Miracle-dz/UniFAR-FTUs huggingface dataset:
Transferring Knowledge by Prior Feature Sampling

The paper presents a novel method for transfer learning through prior variable sampling. A set of problems defined in the same feature space with similar dependencies of target on features is considered. We suggest a method for learning a decision tree ensemble on each of the problems by prior estimation of variable importance on other problems in the set and using it for regularizing model learning for a small amount of training samples. The method is tested on several simulated and real datasets. In particular, we apply our method for a set of time series classification (TSC) problems. Our analysis demonstrates an intriguing result: a model trained on several TSC problems can learn a new problem with high accuracy from a low number of samples.
Information power grid: The new frontier in parallel computing?

ASA’s Information Power Grid is an example of an emerging, exciting concept that can potentially make high-performance computing power accessible to general users as easily and seamlessly as electricity from an electrical power grid. In the IPG system, high-performance computers located at geographically distributed sites will be connected via a high-speed interconnection network. Users will be able to submit computational jobs at any site, and the system will seek the best available computational resources, transfer the user’s input data sets to that system, access other needed data sets from remote sites, perform the specified computations and analysis, and then return the resulting data sets to the user. Systems such as the IPG will be able to support larger applications than ever before. New types of applications will also be enabled, such as multidisciplinary collaboration environments that couple geographically dispersed compute, data, scientific instruments, and people resources together using a suite of grid-wide services. IPG’s fundamental technology comes from current research results in the area of large-scale computational grids. Figure 1 provides an intuitive view of a wide-area computational grid.
Performance analysis of token-based fast TCP in systems supporting large windows

Since TCP can only detect congestion after packet losses have already happened, various forms of fast TCP (FTCP) have been proposed to notify congestion early and avoid packet losses in intermediate nodes by effectively controlling backward ACK flows traversing the same nodes as the forward data packets. Among them, token-based FTCP (TB-FTCP) is a promising approach as it does not need to determine the rate of delaying ACK. The effectiveness of TB-FTCP has been proven for networks in which window size is limited by the bandwidth-delay product (see Peng, F. et al., Proc. 9th Int. Conf. on Computer Commun. and Networks, 2000). A mathematical model for the TB-FTCP is now presented and analyzed numerically for networks in which window size can be larger than the bandwidth-delay product. Results indicate that the proposed method performs extremely well compared to traditional TCP implementations. It is important to note that TCP behavior at end-nodes does not have to be modified in any way.
High-resolution profiling of the LEDGF/p75 chromatin interaction in the ENCODE region

Lens epithelium-derived growth factor/p75 (LEDGF/p75) is a transcriptional coactivator involved in stress response, autoimmune disease, cancer and HIV replication. A fusion between the nuclear pore protein NUP98 and LEDGF/p75 has been found in human acute and chronic myeloid leukemia and association of LEDGF/p75 with mixed-lineage leukemia (MLL)/menin is critical for leukemic transformation. During lentiviral replication, LEDGF/p75 tethers the pre-integration complex to the host chromatin resulting in a bias of integration into active transcription units (TUs). The consensus function of LEDGF/p75 is tethering of cargos to chromatin. In this regard, we determined the LEDGF/p75 chromatin binding profile. To this purpose, we used DamID technology and focused on the highly annotated ENCODE (Encyclopedia of DNA Elements) regions. LEDGF/p75 primarily binds downstream of the transcription start site of active TUs in agreement with the enrichment of HIV-1 integration sites at these locations. We show that LEDGF/p75 binding is not restricted to stress response elements in the genome, and correlation analysis with more than 200 genomic features revealed an association with active chromatin markers, such as H3 and H4 acetylation, H3K4 monomethylation and RNA polymerase II binding. Interestingly, some associations did not correlate with HIV-1 integration indicating that not all LEDGF/p75 complexes on the chromosome are amenable to HIV-1 integration.
An Uncertainty Framework for Classification

We define a generalized likelihood function based on uncertainty measures and show that maximizing such a likelihood function for different measures induces different types of classifiers. In the probabilistic framework, we obtain classifiers that optimize the cross-entropy function. In the possibilistic framework, we obtain classifiers that maximize the interclass margin. Furthermore, we show that the support vector machine is a sub-class of these maximummargin classifiers.
Robust PCA neural networks for random noise reduction of the data

The paper presents a principal component analysis (PCA) approach to the reduction of noise contaminating the data. The PCA performs the role of lossy compression and decompression. The compression/decompression provides the means of coding the data and then recovering it with some losses, dependent on the realized compression ratio. In this process some part of the information contained in the data is lost. When the loss tolerance is equal to the noise strength, the noise and the loss tolerance are augmented and the decompressed signal is deprived of noise. This way of noise filtering has been checked on examples of 1-dimensional and 2-dimensional data and the results of numerical experiments are included.
Hindi speech synthesis by concatenation of recognized hand written devnagri script using support vector machines classifier

