Miracle-dz/UniFAR-FTUs huggingface dataset:
Implementation and assessment of a carbonate system model (Eco3M-CarbOx v1.1) in a highly dynamic Mediterranean coastal site (Bay of Marseille, France)

Abstract. A carbonate chemistry balance module was implemented into a biogeochemical model of the planktonic food web. The model, named Eco3M-CarbOx, includes 22 state variables that are dispatched into 5 compartments: phytoplankton, heterotrophic bacteria, detrital particulate organic matter, labile dissolved organic, and inorganic matter. This model is applied to and evaluated in the Bay of Marseille (BoM, France), which is a coastal zone impacted by the urbanized and industrialized Aix–Marseille Metropolis, and subject to significant increases in anthropogenic emissions of CO2. The model was evaluated over the year 2017, for which in situ data of the carbonate system are available in the study site. The biogeochemical state variables of the model only change with time, to represent the time evolution of a sea surface water cell in response to the implemented realistic forcing conditions. The model correctly simulates the value ranges and seasonal dynamics of most of the variables of the carbonate system except for the total alkalinity. Several numerical experiments were conducted to test the response of carbonate system to (i) a seawater temperature increase, (ii) wind events, (iii) Rhône River plume intrusions, and (iv) different levels of atmospheric CO2 contents. This set of numerical experiments shows that the Eco3M-CarbOx model provides expected responses in the alteration of the marine carbonate balance regarding each of the considered perturbation. When the seawater temperature changes quickly, the behavior of the BoM waters alters within a few days from a source of CO2 to the atmosphere to a sink into the ocean. Moreover, the higher the wind speed is, the higher the air–sea CO2 gas exchange fluxes are. The river intrusions with nitrate supplies lead to a decrease in the pCO2 value, favoring the conditions of a sink for atmospheric CO2 into the BoM. A scenario of high atmospheric concentrations of CO2 also favors the conditions of a sink for atmospheric CO2 into the waters of the BoM. Thus the model results suggest that external forcings have an important impact on the carbonate equilibrium in this coastal area.
Constructing fast and representative analytical models of wind turbine main bearings

This paper considers the modelling of wind turbine main bearings using analytical models. The validity of simplified analytical representations used in existing work is explored by comparing main-bearing force reactions with those obtained from higher-fidelity 3D finite-element models. Results indicate that there is good agreement between the analytical and 3D models in the case of a non-moment-reacting support (such as for double-row spherical roller bearings), but the same does not hold in the moment-reacting case (such as for double-row tapered roller bearings). Therefore, a new analytical model is developed in which moment reactions at the main bearing are captured through the addition of torsional springs. This latter model is shown to significantly improve the agreement between analytical and 3D models in the moment-reacting case. The new analytical model is then used to investigate load characteristics, in terms of forces and moments, for this type of main bearing across different operating points and wind conditions.
Synthesis and Characterization of Bidentate Isonitrile Iron Complexes

The divalent iron complexes trans-[FeBr2(BINC)2], [Cp*FeCl(BINC)] (Cp* = Me5C5), and [FeBr2(CNAr3NC)2] with the chelating bis(isonitrile) ligands BINC (bis(2-isocyanophenyl)phenylphosphonate) and CNAr3NC (2,2″-diisocyano-3,5,3″,5”tetramethyl-1,1′:3′,1″-terphenyl) have been prepared and characterized. Their subsequent reduction yields the di- and trinuclear compounds [Fe3(BINC)6], [Cp*Fe(BINC)]2, [Fe(CNAr3NC)2]2, and [K(Et2O)]2[Fe(CNAr3NC)2]2. The molecular structures of all new species were determined by X-ray crystallography and compared to those of related iron carbonyl complexes, demonstrating that the bidentate isonitrile ligands are capable surrogates for two CO ligands with only minimal distortion of the tetrahedral or octahedral geometry of the parent complexes. The complexes were further characterized by NMR and IR spectroscopy, and the electrochemical properties of selected compounds were analyzed by UV–vis–NIR spectroelectrochemistry.
The use of ASH-15 flowstone as a matrix-matched reference material for laser-ablation U − Pb geochronology of calcite

