Miracle-dz/UniFAR-FTUs huggingface dataset:
Nonparametric Bayesian Sparse Factor Models with application to Gene Expression modelling

A nonparametric Bayesian extension of Factor Analysis (FA) is proposed where observed data $\mathbf{Y}$ is modeled as a linear superposition, $\mathbf{G}$, of a potentially infinite number of hidden factors, $\mathbf{X}$. The Indian Buffet Process (IBP) is used as a prior on $\mathbf{G}$ to incorporate sparsity and to allow the number of latent features to be inferred. The model's utility for modeling gene expression data is investigated using randomly generated data sets based on a known sparse connectivity matrix for E. Coli, and on three biological data sets of increasing complexity.
Hepatocellular carcinoma in patients with autoimmune hepatitis – a systematic review of the literature published between 1989–2016

Background and aims Liver cancer is one of the most common cause of deaths from cancer. Hepatocellular carcinoma (HCC) was reported at a frequency of 7% of patients with autoimmune hepatitis (AIH) - related cirrhosis in 1988. We aimed to provide a systematic literature review on the frequency of HCC in patients with AIH, after the discovery of hepatitis C virus (HCV), in order to avoid any possible confounding etiology. Methods A literature search of the PubMed database between 1989–2016 was performed, using the relevant keywords “hepatocellular carcinoma” and “autoimmune hepatitis”. We followed the PRISMA statement guidelines during the preparation of this review. Results Eleven studies (n=8,460 patients with AIH) were retained for the final analysis. HCC was diagnosed in 0–12.3% of the AIH patients included in these studies. The overall occurrence of HCC in patients with AIH was estimated in two studies, at 5.1% and 6.2%, respectively. In patients with AIH and cirrhosis, the percentage of HCC varied between 0.2%–12.3%. The proportion of HCC in patients with AIH without cirrhosis was estimated at 1.03%. The percentage of cirrhosis in AIH patients varied from 18.7% to 83.3% in Japan, and from 12% to 50.2% in the other areas. The mean follow-up of the patients with AIH was of 10 years. Conclusions The development of HCC in patients with AIH appeared to be similar before and after the discovery of HCV, and it was mainly associated to cirrhosis. The number of patients developing cirrhosis in relation with AIH was impressive. The long evolution of AIH to cirrhosis and, eventually, to HCC, has been be suggested.
Almost separating and almost secure frameproof codes

The theory of separating codes has been applied in several areas of science ranging from automata synthesis to the protection of distribution rights. In this paper, we introduce a relaxed version of separating and secure frameproof codes and show that for the relaxed definitions these two notions are different, as opposed to the original definitions when these notions coincide. Moreover, we also discuss how this new relaxed versions of the codes can be used to construct a family of fingerprinting codes.
Searching for the True Diet of Marine Predators: Incorporating Bayesian Priors into Stable Isotope Mixing Models

Reconstructing the diet of top marine predators is of great significance in several key areas of applied ecology, requiring accurate estimation of their true diet. However, from conventional stomach content analysis to recent stable isotope and DNA analyses, no one method is bias or error free. Here, we evaluated the accuracy of recent methods to estimate the actual proportion of a controlled diet fed to a top-predator seabird, the Little penguin (Eudyptula minor). We combined published DNA data of penguins scats with blood plasma δ15N and δ13C values to reconstruct the diet of individual penguins fed experimentally. Mismatch between controlled (true) ingested diet and dietary estimates obtained through the separately use of stable isotope and DNA data suggested some degree of differences in prey assimilation (stable isotope) and digestion rates (DNA analysis). In contrast, combined posterior isotope mixing model with DNA Bayesian priors provided the closest match to the true diet. We provided the first evidence suggesting that the combined use of these complementary techniques may provide better estimates of the actual diet of top marine predators- a powerful tool in applied ecology in the search for the true consumed diet.
The (1+λ) evolutionary algorithm with self-adjusting mutation rate

