Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance Study of Convolutional Neural Network Architectures for 3D Incompressible Flow Simulations

Recently, correctly handling spatial information from multiple scales has proven to be essential in Machine Learning (ML) applications on Computational Fluid Dynamics (CFD) problems. For these type of applications, Convolutional Neural Networks (CNN) that use Multiple Downsampled Branches (MDBs) to efficiently encode spatial information from different spatial scales have proven to be some of the most successful architectures. However, not many guidelines exist to build these architectures, particularly when applied to more challenging 3D configurations. Thus, this work focuses on studying the impact of the choice of the number of down-sampled branches, accuracy and performance-wise in 3D incompressible fluid test cases, where a CNN is used to solve the Poisson equation. The influence of this parameter is assessed by performing multiple trainings of Unet architectures with varying MDBs on a cloud-computing environment. These trained networks are then tested on two 3D CFD problems: a plume and a Von Karman vortex street at various operating points, where the solution of the neural network is coupled to a nonlinear advection equation.
Evaluation of Price Prediction Models for Cryptocurrencies based on convolutional neural networks trained on Candlestick Charts

In the past few years, there has been a growing interest in cryptocurrencies. However, the risk of incurring losses is high due to their large price fluctuations. Therefore, we want to reduce this risk by predicting the rise and fall of their prices. In this study, we use a convolutional neural network model trained on candlestick charts to make price predictions. In this experiment, the system was trained on the image pattern data of a set of five candlesticks, and predictions were made on whether the price would go up or down. The novelty of this research is that we apply the stock price prediction method using visual candlestick patterns, which has been empirically judged, to virtual currency prediction based on their visual pattern transition model with deep learning. The model trained on the data from 1-minute intervals gave the best results, with a predictive accuracy of 58.69% and a bankruptcy probability of only 1.678%.
Assis: Online Semi-Automatic Dialog Annotation Tool

Context: Task-oriented conversational systems demand a high volume of data to understand human language. One of the major challenges of Natural Language Processing (NLP) is the lack of structured annotated data to improve and refine language models, therefore, institutions often generate or mine their own data and have to annotate it themselves. Problem: The annotation process is time-consuming and costly process that usually results in errors due to human fatigue and often acts as the blocking phase for many smaller teams developing AI. Companies frequently report scarcity and poor data quality when developing these systems. Solution: This paper presents Assis, a modular, adaptable tool for semi-automatic annotation (manual and AI annotation). The tool automates and organizes the intentions and entities in task-oriented conversations. Our proposal combines components that facilitate the visual assimilation of the annotation process. Assis can be embedded with continuously refined language models based on previously annotated sentences. IS theory: Assis was developed with the idea of Design Theory in mind, using its base of knowledge to evaluate the existing and proposed tools to its goal of facilitating annotation. Method: Empirical results from user experience in real-life case studies and satisfaction with both the annotation results as well as the user experience, in comparison to the same study groups conducting the annotation without tools or in another software, using a feedback form after use. Results: During one of the case studies, the tool was used to annotate more than 800 messages, with user feedback relating a high satisfaction with the reduction of the required time. Contributions and Impact in the IS area: The tool innovates with its deployless architecture, modularity and adaptability, while introducing two new concepts for text annotation: dialogue topics and entity propagation.
Fluctuation Trend Prediction and Investment Allocation Optimization of Risk Assets and Safe-haven Assets

