Miracle-dz/UniFAR-FTUs huggingface dataset:
Ecosystem accounting in support of the transition to sustainable societies – the case for a parsimonious and inclusive measurement of ecosystem condition

The development of ecosystem accounting systems at national levels to complete current wealth indicators with robust information on ecosystem degradation or enhancement is a crucial challenge, recognized in international strategies. However, the methodologies remain under development building, at the global level, on an experimental ecosystem accounting framework (the SEEA-EEA). Building on this framework and current academic discussions, this article aims at proposing a methodological advance for aligning the SEEA-EEA with the needs of ecosystem management and the principles of strong sustainability. It consists in structuring ecosystem condition measurement into a parsimonious and inclusive set of characteristics, indicators and reference levels with an explicit and inclusive value basis. This sets the grounds for the development of sound and policy-relevant ecosystem monitoring systems and the production of meaningful macro-aggregate indicators of ecosystem degradation at national levels.
The Merits of the High-temperature Combustion Method for Determining the Amount of Natural Organic Carbon in Surface Freshwater Samples

The merits of the method for the quantitative analysis of carbon of natural organic matter (NOM) in aquatic freshwater samples, using the high-temperature catalytic combustion technique, have to our knowledge not previously been documented. Although a large intercalibration study concerning marine NOM have documented and improved the analytical merits of this technique in marine chemistry, these results cannot directly be adapted to freshwater analysis. This article presents the findings from an intercalibration on the determination of carbon in freshwater NOM, between 25 laboratories participated in using the high-temperature catalytic combustion technique. The laboratories analysed the amount of total- and dissolved organic carbon (i.e. TOC and DOC, respectively) on a set of 10 samples. The samples consisted of fresh surface water, reverse osmosis (RO) isolates of dissolved NOM from natural freshwater locations, as well as synthetic standards. General merits (i.e. accuracy, precision etc.) of the analytical method are presented and problems with detection limits, high blank values, contamination from filters, experience in the use of RO-isolates and analysis of the more refractory (i.e. not readily oxidizable) NOM are discussed. The focus of the study is on the analytical merits achieved on the natural samples compared to the commonly used and possibly readily oxidizable standard material (i.e. potassium hydrogen phthalate). It is demonstrated that the method's merits generated using the readily oxidizable potassium hydrogen phthalate are too optimistic when applied to the analysis of natural aquatic samples.
Assessing the reliability of wind power operations under a changing climate with a non-Gaussian bias correction

Facing increasing societal and economic pressure, many countries have established strategies to develop renewable energy portfolios, whose penetration in the market can alleviate the dependence on fossil fuels. In the case of wind, there is a fundamental question related to the resilience, and hence profitability, of future wind farms to a changing climate, given that current wind turbines have lifespans of up to thirty years. In this work we develop a new non-Gaussian method to adjust assimilated observational data to simulations and to estimate future wind, predicated on a transGaussian transformation and a cluster-wise minimization of the Kullback–Leibler divergence. Future winds abundance will be determined for Saudi Arabia, a country with a recently established plan to develop a portfolio of up to 16 GW of wind energy. Further, we estimate the change in profits over future decades using additional high-resolution simulations, an improved method for vertical wind extrapolation and power curves from a collection of popular wind turbines. We find an overall increase in daily profit of $272,000 for the wind energy market for the optimal locations for wind farming in the country.
A Limited-Capacity Minimax Theorem for Non-Convex Games or: How I Learned to Stop Worrying about Mixed-Nash and Love Neural Nets

Adversarial training, a special case of multi-objective optimization, is an increasingly prevalent machine learning technique: some of its most notable applications include GAN-based generative modeling and self-play techniques in reinforcement learning which have been applied to complex games such as Go or Poker. In practice, a \emph{single} pair of networks is typically trained in order to find an approximate equilibrium of a highly nonconcave-nonconvex adversarial problem. However, while a classic result in game theory states such an equilibrium exists in concave-convex games, there is no analogous guarantee if the payoff is nonconcave-nonconvex. Our main contribution is to provide an approximate minimax theorem for a large class of games where the players pick neural networks including WGAN, StarCraft II, and Blotto Game. Our findings rely on the fact that despite being nonconcave-nonconvex with respect to the neural networks parameters, these games are concave-convex with respect to the actual models (e.g., functions or distributions) represented by these neural networks.
A Comparative Study Between Spray-Drying and Fluidized Bed Coating Processes for the Preparation of Pramipexole Controlled-Release Microparticles for Orally Disintegrating Tablets

