Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance modelling of wormhole-routed hypercubes with bursty traffice and finite buffers

An open queueing network model (QNM) is proposed for wormhole-rout ed hypercubes with finite buffers and deterministic routing subject to a compound Poisson arri val process (CPP) with geometrically distributed batches or, equivalently, a generalised exponential (GE) int erarrival time distribution. The GE/G/1/K queue and appropriate GE-type flow formulae are adopted, as cost-effective building blocks, in a queue-by-queue decomposition of the entire network. Consequently, analytic express ions for the channel holding time, buffering delay, contention blocking and mean message latency are determined. The validi ty of the analytic approximations is demonstrated against results obtained through simulation experimen ts. Moreover, it is shown that the wormholerouted hypercubes suffer progressive performance degradation with incr easing traffic variability (burstiness).
An environment for evaluating browsing in hypermedia systems

Hypermedia systems encourage exploratory information acquisition. Browsing, an exploratory behavior, is therefore crucial in navigating in such systems. The study of browsing can help designers to create systems which are compatible with users navigation pattern. However, browsing behavior is very difficult to measure because the experimenter cannot foretell where in a information space a user will browse and no two users browse the same material the same way. In this paper, we report the creation of an environment in which browsing can be studied efficiently. In this environment, three tools were implemented: An automatic logging tool to record in log files how and where users browse, a visualization tool to scan these files and plot useful charts automatically, and a dynamic questionnaire customization mechanism to retrieve questions from a question bank according to location browsed and time spent on each location. With this environment, browsing patterns and knowledge retention can be assessed effectively.
Incorporating DEM Uncertainty in Coastal Inundation Mapping

Coastal managers require reliable spatial data on the extent and timing of potential coastal inundation, particularly in a changing climate. Most sea level rise (SLR) vulnerability assessments are undertaken using the easily implemented bathtub approach, where areas adjacent to the sea and below a given elevation are mapped using a deterministic line dividing potentially inundated from dry areas. This method only requires elevation data usually in the form of a digital elevation model (DEM). However, inherent errors in the DEM and spatial analysis of the bathtub model propagate into the inundation mapping. The aim of this study was to assess the impacts of spatially variable and spatially correlated elevation errors in high-spatial resolution DEMs for mapping coastal inundation. Elevation errors were best modelled using regression-kriging. This geostatistical model takes the spatial correlation in elevation errors into account, which has a significant impact on analyses that include spatial interactions, such as inundation modelling. The spatial variability of elevation errors was partially explained by land cover and terrain variables. Elevation errors were simulated using sequential Gaussian simulation, a Monte Carlo probabilistic approach. 1,000 error simulations were added to the original DEM and reclassified using a hydrologically correct bathtub method. The probability of inundation to a scenario combining a 1 in 100 year storm event over a 1 m SLR was calculated by counting the proportion of times from the 1,000 simulations that a location was inundated. This probabilistic approach can be used in a risk-aversive decision making process by planning for scenarios with different probabilities of occurrence. For example, results showed that when considering a 1% probability exceedance, the inundated area was approximately 11% larger than mapped using the deterministic bathtub approach. The probabilistic approach provides visually intuitive maps that convey uncertainties inherent to spatial data and analysis.
Minimizing mobile phone disruption via smart profile management

In this paper, we describe the Smart Profile Management application that was designed to help minimize mobile phone disruptions. The system achieves the goal using a machine learning based algorithm that switches the phone profile automatically. A prototype was developed in Python for S60 and an informal usability test was run for a period of 7 days. Results show that a large proportion of profile suggestion changes were accepted by subjects, suggesting that such an application can in fact lower disruptions.
Data Sonification for Users with Visual Impairment: A Case Study with Georeferenced Data

We describe the development and evaluation of a tool, iSonic, to assist users with visual impairment in exploring georeferenced data using coordinated maps and tables, augmented with nontextual sounds and speech output. Our in-depth case studies with 7 blind users during 42 hours of data collection, showed that iSonic enabled them to find facts and discover trends in georeferenced data, even in unfamiliar geographical contexts, without special devices. Our design was guided by an Action-by-Design-Component (ADC) framework, which was also applied to scatterplots to demonstrate its generalizability. Video and download is available at www.cs. umd.edu/hcil/iSonic/.
Evaluation of Intradural Stimulation Efficiency and Selectivity in a Computational Model of Spinal Cord Stimulation

