Miracle-dz/UniFAR-FTUs huggingface dataset:
Optical Coherence Tomography Angiography Vessel Density in Healthy, Glaucoma Suspect, and Glaucoma Eyes

Purpose The purpose of this study was to compare retinal nerve fiber layer (RNFL) thickness and optical coherence tomography angiography (OCT-A) retinal vasculature measurements in healthy, glaucoma suspect, and glaucoma patients. Methods Two hundred sixty-one eyes of 164 healthy, glaucoma suspect, and open-angle glaucoma (OAG) participants from the Diagnostic Innovations in Glaucoma Study with good quality OCT-A images were included. Retinal vasculature information was summarized as a vessel density map and as vessel density (%), which is the proportion of flowing vessel area over the total area evaluated. Two vessel density measurements extracted from the RNFL were analyzed: (1) circumpapillary vessel density (cpVD) measured in a 750-μm-wide elliptical annulus around the disc and (2) whole image vessel density (wiVD) measured over the entire image. Areas under the receiver operating characteristic curves (AUROC) were used to evaluate diagnostic accuracy. Results Age-adjusted mean vessel density was significantly lower in OAG eyes compared with glaucoma suspects and healthy eyes. (cpVD: 55.1 ± 7%, 60.3 ± 5%, and 64.2 ± 3%, respectively; P < 0.001; and wiVD: 46.2 ± 6%, 51.3 ± 5%, and 56.6 ± 3%, respectively; P < 0.001). For differentiating between glaucoma and healthy eyes, the age-adjusted AUROC was highest for wiVD (0.94), followed by RNFL thickness (0.92) and cpVD (0.83). The AUROCs for differentiating between healthy and glaucoma suspect eyes were highest for wiVD (0.70), followed by cpVD (0.65) and RNFL thickness (0.65). Conclusions Optical coherence tomography angiography vessel density had similar diagnostic accuracy to RNFL thickness measurements for differentiating between healthy and glaucoma eyes. These results suggest that OCT-A measurements reflect damage to tissues relevant to the pathophysiology of OAG.
Characterization of globin RNA interference in gene expression profiling of whole-blood samples.

BACKGROUND Blood-based biomarker discovery with gene expression profiling has been hampered by interference from endogenous, highly abundant alpha- and beta-globin transcripts. We describe a means to quantify the interference of globin transcripts on profiling and the effectiveness of globin transcript mitigation by (a) defining and characterizing globin interference, (b) reproducing globin interference with synthetic transcripts, and (c) using ROC curves to measure sensitivity and specificity for a protocol for removing alpha- and beta-globin transcripts. METHODS We collected blood at 2 sites and extracted total RNA in PreAnalytiX PAXgene tubes. As a reference for characterizing interference, we supplemented aliquots of total RNA with synthesized globin transcripts and total RNA from human brain. Selected aliquots were processed with Ambion GLOBINclear to remove globin transcripts. All aliquots were labeled and hybridized to Agilent DNA microarrays by means of pooling schemes designed to quantify the mitigation of globin interference and to titrate gene expression signatures. Quantitative reverse transcription-PCR data were generated for comparison with microarray results. RESULTS Our supplementation and pooling strategy for comparing the microarray data among samples demonstrated that mitigation could reduce an interference signature of >1000 genes to approximately 200. Analysis of samples of endogenous globin transcripts supplemented with brain RNA indicated that results obtained with the GLOBINclear treatment approach those of peripheral blood mononuclear cell preparations. CONCLUSION We confirmed that both the absolute concentrations of globin transcripts and differences in transcript concentrations within a sample set are factors that cause globin interference (Genes Immun 2005;6:588-95). The methods and transcripts we have developed may be useful for quantitatively characterizing globin mRNA interference and its mitigation.
An Investigation of SRSD to Teach Fractions to Students With Disabilities

In the current study we examined the effect of a self-regulated strategy development intervention on the fraction calculations of students with or at risk for disabilities using a multiple-baseline-across-sites, single-case design. Specifically, the intervention package addressed the following skills: adding and subtracting fractions with unlike denominators, simplifying fractions, and converting improper fractions to mixed numbers. The intervention was implemented with high levels of treatment fidelity and social validity across three separate intervention agents. Results of the study showed the intervention to be effective, with 15 of the 16 participants making marked gains on fraction probes. We discuss the results of the study with respect to the research questions, provide limitations to the study, and propose areas for future research.
Potential benefits of hierarchical modes of the DVB-T specification

