Miracle-dz/UniFAR-FTUs huggingface dataset:
Measures of Ignorance on the Web

The hyperlink is a crucial element that makes up the web. While the hyperlink is seen as an element of recommendation, it also represents something more fundamental – namely, knowledge. A page A linking to another page B on the web, indicates knowledge about page B that the author of page A, wishes to make public. If either the author doesn’t know about page B or does not wish to make this knowledge public, it contributes to “ignorance” on the web. This ignorance is on part of the user or a web crawler or a ranking algorithm considering page A. In this work we investigate whether hyperlinks reflect the latent knowledge in the web, by measuring how much they adhere to the well known “cluster hypothesis. ” That is, if a user reading page A is very likely to read page B then page B is linked by a hyperlink from A. Various interpretations are considered for the term “very likely to read” and ignorance is measured as the extent to which the cluster hypothesis is violated. The results show a highly entrenched web, with distinct hyperlinkbased communities of pages unaware of other related pages elsewhere on the web. The results also highlight the role of collaborative content-management environments like Wikipedia to mitigate this entrenchment effect.
Prevalence of adjacent segment disease following cervical spine surgery

AbstractPrevalence estimates of adjacent segment degeneration (ASD) following cervical spine surgery varied greatly in current studies. We conducted a systematic review and meta-analysis to summarize the point prevalence of ASD after cervical spine surgery. MethodsComprehensive electronic searches of PubMed, Embase, Web of Knowledge, and Cochrane Library databases were conducted to identify any study published from initial state to January 2016. Those reporting the prevalence of ASD after cervical surgery were included. A random-effects model was used to estimate the prevalence of radiographic ASD, symptomatic ASD, and reoperation ASD. Univariate meta-regression analyses were conducted to explore the potential associations between prevalence and length of follow-up. All analyses were performed using R version 3.2. 3 (R Foundation for Statistical Computing). ResultsA total of 83 studies were included in the meta-analysis. The prevalence of radiographic ASD, symptomatic ASD, and reoperation ASD after cervical surgery was 28.28% (95% confidence interval [CI], 20.96–36.96), 13.34% (95% CI, 11.06–16.00), and 5.78% (95% CI, 4.99–6.69), respectively, in a general analysis. It was found 2.79%, 1.43%, and 0.24% additions per year of follow-up in the incidence of radiographic ASD, symptomatic ASD, and reoperation ASD, respectively. ConclusionThis meta-analysis provides some details about the prevalence of radiographic ASD, symptomatic ASD, and reoperation ASD after cervical spine surgery. However, the results of this meta-analysis should be interpreted with caution because of the heterogeneity among the studies.
An ellipse feature tracking method based on the Kalman filter

In this paper, a method of ellipse feature tracking based on the Kalman filter is proposed. In the application of the video analysis, this method can predict the results of the next frame, eliminate the false detection of the ellipse, and estimate ellipse parameters using prediction results and ellipse detection results. Through computer simulation experiments and semi physical simulation experiments, it is proved that the ellipse parameter accuracy is further improved compared with the single frame ellipse detection algorithm, and the method does not affect the ellipse detection in real time.
Quantitative automata-based controller synthesis for non-autonomous stochastic hybrid systems

This work deals with Markov processes that are defined over an uncountable state space (possibly hybrid) and embedding non-determinism in the shape of a control structure. The contribution looks at the problem of optimization, over the set of allowed controls, of probabilistic specifications defined by automata - in particular, the focus is on deterministic finite-state automata. This problem can be reformulated as an optimization of a probabilistic reachability property over a product process obtained from the model for the specification and the model of the system. Optimizing over automata-based specifications thus leads to maximal or minimal probabilistic reachability properties. For both setups, the contribution shows that these problems can be sufficiently tackled with history-independent Markov policies. This outcome has relevant computational repercussions: in particular, the work develops a discretization procedure leading into standard optimization problems over Markov decision processes. Such procedure is associated with exact error bounds and is experimentally tested on a case study.
What Is a Savitzky-Golay Filter?

