Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Learning for Hyperspectral Image Classification: An Overview

Hyperspectral image (HSI) classification has become a hot topic in the field of remote sensing. In general, the complex characteristics of hyperspectral data make the accurate classification of such data challenging for traditional machine learning methods. In addition, hyperspectral imaging often deals with an inherently nonlinear relation between the captured spectral information and the corresponding materials. In recent years, deep learning has been recognized as a powerful feature-extraction tool to effectively address nonlinear problems and widely used in a number of image processing tasks. Motivated by those successful applications, deep learning has also been introduced to classify HSIs and demonstrated good performance. This survey paper presents a systematic review of deep learning-based HSI classification literatures and compares several strategies for this topic. Specifically, we first summarize the main challenges of HSI classification which cannot be effectively overcome by traditional machine learning methods, and also introduce the advantages of deep learning to handle these problems. Then, we build a framework that divides the corresponding works into spectral-feature networks, spatial-feature networks, and spectral–spatial-feature networks to systematically review the recent achievements in deep learning-based HSI classification. In addition, considering the fact that available training samples in the remote sensing field are usually very limited and training deep networks require a large number of samples, we include some strategies to improve classification performance, which can provide some guidelines for future studies on this topic. Finally, several representative deep learning-based classification methods are conducted on real HSIs in our experiments.
Interpreting mobilization dynamics through art: A look at the DREAMers Movement

Most research on art and social movements focuses on the representational aspect of art, leaving untouched how artwork informs audiences and actors on the mobilization dynamics permeating the field of action. In this article the authors contend that the analysis of art in social movements should pursue the ways in which collective actors place art as part of their interpretative lens to act upon changing social conditions. The article’s analysis of art around the DREAMers Movement suggests that artwork allows for movement actors to change the limits of their cultural context and affect the repertoires of articulations conceived within that context. This analysis moves beyond the notions of art as representation and presents art as a field to challenge the forms and dynamics of mobilization.
The Zero Bound on Nominal Interest Rates: How Important Is It?

This paper surveys the literature on the zero bound on the nominal interest rate. It addresses questions ranging from the conditions under which the zero bound on the nominal interest rate might occur to policy options to avoid or use to exit from such a situation. We discuss literature that examines historical and country evidence, and literature that uses models to generate evidence on this question. There seems to be a consensus that the probability of encountering the zero bound is relatively low, especially if the inflation target established for the monetary authority is 2 per cent or more. There is some indication in the model work that the likelihood of encountering the zero bound increases at an increasing rate for lower inflation targets. Various caveats apply to drawing conclusions for Canada from the papers we surveyed, among them the paucity of historical experience where a zero bound has occurred, the risk of making inferences using data (or parameters) from different monetary regimes, the relatively few studies using Canadian data, and the weight to give to credibility.
The effect of stress coping on resilience of firefighters.

This study was attempted to provide basic data on mental health by identifying factors affecting the resilience of firefighting officers. The data collection was conducted from May 1st, 2018 to May 31st, 2018, with the consent of the target person and the questionnaire was conducted. The number of participants used in the final analysis was 147. The questionnaire consisted of demographic characteristics, stress coping, and resilience. The collected data were analysed using SPSS 21.0 statistical program using descriptive statistics, t-test, ANOVA, Pearson's correlation, and Multiplier regression. The results of this study showed that the difference in resilience according to general characteristics was the result of subjective health condition, stress relief method, and subjective health condition in stress coping. Resilience showed a static correlation with stress coping, and stress coping showed a positive correlation between active coping and passive coping. In addition, active coping showed a positive correlation with passive coping. Based on the above results, regression analysis showed that factors affecting resilience were stress coping and subjective health status, and the factors used in the analysis explained 49.2% of resilience.
iKernels-Core: Tree Kernel Learning for Textual Similarity

