Miracle-dz/UniFAR-FTUs huggingface dataset:
Text identification in noisy document images using Markov random model

In this paper we address the problem of the identification of text from noisy documents. We segment and identify handwriting from machine printed text because 1) handwriting in a document often indicates corrections, additions or other supplemental information that should be treated differently from the main body or body content, and 2) the segmentation and recognition techniques for machine printed text and handwriting are significantly different. Our novelty is that we treat noise as a separate class and model noise based on selected features. Trained Fisher classifiers are used to identify machine printed text and handwriting from noise. We further exploit context to refine the classification. A Markov random field (MRF) based approach is used to model the geometrical structure of the printed text, handwriting and noise to rectify the mis-classification. Experimental results show our approach is promising and robust, and can significantly improve the page segmentation results in noise documents.
Dynamic Control of Mobile Multirobot Systems: The Cluster Space Formulation

The formation control technique called cluster space control promotes simplified specification and monitoring of the motion of mobile multirobot systems of limited size. Previous paper has established the conceptual foundation of this approach and has experimentally verified and validated its use for various systems implementing kinematic controllers. In this paper, we briefly review the definition of the cluster space framework and introduce a new cluster space dynamic model. This model represents the dynamics of the formation as a whole as a function of the dynamics of the member robots. Given this model, generalized cluster space forces can be applied to the formation, and a Jacobian transpose controller can be implemented to transform cluster space compensation forces into robot-level forces to be applied to the robots in the formation. Then, a nonlinear model-based partition controller is proposed. This controller cancels out the formation dynamics and effectively decouples the cluster space variables. Computer simulations and experimental results using three autonomous surface vessels and four land rovers show the effectiveness of the approach. Finally, sensitivity to errors in the estimation of cluster model parameters is analyzed.
Using fuzzy rough feature selection for image retrieval system

Feature selection is an important step in processing the images especially for applications such as content based image retrieval. In large multimedia databases, it may not be practical to search through the entire database in order to retrieve similar images from a query. Good data structures for similarity search and indexing are needed, and the existing data structures do not scale well for the high dimensional multimedia descriptors. Thus feature selection is an important step. Fuzzy rough feature selection method has many advantages in determining the relevant features. In this paper, five feature selection methods are compared with the fuzzy rough method. These five feature selection methods are Relief-F, Information Gain, Gain Ratio, OneR and the statistical measure χ2. The main purpose of the comparison is to rank the image features and see which method provides better results. An image retrieval dataset (COREL dataset) was used in the comparison. In order to evaluate the performance of the six methods, ranking of the important features is defined. This is then used to compare with the automated ranking produced by the aforesaid feature selection methods. Results show that the retrieval system using fuzzy rough feature selection has better retrieval accuracy and provide good Precision Recall performance. The advantages of the use of fuzzy rough feature selection will also be discussed in the paper.
FAST AND EFFECTIVE BACKGROUND SUBTRACTION BASED ON ε LBP

Background subtraction is an important component of many computer vision systems. In this paper, we present a fast and efficient texture-based method for background subtraction in a video sequence. At first, a novel LBP (Local Binary Pattern) operator called εLBP is applied to extract the local description of each pixel. Then, the kernel of background subtraction is performed based on two principle phases, i.e. the background building phase and the foreground detection phase. In the former phase, the probability that a pixel belongs to foreground is calculated based on the difference between the current εLBP and the adaptive mean εLBP. In the later phase, a thresholding operator is applied on the probability to determine whether a pixel can be classified as foreground, and the adaptive mean εLBP is updated by a user-settable learning rate. Finally, our approach is evaluated against several video sequences compared with the traditional MoG model. Experiments show that our method is suitable for various scenes and is appealing with respect to robustness.
A weighted pseudo-Zernike feature for face recognition

Pseudo-Zernike polynomials are well known and widely used in the analysis of optical systems. In this paper, we introduce a weighted pseudo-Zernike feature for face recognition. The EA strategy is used to maximize the Fisher linear discriminant function (FLD) over the Pseudo-Zernike moments. The argument, which maximizes the FLD criteria, is selected as the proposed weight function. To evaluate the performance of the proposed feature, experimental studies are carried out on the ORL database images of Cambridge University. The numerical results show 97.75% recognition rate on the ORL database with the weighted pseudo-Zernike feature (with order 10) and 65, 146,40 neurons for the input, hidden, and output layers while this amount for the original pseudo-Zernike is 96.5%
Fast Boolean Calculations Using the GPU

The growing number of Boolean variables requires very efficient approaches to solve the given tasks. We explore the utilization of the GPU for fast parallel Boolean calculations in this paper. Hundreds of processor cores of the GPU offer a significant potential for improvements. Constraints in their application may restrict the reachable speedup. This paper summarizes alternative approaches for utilizing the GPU in the Boolean domain and improvements of several orders of magnitudes.
Quaternion-Valued Nonlinear Adaptive Filtering

