Miracle-dz/UniFAR-FTUs huggingface dataset:
Jointly Learning and Selecting Features via Conditional Point-wise Mixture RBMs

Feature selection is an important technique for ﬁnding relevant features from high-dimensional data. However, the performance of feature selection methods is often limited by the raw feature representation. On the other hand, unsupervised feature learning has recently emerged as a promising tool for extracting useful features from data. Although supervised information can be exploited in the process of supervised ﬁne-tuning (preceded by unsupervised pre-training), the training becomes challenging when the unlabeled data contain signiﬁcant amounts of irrelevant information. To address these issues, we propose a new generative model, the conditional point-wise mixture restricted Boltzmann machine , which attempts to perform feature grouping while learning the features. Our model represents each input coordinate as a mixture model when conditioned on the hidden units, where each group of hidden units can generate the corresponding mixture component. Furthermore, we present an extension of our method that combines bottom-up feature learning and top-down feature selection in a uniﬁed way, which can effectively handle irrelevant input patterns by focusing on relevant signals and thus learn more informative features. Our experiments show that our model is effective in learning separate groups of hidden units (e.g., that correspond to informative signals vs. irrelevant patterns) from complex, noisy data.
“Convolutional Neural Networks For Galaxy Morphology Classiﬁcation” Machine Learning (course 1DT071) Uppsala University – Spring 2018

In 2014, Kaggle organized the Galaxy Zoo challenge to analyze images of galaxies from the Sloan Digital Sky Survey in order to automate the tagging of morphological attributes. For a machine to automatically learn the features directly from data, a deep convolutional neural network was implemented based on an existing architecture that placed highly in a competitive image classiﬁcation competition a number of years prior. The dataset consisted of 79 975 test images and 61 578 training images which were cropped and downsampled for dimensionality reduction and memory eﬃciency. The model was trained with a variety of batch sizes and input formats. These input formats included further dimensionality reductions, data augmentation, and color space conversions. One of the methods ultimately attained a Top 100 score on the Kaggle leaderboards. Further advances can be done to improve the performance of the model by tuning other hyperparameters such as learning rate and number of epochs.
An Exact MaxSAT Algorithm: Further Observations and Further Improvements

In the maximum satisfiability problem (MaxSAT), given a CNF formula with m clauses and n variables, we are asked to find an assignment of the variables to satisfy the maximum number of clauses. Chen and Kanj showed that this problem can be solved in O*(1.3248^m) time (DAM 2004) and the running time bound was improved to O*(1.2989^m) by Xu et al. (IJCAI 2019). In this paper, we further improve the result to O*(1.2886^m). By using some new reduction and branching techniques we can avoid several bottlenecks in previous algorithms and get the improvement on this important problem.
SAR-to-Optical Image Translation via Neural Partial Differential Equations

Synthetic Aperture Radar (SAR) becomes prevailing in remote sensing while SAR images are challenging to interpret by human visual perception due to the active imaging mechanism and speckle noise. Recent researches on SAR-to-optical image translation provide a promising solution and have attracted increasing attentions, though still suffering from low optical image quality with geometric distortion due to the large domain gap. In this paper, we mitigate this issue from a novel perspective, i.e., neural partial differential equations (PDE). First, based on the efficient numerical scheme for solving PDE, i.e., Taylor Central Difference (TCD), we devise a basic TCD residual block to build the backbone network, which promotes the extraction of useful information in SAR images by aggregating and enhancing features from different levels. Furthermore, inspired by the Perona-Malik Diffusion (PMD), we devise a PMD neural module to implement feature diffusion through layers, aiming at removing the noises in smooth regions while preserving the geometric structures. Assembling them together, we propose a novel SAR-to-Optical image translation network named S2O-NPDE, which delivers optical images with finer structures and less noise while enjoying an explainability advantage from explicit mathematical derivation. Experiments on the popular SEN1-2 dataset show that our model outperforms state-of-the-art methods in terms of both objective metrics and visual quality.
Fat Quantitation in Liver Biopsies Using a Pretrained Classification Based System

