Miracle-dz/UniFAR-FTUs huggingface dataset:
Synthesis of Function-Described Graphs and Clustering of Attributed Graphs

Function-Described Graphs (FDGs) have been introduced by the authors as a representation of an ensemble of Attributed Graphs (AGs) for structural pattern recognition alternative to first-order random graphs. Both optimal and approximate algorithms for error-tolerant graph matching, which use a distance measure between AGs and FDGs, have been reported elsewhere. In this paper, both the supervised and the unsupervised synthesis of FDGs from a set of graphs is addressed. First, two procedures are described to synthesize an FDG from a set of commonly labeled AGs or FDGs, respectively. Then, the unsupervised synthesis of FDGs is studied in he context of clustering a set of AGs and obtaining an FDG model for each cluster. Two algorithms based on incremental and hierarchical clustering, respectively, are proposed, which are parameterized by a graph matching method. Some experimental results both on synthetic data and a real 3D-object recognition application show that the proposed algorithms are effective for clustering a set of AGs and synthesizing the FDGs that describe the classes. Moreover, the synthesized FDGs are shown to be useful for pattern recognition thanks to the distance measure and matching algorithm previously reported.
Genetic Algorithm Optimization of Adaptive Multi-Scale GLCM Features

We introduce a new second-order method of texture analysis called Adaptive Multi-Scale Grey Level Co-occurrence Matrix (AMSGLCM), based on the well-known Grey Level Co-occurrence Matrix (GLCM) method. The method deviates significantly from GLCM in that features are extracted, not via a fixed 2D weighting function of co-occurrence matrix elements, but by a variable summation of matrix elements in 3D localized neighborhoods. We subsequently present a new methodology for extracting optimized, highly discriminant features from these localized areas using adaptive Gaussian weighting functions. Genetic Algorithm (GA) optimization is used to produce a set of features whose classification worth is evaluated by discriminatory power and feature correlation considerations. We critically appraised the performance of our method and GLCM in pairwise classification of images from visually similar texture classes, captured from Markov Random Field (MRF) synthesized, natural, and biological origins. In these cross-validated classification trials, our method demonstrated significant benefits over GLCM, including increased feature discriminatory power, automatic feature adaptability, and significantly improved classification performance.
Correcting honest pervasive errors in the scientific literature

In the June 6 issue of Neurology®, we published a retraction of an article,1 a replacement article,2 and a copy of the original published version of the article with the changes highlighted. We had been alerted by a reader's submission to our WriteClick® Rapid Online Correspondence section that the authors had misdiagnosed a medical condition.3,4 Conversations with the authors revealed that the use of an erroneous term for the diagnosis was an honest mistake caused by an error in translation from another language. Issuing a retraction and a replacement version were vitally important to prevent misinforming readers and to correct the literature.
An Approach To Natural Stroke Extraction For Off-Line Loosely-Constrained Handwritten Chinese Characters

This paper proposes a new approach to extracting natural strokes from the skeletons of loosely-constrained, off-line handwritten Chinese characters. It admits the output substrokes from a previously proposed fuzzy substroke extractor as its inputs. By identifying a number of expected ambiguities which include mutual similarities, unstable touches and joint/cross distortions, fuzzy stroke models are constructed and a "hit-all" fuzzy stroke matching strategy is pursued. Fuzzy partitioning technique is used to generate a ranked list of consistent stroke sets from the set of fuzzy strokes being identified. With this approach, a maximum of 20 distinct natural stroke classes can be extracted from each input character, together with an estimate on the actual count of strokes which compose the character. Our system offers a number of performance tuning capabilities such as the computation of the fuzzy scores of each extracted stroke, the adjustment on the fuzzy stroke model parameters, and the potential of incorporating one's personal writing styles into our methodology.
Creating a safe haven in Pakistan

A case study of a women’s shelter in Pakistan is described, using in-depth interviews with the founders and staff. This article examines how a shelter program based on a human rights framework operates in the Pakistani cultural context. Findings demonstrate the considerable challenges faced by the organization, especially in reintegrating women back into society, as well as growing acceptance of shelter programs and women’s right to make life choices as a result of the organization’s work.
Biological age is better than chronological as predictor of 3-month outcome in ischemic stroke