A class of nonlinear quaternion-valued adaptive filtering algorithms is proposed based on locally analytic nonlinear activation functions. To circumvent the stringent standard analyticity conditions which are prohibitive to the development of nonlinear adaptive quaternion-valued estimation models, we use the fact that stochastic gradient learning algorithms require only local analyticity at the operating point in the estimation space. It is shown that the quaternion-valued exponential function is locally analytic, and, since local analyticity extends to polynomials, products, and ratios, we show that a class of transcendental nonlinear functions can serve as activation functions in nonlinear and neural adaptive models. This provides a unifying framework for the derivation of gradient-based learning algorithms in the quaternion domain, and the derived algorithms are shown to have the same generic form as their real- and complex-valued counterparts. To make such models second-order optimal for the generality of quaternion signals (both circular and noncircular), we use recent developments in augmented quaternion statistics to introduce widely linear versions of the proposed nonlinear adaptive quaternion valued filters. This allows full exploitation of second-order information in the data, contained both in the covariance and pseudocovariances to cater rigorously for second-order noncircularity (improperness), and the corresponding power mismatch in the signal components. Simulations over a range of circular and noncircular synthetic processes and a real world 3-D noncircular wind signal support the approach.
Molecular gas and a new young stellar cluster in the far outer Galaxy

Aims. We investigate the star-formation ocurring in the region towards IRAS 07527-3446 in the molecular cloud [MAB97]250.633. 63, in the far outer Galaxy. We report the discovery of a new young stellar cluster, and describe its properties and those of its parent molecular cloud. Methods. Near-infrared JHKS images were obtained with VLT/ISAAC, and millimetre line CO spectra were obtained with the SEST telescope. VLA archive date were also used. Results. The cloud and cluster are located at a distance of 10.3 kpc and a Galactocentric distance of 15.4 kpc, in the far outer Galaxy. Morphologically, IRAS 07527-3446 appears as a young embedded cluster of a few hundred stars seen towards the position of the IRAS source, extending for about 2–4 pc and exhibiting sub-clustering. The cluster contains low and intermediate-mass young reddened stars, a large fraction having cleared the inner regions of their circumstellar discs responsible for (H −KS) colour excess. The observations are compatible with a ≤5 Myr cluster with variable spatial extinction of between AV = 5a ndAV = 11. Decomposition of CO emission in clumps, reveals a clump clearly associated with the cluster position, of mass 3.3 × 10 3 M� . Estimates of the slopes of the KS-band luminosity function and of the star-formation efficiency yield values similar to those seen in nearby star-formation sites. These findings reinforce previous results that the distant outer Galaxy continues to be active in the production of new and rich stellar clusters, with the physical conditions required for the formation of rich clusters continuing to be met in the very distant environment of the outer Galactic disc.
Identification of Essential Sequences for Cellular Localization in BRMS1 Metastasis Suppressor

Background Breast cancer metastasis suppressor 1 (BRMS1) reduces the number and the size of secondary tumours in a mouse model without affecting the growth of the primary foci upon its re-expression. Knockdown of BRMS1 expression associates with metastasis. The molecular details on BRMS1 mechanism of action include its ability to function as a transcriptional co-repressor and consistently BRMS1 has been described as a predominantly nuclear protein. Since cellular distribution could represent a potential mechanism of regulation, we wanted to characterize BRMS1 sequence motifs that might regulate its cellular distribution. According to its amino acids sequence, BRMS1 contain two putative nuclear localization signals, however none of them has been proved to work so far. Methodology/Principal Findings By using well known in vivo assays to detect both nuclear import and export signal, we have characterized, in the present study, one functional nuclear localisation signal as necessary and sufficient to promote nuclear transport. Additionally, the outcome of a directed yeast two-hybrid assay identify importin α6 as a specific partner of BRMS1 thus speculating that BRMS1 nuclear import could be specifically mediated by the reported nuclear transporter. Besides, the combination of a computational searching approach along the utilization of a nuclear export assay, identified a functional motif within the BRMS1 sequence responsible for its nuclear export, that resulted not affected by the highly specific CRM1 inhibitor Leptomycin-B. Interspecies heterokaryon assay demonstrate the capability of BRMS1 to shuttle between the nuclear and cytosolic compartments Conclusions/Significance Our results show for the first time that BRMS1 contains both nuclear import and export signals enabling its nucleo-cytoplasmic shuttling. These findings contributes new data for the understanding of the BRMS1 functions and allow us to speculate that this phenomenon could represent a novel mechanism for regulating the activity of BRMS1 or its associated cytosolic partners
MrCAR: A Multi-relational Classification Algorithm Based on Association Rules

