Miracle-dz/UniFAR-FTUs huggingface dataset:
Improved 21st century projections of sub-daily extreme precipitation by spatio-temporal recalibration

This project will provide more robust projections of future changes in local extremes by developing and applying a new statistical framework that combines for the first time high-resolution climate model outputs and historical observations in a joint model, providing unprecedented information on extreme sub-daily precipitation. The project will deliver this statistical method and bias-corrected projections of extreme precipitation across the UK on a 5 x 5 km grid for 20212040 and 2061-2080, and these will be made easily accessible for decision makers, stakeholders and others. As an important output, the project will improve predictions of intense hourly precipitation. These predictions are crucial for understanding future changes in flash floods that are one of the costliest and most critical of climate change impacts, and are known to be the cause of fatalities, water resource and transportation disruption, and cause damage to infrastructure and ecosystems. The project will result in: (i) a computationally feasible statistical model that scales well with the spatial dimension. It is based on a hierarchical structure equipped with a copula at the data level, able to take account of extremal spatial dependence and time-varying covariates, and designed to handle block maxima. Its inference scheme is an extension of the recently developed Max-and-Smooth method (Hrafnkelsson et al., 2021). The project will also provide: (ii) freely-
Employment Creation and Extreme Poverty Eradication through Open and Distance eLearning and Technical-Vocational Education and Training in Zimbabwe

This study explored how Open and Distance eLearning (ODeL) system is enhancing employment creation and extreme poverty eradication in Zimbabwe. Poverty is a challenge in developing countries in the presence of unemployment and lack of education. This study aimed to put into perspective the strides made by ODeL institutions in Zimbabwe towards extreme poverty eradication through TVET in the education system. Data were collected from purposively selected 20 ODeL graduates including; entrepreneurs, street vendors, and individuals, formally and informally employed, by using interviews and observation, in this case study. The study employed thematic analysis for the narrative qualitative data. The study revealed that poverty can be reduced through skills empowerment to learners at all educational levels. Exposure to a bouquet of vocational subjects in technical-vocational institutions has enabled entrepreneurs to create employment, applying acquired skills. The major barriers to effective entrepreneurship and employment creation were the scarcity of resources and natural disasters. The study is also significant that educational planners and policy-makers may formulate policies that encourage school and college graduates to be innovative, self-reliant, and may guard against extreme poverty in their communities. It can be concluded that possession and application of vocational skills may create employment and eradicate extreme poverty. It is, therefore, recommended that TVET starts early in life for innovativeness and effective use of available resources. Further research could be carried out on the nature of programmes in learning institutions that empower learners with skills for employment creation, sustainable development, and extreme poverty eradication.
A Succinct Exploration on Bilwa (Aegle marmelos) Plant – A Literature Review

Aegle marmelos (L) Correa (family: Rutaceae) is used to treat a wide range of ailments. This plant has been around since prehistoric time. As it produces a number of alkaloids, all aspects of the plant, including fruits, leaves, bark, stem, and root, are used to treat variety of diseases. Antidiarrheal, antidysenteric, antipyretic, and anti-inflammatory properties are some of the most important medicinal properties of Bilwa. Fruit-derived compounds have been shown to have biological promise in the treatment of diseases such as diabetes, gastric ulcers, and hyperlipidaemia. Many experiments have been conducted on its medical properties and uses which demonstrate its importance in today's environment. The anatomy, distribution, dietary action, Ayurveda applications, and pharmacological properties of this plant is outlined in this review manuscript.
Sentiment Analysis of Consumers for Determining the Packaging Features of Eucalyptus Oil Products

This study aims to accurately predict eucalyptus oil packaging features and extract the most features to be improved for redesigning eucalyptus oil packaging. This research begins with taking consumer comments using a power query and then processing it using the data mining method and processed using WEKA to find sentiment analysis and accuracy of consumer comments regarding eucalyptus oil products. This study obtained the tendency of comments on each attribute with an assessment of the accuracy for all classes of 83% and each positive sentiment 3% of comments and 57% of comments for negative courses. The sentiment that shows the packaging tends to be normal at 20%, which is interpreted as neutral. This research can provide a suggestion to redesign the packaging based on the commentary sentiment of eucalyptus oil.
Story chasers: using storytelling in the classroom to incentivise collective creativity

