols, respectively. Simulation results are provided, under various opportunistic routing attack scenarios, showing that our proposed mechanism helps defending against such attacks, while yielding an increased average performance in terms of delivery ratio and delay in message delivery by 21% and 19% for Epidemic and ProPHet, respectively.
Segmentation of Subspaces in Sequential Data

We propose Ordered Subspace Clustering (OSC) to segment data drawn from a sequentially ordered union of subspaces. Similar to Sparse Subspace Clustering (SSC) we formulate the problem as one of finding a sparse representation but include an additional penalty term to take care of sequential data. We test our method on data drawn from infrared hyper spectral, video and motion capture data. Experiments show that our method, OSC, outperforms the state of the art methods: Spatial Subspace Clustering (SpatSC), Low-Rank Representation (LRR) and SSC.
A simple modified fixed step size power control algorithm for CDMA cellular systems

CDMA is interference limited multiple access system. Power control is an effective way to reduce co-channel interference. Consequently, it can improve the system capacity. CDMA employs fast closed-loop power control in uplink in which signal-to-interference ratio (SIR) is estimated. Transmitting power is adjusted by comparing estimated SIR with desired SIR. In this paper a modified fixed step power control algorithm has been proposed, which can improve the performance of a CDMA system. The proposed algorithm produced a faster convergence and low outage probability compared to other power control algorithms discussed and analyzed to maintain the desired SIR target.
Short Cognitive/Neuropsychological Test Battery for First-Tier Fitness-To-Drive Assessment of Older Adults

Since fitness-to-drive evaluation of elderly drivers has become an important issue, we developed a short first-tier screening battery to evaluate the necessity for further referral to specialised centres. Our sample consisted of 84 subjects between 65 and 96 years who came to the Belgian Road Safety institute for a fitness-to-drive evaluation. Using cross-validated discriminant analyses, the predictive power of a battery consisting of the TRAIL Making Test, Part A, a visual acuity test, a clock drawing test, the Mini-Mental State Examination (MMSE) and age was analysed. The judgement by an independent driver instructor (fit-to-drive vs. not unconditional fit-to-drive), based on a real world road test was used as the dependent variable. Classification functions based on the significant discriminant function yielded a specificity score of 85 (subjects fit-to-drive correctly classified) and a sensitivity score of 80 (subjects as not unconditional fit-to-drive correctly classified). These results highlight the potential value of a short screening instrument that can be used in primary health care settings. This instrument may be useful as a first step in a multi-tier assessment procedure.
Detecting Community Structure in Complex Networks Using Bacterial Chemotaxis with Fuzzy C-means Clustering

Identification of (overlapping) communities/clusters in a complex network is a general problem in data mining of network data sets. In this paper, the bacterial chemotaxis (BC) strategy is used to maximize the modularity of a network, associating with a dissimilarity-index-based and with a diffusion-distance-based fuzzy c-means clustering iterative procedure. The proposed algorithm outperforms most existing methods in the literature as regards the optimal modularity found. Experimental results indicate that the new algorithm is efficient at detecting both good clusterings and the appropriate number of clusters. Copyright © 2014 IFSA Publishing, S. L.
A design approach for digital controllers using reconfigurable network-based measurements

In this paper the authors propose and analyze a network-based control architecture for power electronics building block (PEBB) devices. The objective of this approach to PEBB control is to create a distributed control system capable of guaranteeing maximum flexibility in the control of a power distribution system. In this control system, controller and controlled device are connected through the network, which affects the measurement and control signals with delays and other effects. This application is therefore subject to the known challenges of networked controllers. The main goal of this work is to outline a design methodology for controllers operating with measurements coming from a network. An approach is proposed here to assess the robustness of the control system in presence of delay and to design the controller for robustness to network delay. This approach is based on uncertainty analysis, assuming that such delay is the main element of uncertainty in the system. The theoretical foundations of this approach are discussed, together with the simulation and implementation of a physical laboratory prototype.
Video-based Sign Language Recognition without Temporal Segmentation

Millions of hearing impaired people around the world routinely use some variants of sign languages to communicate, thus the automatic translation of a sign language is meaningful and important. Currently, there are two sub-problems in Sign Language Recognition (SLR), i.e., isolated SLR that recognizes word by word and continuous SLR that translates entire sentences. Existing continuous SLR methods typically utilize isolated SLRs as building blocks, with an extra layer of preprocessing (temporal segmentation) and another layer of post-processing (sentence synthesis). Unfortunately, temporal segmentation itself is non-trivial and inevitably propagates errors into subsequent steps. Worse still, isolated SLR methods typically require strenuous labeling of each word separately in a sentence, severely limiting the amount of attainable training data. To address these challenges, we propose a novel continuous sign recognition framework, the Hierarchical Attention Network with Latent Space (LS-HAN), which eliminates the preprocessing of temporal segmentation. The proposed LS-HAN consists of three components: a two-stream Convolutional Neural Network (CNN) for video feature representation generation, a Latent Space (LS) for semantic gap bridging, and a Hierarchical Attention Network (HAN) for latent space based recognition. Experiments are carried out on two large scale datasets. Experimental results demonstrate the effectiveness of the proposed framework.
Reducing the Dimensionality of Face Space in a Sparse Distributed Local-Features Representation