Safe-haven assets are a safe and effective value storage and risk hedging tool in the period of market turbulence, while risk assets show multiple characteristics such as the coexistence of high risk and high return, great variability, strong volatility and so on. Taking gold and bitcoin, two typical safe-haven and risk assets, as examples, this paper constructs the ARIMA-XGBoost joint prediction model and predicts the future fluctuation trend of gold and bitcoin; At the same time, the prediction results are used to optimize the parameter allocation of the mean variance model, and the effective frontier of the portfolio is calculated under different constraints. The results show that the RMSE of ARIMA-Xgboost model is 5.3 and 83.6 respectively, and the MAPE is 0.35% and 0.80% respectively; The efficient allocation frontier of the portfolio is its Pareto optimal solution, and when the allocation proportion of a single asset is limited, the overall yield of the portfolio is significantly reduced, but it is better than the result of equal weight allocation; ARIMA-Xgboost model has high prediction accuracy, good stability and strong self-learning and self-adaptive ability, which can provide a certain reference for investors or salespeople to make investment decisions.
Estimation of Aboveground Vegetation Carbon Storage in Coastal Salt Marshes based on ZhuHai-1 Hyperspectral Satellite Images

Since the carbon reserve of salt marsh vegetation is a key parameter for studying the carbon cycle of coastal wetland ecosystem, it is of great practical significance to study the carbon reserve of multi-species salt marsh vegetation under the background of "carbon peak and carbon neutrality". In this paper, the image of ZhuHai-1 in the core area of Yancheng Wetland Rare Birds National Nature Reserve in Jiangsu, China was taken as the data source. In this paper, Support Vector Machine was used to distinguish vegetation types. Based on PROSAIL radiation transfer model, the forward relationship between vegetation carbon reserve and reflectivity of canopy spectral response was quantitatively analyzed, and the paired data set of the two was constructed. In addition, this paper quantified the relationship between simulated carbon reserve and simulated reflectivity of canopy spectral through neural network model, which was used in the remote sensing image of ZhuHai-1, so as to obtain the spatial and temporal distribution of carbon reserve of salt marsh. According to the statistics, the total carbon reserve of the Spartina alterniflora was 5.74×104 tons, the total carbon reserve of the suaeda was 0.21×104 tons, and the total carbon reserve of the breed was 2.9×104 tons. This study can provide technical support for ecological protection and restoration of coastal salt marshes and carbon sequestration and storage by vegetation.
Exploiting symmetries for preconditioning Poisson's equation in CFD simulations

Divergence constraints are present in the governing equations of many physical phenomena, and they usually lead to a Poisson equation whose solution is one of the most challenging parts of scientific simulation codes. Indeed, it is the main bottleneck of incompressible Computational Fluid Dynamics (CFD) simulations, and developing efficient and scalable Poisson solvers is a critical task. This work presents an enhanced variant of the Factored Sparse Approximate Inverse (FSAI) preconditioner. It arises from exploiting s spatial reflection symmetries, which are often present in academic and industrial configurations and allow transforming Poisson's equation into a set of 2s fully-decoupled subsystems. Then, we introduce another level of approximation by taking advantage of the subsystems' close similarity and applying the same FSAI to all of them. This leads to substantial memory savings and notable increases in the arithmetic intensity resulting from employing the more compute-intensive sparse matrix-matrix product. Of course, recycling the same preconditioner on all the subsystems worsens its convergence. However, this effect was much smaller than expected and made us introduce relatively cheap but very effective low-rank corrections. A key feature of these corrections is that thanks to being applied to each subsystem independently, the more symmetries being exploited, the more effective they become, leading to up to 5.7x faster convergences than the standard FSAI. Numerical experiments on up to 1.07 billion grids confirm the quality of our low-rank corrected FSAI, which, despite being 2.6x lighter, outperforms the standard FSAI by a factor of up to 4.4x.
Discovery of breakout patterns in financial tick data via parallel stream processing with in-order guarantees

In this paper we describe a parallel solution to the problem of discovering breakout patterns in continuously evaluated exponential-moving averages (EMA) of financial tick data streams over tumbling windows, achieving in-order processing of stock quotes. Our solution extends a first system we developed for this problem that used sequential ingestion of tick data to correctly preserve the per symbol ordering while placing them to their corresponding time window. The solution described in this paper allows for parallel ingestion of tick data streams to increase throughput, while ensuring order via a broadcast mechanism that propagates necessary ordering metadata to downstream operators. Evaluation of our prototype on a 32-core cluster of high-performance servers shows that our parallel prototype scales well (although sublinearly, given the constraint of maintaining in-order guarantees), and yields up to ~1.73x faster processing compared to the sequential-ingest version when each is deployed on 32 cores over 4 servers.
A Survey of Learning Causality with Data: Problems and Methods