In this article, we explore the implementation of storytelling techniques in the classroom and analyze the processes of collaborative story creation. This is done within the framework of educational and innovation research project Story Chasers. Story Chasers was funded by the Barcelona City Council and was developed in two editions (between 2017 and 2019) in primary and secondary schools in Barcelona, as well as in other non-formal educational contexts. Based on a board game created specifically for the project, a series of workshops to experiment with the exchange of narrative worlds between the participants were implemented. The game is the starting point of a co-creation methodology with the aim of fostering the co-construction of fictional stories based on premises that stimulate reflection on challenges such as the climate emergency, the fight against discrimination based on ethnicity, functional diversity or gender. The dynamics of Story Chasers allow for the observation of a series of strategies, in which the students combine the premises with their own personal experiences, their cultural background and their social environment. This way, the generation of empathy and possible responses to challenges posed to the students are fostered. Through this experience, we conclude that storytelling and fiction are a valuable creative methodology and an effective tool to address social challenges in a plural way and to promote collective and collaborative creation processes in the classroom.
Correction figure 4 for "Divergent historical GPP trends among state-of-the-art multi-model simulations and satellite-based products"

Understanding historical changes in gross primary productivity (GPP) is essential for better predicting the future global carbon cycle. However, the historical trends of terrestrial GPP, owing to the CO2 fertilization effect, climate, and land25 use change, remain largely uncertain. Using long-term satellite-based near-infrared radiance of vegetation (NIRv), a proxy for GPP, and multiple GPP datasets derived from satellite-based products, Dynamic Global Vegetation Model (DGVM) simulations, and machine learning techniques, here we comprehensively investigated their trends and analyzed the causes for any discrepancies during 1982–2015. Although spatial patterns of climatological annual GPP from all products and NIRv are highly correlated (r > 0.84), the spatial correlation coefficients of trends between DGVM GPP and NIRv significantly 30 decreased (with the ensemble mean of r = 0.49) and even the spatial correlation coefficients of trends between other GPP products and NIRv became negative. By separating the global land into the tropics plus extra-tropical southern hemisphere (Trop+SH) and extra-tropical northern hemisphere (NH), we found that, during 1982–2015, simulated GPP from most of the models showed a stronger increasing trend over Trop+SH than NH. In contrast, the satellite-based GPP products indicated a substantial increase over NH. Mechanistically, model sensitivity experiments indicated that the increase of annual GPP was 35 dominated by the CO2 fertilization effect (Global: 83.9%), albeit a large uncertainty in magnitude among individual simulations. However, the spatial distribution of inter-model spreads of GPP trends resulted mainly from climate and land-use change rather https://doi.org/10.5194/esd-2021-69 Preprint. Discussion started: 23 September 2021 c © Author(s) 2021. CC BY 4.0 License.
Genetic variation and path analysis for yield and other agronomic traits in Nigella sativa L. germplasm

Thirty two genotypes of Nigella sativa L. were evaluated for three consecutive years which showed significant differences for all the traits indicating high level of genetic variation. Heritability in broad sense ranged from 0.28 to 0.98 and the highest heritability was calculated for days to maturity and days to flowers. Grain yield was positively associated with plant height, capsule weight, capsule length, root length, whereas negatively with capsule width and 1000-seed weight that required the use of novel breeding techniques to break this undesired linkage to improve grain yield in N. sativa. Path coefficient indicated that direct effects of all the traits were positive except days to first flower, days to 50% flowers, flowering duration, number of capsules, root weight and harvest index. The characters exhibiting correlation along with direct effect towards grain yield viz., days to maturity, capsule weight, capsule length and root length should be given more preference while selecting high yielding N. sativa genotypes for future crop improvement programs.
Avoiding a financial epidemic – the role of macroprudential policies

Macroprudential policies are also inspired by this philosophy of focusing on prevention to safeguard the system as a whole – in this case, the financial system. [ ] This entails strengthening the resilience of the financial system and suppressing the build-up of vulnerabilities. By smoothing the financial cycle, macroprudential policies are meant to ensure a sustainable contribution of the financial sector to economic growth[ ].
Online Transaction Fraud Detection System Based on Machine Learning

With the rapid development of Internet technology, the scale of online transactions is constantly expanding. At the same time, the related network transaction fraud problem has become more significant. Compared with the credit card transaction, the network transaction has the characteristics of low cost, wide coverage and high frequency, which makes the detection of fraud more complex. Aiming at the problem of difficult fraud detection in network transactions, this paper designed two fraud detection algorithms based on Fully Connected Neural Network and XGBoost, whose AUC values can achieve 0.912 and 0.969 respectively. Meanwhile, we designed an interactive online transaction fraud detection system based on XGBoost model, which can automatically analyze the transaction data uploaded and return the fraud detection results to users.
WOMEN , SEXUAL IDENTITY AND LANGUAGE

The earliest studies which were concerned with the relationship between language and women’s social status can be traced back to the nineteenth century women’s movement (on this see, for example, Beauvoir, 1952; Stannard, 1977), and also to linguistics and to feminist theories in the earlier twentieth century. Those publications established and led to a debate on the fact that language reflected gender differences, socially associated with women and men, but they are also said to have contributed to maintaining negative attitudes towards women and to perpetuating their secondary social status. Thus, the relationship between gender and language or linguistic variation, that is, gender differences in language use (see Coates, 1986; Jespersen, 1922; Key, 1975), on the one hand, and the significance of primera Author / Autora: Isabel Balteiro Universidad de Alicante, Alicante, Spain balteiro@ua.es https://orcid.org/0000-0002-9771-5883
Omnichannel: Factors that Determine Adoption of Webrooming and Showrooming for Three Product Categories

