Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluating Di ↵ erent Approaches to Calibrating Conformal Predictive Systems

Conformal predictive systems (CPSs) provide probability distributions for real-valued labels of test examples, rather than point predictions (as output by regular regression models) or conﬁdence intervals (as output by conformal regressors). The performance of a CPS is dependent on both the underlying model and the way in which the quality of its predictions is estimated; a stronger underlying model and a better quality estimation can signiﬁcantly improve the performance. Recent studies have shown that conformal regressors that use random forests as the underlying model may beneﬁt from using out-of-bag predictions for the calibration, rather than setting aside a separate calibration set, allowing for more data to be used for training and thereby improving the performance of the underlying model. These studies have furthermore shown that the quality of the individual predictions can be e ↵ ectively estimated using the variance of the predictions or by k -nearest-neighbor models trained on the prediction errors. It is here investigated whether these methods are also effective in the context of split conformal predictive systems. Results from a large empirical study are presented, using 33 publicly available datasets. The results show that by using either variance or the k -nearest-neighbor method for estimating prediction quality, a significant increase in performance, as measured by the continuous ranked probability score, can be obtained compared to omitting the quality estimation. The results furthermore show that the use of out-of-bag examples for calibration is competitive with the most e ↵ ective way of splitting training data into a proper training set and a calibration set, without requiring tuning of the calibration set size.
Conformal anomaly detection for visual reconstruction using gestalt principles

In this paper, we combine a modern machine learning technique called conformal predictors (CP) with elements of gestalt detection and apply them to the problem of visual perception in digital images. Our main task is to quantify several gestalt principles of visual reconstruction. We interpret an image/shape as being perceivable (meaningful) if it sufﬁciently deviates from randomness - in other words, the image could hardly happen by chance. These deviations from randomness are measured by using conformal prediction technique that can guarantee the validity under certain assumptions. The technique describes the detection of perceivable images that allows to bound the number of false alarms, i.e. the proportion of non-perceivable images wrongly detected as perceivable.
Factors influencing metabolic syndrome perception and exercising behaviors in Korean adults: Data mining approach

This study was conducted to determine which factors would predict metabolic syndrome (MetS) perception and exercise by applying a machine learning classifier, or Extreme Gradient Boosting algorithm (XGBoost) from July 2014 to December 2015. Data were obtained from the Korean Community Health Survey (KCHS), representing different community-dwelling Korean adults 19 years and older, from 2009 to 2013. The dataset includes 370,430 adults. Outcomes were categorized as follows based on the perception of MetS and physical activity (PA): Stage 1 (no perception, no PA), Stage 2 (perception, no PA), and Stage 3 (perception, PA). Features common to all questionnaires for the last 5 years were selected for modeling. Overall, there were 161 features, categorical except for age and the visual analogue scale (EQ-VAS). We used the Extreme Boosting algorithm in R programming for a model to predict factors and achieved prediction accuracy in 0.735 submissions. The top 10 predictive factors in Stage 3 were: age, education level, attempt to control weight, EQ mobility, nutrition label checks, private health insurance, EQ-5D usual activities, anti-smoking advertising, EQ-VAS, education in health centers for diabetes, and dental care. In conclusion, the results showed that XGBoost can be used to identify factors influencing disease prevention and management using healthcare bigdata. 요 약 본 연구는 기계 학습법 중 하나인 XGBoost를 이용하여 대사증후군을 인지하고 신체활동을 수행하는 집단을 예측하 고자 2014년 7월부터 2015년 12월까지 시도되었다. 이에 2009-2013년 지역사회건강조사를 연구자료로 사용하였고 370,430 명의 성인을 분석에 포함하였다. 본 연구의 종속변수는 대사증후군의 인지 및 신체활동 실천정도에 따른 단계로 3단계로 구분하였다:Stage 1(무인지, 무 신체활동), Stage 2(인지, 무 신체활동), and Stage 3(인지, 신체활동). 예측변수로는 5년간의 지역사회건강조사 중 공통으로 수집된 문항으로부터 161개의 특성을 선택하였다. 자료 분석을 위해 R program을 이용하여 XGBoost 알고리즘을 적용하였다. 분석 결과 정확도는 0.735 이었으며, 가장 영향을 미치는 10개의 특성은 나이, 교육수준, 체중조절시도 경험, EQ-5D 운동능력, 영양표시 확인, 개인 건강보험가입 유무, EQ-5D 일상활동, 금연광고경험 여부, 통증 유무, 당뇨에 대한 보건기관의 교육 경험 순으로 확인되었다. 본 연구결과는 XGBoost가 보건의료빅데이터를 이용한 질병의 예방과 관리에 영향을 주는 요인을 확인하는데 유용한 도구임을 보여주었다. 또한, 본 연구를 통해 대사증후군에 취약한 계층 을 확인하고 이를 위한 교육프로그램 개발에 도움을 줄 수 있을 것으로 보인다.
Xiurenbagrus, a New Genus of Amblycipitid Catfishes (Teleostei: Siluriformes), and Phylogenetic Relationships among the Genera of Amblycipitidae