The era of big data provides researchers with convenient access to copious data. However, we often have little knowledge of such data. The increasing prevalence of massive data is challenging the traditional methods of learning causality because they were developed for the cases with limited amount of data and strong prior causal knowledge. This survey aims to close the gap between big data and learning causality with a comprehensive and structured review of both traditional and frontier methods followed by a discussion about some open problems of learning causality. We begin with preliminaries of learning causality. Then we categorize and revisit methods of learning causality for the typical problems and data types. After that, we discuss the connections between learning causality and machine learning. At the end, some open problems are presented to show the great potential of learning causality with data
Association between extremely high-density lipoprotein cholesterol and adverse cardiovascular outcomes: a systematic review and meta-analysis

Background The association between high-density lipoprotein cholesterol (HDL-C) and adverse cardiovascular outcomes is understudied. Based on cohort studies, the current study aimed to investigate the association of extremely high HDL-C with all-cause, atherosclerotic cardiovascular disease (CVD) mortality, and stroke risk. Methods A systematic literature search in Embase, PubMed, Cochrane Library, and Web of Science was performed to collect relevant cohort studies published before August 20, 2022. A random-effects model was used to pool relative risks (RRs) and 95% confidence intervals (CIs). Results A total of 17 cohort studies involving 19,630,829 participants were included, encompassing 18,547,132 total deaths (1,328,036 CVD deaths). All-cause mortality, CVD mortality, and stroke risk in the extremely high HDL-C group were increased by 15% (RR = 1.15, 95% CI:1.05–1.25), 14% (RR = 1.14, 95% CI:0.96–1.35) and 14% (RR = 1.14, 95% CI:0.82–1.58), compared to the normal HDL-C group. In subgroup analyses, extremely high HDL-C was associated with a reduced risk of CVD mortality in women and a lower risk of stroke in men compared to normal HDL-C levels. Conclusions The extremely high levels of HDL-C were associated with elevated risks of all-cause mortality, CVD mortality, and stroke. More well-designed studies are needed to confirm our findings. Systematic Review Registration https://www.crd. york.ac. uk/PROSPERO/display_record. php? RecordID=370201, identifier: CRD42022370201.
An open-source human-in-the-loop BCI research framework: method and design

Brain-computer interfaces (BCIs) translate brain activity into digital commands for interaction with the physical world. The technology has great potential in several applied areas, ranging from medical applications to entertainment industry, and creates new conditions for basic research in cognitive neuroscience. The BCIs of today, however, offer only crude online classification of the user's current state of mind, and more sophisticated decoding of mental states depends on time-consuming offline data analysis. The present paper addresses this limitation directly by leveraging a set of improvements to the analytical pipeline to pave the way for the next generation of online BCIs. Specifically, we introduce an open-source research framework that features a modular and customizable hardware-independent design. This framework facilitates human-in-the-loop (HIL) model training and retraining, real-time stimulus control, and enables transfer learning and cloud computing for the online classification of electroencephalography (EEG) data. Stimuli for the subject and diagnostics for the researcher are shown on separate displays using web browser technologies. Messages are sent using the Lab Streaming Layer standard and websockets. Real-time signal processing and classification, as well as training of machine learning models, is facilitated by the open-source Python package Timeflux. The framework runs on Linux, MacOS, and Windows. While online analysis is the main target of the BCI-HIL framework, offline analysis of the EEG data can be performed with Python, MATLAB, and Julia through packages like MNE, EEGLAB, or FieldTrip. The paper describes and discusses desirable properties of a human-in-the-loop BCI research platform. The BCI-HIL framework is released under MIT license with examples at: bci.lu. se/bci-hil (or at: github.com/bci-hil/bci-hil).
Editorial: The links between space plasma physics and planetary science