Spinal cord stimulation (SCS) is an alternative or adjunct therapy to treat chronic pain, a prevalent and clinically challenging condition. Although SCS has substantial clinical success, the therapy is still prone to failures, including lead breakage, lead migration, and poor pain relief. The goal of this study was to develop a computational model of SCS and use the model to compare activation of neural elements during intradural and extradural electrode placement. We constructed five patient-specific models of SCS. Stimulation thresholds predicted by the model were compared to stimulation thresholds measured intraoperatively, and we used these models to quantify the efficiency and selectivity of intradural and extradural SCS. Intradural placement dramatically increased stimulation efficiency and reduced the power required to stimulate the dorsal columns by more than 90%. Intradural placement also increased selectivity, allowing activation of a greater proportion of dorsal column fibers before spread of activation to dorsal root fibers, as well as more selective activation of individual dermatomes at different lateral deviations from the midline. Further, the results suggest that current electrode designs used for extradural SCS are not optimal for intradural SCS, and a novel azimuthal tripolar design increased stimulation selectivity, even beyond that achieved with an intradural paddle array. Increased stimulation efficiency is expected to increase the battery life of implantable pulse generators, increase the recharge interval of rechargeable implantable pulse generators, and potentially reduce stimulator volume. The greater selectivity of intradural stimulation may improve the success rate of SCS by mitigating the sensitivity of pain relief to malpositioning of the electrode. The outcome of this effort is a better quantitative understanding of how intradural electrode placement can potentially increase the selectivity and efficiency of SCS, which, in turn, provides predictions that can be tested in future clinical studies assessing the potential therapeutic benefits of intradural SCS.
Doença crónica: intervenção do médico de família para limitar as repercussões na pessoa e na família

Given the chronic illness of one of its elements, the family has struggled and is pressed to mobilize mechanisms that do not belong to their usual repertoire. During the process of adapting to this situation, the reactions of the sick person and other members of his family influence each other, a process that can become in itself a problem, leading to new risks for the family and for the ill person. Thus, the family should not be excluded from the process of care. This dissertation focuses on the impact of chronic disease in the sick person and his family, it analyzes the context of factors that influence and identifies types of medical interventions that promote adaptive responses to disease. Structured into seven parts, the first part of this dissertation demonstrates the importance of chronic diseases, focusing its weight in the pattern of current morbidity and its impact on health care costs, direct and indirect, in the organization of health care, and in the family and the sick person, the latter two levels of impact which focuses this thesis. In the second part, we review the definitions in question in this dissertation and the theories that fall, including the general systems theory and the Bronfenbrenner ́s bioecological model of development. In the third part, we justified the methodological options of this dissertation. Inserted in the interpretive paradigm is grounded and uses qualitative and quantitative data, a triangulation of methods and analysis. To support the rationale for choices made, described the dialogue between alternative paradigms, including the definition of the respective concepts and standards. Given the rhetoric not be limited to quantitative research versus qualitative research, but also occur between the different currents of the latter, passed in review their distinct historical moments, since persist and coexist on
Metric properties of the braided Thompson's groups

Braided Thompson's groups are finitely presented groups introduced by Brin and Dehornoy which contain the ordinary braid groups $B_n$, the finitary braid group $B_{\infty}$ and Thompson's group $F$ as subgroups. We describe some of the metric properties of braided Thompson's groups and give upper and lower bounds for word length in terms of the number of strands and the number of crossings in the diagrams used to represent elements.
Automatic Musical Instrument Recognition Using K-NN and MLP Neural Networks

In this paper, we proposed an approach to Musical Instrument Automatic Recognition. We used seven different musical instruments to be played simultaneously from solos to quartets. Our data have 296 feature vectors that used in audio signal classification by MLP neural networks and K-NN algorithm. Finally, MLP achieved as the best neural network in musical instrument recognition.
The Importance Of Reading - How Perspective-Taking InfluencesOur (Prosocial) Behaviour

Helping has become a matter of course for many of us. Be it through helping picking up dropped purchases or donating money to charity, some sort of prosocial behaviour has become intuitive to almost anyone. In addition, research has shown that we are experiencing the most peaceful era in the existence of the human species: A global decline in violence and an increased inclination towards prosocial behaviour can be recognized, which, amongst others, seems to result from both empathy and improved literacy. It is said that reading helps to learn and develop social competences like empathy which in turn promotes prosocial behaviour. To investigate the relationships between reading, prosocial behaviour and empathy, a true online experiment, including a questionnaire, with 112 participants was conducted. The results have shown that reading a factual text was related to more empathy than reading a personal text. All further relationships between reading, empathy, acceptance and prosocial willingness were not significant, just age had a little effect on acceptance. It is unclear if those findings result from wrong measurement, wrong manipulation or if there were indeed no relationships between the constructs. Further research is recommended.
Swimming Exercise Changes Hemodynamic Responses Evoked by Blockade of Excitatory Amino Receptors in the Rostral Ventrolateral Medulla in Spontaneously Hypertensive Rats