The objective of this study was to analyze how consumers adopt webrooming and showrooming purchasing processes for different products (textile-fashion, home electronics and groceries). A crossover study was carried out in two countries, Spain and Colombia, applying an exploratory model through the analysis of PLS structural equations. The results showed differences in the adoption of this type of purchase depending on the type of product. The contributions of this study allow companies to choose and manage omnichannel processes depending on the type of products they sell.
Fairness Concerns and Job Assignment to Positions with Different Surplus

How does job assignment to positions with different surplus affect fairness concerns? We experimentally examine agents’ fairness concerns in a three-person ultimatum game in which all agents are asked to complete a general knowledge quiz before being assigned to a high-stake or low-stake position. We disentangle two possible channels through which job assignment impacts fairness concerns, wage differences and the principal’s intentions, by comparing cases in which the job assignment is determined randomly or by the principal. The knowledge quiz, which mimics performance evaluation, signifies the distinction between the two cases as it provides a basis on which the principal can make the assignment decision. We find that the principal’s intentions significantly impact fairness concerns of the agents assigned to the low-stake position, but wage differences themselves do not. We elaborate on managerial implications of our findings.
Robust Stochastic Stability in Dynamic and Reactive Environments

The theory of learning in games has extensively studied situations where agents respond dynamically to each other by optimizing a fixed utility function. However, in many settings of interest, agent utility functions themselves vary as a result of past agent choices. The ongoing COVID-19 pandemic provides an example: a highly prevalent virus may incentivize individuals to wear masks, but extensive adoption of mask-wearing reduces virus prevalence which in turn reduces individual incentives for mask-wearing. This paper develops a general framework using probabilistic coupling methods that can be used to derive the stochastically stable states of log-linear learning in certain games which feature such game-environment feedback. As a case study, we apply this framework to a simple dynamic game-theoretic model of social precautions in an epidemic and give conditions under which maximally-cautious social behavior in this model is stochastically stable.
Accelerating Fully Connected Neural Network on Optical Network-on-Chip (ONoC)

Fully Connected Neural Network (FCNN) is a class of Artificial Neural Networks widely used in computer science and engineering, whereas the training process can take a long time with large datasets in existing many-core systems. Optical Network-on-Chip (ONoC), an emerging chip-scale optical interconnection technology, has great potential to accelerate the training of FCNN with low transmission delay, low power consumption, and high throughput. However, existing methods based on Electrical Network-on-Chip (ENoC) cannot fit in ONoC because of the unique properties of ONoC. In this paper, we propose a fine-grained parallel computing model for accelerating FCNN training on ONoC and derive the optimal number of cores for each execution stage with the objective of minimizing the total amount of time to complete one epoch of FCNN training. To allocate the optimal number of cores for each execution stage, we present three mapping strategies and compare their advantages and disadvantages in terms of hotspot level, memory requirement, and state transitions. Simulation results show that the average prediction error for the optimal number of cores in NN benchmarks is within 2.3%. We further carry out extensive simulations which demonstrate that FCNN training time can be reduced by 22.28% and 4.91% on average using our proposed scheme, compared with traditional parallel computing methods that either allocate a fixed number of cores or allocate as many cores as possible, respectively. Compared with ENoC, simulation results show that under batch sizes of 64 and 128, on average ONoC can achieve 21.02% and 12.95% on reducing training time with 47.85% and 39.27% on saving energy, respectively.
Lax-Oleinik-type formulas and efficient algorithms for certain high-dimensional optimal control problems

Two of the main challenges in optimal control are solving problems with state-dependent running costs and developing efficient numerical solvers that are computationally tractable in high dimension. In this paper, we provide analytical solutions to certain optimal control problems whose running cost depends on the state variable and with constraints on the control. We also provide Lax-Oleinik-type representation formulas for the corresponding Hamilton-Jacobi partial differential equations with state-dependent Hamiltonians. Additionally, we present an efficient, grid-free numerical solver based on our representation formulas, which is shown to scale linearly with the state dimension, and thus, to overcome the curse of dimensionality. Using existing optimization methods and the min-plus technique, we extend our numerical solvers to address more general classes of convex and nonconvex initial costs. We demonstrate the capabilities of our numerical solvers using implementations on a central processing unit (CPU) and a field-programmable gate array (FPGA). In several cases, our FPGA implementation obtains over a 10 times speedup compared to the CPU, which demonstrates the promising performance boosts FPGAs can achieve. Our numerical results show that our solvers have the potential to serve as a building block for solving broader classes of high-dimensional optimal control problems in real-time.
Drying of Matricaria recutita Flowers in Vibrofluidized Bed Dryer: Optimization of Drying Conditions Using Response Surface Methodology