Xiurenbagrus, a new genus of the Amblycipitidae, is established for the single species Liobagrus xiurenensis Yue 1981. This taxon differs from confamilials by having two patches of vomerine teeth, lack of deep grooves along shafts of dorsaland pectoral-fin spines, relatively acute snout, and short barbels. Xiurenbagrus xiurenensis is redescribed to include new data on its osteology and external morphology. The three genera of the Amblycipitidae, Xiurenbagrus, Amblyceps, and Liobagrus, are a monophyletic group. Monophyly of each genus is well supported by several synapomorphies of the skeleton and external soft anatomy. Within the Amblycipitidae, Amblyceps is the sister taxon of Liobagrus, a relationship supported by 10 synapomorphies.
Real-Time Age and Gender Estimation from Face Images

: In this paper, we describe an automated real-time system that estimates age and gender by utilizing a set of facial image sequences from a video camera. The age and gender estimation system consists of four steps: i) detection and extraction of the facial region from input video; ii) selection of the frontal face images from the extracted facial regions using head pose estimation; iii) duplicated face detection and removal by tracking the faces; and iv) age and gender estimation using statistical facial features. Here, LBP features with AdaBoost classifiers are used to detect the face region in a video frame, and the frontal face images are selected using a 3D pose estimation method. In addition, a particle filter-based tracking framework is employed to remove duplicated faces and to improve the accuracy of people counting, and Gabor-LBP features are used to estimate age and gender using a linear SVM and Adaboost classifiers. In experiments, a large number of face datasets are used to train and evaluate the proposed method, and higher performance is achieved in terms of age and gender estimation: 72.53% for age and 98.90% for gender.
Rubiks Cube Localization, Face Detection, and Interactive Solving

While Rubiks cubes are highly mathematical puzzles with a vast assortment of existing software, the task of transferring the cubes state into the computer has historically been quite tedious. Here we approach this problem from a computer vision perspective: how might one go about automatically recognizing and extracting the state of a Ru-biks cube at any given point in time using a front-facing web camera on commercial hardware, robust to a variety of lighting conditions, scales and motion? We reason that since the cubes face is highly structured and presents a distinct pattern and texture, this problem is an excellent candidate for convolutional neural networks. We present an approach that relies upon two separately trained convolutional neural networks for (1) localization of the cube within live video feeds and (2) subsequent detection of the centers of the cube faces from cropped images. In addition, we present a set of techniques grounded in classical computer vision to extract the Rubiks cube state from the output of the aforementioned convolutional neural networks, featuring SLIC superpixel segmentation, projective transforms and color histogram classiﬁcation. Finally, we present a technique for gathering large amounts of labeled data for similar problems relying upon an iterative training then assisted labeling procedure and analyze the resultant performance gains. Our code is open source and can be viewed at https://github.com/jayhack/ConvCube
Acoustic–gravity waves during solar eclipses: Detection and characterization using wavelet transforms