[ lecture NOTES ] R ecently, while reading a paper on heart rate monitoring using an accelerometer [1], I found myself asking the question posed by the above title. While searching for the answer, I discovered many things that seemed to be well known to others outside the field of digital signal processing (DSP) but not to me. After adding some results of my own, I presented what I had learned at a poster session at the 2011 DSP/SPE Workshop [2]. As I interacted with people at the poster session, I asked if they had ever heard of Savitzky-Golay (S-G) filters. Given my own ignorance, it was comforting that only one out of about 20 had heard of them. What is remarkable about this is that Savitzky and Golay's paper [3], published in 1964, was described in 2000 by editors of the journal Analytical Chemistry as number five among the top ten papers ever published in that journal [4]. They stated, " It can be argued that the dawn of computer-controlled analytical chemistry can be traced to this article. " For this reason, I feel that it could be useful to use the "Lecture Notes" forum to introduce (or reintroduce) my colleagues in signal processing to the S-G filters. RELEVANCE In their " seminal " [4] paper [3], Savitzky and Golay proposed a method of data smoothing based on local least-squares polynomial approximation. They showed that fitting a polynomial to a set of input samples and then evaluating the resulting polynomial at a single point within the approximation interval is equivalent to discrete convolution with a fixed impulse response. The lowpass filters obtained by this method are widely known (in some sectors) as Savitzky-Golay filters. Savitzky and Golay were interested in smoothing noisy data obtained from chemical spectrum ana-lyzers, and they demonstrated that least-squares smoothing reduces noise while maintaining the shape and height of waveform peaks (in their case, Gaussian-shaped spectral peaks). In researching this topic, I did find some awareness of S-G filters in the signal processing community. Hamming's book [7] has a discussion of the use of least-squares in data smoothing, and Orfanidis has a detailed discussion of S-G filters in his book, which is now out of print but available for free download [8]. Some researchers have found the peak shape preservation property of the S-G filters to be attractive in applications such as electrocardiogram processing [1] …
N-ification of forces: a holographic perspective on D-brane model building

We study geometric aspects of extensions of the supersymmetric standard model that exhibit a periodic duality cascade. Via the holographic correspondence, the growth of the gauge group rank towards the UV is interpreted as a gradual decompactification transition. We show that this class of models typically develop a duality wall in the UV, and present an efficient method for estimating the hierarchy between the on-set of the cascade and the formation of the wall. As an illustrative example, we study the model introduced by Cascales, Saad and Uranga in [14], which has an known geometric realization in terms of D-branes on an SPP/3 singularity.
The influence of dual credit on Latinx students’ college aspirations and choices

ABSTRACT This study employed social capital and chain migration theory to understand the development of college aspirations and the college choice process of Latinx dual credit earners enrolled in engineering programs. Through semi-structured, one-on-one interviews, participants described the key individuals who inspired their college aspirations and shared the reasons they selected a four-year, public university. The findings indicate that dual credit was not a substantial influence in their college-going decision making. Instead, family, peers, and financial aid resources informed the participants’ aspirations and choices. Recommendations for practice and future research are offered.
Assessment of the effect of medical education on the functional connectivity of the brain using resting statefMRI

BACKGROUND: The brain is a very complex organ and the seat of many important functions, hence much effort and research has gone into understanding how the brain functions and coordinates various activities. Of the various aspects which can affect the brain networks, there have been few studies which show that education had a significant effect on various networks of the brain involved in memory, intelligence and learning. Through these studies we learn more about the networks which are involved in the process of memory, learning etc. and in turn apply this knowledge into the deeper understanding of various cognitive disorders. In this study we aimed to show the affect of medical education on the functional networks of the brain. OBJECTIVE: To determine the differences in the functional networks of the brain in a group of individuals who completed four and half years of medical training as compared to a group of individuals with no medical training and to map out the regions of interest where these differences were significant METHODS: Resting state fMRI was performed in 3 Tesla Philips MRI scanner on two groups of medical students, first group consisting of 22 individuals who completed four and half years of medical training in CMC, Vellore and the second group consisting of 26 individuals who newly joined the medical course in CMC. The fMRI data was pre processed using SPM8 software and seed-to-voxel based analysis was done using CONN version 16.L implemented in SPM8. RESULTS: There were significant differences in the right inferior temporal gyrus (MNI coordinates, 50, -6, -38), right supplementary motor cortex (MNI coordinates, -6, -7, -61) and left cerebellum (MNI coordinates, -46, -52, -40) of the final year brain connectivity as compared to the first years. The above mentioned regions showed increased connectivity and interactions with different regions in the fronto-parietal lobes and the cerebellum. CONCLUSIONS: Significant increased brain connectivity in the group which completed four and half years of medical training was seen in the temporal lobe, which is known to be the seat of long term memory and recall, the cerebellum, which is the seat of language and motor coordination and the supplementary motor cortex which is responsible for fine motor movements and the ability to initiate a motor function.
Does Corporate Governance Influence Corporate Risk-Taking? Evidence from the Institutional Shareholders Services (ISS)

We provide evidence on the effect of corporate governance on the extent of corporate risk-taking. Provided by the Institutional Shareholder Services (ISS), our governance metrics are among the most comprehensive in the literature. Our results show that firms with more effective governance exhibit corporate strategies that are significantly less risky. Left to their own devices, managers tend to take excessive risk. Effective governance, however, reduces the degree of risk-taking significantly. Exploiting the passage of the Sarbanes–Oxley Act of 2002 as an exogenous shock that improves governance quality, we show that the effect of corporate governance on risk-taking is likely causal.
21st Century Skills through Film Production in Tertiary Education: A Transformative Assessment in a Literature and Media Course

