Reviewerly/exHarmony huggingface dataset:
Suitability evaluation of selected sand geological formations for synthesis high purity crystalline silica, Iraq

Authors:
raw_author_name: H A Al-Saad
raw_affiliation_string: Department of Geology, College of science, University of Basrah, Iraq

Abstract: Abstract This study aims determination if the sand collected from Injana and Dibdibba Formations are suitable for use as low-cost raw materials in the manufacture of high-purity crystalline silica. Sand is distributed in vast quantities within the two Formations, which occupy large portions of Iraq’s southern desert. Quartz is the predominant mineral phase, followed by calcite, feldspar, and dolomite respectively, based on mineral analysis data. SiO 2 is the main oxide in sand with an average of (82.76%), (90.24%) and the rest of oxides are CaO (6.28%), (1.64%), Al 2 O 3 (4.02%),(3.19%), Fe 2 O 3 (2.73%), (0.86%), K 2 O+ Na 2 O (2.05%), (2.51%), SO 3 (0.05%), (0.47%), MnO (0.01%), (0.03%) P 2 O 2 (0.23%),(0.03%), and MgO (1.75%), (1.01%) in Injana and Dibdibba Formations respectively, which are considered as impurities impact the manufacturing properties of sand. The coprecipitation method, which produces high purity crystalline silica at a low cost, involves three steps: the first involves soaking raw sand powder in 2M HCl for 24 hours; The second involves mix purified sand with NaOH while stirring and heating it to 95C° to produce Na 2 SiO 3 ; and the third involves adding 3M HCl to sodium silicate solution to produce Si(OH) 4 and drying it to produce high purity of silica. The prepared silica got mineral analysis and chemical analysis. The synthesized silica was in the crystalline phase, according to the results of the mineral characterization. In synthetic silica of Injana and Dibdibba sand, The amount of SiO 2 increased according to chemical analysis, reaching 96.62%, and 97.69% respectively, while all other oxides (impurities) were lowered to their lowest levels. FE-SEM revealed the existence of spherical, well-agglomerated synthetic silica particles within the size 28.6 nm and 24.76 nm in synthetic silica particles of Injana sand and Dibdibba sand respectively.
Unsupervised texture classification of 3D X-ray Micro-computed Tomography images

Authors:
raw_author_name: Mohamed Soufiane Jouini
raw_affiliation_string: Department of Mathematics, Khalifa University, Abu Dhabi, United Arab Emirates
raw_author_name: Fawaz Hjouj
raw_affiliation_string: Department of Mathematics, Khalifa University, Abu Dhabi, United Arab Emirates

Abstract: Abstract Characterizing rock proprieties is crucial in the oilfield to evaluate hydrocarbon reserves. Several studies showed a high correlation between rock properties and textures. Therefore, we propose integrating texture information in the images to identify precisely the most representative textures in highly heterogeneous rocks to estimate their properties. First, we implemented a steerable pyramid decomposition to extract the texture features. Then, those parameters were used as input for the Self-organizing map to classify the textures. Finally, by applying several models and comparing their results, we suggested the best approach to implement for texture classification.
Gaze analysis: A survey on its applications

Authors:
raw_author_name: Carmen Bisogni
raw_affiliation_string: University of Salerno, Via Giovanni Paolo II 132, Salerno 84084, Fisciano, Italy
raw_author_name: Michele Nappi
raw_affiliation_string: University of Salerno, Via Giovanni Paolo II 132, Salerno 84084, Fisciano, Italy
raw_author_name: Genoveffa Tortora
raw_affiliation_string: University of Salerno, Via Giovanni Paolo II 132, Salerno 84084, Fisciano, Italy
raw_author_name: Alberto Del Bimbo
raw_affiliation_string: University of Firenze, Via Santa Marta 3, Firenze 50139, Italy

Abstract: The examination of ocular movements has a wide range of applications due to the current developments in sensors that are now able to collect this biometric. This type of investigation is known as “gaze analysis”. The gaze has successfully examined a subject's physical and mental status in the past. As a result, over the last few decades, a large and diverse amount of literature on this subject has been generated and presented. The aim of this study is to collect and debate current gaze analysis methods based on their application field. Due to the context-specific needs for performance and efficiency, the eye movements under research are frequently evaluated from completely distinct perspectives. As a result, a collection of data, methods, and discussions ranging from the medical community to virtual and augmented reality, as well as human computer interface and remote learning, has been produced. In addition to providing a peek of novel observation on the issue of gaze analysis, the gaps between and within areas are also discussed to provide points for researchers to pursue.
Reprint of: Some thoughts about transfer learning. What role for the source domain?

