Miracle-dz/UniFAR-FTUs huggingface dataset:
STANDARDIZED CATCH RATES FOR LARGE BLUEFIN TUNA, Thunnus thynnus, FROM THE U.S. PELAGIC LONGLINE FISHERY IN THE GULF OF MEXICO AND OFF THE FLORIDA EAST COAST.

SUMMARY Relative abundance of bluefin tuna (Thunnus thynnus) was estimated by a delta lognormal model approach of catch rates from a selected subset of 21 longline vessels in the Gulf of Mexico from 1987 through 1999. Variables included in the analyses were year, month, area, depth of hooks, hooks per mile and vessel. The random effects models accounted for 57 % of the variability of the portion positives and 68 % of the variability of the positive sets. The fixed effects models accounted for 50 % of the variability of the portion positives and 51 % of the variability of the positive sets Both the fixed effects and random effects composite indices indicated an upward trend in model predicted mean CPUEs in recent years.
CREDIT CARD FRAUDS SCORING MODEL BASED ON DEEP LEARNING ENSEMBLE

Credit card frauds can result in substantial financial losses, particularly when fraudulent transactions have large values. Thus, it is essential to detect fraudulent transactions prior to their authorization by card issuers. Most conventional fraud detection systems are based on machine learning models. Recent studies explored utilizing deep learning (DL) models to detect fraudulent transactions efficiently. However, such studies depend merely on a single DL model. In this paper, we present various deep learning and ensemble methods for detecting credit card fraudulent transactions. The main motivation behind this presented work is to contribute toward reducing both missed frauds and false alarms, where our contribution in this work lies specifically in combining the resulting scores of three different distinct DL models, namely, convolutional neural networks (CNN), autoencoders (AE), and recurrent neural networks (RNN). Experiments on a public credit-card dataset demonstrated that, for the single DL-based models, AE has the best validation accuracy (93.4%) compared to CNN (91.4%) and RNN (91.8%). For the ensemble results, the validation accuracy (94.9%) was superior to all the three implemented DL-based models.
Uncovering the In�uence of Hydrological and Climate Variables in Chlorophyll-A Concentration in Tropical Reservoirs with Machine Learning

Climate variability and change, associated with increasing water demands, can have signi�cant implications for water availability. In the Brazilian semi-arid, eutrophication in reservoirs raises the risk of water scarcity. The reservoirs have also a high seasonal and annual variability of water level and volume, which can have important effects on Chlorophyll-a concentration (Chla). Assessing the in�uence of climate and hydrological variability on phytoplankton growth can be important to �nd strategies to achieve water security in tropical regions with similar problems. This study explores the potential of machine learning models to predict Chla in reservoirs and to understand their relationship with hydrological and climate variables. The model is based mainly on satellite data, which makes the methodology useful for data-scarce regions. Tree-based ensemble methods had the best performances among six machine learning methods and one parametric model. This performance can be considered satisfactory as classical empirical relationships between Chla and phosphorus may not hold for tropical reservoirs. Water volume and the mix-layer depth are inversely related to Chla, while mean surface temperature, water level, and surface solar radiation have direct relationships with Chla. These �ndings provide insights on how seasonal climate prediction and reservoir operation might in�uence water quality in regions supplied by super�cial reservoirs.
Opleiding DSAI Hypernetworks for binary neural networks

Binary neural networks are neural network with binary weights and activations. The weights and activations only have two possible values, and only one bit is needed to represent the values. This can be very useful for devices that have limited computational power and memory. This paper discusses the performance of a binary neural network on two classification problems, compared to the performance of a full precision neural network. Finding the optimal parameters through training a neural network with backpropagation is not the only way. Another way is through the use of hypernetworks. Those are neural networks that find the weights of another network, called the target network. This thesis discusses how the weights of a binary neural network, which functions as the target network, can be found using a hypernetwork.
Variations in infant feeding practice are associated with body composition in childhood: a prospective cohort study.