Many engineers and broadcasters are very familiar with the concept of digital terrestrial television (DTT), and hopefully will all have seen live reception of the signals at some time. However a number of those people will not have encountered the idea of hierarchical modulation, which is a part of the ETSI standard-the definition of the RF transmission standard for DVB-T signals. This paper introduces the ideas behind the hierarchical modes, and shows one of the ways in which they could be used at some time in the future, or perhaps by countries still in the process of planning their DTT services at the moment. This paper is not intended to suggest that the existing UK DTT services should be replaced by hierarchical services, but instead it points out a potential way in which the DTT system could be used, if additional frequencies become available in the future.
Forage Fish: From Ecosystems to Markets

Fisheries targeting small-to-medium pelagic, so-called forage fish, impact on human food security and marine ecosystems. Because their operations are shrouded by the myth that forage fish are unsuitable for human consumption, the role of these fisheries in intensive food production is not well understood or appreciated. Thus, although they account for over 30% of global landings of marine fish annually, our knowledge of how these levels of removal impact on marine ecosystems is limited. Nevertheless, there is considerable scope for policy makers to change the current management of these fisheries and to enhance their contribution to food security and economic development. Industry and consumers also have an important role in finding the balance between these fisheries contributing to human food security and poverty alleviation on the one hand, and sustaining intensive animal food production systems, especially aquaculture, on the other.
Feedforward Initialization for Fast Inference of Deep Generative Networks is biologically plausible

We consider deep multi-layered generative models such as Boltzmann machines or Hopfield nets in which computation (which implements inference) is both recurrent and stochastic, but where the recurrence is not to model sequential structure, only to perform computation. We find conditions under which a simple feedforward computation is a very good initialization for inference, after the input units are clamped to observed values. It means that after the feedforward initialization, the recurrent network is very close to a fixed point of the network dynamics, where the energy gradient is 0. The main condition is that consecutive layers form a good auto-encoder, or more generally that different groups of inputs into the unit (in particular, bottom-up inputs on one hand, top-down inputs on the other hand) are consistent with each other, producing the same contribution into the total weighted sum of inputs. In biological terms, this would correspond to having each dendritic branch correctly predicting the aggregate input from all the dendritic branches, i.e., the soma potential. This is consistent with the prediction that the synaptic weights into dendritic branches such as those of the apical and basal dendrites of pyramidal cells are trained to minimize the prediction error made by the dendritic branch when the target is the somatic activity. Whereas previous work has shown how to achieve fast negative phase inference (when the model is unclamped) in a predictive recurrent model, this contribution helps to achieve fast positive phase inference (when the target output is clamped) in such recurrent neural models.
Channel aggregation with guard-band in D-OFDM based CRNs: Modeling and performance evaluation

Channel aggregation (CA) techniques can offer flexible channel allocation and improve overall system performance in multi-channel cognitive radio networks (CRNs). Although many CA techniques have been proposed and studied, the impact of guard-band on CA for channel access has not been addressed in-depth. In this paper, we study the guard-band allocation mechanisms in discontinuous-orthogonal frequency division multiplexing (D-OFDM) based CRNs, and investigate the impact of guard-band sharing on SU flows when CA is enabled. Continuous time Markov chain (CTMC) based models have been developed in order to investigate the stochastic behavior of PU and SU flows. Based on our mathematical analysis and simulation results, we observe that when guard-band is considered together with CA, the performance of the network varies according to different access schemes with distinct guard-band allocation mechanisms.
Geometry synthesis

Inspired by texture synthesis techniques, we present in this paper a method for geometry synthesis. Given an example of input geometry, we synthesize new output geometry that is perceived similar to the input geometry, but at the same time differs in its local appearance. We assume our input geometry satisfies the constraints of a Markov Random Field model, and represent the input by a hierarchical distance field. This allows us to perform fast matching queries between a target distance field that is partially synthesized, and the input distance field. Once the target distance field is completed, we copy the original corresponding geometry elements to our synthesized result. We show that automatically generating geometry by example can be achieved within reasonable computing times, and is able to produce convincing results.
Using logistic regression method to classify tweets into the selected topics

Topics about health, music, sport, and technology are widely discussed in social network sites, especially in Twitter. Sharing information about those topics can enrich one's knowledge as well as increase the awareness of the current trends pertinent to the area of interests. Hence, this research aims to develop a web-based application that can classify tweets of netizens into these four categories of topics using one of machine learning methods called Logistic Regression. There are four main processes applied in this application that are fetching tweets, preprocessing, text feature extraction and machine learning. There are 1800 labeled tweets for each topic used as training data. Several processes were done in the pre-processing phase, including removal of URLs, punctuation, and stop words, tokenization, and stemming. Later, the application automatically converted the pre-processed tweets into set of features vector using Bag of Words. The set of features vector was applied to the Logistic Regression algorithm for the classification task. The trained classifier was then evaluated using 1800 tweets with 450 for each topic. Using Confusion Matrix, the results showed the accuracy of tweets classification into the selected topics is 92% which is considered very high.
Software defect association mining and defect correction effort prediction