The 21 st century lifestyle demands that individuals engage different communication modes and intelligences in every domain from learning, employment, communication and recreation and entertainment. In order to produce individuals that are competent and able to function competitively in the 21 st century global field, tertiary education and assessments must move beyond traditional methods of essays, quizzes and exams that only test discipline related knowledge and memorization skills. Holistic, transformative assessments that can develop knowledge and skills and maximize an individual’s potential is important in creating competent and marketable graduates. This study discusses the development of 21 st century skills through a short film production assessment in the Literature and the Media course taught at The National University of Malaysia. The core principles of formative assessments are used to guide the development, design and implementation of the short film production assessment. A distinctive aspect of the assessment is the inclusion of pitching and short film premiere event organization elements which were found to be very beneficial in helping students develop skills such as decision making, problem-solving and public speaking. The tasks involved in all three stages of the short film production assessment were found to be instrumental in developing fourteen 21 st skills in students. Students’ reflections also provided an insight into the various skills developed through the assessment. This study reinforces the potential of filmmaking as a holistic and transformative assessment tool to equip tertiary students with the necessary skills to meet the demands of 21 st century work and life environments. Keywords: 21 st century skills; short film production; transformative; formative assessment; literature
Data Preparation by CFS: An Essential Approach for Decision Making Using C 4.5 for Medical Data Mining

Trauma has become the leading cause of death in day to day life. Every year millions of people die and many more are handicapped due to various types of accidents caused by Trauma and many people become handicapped for the rest of their lives. It is necessary to develop a tool for predicting and preventing trauma. Reducing mortality rate and increasing the Health awareness is the aim. We have used the data mining process, to extract the useful data from large datasets. Feature subset selection is of immense importance in the field of data mining. The increased dimensionality of data makes testing and training of general classification method difficult. Mining on the reduced set of attributes reduces computation time and also helps to make the patterns easier to understand. The CFS approach for feature selection is proposed. As a part of feature selection step we used filter approach algorithm as random search technique for subset generation, wrapped with different classifiers/ induction algorithm namely decision tree C 4.5, Naïve Bayes, as subset evaluating mechanism on standard datasets. It is mandatory to obtain ethical and legal clearance from regional as well as Institutional Ethics Review Board (IERB), before using data mining tools in health care research. We got Ethical clearance from BGS Hospital for using the datasets. These datasets were gathered from the patient files which were recorded in the medical record section of the BGS Hospital Bangalore. Further the relevant attributes identified by proposed filter are validated using classifiers. Experimental results illustrate, employing feature subset selection using proposed filter approach has enhanced classification accuracy. Applying [DM ] techniques to the data brings about very interesting and valuable results. It is concluded that in this case, comparing the result of evaluating the models on test set, decision tree works better than NaiveBayes. In this paper, we have also used WEKA Tool for creating the models.
Forecasting Tornado Pathlengths Using a Three-Dimensional Object Identification Algorithm Applied to Convection-Allowing Forecasts

AbstractA three-dimensional (in space and time) object identification algorithm is applied to high-resolution forecasts of hourly maximum updraft helicity (UH)—a diagnostic that identifies simulated rotating storms—with the goal of diagnosing the relationship between forecast UH objects and observed tornado pathlengths. UH objects are contiguous swaths of UH exceeding a specified threshold. Including time allows tracks to span multiple hours and entire life cycles of simulated rotating storms. The object algorithm is applied to 3 yr of 36-h forecasts initialized daily from a 4-km grid-spacing version of the Weather Research and Forecasting Model (WRF) run in real time at the National Severe Storms Laboratory (NSSL), and forecasts from the Storm Scale Ensemble Forecast (SSEF) system run by the Center for Analysis and Prediction of Storms for the 2010 NOAA Hazardous Weather Testbed Spring Forecasting Experiment. Methods for visualizing UH object attributes are presented, and the relationship between pathlen.
SHOOT2.0: An indirect grid shooting package for optimal control problems, with switching handling and embedded continuation

The SHOOT2.0 package implements an indirect shooting method for optimal control problems. It is specifically designed to handle control discontinuities, with an automatic switching detection that requires no assumptions concerning the number of switchings. Special care is also devoted to the computation of the Jacobian matrix of the shooting function, using the variational system instead of classical finite differences. The package also features an embedded continuation method and an automatic (parallel) grid shooting in order to reduce the dependency to the initialization.
Reliability and validity of instruments measuring job satisfaction--a systematic review.