Authors:
raw_author_name: A. Cornuéjols
raw_affiliation_string: UMR MIA-Paris-Saclay, AgroParisTech, INRAe, Université Paris-Saclay, 91120, Palaiseau, France

Abstract: Transfer learning is called for when the training and test data do not share the same input distributions (PXS≠PXT) or/and not the same conditional ones (PY|XS≠PY|XT). In the most general case, the input spaces and/or output spaces can be different: XS≠XT and/or YS≠YT. However, most work assume that XS=XT. Furthermore, a common held assumption is that it is necessary that the source hypothesis be good on the source training data and that the “distance” between the source and the target domains be as small as possible in order to get a good (transferred) target hypothesis. This paper revisits the reasons for these beliefs and discusses the relevance of these conditions. An algorithm is presented which can deal with transfer learning problems where XS≠XT, and that furthermore brings a fresh perspective on the role of the source hypothesis (it does not have to be good) and on what is important in the distance between the source and the target domains (translations between them should belong to a limited set). Experiments illustrate the properties of the method and confirm the theoretical analysis. Determining beforehand a relevant source hypothesis remains an open problem, but the vista provided here helps understanding its role.
Image Segmentation and Classification Fields Are Generated Using Binary Random Fields Based on Planar Graph and Neighborhood Spanning Tree

Authors:
raw_author_name: None Dr. G. Thippanna

Abstract: Image analysis plays a pivotal role in computer vision, with image segmentation and classification being fundamental tasks in this domain. This abstract presents a novel approach to image processing that leverages Binary Random Fields (BRF) with a foundation in planar graphs and neighborhood spanning trees. This innovative methodology seeks to enhance the accuracy and efficiency of image segmentation and classification, addressing key challenges in computer vision applications. Binary Random Fields (BRF) is probabilistic graphical models that have proven effective in capturing spatial dependencies and contextual information within images. Our proposed method extends the utility of BRF by incorporating planar graph theory and neighborhood spanning trees to refine the segmentation and classification processes. Planar graphs offer a structured representation of image data, preserving topological relationships among pixels, while neighborhood spanning trees provide a hierarchical framework for modeling image regions
Detailed Report on the Measurement of the Positive Muon Anomalous Magnetic Moment to 0.20 ppm

Authors:
raw_author_name: Bedeschi, F.
raw_affiliation_string: INFN, Pisa
raw_author_name: Bowcock, T.
raw_affiliation_string: U. Liverpool (main)
raw_author_name: Braun, S.
raw_affiliation_string: Pennsylvania U.
raw_author_name: Casey, B. C. K.
raw_affiliation_string: Fermilab
raw_author_name: Chu, Z.
raw_affiliation_string: SKLPPC, Shanghai; Shanghai Jiaotong U.
raw_author_name: Donati, S.
raw_affiliation_string: INFN, Pisa; Pisa U.
raw_author_name: Galati, M. D.
raw_affiliation_string: INFN, Pisa; Pisa U.
raw_author_name: Haciomeroglu, S.
raw_affiliation_string: U. Liverpool (main)
raw_author_name: Halewood-Leagas, T.
raw_affiliation_string: U. Liverpool (main)
raw_author_name: Hu, T.
raw_affiliation_string: Shanghai Jiao Tong U.
raw_author_name: Hu, Y.
raw_affiliation_string: SKLPPC, Shanghai; Shanghai Jiaotong U.
raw_author_name: Logashenko, I.
raw_affiliation_string: Novosibirsk State U.
raw_author_name: Lusiani, A.
raw_affiliation_string: Pisa, Scuola Normale Superiore
raw_author_name: Lyon, A. L.
raw_affiliation_string: Fermilab
raw_author_name: Omarov, Z.
raw_affiliation_string: KAIST, Taejon
raw_author_name: Santi, L.
raw_affiliation_string: INFN, Udine
raw_author_name: Semertzidis, Y. K.
raw_affiliation_string: IBS, Daejeon, CAPP
raw_author_name: Stoughton, C.
raw_affiliation_string: Fermilab
raw_author_name: Zeng, Y.
raw_affiliation_string: Shanghai Jiao Tong U.

Abstract: We present details on a new measurement of the muon magnetic anomaly, $a_\mu = (g_\mu -2)/2$. The result is based on positive muon data taken at Fermilab's Muon Campus during the 2019 and 2020 accelerator runs. The measurement uses $3.1$ GeV$/c$ polarized muons stored in a $7.1$-m-radius storage ring with a $1.45$ T uniform magnetic field. The value of $ a_{\mu}$ is determined from the measured difference between the muon spin precession frequency and its cyclotron frequency. This difference is normalized to the strength of the magnetic field, measured using Nuclear Magnetic Resonance (NMR). The ratio is then corrected for small contributions from beam motion, beam dispersion, and transient magnetic fields. We measure $a_\mu = 116 592 057 (25) \times 10^{-11}$ (0.21 ppm). This is the world's most precise measurement of this quantity and represents a factor of $2.2$ improvement over our previous result based on the 2018 dataset. In combination, the two datasets yield $a_\mu(\text{FNAL}) = 116 592 055 (24) \times 10^{-11}$ (0.20 ppm). Combining this with the measurements from Brookhaven National Laboratory for both positive and negative muons, the new world average is $a_\mu$(exp) $ = 116 592 059 (22) \times 10^{-11}$ (0.19 ppm).
A Framework for the Interoperability of Cloud Platforms: Towards FAIR Data in SAFE Environments

