Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Theoretical Model for the Pseudo Affine Projection Algorithm for Unity Step Size and Autoregressive Inputs

The behavior of the Pseudo Affine Projection (PAP) adaptive algorithm is based on the projection of the present input vector onto a subspace defined by a collection of previous input vectors. Existing analytic models for PAP behavior have considered such a projection using expressions that are in fact valid only after a short initialization period. As a consequence, such models are capable of accurately predict the algorithm convergence behavior whenever the parameters of the problem render the effects of this initialization period unimportant. When this is not the case, the PAP behavior predicted by these models can deviate significantly from reality. This work studies the effect of the initialization on the convergence behavior of the PAP algorithm. The analysis is performed for real-valued signals and for unity step-size (fastest convergence). A new analytical model is derived that incorporates a deterministic initial transient phase at the very beginning of the adaptation process. This phase is due both to the arbitrary initialization of the coefficient vector and to the projection subspace, and is responsible for the modified algorithm behavior. Recursive deterministic equations are derived for the mean weight and mean-square error behaviors for a large number of adaptive filter taps, when compared to the algorithm order. Steady-state theoretical equations are also derived. Monte Carlo simulations show significant modeling improvements for specific parameter sets, both during transient and in steady-state, when compared to the most accurate existing PAP model.
Building a Concept Hierarchy Using Frequent Tag Sequences

Web sites that allow collaborative tagging of resources have become a commonplace development. As part of the second generation of applications available on the Web, these sites provide a tremendous amount of user-generated taxonomic information. However, information seekers are hindered by the lack of organization within these tags. To address this issue, several methods have been proposed for creating an organizational structure from the tags. Despite their benefits, the current methods do not directly represent an organization of concepts, as a concept is often composed of more than one tag. In this paper, we propose a new approach to generating a concept hierarchy from the user-generated tags. Exploiting the fact that users often express a concept over a set of sequential tags, we propose a two-step approach for generating a hierarchy of concepts. We first discover concepts through tag sequences with sufficient support. Using these concepts, we then calculate conditional probabilities to discover the existing hierarchical relationships. The key benefit of the hierarchy produced through our approach is that it is topic-based, as opposed to existing related work, which only produce hierarchies of tags. Our findings are illustrated on a domain-specific dataset of tags supplied by a popular collaborative tagging Web site.
Reusing Software Libraries Using Semantic Graphs

This paper describes a two-part system that helps developers understand how to search and reuse complex software libraries. First, we present an approach for automatic retrieval of software components in reuse libraries. Second, the system implements a source-code recommendation approach which automatically constructs and delivers relevant code examples that demonstrate how the retrieved components can be used to solve particular programming problems. In arriving at such solutions, we utilize ontological modeling to provide semantic representation of the conceptual source-code knowledge in software libraries. This representation is the basis for computing entailments and enabling semantic reasoning. Our experiments show evidence that combining ontology formalisms with context-sensitive techniques enhance precision when retrieving and recommending reusable code even without mining a carefully crafted corpus of similar code.
Dynamic Distribution of Nuclear Coactivator 4 during Mitosis: Association with Mitotic Apparatus and Midbodies

The cytoplasmic localization of Nuclear Receptor Coactivator 4 (NcoA4), also referred to as androgen receptor associated protein 70 (ARA70), indicates it may possess activities in addition to its role within the nucleus as a transcriptional enhancer. Towards identifying novel functions of NcoA4, we performed an in silico analysis of its amino acid sequence to identify potential functional domains and related proteins, and examined its subcellular distribution throughout the cell cycle. NcoA4 has no known or predicted functional or structural domains with the exception of an LxxLL and FxxLF nuclear receptor interaction motif and an N-terminal putative coiled-coil domain. Phylogenetic analysis indicated that NcoA4 has no paralogs and that a region referred to as ARA70-I family domain, located within the N-terminus and overlapping with the coiled-coil domain, is evolutionarily conserved in metazoans ranging from cnidarians to mammals. An adjacent conserved region, designated ARA70-II family domain, with no significant sequence similarity to the ARA70-I domain, is restricted to vertebrates. We demonstrate NcoA4 co-localizes with microtubules and microtubule organizing centers during prophase. Strong NcoA4 accumulation at the centrosomes was detected during interphase and telophase, with decreased levels at metaphase and anaphase. NcoA4 co-localized with tubulin and acetylated tubulin to the mitotic spindles during metaphase and anaphase, and to midbodies during telophase. Consistent with these observations, we demonstrated an interaction between NcoA4 and α-tubulin. Co-localization was not observed with microfilaments. These findings indicate a dynamic distribution of NcoA4 with components of the mitotic apparatus that is consistent with a potential non-transcriptional regulatory function(s) during cell division, which may be evolutionarily conserved.
Activation of the TrkB Neurotrophin Receptor Is Induced by Antidepressant Drugs and Is Required for Antidepressant-Induced Behavioral Effects