Classification is an important subject in data mining and machine learning, which has been studied extensively and has a wide range of applications. Classification based on association rules is one of the most effective classification method, whose accuracy is higher and discovered rules are easier to understand comparing with classical classification methods. However, current algorithms for classification based on association rules is single table oriented, which means they can only apply to the data stored in a single relational table. Directly applying these algorithms in multi-relational data environment will result in many problems. This paper proposes a novel algorithm MrCAR for classification based on association rules in multi-relational data environment. MrCAR mines relevant features in each table to predict the class label. Close item sets technique and Tuple ID Propagation method are used to improve the performance of the algorithm. Experimental results show that MrCAR has higher accuracy and better understandability comparing with a typical existing multi relational classification algorithm.
Establishment of a sandwich ELISA for human megsin, a kidney-specific serine protease inhibitor.

BACKGROUND We previously identified a novel serine protease inhibitor (serpin), megsin, which is predominantly expressed in the kidney. Megsin expression is up-regulated in human and experimental renal diseases associated with mesangial proliferation and expansion, suggesting that urinary megsin may be a novel diagnostic marker for some renal diseases. METHODS We established a specific and sensitive sandwich enzyme-linked immunosorbent assay (ELISA) for megsin and measured urinary megsin of patients with various renal diseases. RESULTS Megsin ELISA specifically detected megsin but not other serpins. The detection limit was 0.04 ng/ml, which allowed detection of urinary megsin in 3.6% of healthy individuals. The antigenic epitope in the urine detected by the ELISA was confirmed as megsin protein by time-of-flight mass spectrometry. Among patients with rapidly progressive glomerulonephritis (n = 18), 55.6% were urinary megsin-positive, while 24.1% in IgA nephropathy (n = 112) and 15.1% in chronic non-IgA glomerulonephritis (n = 245) were urinary megsin-positive, respectively. Among patients with chronic renal failure due to unknown causes (n = 74), 18.9% were positive for urinary megsin. In diabetic patients with or without nephropathy (n = 1073), 12.3% were urinary megsin-positive, while positivity of urinary megsin in patients with non-renal diseases (n = 768) was equivalent (3.3%) to that of healthy individuals. Of note, when urinary megsin-positive patients with diabetic nephropathy (n = 71) were classified into four stages by their proteinuria and estimated glomerular filtration rate, urinary megsin excretion increased as the stage progressed up to stage 3A, suggesting correlation of that with mesangial expansion level. Urinary megsin decreased in the advanced stage, probably reflecting development of glomerulosclerosis. CONCLUSION We established a high-sensitive megsin ELISA, which detects urinary megsin in some patients with renal diseases and in only a few healthy subjects. Megsin ELISA may be a novel diagnostic tool for renal diseases.
The Cerebral Blood Flow Biomedical Informatics Research Network (CBFBIRN) database and analysis pipeline for arterial spin labeling MRI data

Arterial spin labeling (ASL) is a magnetic resonance imaging technique that provides a non-invasive and quantitative measure of cerebral blood flow (CBF). After more than a decade of active research, ASL is now emerging as a robust and reliable CBF measurement technique with increased availability and ease of use. There is a growing number of research and clinical sites using ASL for neuroscience research and clinical care. In this paper, we present an online CBF Database and Analysis Pipeline, collectively called the Cerebral Blood Flow Biomedical Informatics Research Network (CBFBIRN) that allows researchers to upload and share ASL and clinical data. In addition to serving the role as a central data repository, the CBFBIRN provides a streamlined data processing infrastructure for CBF quantification and group analysis, which has the potential to accelerate the discovery of new scientific and clinical knowledge. All capabilities and features built into the CBFBIRN are accessed online using a web browser through a secure login. In this work, we begin with a general description of the CBFBIRN system data model and its architecture, then devote the remainder of the paper to the CBFBIRN capabilities. The latter part of our work is divided into two processing modules: (1) Data Upload and CBF Quantification Module; (2) Group Analysis Module that supports three types of analysis commonly used in neuroscience research. To date, the CBFBIRN hosts CBF maps and associated clinical data from more than 1,300 individual subjects. The data have been contributed by more than 20 different research studies, investigating the effect of various conditions on CBF including Alzheimer’s, schizophrenia, bipolar disorder, depression, traumatic brain injury, HIV, caffeine usage, and methamphetamine abuse. Several example results, generated by the CBFBIRN processing modules, are presented. We conclude with the lessons learned during implementation and deployment of the CBFBIRN and our experience in promoting data sharing.
Scaling laws of coverage and capacity in downlink ultra dense cellular networks