CONTEXT Most studies of infant diet and later body composition focus on milk feeding; few consider the influence of variations in the weaning diet. OBJECTIVE Our objective was to examine how variations in milk feeding and the weaning diet relate to body composition at 4 yr. STUDY POPULATION A total of 536 children participating in a prospective birth cohort study. DESIGN Diet was assessed at 6 and 12 months of age. Compliance with weaning guidance was defined by the infant's score for a principal component analysis-defined dietary pattern (infant guidelines) at 12 months. Infants with high infant guidelines scores had diets characterized by high consumption of fruit, vegetables, and home-prepared foods. Body composition was assessed at 4 yr by dual x-ray absorptiometry. RESULTS Longer duration of breastfeeding was associated with lower fat mass at 4 yr [4.5 kg, 95% confidence interval (CI) of 4.3-4.7 kg, in children breastfed for 12 months or more, compared with 5.0 (95% CI 4.7-5.3) kg in children never breastfed (P = 0.002)] but was not related to body mass index. Children with high infant guidelines scores had a higher lean mass [12.6 (95% CI 12.3-12.9) kg in children in the top quarter of the distribution, compared with 12.0 (95% CI 11.7-12.4) kg in children in the bottom quarter (P = 0.001)]. These associations were independent and were little changed by adjustment for confounding factors. CONCLUSIONS These data suggest that variations in both milk feeding and in the weaning diet are linked to differences in growth and development, and they have independent influences on body composition in early childhood.
Improvement of the real-time gesture analysis by a new mother wavelet and the application for the navigation inside a scale-one 3D system

This paper proposes a navigation technique for traveling inside a real-scale 3D model based on human gesture analysis. In the first step, a simple threshold is used as a criterion to analyze gestures. In the next step, a complex criterion will be imposed to the analysis to improve the navigation technique. Walking is a periodic signal and moving feet up and down is a part which is repeated. A mother wavelet is allocated to the selected pattern. Then the position of the pattern is recognized by applying Multi Resolution Analysis (MRAs). The movement command is generated and sent to the graphic render (as a movement command). Analytical results show a very high precision performance in the presence of noise, scale variation and superposition of other signals. Practical experiments also verify the same promising results.
FACTORS AFFECTING THE INTENTION TO USE MOBILE APPLICATION ONLINE TRAVEL AGENCY

E-commerce has become a new shopping trend in the last few years. A type of e-commerce selling and buying objects in travel services is known as an online travel agency (OTA). OTA's presence is intended to make it easier for customers and travel suppliers to do transactions regarding travel services. It is widely predicted that the OTA ecosystem will continue to grow, seeing the increasingly massive internet use trend and the increasing use of smartphones. Even so, this does not mean that OTA companies are not experiencing challenges. It is proven that some companies cannot survive and eventually out of business. It happened because the company is unable to meet the needs and desires of its buyers. This research aims to analyze the factors that influence the intention to use the OTA mobile application using PLS-SEM. The research model was built based on a combination of TAM, UTAUT, ISS model, and perceived risk. The research results on 261 samples showed that the intention of using the OTA mobile application was influenced positively and significantly by perceived usefulness and hedonic motivation. This study's other constructs include social influence, information quality, system quality, service quality, perceived risk, and habit. However, they do not significantly positively affect the intention to use the OTA mobile application.
Validation of the GRAPE single view aerosol retrieval for ATSR-2 and insights into the long term global AOD trend over the ocean