We propose a new way to self-adjust the mutation rate in population-based evolutionary algorithms. Roughly speaking, it consists of creating half the offspring with a mutation rate that is twice the current mutation rate and the other half with half the current rate. The mutation rate is then updated to the rate used in that subpopulation which contains the best offspring. We analyze how the (1 + λ) evolutionary algorithm with this self-adjusting mutation rate optimizes the OneMax test function. We prove that this dynamic version of the (1 + λ) EA finds the optimum in an expected optimization time (number of fitness evaluations) of O(nλ/log λ + n log n). This time is asymptotically smaller than the optimization time of the classic (1 + λ) EA. Previous work shows that this performance is best-possible among all λ-parallel mutation-based unbiased black-box algorithms. This result shows that the new way of adjusting the mutation rate can find optimal dynamic parameter values on the fly. Since our adjustment mechanism is simpler than the ones previously used for adjusting the mutation rate and does not have parameters itself we are optimistic that it will find other applications.
Using Incremental Gesture Recognition to Provide Immediate Feedback while Drawing Pen Gestures

As recognition of pen gestures tends to be ambiguous and error-prone, it is essential that users get feedback about recognized shapes as early as possible. This is especially important in modeless user interfaces with no separate modes for writing and gesturing. In this paper, we propose to give feedback continuously while users draw, as this allows immediate correction of minor mistakes. To be able to provide the speed necessary for fluent writing, an incremental algorithm is used for gesture recognition.
RIDGE BASED DECOMPOSITION OF COMPLEX BUILDINGS FOR 3D MODEL GENERATION FROM HIGH RESOLUTION DIGITAL SURFACE MODELS

3D city models have a wide range of applications such as mobile navigation, urban planning, telecommunication, and tourism. The generation of high resolution Digital Surface Models (DSMs), in particular from airborne LIDAR data have found a major attraction of researchers in this field due to their high accuracy and high density of points. In this paper, a new approach is proposed for the generation of 3D models of buildings that are extracted from a high resolution DSM. Particularly the complex buildings containing several smaller building parts are discussed. The building parts are individually modeled by a projection-based method and merged to form the final 3D model. The following building parts are considered to be modeled in the proposed approach: (1) flat roof, (2) gable roof, (3) hipped roof, and (4) mansard-shaped roof buildings. The complex buildings are decomposed into several parts according to the number of existing ridge lines. The ridge line extraction begins with image reconstruction by geodesic morphology (Arefi and Hahn, 2005) extracting the pixels of the highest part of the building segment, i.e., local maxima. Next, a Random SAmple Consensus (RANSAC) (Hartley and Zisserman, 2004) based technique is used to fit straight lines to the ridge points. The pixels of the building part regarding to each ridge line are projected onto a plane which is defined based on ridge direction. According to the type of the roof, a 2D model is fitted to the projected points. The 2D model is converted back to 3D by extruding it orthogonally to the projection plane. After reconstructing 3D models for all building parts, they are merged to form the overall 3D model of the building.
Know Thy Selfie: using contemporary art to teach adolescent identity exploration

Purpose The purpose of this paper is to highlight how one high school psychology teacher helped students explore the concept of identity exploration and express their own personal identity through the use of contemporary art in a high school psychology course. Design/methodology/approach In this paper, techniques one high school teacher used for utilizing the visual arts to teach identity exploration in a high school psychology course are shared, including student discussion surrounding the visual analysis of contemporary artwork, thoughtful student application of developmental theories and the student production of original artwork to express one’s identity. Findings Students participating in the lesson engaged enthusiastically in the discussion of the use of selfies in contemporary art and demonstrated thoughtful reflection in the creation of their own selfies. Research limitations/implications Future research is needed to systematically investigate the effectiveness of incorporating contemporary art as a means of teaching identity exploration to adolescents as part of a high school psychology curriculum. Practical implications Adolescent exploration is a key feature of the adolescent experience and is part of the psychology curriculum at the high school level. Such courses afford students the unique opportunity to apply developmental theories and theories of identity exploration to recent occurrences in their lives. One possibility for teaching identity exploration is through the visual arts. Originality/value This lesson advances psychology instruction through the purposeful scaffolding of identity exploration as both content and process using contemporary art.
Movement of Iranian Academic Research Centers towards Knowledge Management: An Exploration of KM Critical Factors