COPYRIGHT © 2023 Dialynas, Allen and Roussos. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Editorial: The links between space plasma physics and planetary science
Assessment of different manufacturing techniques for the production of bioartificial scaffolds as soft organ transplant substitutes

Introduction: The problem of organs’ shortage for transplantation is widely known: different manufacturing techniques such as Solvent casting, Electrospinning and 3D Printing were considered to produce bioartificial scaffolds for tissue engineering purposes and possible transplantation substitutes. The advantages of manufacturing techniques’ combination to develop hybrid scaffolds with increased performing properties was also evaluated. Methods: Scaffolds were produced using poly-L-lactide-co-caprolactone (PLA-PCL) copolymer and characterized for their morphological, biological, and mechanical features. Results: Hybrid scaffolds showed the best properties in terms of viability (>100%) and cell adhesion. Furthermore, their mechanical properties were found to be comparable with the reference values for soft tissues (range 1–10 MPa). Discussion: The created hybrid scaffolds pave the way for the future development of more complex systems capable of supporting, from a morphological, mechanical, and biological standpoint, the physiological needs of the tissues/organs to be transplanted. Graphical Abstract
ATHLETIQUE: interest of an adapted physical activity program in patients with juvenile idiopathic arthritis: a feasibility and preliminary effectiveness study

Background Juvenile Idiopathic Arthritis (JIA) is associated with joint inflammation, pain and limited joint mobility, impacting the practice of physical activities. Adapted Physical Activities (APA) are an increasingly used method of rehabilitation, but additional studies are needed to define the nature of the most appropriate physical activity for patients with JIA. The “ATHLETIQUE” project aims to evaluate the impact of a program integrating APA sessions with use of a pedometer watch, on disease activity in patients with JIA. Methods This study will be a randomized, multicenter, open-label, controlled clinical trial with 2 parallel arms. The patients included in this study will be children and adolescents with JIA, aged 6 to 17 years. The experimental group (30 patients) will participate in an APA program for 3 months and will use a pedometer watch for one year. We will evaluate and compare the change in disease activity measurements (primary objective), fatigue, pain, quality of life, level of physical activity, functional capacities, and muscle strength (secondary objectives) after 14, 26 and 50 weeks. The control group (10 patients) will undergo the same evaluations as the experimental group but will not participate in the APA program and will not wear the pedometer watch. Expected results The APA program may help to promote an active lifestyle with regular physical activity, preventing comorbidities and motor disability. Promising results on disease activity, functional capacities and quality of life would enable us to envisage a larger research program with a view to optimizing and assessing APA for children with JIA. Discussion This study will be conducted in the short and medium-term, with one-year follow-up, including 3 months of APA sessions for the experimental group. The sessions proposed during the APA program will mainly be aerobic and bodyweight exercises. Furthermore, in contrast to previous studies on this topic, our study will integrate a novel element, namely the use of a pedometer watch. This watch will help to implement strategies to address motivation. This study aims to improve physical and mental well-being, provide a basis for the design of a larger study, and propose recommendations adapted to children with JIA. Trial registration Registered with ClinicalTrials.gov under the number NCT05572424
Protective mechanisms of a microbial oil against hypercholesterolemia: evidence from a zebrafish model