Handwritten optical character recognition is one of the major research area due to its complexity in segmenting the character which increases in the case of Devnagri Script due to Modifiers and compound characters. Thus this paper shows an adaptive segmentation technique which shows less error. This paper shows an implementation of Handwritten Devanagri character recognition system. Keeping in mind the impairment of blind people, OCR system is extended to Text to Speech System. As by the surveys Support Vector Machines found to be very efficient and robust in handling large amount of features, SVM was used for the purpose of classification. The learning was done in Multi domain feature. In Transform domain, wavelet transform was used because of its ability to keep global feature in different scale. In structural domain, gradient and distance profile features were used as they represent local characteristics of the characters. Here the handwritten document was segmented adaptively in 3 levels; line, word and character. Preprocessing was done using various Morphological operation and thinning techniques. All the training was done using self-created database consists of 50 samples per character from 10 different individuals. Further, the recognized characters were digitized using Unicode and their corresponding phoneme present in the database were concatenated to form the speech signal. A self-created database of all the possible phonemes was created.
Selection and Ordering of Linear Online Video Ads

This paper studies the selection and ordering of in-stream ads in videos shown in online content publishers. We propose an allocation algorithm that uses a collective measure of price and quality for each ad and factors in slot-specific continuation probabilities to maximize publisher revenue. The algorithm is based on cascade models and uses a dynamic programming method to assign linear (video) ads to slots in an online video. The approach accounts for the negative externality created by lower quality ads placed in a video, leading to viewer exit and thereby preventing the publisher from showing the subsequent ads scheduled in that session. Our algorithm is scalable and suited for real-time applications. A large log of viewer activity from a video ad platform is used to empirically test the algorithm. A series of simulations show that our algorithm, when compared to other algorithms currently practiced in industry, generates more revenue for the publisher and increases viewer retention.
Routing and Scheduling in Wireless Mesh Network

Wireless networks over the year have undergone huge technological advancements. Due to integration of all three types of services viz. voice, data, video, during improvement in wireless technologies, bandwidth has become the most crucial network resource that needs to be optimized for these bandwidth hungry services. In multihop scenarios (in WMNs), performance depends on the routing protocol to properly choose routes, given the current network conditions. To efficiently optimize these wireless networks, efficient routing and scheduling techniques needs to be incorporated to improve Quality of Service (QoS). Routing and scheduling techniques have been analyzed. Different techniques in various technologies are discussed to improve network performance.
How to Summarize an OWL Domain Ontology

With the Semantic Web Initiative, the Internet more and more becomes an Internet which contains ontologies for different domains. Such ontologies were mainly generated to be machine interpretable. Nevertheless, in a digital Society also the human reader must have the possibility to decide if an ontology is usable for his purpose. This paper will present an approach which allows getting a quick overview of the focus of an ontology. In order to fulfill this, the paper describes how to generate a controlled natural language summary of the ontology for the human reader.
The Gilles De La Tourette Syndrome-Quality of Life Scale for Children and Adolescents (C&A-GTS-QOL): Development and Validation of the Italian Version

Background: Gilles de la Tourette syndrome (GTS) is a chronic childhood-onset neuropsychiatric disorder with a significant impact on patients’ health-related quality of life (HR-QOL). Cavanna et al. (Neurology 2008; 71: 1410–1416) developed and validated the first disease-specific HR-QOL assessment tool for adults with GTS (Gilles de la Tourette Syndrome-Quality of Life Scale, GTS-QOL). This paper presents the translation, adaptation and validation of the GTS-QOL for young Italian patients with GTS. Methods: A three-stage process involving 75 patients with GTS recruited through three Departments of Child and Adolescent Neuropsychiatry in Italy led to the development of a 27-item instrument (Gilles de la Tourette Syndrome-Quality of Life Scale in children and adolescents, C&A-GTS-QOL) for the assessment of HR-QOL through a clinician-rated interview for 6–12 year-olds and a self-report questionnaire for 13–18 year-olds. Results: The C&A-GTS-QOL demonstrated satisfactory scaling assumptions and acceptability. Internal consistency reliability was high (Cronbach’s alpha > 0.7) and validity was supported by interscale correlations (range 0.4–0.7), principal-component factor analysis and correlations with other rating scales and clinical variables. Conclusions: The present version of the C&A-GTS-QOL is the first disease-specific HR-QOL tool for Italian young patients with GTS, satisfying criteria for acceptability, reliability and validity.
Discrete-Time Nonlinear HJB Solution Using Approximate Dynamic Programming: Convergence Proof