Abstract. Latest advances in laser ablation inductively coupled plasma mass spectrometer (LA-ICPMS) allow for accurate in situ U−Pb dating of carbonate material, with final age uncertainties usually >3 % 2σ . Cross-laboratory reference materials (RMs) used for sample-bracketing are currently limited to WC1 calcite with an age of 254.4±6.5 ( 2σ ). The minimum uncertainty on any age determination with the LA-ICPMS method is therefore ≥2.5 %, and validation by secondary RMs is usually performed on in-house standards. This contribution presents a new reference material, ASH-15, a flowstone that is dated here by isotope dilution (ID) thermal ionization mass spectrometry (TIMS) analysis using 37 sub-samples, 1–7 mg each. Age results presented here are slightly younger compared to previous ID isotope ratio mass spectrometry (IRMS) U−Pb dates of ASH-15 but within uncertainties and in agreement with in situ analyses using WC1 as the primary RM. We provide new correction parameters to be used as primary or secondary standardization. The suggested 238U∕206Pb apparent age, not corrected for disequilibrium and without common-lead anchoring, is 2.965±0.011 Ma (uncertainties are 95 % confidence intervals). The new results could improve the propagated uncertainties on the final age with a minimal value of 0.4 %, which is approaching the uncertainty of typical ID analysis on higher- U materials such as zircon. We show that although LA-ICPMS spot analyses of ASH-15 exhibit significant scatter in their isotopic ratios, the down-hole fractionation of ASH-15 is similar to that of other reference materials. This high- U ( ≈1 ppm ) and low- Pb ( ) calcite is most appropriate as a reference material for other speleothem-type carbonates but requires more-sensitive ICP-MS instruments such as the new generation of single-collector and multi-collector ICP-MS. Reference materials with high- Pb and low- U or both low- U and low- Pb compositions are still needed to fully cover the compositional range of carbonate material but may introduce analytical challenges.
Efficient removal of cathepsin L from active cathepsin X using immunoprecipitation technique

Cathepsin X is a cysteine protease which is involved in various important physiological and pathological processes. For the purpose of biochemical and structural studies, pure and active cathepsin X is required without contamination with other related proteases. Recombinant cathepsin X was obtained by expression in methylotropic yeast Pichia pastoris. During purification, cathepsin X has to be activated with cysteine protease cathepsin L, however, separation methods, used so far, can not completely remove cathepsin L from the activated cathepsin X. Here we describe a new purification procedure which provides active recombinant cathepsin X without the presence of residual cathepsin L.
Corpus-based technique for improving Arabic OCR system

An optical character recognition (OCR) refers to a process of converting the text document images into editable and searchable text. OCR process poses several challenges in particular in the Arabic language due to it has caused a high percentage of errors. In this paper, a method, to improve the outputs of the Arabic Optical character recognition (AOCR) Systems is suggested based on a statistical language model built from the available huge corpora. This method includes detecting and correcting non-word and real words error according to the context of the word in the sentence. The results show that the percentage of improvement in the results is up to (98%) as a new accuracy for AOCR output.
A review of IoT-based smart waste level monitoring system for smart cities

Smart cities are covering the population that are seeking the best lifestyle and fulfilling their needs. Through smart cities, necessary modern facilities using ICT emerging technologies such as the internet of things (IoT) had been installed to ensure the sustainability of the city. In the perspective of waste management, several different IoT-based solutions also had been proposed as an alternative to monitor and to ensure the health of communities. This paper reviews existing IoT-based solutions in smart cites’ waste level management system to bring together the state-of-the-art. We performed reviews on 16 research articles from the past 5 years in the literature to provide a comprehensive review of different works on IoT-based solutions related to the smart waste level monitoring system, possible solutions and technologies used. The results obtained shows that existing solutions were similar in the platform used to integrate with the IoT technologies but had some differences in term of the used of sensors and communication technologies. The study also shows that many of the prior studies used Arduino Uno. Results from this study will assist the researcher, focusing on expanding further the used of different technologies or improved the existing system.
Integrated planning framework for urban stormwater management: one water approach

