Miracle-dz/UniFAR-FTUs huggingface dataset:
Light-weight Reinforcement Learning with Function Approximation for Real-life Control Tasks

Despite the impressive achievements of reinforcement learning (RL) in playing Backgammon already in the beginning of the 90’s, relatively few successful real-world applications of RL have been reported since then. This could be due to the tendency of RL research to focus on discrete Markov Decision Processes that make it difficult to handle tasks with continuous-valued features. Another reason could be a tendency to develop continuously more complex mathematical RL models that are difficult to implement and operate. Both of these issues are addressed in this paper by using the gradient-descent Sarsa(λ) method together with a Normalised Radial Basis Function neural net. The experimental results on three typical benchmark control tasks show that these methods outperform most previously reported results on these tasks, while remaining computationally feasible to implement even as embedded software. Therefore the presented results can serve as a reference both regarding learning performance and computational applicability of RL for real-life applications.
Reductions in India's crop yield due to ozone

This bottom‐up modeling study, supported by emission inventories and crop production, simulates ozone on local to regional scales. It quantifies, for the first time, potential impact of ozone on district‐wise cotton, soybeans, rice, and wheat crops in India for the first decade of the 21st century. Wheat is the most impacted crop with losses of 3.5 ± 0.8 million tons (Mt), followed by rice at 2.1 ± 0.8 Mt, with the losses concentrated in central and north India. On the national scale, this loss is about 9.2% of the cereals required every year (61.2 Mt) under the provision of the recently implemented National Food Security Bill (in 2013) by the Government of India. The nationally aggregated yield loss is sufficient to feed about 94 million people living below poverty line in India.
Computational Study on the Performance of Multiple-Gate Nanowire Schottky-Barrier MOSFETs

Quantum simulations of multiple-gate nanowire Schottky-barrier (SB) MOSFETs in the ballistic transport regime have been performed by self-consistently solving the nonequilibrium Green's function transport equation and the Poisson's equation. The device characteristics have been examined as the channel length of the nanowire SB-MOSFETs was aggressively reduced, and their scaling behaviors were compared to planar SB devices and also to devices with doped source/drain. The enhancement of the device performance due to the multiple-gate effects has been assessed quantitatively. A limited improvement of the off-state performance has been observed, whereas ON-state currents increase significantly despite the size quantization effect.
Cardiac disease classification using total variation denoising and morlet continuous wavelet transformation of ECG signals

Cardiac diseases (CDs) are a leading cause of death, and electrocardiogram (ECG) is the most important noninvasive method for the diagnosis of CDs. The objective of this study was to diagnose CDs using Morlet continuous wavelet transform from 1-channel ECG. Four CDs were studied: heart failure (HF), sudden cardiac death (SCD), ventricular fibrillation (VF), and atrial fibrillation (AF). One hundred ECG signals were obtained from the open source MIT-BIH cardiac arrhythmia database. The distribution of patients' diseases was 20 HF, 20 SCD, 20 VF, 20 AF, and 20 normal. Total variation denoising (TVD) was used to filter ECG signals without smoothing sharp edges, and then Morlet continuous wavelet coefficient matrices were calculated. Five features were calculated from each row in the coefficient matrix (5 features × 9 rows), based on statistical parameters. The classification of CDs versus normal was based on binary logistic regression, and the classification of specific CDs was by multinomial logistic regression. The accuracy of detecting HF, SCD, VF, and AF with respect to normal was 95%, 100%, 100%, and 100%, respectively. The overall accuracy of identifying the correct CD was 98%. In conclusion, features extracted from wavelet transform of a TVD filtered ECG signal successfully distinguished CD patients from normal cardiac patients, and identified the type of CD. This method could be implemented in ECG machines for auto-detection of CDs.
Condition-Dependent Transcriptome Reveals High-Level Regulatory Architecture in Bacillus subtilis