Recent studies have indicated that exogenously administered neurotrophins produce antidepressant-like behavioral effects. We have here investigated the role of endogenous brain-derived neurotrophic factor (BDNF) and its receptor trkB in the mechanism of action of antidepressant drugs. We found that trkB.T1-overexpressing transgenic mice, which show reduced trkB activation in brain, as well as heterozygous BDNF null (BDNF+/−) mice, were resistant to the effects of antidepressants in the forced swim test, indicating that normal trkB signaling is required for the behavioral effects typically produced by antidepressants. In contrast, neurotrophin-3+/− mice showed a normal behavioral response to antidepressants. Furthermore, acute as well as chronic antidepressant treatment induced autophosphorylation and activation of trkB in cerebral cortex, particularly in the prefrontal and anterior cingulate cortex and hippocampus. Tyrosines in the trkB autophosphorylation site were phosphorylated in response to antidepressants, but phosphorylation of the shc binding site was not observed. Nevertheless, phosphorylation of cAMP response element-binding protein was increased by antidepressants in the prefrontal cortex concomitantly with trkB phosphorylation and this response was reduced in trkB.T1-overexpressing mice. Our data suggest that antidepressants acutely increase trkB signaling in a BDNF-dependent manner in cerebral cortex and that this signaling is required for the behavioral effects typical of antidepressant drugs. Neurotrophin signaling increased by antidepressants may induce formation and stabilization of synaptic connectivity, which gradually leads to the clinical antidepressive effects and mood recovery.
A New Approach to Spatial Power Spectral Density Estimation for Multiple Incoherently Distributed Sources

In this paper, a new technique is proposed for estimating the total spatial power spectral density (PSD) caused by multiple incoherently distributed sources. Our approach is based on the fact that the array covariance matrix can be represented through the moments of the spatial PSD. Based on this representation, we develop a computationally efficient technique to estimate the moments from the array covariance matrix. The so-obtained moments are then used to estimate the total spatial PSD.
A new timing estimation and compensation scheme for ultra-wideband MB-OFDM communications

A low-complexity correlation based symbol timing synchronization (CBTS) algorithm for UWB MB-OFDM systems is presented here. The scheme attempts to locate the start of the fast Fourier transformation (FFT) window during frame synchronization (FS) sequence of the received preamble by firstly correlating the received samples with known training samples and then identifying the first significant multi-path component by comparing the correlated samples of two consecutive OFDM symbols against a predefined threshold. The algorithm is tested by computer simulation over all UWB channel models (CM), CM1-CM4; each for 100,000 noisy channel realizations under 17 dB signal-to-noise ratio (SNR). Performance of the proposed algorithm is measured through mean-squared error (MSE) of timing estimation and probability of synchronization.
Smart Task Distributor for MapReduce on Cloud Computing

A MapReduce system is widely used to implement the large-scale computation on cloud computing. A MapReduce system currently defines computation resources in a node as a roughly configurable slot number, and distributes tasks over nodes according to the slot number. However, a MapReduce system may make computation resources of clusters in the underutilization or overutilization condition, because a task of different applications unlikely uses the same computation resources and because a node may have different CPUs with different capabilities. A MapReduce system can use Smart Task Distributor (STD) proposed in this paper to solve the problem of the computation resource underutilization or overutilization in clusters. Technically, a MapReduce system can use STD to smartly distribute tasks over nodes in clusters, because STD on the one hand gradually assigns tasks to a node in order to fully utilize computation resources in the node and on the other hand dynamically estimates the remaining computation resources in the node for toggling the assignment of tasks on demand without overloading it. In experiments, a MapReduce system is proved to get better performances with STD than with other ways.
Recognizing Intrusive Intention Based on Dynamic Bayesian Networks

Knowing an attacker’s intentions can significantly improve the effectiveness of a decision-making system. However, recognition such intentions and the attacker’s intended plans for achieving them is not an easy task because there are too many uncertain and dynamic factors in network environment. In this paper, intrusive intention recognition using dynamic Bayesian network is proposed to cope with uncertainty and dynamics in network security awareness. Furthermore, attack actions forecast based on goal recognition is given and discussed. Finally, feasibility and validity of this method are proved from the experiments.
On-line Scheduling Method of Manufacturing System Based on Virtual supervisory Algorithm for Reference Pattern