Objective: To analyze the effect of age-related DNA methylation changes in multiple cytosine-phosphate-guanine (CpG) sites (biological age [b-age]) on patient outcomes at 3 months after an ischemic stroke. Methods: We included 511 patients with first-ever acute ischemic stroke assessed at Hospital del Mar (Barcelona, Spain) as the discovery cohort. Demographic and clinical data, including chronological age (c-age), vascular risk factors, initial stroke severity, recanalization treatment, and previous and 3-month modified Rankin Scale (p-mRS and 3-mRS, respectively) were registered. B-age was estimated with an algorithm, based on DNA methylation in 71 CpGs. Bivariate analysis determined variables associated with 3-mRS for inclusion in ordinal multivariate analysis. Results: After ordinal regressions for 3-month ischemic stroke outcome (3-mRS), b-age was associated with outcome (odds ratio 1.04 [95% confidence interval 1.01–1.07]), nullifying c-age. Stepwise regression kept b-age, basal NIH Stroke Scale, sex, p-mRS, and recanalization treatment as better explanatory variables, instead of c-age. These results were successfully replicated in an independent cohort. Conclusions: B-age, estimated by DNA methylation, is an independent predictor of ischemic stroke outcome regardless of chronological years.
Lum Processor with Neural Decision

This paper focuses on impulsive noise filtering and outliers rejection in gray-scale images. The proposed method combines neural networks, lower-upper-middle (LUM) smoothers and adaptive switching operations to produce a high-quality enhanced image. Extensive experimentation reported in this paper indicates that the proposed method is sufficiently robust, achieves an excellent balance between noise suppression and signal-detail preservation, and outperforms some well-known filters both subjectively and objectively.
Gaze Tracking System Model Based on Physical Parameters

In the past years, research in eye tracking development and applications has attracted much attention and the possibility of interacting with a computer employing just gaze information is becoming more and more feasible. Efforts in eye tracking cover a broad spectrum of fields, system mathematical modeling being an important aspect in this research. Expressions relating to several elements and variables of the gaze tracker would lead to establish geometric relations and to find out symmetrical behaviors of the human eye when looking at a screen. To this end a deep knowledge of projective geometry as well as eye physiology and kinematics are basic. This paper presents a model for a bright-pupil technique tracker fully based on realistic parameters describing the system elements. The system so modeled is superior to that obtained with generic expressions based on linear or quadratic expressions. Moreover, model symmetry knowledge leads to more effective and simpler calibration strategies, resulting in just two calibration points needed to fit the optical axis and only three points to adjust the visual axis. Reducing considerably the time spent by other systems employing more calibration points renders a more attractive model.
Unconventional Computing — Reversible Signed Digit Adders for Future Nanocomputing Devices

Abstract Unconventional Computing (UC) denotes new and alternative methods and technologies for computer technologies, e. g. , nanocomputing or DNA computing. This paper investigates the importance of UC for the Grand Challenges of Computer Engineering. At the moment nanocomputing appears to be the most promising UC discipline. As example for an UC method the use of redundant arithmetic and reversible logic for nanocomputing is presented.
Image Matching with a Car-Mounted Camera Robust to Changes in Imaging Conditions

We propose a matching method for images captured at different times and under different capturing conditions. Our method is designed for change detection in streetscapes using normal automobiles that have an off-the-shelf car mounted camera and a GPS. Therefore, we are able to analyze low-resolution and low frame-rate images captured asynchronously. To cope with this difficulty, previous and current panoramic images are created from sequential images which are rectified based on the view direction of a camera, and are then compared. In addition, in order to allow the matching method to be applicable to images captured under varying conditions, (1) for different lanes, enlarged/reduced panoramic images are compared with each other, and (2) robustness to noises and changes in illumination is improved by the edge features. To confirm the effectiveness of the proposed method, we conducted experiments matching real images captured under various capturing conditions.
Cortico-juxtacortical and periventricular lesions and MS diagnostic criteria

The art of diagnosing multiple sclerosis (MS) has changed considerably during recent decades, involving more radiology interpretation rather than purely clinical evaluations. The advent of MRI on the clinical scene during the 1980s changed the focus from repeated clinical assessments to an analysis of the number and distribution of white matter MRI lesions.
K‐Lysine acetyltransferase 2a regulates a hippocampal gene expression network linked to memory formation

Neuronal histone acetylation has been linked to memory consolidation, and targeting histone acetylation has emerged as a promising therapeutic strategy for neuropsychiatric diseases. However, the role of histone‐modifying enzymes in the adult brain is still far from being understood. Here we use RNA sequencing to screen the levels of all known histone acetyltransferases (HATs) in the hippocampal CA1 region and find that K‐acetyltransferase 2a (Kat2a)—a HAT that has not been studied for its role in memory function so far—shows highest expression. Mice that lack Kat2a show impaired hippocampal synaptic plasticity and long‐term memory consolidation. We furthermore show that Kat2a regulates a highly interconnected hippocampal gene expression network linked to neuroactive receptor signaling via a mechanism that involves nuclear factor kappa‐light‐chain‐enhancer of activated B cells (NF‐κB). In conclusion, our data establish Kat2a as a novel and essential regulator of hippocampal memory consolidation.
Structural intermediates in the fusion‐associated transition of vesiculovirus glycoprotein