In the present contribution, we first propose a methodology that enables to detect wave-like structures propagating in ionosphere, by tracking the local maxima of the modulus of continuous complex wavelet transform coefficients with respect to heights. From the derivation of the phases of the wavelet transform, we measure the corresponding propagation parameters. These tools are applied to measurements collected by vertical ionospheric sounding at high-time resolution sampling regime (sampling periods ranged from 1 to 3min) in the observatory Průhonice (49.9N, 14.5E, Czech Republic). The aim of these experiments is to analyze the changes in the ionospheric plasma induced by three different solar eclipse events (total solar eclipses, 11 August 1999, 29 March 2006, and annular solar eclipse, 3 October 2005) and to detect and analyze the propagation of the generated acoustic–gravity waves (AGWs). Second, injecting wave vector components measured from the data into the AGW propagation equations, we obtain a full description of the propagation of the waves. This enables us to differentiate AGWs from others wave-like oscillations and to discuss similarities and differences of the waves detected during these three particular events. These procedures also enabled us to detect acoustic waves. We believe that the methodology proposed here brings significant improvement in detecting and characterizing AGW propagations from empirical data and can be readily used in the ionosphere community. r 2007 Elsevier Ltd. All rights reserved.
Use in Late Adolescence and Early Adulthood: The Role of Generalised Anxiety Disorder and Drinking to Cope Motives. Drug and Alcohol Dependence, 204, [107480]

Background: The relationship between anxiety and alcohol use is unclear, and moderating factors, such as drinking to cope (DTC) motives, may explain mixed findings. Methods: Using the Avon Longitudinal Study of Parents and Children (ALSPAC), we examined associations between generalized anxiety disorder (GAD) at age 18 and frequent drinking, frequent bingeing, hazardous drinking, and harmful drinking at ages 18 (unadjusted n= 3462) and 21 (unadjusted n=2076), in a sample of late adolescent drinkers. Analyses were adjusted for sociodemographic, parental, and adolescent confounders. We also examined whether DTC motives influenced the strength and direction of associations between GAD and alcohol use. Results: GAD was positively associated with all alcohol outcomes at baseline (unadjusted OR (95% CI): frequent drinking 1.40 (1.02–1.91); frequent bingeing 1.40 (0.96–2.04); hazardous drinking 1.44 (1.08–1.92); harmful drinking 1.98 (1.22–3.23)). GAD increased the odds of harmful drinking at age 21 (unadjusted OR 1.72, 95% CI 1.09–2.73), but there was no clear evidence of a longitudinal relationship between GAD and the other alcohol use outcomes. There was no clear evidence of a GAD x DTC interaction on alcohol use at ages 18 or 21. Findings were consistent across various multiply imputed datasets. Conclusions: In adolescence, GAD symptoms are associated with frequent drinking, frequent bingeing, hazardous drinking, and harmful drinking. In early adulthood, associations remain for harmful drinking only. DTC motives do not appear to moderate the relationship at either age.
CONTINUOUS REAL-TIME ACOUSTIC SURVEILLANCE OF FAST SURFACE VESSELS

Passive underwater acoustic sensing technologies applied to mobile autonomous maritime systems allow for minimum environmental impact, covertness, long endurance, wide area coverage, near real-time, continuous (’24/7’) monitoring, and the availability of several capabilities, ranging from detection to the classification of acoustic noise sources. Gliders – both as unmanned underwater and unmanned (sea) surface platforms – are particularly appropriate for this kind of application, as their features include persistence, discreteness, portability, scalability and remote control. NATO STO CMRE developed a new low-power passive acoustic system to be hosted on persistent platforms such as underwater gliders and wave gliders. The system is able to detect, track and classify surface vessels by using a compact volumetric array of hydrophones and processing acoustic data in real time, directly on board the gliders, through the application of advanced algorithms. The signal processing chain of the passive acoustic surveillance system has been recently extended to the capability of multitarget tracking by means of a Bayesian tracking algorithm. In this paper, we present post-analysis results, were two fast surface vessels are successfully tracked at the same time.
Bacterial microflora in the gastrointestinal tract of Nile tilapia, Oreochromis niloticus, cultured in a semi-intensive system