This paper is to discuss the essential issues of knowledge management (KM), in order to explore knowledge management primary steps in some Iranian academic research centers (ARC). So, the critical success factors of knowledge management (KM-CSF) through some Iranian academic research centers have been identifies based on a comprehensive review of KM literature. For this purpose, a questionnaire was designed, applied and then analyzed by some statistical methods.
TOWARDS ROBUST SPEAKER SEGMENTATION: THE ICSI-SRI FALL 2004 DIARIZATION SYSTEM

We describe the ICSI-SRI entry in the Fall 2004 DARPA EARS Metadata Evaluation. The current system was derived from ICSI’s Fall 2003 Speaker-attributed STT system. Our system is an agglomerative clustering system that uses a BIC-like measure to determine when to stop merging clusters and to decide which pairs of clusters to merge. The main advantage of this approach is that it does not require pre-trained acoustic models, providing robustness and portability. Changes for this year’s system include: different front-end features, the addition of SRI’s Broadcast News speech/non-speech detector, and modifications to the segmentation routine. In post-evaluation work, we found further improvement by changing the stopping criterion from the BIC-like measure to a Viterbi measure. Additionally, we have explored issues related to pruning and improved initialization.
Improved Vector Space Model TF/IDF Using Lexical Relations

Current vector space model, for instance TF/IDF, has not yet taken into account the relations between terms; it only combines the term frequency in a document and the inverse document frequency in whole database to identify importance-score (weight) of a term respect with the document. Here we discover lexical relations among terms in the document to improve the vector space model TF/IDF. The weight generated from TF/IDF for each term, which is improved by lexical relations among related terms in the document. We evaluate the proposed method using documents selected from Wikipedia. The result shown that the proposed method is significant effective.
Robust Estimation of Feature Weights in Statistical Machine Translation

Weights of the various components in a standard Statistical Machine Translation model are usually estimated via Minimum Error Rate Training. With this, one finds their optimum value on a development set with the expectation that these optimal weights generalise well to other test sets. However, this is not always the case when domains differ. This work uses a perceptron algorithm to learn more robust weights to be used on out-of-domain corpora without the need for specialised data. For an Arabic-to-English translation system, the generalisation of weights represents an improvement of more than 2 points of BLEU with respect to the MERT baseline using the same information.
Squeeze box filter for contrast enhancement in ultrasound despeckling

Ultrasound imaging is a widely used and safe medical diagnostic technique, due to its noninvasive nature, low cost, real time imaging. However the usefulness of ultrasound imaging is degraded by the presence of signal depended noise known as speckle‥ The speckle pattern depends on the structure of the image tissue and various imaging parameters. There are two main purposes for speckle reduction in medical ultrasound imaging (1) to improve the human interpretation of ultrasound images (2) despeckling is the preprocessing step for many ultrasound image processing tasks such as segmentation and registration‥ The recent advancements in ultrasound devices necessitate the need of more robust despeckling techniques for enhancing ultrasound medical imaging in routine clinical practice. The method is developed to contrast enhance B-mode ultrasound images. Despeckle filter is very important in the enhancement of ultrasound imaging. This method is motivated by viewing the pixel values as a speckle and removing outliers and enhances the image contrast. Several speckle reduction techniques are applied to ultrasound images in order to reduce the speckle and improve the visual quality for better diagnoses. A comparison of the proposed despeckling filter is compared with other well knowndespeckling filter. The essence of this despeckling method is to remove highly dense impulse noise using Squeeze Box Filter. The despeckling performance based upon improvements to contrast enhancement, structural similarity, and segmentation.
Impact of MODIS atmospheric motion vectors on a global NWPsystem