Vesiculoviruses enter cells by membrane fusion, driven by a large, low‐pH‐induced, conformational change in the fusion glycoprotein G that involves transition from a trimeric pre‐fusion toward a trimeric post‐fusion state via monomeric intermediates. Here, we present the structure of the G fusion protein at intermediate pH for two vesiculoviruses, vesicular stomatitis virus (VSV) and Chandipura virus (CHAV), which is responsible for deadly encephalopathies. First, a CHAV G crystal structure shows two intermediate conformations forming a flat dimer of heterodimers. On virions, electron microscopy (EM) and tomography reveal monomeric spikes similar to one of the crystal conformations. In solution, mass spectrometry shows dimers of G. Finally, mutations at a dimer interface, involving fusion domains associated in an antiparallel manner to form an intermolecular β‐sheet, affect G fusion properties. The location of the compensatory mutations restoring fusion activity strongly suggests that this interface is functionally relevant. This work reveals the range of G structural changes and suggests that G monomers can re‐associate, through antiparallel interactions between fusion domains, into dimers that play a role at some early stage of the fusion process.
A GA-Based Approach for Epipolar Geometry estimation

In this paper, a genetic algorithm (GA)-based approach to estimate the fundamental matrix is presented. The aim of the proposed GA-based algorithm is to reduce the effect of noise and outliers in the corresponding points which affect the accuracy of the estimated fundamental matrix. Although in the proposed approach the GA is allowed to select the significant among all detected points, on the average half of the matched points have been determined to give optimum estimation of the fundamental matrix. Experiments with synthetic and real data show that the proposed approach is accurate especially in the presence of a high percentage of outliers. The proposed GA can always obtain good results in both high and low detailed images. Even for low detailed images which have a small number of matched points available to estimate the fundamental matrix, the proposed GA outperformed other methods.
An Adaptive Fuzzy Fusion Framework for Face Recognition under Illumination variation Based on Local Multiple Patterns

Local binary pattern (LBP) operator offers an efficient way to recognize face under varying illumination, while it has the drawback of abandoning some important texture features. Local multiple patterns (LMP) has alleviated the problem by a hierarchical model. However, the LMP method can bring out the rapid expansion of feature dimension, so a special feature encoding method is adopted by this paper. Meanwhile, we find that the LMP features of different layers can be used to recognize face independently so that it would preserve more abundant recognition information. Most importantly, the contribution of the LMP features from different layers is blurry under varying illumination. We propose a fuzzy framework to fuse the recognition result of different layers and use adaptive weights to calculate contribution rates of different layers under varying illumination. Experimental results demonstrate that the proposed method outperforms other state-of-the-art methods on four databases such as Yale B, Extended Yale B, CMU PIE and Outdoor.
Illumination Invariant Face Recognition in quaternion Domain

The performance of face recognition systems tends to decrease when images are affected by illumination. Feature extraction is one of the main steps of a face recognition process, where it is possible to alleviate the illumination effects on face images. In order to increase the accuracy of recognition tasks, different methods for obtaining illumination invariant features have been developed. The aim of this work is to compare two different ways to represent face image descriptions in terms of their illumination invariant properties for face recognition. The first representation is constructed following the structure of complex numbers and the second one is based on quaternion numbers. Using four different face description approaches both representations are constructed, transformed into frequency domain and expressed in polar coordinates. The most illumination invariant component of each frequency domain representation is determined and used as the representative information of the face image. Verification and identification experiments are then performed in order to compare the discriminative power of the selected components. Representative component of the quaternion representation overcame the complex one.
Physical Activity and Socioeconomic Status Explain Rural-Urban Differences in Obesity: a Cross-Sectional Study in Benin (West Africa)

To explore the relationships between obesity, socioeconomic status (SES), and physical activity (PA), we conducted a study with 341 subjects of a medium-size city of Benin and its semi-rural outskirts. PA was appraised with 24-hour recalls and SES, with a questionnaire. The overall prevalence of obesity (body mass index [BMI] ≥ 30), abdominal obesity (waist circumference) and elevated percent of body fat (%BF) were 10.6%, 23.8% and 20.2%, respectively. However, 14.6% were underweight (BMI < 18.5). Overweight (BMI ≥ 25) was significantly higher in urban than rural areas and in women than men. SES and PA were independently associated with obesity indicators, while urbanization variables were not.
Life expectancy in Parkinson disease

As of 1967, the answer to the question, “How long have I got, Doc? ” posed by a patient with Parkinson disease (PD) was 9.4 years from onset to death.1 This was the era before levodopa and before most of the “atypical parkinsonisms” other than postencephalitic parkinsonism were distinguishable from PD. By 1993, post levodopa but pre–deep brain stimulation, the figure had improved to 13.1 years,2 and by 2016 to 14.6 (±7.7) years.3 With an average PD onset age of 60 years, the relevant comparator is 23.3 years, the general population's life expectancy from that point.4
A Novel Minority Cloning Technique for Cost-Sensitive Learning