Low-dimensional representations of sensory signals are key to solving many of the computational problems encountered in high-level vision. Principal Component Analysis (PCA) has been used in the past to provide a global representation with a dimensionality as small as 400 of both images of human faces and their probabilities. Local Feature Analysis (LFA) is a biologically-motivated method tha t has been shown to preserve all the desirable properties of PCA, and also build a more intuitive and efficient, sparsedistributed representation of faces in terms of flexible tem plates of local features. Here we study the properties of LFA as a probability model, and show that the dimensionality of the LFA representation is three times smaller than that of PCA. We discuss the implications for both compression and recognition algorithms.
Characterizing Multi-Hop Communication in Vehicular Networks

Majority of characterization studies on Vehicular Networks are based on simulations that assume little network errors and consistent signal strength. Existing VANET experiments, on the other hand, focus mostly on single-hop communication. In this paper, we present the results of an extensive measurement campaign involving up to six vehicles and evaluating the performance of IEEE 802.11 in different vehicular communication scenarios: vehicle-to-vehicle (V2V) and infrastructure-to-vehicle (I2V). We concentrate our evaluation on multi-hop communication in these two scenarios. We found that distance and line of sight communication are the two main factors affecting the multi-hop inter-vehicle communication. The experimental results confirm also the feasibility of using ad hoc networks to extend the transmission range of the infrastructure and the connection time for cars in motion.
Measures of Simmelian Tie Strength, Simmelian Brokerage, and, the Simmelianly Brokered

A set of measures for Simmelian tie strength, Simmelian brokerage, and, being Simmelianly brokered are introduced. The measures are derived from interpretations of a quote from Simmel (1950). The theoretically most informative measure of Simmelian brokerage is based on a complex value measure of Simmelian tie strength reflected in an Hermitian matrix. Also measures based on weight matrices and hypergraphs are discussed. A maximum for the number of ties one node could Simmelian broker in a network of n nodes is determined.
NVIZ: An Integrated Environment for Simulation, Visualization and Analysis of Spinal Neuronal Dynamics

In this article we describe NVIZ, an interactive environment for system level investigation of neural function. NVIZ combines simulation, visualization and analysis in a single software system that encompasses five biological levels of organization, from ion channels to generated behavior. Once a simulation has been completed, cell-level output data can be visualized as height fields (presenting data from an entire population in a compact representation), or within an anatomical model of cat spinal cord. NVIZ provides a set of numerical analysis tools such as histograms, and a variety of 2D plots that permit both population and cell level analysis within the software system. Movement of a visualized limb segment is generated from activity ofmotoneurons, using a novel new algorithm called Net Neural Drive. The linked visualizations in NVIZ provide a powerful means to comprehend neuronal activity generated in complex models involving thousands of cells. The ability to design or modify neural circuits involving multiple populations, followed by simulation, visualization and analysis promotes rapid experimentation and the ability to digest massive amounts of time-varying simulation data. NVIZ is highly scalable, in terms of the number of populations, neurons, and limb segments. Using interactive tools, the visualization can be easily customized to focus on neural activity of interest. We demonstrate the application of NVIZ to understanding locomotion of a single limb joint, using a central pattern generator model.
An upper limb robot model of children limb for cerebral palsy neurorehabilitation

Robot therapy has emerged in the last few decades as a tool to help patients with neurological injuries relearn motor tasks and improve their quality of life. The main goal of this study was to develop a simple model of the human arm for children affected with cerebral palsy (CP). The Simulink based model presented here shows a comparison for children with and without disabilities (ages 6-15) with normal and reduced range of motion in the upper limb. The model incorporates kinematic and dynamic considerations required for activities of daily living. The simulation was conducted using Matlab/Simulink and will eventually be integrated with a robotic counterpart to develop a physical robot that will provide assistance in activities of daily life (ADLs) to children with CP while also aiming to improve motor recovery.
Testing Models of Change in Life Goals After a Cancer Diagnosis

This study analyzes goals of 159 cancer patients before the start of therapy and goals of 160 healthy controls. Follow-up data were collected at about 9 and 18 months after the first assessment. Patients reported fewer achievement-related goals and leisure goals than controls and had a shorter time perspective for their goals; these differences persisted over the study interval. Whereas the number of health-related goals was similar in both groups at the Time 1, patients showed an increase in this goal category thereafter. In addition, patients reported more health-related barriers to goal pursuit than their healthy peers at Time 1, but this difference declined over time. We conclude that cancer influences loss-based goal selection in the first days after being diagnosed and that the changed goal perspective is widely maintained for one and a half years.