In this paper, a scheduling method is developed provide planning for manufacturing plants with multiple coordinating cells. The controls for reconfigurable manufacturing systems have to be capable not only of identifying exceptions on-line, but also simultaneously developing on-line strategies for unpredictable customer order changes or inaccurate estimate of processing times. The approach exploits virtual supervisor (VS) concept developing an algorithm which provides access to all system information during program execution and thus can readily monitor the overall system performance creating reference pattern. The goal is to minimize expected costs of part tardiness and/or earliness. A solution methodology based on a combined Lagrangian relaxation, VS-Patterns, Maxwell equations and temporal difference is developed to reduce the computational requirements for large problems. Sequences pattern shows that near optimal schedules can be obtained a dual solution for on-line implementation.
Automatic segmentation of MR brain images

Intensity based and edge based segmentation methods have its own limitations. Multiscale representation employing Gradient dependent diffusion has been proposed in the literature to get rid of these limitations to some extend. In the present work, a fully automatic segmentation method is proposed. A 'trous algorithm is employed to achieve a multiscale representation of the image to be segmented. Intensity based linking model is used to group pixels into a number of segments. This approach is employed successfully to segment 2-D MR Brain images into White Matter (WM), Gray Matter (GM), and Cerebral Spinal Fluid (CSF) with no user interaction. Faster and better segmentation performances were observed when compared to Gradient dependent diffusion approach proposed in the literature.
How Does Government Ownership Affect Firm Performance? Evidence from China's Privatization Experience

The effect of government ownership on firm performance remains a controversial issue, especially in a transitional economy like China. Government ownership is typically viewed as adversely affecting firm performance. This study of that of Mainland China's privatization experience indicates the opposite. No matter whether it is in the form of state ownership or legal person ownership, government ownership has a positive impact on partially privatized state-owned enterprises. However, this relationship is nonlinear and shows an inverted U-shape. Given the situation of highly indebted, non-performing state-owned enterprises, we argue that too much government control is indeed bad for enterprises. But too little government ownership may not be good either. It might mean a lack of the government's political support and business connections, which are valuable and necessary to vitalize performance. Copyright Blackwell Publishers Ltd 2002.
Research Note - Information Technology in Supply Chains: The Value of IT-Enabled Resources Under Competition

In this study, we seek to better understand the value of information technology (IT) in supply chain contexts. Grounded in the resource-based theory in conjunction with transaction cost economics, we develop a conceptual model that links three IT-related resources (backend integration, managerial skills, and partner support) to firm performance improvement. The model differs from previous studies by proposing a moderating effect of competition on the resource-performance relationships. Using data of 743 manufacturing firms, our analysis indicates significant contribution of IT to supply chains, which is generated through development of the digitally enabled integration capability and manifested at the process level along the supply chain. The technological resource alone, however, does not hold the answer to IT value creation. In fact, managerial skills, which enable adaptations on supply chain processes and corporate strategy to accommodate the use of IT, are shown to play the strongest role in IT value creation. Furthermore, backend integration and managerial skills are found to be more valuable in more competitive environments. While commodity-like resources have diminishing value under competition, integrational and managerial resources become even stronger. Overall, our results shed light on the key drivers of IT-enabled supply chains, and provide insights into how competition shapes IT value.
The Association of Calcium-Phosphorus Product With the Severity of Cardiac Valves Failure in Patients Under Chronic Hemodialysis

Background Recent studies have mainly focused on the roles of serum calcium and phosphorus product in the development of valvular heart failure. We determined the association of calcium-phosphorus product with the severity of heart valve failure in patients under chronic hemodialysis (being under hemodialysis for 6 months or more) in Boo-Ali Hospital in 2012 and 2013. Methods It was a cross-sectional, descriptive, comparative study. Thirty-three patients undergoing chronic hemodialysis were recruited to the study. All the patients were hospitalized at Boo-Ali Hospital. The study was done in a 2 years long time frame and the association of calcium-phosphorus product and severity of heart valve failure was evaluated among them. Results The results demonstrated that there was no significant association between age, gender, renal failure cause and hemodialysis duration (P > 0.05). Our results showed a negative correlation between the severity of cardiac valves failure and CA × P level. It was not a meaningful correlation though (P > 0.05). Conclusions Based on the obtained results, it is concluded that there is not any association between calcium-phosphorus product and the severity of heart valve failure in patients under chronic hemodialysis.
Evolution of Hsp70 Gene Expression: A Role for Changes in AT-Richness within Promoters