Convergence of the value-iteration-based heuristic dynamic programming (HDP) algorithm is proven in the case of general nonlinear systems. That is, it is shown that HDP converges to the optimal control and the optimal value function that solves the Hamilton-Jacobi-Bellman equation appearing in infinite-horizon discrete-time (DT) nonlinear optimal control. It is assumed that, at each iteration, the value and action update equations can be exactly solved. The following two standard neural networks (NN) are used: a critic NN is used to approximate the value function, whereas an action network is used to approximate the optimal control policy. It is stressed that this approach allows the implementation of HDP without knowing the internal dynamics of the system. The exact solution assumption holds for some classes of nonlinear systems and, specifically, in the specific case of the DT linear quadratic regulator (LQR), where the action is linear and the value quadratic in the states and NNs have zero approximation error. It is stressed that, for the LQR, HDP may be implemented without knowing the system A matrix by using two NNs. This fact is not generally appreciated in the folklore of HDP for the DT LQR, where only one critic NN is generally used.
A new algebraization of the Laméequation

We develop a new way of writing the LameHamiltonian in Lie-algebraic form. This yields, in a natural way, an explicit formula for both the Lamepolynomials and the classical non-meromorphic Lame functions in terms of Chebyshev polynomials and of a certain family of weakly orthogonal polynomials.
Weight functions impact on LSA performance

This paper presents experimental results of usage of LSA for analysis of English literature texts. Several preliminary transformations of the frequency text-document matrix with different weight functions are tested on the basis of control subsets. Additional clustering based on correlation matrix is applied in order to reveal the latent structure. The algorithm creates a shaded form matrix via singular values and vectors. The results are interpreted as a quality of the transformations and compared to the control set tests.
Universität Des Saarlandes Fachrichtung 6.1 – Mathematik Generalised Nonlocal Image Smoothing Generalised Nonlocal Image Smoothing

We propose a discrete variational approach for image smoothing consisting of nonlocal data and smoothness contraints that penalise general dissimilarity measures defined on image patches. One of such dissimilarity measures is the weighted í µí°¿ 2 distance between patches. In such a case we derive an iterative neighbourhood filter that induces a new similarity measure in the photometric domain. It can be regarded as an extended patch similarity measure that evaluates not only the patch similarity of two chosen pixels, but also the similarity of their corresponding neighbours. This leads to a more robust smoothing process since the pixels selected for averaging are more coherent with the local image structure. The suggested approach includes two recently proposed filters as special cases: The NL-means filter of Buades et al. and the NDS filter of Mrázek et al. In fact, the approach introduced here can be considered as a generalisation of the latter filter. We evaluate our method for the task of denoising greyscale and colour images degraded by Gaussian and impulse noise, demonstrating that it compares very well to other more sophisticated patch-based approaches.
Identification of exudates for Diabetic Retinopathy based on morphological process and PNN classifier

Diabetic Retinopathy is an ocular systemic disease caused by complication of diabetes. It is a major cause of blindness in both middle and advanced age group. Earlier detection of diabetic retinopathy protects patient from vision loss. The foremost symptom of this blindness is the exudates. Exudates are the liquefied fluid comprising solutes, proteins, cells, or cellular debris leaked from the damaged blood vessels into nearby tissues or on tissue surfaces in the retina. The leakage of these proteins or lipids causes vision loss to the patients. Identifying the exudates in advance can safeguard the diabetic patients from blindness. Dilation method is used by the ophthalmologists to detect the exudates. But it causes the irritation to the patients' eyes. This paper focuses on an automated method which detects the diabetic retinopathy through detecting exudates by Morphological process in colour fundus retinal images and then segregates the severity of the lesions. The severity level of the disease was achieved by Probabilistic Neural Network (PNN) classifier.
The Role of Autoantibodies in Diagnosis of Multiple Sclerosis

 Abstract- Multiple sclerosis (MS) is a severe chronic inflammatory disorder of the central nervous system (CNS) manifested by chronic inflammation, myelin loss, gliosis, varying degrees of axonal and oligodendrocyte pathology and progressive neurological dysfunction and large infiltration of a heterogeneous population of cellular and mediators of the immune system. The inflammatory lesions are manifestation of a heterogeneous population of cellular and soluble mediators of the immune system, such as T cells, B cells, macrophages and microglia, as well as a broad range of cytokines, chemokines, antibodies, complement and other toxic substances. Identification of autoantigens and autoantibodies in demyelinating diseases is essential for understanding the immunopathogenesis mechanism of MS. Recently, CSF analysis in MS has been recommended as a tool for monitoring disease activity and diagnosis. In this review we discuss the most significant autoantibodies which could be used as biomarkers for diagnosis, prognosis and treatment in MS and the hope is that biomarkers speed up the MS monitoring.
The cardiff Mk2b MIT head array: Optimising the coil configuration

