Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of Doubly-Generalized LDPC Codes with Random Component Codes for the Binary Erasure Channel

A method for the asymptotic analysis of doublygeneralized low-density parity-check (D-GLDPC) codes on the binary erasure channel (BEC) is described. The proposed method is based on extrinsic information transfer (EXIT) charts. It permits to overcome the impossibility to evaluate the EXIT function for the check or variable component codes, in situations where the information functions or split information functions for the component code are unknown. According to the proposed method, D-GLDPC codes where the check and variable component codes are random codes from an expurgated ensemble, are considered. A technique is then developed which permits to obtain the EXIT chart for the overall D-GLDPC code, by evaluating the expected EXIT function for each check and variable component code. This technique is then combined with differential evolution (DE) algorithm in order to generate some optimal D-GLDPC degree distributions. Numerical results on long, random codes, are presented which reveal how DGLDPC codes can outperform standard LDPC codes in terms of both waterfall performance and error floor.
Linking internal marketing with customer outcomes

Purpose – The purpose of this study is to explore how the adoption of an internal marketing (IM) programme in a retail banking setting enhances some positive employee behaviours that promote customer perceived service quality. Design/methodology/approach – A multilevel research design is adopted which draws evidence from branch managers, employees and customers in order to investigate whether branch manager’s adoption of an IM philosophy affects front-line employee responsiveness to the firm’s IM strategies and their levels of motivation, empowerment and organizational identification (OI), respectively. Findings – Results indicate that manager’s IM adoption can enhance employee adoption of IM and raises their levels of motivation, empowerment and OI. The moderating role of manager-employee dissimilarity is also discussed. Finally, findings confirm that employee motivation, empowerment and OI affect customer perceptions of service quality. Originality/value – This study provides an important shift by forma.
LC Snubber Designing for DC-DC Converter by Genetic Algorithm and Taguchi Method

In this paper, it is first proposed that genetic algorithm and Taguchi method can be employed in the optimal design of DC-DC converter with LC snubber. The purpose of this optimal design is to lower the spike voltage dsp V across power switch and hence reduce the manufacturing cost. For the first step, we investigate the circuit parameters which will affect ds V and subsequently converge the range of circuit parameter value by means of genetic algorithm, and conduct the optimal design of prototype converter with Taguchi method. Compared with spike voltage dsp V of non-optimal design circuit, the effect of optimal design is revealed. In suppressing spike voltage, the dsp V measured from optimal design circuit is 115V, and is actually 28.1% reduced. Therefore, using genetic algorithm and Taguchi method in the optimal design of converter with LC snubber is a more economic, practical and efficient circuit design, which meanwhile can be easily applied to other electronic circuits and accomplishes the optimal design of various quality characteristics.
Fear learning induces persistent facilitation of amygdala synaptic transmission

In the maintenance phase of fear memory, synaptic transmission is potentiated and the stimulus requirements and signalling mechanisms are altered for long‐term potentiation (LTP) in the cortico‐lateral amygdala (LA) pathway. These findings link amygdala synaptic plasticity to the coding of fear memories. Behavioural experiments suggest that the amygdala serves to store long‐term fear memories. Here we provide electrophysiological evidence showing that synaptic alterations in rats induced by fear conditioning are evident in vitro 10 days after fear conditioning. We show that synaptic transmission was facilitated and that high‐frequency stimulation dependent LTP (HFS–LTP) of the cortico‐lateral amygdala pathway remained attenuated 10 days following fear conditioning. Additionally, we found that the low‐frequency stimulation dependent LTP (LFS–LTP) measured 24 h after fear conditioning was absent 10 days post‐training. The persistent facilitation of synaptic transmission and occlusion of HFS–LTP suggests that, unlike hippocampal coding of contextual fear memory, the cortico‐lateral amygdala synapse is involved in the storage of long‐term fear memories. However, the absence of LFS–LTP 10 days following fear conditioning suggests that amygdala physiology 1 day following fear learning may reflect a dynamic state during memory stabilization that is inactive during the long‐term storage of fear memory. Results from these experiments have significant implications regarding the locus of storage for maladaptive fear memories and the synaptic alterations induced by these memories.
Naming faces in broadcast news video by image google