In many real-world applications, it is often the case that the class distribution of instances is imbalanced and the costs of misclassification are different. Thus, the class-imbalanced cost-sensitive learning has attracted much attention from researchers. Sampling is one of the widely used techniques in dealing with the class-imbalance problem, which alters the class distribution of instances so that the minority class is well represented in the training data. In this paper, we propose a novel Minority Cloning Technique (MCT) for class-imbalanced cost-sensitive learning. MCT alters the class distribution of training data by cloning each minority class instance according to the similarity between it and the mode of the minority class. The experimental results on a large number of UCI datasets show that MCT performs much better than Minority Oversampling with Replacement Technique (MORT) and Synthetic Minority Oversampling TEchnique (SMOTE) in terms of the total misclassification costs of the built classifiers.
Mapping Collaborative Work and Innovation in Biomedicine

This paper analyses a major episode in contemporary biomedical research using a new semi-quantitative approach. In the late 1970s, immunologists began producing new kinds of antibodies targeting molecules on the surface of normal and malignant blood cells. These tools quickly transformed biomedical research in immunology and oncology-hematology. Laboratories worldwide produced thousands of these new reagents and reorganized the classification, diagnosis, and prognosis of diseases such as leukemia and the lymphomas. The rapid development of these reagents initially generated considerable confusion. To avoid the impending chaos, researchers in the field, officially supported by the World Health Organization and the International Union of Immunological Societies, launched an ongoing series of distributed workshops that led to the establishment of a nomenclature of antibody reagents and cell surface molecules. The First Workshop (1981-82) mobilized 54 research groups from 14 countries and resulted in the establishment of 15 antibody/molecule categories. By the late 1990s the number of these categories had increased to more than 247 and the number of participating laboratories had risen to more than 500. Sociological analyses of this kind of large-scale collaborative research usually adopt one of two equally unsatisfactory alternatives: either they provide thick descriptions of selected sites, thus missing the figurational dimension of the collaborative network, or they attempt to account for figurational complexity by reducing it to a few quantitative indicators, thus destroying for all practical purposes the very phenomena under investigation. To avoid these two alternatives, we opted for a combination of ethnographic methods (interviews, content analysis) and a computer-based analysis of the more than 6000 antibodies examined during the first six workshops, using Réseau-Lu, a software program specifically designed for the treatment of heterogeneous relational data.
Incorporating Trust Relation with PMF to Enhance Social Network Recommendation Performance

In view of the exponential growth of information generated by social networks, social network analysis and recommendation have become important for many web applications. This paper examines the problem of social collaborative filtering to recommend items of interest to users in a social network setting. Many social networks capture the relationships among the nodes by using trust scores to label the edges. The bias of a node denotes its propensity to trust/mistrust its neighbors and is closely related to truthfulness. It is based on the idea that the recommendation of a highly biased node should be removed. In this paper, we propose a model-based approach for recommendation employing matrix factorization after removing the bias nodes from each link, which naturally fuses the users’ tastes and their trusted friends’ favors together. The empirical analysis on real large datasets demonstrate that our approaches outperform other state-of-the-art methods.
A Shape-Aware Road Detection Method for Aerial Images

Road detection in aerial images is a crucial technique for visual navigation and scene understanding in relation to unmanned aerial vehicles (UAVs). A shape-aware road detection method for aerial images is proposed in this paper. It first employs the stroke width transform (SWT) and a geodesic distance based superpixel clustering to generate proposal regions. Then, a shape classification is responsible for selecting all potential road segments from the proposal regions which appear to be long and with consistent width. All road segments selected are clustered into several groups based on width and color features. A global graph based labeling model is then applied based on each group to remove potential background clutters, as well as to generate the final output. Experiments on two public datasets demonstrate that the proposed method can handle more diverse and challenging road scenes and needs less pre-training, leading to better performance compared to conventional methods.
A Mismatch Detection Method Based on Affine Transformation for Stereo Light Microscopy Stereo Matching

For the light microscopy images that have the characteristics of shallow depth of field, serious distortion and poor resolution, mismatch is a ubiquitous phenomenon. The paper presents a mismatch detection method for the stereo light microscopy stereo matching. Affine transformation matrix and matching constraint condition are calibrated by the calibration board which has the precision solid dots and the motorized stage. Bias vector of affine transformation of each matching pair is taken as the criteria to apply mismatch detection. The experimental results show that the method can detect more mismatching pairs and preserve more matching pairs than the traditional RANSAC method and the epipolar rectification method.