A hemispherical MIT helmet coil array for imaging cerebral haemorrhage has been designed using a realistic 12-tissue finite-difference model of the head including a large peripheral haemorrhage (volume 49 ml). The coil array was first optimised by reaching a compromise between the quality of the reconstructed images and the financial cost of the digital detection system. The practical implementation of the helmet is partially complete.
Hopscotch Methods for Two State Financial Models

In this paper, we consider Hopscotch methods for solving two - state financial models. We first derive a solution algorithm for two - dimensional partial differential equations with mixed boundary conditions. We then consider a number of financial applications including stochastic volatility option pricing, term structure modelling with two states and elliptic irreversible investment problems.
Sexual well-being of older men and women

Most older adults experience sexual desire and engage in sexual activity, however, our knowledge about the sexual well-being (SWB) of older adults is still scarce. The present study aimed to examine the SWB of older adults as well as differences in their SWB based on gender and relationship status. The final sample consisted of 297 adults (149 men and 148 women) who ranged in age from 65 to 75 years; 63% (n = 188) were currently in a romantic relationship. Participants were recruited using the crowdsourcing platform Amazon’s M-Turk. Participants completed a background questionnaire, as well as measures of sexual behaviours (frequency of genital and non-genital activity and masturbation alone), sexual affect and cognitions (sexual esteem, attitudes, and satisfaction), sexual motivation (sexual interest), and sexual functioning. Our participants reported overall positive SWB; specifically, positive sexual self-esteem and attitudes toward sex as well as high sexual satisfaction and interest. Furthermore, participants who were in a relationship engaged in frequent genital and non-genital sexual activity. About two-thirds of the participants in a current relationship had experienced at least one sexual difficulty in the last three months, but only one-quarter were distressed about it. Although some gender and relationship status differences emerged, in general both the men and women and participants who were and were not in a relationship reported positive SWB. These results are discussed in terms of the ways in which they extend our knowledge on the SWB of older adults and challenge popular myths about sexuality in later life.
Location-Based Learning Management System for Adaptive Mobile Learning

 Abstract — E-learning and distance learning are all forms of learning that take place outside of a traditional learning environment and can be alternatives for learners who are not able to study in a traditional environment for various reasons. With advancement in technologies and increased use of smart phone, mobile learning has gained popularity as another form of learning and has enabled learners to learn anywhere and anytime. Ubiquitous learning takes mobile learning to another level by providing contents that are context and location aware. There is therefore the need to provide mobile devices with the right learning contents for the right users. The right learning contents should be adaptive to the learner’s location, as well as learning style and device etc. To be able to implement the learning, learning management systems play the important role in creating, managing, and delivering the learning contents. In this paper, a location-based Learning Management System for adaptive and personalized mobile learning is presented. The systems makes use of 5R Adaptation Framework for Location based Mobile learning, the location-based dynamic grouping algorithm, and concepts of the IMS Learning Design model to produce a location-based adaptive mobile learning setting.
An adaptive threshold for the Canny Operator of edge detection

The thresholds play an important role in the Canny Operator which used in the image edge detection. Many self-adaptive threshold algorithms have been proposed to improve the performance of Canny Operator. The Otsu method is one of the most popular improvements. However the Otsu method can not automatically set the low threshold according to the different image intensity adaptively. In order to overcome this defect, an adaptive threshold algorithm for the Canny Operator was proposed which calculated the low threshold adaptively based on a probability model. Experiments show that this method produces better edge detection results both on objective and subjective evaluations than the Otsu method.
Bounds on polynomial-time list decoding of rank metric codes

This contribution provides bounds on the list size of rank metric codes in order to understand whether polynomial-time list decoding is possible or not. First, an exponential upper bound is derived, which holds for any rank metric code of length n and minimum rank distance d. Second, a lower bound proves that there exists a rank metric code over Fqm of length n ≤ m such that the list size is exponential in the length of the code for any radius greater than half the minimum distance. This implies that in rank metric there cannot exist a polynomial upper bound depending only on n and d as the Johnson bound for Hamming metric. These bounds reveal significant differences between codes in Hamming and rank metric.
DCProposal : Online Unsupervised Coreference Resolution for Semi-Structured Heterogeneous Data ∗

A pair of RDF instances are said to corefer when they are inten ded to denote the same thing in the world, for example, when two nodes of type foaf:P erson describe the same individual. This problem is central to integrating and inte r-linking semi-structured datasets. We are developing an online, unsupervised corefe renc resolution framework for heterogeneous, semi-structured data. The online a sp ct requires us to process new instances as they appear and not as a batch. The instances are heterogeneous in that they may contain terms from different ontologies whose alignments are not known in advance. Our framework encompasses a two-phased cluster ing algorithm that is both flexible and distributable, a probabilistic multidime nsional attribute model that will support robust schema mappings, and a consolidation al gorithm that will be used to perform instance consolidation in order to improve recal l measures over time by addressing data spareness.