Outside In Acquisition and analysis of large data sets promises to move us toward a greater understanding of the mechanisms by which biological systems are dynamically regulated to respond to external cues. Now, two papers explore the responses of a bacterium to changing nutritional conditions (see the Perspective by Chalancon et al.). Nicolas et al. (p. 1103) measured transcriptional regulation for more than 100 different conditions. Greater amounts of antisense RNA were generated than expected and appeared to be produced by alternative RNA polymerase targeting subunits called sigma factors. One transition, from malate to glucose as the primary nutrient, was studied in more detail by Buescher et al. (p. 1099) who monitored RNA abundance, promoter activity in live cells, protein abundance, and absolute concentrations of intracellular and extracellular metabolites. In this case, the bacteria responded rapidly and largely without transcriptional changes to life on malate, but only slowly adapted to use glucose, a shift that required changes in nearly half the transcription network. These data offer an initial understanding of why certain regulatory strategies may be favored during evolution of dynamic control systems. A horizontal analysis reveals the breadth of genes turned on and off as nutrients change. Bacteria adapt to environmental stimuli by adjusting their transcriptomes in a complex manner, the full potential of which has yet to be established for any individual bacterial species. Here, we report the transcriptomes of Bacillus subtilis exposed to a wide range of environmental and nutritional conditions that the organism might encounter in nature. We comprehensively mapped transcription units (TUs) and grouped 2935 promoters into regulons controlled by various RNA polymerase sigma factors, accounting for ~66% of the observed variance in transcriptional activity. This global classification of promoters and detailed description of TUs revealed that a large proportion of the detected antisense RNAs arose from potentially spurious transcription initiation by alternative sigma factors and from imperfect control of transcription termination.
Increase in Oxidative Stress in Kidneys of Diabetic Akita Mice

New biomarkers for oxidative damage, were used to identify whether hyperglycemia caused oxidative stress in diabetic Akita mice. At 13 weeks of age, the tissues of these mice were obtained, and the levels of Nε-(hexanonyl)lysine (HEL) and dityrosine (DT) were measured, these being related to lipid peroxide-derived protein covalent modification and protein cross-linking. The levels of HEL and DT in the kidneys of Akita mice were significantly increased compared with the control mice without any accumulation of thiobarbituric acid reactive substances and 4-hydroxy-2-nonenal-modified protein. Immunopositive staining was clearly observed in the kidneys of the Akita mice when using the anti-HEL antibody or anti-DT antibody. These results suggest that hyperglycemia in Akita mice induced oxidative stress and increased these markers in the kidneys.
Asynchronous Piecewise Output-Feedback Control for Large-Scale Fuzzy Systems via Distributed Event-Triggering Schemes

This paper examines the problem of distributed event-triggering output-feedback control for discrete-time large-scale fuzzy systems in the network scenario. A novel distributed event-triggering control scheme is proposed, where each subsystem transmits its output information and premise variables only when its local output error exceeds a specified threshold. The resulting closed-loop fuzzy control system subject to nonsynchronous grades of membership is explored under such circumstances. In virtue of a piecewise Lyapunov function together with some matrix inequality convexification techniques, the solution to the distributed event-triggering output-feedback control problem is derived in the form of linear matrix inequalities. It will be shown that the closed-loop control system is asymptotically stable while significantly reducing the communications via networks. Finally, a direct current microgrid with solar photovoltaic arrays is utilized to illustrate the validity and practicability of the proposed results.
Rigorous closed-form formulas for physical optics scattering by dielectric circular sectors

A closed-form exact representation of the physical optics (PO) field scattered in the far zone by penetrable circular sectors of arbitrary opening angle is derived in terms of incomplete cylindrical functions. The proposed expressions in combination with PO formulas for the scattering from polygonal plates can constitute a powerful tool to evaluate the scattering from flat surfaces with curved edges in a very computationally efficient manner. In order to check the correctness of this scattered field solution, first comparisons with an accurate numerical PO approach are provided
Neural Networks by using Self-Reinforcement Reactions

We consider a chemical reaction network model in which selections of reaction are stochastic and depend on past history. In this chemical reaction network, we found the emergence of Auto-Catalytic Sets (ACS) and complex dynamics in which ACS are repeatedly created and destroyed; we have called this reaction system as the SelfReinforcement Reactions, SRR. We developed a neural-networks system by using SRR and confirm the neural network of SRR can solve a linear classification problem.
The effects of surfactants on drop deformation and breakup By

The effects of surface-active agents on drop deformation and breakup in extensional flows at low Reynolds numbers are described. In this free-boundary problem, determination of the interfacial velocity requires knowledge of the distribution of surfactant, which, in turn, requires knowledge of the interfacial velocity field. We account for this explicit coupling of the unknown drop shape and the evolving surfactant distribution. An analytical result valid for nearly spherical distortions is presented first. Finite drop deformation is studied numerically using the boundaryintegral method in conjunction with the time-dependent convective-diffusion equation for surfactant transport. This procedure accurately follows interfacial tension variations, produced by non-uniform surfactant distribution, on the evolving interface. The numerical method allows for an arbitrary equation of state relating interfacial tension to the local concentration of surfactant, although calculations are presented only for the common linear equation of state. Also, only the case of insoluble surfactant is studied. The analytical and numerical results indicate that a t low capillary numbers the presence of surfactant causes larger deformation than would occur for a drop with a constant interfacial tension equal to the initial equilibrium value. The increased deformation occurs owing to surfactant being swept to the end of the drop where i t acts to locally lower the interfacial tension, which therefore requires increased deformation to satisfy the normal stress balance. However, a t larger capillary numbers and finite deformations, this convective effect competes with ‘dilution ’ of the surfactant due to interfacial area increases. These two different effects of surfaceactive material are illustrated and discussed and their influence on the critical capillary number for breakup is presented.
Software requirement analysis of “family tracking mobile application” on cross platform with hybrid approach