Abstract. The Global Retrieval of ATSR Cloud Parameters and Evaluation (GRAPE) project has produced a global data-set of cloud and aerosol properties from the Along Track Scanning Radiometer-2 (ATSR-2) instrument, covering the time period 1995–2001. This paper presents the validation of aerosol optical depths (AODs) over the ocean from this product against AERONET sun-photometer measurements, as well as a comparison to the Advanced Very High Resolution Radiometer (AVHRR) optical depth product produced by the Global Aerosol Climatology Project (GACP). The GRAPE AOD over ocean is found to be in good agreement with AERONET measurements, with a Pearson's correlation coefficient of 0.79 and a best-fit slope of 1.0±0.1, but with a positive bias of 0.08±0.04. Although the GRAPE and GACP datasets show reasonable agreement, there are significant differences. These discrepancies are explored, and suggest that the downward trend in AOD reported by GACP may arise from changes in sampling due to the orbital drift of the AVHRR instruments.
Review Article Successive Approximation Register Analog-to-Digital Converter (SAR ADC) for Biomedical Applications

Tis study presents a survey of the most promising reported SAR ADC designs for biomedical applications, stressing advantages, disadvantages, and limitations, and concludes with a quantitative comparison. Recent progress in the development of a single SAR ADC architecture is reviewed. In wearable and biosensor systems, a very small amount of total power must be devoured by portable batteries or energy-harvesting circuits in order to function correctly. During the past decade, implementation of the high energy efciency of SAR ADC has become the most necessary. So, several diferent implementation schemes for the main components of the SAR ADC have been proposed. In this review study, the various circuit architectures have been explained, beginning with the sample and hold (S/H) switching circuits, the dynamic comparator, the internal digital-to-analog converter (DAC), and the SAR control logic. In order to achieve low power consumption, numerous diferent confgurations of dynamic comparator circuits are revealed. At the end of this overview, the evolutions of DAC architecture in distinct biomedical applications today can make a tradeof between resolution, speed, and linearity, which represent the challenges of a single SAR ADC. For high resolution, the dual split capacitive DAC (CDAC) array technique and hybrid capacitor technique can be used. Also, for ultralow power consumption, various voltage switching schemes are achieved to reduce the number of switches. Tese schemes can save switching energy and reduce capacitor array area with high linearity. Additionally, to increase the speed of the conversion process, a prediction-based ADC design is employed. Terefore, SAR ADC is considered the ideal solution for biomedical applications.
Natural statistics of human head orientation constrain models of vestibular processing

Head orientation relative to gravity determines how gravity-dependent environmental structure is sampled by the visual system, as well as how gravity itself is sampled by the vestibular system. Therefore, both visual and vestibular sensory processing should be shaped by the statistics of head orientation relative to gravity. Here we report the statistics of human head orientation during unconstrained natural activities in humans for the first time, and we explore implications for models of vestibular processing. We find that the distribution of head pitch is more variable than head roll and that the head pitch distribution is asymmetrical with an over-representation of downward head pitch, consistent with ground-looking behavior. We further show that pitch and roll distributions can be used as empirical priors in a Bayesian framework to explain previously measured biases in perception of both roll and pitch. We also analyze the dynamics of human head orientation to better understand how gravitational and inertial acceleration are processed by the vestibular system. Gravitational acceleration dominates at low frequencies and inertial acceleration dominates at higher frequencies. The change in relative power of gravitational and inertial components as a function of frequency places empirical constraints on dynamic models of vestibular processing. We conclude with a discussion of methodological considerations and scientific and applied domains that will benefit from continued measurement and analysis of natural head movements moving forward.
IMAGE FEATURE ENCODING USING LOWNERIZATION TENSOR