Much current software defect prediction work focuses on the number of defects remaining in a software system. In this paper, we present association rule mining based methods to predict defect associations and defect correction effort. This is to help developers detect software defects and assist project managers in allocating testing resources more effectively. We applied the proposed methods to the SEL defect data consisting of more than 200 projects over more than 15 years. The results show that, for defect association prediction, the accuracy is very high and the false-negative rate is very low. Likewise, for the defect correction effort prediction, the accuracy for both defect isolation effort prediction and defect correction effort prediction are also high. We compared the defect correction effort prediction method with other types of methods - PART, C4.5, and Naive Bayes - and show that accuracy has been improved by at least 23 percent. We also evaluated the impact of support and confidence levels on prediction accuracy, false-negative rate, false-positive rate, and the number of rules. We found that higher support and confidence levels may not result in higher prediction accuracy, and a sufficient number of rules is a precondition for high prediction accuracy.
Autonomous Workflow Reconstruction for Command and Control Experimentation

This paper describes a semi-autonomous system that has been developed to support the mining, visualization, and analysis of the process as performed by a team of operators when fulfilling specific military command and control tasks. The system focuses on operational centre activities. It is composed of several independent modules. Two primary modules provides the basic input to the system: a monitoring module that logs all keystrokes, mouse clicks and screen snapshots on each workstation used by the operators; and, a reference module providing a repository of activity models describing expected sequence of activities. In addition, the system uses an optical character recognition (OCR) application as well as pattern recognition algorithms. It provides information on the structure of the workflow as performed by the operators and support the comparison between the expected and observed activity flow. The system has been used to support Joint Fires Support experiments and although improvements to the OCR are required, the developed methodology appears very promising.
The Variation of Integrated Star Initial Mass Functions among Galaxies

The integrated galaxial initial mass function (IGIMF) is the relevant distribution function containing the information on the distribution of stellar remnants, the number of supernovae, and the chemical enrichment history of a galaxy. Since most stars form in embedded star clusters with different masses, the IGIMF becomes an integral of the assumed (universal or invariant) stellar IMF over the embedded star cluster mass function (ECMF). For a range of reasonable assumptions about the IMF and the ECMF we find the IGIMF to be steeper (containing fewer massive stars per star) than the stellar IMF, but below a few solar masses it is invariant and identical to the stellar IMF for all galaxies. However, the steepening sensitively depends on the form of the ECMF in the low-mass regime. Furthermore, observations indicate a relation between the star formation rate of a galaxy and the most massive young stellar cluster in it. The assumption that this cluster mass marks the upper end of a young-cluster mass function leads to a connection of the star formation rate and the slope of the IGIMF above a few solar masses. The IGIMF varies with the star formation history of a galaxy. Notably, large variations of the IGIMF are evident for dE, dIrr, and LSB galaxies with a small to modest stellar mass. We find that for any galaxy the number of supernovae per star (NSNS) is suppressed relative to that expected for a Salpeter IMF. Dwarf galaxies have a smaller NSNS than massive galaxies. For dwarf galaxies the NSNS varies substantially depending on the galaxy assembly history and the assumptions made about the low-mass end of the ECMF. The findings presented here may be of some consequence for the cosmological evolution of the number of supernovae per low-mass star and the chemical enrichment of galaxies of different mass.
Performance prediction for concurrent database workloads

Current trends in data management systems, such as cloud and multi-tenant databases, are leading to data processing environments that concurrently execute heterogeneous query workloads. At the same time, these systems need to satisfy diverse performance expectations. In these newly-emerging settings, avoiding potential Quality-of-Service (QoS) violations heavily relies on performance predictability, i.e., the ability to estimate the impact of concurrent query execution on the performance of individual queries in a continuously evolving workload. This paper presents a modeling approach to estimate the impact of concurrency on query performance for analytical workloads. Our solution relies on the analysis of query behavior in isolation, pairwise query interactions and sampling techniques to predict resource contention under various query mixes and concurrency levels. We introduce a simple yet powerful metric that accurately captures the joint effects of disk and memory contention on query performance in a single value. We also discuss predicting the execution behavior of a time-varying query workload through query-interaction timelines, i.e., a fine-grained estimation of the time segments during which discrete mixes will be executed concurrently. Our experimental evaluation on top of PostgreSQL/TPC-H demonstrates that our models can provide query latency predictions within approximately 20% of the actual values in the average case.
Coworking spaces as places where economic diversity can be articulated: Towards a theory of syntopia