Atmospheric motion vectors (AMVs) generated using observations in the infrared (11 μm) and the water vapour absorption band (6.7 μm) taken by the Moderate Resolution Imaging Spectrometer (MODIS) instrument on the National Aeronautics and Space Administration (NASA) Aqua and Terra satellites have been assimilated into the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) and have been shown to improve global forecasts. These AMVs provide vital wind information in the data-sparse polar regions of the globe, where the use of remote sensing data is often difficult. Several periods have been examined including August and September 2004 and the International MOWSAP (MODIS Winds Special Acquisition Period), January and February 2004. This assimilation study was part of the initial testing for the operational National Oceanic and Atmospheric Administration/National Environmental Satellite Data Information Service (NOAA/NESDIS) MODIS AMV system, which is now used to generate these winds operationally. The MODIS AMVs are distributed via the Global Telecommunication System (GTS) for the international community. The MODISbased AMVs have been characterised in terms of error and correlated error and data impact tests have also been undertaken using MODIS wind data in analyses up to the second-last analysis in the assimilation cycle, as this represents the current operational availability of the wind data at NCEP. The tests summarised here show positive forecast impact on the GFS at middle and high latitudes in both hemispheres. It was also noted use of the AMVs provided an impact in tropical latitudes and in the Atlantic basin which resulted in improved tropical cyclone track forecasts during the 2004 hurricane season. The impact of these winds, used up to the secondlast analysis in the GFS forecast cycle, has resulted in their introduction into the Operational Global Forecast Suite at NCEP. In effect this study has not only demonstrated the utility of the winds for operational numerical weather prediction (NWP), but also the considerable transfer of information through the operational analysis background fields.
A fingerprint and threshold scheme-based key generation method

The biometric cryptographic system can provide the organic integration of biometric features and the traditional cryptology. The application of biometric keys can not only hide the information of users' biometric features, but also securely realize secret key production, recovery and certification. This paper proposes a new scheme of cryptographic key generation method based on fingerprint features and the (t,n)- threshold scheme. Our method can not only protect the user's key but can also effectively prevent the legitimate user's biometric features revealed. Our experiment results show that the key generation and recovery are efficient, and our proposed algorithm can achieve the anticipated results.
Effectively Transporting a Spiritually Based Intervention for Reducing Subsyndromal Anxiety and Increasing Coping Skills in Urban Adolescents: A Multiple Baseline Design

A critical review of literature revealed a gap in the literature of spiritually based interventions, most notably the subject of a spiritually based intervention for reducing subsyndromal anxiety and increasing coping skills in urban adolescents. The purpose of this study was to investigate the relationship between a spiritually based intervention, a triangulation approach, subsyndromal anxiety, and coping skills in urban adolescents, employing the add-on/integration of a Prayer Wheel intervention. Utilizing a multiple baseline design across subjects to demonstrate the controlling effects of spiritually based intervention/prayer wheel, research revealed a decrease in subsyndromal anxiety levels, and an increase in coping skills in urban adolescent subjects. Although the correlations between a spiritually based intervention, a triangulation approach, subsyndromal anxiety, and coping skills in urban adolescents was not as strong as hypothesized, the current study provides empirical (quantitative and qualitative) evidence that a Prayer Wheel intervention’s potential impact has tripartite significance: in research, clinical, and Biblical practice, and suggests that THE PRAYER WHEEL TM Program may be an effective tool for the alleviation of symptoms of subsyndromal anxiety among urban adolescents.
Semiparametric GEE analysis in partially linear single-index models for longitudinal data