Exercise training reduces sympathetic activity in hypertensive humans and rats. We hypothesized that the swimming exercise would change the neurotransmission in the rostral ventrolateral medulla (RVLM), a key region involved in sympathetic outflow, and hemodynamic control in spontaneously hypertensive rats (SHR) and Wistar-Kyoto (WKY) rats. Bilateral injections of kynurenic acid (KYN) were carried out in the RVLM in sedentary- (S-) or exercised- (E-) SHR and WKY rats submitted to swimming for 6 weeks. Rats were α-chloralose anesthetized and artificially ventilated, with Doppler flow probes around the lower abdominal aorta and superior mesenteric artery. Injections into the RVLM were made before and after i.v. L-NAME (nitric oxide synthase, NOS, inhibitor). Injections of KYN into the RVLM elicited a major vasodilation in the hindlimb more than in the mesenteric artery in E-SHR compared to S-SHR, but similar decrease in arterial pressure was observed in both groups. Injections of KYN into the RVLM after i.v. L-NAME attenuated the hindlimb vasodilation evoked by KYN and increased the mesenteric vasodilation in E-SHR. Swimming exercise can enhance the hindlimb vasodilation mediated by peripheral NO release, reducing the activation of neurons with EAA receptors in the RVLM in SHR.
Using full-rank spatial covariance models for noise-robust ASR

We present a joint spatial and spectral denoising front-end for Track 1 of the 2nd CHiME Speech Separation and Recognition Challenge based on the Flexible Audio Source Separation Toolbox (FASST). We represent the sources by nonnegative matrix factorization (NMF) and full-rank spatial covariances, which are known to be appropriate for the modeling of small source movements. We then learn acoustic models for automatic speech recognition (ASR) on the enhanced training data. We obtain 40% average error rate reduction due to speech separation compared to multicondition training alone.
Family Values and the Star Phenomenon

We examine the extent to which a fund's cash flows are affected by the stellar performance of other funds in its family - and consequences of such spillovers. We show that star performance results in greater cash inflow to the fund and to other funds in its family. Moreover, families with higher variation in investment strategies across funds are shown to be more likely to generate star performance. We argue that spillovers may induce lower ability families to pursue star creating strategies. Consistent with our conjecture, families with high variation in investment strategies across funds significantly underperform low variation families.
A Self-Adaptive Multi-Agent System Approach for Collaborative Mobile Learning

Mobile technologies have emerged as facilitators in the learning process, extending traditional classroom activities. However, engineering mobile learning applications for outdoor usage poses severe challenges. The requirements of these applications are challenging, as many different aspects need to be catered, such as resource access and sharing, communication between peers, group management, activity flow, etc. Robustness is particularly important for learning scenarios to guarantee undisturbed and smooth user experiences, pushing the technological aspects in the background. Despite significant research in the field of mobile learning, very few efforts have focused on collaborative mobile learning requirements from a software engineering perspective. This paper focuses on aspects of the software architecture, aiming to address the challenges related to resource sharing in collaborative mobile learning activities. This includes elements such as autonomy for personal interactive learning, richness for large group collaborative learning (indoor and outdoor), as well as robustness of the learning system. Additionally, we present self-adaptation as a solution to mitigate risks of resource unavailability and organization failures that arise from environment and system dynamism. Our evaluation provides indications regarding the system correctness with respect to resource sharing and collaboration concerns, and offers qualitative evidence of self-adaptation benefits for collaborative mobile learning applications.
Fever of unknown origin in adults.

In 1907 Richard C. Cabot, a physician at the Massachusetts General Hospital (Boston) and founder of its clinicopathological conferences, published a paper entitled "The Three LongContinued Fevers of New England" [1]. Reviewing hospital records, autopsies, and cases from private practice, he found 784 patients with >2 weeks of continuous temperatures above 99? F and reported that 91% suffered from one of three disorders-typhoid fever (75%), tuberculosis (7%), and "sepsis" (i.e., pyogenic bacterial infections such as abscesses and endocarditis) (9%). Most of the remaining cases were due to other infectious diseases, including meningitis, influenza, syphilis, and gonorrhea. Through the 1950s, clinicians in the United States and western Europe continued to describe patients with lengthy or unexplained fevers, but these series were retrospective, had differing or undefined criteria for fever, involved various intensities of evaluation, and typically failed to identify the etiology in most cases. Among those with prolonged fever for whom a specific diagnosis emerged, the most common causes continued to be a wide variety of infections, including syphilis, brucellosis, endocarditis, and, especially, tuberculosis. Believing that most patients with protracted, perplexing fever had non-infectious diseases, Paul B. Beeson conducted a prospective study from 1952 to 1957 at Yale University, and, with Robert G. Petersdorf, published this experience with 100 patients in 1961 [2]. The intent of this series was to study genuinely perplexing cases of prolonged fever: those included in it had to satisfy three prerequisites that have since formed the definition of fever of unknown (or undetermined) origin (FUO): (1) illness of >3 weeks' duration; (2) fever higher than 101? F (38.3? C) on several occasions; (3) an uncertain diagnosis despite 1 week of study in a hospital. The purpose of these criteria was to exclude from consideration acute, self-limited illnesses, healthy people whose temperatures slightly exceeded the normal range (those with "habitual hyperthermia"), and disorders readily identifiable after a brief evaluation. Since hospitalization is often unnecessary, Petersdorf has suggested revising the third criterion to: no diagnosis established despite 1 week of intensive evaluation [3].
Complete developmental cycle of Leishmania mexicana in axenic culture