In the present study, controlled-release microparticles for orally disintegrating tablets (ODT) were prepared using two different processes, spray drying and fluidized bed coating processes. Pramipexole dihydrochloride monohydrate (PRM), an anti-Parkinson's disease agent, was selected as a model drug. The in vitro release rate and morphology of microparticles were evaluated and compared. The size of microparticles prepared by spray drying (SD microparticles) and fluidized bed coating (FC microparticles) was around 10 and 200 µm, respectively. The latter size was defined by the size of an inert core bead. The release behavior of SD microparticles was characterized by a large initial burst release prior to slow release. In the case of FC microparticles, the initial burst release was smaller than that of SD microparticles and the compression process damaged the release-controlling layer, which led to a change in release rate. The results indicated the importance of carefully considering the manufacturing process for microparticles during the design of controlled-release ODT.
Factors affecting secondary nucleation rate of sodium chloride in an evaporative crystallizer

Crystallization kinetics of sodium chloride have been studied in a Continuous Mixed Suspension Mixed Product Removal evaporative crystallizer at 50°C. A marine propeller was found to give a substantially lower nucleation rate than a pitched blade impeller under identical conditions. The crystal turnover time did not affect the effective nucleation rate.
A Statistical Analysis of Wind Speed Distributions in the Area of Western Greece

The wind energy potential in Western Greece was evaluated from measurements of wind speed and direction at four weather stations. Data collected over a period of years were used to statistically analyze wind speed distributions. Weibull parameters, at the selected stations, were calculated using three different methods. The theoretical values calculated from the analysis of the collected data performed well with the measured ones. Finally, the correlation between Weibull parameters and the measured wind speed values was performed and analysed for each station.
Multi-Prize Lottery Ticket Hypothesis: Finding Accurate Binary Neural Networks by Pruning A Randomly Weighted Network

Recently, Frankle&Carbin (2019) demonstrated that randomly-initialized dense networks contain subnetworks that once found can be trained to reach test accuracy comparable to the trained dense network. However, finding these high performing trainable subnetworks is expensive, requiring iterative process of training and pruning weights. In this paper, we propose (and prove) a stronger Multi-Prize Lottery Ticket Hypothesis: A sufficiently over-parameterized neural network with random weights contains several subnetworks (winning tickets) that (a) have comparable accuracy to a dense target network with learned weights (prize 1), (b) do not require any further training to achieve prize 1 (prize 2), and (c) is robust to extreme forms of quantization (i.e., binary weights and/or activation) (prize 3). This provides a new paradigm for learning compact yet highly accurate binary neural networks simply by pruning and quantizing randomly weighted full precision neural networks. We also propose an algorithm for finding multi-prize tickets (MPTs) and test it by performing a series of experiments on CIFAR-10 and ImageNet datasets. Empirical results indicate that as models grow deeper and wider, multi-prize tickets start to reach similar (and sometimes even higher) test accuracy compared to their significantly larger and full-precision counterparts that have been weight-trained. Without ever updating the weight values, our MPTs-1/32 not only set new binary weight network state-of-the-art (SOTA) Top-1 accuracy -- 94.8% on CIFAR-10 and 74.03% on ImageNet -- but also outperform their full-precision counterparts by 1.78% and 0.76%, respectively. Further, our MPT-1/1 achieves SOTA Top-1 accuracy (91.9%) for binary neural networks on CIFAR-10. Code and pre-trained models are available at: https://github.com/chrundle/biprop.
OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs

Enabling effective and efficient machine learning (ML) over large-scale graph data (e.g., graphs with billions of edges) can have a great impact on both industrial and scientific applications. However, existing efforts to advance large-scale graph ML have been largely limited by the lack of a suitable public benchmark. Here we present OGB Large-Scale Challenge (OGB-LSC), a collection of three real-world datasets for facilitating the advancements in large-scale graph ML. The OGB-LSC datasets are orders of magnitude larger than existing ones, covering three core graph learning tasks -- link prediction, graph regression, and node classification. Furthermore, we provide dedicated baseline experiments, scaling up expressive graph ML models to the massive datasets. We show that expressive models significantly outperform simple scalable baselines, indicating an opportunity for dedicated efforts to further improve graph ML at scale. Moreover, OGB-LSC datasets were deployed at ACM KDD Cup 2021 and attracted more than 500 team registrations globally, during which significant performance improvements were made by a variety of innovative techniques. We summarize the common techniques used by the winning solutions and highlight the current best practices in large-scale graph ML. Finally, we describe how we have updated the datasets after the KDD Cup to further facilitate research advances. The OGB-LSC datasets, baseline code, and all the information about the KDD Cup are available at https://ogb.stanford. edu/docs/lsc/ .
Multimodal End-to-End Sparse Model for Emotion Recognition