ABSTRACT Flooded streets and homes, polluted surface waters, degraded aquatic habitats, stressed aquifers, and expensive drainage infrastructures are evidence that Canada’s current approach to stormwater management requires rethinking. To address these challenges, the authors developed an integrated community-level urban stormwater decision support framework. The framework assesses low impact development infrastructure, traditional stormwater infrastructure, and community-level stormwater reuse schemes to better understand the interactions between various water resources in the context of various stormwater management strategies. The framework also incorporates stochastic variation in natural processes to predict a community’s water footprint using the One Water Approach. A climate change sensitivity analysis is also incorporated to test proposed stormwater systems for resiliency to climate change. Fuzzy clustering analysis was then performed to identify the most appropriate stormwater management strategy. A case study demonstration of the framework was performed for a hypothetical 80-hectare mixed-use urban community in Kelowna, British Columbia.
Adversarial examples attack based on random warm restart mechanism and improved Nesterov momentum

The deep learning algorithm has achieved great success in the field of computer vision, but some studies have pointed out that the deep learning model is vulnerable to attacks adversarial examples and makes false decisions. This challenges the further development of deep learning, and urges researchers to pay more attention to the relationship between adversarial examples attacks and deep learning security. This work focuses on adversarial examples, optimizes the generation of adversarial examples from the view of adversarial robustness, takes the perturbations added in adversarial examples as the optimization parameter. We propose RWR-NM-PGD attack algorithm based on random warm restart mechanism and improved Nesterov momentum from the view of gradient optimization. The algorithm introduces improved Nesterov momentum, using its characteristics of accelerating convergence and improving gradient update direction in optimization algorithm to accelerate the generation of adversarial examples. In addition, the random warm restart mechanism is used for optimization, and the projected gradient descent algorithm is used to limit the range of the generated perturbations in each warm restart, which can obtain better attack effect. Experiments on two public datasets show that the algorithm proposed in this work can improve the success rate of attacking deep learning models without extra time cost. Compared with the benchmark attack method, the algorithm proposed in this work can achieve better attack success rate for both normal training model and defense model. Our method has average attack success rate of 46.3077%, which is 27.19% higher than I-FGSM and 9.27% higher than PGD. The attack results in 13 defense models show that the attack algorithm proposed in this work is superior to the benchmark algorithm in attack universality and transferability.
Towards a Model for LSH

As data volumes continue to grow, clustering and outlier detection algorithms are becoming increasingly time-consuming. Classical index structures for neighbor search are no longer sustainable due to the"curse of dimensionality". Instead, approximated index structures offer a good opportunity to significantly accelerate the neighbor search for clustering and outlier detection and to have the lowest possible error rate in the results of the algorithms. Locality-sensitive hashing is one of those. We indicate directions to model the properties of LSH.
Classical unique continuation property for multi-terms time fractional diffusion equations

As for the unique continuation property (UCP) of solutions in $(0,T)\times\Omega$ with a domain $\Omega\subset{\mathbb R}^n,\,n\in{\mathbb N}$ for a multi-terms time fractional diffusion equation, we have already shown it by assuming that the solutions are zero for $t\le0$ (see \cite{LN2019}). Here the strongly elliptic operator for this diffusion equation can depend on time and the orders of its time fractional derivatives are in $(0,2)$. This paper is a continuation of the previous study. The aim of this paper is to drop the assumption that the solutions are zero for $t\le0$. We have achieved this aim by first using the usual Holmgren transformation together with the argument in \cite{LN2019} to derive the UCP in $(T_1,T_2)\times B_1$ for some $0<T_1<T_2<T$ and a ball $B_1\subset\Omega$. Then if $u$ is the solution of the equation with $u=0$ in $(T_1,T_2)\times B_1$, we show $u=0$ also in $((0,T_1]\cup[T_2,T))\times B_r$ for some $r<1$ by using the argument in \cite{LN2019} which uses two Holmgren type transformations different from the usual one. This together with spatial coordinates transformation, we can obtain the usual UCP which we call it the classical UCP given in the title of this paper for our time fractional diffusion equation.
Optimization of model independent gravitational wave search for binary black hole mergers using machine learning