This paper describes the participation of iKernels system in the Semantic Textual Similarity (STS) shared task at *SEM 2013. Different from the majority of approaches, where a large number of pairwise similarity features are used to learn a regression model, our model directly encodes the input texts into syntactic/semantic structures. Our systems rely on tree kernels to automatically extract a rich set of syntactic patterns to learn a similarity score correlated with human judgements. We experiment with different structural representations derived from constituency and dependency trees. While showing large improvements over the top results from the previous year task (STS-2012), our best system ranks 21st out of total 88 participated in the STS2013 task. Nevertheless, a slight refinement to our model makes it rank 4th.
Fundus imagemosaic based on the SIFT feature

Fundus image mosaic can expand the visual field of view and provide auxiliary diagnosis for fundus diseases. Due to the low contrast and uneven grayscale of fundus images, traditional methods based on gray characteristic are no longer applicable. We propose a fundus image mosaic algorithm based on Scale-invariant feature transform (SIFT), along with a quantitative evaluation criterion. We detect feature points in Gaussian scale space and generate feature descriptors with scale invariance for all feature points. The matching feature point pairs are obtained by evaluating the similarity of their feature descriptors and outliers are eliminated using Random sample consensus (RANSAC) algorithm employed the projective transformation model. After that, the transformation matrix can be calculated based on the obtained matching point pairs. Finally, the multi-view fundus images are fused based on the transformation matrix. To the best of our knowledge, there is no quantitative evaluation reported for fundus image mosaic. Here we propose a criterion based on manual marker to evaluate the mosaic results. The experimental results show that the average accuracy is 2.27±0.5 pixels. Our proposed method has great potential to be applied in existing computer-assisted diagnostic system of ophthalmologic diseases.
A realistic anthropomorphic dynamic heart phantom

This paper presents a realistic numerical beating heart phantom. The development of such a model is motivated by the need of an appropriate ground truth reference for the evaluation of cardiac image processing algorithms. An anatomical model has been developed by extracting cardio-vascular structures contours from a 3D acquisition in magnetic resonance imaging (MRI) of a human healthy subject. Cardiac motion has been tracked by 3D non rigid registration between the consecutive linearly interpolated volumes from dynamic MRI sequences. The so-obtained numerical phantom is structurally rich and its motion realistic. As it issues from the same MRI acquisition on the same human healthy subject, it presents a very good overall consistency and will be use for cardiac image processing algorithms evaluation
Giving young people a well-rounded education: a study of the educational goals parents attach to the leisure activities of their children

ABSTRACT Policymakers strongly stimulate young people’s participation in organised leisure activities because it is believed to provide young people with important ‘work’ skills and attitudes. Special attention is paid to the underrepresented working-class youth. To stimulate (especially working-class) parents to play an active ‘guiding’ role, policymakers often assume that they have to be made aware of the educational benefits of engagement in leisure activities. The empirical basis of this assumption is, however, thin. Therefore, this study examined the educational goals middle- and working-class parents attach to their adolescents’ leisure activities on the basis of 32 interviews conducted in a small city in Belgium. Although there were subtle socio-economic differences in some goals parents pursued through their children’s leisure activities (i.e. teaching skills and societal values and norms, belonging to a peer group, independent thinking and relaxation), these were not of a magnitude that they could contribute to our understanding of socio-economic differences in parents’ role and in turn, in young people’s leisure use. We argue that further research needs to focus on socio-economic differences in available resources from which parents construct particular strategies to shape their young people’s leisure spending.
Analysis of Different Text Steganography Techniques: A Survey

Steganography is helping individual to send confidential data between two parties. It enables user to hide data in different digital mediums. Steganography is of many types such as image steganography, text steganography, audio/video steganography etc. Text Steganography is quite difficult than other techniques because of less amount of redundancy and changes can be detected quite easily. Some of the techniques of text steganography has been discussed along with characteristics and working.
Hybrid system for cardiac arrhythmia classification with fuzzy k-nearest neighbors and Multi Layer Perceptrons combined by a fuzzy inference system