Existing works in multimodal affective computing tasks, such as emotion recognition and personality recognition, generally adopt a two-phase pipeline by first extracting feature representations for each single modality with hand crafted algorithms, and then performing end-to-end learning with extracted features. However, the extracted features are fixed and cannot be further fine-tuned on different target tasks, and manually finding feature extracting algorithms does not generalize or scale well to different tasks, which can lead to sub-optimal performance. In this paper, we develop a fully end-to-end model that connects the two phases and optimizes them jointly. In addition, we restructure the current datasets to enable the fully end-to-end training. Furthermore, to reduce the computational overhead brought by the end-to-end model, we introduce a sparse cross-modal attention mechanism for the feature extraction. Experimental results show that our fully end-to-end model significantly surpasses the current state-of-the-art models based on the two-phase pipeline. Moreover, by adding the sparse cross-modal attention, our model can maintain the performance with around half less computation in the feature extraction part of the model.
Micromagnetic description of twisted spin spirals in the B20 chiral magnet FeGe from first principles

Using the model of classical Heisenberg exchange and Dzyaloshinskii-Moriya (DM) interaction, we show that the ground state of the B20 FeGe chiral magnet is a superposition of twisted helical spin-density waves formed by different sublattices of the crystal. Such twisted spin-density waves propagate in the same direction but with different phases and different directions of the rotation axes. We derive an advanced micromagnetic expression describing the exchange and DM interaction for such magnetic structures. In particular, we show that such magnetic order gives rise to new contributions to the micromagnetic energies of the exchange and DM interactions. By employing first-principles calculations based on density functional theory and using our micromagnetic model we show that the magnitude of the spin-spiral twist in B20 FeGe is of the same order as global spiraling. While the energy difference between the ground state of twisted spirals and the ferromagnetic state is in good agreement with the experimental results, for the spin spirals without a twist it is smaller by a factor of 3. In addition, we verify our results by employing spin-dynamics simulations. This calls for new experiments exploring the ground state properties of B20 chiral magnets.
A Survey of Forex and Stock Price Prediction Using Deep Learning

Predictions of stock and foreign exchange (Forex) have always been a hot and profitable area of study. Deep learning applications have been proven to yield better accuracy and return in the field of financial prediction and forecasting. In this survey, we selected papers from the Digital Bibliography & Library Project (DBLP) database for comparison and analysis. We classified papers according to different deep learning methods, which included Convolutional neural network (CNN); Long Short-Term Memory (LSTM); Deep neural network (DNN); Recurrent Neural Network (RNN); Reinforcement Learning; and other deep learning methods such as Hybrid Attention Networks (HAN), self-paced learning mechanism (NLP), and Wavenet. Furthermore, this paper reviews the dataset, variable, model, and results of each article. The survey used presents the results through the most used performance metrics: Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), Mean Square Error (MSE), accuracy, Sharpe ratio, and return rate. We identified that recent models combining LSTM with other methods, for example, DNN, are widely researched. Reinforcement learning and other deep learning methods yielded great returns and performances. We conclude that, in recent years, the trend of using deep-learning-based methods for financial modeling is rising exponentially.
Monoceros OB4: a new association in Gaia DR2

We use Gaia DR2 data to survey the classic Monoceros OB1 region and look for the existence of a dispersed young population, co-moving with the cloud complex. An analysis of the distribution of proper motions reveals a 20–30 Myr association of young stars, about 300–400 pc away from the far side of the Mon OB1 complex, along the same general line of sight. We characterize the new association, Monoceros OB4, and estimate it contains between 1400 and 2500 stars, assuming a standard initial mass function, putting it on par in size with NGC 2264. We find from the internal proper motions that Mon OB4 is unbound and expanding. Our results seem to unveil a larger and more complex Monoceros star formation region, suggesting an elongated arrangement that seems to be at least 300 × 60 pc.
Graph Convolutional Network for Swahili News Classification

This work empirically demonstrates the ability of Text Graph Convolutional Network (Text GCN) to outperform traditional natural language processing benchmarks for the task of semi-supervised Swahili news classification. In particular, we focus our experimentation on the sparsely-labelled semi-supervised context which is representative of the practical constraints facing low-resourced African languages. We follow up on this result by introducing a variant of the Text GCN model which utilises a bag of words embedding rather than a naive one-hot encoding to reduce the memory footprint of Text GCN whilst demonstrating similar predictive performance.
Resolving Galactic binaries in LISA data using particle swarm optimization and cross-validation

Xue-Hao Zhang, 2, 3 Soumya D. Mohanty, 3 Xiao-Bo Zou, 2, 3 and Yu-Xiao Liu 2 Institute of Theoretical Physics & Research Center of Gravitation, Lanzhou University, Lanzhou 730000, China Lanzhou Center for Theoretical Physics, Lanzhou University, Lanzhou 730000, China Morningside Center of Mathematics, Academy of Mathematics and System Science, Chinese Academy of Sciences, 55, Zhong Guan Cun Donglu, Beijing 100190, China Dept. of Physics and Astronomy, University of Texas Rio Grande Valley, One West University Blvd., Brownsville, Texas 78520, USA
General theory of robustness against disorder in multiband superconductors