SUMMARY A complete developmental sequence of Leishmania mexicana has been produced in axenic culture for the first time. This was achieved by manipulation of media, pH and temperature conditions over a period of 16 days. All experiments were initiated with lesion amastigotes that were transformed to multiplicative promastigotes by culture in HOMEM, 10% foetal calf serum, pH 7·5, at 25 °C. Metacyclogenesis was induced by subpassage in Schneider's Drosophila medium, 20% foetal calf serum, pH 5·5, and the resulting forms transformed to axenically growing amastigotes by subpassage in the same medium and raising the temperature to 32 °C. Parasites from each day were characterized with respect to their general morphology using light microscopy of Giemsa-stained smears, and biochemically by analysis of total protein content, proteinases, nucleases and secretory acid phosphatase. The results demonstrated that the three main stages identified - amastigotes, multiplicative promastigotes and metacyclic promastigotes - each exhibited the expected suite of biochemical properties. Further, the changes in morphology observed as the developmental sequence proceeded from stage to stage were accompanied by appropriate changes in biochemical properties. These results provide both useful biochemical markers and a culture system in which to examine the regulation of differentiation and transformation during the Leishmania life-cycle.
A Research Roadmap for Bringing Continuous Quality Assurance and Optimization to Cloud Service Brokers

: Cloud service intermediation is becoming increasingly recognized as a key component of the cloud computing value chain. Existing cloud service intermediaries already offer capabilities such as integration, customization or aggregation brokerage, but in the future, enterprises will require much more sophisticated capabilities going far beyond what is available today. The types of brokerage capabilities foreseen to be most valuable for service consumers, and at the same time most demanding for future enterprise service brokers to implement, are those addressing continuous quality assurance and optimization of cloud services. In this paper, we present a research roadmap for bringing continuous quality assurance and optimization capabilities closer to the reach of enterprise cloud service brokers. We present a scenario for motivating the need for such capabilities, as well as a discussion of the dominant views on cloud service brokerage
Skin Recognition Based on Color Image Enhance Algorithm

Traditional skin recognition process include parameters ensuring and recognition. Most recognition focused on the accurate of parameters and taked out method of filter light information of image. Experiment proved that the light imformation can not be filter directly from image. This paper take out algorithm of skin recognition based on image enhance method. Firstly, image is enhanced non-linear transform to enhance the dark pixels. Then adjust the contrast with diagram banlance and local enhance method. In the end, renew the image with linear transform. After this process, we recognize the image with ellipse clusterring skin color model. This method has the advantage of high recogniton rate and strong robust.
Periodic points of endomorphisms on solenoids and related groups

This paper investigates the problem of finding the possible sequences of periodic point counts for endomorphisms of solenoids. For an ergodic epimorphism of a solenoid, a closed formula is given that expresses the number of points of any given period in terms of sets of places of finitely many algebraic number fields and distinguished elements of those fields. The result extends to more general epimorphisms of compact abelian groups.
Automatic aerial image segmentation using a modified Chan-Vese algorithm

Automatic segmentation of aerial images has been a challenging area of research in recent years. Among numerous image segmentation methods, the level set method has received a great deal of attention which could represent contours or surfaces with complex topology and change their topology in a natural way. The solution of classic level set model, however, can be easily trapped into a local minimum. To overcome this problem, a novel modified dual Chan-Vese model is proposed in this paper. This proposed model is composed of two contours, which evolve towards the edges of objects from inside of the objects and outside of the objects. By reducing the differences between the interior contour and the external contour, the proposed model can partly prevent the solution of the level set method from a local minimum. Experiments show that the proposed model can obtain exact aerial image segmentation.