Miracle-dz/UniFAR-FTUs huggingface dataset:
A matrix-free reformulation of the multi-parameter descent and conjugate-gradient method for isotropic elastic iterative reverse-time migration

Multi-parameter inversion of linear systems appears in many problems. The focus here is on isotropic elastic iterative reverse-time migration for three position-dependent subsurface model parameters, which amounts to data fitting of processed seismic data with synthetics from the Born approximation of the elastic wave equation. In that case, the matrix of the linear system is the hessian. As it is impractical to form, a matrix-free formulation is needed, which is readily derived for the gradient descent method. For single-parameter inversion, the conjugate-gradient (CG) method is generally more efficient than simple descent. However, the multiple-parameter CG method has a significantly higher cost than the descent method. Here, first a matrix-free data-domain reformulation is derived. Then, its performance is compared to the simple descent method to see of its faster convergence justifies the higher cost. A comparison on a marine 2-D toy problem with a salt body and sea-bottom receivers shows that the multiple-parameter descent method wins in terms of efficiency if the number of iterations is limited and that the single-parameter CG method is even faster.
Learning Disagreement Regions with Deep Neural Networks to Reduce Practical Complexity of Mixed-Integer MPC

Efficiently computing solutions to mixed-integer optimization-based control problems, such as in model predictive control (MPC) of hybrid systems, is extremely challenging due to the exponential worst-case complexity. The practical time-complexity of computing good control actions can be reduced by using a combination of two solvers: a strong solver that generates optimal or near-optimal closed-loop solutions with a large number of iterations, and a weak solver that converges quickly to suboptimal closed-loop solutions. In this paper, we propose the use of deep neural networks to learn sub-regions of the admissible state-space where replacing the strong solver with the weak solver maintains constraint satisfaction properties and does not result in a significant deterioration of performance. We illustrate the practical time-complexity reduction of the proposed solver selection mechanism on a station-keeping problem for a satellite.
Reinforcement Learning-based Smart Inverter Control with Polar Action Space in Power Distribution Systems

To tackle the challenge of voltage regulation under high solar photovoltaics (PV) penetration, the slow timescale control of conventional voltage regulating devices can be combined with fast timescale control of smart inverters. In this paper, we develop a two-timescale Volt-VAR control (VVC) framework. The slow time-scale control of voltage regulating devices is achieved by a model-based approach. The fast timescale control of smart inverters is attained with a reinforcement learning-based method. The deep deterministic policy gradient (DDPG) algorithm is adopted to control the setpoints of both real and reactive power of smart inverters. The control policy of smart inverters is learned from the historical operational data without relying on accurate distribution network secondary circuit parameters. Simulation results on the IEEE 34-bus feeder show that the proposed framework can determine near optimal set points of smart inverters in real-time operations. Compared with existing reinforcement learning based smart inverter control, our approach achieves lower line losses, voltage deviations, and active power curtailment.
Old and new adventures with fatty acids and their oxylipins: The road towards personalised clinical nutrition

After paying homage to the work of E. Chevreul, Prof. Robert Gibson went on in a lighthearted way to find similarities in the way they both approached their respective fields of research, as well as their way of life. Prof. Robert Gibson, who was awarded the 2021 Chevreul Medal, reported that “his huge delight was to witness the massive growth of lipid research and to have played a role in elucidating the role of dietary fats in the health of mothers and their babies”. Prof. Gibson highlighted some of the major results he collected from Australian clinical studies conducted on the role of omega-3 fatty acids on the health outcomes of mothers and their infants. He first discussed the role of fish oil on visual acuity of babies and demonstrated that infant formulas supplying more than 1% of linolenic acid (ALA) seemed adequate to ensure optimal visual and cognitive development of term infants. However, in preterm infants, whether there is a specific need for DHA above the benefit provided by ALA, still needs to be clarified. He reported a small beneficial impact on the cognitive development of preterm infants receiving DHA enriched breast milk of their mothers (1% of total fatty acids). He then discussed data from his large randomised clinical trials conducted on pregnant women receiving a DHA dietary treatment (800 mg/d DHA) or placebo, that suggested that DHA may decrease the risk of preterm birth (DOMInO trial, 2400 women). This effect was confirmed in the ORIP trial (5400 women) which found that preterm birth could be prevented by a DHA supplement treatment mainly in women with a singleton pregnancy who had a low omega-3 status in the first trimester. In the last part of his review, Robert Gibson described the use of a new low cost, rapid and efficient method to monitor changes in blood levels of omega-3 fatty acids with clinical outcomes: the Dried Blood Spots (DBS) technology. The validation of this technique has been demonstrated in large trials like N3RO and ORIP involving large cohorts of women, which could not have been obtained easily by classical analysis of lipids. He went on to point out that free fatty acids (generally not explored despite their importance in many metabolic disorders) and oxylipins, are both stable and easily identified when they are preserved in a dry state on a paper matrix (DBS), thus opening new fields of research. To conclude, the major impact of Prof. Robert Gibson’s work was identifying and overcoming one of the causes of early preterm birth (omega-3 deficiency), developing a tool to rapidly assess omega-3 status (the DBS technique) that together is close to being implemented into the world health system.
Long-Term Ship Position Prediction Using Automatic Identification System (AIS) Data and End-to-End Deep Learning