We investigate the influence of general forms of disorder on the robustness of superconductivity in multiband materials. Specifically, we consider a general two-band system where the bands arise from an orbital degree of freedom of the electrons. Within the Born approximation, we show that the interplay of the spin-orbital structure of the normal-state Hamiltonian, disorder scattering, and superconducting pairing potentials can lead to significant deviations from the expected robustness of the superconductivity. This can be conveniently formulated in terms of the so-called “superconducting fitness”. In particular, we verify a key role for unconventional s-wave states, permitted by the spin-orbital structure and which may pair electrons that are not time-reversed partners. To exemplify the role of Fermi surface topology and spin-orbital texture, we apply our formalism to the candidate topological superconductor CuxBi2Se3, for which only a single band crosses the Fermi energy, as well as models of the iron pnictides, which possess multiple Fermi pockets.
Quantitative De Giorgi methods in kinetic theory

We consider hypoelliptic equations of kinetic Fokker-Planck type, also known as Kolmogorov or ultraparabolic equations, with rough coefficients in the drift-diffusion operator. We give novel short quantitative proofs of the De Giorgi intermediate-value Lemma as well as weak Harnack and Harnack inequalities. This implies H{\"o}lder continuity with quantitative estimates. The paper is self-contained.
The role of planetary interior in the long-term evolution of atmospheric CO2 on Earth-like exoplanets

Context. The long-term carbonate silicate cycle plays an important role in the evolution of Earth’s climate and, therefore, may also be an important mechanism in the evolution of the climates of Earth-like exoplanets. However, given the large diversity in the possible interiors for Earth-like exoplanets, the ensuing evolution of the atmospheric CO2 pressure may be widely different. Aims. We assess the role of the thermal evolution of the planetary interior on the long-term carbon cycle of Earth-like exoplanets. In particular, we investigate the effects of radiogenic mantle heating, core size, and planetary mass on the atmospheric partial CO2 pressure, and the ability of a long-term carbon cycle driven by plate tectonics to control the atmospheric CO2 pressure. Methods. We developed a box-model which connects carbon cycling to parametrized mantle convection. Processes considered in the carbon cycle are temperature-dependent continental weathering, seafloor weathering, subduction, and degassing through ridge and arc volcanism. The carbon cycle was coupled to the thermal evolution via the plate speed, which was parametrized in terms of the global Rayleigh number. Results. We find decreasing atmospheric CO2 pressure with time, up to an order of magnitude over the entire main sequence lifetime of a solar-type star. High abundances of radioactive isotopes allow for more efficient mantle degassing, resulting in higher CO2 pressures. Within the spread of abundances found in solar-type stars, atmospheric CO2 pressures at 4.5 Gyr were found to vary from 14 Pa to 134 Pa. We find a decreasing Rayleigh number and plate speed toward planets with larger core mass fractions fc, which leads to reduced degassing and lower atmospheric CO2 pressure. In particular for fc ≳ 0.8, a rapid decrease of these quantities is found. Variations in planet mass have more moderate effects. However, more massive planets may favor the development of more CO2 rich atmospheres due to hotter interiors. Conclusions. The dependence of plate tectonics on mantle cooling has a significant effect on the long-term evolution of the atmospheric CO2 pressure. Carbon cycling mediated by plate tectonics is efficient in regulating planetary climates for a wide range of mantle radioactive isotope abundances, planet masses and core sizes. More efficient carbon cycling on planets with a high mantle abundance of thorium or uranium highlights the importance of mapping the abundances of these elements in host stars of potentially habitable exoplanets. Inefficient carbon recycling on planets with a large core mass fraction (≳0.8) emphasizes the importance of precise mass-radius measurements of Earth-sized exoplanets.
New Insights Into the Effect of Calcium Hydroxide Precipitation on the Kinetics of Tricalcium Silicate Hydration

After decades of research, there still is no universal agreement on the basic mechanisms that are responsible for the induction period (Stage 2) in cement hydration at early times. This paper surveys several theories and critical experimental data on early-age hydration kinetics of tricalcium silicate, the most abundant phase in Portland cement clinker. Using HydratiCA, a kinetic cellular automaton model of cement hydration and microstructure development, we investigate two of the leading hypotheses on the mechanism of slow hydration in Stage 2, and see for each one the implications of suppressing the growth of calcium hydroxide formation. Each hypothesized mechanism leads to quite different kinetic behavior when calcium hydroxide is suppressed, a result which indicates a line of future experimental inquiry that could decisively determine the Stage 2 mechanism. Furthermore, the simulations demonstrate how the early theory of calcium hydroxide triggering might be reconciled to the more modern theoretical models and to the experimental data.