In recent days, technology plays a major role in real-time applications of current usage and at the same time security is one of the critical task in order to utilize and access data as well applications/tools. In this scenario, vulnerability is the most important factor for any real-time application. There is a need to minimize the vulnerability of any application up to date. In majority applications, traditional human traits like Face and fingerprint can be treated as one of the prime data to provide security, through these patterns with conventional methods of access is major risk to deal. A multimodal authentication method has been proposed by these two human traits for authentication by applying a two level of process for the analysis, encoding and Decoding, 1) to preprocess the images and then extract the features using Non-Negative Matrix Factorization (NNMF) and 2) to encode and decode the extracted features using Tensor based Lownerization method. The major contribution of this method is to minimize the vulnerability of traits/patterns which extracted the features by applying NNMF method. The extent of the proposed method has been validated with the results that are obtained in two ways. In one way, after extracting the features fusion can be applied. In another way fusion is not applied. Here the two distinct metrics viz., Euclidean Distance and Mean Square Error are used. When compared to the existing papers the proposed encoding and decoding method gives better security. Mean Square Error Distance gives better results when comparing to the Euclidean Distance.
SUBSTRA: Supervised Bayesian Patient Stratiﬁcation

Motivation: Patient stratiﬁcation methods are key to the vision of precision medicine. Here, we consider transcriptional data to segment the patient population into subsets relevant to a given phenotype. Whereas most existing patient stratiﬁcation methods focus either on predictive performance or interpretable features, we developed a method striking a balance between these two important goals. Results: We introduce a Bayesian method called SUBSTRA that uses regularized biclustering to identify patient subtypes and interpretable subtype-speciﬁc transcript clusters. The method iteratively re-weights feature importance to optimize phenotype prediction performance by producing more phenotype-relevant patient subtypes. We investigate the performance of SUBSTRA in ﬁnding relevant features using simulated data and successfully benchmark it against state-of-the-art unsupervised stratiﬁcation methods and supervised alternatives. Moreover, SUBSTRA achieves predictive performance competitive with the supervised benchmark methods and provides interpretable transcriptional features in diverse biological settings, such as drug response prediction, cancer diagnosis, or kidney transplant rejection.
Validation of subscales of the Severe Asthma Questionnaire (SAQ) using exploratory factor analysis (EFA) Authors

1. University of Plymouth, Plymouth, UK. 2. Plymouth Marjon University, Plymouth, UK 3. Royal Brompton Hospital, London, UK 4. Heartlands Hospital, University Hospitals Birmingham and University of Birmingham, Birmingham, UK 5. Academic Respiratory Unit, Southmead Hospital, North Bristol Hospital Trust, Bristol, UK 6. School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre and NIHR Manchester Biomedical Research Centre, Manchester University Hospitals NHS Foundation Trust, Manchester, UK 7. Royal Devon and Exeter Hospital, Exeter, UK and University of Exeter, UK
synthesis in hypothalamic astrocytes is associated with a pubertal 2 increase in membrane ER α levels 3

33 The development of estrogen positive feedback is a hallmark of female puberty. Both estrogen 34 and progesterone signaling are required for the functioning of this neuroendocrine feedback 35 loop but the physiological changes that underlie the emergence of estrogen positive feedback 36 remain unknown. Only after puberty does estradiol (E2) facilitate progesterone synthesis in 37 female hypothalamic astrocytes (neuroP) (Mohr et al. 2018), an event critical for estrogen 38 positive feedback and the LH surge. We hypothesize that prior to puberty, these astrocytes 39 have low levels of membrane estrogen receptor alpha (ER α ), making them unable to respond to 40 E2 with increased neuroP synthesis prior to puberty. To test this hypothesis, pure populations of 41 primary astrocyte cultures were derived from female mice at three different stages of 42 development: pre-puberty (postnatal week 3), pubertal onset (week 5), and post-puberty (week 43 8). Hypothalamic astrocyte responses were measured after treatment with E2. Hypothalamic 44 astrocytes increased progesterone synthesis across pubertal development. Prior to puberty, 45 mER α expression was low in hypothalamic astrocytes, but expression increased across 46 puberty. The increase in mER α expression in hypothalamic astrocytes also corresponded with 47 an increase in caveolin-1 protein, PKA phosphorylation, and a more rapid [Ca 2+ ] i flux in 48 response to E2. Together, these results indicate that increased mER α in hypothalamic 49 astrocytes contributed to the post-pubertal response to E2 that results in neuroP synthesis, 50 critical for ovulation.
Exploring the factors affecting the adoption of mobile financial services among the rural under-banked