In this paper we describe a hybrid architecture for classification of cardiac arrhythmias taking as a source the ECG records MIT-BIH Arrhythmia database. The Samples were taken from the LBBB, RBBB, PVC and Fusion Paced and Normal arrhythmias, as well as the normal beats. These were segmented and normalized and three methods of classification were used: Fuzzy KNN, Multi Layer Perceptron with Gradient Descent with Momentum Backpropagation, and Multi Layer Perceptron with Scaled Conjugate Gradient Backpropagation. Finally, we used a Mamdani fuzzy inference system to combine the outputs of each classifier, and we achieved a very high classification rate of 98%.
A low-cost, dual-band RF loop antenna and energy harvester

This paper presents the design of a Dickson charge pump circuit paired with a compact, multi-resonant loop antenna for RF energy harvesting at 900 MHz and 2.4 GHz. It enables the RF harvester to operate efficiently both indoors and outdoors. A prototype design using surface mount components is simulated using ADS. It achieves a sensitivity of −21.2 dBm and −17.1 dBm at 900 MHz and 2.4 GHz, respectively. The simulated Vout >1 V is achieved in < 60µs. The prototype design, including antenna, requires an area of 55.9×38.1 mm2.
A Home Environment Posture and Behavior Recognition System

In this paper, we propose a home environment health care surveillance system. This system can recognize six static postures and four activities, and send out informing SMS messages when abnormal actions are detected. The system contains two parts: static posture recognition and human behavior analysis. We use a modified shape context method which we call the area-based shape context to recognize static postures and Hidden Markov Models are used to build motion models.
LeoXSS: An Efficient XML Stream System for Processing Complex XPaths

Stream-based continuous query processing fits a large class of new applications, such as sensor networks, location tracking, network management and financial data analysis. In this paper, we propose an efficient system called LeoXSS that processes complex twig patterns with OR-predicates and AND-predicates over XML document streams. In LeoXSS, a set of twig patterns is processed in a single document-order pass over the input document stream avoiding the post-processing of nested paths.
L'ingénierie pédagogique : Pour construire l'apprentissage en réseau

Les nouvelles technologies de formation, si elles se limitent a faciliter l'acces aux informations, pourront certes jouer un role utile, mais elles ne suffiront pas a assurer un apprentissage de qualite. Progres technologique et progres pedagogique ne vont pas automatiquement de pair. Cet ouvrage a pour but d'aider les educateurs et les chercheurs a faire une plus large et une meilleure utilisation des extraordinaires moyens qui sont maintenant mis a leur disposition sur les reseaux pour apprendre, aider a apprendre ou concevoir de nouveaux moyens d'apprendre. Il vise a combler l'ecart qui existe entre les methodes traditionnelles de design pedagogique et les possibilites qu'apportent les nouvelles technologies de formation sur le plan pedagogique. Contenu de l'ouvrage : Defi du teleapprentissage; Technologies de teleapprentissage; Centres virtuels d'apprentissage; Ingenierie pedagogique; Modelisation des connaissances et des competences; Traitement pedagogique; Traitement mediatique; Planification de la diffusion; Etudes de cas d'ingenierie pedagogique; Methode, theorie et pratique. L'ouvrage compagnon, Modelisation des connaissances et des competences : un langage graphique pour concevoir et apprendre, permet d'approfondir ces questions centrales pour l'ingenierie pedagogique.
The effects of the electrical double layer on giant ionic currents through single-walled carbon nanotubes

We developed a computational model for investigating the cause for the high ionic current through a single-walled carbon nanotube nanofluidic device by considering the electrical double layer at a solid–liquid interface. With this model, we were able to examine the influence of the Gouy–Chapman–Stern electrical double layer and the solution concentration on the ionic conductance in the device. Results showed that the conductance–concentration relationship predicted from our model agreed well with experimental observation. Moreover, our model showed that the compact layer thickness increased with the increase of the bulk solution concentration, reducing the internal volume of the nanotube channel available for fluid transport. Fluid within the channel had an enhanced concentration and a net charge which increased the electroosmotic and electrophoretic transport properties of the device, increasing the total ionic conductance of the system.
CMV-Independent Lysis of Glioblastoma by Ex Vivo Expanded/Activated Vδ1+ γδ T Cells