This experiment measured total bacterial numbers in the gastrointestinal regions of semi-intensively cultured tilapia, Oreochromis niloticus L. (Cichlidae). Mean bacterial numbers were higher in both anterior and posterior gut than in stomach. The percentage of isolated species and the percentage of isolates from any particular species varied significantly among gastrointestinal tract regions. Aeromonas hydrophila, Aeromonas veronii, Burkholderia cepacia, Chromobacterium violaceum, Citrobacter freundii, Escherichia coli, Flavimonas oryzihabitans and Plesiomonas shigelloides were the most frequently isolated Gram- negative bacilli. From these samples, only Plesiomonas shigelloides was present in both gastrointestinal regions, with higher number in the posterior gut (76%), when compared to anterior gut (4.8%) and stomach (0.6%). Aeromonas hydrophila (0.6%), Escherichia coli (7.4%), and Flavimonas oryzihabitans were isolated only in the stomach, and Citrobacter freundii and Burkholderia cepacia were found only in the posterior gut. Chromobacterium violaceum was the dominant bacteria isolated from stomach and anterior gut, with 90% and 55%, respectively. Unidentified organisms comprised 0 - 39.3% of the gastrointestinal microflora.
Ecole de biologie What drives the success of the invasive tree Robinia pseudoacacia: climate suitability, disturbance or land management?

Biological invasions are a major component of global change. For this reason, many studies have investigated the drivers and impacts of alien species invasions, but the variability in invasion rates between and within different areas has been poorly studied. Our aim was to better understand the anthropogenic and ecological factors that drive the invasion by a major invasive alien species, the tree R. pseudoacacia, and to try to explain the apparent difference in invasion rates between three different countries: Switzerland, Italy and France. To do this, we applied different methods: we modelled climatic suitability of R. pseudoacacia at a global level and then used it to conduct modelbased fieldwork to assess presence-absence and population dynamics at a local level. Habitat suitability models, univariate analysis and multivariate mixed models were used to analyse the data. Climate was the most important predictor in explaining presence of R. pseudoacacia and native tree species richness seemed to play a role in the difference in density reached by it. However, none of the studied predictors could explain entirely the difference in invasion rates among countries. History of introduction (with its effect on propagule pressure), different in the three countries, could likely have played a role.
PRACTICAL APPLICATION OF COHERENT LIGHT SCATTERING ON NANOFLUIDS DYNAMICS SIMULATION

Nanofluids are suspensions of nano to micrometer sized particles in liquids. The nanoparticles act as scattering centers when light passes through the medium. In a suspension the SCs have a complex movement of both sedimentation and the Brownian motion. As a result of the SCs permanent position changes the speckle image is not static but presents time fluctuations. A computer code to simulate the dynamics of the coherent light scattering on nanofluids was written, tested and used to calculate the far field intensity variation for nanofluids having different particle size. The results are discussed and an experimental method for fast nanoparticle size assessing is suggested.
A performance comparison using principal component analysis and differential evolution on fuzzy c-means and k-harmonic means

Several clustering researches have attempted to optimize the clustering approaches regarding initial clusters. The purpose is to alleviate local optima traps. However, such an optimization may possibly not significantly improve the accuracy rate; contrarily it usually generates abundant runtime consumption. In addition, it may cause the emergence of local traps rather than providing the proper clusters initialization. One may turn to focus on the problems of high dimensional, noisy data and outliers hidden in real-world data. Such difficulties can seriously spoil the computation of several types of learning, including clustering. Feature reduction is one of the approaches to relieve such problems. Thereby, this paper proposes a performance comparison using principal component analysis (PCA) and differential evolution (DE) on fuzzy clustering. The purpose relates to evaluating the consequences of feature reduction, compared to those of optimization of the clustering environment. Here, the fuzzy clustering approaches, fuzzy c-means (FCM) and k-harmonic means (KHM) are experimented. FCM and KHM are soft clustering algorithms that retain more information from the original data than those of crisp or hard. PCA, the feature reduction method, is employed as a preprocessing of FCM and KHM for relieving the curse of high-dimensional, noisy data. The performance of the FCM and KHM based on PCA feature extraction, called PCAFCM and PCAKHM are compared with related algorithms, including the FCM and KHM optimized by differential evolution (DE) method. Comparison tests are performed related to 7 well-known benchmark real-world data sets. Within the scope of this study, the superiority of the feature reduction using PCA over DE optimization on FCM and KHM
ON OBTAINING OF ERGODICITY RESULTS AND PERFORMANCE MEASURES OF HYPO-EXPONENTIAL DISTRIBUTED QUEUING MODEL WITH NEUTS MATRIX-GEOMETRIC METHOD