The establishment of maritime safety and security is an important concern. Ship position prediction for maritime situational awareness (MSA), as a critical aspect of maritime safety and security, requires a longer time interval than collision avoidance and maritime traffic monitoring. However, previous studies focused mainly on shorter time-interval predictions ranging from 30 min to 10 h. A longer time-interval ship position prediction is required not only for MSA, but also for efficient allocation of ships by shipping companies in accordance with global freight demand. This study used an end-to-end tracking method that inputs the previous position of a vessel to a trained deep learning model to predict its next position with an average 24-h interval. An AIS dataset with a long-time-interval distribution in a nine-year timespan for capesize bulk carriers worldwide was used. In the first experiment, a deep learning model of the Indian Ocean was examined. Subsequently, the model performance was compared for six different oceans and six primary maritime chokepoints to investigate the influence of each area. In the third experiment, a sample location within the Malacca Strait area was selected, and the number of ships was counted daily. The results indicate that the ship position can be predicted accurately with an average time interval of 24 h using deep learning systems with AIS data.
A Novel Standard-Cell-Based Implementation of the Digital OTA Suitable for Automatic Place and Route

This paper presents a novel implementation of a digital-based Operational Transconductance Amplifier (OTA) which has been recently introduced in the technical literature as a fully digital alternative to the conventional differential pair to implement low voltage analog amplifiers and comparators. The proposed implementation does not make use of resistors, floating gate resistors nor C-Muller elements and is made up of only digital gates usually available in the standard cell libraries. The resulting analog circuit schematic can be described using structural VHDL or Verilog languages and is suitable to be integrated in an automatic synthesis and place and route flow for digital circuits. The proposed digital-based amplifier has been implemented in a commercial 130 nm CMOS process by using an automatic place and route flow for layout generation starting from the Verilog netlist. Post layout simulations are presented to show the performance of the proposed circuit and compare it against the state of the art.
Evaluation of Blackgram [(Vigna mungo (L)] Genotypes for Saline Tolerance at Seedling Stage Using Sea Water

Blackgram is one of the most highly prized pulse crops, cultivated in almost all parts of India.Soil salinity is one ofthe major factors responsible for losses in agriculturalproduction in most of the arid and semi-arid regions in theworld leading to loss in yield. The experiment was maintained for 10 days and all the observations from the seedlings namely germination percentage, shoot length, root length, dry matter production, vigour index I and vigour index II were recorded from each replicate and mean was worked out.On the basis of physiological parameters, the blackgram genotypes were discriminated into tolerant, moderate and sensitive to salinity.Germination per cent decreased by 85.5%, root length reduced by 75.68%, shoot length reduced by 61.73% while seedling length decreased by 22.30% in T4 treatment as compared to the T1 (control) treatment among all the blackgram genotypes. Seedling dry weight reduced by 40.04 folds in T4 treatment as compared to the T1 (control) treatment. Looking to the vigour index, seedling vigour index-I (length basis) and seedling vigour index-II (Dry weight basis) decreased by 0.03 and 1.18 folds, respectively. Out of 20 genotypes, four genotypes viz., IC-204869, TPU-94-2, IC-21485, and IC-214844 were found to be tolerant to salinity. thirteengenotypes were found to be moderately tolerant and three genotype viz.,SKNU-03-03, SKNU-0703, and SKNU-06-03 are sensitive to salinity.
Roles of Exogenous α-Lipoic Acid and Cysteine in Mitigation of Drought Stress and Restoration of Grain Quality in Wheat