Vδ2neg γδ T cells, of which Vδ1+ γδ T cells are by far the largest subset, are important effectors against CMV infection. Malignant gliomas often contain CMV genetic material and proteins, and evidence exists that CMV infection may be associated with initiation and/or progression of glioblastoma multiforme (GBM). We sought to determine if Vδ1+ γδ T cells were cytotoxic to GBM and the extent to which their cytotoxicity was CMV dependent. We examined the cytotoxic effect of ex vivo expanded/activated Vδ1+ γδ T cells from healthy CMV seropositive and CMV seronegative donors on unmanipulated and CMV-infected established GBM cell lines and cell lines developed from short- term culture of primary tumors. Expanded/activated Vδ1+ T cells killed CMV-negative U251, U87, and U373 GBM cell lines and two primary tumor explants regardless of the serologic status of the donor. Experimental CMV infection did not increase Vδ1+ T cell - mediated cytotoxicity and in some cases the cell lines were more resistant to lysis when infected with CMV. Flow cytometry analysis of CMV-infected cell lines revealed down-regulation of the NKG2D ligands ULBP-2, and ULBP-3 as well as MICA/B in CMV-infected cells. These studies show that ex vivo expanded/activated Vδ1+ γδ T cells readily recognize and kill established GBM cell lines and primary tumor-derived GBM cells regardless of whether CMV infection is present, however, CMV may enhance the resistance GBM cell lines to innate recognition possibly contributing to the poor immunogenicity of GBM.
The Adaptive Evolution of Plasticity: Phytochrome-Mediated Shade Avoidance Responses1

Abstract Many plants display a characteristic suite of developmental “shade avoidance” responses, such as stem elongation and accelerated reproduction, to the low ratio of red to far-red wavelengths (R:FR) reflected or transmitted from green vegetation. This R:FR cue of crowding and vegetation shade is perceived by the phytochrome family of photoreceptors. Phytochrome-mediated responses provide an ideal system for investigating the adaptive evolution of phenotypic plasticity in natural environments. The molecular and developmental mechanisms underlying shade avoidance responses are well studied, and testable ecological hypotheses exist for their adaptive significance. Experimental manipulation of phenotypes demonstrates that shade avoidance responses may be adaptive, resulting in phenotypes with high relative fitness in the environments that induce those phenotypes. The adaptive value of shade avoidance depends upon the competitive environment, resource availability, and the reliability of the R:FR cue for predicting the selective environment experienced by an induced phenotype. Comparative studies and a reciprocal transplant experiment with Impatiens capensis provide evidence of adaptive divergence in shade avoidance responses between woodland and clearing habitats, which may result from population differences in the frequency of selection on shade avoidance traits, as well as differences in the reliability of the R:FR cue. Recent rapid progress in elucidating phytochrome signaling pathways in the genetic model Arabidopsis thaliana and other species now provides the opportunity for studying how selection on shade avoidance traits in natural environments acts upon the molecular mechanisms underlying natural phenotypic variation.
Design considerations for fractional-slot winding configurations of synchronous machines

This paper presents some design considerations for synchronous machines characterized by a fractional number of slots per pole per phase. The main advantage of this configuration is a smooth torque, which is due to the elimination of periodicity between slots and poles. A second advantage is a higher fault-tolerant capability, making the machine able to work even in faulty conditions. However, the fractional-slot configuration presents a high content of MMF harmonics that may cause an unbalanced saturation and thus an unbearable torque ripple. A method to design fractional-slot machines is illustrated in this paper, including double-layer and single-layer windings. The analytical computation is extended to determine the harmonics of MMF distribution. Their effect is highlighted in isotropic as well as anisotropic machines. Finally, some considerations are reported to avoid unsuitable configurations
Chidamber and Kemerer's Metrics Suite: A Measurement Theory Perspective