Naming faces is important for news videos browsing and indexing. Although some research efforts have been contributed to it, they only use the concurrent information between the face and name or employ some clues as features and use simple heuristic method or machine learning approach to finish the task. They use little extra knowledge about the names and faces. Different from previous work, in this paper we present a novel approach to name the faces by exploring extra knowledge obtained from image google. The behind assumption is that the faces of those important persons will turn out many times in the web images and could be retrieved from image google easily. Firstly, faces are detected in the video frames; and the name entities of candidate persons are extracted from the textual information by automatic speech recognition and close caption detection. Then, these candidate person names are used as queries to find the name related person images through image google. After that, the retrieved result is analyzed and some typical faces are selected through feature density estimation. Finally, the detected faces in the news video are matched with the faces selected from the result returned by image google to label each face. Experimental results on MSNBC news and CNN news demonstrate that the proposed approach is effective.
Learning adaptive multiscale approximations to data and functions near low-dimensional sets

In the setting where a data set in ℝD consists of samples from a probability measure ρ concentrated on or near an unknown d-dimensional set M, with D large but d ≪ D, we consider two sets of problems: geometric approximation of M and regression of a function f on M. In the first case we construct multiscale low-dimensional empirical approximations of M, which are adaptive when M has geometric regularity that may vary at different locations and scales, and give performance guarantees. In the second case we exploit these empirical geometric approximations to construct multiscale approximations to f on M, which adapt to the unknown regularity of f even when this varies at different scales and locations. We prove guarantees showing that we attain the same learning rates as if f was defined on a Euclidean domain of dimension d, instead of an unknown manifold M. All algorithms have complexity O(n log n), with constants scaling linearly in D and exponentially in d.
Towards a Software Product Line Framework to Support Cross-domain Component Composition

Software product lines (SPL) are of great significance to the software development process as they rely on identifying commonalities within an application domain in order to facilitate component integration to build new software products. The main focus of SPL is the development of software systems in a single domain where some basic functionality is reused over the developed systems. However, when it comes to cross-domain component reuse, SPL development processes always encounter difficulties in integrating heterogeneous components due to different architectural assumptions. This paper establishes a significant distinction of component interfaces and utilises it to derive the building block of a SPL that can support developing cross-domain software reuse.
A Systematic Bayesian Integration of Epidemiological and Genetic Data

Genetic sequence data on pathogens have great potential to inform inference of their transmission dynamics ultimately leading to better disease control. Where genetic change and disease transmission occur on comparable timescales additional information can be inferred via the joint analysis of such genetic sequence data and epidemiological observations based on clinical symptoms and diagnostic tests. Although recently introduced approaches represent substantial progress, for computational reasons they approximate genuine joint inference of disease dynamics and genetic change in the pathogen population, capturing partially the joint epidemiological-evolutionary dynamics. Improved methods are needed to fully integrate such genetic data with epidemiological observations, for achieving a more robust inference of the transmission tree and other key epidemiological parameters such as latent periods. Here, building on current literature, a novel Bayesian framework is proposed that infers simultaneously and explicitly the transmission tree and unobserved transmitted pathogen sequences. Our framework facilitates the use of realistic likelihood functions and enables systematic and genuine joint inference of the epidemiological-evolutionary process from partially observed outbreaks. Using simulated data it is shown that this approach is able to infer accurately joint epidemiological-evolutionary dynamics, even when pathogen sequences and epidemiological data are incomplete, and when sequences are available for only a fraction of exposures. These results also characterise and quantify the value of incomplete and partial sequence data, which has important implications for sampling design, and demonstrate the abilities of the introduced method to identify multiple clusters within an outbreak. The framework is used to analyse an outbreak of foot-and-mouth disease in the UK, enhancing current understanding of its transmission dynamics and evolutionary process.
A Robust Proximal Support Vector Machines for Classification

Proximal support vector machines (PSVMs) are support vector machines (SVMs) version which involve equality instead of inequality constraints assigning data points to one of two classes, and have a significant speedup in comparison with traditional SVM-type classifiers. However, PSVMs are very sensitive to outliers or noises because of overfitting problem. In this paper, a robust proximal support vector machines (RPSVMs) is proposed to deal with the problem of overfitting in PSVMs for two-class classification. In experiments, two methods are used to give membership values to training data points according to their relative importance in the training set. Results show that the proposed RPSVMs actually reduce the effect of outliers and yield higher classification rate than PSVMs do
Momentum transfer in a turbulent, particle-laden Couette flow