The number of smartphone users in Indonesia has increased every year. This country belongs to the 4 countries most smartphone users in the world. The diversity of devices and platforms in mobile phone / smartphone creates a challenge for mobile application developers, such as context-aware features and reusability aspects, so that built applications can run on a cross-platform basis. Good quality software was built exactly to suit of its user requirement. So the mobile application developers need quality requirement as a reference. By using the quality requirement, developers will know what to follow or should be prioritized when developing application for good quality. In this research, we proposed the quality requirement that has been validated with triangulation of 3 methods of data collection such as interviews, surveys and observations. The quality requirement was obtained based on the results from a qualitative approach that identifies the functional and non-functional requirement that can be compiled and adapted to mobile applications that running on cross platforms, so developers can use them to build the them with good quality.
Distance metric learning using each category centroid with nuclear norm regularization

The development in information technology has resulted in more diverse data characteristics and a larger data scale. Therefore, pattern recognition techniques have received significant interest in various fields. In this study, we focus on a pattern recognition technique based on distance metric learning, which is known as the learning method in metric matrix under an arbitrary constraint from the training data. This method can acquire the distance structure, which takes account of the statistical characteristics of the training data. Most distance metric learning methods estimate the metric matrix from pairs of training data. One of the problem of the distance metric learning is that the computational complexity for prediction (i. e. distance calculation) is relatively high especially when the dimension of input data becomes large. To calculate the distance effectively, we propose the way to derive low rank metric matrix with nuclear norm regularization. When solving the optimization problem, we use the alternating direction method of multiplier and proximal gradient. To verify the effectiveness of our proposed method from the viewpoint of classification accuracy and rank reduction, simulation experiments using benchmark data sets are conducted.
Sizing up community structure : Exploring latitudinal gradients in Northeastern Pacific Patellogastropoda ( Mollusca , Gastropoda ) body size with high-throughput morphometric imaging

Individual body size can vary widely between different communities, many of which are spatially adjacent. This variation is particularly important to ecologists as the size structure of these communities is closely tied to their ecology (i.e. their dynamics and energetics) as well as to individual life and evolutionary histories. Although ecologists commonly refer to Bergmann’s rule (an increase in body size with latitude), community body size is rarely directly measured in marine invertebrates due to the difficulty of obtaining body size data for entire assemblages. Here we examine individual variation in an understudied group, Patellogastropoda (true limpets), across a latitudinal gradient (namely, the Northeastern Pacific). The transect studied, which ranges from Alaska to Baja, California, showcases large amounts of environmental variation and encompasses a number of environmental and faunal provinces. Thanks to recent advancements in the speed and quality of morphological measurements, collection of large, individual-level datasets is no longer infeasible. However, it has previously only been possible for microorganisms. In light of this, we apply a novel method, macroscopic high-throughput imaging, that allows for automated collection of morphological data for entire assemblages, to the Northeastern Pacific Patellogastropoda. Previous studies of this system for a similar group (Bivalvia) which utilized exemplar measurements found that size structure was invariant across the transect. Using individual-level, community data, we find that size structure does vary along the transect, and that body size increases with latitude, confirming Bergmann’s rule for Patellogastropoda. However, we find that the resolution at which data is collected influences the results obtained from the data, as intraspecific trends do not match broad inter-specific trends in size. In addition, the scale at which measurements are taken (i.e. whether they are empirical versus exemplar) greatly alters the distributions obtained. We also find that the greatest predictors of body size variation are biotic correlates (i.e. community structure and species present), rather than environmental variables. As such, Patellogastropod community body size varies only weakly with latitude and environment, but is closely related to community composition. This finding sheds light on the importance of interspecific interactions, which are especially strong in Patellogastropod communities, as a determinant of faunal body size trends.
Line x tester analysis in sugarcane (Saccharum officinarum)