Driven by new types of wireless devices and the proliferation of bandwidth-intensive applications, user data traffic and the corresponding network load are increasing dramatically. Network densification has been recognized as one of the most promising and efficient ways to provide higher network capacity and enhanced coverage. Most prior work on analyzing the performance of ultra dense networks (UDNs) has focused on random spatial deployment with idealized, singular path loss models and Rayleigh fading. In this paper, we consider a more precise and general model, which incorporates multi-slope path loss and general fading distributions. We derive the tail behavior and scaling laws on the coverage probability and the average rate assuming strongest base station association. Our analytical results identify the regimes in which the signal-to-interference-plus-noise ratio (SINR) monotonically grows, saturates, or decreases with increasing network density. We establish general results on when ultra densification leads to worse or even zero SINR coverage and rate, providing useful insights on the ultimate performance limits of network densification.
A Survey of Tools for Community Detection and Mining in Social Networks

Online Social Networks (OSNs) namely Facebook, Twitter and LinkedIn are the most popularly visited sites on the internet. These sites contain large voluminous data about the people and relationships among them. Community structure is an important property of social networks. It is a topic of considerable interest in many areas due to its wide range of applications in multiple disciplines including biology, computer science, social sciences and so on. Detection of communities reveals how the structure of ties affects the peoples and their relationships. To facilitate community discovery a wide range of tools have been developed over years. This paper surveys several tools available for detection and mining of communities and presents a comparative study. In addition, we discussed various visualization layouts of social networks in order to perceive network data and to communicate the result of analysis.
Multilinugal patent knowledge analysis

Knowledge extraction for patent requests depends on analyzing current state of the art in multiple languages. Currently the process is usually limited to the languages the patent seeker knows. The paper describes a model for representing the patent request by a set of concepts related to existing multilingual knowledge ontology. The search for patent information is based on Fuzzy Logic decision support, allowing a multilingual search. The model was analyzed in assisting the decision process in the Korean Patent Office based on patents in the Korean, English, and Chinese languages.
Estimating Option Implied Risk-Neutral Densities Using Spline and Hypergeometric Functions

We examine the ability of two recent methods -- the smoothed implied volatility smile method (SML) and the density functionals based on confluent hypergeometric functions (DFCH) -- for estimating implied risk-neutral densities (RNDs) from European-style options. Two complementary Monte Carlo experiments are conducted and the performance of the two RND estimators is evaluated by the root mean integrated squared error (RMISE) criterion. Results from both experiments show that the DFCH method outperforms the SML method for the overall quality of the estimated RNDs concerning both accuracy and stability. An application of the two methods to the OTC currency options market is also presented. Copyright Royal Economic Society 2007
Host Factors in the Infection Cycle of Bamboo mosaic virus

To complete the infection cycle efficiently, the virus must hijack the host systems in order to benefit for all the steps and has to face all the defense mechanisms from the host. This review involves a discussion of how these positive and negative factors regulate the viral RNA accumulation identified for the Bamboo mosaic virus (BaMV), a single-stranded RNA virus. The genome of BaMV is approximately 6.4 kb in length, encoding five functional polypeptides. To reveal the host factors involved in the infection cycle of BaMV, a few different approaches were taken to screen the candidates. One of the approaches is isolating the viral replicase-associated proteins by co-immunoprecipitation with the transiently expressed tagged viral replicase in plants. Another approach is using the cDNA-amplified fragment length polymorphism technique to screen the differentially expressed genes derived from N. benthamiana plants after infection. The candidates are examined by knocking down the expression in plants using the Tobacco rattle virus-based virus-induced gene silencing technique following BaMV inoculation. The positive or negative regulators could be described as reducing or enhancing the accumulation of BaMV in plants when the expression levels of these proteins are knocked down. The possible roles of these host factors acting on the accumulation of BaMV will be discussed.
No Representation without Taxation ? Rents , development and democracy

It is widely thought that oil and democracy do not mix. Rentier states, it is argued, need not tax their citizens, thus breaking a crucial link between citizens and their governments, and dimming the prospects for democracy. In this paper I examine the link between rentierism and democracy using a cross-regional dataset. I pay particular attention to the possibility that there are both positive and negative effects of rentierism on democracy. I do not find consistent support for the notion that there is a net negative effect of rentierism on the prospects that a country will be democratic. I do find that democracy scores in the surrounding region are strongly correlated with a country’s own democracy score.
Stratification Learning through Homology Inference

We develop a topological approach to stratification learning. Given point cloud data drawn from a stratified space, our objective is to infer which points belong to the same strata. First we define a multi-scale notion of a stratified space, giving a stratification for each radius level. We then use methods derived from kernel and cokernel persistent homology to cluster the data points into different strata, and we prove a result which guarantees the correctness of our clustering, given certain topological conditions. We later give bounds on the minimum number of sample points required to infer, with probability, which points belong to the same strata. Finally, we give an explicit algorithm for the clustering and apply it to some simulated data.