A point-force model is used to study turbulent momentum transfer in the presence of moderate mass loadings of small (relative to Kolmogorov scales), dense (relative to the carrier phase density) particles. Turbulent Couette flow is simulated via direct numerical simulation, while individual particles are tracked as Lagrangian elements interacting with the carrier phase through a momentum coupling force. This force is computed based on the bulk drag of each particle, computed from its local slip velocity. By inspecting a parameter space consisting of particle Stokes number and mass loading, a general picture of how and under what conditions particles can alter near-wall turbulent flow is developed. In general, it is found that particles which adhere to the requirements for the point-particle approximation attenuate small-scale turbulence levels, as measured by wall-normal and spanwise velocity fluctuations, and decrease turbulent fluxes. Particles tend to weaken near-wall vortical activity, which in turn, .
Nitric Oxide Affects ERK Signaling through Down-Regulation of MAP Kinase Phosphatase Levels during Larval Development of the Ascidian Ciona intestinalis

In the ascidian Ciona intestinalis larval development and metamorphosis require a complex interplay of events, including nitric oxide (NO) production, MAP kinases (ERK, JNK) and caspase-3 activation. We have previously shown that NO levels affect the rate of metamorphosis, regulate caspase activity and promote an oxidative stress pathway, resulting in protein nitration. Here, we report that NO down-regulates MAP kinase phosphatases (mkps) expression affecting positively ERK signaling. By pharmacological approach, we observed that the reduction of endogenous NO levels caused a decrease of ERK phosphorylation, whereas increasing levels of NO induced ERK activation. We have also identified the ERK gene network affected by NO, including mpk1, mpk3 and some key developmental genes by quantitative gene expression analysis. We demonstrate that NO induces an ERK-independent down-regulation of mkp1 and mkp3, responsible for maintaining the ERK phosphorylation levels necessary for transcription of key metamorphic genes, such as the hormone receptor rev-erb and the van willebrand protein vwa1c. These results add new insights into the role played by NO during larval development and metamorphosis in Ciona, highlighting the cross-talk between different signaling pathways.
Emotional Intelligence and Academic Achievement: A Meta-Analysis

A meta-analytic review investigated the relationship between emotional intelligence and Academic achievement. In this study, the sample consists of 25 reviews from different journal articles. The sample of the reviews based on inclusion criteria such as emotional intelligence and academic achievement. Meta-analysis related detail was collected by quantitative method reviews. Author name, Title of the study, sample size, correlation value and findings of each review was noted in table. The meta-analysis is on the relationship between emotional intelligence and academic achievement. Therefore the researchers categorized the quantitative studies based on criteria. A meta- analysis of research papers were formed the conclusion.
Web page classification using visual layout analysis

Automatic processing of Web documents is an important issue in the design of search engines, of Web mining tools, and of applications for Web information extraction. Simple text-based approaches are typically used in which most of the information provided by the page visual layout is discarded. Only some visual features, as the font face and size, are effectively used to weigh the importance of the words in the page. In this paper, we propose to use a hierarchical representation, which includes the visual screen coordinates for every HTML object in the page. The use of the visual layout allows us to identify common page components such as the header, the navigation bars, the left and right menus, the footer, and the informative parts of the page. The recognition of the functional role of each object is performed by a set of heuristic rules. The experimental results show that page areas are correctly classified in 73% of the cases. The identification of different functional areas on the page allows the definition of a more accurate method for representing the page text contents, which splits the text features into different subsets according to the area they belong to. We show that this approach can improve the classification accuracy for page topic categorization by more than 10% with respect to the use of a flat “bag-of-words” representation.
Experiments in retrieval of mineral information