The metrics suite for object-oriented design put forward by Chidamber and Kemerer (1994) is partly evaluated by applying principles of measurement theory. Using the object coupling measure (CBO) as an example, it is shown that failing to establish a sound empirical relation system can lead to deficiencies of software metrics. Similarly, for the object-oriented cohesion measure (LCOM) it is pointed out that the issue of empirically testing the representation condition must not be ignored, even if other validation principles are carefully obeyed. As a by-product, an alternative formulation for LCOM is proposed.
Association of β-blocker therapy with long-term clinical outcomes in patients with coronary chronic total occlusion

AbstractThere are limited data regarding the efficacy of &bgr;-blockers for secondary prevention in patients with coronary chronic total occlusion (CTO). Therefore, we investigated the association of &bgr;-blocker therapy with long-term clinical outcomes in CTO patients. From March 2003 to February 2012, a total of 2024 CTO patients treated with either medical therapy alone or revascularization were enrolled in the study. We assessed 1596 patients with stable ischemic heart disease and divided them into the &bgr;-blocker group (n = 932) and the no-&bgr;-blocker group (n = 664). The primary outcome was all-cause death. The median follow-up duration was 3.9 (interquartile range: 2.0–6.2) years. All-cause death occurred in 11.6% patients in the &bgr;-blocker group and 13.6% patients in the no-&bgr;-blocker group (hazard ratio [HR]: 0.81, 95% confidence interval [CI]: 0.61–1.08; P = 0.15). In the propensity score-matched population (570 pairs), all-cause death occurred in 12.3% patients in the &bgr;-blocker group and 12.8% patients in the no-&bgr;-blocker group (HR: 0.93, 95% CI: 0.67–1.29; P = 0.66). In subgroup analysis, &bgr;-blocker therapy was associated with better outcome, in terms of all-cause death, in patients with CTO of the left anterior descending coronary artery and Synergy Between PCI with Taxus and Cardiac Surgery (SYNTAX) score ≥23 (P for interaction = 0.01 and 0.02, respectively). In conclusion, &bgr;-blocker therapy was not associated with favorable long-term clinical outcomes in stable CTO patients, regardless of treatment strategy. However, &bgr;-blocker therapy might be beneficial in a highly selective group of CTO patients with a high ischemic burden.
Association of Polymorphic Variants of miRNA Processing Genes with Larynx Cancer Risk in a Polish Population

Laryngeal cancer (LC) is one of the most prevalent types of head and neck cancer. An increasing interest has been focused on the role of microRNA (miRNAs) in LC development. The study group consisted of 135 larynx cancer patients and 170 cancer-free individuals. Nine polymorphisms of pre-miRNA processing genes, DROSHA (rs6877842), DGCR8 (rs3757, rs417309, and rs1640299), RAN (rs14035), XPO5 (rs11077), DICER1 (rs13078 and rs3742330) and TARBP2 (rs784567), were performed by TaqMan SNP Genotyping Assay. It was found that the frequency of the GT and the TT polymorphic variants of XPO5 gene were higher in LC patients than in controls (p < 0.0001 and p = 0.000183, resp.). In turn, the frequency of the CT genotype of RAN gene was higher in controls than in LC patients (p < 0.0001). The TT and the AG of DICER1 gene (p = 0.034697 for rs13078 and p = 0.0004 for rs3742330) as well as the AG and the GG genotypes of TARBP2 gene (p = 0.008335 and p < 0.0001, resp.) were associated with higher risk of LC occurrence. Our data suggested that polymorphisms of miRNA processing genes might be useful as predictive factors for the LC development.
A Randomized Approach for Approximating the Number of Frequent Sets

We investigate the problem of counting the number of frequent (item)sets - a problem known to be intractable in terms of an exact polynomial time computation. In this paper, we show that it is in general also hard to approximate. Subsequently, a randomized counting algorithm is developed using the Markov chain Monte Carlo method. While for general inputs an exponential running time is needed in order to guarantee a certain approximation bound, we empirically show that the algorithm still has the desired accuracy on real-world datasets when its running time is capped polynomially.