Cysteine (Cys) and α-lipoic acid (ALA) are naturally occurring antioxidants (sulfur-containing compounds) that can protect plants against a wide spectrum of environmental stresses. However, up to now, there are no conclusive data on their integrative roles in mitigation of drought stress in wheat plants. Here, we studied the influence of ALA at 0.02 mM (grain dipping pre-cultivation treatment) and Cys (25 and 50 ppm as a foliar application) under well watered and deficit irrigation (100% and 70% of recommended dose). The results showed that deficit irrigation markedly caused obvious cellular oxidative damage as indicated by elevating the malondialdehyde (MDA) and hydrogen peroxide content (H2O2). Moreover, water stressed plants exhibited multiple changes in physiological metabolism, which affected the quantitative and qualitative variables of grain yield. The enzymatic antioxidants, including superoxide dismutase (SOD), ascorbate peroxidase (APX), catalase (CAT) and peroxidase (POX) were improved by Cys application. SOD and APX had the same response when treated with ALA, but CAT and POX did not. Moreover, both studied molecules stimulated chlorophyll (Chl) and osmolytes’ biosynthesis. In contrast, the Chl a/b ratio was decreased, while flavonoids were not affected by either of the examined molecules. Interestingly, all above-mentioned changes were associated with an improvement in the scavenging capacity of reactive oxygen species (ROS), leaf relative water content (RWC), grain number, total grain yield, weight of 1000 kernels, gluten index, falling number, and alveographic parameters (P, W, and P/L values). Furthermore, heatmap plot analysis revealed several significant correlations between different studied parameters, which may explore the importance of applied Cys and ALA as effective compounds in wheat cultivation under water deficit conditions.
Commercial Sentiment Analysis Solutions: A Comparative Study

: Empirical insights into high-promising commercial sentiment analysis solutions that go beyond their vendors’ claims are rare. Moreover, due to ongoing advances in the field, earlier studies are far from reflecting the current situation due to the constant evolution of the field. The present research aims to evaluate and compare current solutions. Based on tweets on the airline service quality, we test the solutions of six vendors with different market power, such as Amazon, Google, IBM, Microsoft, and Lexalytics, and MeaningCloud, and report their measures of accuracy, precision, recall, (macro) F1, time performance, and service level agreements (SLA). For positive and neutral classifications, none of the solutions showed precision of over 70%. For negative classifications, all of them demonstrate high precision of around 90%, however, only IBM Watson NLU and Google Cloud Natural Language achieve recall of over 70% and thus can be seen as worth considering for application scenarios where negative text detection is a major concern. Overall, our study shows that an independent, critical experimental analysis of sentiment analysis services can provide interesting insights into their general reliability and particular classification accuracy beyond marketing claims to critically compare solutions based on real-world data and analyze potential weaknesses and margins of error before making an investment.
Data-augmentation de dados de radiografia de torax no contexto de aprendizagem profunda

Este artigo discute técnicas de aprendizagem profunda aplicadas ao problema de triagem de tuberculose ativa em radiografias de tórax. O objetivo é contribuir para a prevenção e para o tratamento desta doença infecciosa, propondo uma rede neural convolucional (CNN) classificadora de tuberculose ativa, tendo como dados de entrada um conjunto de radiografias de tórax de indivíduos com ou sem tuberculose. Para melhor ajustar os parâmetros da CNN, técnicas de data-augmentation, utilizando modelos generativos adversariais (GAN), são aplicadas e avaliadas.
Parallel Multi‐Scale Network with Attention Mechanism for Pancreas Segmentation

In this paper, we address the task of segmenting small organs (i.e., the pancreas) from abdominal CT scans. As the target often occupies a relatively small region in the input image, deep neural networks can be easily confused by complex and variable backgrounds. We propose a method that uses a parallel multi‐scale network with an attention mechanism for pancreas segmentation, which can better grasp the balance between the semantic segmentation, classification, and localization tasks. We use a parallel network to connect the feature maps between different bottleneck layers, which contain rich semantic information and complete spatial information. We apply an attention module to enhance the key features of semantic information. Then, we fuse the two modules and apply the fused module as attention information on the feature map to ensure the full fusion between contextual semantic information and spatial information, thereby improving segmentation accuracy. We conduct extensive experiments on the NIH pancreas segmentation data set. In particular, our model achieves a mean coefficient Dice of 86.6. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.
Towards Services Profiling for Energy Management in Service-oriented Architectures