A Western diet elevates the circulating lipoprotein and triglyceride levels which are the major risk factors in cardiovascular disease (CVD) development. Consumption of long-chain omega-3 fatty acids can stall the disease progression. Although these fatty acids can significantly impact the intestine under a hypercholesterolemic condition, the associated changes have not been studied in detail. Therefore, we investigated the alterations in the intestinal transcriptome along with the deviations in the plasma lipids and liver histomorphology of zebrafish offered DHA- and EPA-rich oil. Fish were allocated to 4 dietary treatments: a control group, a high cholesterol group and microbial oil groups with low (3.3%) and high (6.6%) inclusion levels. We quantified the total cholesterol, lipoprotein and triglyceride levels in the plasma. In addition, we assessed the liver histology, intestinal transcriptome and plasma lipidomic profiles of the study groups. The results suggested that higher levels of dietary microbial oil could control the CVD risk factor indices in zebrafish plasma. Furthermore, microbial oil-fed fish had fewer liver vacuoles and higher mRNA levels of genes involved in β-oxidation and HDL maturation. Analyses of the intestine transcriptome revealed that microbial oil supplementation could influence the expression of genes altered by a hypercholesterolemic diet. The plasma lipidomic profiles revealed that the higher level of microbial oil tested could elevate the long-chain poly-unsaturated fatty acid content of triglyceride species and lower the concentration of several lysophosphatidylcholine and diacylglycerol molecules. Our study provides insights into the effectiveness of microbial oil against dyslipidemia in zebrafish.
Lymphoplasmapheresis versus plasma exchange in severe myasthenia gravis: a retrospective cohort study

Background Lymphoplasmapheresis (LPE) is a new therapy developed on the basis of traditional plasma exchange (PE) in combination with leukapheresis. Currently, it remains unclear whether PE and LPE show differences in efficacy for severe MG. Methods A retrospective analysis was conducted on 198 MG patients, 75 in the PE group and 123 in the LPE group, and the patients’ Myasthenia Gravis Foundation of America (MGFA) Clinical Classification was Class IV. The treatment outcome was the change in Quantitative Myasthenia Gravis Score (QMGS) from baseline to the end of treatment. Propensity score matching (PSM) was applied for the balance of confounders between the two groups. Results In this study cohort, the safety profile of LPE and PE was good and no serious adverse events were observed. Based on PSM, 62 patients treated with LPE and 62 patients treated with PE were entered into a comparative efficacy analysis. In the PE group, patients underwent a total of 232 replacements, with a mean of 3.74. PE significantly improved the patients’ QMGS performance, with the mean QMGS decreasing from 22.98 ± 4.03 points at baseline to 18.34 ± 5.03 points after treatment, a decrease of 4.68 ± 4.04 points (p < 0.001). A decrease of ≥3 points in QMGS was considered a significant improvement, with a treatment response rate of 67.7% in the PE group. In the LPE group, patients received a total of 117 replacements, with a mean of 1.89. The patients’ mean QMGS was 23.19 ± 4.11 points at baseline and was 16.94 ± 5.78 points after treatment, a decrease of 6.26 ± 4.39 points (p < 0.001). The improvement in QMGS was more significant in patients treated with LPE compared to the PE group (p = 0.039). The treatment response rate in the LPE group was 79%, which was not significantly different compared to the PE group (p = 0.16). The LEP group had a shorter mean length of stay compared to the PE group (10.86 ± 3.96 vs. 12.14 ± 4.14 days), but the difference was not statistically significant (p = 0.13). During the 2-month follow-up period, LPE may be associated with better functional outcomes for patients, with lower QMG score and relapse rate. LPE and PE were both effective in reducing the levels of inflammatory cytokines (TNF-α, IL-1β, and IL-6) and AChR-Ab. Compared to PE, LPE was superior in the reduction of AChR-Ab titer. Conclusion In severe MG, LPE may be a more preferred treatment option than PE. It achieves treatment outcomes that are not inferior to or even better than PE with fewer replacements. This study provides further evidence to support the application of LPE as a new treatment option for MG.
New algebraic-geometric conditions for the linearization by input-output injection

The goal of this paper is twofold. It unifies and generalizes existing results on input-output injection linearization of nonlinear systems. The problem is solved as a realization problem since it is based on the analysis of the structure of the input-output differential equation. The necessary and sufficient conditions are derived from a simplified and constructive procedure. For clarity, the paper is limited to the case of single output systems. Exterior differential systems are extensively used throughout the paper, giving constructive conditions.