Non-Alcoholic Fatty Liver Disease (NAFLD) is a common syndrome that mainly leads to fat accumulation in liver and steatohepatitis. It is targeted as a severe medical condition ranging from 20% to 40% in adult populations of the Western World. Its effect is identified through insulin resistance, which places patients at high mortality rates. An increased fat aggregation rate, can dramatically increase the development of liver steatosis, which in later stages may advance into fibrosis and cirrhosis. During recent years, new studies have focused on building new methodologies capable of detecting fat cells, based on the histology method with digital image processing techniques. The current study, expands previous work on the detection of fatty liver, by identifying once more a number of diverse histological findings. It is a combined study of both image analysis and supervised learning of fat droplet features, with a specific goal to exclude other findings from fat ratio calculation. The method is evaluated in a total set of 40 liver biopsy images with different magnification capabilities, performing satisfyingly (1.95% absolute error).
Big Data Perspective for Driver/Driving Behavior

There are many articles published in driving/driver behavior (DDB) but few of them have focused on DDB with big data in the literature. The reasons for this might be the lack of media coverage, data, expertise or big data perspectives. This paper presents a big data perspective for investigating the DDB based on models, data features, and experiences. For this purpose, DDB studies were reviewed and grouped into six perspectives. 6V's of big data (volume, velocity, variety, veracity, vulnerability and value) were also revised and discussed how these V's were compatible with DDB data. Finally, the use of big data in DDB analysis was discussed and some suggestions were presented. The lack of big data perception on DDB research was also overviewed and a new perspective was presented for researchers, applicants or business managers to do more effective and suitable studies in the field of big data DDB.
Investment decisions based on EEG emotion recognition

In the recent years, computational neuroscience which is a study on the brain functions was frequently applied to discover interesting patterns in the investment decisions. Emotions in neurofinance study have been measured by sentiments analysis but not measured by biosignal. Behavioural finance affects investors‘ performance which is also influenced by their emotional or cognitive errors in taking the investment decisions. This paper focused on the EEG-based emotion recognition recorded while making decisions that can also be helpful in investment’s returns. The features were extracted by using Mel Frequency Cepstal Coefficient (MFCC) and the classification used the Multi-Layer Perceptron (MLP) classifier. The EEG-based emotion recognition was tested by using the dimensional models of emotions, 12-PAC and rSASM, and also the Radboud Faces Database (RaFD). Results show that investment decisions can be driven by the emotions of the investor and some measurement should be taken before they lose their money.
Flexible manufacturing systems performance in U.S. automotive manufacturing plants: a case study

Abstract Flexible manufacturing systems (FMS) philosophy is a key weapon in achieving global manufacturing competitiveness. It encompasses a wide range of dimensions to improve all aspects of operational performance metrics. The aim of the study is to examine the current state of flexibility adoption in U.S. automotive manufacturing facilities and its impact on operational performance metrics. It utilizes survey questionnaire developed based on previous work in U.S. manufacturing industry. The survey was originally distributed to 420 facility managers in the U.S. domestic automotive industry. It was revealed that 70% of the respondents had implemented all 15 flexibility dimensions listed in the questionnaire. The data analysis conducted shows that implementation of certain flexibility dimensions will lead to significant improvement in specific operational performance metrics. This considerable finding can be used as a guide for manufacturing managers to achieve certain objectives in operational performance improvement in a rapidly changing environment.
Observational study on cases of sepsis in the State of Pará from 2015 to 2019