In this study, a queuing model with exponential distribution and inter-service hypo-exponential distribution with inter-arrival time parameter was studied. When the customer arrives in the system, if there are customers in the queue or service channels, they start to wait in the queue to get service, and the customer is not allowed to lose. In this model where FIFO method is used as queue discipline, the diagram shows the number of customers in the system and the transition rate diagram for the showing which phase the client receiving the service is.
doping on structural, morphological, optical properties of ZnO nanoparticles: Enhanced photo-catalytic and antibacterial activity

Different concentration of cerium doped zinc oxide (CexZn1-xO 0.0 ≤ x ≤ 0.07) nanoparticles (NPs) were synthesized via co-precipitation method using zinc acetate, cerium nitrate as precursors, octylamine as capping and reducing agent. The structures, morphologies, optical activity (photoluminescence) and antibacterial properties of CexZn1-xO were analyzed by Fourier transform infrared (FT-IR) spectroscopy, X-ray powder diffraction (XRD), High resolution scanning electron microscopy (HR-SEM), Energy dispersive X-ray (EDX), High resolution transmission electron microscopy (HR-TEM), UV-Visible, Photoluminescence (PL) spectroscopy. The antibacterial activities of CexZn1-xO NPs were tested by modified Disc diffusion method. The XRD results showed that the Ce 3+ ions were successfully incorporated into the ZnO host, and the products were well-crystalline nature. The average crystallite size of 2 CexZn1-xO (0.0 ≤ x ≤ 0.07) NPs were found to be in the range 15.64 nm -12.33 nm. In addition, the sphere-like morphology of CexZn1-xO NPs was confirmed by HR-SEM and HR-TEM images. The band gap of products was varying with the cerium substitution (x) and it increases the defects in ZnO and hence resulting red shift in UV emission which indicate the presence of narrow band gap in the products. In addition the as-synthesized CexZn1-xO NPs have important antibacterial activity against P.mirabilis, S.typhi.
Isolation, characterization and decolorization of textile dyes by fungal isolates from textile effluents

Synthetic dyes have an adverse effect on the aquatic ecosystem and their toxic component has to be removed from the effluent before their exoneration. Microorganisms have the potentials to degrade complex and recalcitrant organic compounds into simpler fragments achieving complete mineralization. Thus, the aim of this work is to isolate and investigate the degradative potential of fungal isolates recovered from textile effluents. The fungal isolates encountered includes; Aspergillus niger (14.3 %), Aspergillus fumigatus (14.3 %), Cryptococcus sp. (28.6 %) and Candida albican (42.9 %). The fungal isolates utilized some selected synthetic dyes (Malachite green, Methylene blue, Eosin Y and Carbol fuchsine) as sole source of carbon and nitrogen. All the fungal isolates exhibited degradative and decolourization potential against the different dyes tested at varying degree. Highest decolourization (79.4%) was obtained by Aspergillus niger for Eosin Y dye, followed by Cryptococcus sp . (77.0%) for the same dye after five days incubation. However the least decolourization efficiency (35%) was obtained by Aspergillus fumigatus for Malachite green. The results have demonstrated the potential of the fungal isolates for the treatment of dye wastewater.
Deep Spherical Panoramic Representation for 3D Shape Recognition

: 3D shape recognition is a hot topic in recent years. This paper proposed a 3D model recognition method with multi-branch convolutional neural network (CNN) to address the problems of 3D shape representation and recognition. The inputs of the proposed method are spherical panoramas by deep spherical projection of 3D models; to improve recognition accuracy, the spherical panorama of the shape first unfolded on various orientations to produce multiple rectified images as input of recognition frame; the recognition system consists of a multi-branch CNN, which analyzes the panoramas as a whole to produce the final recognition result. The experiment results of retrieval and classification on various of 3D dataset showed that the performance of our method is better than the state-of-the-art methods, and the retrieval accuracy outperforms that of multi-view method.