In disparate organisms adaptation to thermal stress has been linked to changes in the expression of genes encoding heat-shock proteins (Hsp). The underlying genetics, however, remain elusive. We show here that two AT-rich sequence elements in the promoter region of the hsp70 gene of the fly Liriomyza sativae that are absent in the congeneric species, Liriomyza huidobrensis, have marked cis-regulatory consequences. We studied the cis-regulatory consequences of these elements (called ATRS1 and ATRS2) by measuring the constitutive and heat-shock-induced luciferase luminescence that they drive in cells transfected with constructs carrying them modified, deleted, or intact, in the hsp70 promoter fused to the luciferase gene. The elements affected expression level markedly and in different ways: Deleting ATRS1 augmented both the constitutive and the heat-shock-induced luminescence, suggesting that this element represses transcription. Interestingly, replacing the element with random sequences of the same length and A+T content delivered the wild-type luminescence pattern, proving that the element's high A+T content is crucial for its effects. Deleting ATRS2 decreased luminescence dramatically and almost abolished heat-shock inducibility and so did replacing the element with random sequences matching the element's length and A+T content, suggesting that ATRS2's effects on transcription and heat-shock inducibility involve a common mechanism requiring at least in part the element's specific primary structure. Finally, constitutive and heat-shock luminescence were reduced strongly when two putative binding sites for the Zeste transcription factor identified within ATRS2 were altered through site-directed mutagenesis, and the heat-shock-induced luminescence increased when Zeste was over-expressed, indicating that Zeste participates in the effects mapped to ATRS2 at least in part. AT-rich sequences are common in promoters and our results suggest that they should play important roles in regulatory evolution since they can affect expression markedly and constrain promoter DNA in at least two different ways.
Generalized Outlier Detection with Flexible Kernel Density Estimates

We analyse the interplay of density estimation and outlier detection in density-based outlier detection. By clear and principled decoupling of both steps, we formulate a generalization of density-based outlier detection methods based on kernel density estimation. Embedded in a broader framework for outlier detection, the resulting method can be easily adapted to detect novel types of outliers: while common outlier detection methods are designed for detecting objects in sparse areas of the data set, our method can be modified to also detect unusual local concentrations or trends in the data set if desired. It allows for the integration of domain knowledge and specific requirements. We demonstrate the flexible applicability and scalability of the method on large real world data sets.
Variable selection for ad prediction

We consider the problem of predicting the probability of a click for an advertisement when the outcome of a click or no-click is expressed by means of a set of a large number of variables. Many, if not most, of these variables are very weakly related to the clicking of the ad. Thus, a traditional approach to address this problem that treats each variable on an equal and blind footing takes away the interpretability in explaining the underlying process of the outcome. Such an approach would be computationally expensive and, further, may suffer from poor generalization. We investigate the forward selection method for variable subset selection in the domain of advertisement click-through-rate prediction. The forward selection method proceeds sequentially in a way that rewards a set of variables by how much information it provides regarding the outcome, but penalizes the set based on the number of variables in it. Concretely, we propose a logistic regression model for estimating the conditional expectation between the outcome and the ensemble of variables. The model obtained compares favorably with that obtained via an exhaustive search through the model space. We also observe that the set of variables selected by the forward selection procedure has better predictive power than that selected by considering their individual statistical significance. Thus we show that the forward-selection method for subset selection serves to produce a good model for predicting ad click-through-rates.
Computational Study of Subdural Cortical Stimulation: Effects of Simulating Anisotropic Conductivity on Activation of Cortical Neurons

Subdural cortical stimulation (SuCS) is an appealing method in the treatment of neurological disorders, and computational modeling studies of SuCS have been applied to determine the optimal design for electrotherapy. To achieve a better understanding of computational modeling on the stimulation effects of SuCS, the influence of anisotropic white matter conductivity on the activation of cortical neurons was investigated in a realistic head model. In this paper, we constructed pyramidal neuronal models (layers 3 and 5) that showed primary excitation of the corticospinal tract, and an anatomically realistic head model reflecting complex brain geometry. The anisotropic information was acquired from diffusion tensor magnetic resonance imaging (DT-MRI) and then applied to the white matter at various ratios of anisotropic conductivity. First, we compared the isotropic and anisotropic models; compared to the isotropic model, the anisotropic model showed that neurons were activated in the deeper bank during cathodal stimulation and in the wider crown during anodal stimulation. Second, several popular anisotropic principles were adapted to investigate the effects of variations in anisotropic information. We observed that excitation thresholds varied with anisotropic principles, especially with anodal stimulation. Overall, incorporating anisotropic conductivity into the anatomically realistic head model is critical for accurate estimation of neuronal responses; however, caution should be used in the selection of anisotropic information.