During the last decade, there has been tremendous growth in mobile penetration in many countries across the globe and most interestingly in a number of developing countries. On the other hand, around half of the world’s population is deprived of banking and financial services. This paper is based on a study that was aimed at identifying drivers and inhibitors for adoption of mobile financial services (MFS) among the rural under-banked population and to compare the results with existing studies. During the study, an extensive review of literature was conducted to identify the factors that were studied and found to significantly affect the adoption of MFS. This was followed by an exploratory qualitative research conducted among the rural under-banked population of three states in India. The findings of the study indicate that the demand for banking and financial services and the amount of hardships faced in availing these services through the existing channels of delivery can act as strong drivers for MFS adoption among the rural under-banked. On the other hand, factors like lack of trust on technology and lack of technology readiness were found to act as barriers to the adoption of MFS.
Encoding Beacon Statements for Code Comment Generation

—High-quality code comment is important to help developer review and comprehend source code. Inspired by the effectiveness of deep learning techniques in the NLP ﬁeld, many studies focus on using the machine translation algorithm to automatically generate comment for the source code. Most of the current studies typically involve in collecting a large number of dataset regarding the function-comment pairs, and then encode the information (e.g., AST) from the whole function for training a generation model. Most of current studies indistinguishably encode every statement in a function, which may not be the best strategy for comment generation because each statement in a function plays different roles, and the non-core code statements may have no effect or even a negative effect on the comment generation. In this paper, we propose a ﬂexible encoding strategy that highlights the most informative statements from the function, called beacon statements, for code comment generation. Speciﬁcally, the beacon statements are detected from the function according to their roles when human understanding program, and then a pre-training model is proposed to encode the knowledge of the beacon statements. At last, the knowledge of the beacon statements is transferring to a basic model for comment generation by sharing the training parameters. The encouraging experimental results demonstrate the feasibility and effectiveness of our comment generation model.
Late Holocene glacier and climate ﬂuctuations in the Mackenzie and Selwyn mountain ranges, northwestern Canada

Over the last century, northwestern Canada experienced some of the highest rates of tropospheric warming globally, which caused glaciers in the region to rapidly re-treat. Our study seeks to extend the record of glacier ﬂuc-tuations and assess climate drivers prior to the instrumental record in the Mackenzie and Selwyn mountains of north-western Canada. We collected 27 10 Be surface exposure ages across nine cirque and valley glacier moraines to constrain the timing of their emplacement. Cirque and valley glaciers in this region reached their greatest Holocene extents in the latter half of the Little Ice Age (1600–1850 CE). Four er-ratic boulders
Food niche overlap between two stingless bee species along a spatio-temporal gradient: implications for keeping and conservation

It is common practice that local stingless beekeepers in Southern Mexico keep their colonies, from two or more species, in a single place, referred to as meliponary. Little is known, however, if such scheme promotes competition for local resources among managed species. This is particularly relevant since in some cases over several dozens of hives are reared in one meliponary. In this study we examined the plant species visited by two culturally and economically important stingless bee species commonly kept together, Scaptotrigona mexicana and Tetragonisca angustula, in order to know the magnitude of the competition between them. Honey samples from managed colonies located in three meliponaries were collected during a year. Using standard melissopalynological techniques we analyzed them and could identify the plant species the colonies collected food from. Overall 84 pollen types were identi�ed: 34 at the species level, 32 at the genus level and 18 at the family level. Samples of both bee species had high indexes of plant diversity; Compositae, Leguminosae and Euphorbiaceae were the most visited plant families. Niche overlap analysis showed that up to 21% of plant species were common to both bee species, but only in some months of the year. The results of this study show low competition between S. mexicana and T. angustula. With these results we provide Mexican stingless beekeepers with valuable information that supports the continuation of ancient bee management practices.