BACKGROUND Although job satisfaction research has been carried out for decades, no recent overview of job satisfaction instruments and their quality is available. Aim The aim of this systematic review is to select job satisfaction instruments of adequate reliability and validity for use as evaluative tools in hospital environments. METHODS Systematic literature searches were performed in the Medline and PsycInfo databases. First, the construct of job satisfaction was operationalized by generating work factors from both theoretical studies and meta-analyses or reviews of empirical studies on job satisfaction. Secondly, emphasis was placed on the internal consistency, construct validity and responsiveness of these instruments.Twenty-nine job satisfaction instruments were retrieved in total. RESULTS Seven instruments met the defined reliability and validity criteria. Of the seven, the 'Measure of Job Satisfaction' had an adequate content validity. Only the 'Job in General Scale' provided data about 'responsiveness' to change. CONCLUSION Few instruments have shown both high reliability and high validity, but little is known about their evaluative potential.
Pharmacologic Stem Cell Based Intervention as a New Approach to Osteoporosis Treatment in Rodents

Background Osteoporosis is the most prevalent skeletal disorder, characterized by a low bone mineral density (BMD) and bone structural deterioration, leading to bone fragility fractures. Accelerated bone resorption by osteoclasts has been established as a principal mechanism in osteoporosis. However, recent experimental evidences suggest that inappropriate apoptosis of osteoblasts/osteocytes accounts for, at least in part, the imbalance in bone remodeling as occurs in osteoporosis. The aim of this study is to examine whether aspirin, which has been reported as an effective drug improving bone mineral density in human epidemiology studies, regulates the balance between bone resorption and bone formation at stem cell levels. Methods and Findings We found that T cell-mediated bone marrow mesenchymal stem cell (BMMSC) impairment plays a crucial role in ovariectomized-induced osteoporosis. Ex vivo mechanistic studies revealed that T cell-mediated BMMSC impairment was mainly attributed to the apoptosis of BMMSCs via the Fas/Fas ligand pathway. To explore potential of using pharmacologic stem cell based intervention as an approach for osteoporosis treatment, we selected ovariectomy (OVX)-induced ostoeporosis mouse model to examine feasibility and mechanism of aspirin-mediated therapy for osteoporosis. We found that aspirin can inhibit T cell activation and Fas ligand induced BMMSC apoptosis in vitro. Further, we revealed that aspirin increases osteogenesis of BMMSCs by aiming at telomerase activity and inhibits osteoclast activity in OVX mice, leading to ameliorating bone density. Conclusion Our findings have revealed a novel osteoporosis mechanism in which activated T cells induce BMMSC apoptosis via Fas/Fas ligand pathway and suggested that pharmacologic stem cell based intervention by aspirin may be a new alternative in osteoporosis treatment including activated osteoblasts and inhibited osteoclasts.
Flow-Based Local Optimization for Image-to-Geometry Projection

The projection of a photographic data set on a 3D model is a robust and widely applicable way to acquire appearance information of an object. The first step of this procedure is the alignment of the images on the 3D model. While any reconstruction pipeline aims at avoiding misregistration by improving camera calibrations and geometry, in practice a perfect alignment cannot always be reached. Depending on the way multiple camera images are fused on the object surface, remaining misregistrations show up either as ghosting or as discontinuities at transitions from one camera view to another. In this paper we propose a method, based on the computation of Optical Flow between overlapping images, to correct the local misalignment by determining the necessary displacement. The goal is to correct the symptoms of misregistration, instead of searching for a globally consistent mapping, which might not exist. The method scales up well with the size of the data set (both photographic and geometric) and is quite independent of the characteristics of the 3D model (topology cleanliness, parametrization, density). The method is robust and can handle real world cases that have different characteristics: low level geometric details and images that lack enough features for global optimization or manual methods. It can be applied to different mapping strategies, such as texture or per-vertex attribute encoding.
Enabling High Performance Computing in Cloud Infrastructure using Virtualized GPUs

With the advent of virtualization and Infrastructure-as-a-Service (IaaS), the broader scientific computing community is considering the use of clouds for their technical computing needs. This is due to the relative scalability, ease of use, advanced user environment customization abilities clouds provide, as well as many novel computing paradigms available for data-intensive applications. However, there is still a notable gap that exists between the performance of IaaS when compared to typical high performance computing (HPC) resources, limiting the applicability of IaaS for many potential scientific users. Most recently, general-purpose graphics processing units(GPGPUs or GPUs) have become commonplace within high performance supercomputers. We propose to bridge the gap between supercomputing and Clouds by providing GPU-enabled virtual machines. Specifically, the Xen hypervisor is utilized to leverage specialized hardware-assisted I/O virtualization tools in order to provide advanced HPC-centric Nvidia GPUs directly in guest VMs. We evaluate this work by measuring the performance of two Nvidia Tesla GPUs and comparing to bare-metal hardware. Results show this method of leveraging GPUs within virtual machines is a viable use case for many scientific computing workflows, and could help support high performance cloud infrastructure in the near future.