In this article, we study a partially linear single-index model for longitudinal data under a general framework which includes both the sparse and dense longitudinal data cases. A semiparametric estimation method based on a combination of the local linear smoothing and generalized estimation equations (GEE) is introduced to estimate the two parameter vectors as well as the unknown link function. Under some mild conditions, we derive the asymptotic properties of the proposed parametric and nonparametric estimators in different scenarios, from which we find that the convergence rates and asymptotic variances of the proposed estimators for sparse longitudinal data would be substantially different from those for dense longitudinal data. We also discuss the estimation of the covariance (or weight) matrices involved in the semiparametric GEE method. Furthermore, we provide some numerical studies including Monte Carlo simulation and an empirical application to illustrate our methodology and theory.
Use of Non-Topological Node Attribute Values for Probabilistic Determination of Link Formation

Here we propose a probabilistic model for determining link formation, using Naive Bayes Classifier on non-topological attribute values of nodes, in a social network. The proposed model gives a score which helps to determine the relationship strength in a non-formed link. In addition to Naive Bayes Classifier, weighted Average of the Attribute value match helps to determine the friendship score of a non-formed link. With the increase in online social networks and its influence on people, more and more individuals are getting wider and enhanced social connect. Everyone tries to connect more to explore more. In this race of more, an individual needs better and definitive tools to help them grow their network. Wider is the network more is the possibility to explore. Here we present a novel approach for predicting a link (friendship) between two individuals (nodes) in a social network. The proposed approach uses non-topological attribute data values of both the nodes and predicts linkage possibility by applying Naive Bayes Classifier on non-topological attribute data values of nodes in existing linkages. A linkage possibility is expressed using one quantitative measure FSCORE. We call it friendship score (FSCORE) between two unconnected individuals. FSCORE is used to predict linkage between two nodes. Higher FSCORE means a higher possibility of linkage between two nodes.
The Global Kinematics of the Globular Cluster M92

We report the determination of high-accuracy radial velocities for 306 members of the globular cluster M92 using the Hydra multiobject spectrograph on the WIYN telescope. We have concentrated on stars outside of the central region of the cluster, located up to 14.4′ from the cluster center. Candidate members were selected for spectroscopy based on a photometric metallicity index determined from three-band Washington photometry, also obtained with the WIYN telescope. The median error in the velocities is 0.35 km s-1. We find the heliocentric radial velocity of the cluster to be -121.2 ± 0.3 km s-1. We have used an improved Bayesian analysis to determine the velocity dispersion profile of M92. The most probable profile is a cored power law with a scale radius of 2′, a velocity dispersion at 1′ of 6.3 km s-1, and an outer power law with a slope of -0.6. We have also reanalyzed the M15 radial velocities of Drukier et al. and find that a pure power law with a 1′ velocity dispersion of 8 km s-1 and a slope of -0.5 and the combination of a power law with a slope of -0.4 and a scale of 7.5 km s-1 inside 9′ and a dispersion of 4 km s-1 outside are equally likely. In both clusters there is evidence that the samples include escaping stars. We present results from a GRAPE-based N-body simulation of an isolated cluster that demonstrates this effect. We suggest additional tests to determine the relative importance of tidal heating and stellar ejection for establishing the velocity field in globular cluster halos.
A Task- and Scenario-Based Analysis and Design Method for User-Centered Systems

Software development processes need to cover usability relevant information. Although several methods have been proposed how to collect usability relevant information, it is still an unsolved problem how to deal with this information and to ensure its availability and proper management at all relevant stages in the development process. This paper provides a framework to structure all relevant aspects concerning hypermedia learning systems and to link usability knowledge with domain knowledge. The framework also supports distributed design teams in framing, storing and communicating knowledge item. It is implemented with an XML-based database and a web-front-end.
Image restoration by regularisation in uncorrelated transform domain

Conventional spatially adaptive regularised image restoration schemes weight the amount of regularisation according to the spatial content of an image. The authors first separately decorrelate the signals under analysis into uncorrelated components and then weight the amount of regularisation performed to these components accordingly. The proposed approach works better than conventional schemes, especially in edge regions.