: Even though hardware architects have managed to incrementally mitigate energy consumption in information and communication technology devices, it will always be a requisite for software execution. This has motivated researchers to develop a limited amount of methodologies that promote green software development and its philosophy, with new assessment methods for calculating the energetic costs of software development and software execution. In spite of this, they have been acknowledged and adopted with limited success, as they try to address highly-volatile variables (like human behavior) and environments with specific hardware/software platforms and language-centric solutions. This has created a conflict between theory and practice where, otherwise, a generic and adaptive approach could manage the discord. In this paper, we present a brief review of available selected research in relation to services’ requirements definition and profiling for energy management, as well as the limitations and advantages of existing proposals in relation to green software development. Furthermore, we present our progress towards a series of properties to define services’ requirements and their resource consumption behavior. Our final goal is to create a proper approach for energy management from the analysis and design phases of the Software Development Life Cycle using Service-Oriented Architectures as a platform for our work.
Heuristics for Course Workspace Design and Evaluation

Course workspace represents the interface between the teacher, the course content, and the student. Both student engagement and student learning are influenced by the course workspace in a similar way as the end-user experience is influenced by system interface. However, course workspaces are typically designed by teachers without a specific input from students, which contrasts the practice in end-user computing and instructional design where users are or should be involved in the early stages of interface design and evaluation. In this paper, we develop a heuristics framework for being applied in a participatory manner involving the student perspective in the evaluation of course workspaces. The framework has been validated on three Moodle course workspaces. The results showed that the framework and the participatory approach provided valuable insights into student experience with the course workspace, while keeping the evaluation effort manageable in terms of data analysis and interpretation. We believe that the heuristics framework and participatory approach could be valuable for teachers, academics, and practitioners that use e-learning platforms for designing course workspaces. The paper provides also examples of improvement areas that have been identified in the evaluation and highlights new research directions in this area.
Synthesis and Photophysical Behaviours of Novel Phenanthro[9,10-d] imidazole Substituted Azo Dyes in Solvent Media

In this study, three novel phenanthroimidazole derivatives containing azo group were synthesized and their photophysical properties were investigated. For this purpose, 2-(4-nitrophenyl)1 H -phenanthro[9,10- d ]imidazole structure obtained from 9,10-phenanthroquinone and 4-nitrobenzaldehyde was reduced with palladium on activated charcoal 10 % and hydrazine hydrate. Phenanthro[9,10- d ]imidazole-2-yl)aniline obtained in this way was converted to phenantroimidazole-azo derivatives containing different aromatic groups. Structural analyses were performed by FT-IR, NMR, LC-MS spectroscopic techniques. Spectral characterizations and photostability studies of phenanthroimidazole derivatives were performed by UV-vis and fluorescence spectroscopy in solvent media. The maximum absorption and emission wavelengths, molar extinction coefficients, singlet energy levels, Stokes’ shift values of phenanthroimidazole-azo derivatives were obtained.
Social Media Analytics: An Overview of Applications and Approaches

Users' activities on social media generate useful and valuable information about the general public that can be used in policymaking and the decision-making processes. In this paper we review social media analysis approaches and its' application areas by sampling and reviewing studies from IEEE Xplore, Science Direct, and SpringerLink. The primary focus of this paper is on the role of social media data in extracting, tracking, and evaluating general public activities, public opinions, and public behaviour. We look at several application areas, including disaster, urban planning, public health, politics, business, and marketing. The frequently used approaches in these areas are topic analysis, sentiment analysis, social network analysis, spatial and temporal analysis. Moreover, this study provides insight for those who wish to learn about social media's role as a data source for research related to our real-world issues and events.
Asymmetry and Parity Violation in Magnetoresistance of Magnetic Diluted Dirac–Weyl Semimetal (Cd0.6Zn0.36Mn0.04)3As2

Orientation dependences of magnetoresistance, ρ (B→) , of single crystal (Cd0.6Zn0.36Mn0.04)3As2 having tetragonal P42/nmc symmetry have been studied. Three orientations between electrical current, J→ , flowing along crystal (100) plane and magnetic field, B→ , have been used as follows: 1) B→ is perpendicular to both J→ and (100) plane; 2) B→ is perpendicular to J→ but parallel to (100) plane; 3) B→ is parallel to both J→ and (100) plane. Asymmetry in curves ρ (B→) is observed for all orientations. For orientation (2), crossover from negative to positive magnetoresistance is found as magnetic field changes its direction from one position corresponding to negative B→ to opposite direction corresponding to positive B→ . So magnetoresistance parity typical for numerous solids is violated for this orientation. The Shubnikov–de Haas (SdH) oscillations are observed for all orientations above ≈1.8 T and below ≈40 K. The effective mass of electrons extracted from SdH oscillations analysis is found to be orientation dependent. Smeared kinks correlated with the SdH oscillations are also observed in the Hall effect study for orientation (1). Magnetoresistance peculiarities can be related to specific properties of the Dirac semimetals and their evolution under magnetic field.