The Coherent WaveBurst (cWB) search algorithm identifies generic gravitational wave (GW) signals in the LIGO-Virgo strain data. We propose a machine learning (ML) method to optimize the pipeline sensitivity to the special class of GW signals known as binary black hole (BBH) mergers. Here, we test the ML-enhanced cWB search on strain data from the first and second observing runs of Advanced LIGO and successfully recover all BBH events previously reported by cWB, with higher significance. For simulated events found with a false alarm rate less than $1\,\mathrm{yr}^{-1}$, we demonstrate the improvement in the detection efficiency of 26% for stellar-mass BBH mergers and 16% for intermediate mass black hole binary mergers. To demonstrate the robustness of the ML-enhanced search for the detection of generic BBH signals, we show that it has the increased sensitivity to the spin precessing or eccentric BBH events, even when trained on simulated quasi-circular BBH events with aligned spins.
Learning to Ask Appropriate Questions in Conversational Recommendation

Conversational recommender systems (CRSs) have revolutionized the conventional recommendation paradigm by embracing dialogue agents to dynamically capture the fine-grained user preference. In a typical conversational recommendation scenario, a CRS firstly generates questions to let the user clarify her/his demands and then makes suitable recommendations. Hence, the ability to generate suitable clarifying questions is the key to timely tracing users' dynamic preferences and achieving successful recommendations. However, existing CRSs fall short in asking high-quality questions because: (1) system-generated responses heavily depends on the performance of the dialogue policy agent, which has to be trained with huge conversation corpus to cover all circumstances; and (2) current CRSs cannot fully utilize the learned latent user profiles for generating appropriate and personalized responses. To mitigate these issues, we propose the Knowledge-Based Question Generation System (KBQG), a novel framework for conversational recommendation. Distinct from previous conversational recommender systems, KBQG models a user's preference in a finer granularity by identifying the most relevant relations from a structured knowledge graph (KG). Conditioned on the varied importance of different relations, the generated clarifying questions could perform better in impelling users to provide more details on their preferences. Finially, accurate recommendations can be generated in fewer conversational turns. Furthermore, the proposed KBQG outperforms all baselines in our experiments on two real-world datasets.
Peculiar-velocity cosmology with Types Ia and II supernovae

We present the Democratic Samples of Supernovae (DSS), a compilation of 775 low-redshift Type Ia and II supernovae (SNe Ia & II), of which 137 SN Ia distances are derived via the newly developed snapshot distance method. Using the objects in the DSS as tracers of the peculiar-velocity field, we compare against the corresponding reconstruction from the 2M++ galaxy redshift survey. Our analysis — which takes special care to properly weight each DSS subcatalogue and cross-calibrate the relative distance scales between them — results in a measurement of the cosmological parameter combination f σ8 = 0.390+0.022 −0.022 as well as an external bulk flow velocity of 195+22 −23 km s −1 in the direction (l, b) = (292+7 −7,−6 +5 −4) deg, which originates from beyond the 2M++ reconstruction. Similarly, we find a bulk flow of 245+32 −31 km s −1 toward (l, b) = (294+7 −7, 3 +6 −5) deg on a scale of ∼ 30 h −1Mpc if we ignore the reconstructed peculiar-velocity field altogether. Our constraint on f σ8 — the tightest derived from SNe to date (considering only statistical error bars), and the only one to utilise SNe II— is broadly consistent with other results from the literature. We intend for our data accumulation and treatment techniques to become the prototype for future studies that will exploit the unprecedented data volume from upcoming wide-field surveys.
Museum Painting Retrieval