Combining ability variances and effects were estimated for important agronomic and biochemical traits in sugarcane using line x tester mating design. Five commercially grown varieties and one line from germplasm collection were involved in this study of which three were used as females (lines) and three as males (testers). The six agronomic characters studied were number of millable stalk per stool, stalk diameter, stalk height, weight of millable stalk, refroctometre brix and plant volume. The five bio-chemical characters studied include polatronic reading (pol), fibre, purity, brix and pure obtainable cane-sugar (POCS). Among the lines, Aiwa and Mana and among the testers Vatu and Mentor were found to be the best general combiners for most of the agronomic and bio-chemical characters. Four out of nine crosses namely Aiwa x Mentor, Kaba x Vatu, Kaba x Yasawa and Mana x Yasawa were found to be the best crosses. Analysis of Variance for bio-chemical characters showed that only one character (% fibre) was significant. The line x tester interaction was not significant for any of the characters studied. Estimates of variance due to gca and sca and their ratio revealed predominantly non-additive gene action for these characters. Non-additive gene action can be exploited in F1 generation. Development of new hybrid varieties with improved agronomic and bio-chemical characters is therefore suggested.
Performance comparison of supervised learning methods for retinal vessel tortuosity characterisation

Retinal vessel tortuosity, one of the morphological features, is an early symptom indicator of retinopathies. While several methods have investigated vessel tortuosity, the needs for improvement necessitate a further investigation into the determination of retinal vessel tortuosity using supervised learning methods. In this study, three different features namely distance metric, normalized hybrid and non-normalized hybrid metrics are extracted from the retinal vessels. Knuth-Morris-Pratt (KMP) algorithm is applied to detect all the vessel twists using the vessel directional changes information. The two hybrid features, normalized hybrid and non-normalized hybrid, combine directional changes information with distance metrics. In this paper, four different classifiers namely k-nearest neighbour (KNN), decision tree (DT), support vector machine (SVM) and naive Bayesian (NB) are applied. We investigate the suitability of the combinati on of any two of the three features and all the three features in determining the state of the vessels as either tortuous or normal. Experimental results show that k-nearest neighbour and naive Bayesian classifiers achieved the highest accuracy rates of 100% for all the feature combinations when the training sample sizes are greater than 70% of the dataset size.
Application of Markov random field model in the design of digital pattern painting

The principle of camouflage pattern painting is to derive the characteristics of surrounding background, such as color and texture, and make the characteristics reappear in the target camouflage. Color and texture are the key elements in the design of pattern painting. We derive major colors of surrounding background using the clustering method, and make the texture parameters reappear in the design of digital pattern painting using the Markov random field method. Then, the digital pattern painting design system is constructed. To validate the effectiveness of the system, the experiment is organized. A piece of satellite optical picture is used as the background, and in the picture, one small district is selected as the target. The digital pattern painting is designed by the constructed system. The experimental results show that the designed pattern painting can blend with the surrounding background, which has good camouflage effectiveness. The pattern painting design system can be referenced in the design of distortion pattern painting.
Prediction of Lithium-ion Battery ' s Remaining Useful Life Based on Multi-kernel Support Vector Machine with Particle Swarm Optimization

The Remaining Useful Life (RUL) of Lithium-ion (Li-ion) batteries estimation is very important for the intelligent Battery Management System (BMS). With the arrival of the era of big data, data mining technology is becoming more and more mature, and the Li-ion batteries RUL estimation based on data-driven prognostics is more accurate. However, the Support Vector Machine (SVM) applying to predict the Li-ion batteries RUL uses the traditional single radial basis kernel function, this single radial basis kernel function classifier is weak in generalization ability, and it is easy to appear the problem of data migration, which leads to the inaccurate prediction of Li-ion batteries RUL. In this work, a novel Multi-kernel SVM (MSVM) based on polynomial kernel and radial basis kernel function is proposed. Moreover, the Particle Swarm Optimization (PSO) algorithm is used to search the kernel parameters, penalty factor and weight coefficient of the MSVM model. Finally, this paper makes use of the NASA battery data set to form the observed data sequence for regression prediction, the calculation results show that the improved algorithm not only has better prediction accuracy and stronger generalization ability, but also decreases the training time and computational complexity.
Energy requirements in top-level DanceSport athletes

Dancesport is a sport of recent history: recognition by the IOC as Olimpic discipline came in 1997. Scientific literature on this new sport is almost nonexistent. The purpose of this study is for define the energy expenditure and the intensity of the two main dancesport disciplines (latin-american and standard dances) in top-level athletes and verify characteristics and differences of these two dance genres. Twenty dancers, 10 male and 10 female (10 couples,), aged between 19 and 31 years, have been enrolled in this study. 5 couples were latin-american dancers while 5 couples standard dancers. Athletes were asked to wear a metabolic holter (Sensewear Armband)for 25 hours and to perform a training session and a simulated competition to record BMR (Basal Metabolic Rate), DEE (Daily Energy Expenditure), TEE (Training Energy Expenditure), MET (Metabolic Equivalent) and PAL (Physical Activity Level). Statistical analysis reveals no differences between the two dance disciplines, our results suggest that dancesport is a moderate/heavy activity that requires a strong energy expenditure. Athletes involved in continuous training programs show a vigorous Physical Activity Level.