Sepsis can be understood as a set of infections throughout the body. Worldwide, its incidence is estimated at around 15 to 19 million cases annually. Recently, deaths have decreased, reaching an estimated value of 20 to 40% of reported cases. However, in Brazil, according to data from the Instituto Latino Americano de Sepsis (Ilas), death from sepsis corresponds to 65% of all deaths, which is much higher than the world estimate. Thus, in this paper, we will evaluate mortality from sepsis in the State of Pará from 2015 to 2019. An ecological observational study was carried out using the data collected in the Mortality Information System and the Hospital Information System, made available by the Department of Informatics of the Unified Health System in Brazil. During the period investigated, a total of 2,547 deaths from sepsis were recorded. The epidemiological profile of mortality from sepsis presented individuals with the following characteristics: male, mixed race, 1 to 3 years of schooling, marital status: a married, place of occurrence: hospital, and age group from 60 to 79 years. It is of fundamental importance to develop more targeted prevention policies, prioritize educational and population awareness measures, and the continuous improvement of health teams regarding the knowledge of sepsis about early identification and more effective care to reduce mortality from sepsis in the state of Pará, Brazil.
Early Haemodynamic Outcomes of Pulmonary Thrombo-endarterectomy: Experience of a Tertiary Referral Center in Egypt

Background: Chronic thromboembolic pulmonary hypertension is a potentially curable condition via pulmonary thromboendarterectomy (PTE) which involves surgical removal of the thromboembolic material through endarterectomy. This is a technically demanding surgery and better results could be achieved only in experienced centers worldwide. The present study aimed to document the hemodynamic outcomes of PTE focusing on the degree of improvement in exercise capacity and hemodynamic parameters after surgery. Subjects and Methods: an interventional prospective and retrospective study was undertaken in the Cardiothoracic Surgery Department at Kasr El-Aini University Hospitals, including 20 patients who underwent PTE. Results: After excluding the 3 mortality cases, all the 15 cases with preoperative NYHA-FC III and IV improved to a better FC while the 2 cases with preoperative NYHA-FC II remained unchanged. Mean pulmonary artery pressure (mPAP) and pulmonary vascular resistance (PVR) decreased by 33.5 (range 35.6 - 31.3) mmHg and 542.6 (range 604 -481.1) dyn.sec. cm -5 respectively. Conclusion: PTE results in a dramatic improvement in the hemodynamic parameters in patients with chronic thromboembolic pulmonary hypertension.
“INDUSTRY 4.0”—TOWARDS OPPORTUNITIES AND CHALLENGES OF IMPLEMENTATION

In the paper the concept of "Industry 4.0", which is based on self-organizing production systems, is shown. The attention is paid to the automation which is "an installation" that provides conditions for a human cooperating with the system as a client, service technician and programmer. The problem of competence understood as a competence of the person and as the power of position, and creating more and more independence of the system, independent from the human, way of his functioning, is also presented. Moreover, the comparison of the assumptions of automation and contemporary solutions is done.
Investigation the Influence of Marketing-mix Efforts on Brand Equity in the Bangladesh Software Industry

: Brand equity is a vital metric for measuring a brand’s health, and monitoring it on a regular basis is an important part of efficient brand management. An already developed model is being used to examine the relationships among marketing-mix efforts (channel/place, price, promotion, and after-sales service), and three dimensions of brand equity (brand awareness, perceived quality, and brand loyalty). The goal of our research was to see how the marketing mix (pricing, product, place, promotion) and after-sales services affected three aspects of brand equity. The model is tested in the context of the Bangladesh software sector. The study focused more on the hypothesis that checks Bangladesh’s software industry perspective. It’s a descriptive survey in which the necessary information was gathered through a questionnaire. 70 responses were selected as sample data to analyze. The correlations between research variables have been investigated utilizing SPSS (correlation and regression) and Amos software. Structural equation modeling is used to test the model and research hypotheses. Cronbach’s alpha was used to confirm reliability. Perceived quality is placed as a mediator between the marketing mix efforts and brand equity. The results show that few of the marketing-mix efforts and threedimensions of brand equity have significant relationships with brand equity.
Energy storage system expansion planning in power systems: a review