To retrieve images based on their content is one of the most studied topics in the field of computer vision. Nowadays, this problem can be addressed using modern techniques such as feature extraction using machine learning, but over the years different classical methods have been developed. In this paper, we implement a query by example retrieval system for finding paintings in a museum image collection using classic computer vision techniques. Specifically, we study the performance of the color, texture, text and feature descriptors in datasets with different perturbations in the images: noise, overlapping text boxes, color corruption and rotation. We evaluate each of the cases using the Mean Average Precision (MAP) metric, and we obtain results that vary between 0.5 and 1.0 depending on the problem conditions. Keywords— Computer vision, Image retrieval, CBIR, Color descriptors, Texture descriptors, Local descriptors, Key points.
Gaussianization of peculiar velocities and bulk flow measurement

The line-of-sight peculiar velocities are good indicators of the gravitational fluctuation of the density field. Techniques have been developed to extract cosmological information from the peculiar velocities in order to test cosmological models. These techniques include measuring cosmic flow, measuring two-point correlation and power spectrum of the peculiar velocity fields, and reconstructing the density field using peculiar velocities. However, some measurements from these techniques are biased due to the non-Gaussianity of the estimated peculiar velocities. Therefore, we rely on the 2MTF survey to explore a power transform that can Gaussianize the estimated peculiar velocities. We find a tight linear relation between the transformation parameters and the measurement errors of log-distance ratio. To show an example for the implementation of Gaussianized peculiar velocities in cosmology, we develop a bulk flow estimator and estimate bulk flow from the Gaussianized peculiar velocities. We use 2MTF mocks to test the algorithm, and we find the algorithm yields unbiased measurements. We also find this technique gives smaller measurement errors compared to other techniques. In Galactic coordinates, at the depth of 30 h −1 Mpc, we measure a bulk flow of 332 ± 27 km s−1 in the direction (l,b) = (293° ± 5°, 13° ± 4°). The measurement is consistent with the ΛCDM prediction.
Counterfactual Explanations for Neural Recommenders

While neural recommenders have become the state-of-the-art in recent years, the complexity of deep models still makes the generation of tangible explanations for end users a challenging problem. Existing methods are usually based on attention distributions over a variety of features, which are still questionable regarding their suitability as explanations, and rather unwieldy to grasp for an end user. Counterfactual explanations based on a small set of the user's own actions have been shown to be an acceptable solution to the tangibility problem. However, current work on such counterfactuals cannot be readily applied to neural models. In this work, we propose ACCENT, the first general framework for finding counterfactual explanations for neural recommenders. It extends recently-proposed influence functions for identifying training points most relevant to a recommendation, from a single to a pair of items, while deducing a counterfactual set in an iterative process. We use ACCENT to generate counterfactual explanations for two popular neural models, Neural Collaborative Filtering (NCF) and Relational Collaborative Filtering (RCF), and demonstrate its feasibility on a sample of the popular MovieLens 100K dataset.
An introduction to causal reasoning in health analytics

A data science task can be deemed as making sense of the data and/or testing a hypothesis about it. The conclusions inferred from data can greatly guide us to make informative decisions. Big data has enabled us to carry out countless prediction tasks in conjunction with machine learning, such as identifying high risk patients suﬀering from a certain disease and taking preventable measures. However, healthcare practitioners are not content with mere predictions – they are also interested in the cause-eﬀect relation between input features and clinical outcomes. Understanding such relations will help doctors treat patients and reduce the risk eﬀectively. Causality is typically identiﬁed by randomized controlled trials. Often such trials are not feasible when scientists and researchers turn to observational studies and attempt to draw inferences. However, observational studies may also be aﬀected by selection and/or confounding biases that can result in wrong causal conclusions. In this chapter, we will try to highlight some of the drawbacks that may arise in traditional machine learning and statistical approaches to analyze the observational data, particularly in the healthcare data analytics domain. We will discuss causal inference and ways to discover the cause-eﬀect from observational studies in healthcare domain. Moreover, we will demonstrate the applications of causal inference in tackling some common machine learning issues such as missing data and model transportability. Finally, we will discuss the possibility of integrating reinforcement learning with causality as a way to counter confounding bias.