Drying of Matricaria recutita flower was investigated experimentally in a VibroFluidized Bed Dryer (VFBD). The aim of the present work was to optimize the best operating conditions for the drying of Matricaria recutita flower in the VFBD based on experimental design techniques. Response Surface Methodology (RSM) and Central Composite Design (CCD) based on 4-variable with 5-level have been employed to achieve the desirable possible combinations of frequency of vibration (7-15 Hz), inlet air temperature (36-68 °C), air flow rate (16-24 m3/h), and drying time (30-70 min) for the highest responses in terms of moisture removal (MR) and thermal efficiency (). A full quadratic model was used to describe the effects of individual and interactive parameters on the responses. The analysis of the obtained results showed that the inlet air temperature has the largest effect on responses. The optimal process parameters were as follows: frequency of vibration of 10.88 Hz, inlet air temperature of 64.08 °C, air flow rate of 20.63 m3/h and drying time of 69.11 min in which the predicted value for the MR (%) and  (%) was 86.76 and 53.05, respectively. The proposed optimal conditions were examined in the laboratory and MR (%) and  (%) achieved as 87.12±0.25% and 52.78±0.34%, respectively. The experimental values agreed with those predicted by RSM models, thus indicating the suitability of the model employed and the success of RSM in optimizing the drying conditions.
Coverage Control in Multi-Robot Systems via Graph Neural Networks

This paper develops a decentralized approach to mobile sensor coverage by a multi-robot system. We consider a scenario where a team of robots with limited sensing range must position itself to effectively detect events of interest in a region characterized by areas of varying importance. Towards this end, we develop a decentralized control policy for the robots-realized via a Graph Neural Network-which uses inter-robot communication to leverage non-local information for control decisions. By explicitly sharing information between multi-hop neighbors, the decentralized controller achieves a higher quality of coverage when compared to classical approaches that do not communicate and leverage only local information available to each robot. Simulated experiments demonstrate the efficacy of multi-hop communication for multi-robot coverage and evaluate the scalability and transferability of the learning-based controllers.
Unified Data Collection for Visual-Inertial Calibration via Deep Reinforcement Learning

Visual-inertial sensors have a wide range of applications in robotics. However, good performance often requires different sophisticated motion routines to accurately calibrate camera intrinsics and inter-sensor extrinsics. This work presents a novel formulation to learn a motion policy to be executed on a robot arm for automatic data collection for calibrating intrinsics and extrinsics jointly. Our approach models the calibration process compactly using model-free deep reinforcement learning to derive a policy that guides the motions of a robotic arm holding the sensor to efficiently collect measurements that can be used for both camera intrinsic calibration and camera-IMU extrinsic calibration. Given the current pose and collected measurements, the learned policy generates the subsequent transformation that optimizes sensor calibration accuracy. The evaluations in simulation and on a real robotic system show that our learned policy generates favorable motion trajectories and collects enough measurements efficiently that yield the desired intrinsics and extrinsics with short path lengths. In simulation, we are able to perform calibrations 10× faster than hand-crafted policies, which transfers to a real-world speed up of 3× over a human expert. The code of this work is publicly available at: https://github.com/ethz-asl/Learn-to-Calibrate.
Variation in the Stellar Initial Mass Function from the Chromospheric Activity of M Dwarfs in Early-type Galaxies

Mass measurements and absorption-line studies indicate that the stellar initial mass function (IMF) is bottom-heavy in the central regions of many early-type galaxies, with an excess of low-mass stars compared to the IMF of the Milky Way. Here we test this hypothesis using a method that is independent of previous techniques. Low-mass stars have strong chromospheric activity characterized by nonthermal emission at short wavelengths. Approximately half of the UV flux of M dwarfs is contained in the λ1215. 7 Lyα line, and we show that the total Lyα emission of an early-type galaxy is a sensitive probe of the IMF with a factor of ∼2 flux variation in response to plausible variations in the number of low-mass stars. We use the Cosmic Origins Spectrograph on the Hubble Space Telescope to measure the Lyα line in the centers of the massive early-type galaxies NGC 1407 and NGC 2695. We detect Lyα emission in both galaxies and demonstrate that it originates in stars. We find that the Lyα to i-band flux ratio is a factor of 2.0 ± 0.4 higher in NGC 1407 than in NGC 2695, in agreement with the difference in their IMFs as previously determined from gravity-sensitive optical absorption lines. Although a larger sample of galaxies is required for definitive answers, these initial results support the hypothesis that the IMF is not universal but varies with environment.