In recent two decades, the power systems have confronted with considerable changes such as the power system restructuring, growth of distributed energy sources and renewable energy sources (RESs), and emergence of smart grid concept. One of the common challenges caused by these changes is flexibility necessity of energy resources. One of the best solutions to mitigate this challenge is energy storage systems (ESSs) utilisation. The main question is how to determine size, site, and type of ESSs to maximise their benefits. This study reviews the answers to this question according to the research studies. This study first classifies the studies related to ESS expansion planning into two main categories from the viewpoint of the power system operators and the investors. Next, the first main category is divided into three subcategories: ESS expansion planning in microgrids, distribution networks, and transmission networks. The second main category is classified into two subcategories: ESS expansion planning aim to smooth RESs output power and to maximise profit. In each subcategory the modelling approaches, solving methods, and the results are evaluated. Finally, based on the existing challenges, the future research directions of ESS expansion planning are outlined.
Improved AHP Model and Neural Network for Consumer Finance Credit Risk Assessment

With the rapid expansion of the consumer financial market, the credit risk problem in borrowing has become increasingly prominent. Based on the analytic hierarchy process (AHP) and the long short-term memory (LSTM) model, this paper evaluates individual credit risk through the improved AHP and the optimized LSTM model. Firstly, the characteristic information is extracted, and the financial credit risk assessment index system structure is established. The data are input into the AHP-LSTM neural network, and the index data are fused with the AHP so as to obtain the risk level and serve as the expected output of the LSTM neural network. The results of the prewarning model after training can be used for financial credit risk assessment and prewarning. Based on LendingClub and PPDAI data sets, the experiment uses the AHP-LSTM model to classify and predict and compares it with other classification methods. Experimental results show that the performance of this method is superior to other comparison methods in both data sets, especially in the case of unbalanced data sets.
Mainstream media use for far-right mobilisation on the alt-tech online platform Gab

Far-right movements tend to have an ambivalent relationship with mainstream media. On the one hand, they often express animosity towards traditional media, accusing them of spreading ‘fake news’ and being part of a hostile conspiracy against ordinary (white) people. On the other hand, this often antagonist perception of the media does not stop far-right actors from using mainstream reporting for their ideological online messaging. This contradictory relationship is situated in a broader societal context shaped by, among other factors, widespread mistrust towards the media, an increasingly polarised media landscape and the recent proliferation of a hyper-partisan online media ecosystem at the far-right fringes. Against this contextual backdrop, this multimethod study analyses over 45,000 posts from Australian users of the far-right alt-tech online platform Gab to examine the use of mainstream media sources for their ideological messaging. This is complemented by a qualitative in-depth analysis of 298 posts that share mainstream media content, identifying posting patterns of incorporating or reframing mainstream media outputs that seek to communicate far-right ideologies within the ideologically charged online community on Gab.
Modelling the Dynamics of Multi-Agent Q-learning: The Stochastic Effects of Local Interaction and Incomplete Information

The theoretical underpinnings of multiagent reinforcement learning has recently attracted much attention. In this work, we focus on the generalized social learning (GSL) protocol --- an agent interaction protocol that is widely adopted in the literature, and aim to develop an accurate theoretical model for the Q-learning dynamics under this protocol. Noting that previous models fail to characterize the effects of local interactions and incomplete information that arise from GSL, we model the Q-values dynamics of each individual agent as a system of stochastic differential equations (SDE). Based on the SDE, we express the time evolution of the probability density function of Q-values in the population with a Fokker-Planck equation. We validate the correctness of our model through extensive comparisons with agent-based simulation results across different types of symmetric games. In addition, we show that as the interactions between agents are more limited and information is less complete, the population can converge to a outcome that is qualitatively different than that with global interactions and complete information.
Construcción de una batería para la evaluación de la salud sexual de los/as niños/as y adolescentes*