This article reports a qualitative study of several coworking spaces conducted over 3 years. We build on Foucault’s reflection concerning heterotopias to develop a new concept – that of ‘syntopia’ – for theorizing this type of space, whose main characteristic is that its alternative potential lies in enabling its users to articulate economic diversity. Our contribution is twofold: on one hand, our theorization of coworking spaces helps better account for their complexity, for the tensions that can arise within them and for their impacts; on the other hand, with the concept of syntopia, we provide a concept that could help identify other places of a post capitalocentric economy, likely to be a source of profound change in our society. We propose to develop a ‘syntopology’ whose object would be to study systematically the different forms of syntopias, their characteristics, potentials and limitations.
Artificial Neural Network Based Decision on Parameter Values in AODV to Enhance the Performance of Mobile Ad Hoc Networks

Routing in mobile ad hoc networks is really a tedious task which should be performed in an intelligent way. The AODV routing protocol depends on fixed parameter values irrespective of the network behaviour in a dynamic environment. The IETF draft of AODV has highlighted that the choice of the default parameters values affect the protocol performance and their values should be determined considering the network behaviour. In this paper, an attempt is made to incorporate Artificial Neural Network decision on parameter values in AODV to enhance the performance of the mobile ad hoc networks. The Percentage of packet delivery ratio was significantly enhanced by 27.27 % in small network sizes and 9.09% in large network sizes through artificial neural network decisions. Keywords— AODV, Artificial Neural Networks, MANETs, NNBNTTPEAODV, Performance.
Univariate and multirater ordinal cumulative link regression with covariate specific cutpoints

The author considers a reparameterized version of the Bayesian ordinal cumulative link regression model as a tool for exploring relationships between covariates and “cutpoint” parameters. The use of this parameterization allows one to fit models using the leapfrog hybrid Monte Carlo method, and to bypass latent variable data augmentation and the slow convergence of the cutpoints which it usually entails. The proposed Gibbs sampler is not model specific and can be easily modified to handle different link functions. The approach is illustrated by considering data from a pediatric radiology study.
Pulmonary Metastasectomy in Adult Patients with Synovial Sarcoma: A Single-Center Experience

Background This study assessed the efficacy of pulmonary metastasectomy for synovial sarcoma in adult patients. Methods Fifty patients, diagnosed with pulmonary metastasis from June 1990 to August 2010, were reviewed retrospectively. Twenty-eight patients underwent complete pulmonary metastasectomy, and their survival was evaluated. Age, sex, time to metastatic progression, laterality, number of tumors, size of largest nodule, and number of metastasectomies were analyzed as potential prognostic factors. Results In all, 29 patients underwent at least one pulmonary metastasectomy, and 51 resections were performed. One intraoperative mortality occurred, and the 5-year survival rate was 58.4%. Bilateral metastases and early metastatic progression were associated with poor survival in multivariate analyses. Conclusion Surgical resection can be a good option for treating pulmonary metastasis in patients with synovial sarcoma. Repeated resection was feasible with low mortality and morbidity.
Integrability vs. information loss: a simple example

The half-BPS sector of Yang-Mills theory with 16 supercharges is integrable: there is a set of commuting conserved charges, whose eigenvalues can completely identify a state. We show that these charges can be measured in the dual gravitational description from asymptotic multipole moments of the spacetime. However, Planck scale measurements are required to separate the charges of different microstates. Thus, semiclassical observers making coarse-grained measurements necessarily lose information about the underlying quantum state.
Modelling Aggressive or Risky Driving: The Effect of Cinematic Portrayals of Risky Driving

The purpose of the current research was to investigate the influence of motion pictures, depicting aggressive or risky driving, on subsequent driving behaviour. Both experimental and descriptive research approaches were used in an attempt to demonstrate the robustness of this relationship. Study 1 employed an experimental design, in which participants drove through a test course on a driving simulator following exposure to either neutral, arousing, or aggressive and risky driving movie content. Various person, situation, and internal factors were assessed, along with various measures of aggressive or risky driving (e.g., speed, acceleration, passing frequency). Study 2 was an event study, which linked automated enforcement speeding data, from the City of Edmonton, to the release of two aggressive or risky driving movies (i.e., Fast and Furious 6 and Furious 7) to investigate changes in the number of speeding infractions and speed differential (i.e., amount the driver exceeded the posted speed limit). Multiple years of speeding infraction data provided a built in replication, allowing for comparisons across different years. The results from Study 1 provided evidence for the contribution of trait aggression, sensation seeking, driving vengeance, a history of violation (i.e., particularly speeding), and a provoking racing scenario to the modelling of aggressive or risky driving. Study 2 revealed an increase in the number of speeding infractions and mean speed differential for the opening weekend and first week post-movie release for Furious 7. The findings from these studies demonstrate the interactivity of person, internal, and situation factors in the modelling of aggressive or risky driving and suggest that movies, which depict this content, can influence realworld speeding behaviour. Public policy implications are addressed, with a strong suggestion for increased enforcement following the release of such movies, particularly during the first week. An emphasis is placed on production companies to provide warnings and address unsafe driving