The problems in image retrieval derive from the difficulty to exactly define and interpret the image content. In most cases, image retrieval techniques are based on the data base system techniques [6], [19] or the information retrieval systems, where the image content is represented in a text form [20]. Other image retrieval techniques require that the images belong to a specific domain which must be described in advance [25]. In this paper, we present the experiments in retrieval of multimedia mineral information using AMCIRS (A Multimedia Cognitive-based Information Retrieval System) [13]. The AMCIRS query based mechanism is based on a multimedia objects content search using the vector model. Each vector is composed of text and image objects. The image objects in the vectors are image object contours, represented by polygonal approximations. The content search process is deduced to the similarity estimation between the MM query and MM index vectors. The similarity function for image objects is based on the polygon similarity estimation. The basic elements of A Multimedia Cognitive-based Information Retrieval System called AMCIRS which integrates image and text information have been described elsewhere [12], [13]. In AMCIRS, the content search process is performed using the vector model [26], where the user query and the MM information are presented by the MM query and index vectors respectively. Each vector contains text and image objects. The image objects in the vectors are image object contours, represented by polygonal approximations [24]. The information selection in AMCIRS is based on the similarity estimation between the MM query and MM index vectors. The similarity function for the image objects is deduced to the polygon similarity estimation [14]. The experimental evaluation of AMCIRS retrieval effectiveness is expressed by the recall and precision parameters. Possible advantages of multiple media retrieval with respect to the single medium retrieval are also investigated and explicitly represented by the recall-precision diagrams.
A Feature-based Categorization of Multi-Level Modeling Approaches and Tools

The traditional two-level modeling approaches produce accidental complexities when modeling multiple abstraction levels. This problem is addressed by the emerging multi-level modeling paradigm that allows an arbitrary number of modeling levels. To date, multiple frameworks and tool implementations have been proposed, but so far there is no comprehensive comparison of commonalities and differences of design choices that have been made in those frameworks and tools. We propose a feature-based comparison framework that covers three core perspectives on multi-level modeling approaches and tools: language engineering, domain modeling and tool support, and evaluated a selected set of existing approaches and tools according to them. The framework highlights research challenges and trends for future research and its results support users to choose a specific approach depending on the application domain. The framework can also be used as a basis to map and transform between approaches.
Novel Roles for α3β1 Integrin as a Regulator of Cytoskeletal Assembly and as a Trans-dominant Inhibitor of Integrin Receptor Function in Mouse Keratinocytes

Previously we found that α3β1 integrin–deficient neonatal mice develop micro-blisters at the epidermal–dermal junction. These micro-blisters were associated with poor basement membrane organization. In the present study we have investigated the effect of α3β1-deficiency on other keratinocyte integrins, actin-associated proteins and F-actin organization. We show that the absence of α3β1 results in an increase in stress fiber formation in keratinocytes grown in culture and at the basal face of the basal keratinocytes of α3-null epidermis. Moreover, we see a higher concentration of actin-associated proteins such as vinculin, talin, and α-actinin at focal contact sites in the α3-deficient keratinocytes. These changes in focal contact composition were not due to a change in steady-state levels of these proteins, but rather to reorganization due to α3β1 deficiency. Apart from the loss of α3β1 there is no change in expression of the other integrins expressed by the α3-null keratinocytes. However, in functional assays, α3β1 deficiency allows an increase in fibronectin and collagen type IV receptor activities. Thus, our findings provide evidence for a role of α3β1 in regulating stress fiber formation and as a trans-dominant inhibitor of the functions of the other integrins in mouse keratinocytes. These results have potential implications for the regulation of keratinocyte adhesion and migration during wound healing.
Identification of Gaussian Process State-Space Models with Particle Stochastic Approximation EM

Gaussian process state-space models (GP-SSMs) are a very flexible family of models of nonlinear dynamical systems. They comprise a Bayesian nonparametric representation of the dynamics of the system and additional (hyper-)parameters governing the properties of this nonparametric representation. The Bayesian formalism enables systematic reasoning about the uncertainty in the system dynamics. We present an approach to maximum likelihood identification of the parameters in GP-SSMs, while retaining the full nonparametric description of the dynamics. The method is based on a stochastic approximation version of the EM algorithm that employs recent developments in particle Markov chain Monte Carlo for efficient identification.
Communication data based user activity recommendations

The amount of information exchanged in the communication increases rapidly as the user plays a more role in her organization and social networks. It has become overwhelming to many people in this communication and information centric era and the need for assistance to manage communicated information and the communication relations has grown. We present a communication data based user activity recommender system that aims to help the user use the communication services more effectively and easily. The communication data of a user provides abundant information about the topics the user is working on, the people the user communicates with, and the communication and information needs of the user. Our system analyzes the communication data and extracts such information, generates recommendations for user's communication services, and provides the information the user needs according to the user's communication context. Machine learning and natural language processing methods are utilized for communication data analysis and the performance of an example of our recommenders -- predictive meeting assistant -- is presented in details. Our recommender system has been implemented for large-scale deployments and its core architecture is presented.