La salud sexual en la infancia y en la adolescencia constituye una prioridad dado el importante impacto que esta ejerce en su salud global. Sin embargo, no existe un fuerte consenso sobre los indicadores que determinan el poseer una buena o una mala salud sexual, y más cuando hablamos de una población de especial vulnerabilidad dados los múltiples cambios físicos, psicológicos y sociales a los que se enfrentan en poco tiempo. En el presente estudio exponemos el proceso de construcción de una batería de instrumentos para la evaluación de la salud sexual de los niños, niñas y adolescentes. Tras la realización de un completo proceso de valoración, guiado por múltiples expertos en el área, se ha concluido que tanto los conocimientos como las actitudes (p.ej. sexismo) y habilidades sexuales (p.ej. asertividad sexual) son un indicador principal. Asimismo, los comportamientos sexuales de riesgo (p. ej. búsqueda de sensaciones sexuales, adicción al sexo, al cibersexo o conductas de sexting), también se consideran variables importantes. Disponer de una batería de instrumentos validados en este grupo poblacional que recoja todos los aspectos relevantes para la valoración de su salud sexual va a permitir, no solo conocer más sobre la misma, sino también probar si la educación afectivo-sexual que reciben es adecuada y suficiente para asegurar un desarrollo sexual saludable.
GraphDIVE: Graph Classification by Mixture of Diverse Experts

Graph classification is a challenging research task in many applications across a broad range of domains. Recently, Graph Neural Network (GNN) models have achieved superior performance on various real-world graph datasets. Despite their successes, most of current GNN models largely suffer from the ubiquitous class imbalance problem, which typically results in prediction bias towards majority classes. Although many imbalanced learning methods have been proposed, they mainly focus on regular Euclidean data and cannot well utilize topological structure of graph (non-Euclidean) data. To boost the performance of GNNs and investigate the relationship between topological structure and class imbalance, we propose GraphDIVE, which learns multi-view graph representations and combine multi-view experts (i.e., classifiers). Specifically, multi-view graph representations correspond to the intrinsic diverse graph topological structure characteristics. Extensive experiments on molecular benchmark datasets demonstrate the effectiveness of the proposed approach.
Prognostic/predictive markers in systemic therapy resistance and metastasis in breast cancer

Breast cancer is a highly heterogeneous group of diseases posing a significant challenge in biomarker-driven research and the development of effective targeted therapies. Especially the treatment of metastatic breast cancer poses even more challenges, as we still lose more than 42,000 women and men each year in the United States alone. New biological insight helps to improve breast cancer treatment through early detection, adaptation to chemotherapy resistance, and tailoring to find the right size of care. This review focuses on existing and new areas of predictive biomarkers under development to tailor the management of breast cancer and the application of integrative approaches that have resulted in the promising candidate biomarker discovery. Furthermore, we review new methods to detect metastatic progression using imaging, and blood-based assays. We hope to increase the attention and awareness of a new generation of therapeutic development strategies in metastatic breast cancer.
Calibration of the Nonlinear Wheel Odometry Model with an Improved Genetic Algorithm Architecture

: To guarantee the required motion estimation accuracy for an autonomous vehicle, the integration of the wheel encoder measurements is an adequate choice besides the generally applied GNSS, inertial and visual-odometry methods. Wheel odometry is a robust and cost-effective technique, but the required calibration of the nonlinear odometry model in the presence of noise remains an open problem in the context of autonomous vehicles. The core problem is that due to the nonlinear behavior of the model, the identiﬁed parameters will be biased even with Gaussian-type measurement noises. The presented method operates with genetic algorithms and utilizes two novel improvements: compensation of the state initialization of the model inside the estimation process, and equilibration of the parameter estimation by an adaptive weighting technique. With these innovations the distortion effects are mitigated and unbiased model calibration can be obtained even when several local minimums exist. The performance of the developed algorithm and the accuracy of parameter estimation are demonstrated with detailed validation and test with a real vehicle.