Authors:
raw_author_name: Robert L. Grossman
raw_affiliation_string: Center for Translational Data Science, University of Chicago, Chicago, IL, USA
raw_author_name: Amanda Haddock
raw_affiliation_string: Dragon Master Initiative, Kechi, KS, USA
raw_author_name: Allison P. Heath
raw_affiliation_string: Children’s Hospital of Philadelphia, Philadelphia, PA, USA
raw_author_name: Deanne M. Taylor
raw_affiliation_string: University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA

Abstract: As the number of cloud platforms supporting scientific research grows, there is an increasing need to support interoperability between two or more cloud platforms. A well accepted core concept is to make data in cloud platforms Findable, Accessible, Interoperable and Reusable (FAIR). We introduce a companion concept that applies to cloud-based computing environments that we call a Secure and Authorized FAIR Environment (SAFE). SAFE environments require data and platform governance structures and are designed to support the interoperability of sensitive or controlled access data, such as biomedical data. A SAFE environment is a cloud platform that has been approved through a defined data and platform governance process as authorized to hold data from another cloud platform and exposes appropriate APIs for the two platforms to interoperate.
Joint wireless resource allocation and bitrate adaptation for QoE improvement in IRS-aided RSMA-enabled IoMT streaming systems

Authors:
raw_author_name: Nam-Phuong Tran
raw_affiliation_string: School of Computer Science and Engineering, Chung-Ang University, Seoul 06974, South Korea
raw_author_name: Quang Tuan Do
raw_affiliation_string: School of Computer Science and Engineering, Chung-Ang University, Seoul 06974, South Korea
raw_author_name: Nhu-Ngoc Dao
raw_affiliation_string: Department of Computer Science and Engineering, Sejong University, Seoul 05006, South Korea
raw_author_name: Sungrae Cho
raw_affiliation_string: School of Computer Science and Engineering, Chung-Ang University, Seoul 06974, South Korea

Abstract: In the context of the increasing demand for Internet of Multimedia Things (IoMT) services, rate splitting multiple access (RSMA) and intelligent reflecting surface (IRS) technologies have been considered potential networking enablers to provide ultra-throughput wireless access. However, challenges arise due to the heterogeneity of IoMT devices and arbitrary network quality changes, resulting in unwanted service quality fluctuations and downgradation. This study addresses this problem by jointly optimizing wireless resource allocation and bitrate adaptation with Deep Reinforcement Learning (DRL)-based QoE management for IRS-aided RSMA-enabled IoMT streaming systems. We formulated the problem as a Markov decision process (MDP) and apply Proximal Policy Optimization (PPO) method to flexibly adjust IoMT bitrate, transmission beamforming, IRS phase shift, and RSMA parameters. As a result, our algorithm mitigates overestimation of client-side bandwidth, leading to smoother playback and reduced quality fluctuations. Simulations show that our approach outperforms baseline methods in terms of video resolution (up to 2.5 times) and achievable sum-rate (up to 50%), contributing to a superior streaming experience in IoMT systems.
Expectation maximization pseudo labels

Authors:
raw_author_name: Moucheng Xu
raw_affiliation_string: Satsuma Lab, University College Londo, 90 High Holborn, WC1V 6LJ, UK; UCL Centre for Medical Image Computing (CMIC), University College London, 90 High Holborn, London, WC1V 6LJ, UK
raw_author_name: Marius de Groot
raw_affiliation_string: GSK, Gunnels Wood Road, Stevenage, SG1 2NY, UK
raw_author_name: Daniel C. Alexander
raw_affiliation_string: UCL Centre for Medical Image Computing (CMIC), University College London, 90 High Holborn, London, WC1V 6LJ, UK; UCL Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, UK
raw_author_name: Yipeng Hu
raw_affiliation_string: UCL Centre for Medical Image Computing (CMIC), University College London, 90 High Holborn, London, WC1V 6LJ, UK; Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, Charles Bell House, 1a 43-45 Foley Street, London, W1 W 7TS, UK
raw_author_name: Joseph Jacob
raw_affiliation_string: Satsuma Lab, University College Londo, 90 High Holborn, WC1V 6LJ, UK; UCL Centre for Medical Image Computing (CMIC), University College London, 90 High Holborn, London, WC1V 6LJ, UK

Abstract: In this paper, we study pseudo-labelling. Pseudo-labelling employs raw inferences on unlabelled data as pseudo-labels for self-training. We elucidate the empirical successes of pseudo-labelling by establishing a link between this technique and the Expectation Maximisation algorithm. Through this, we realise that the original pseudo-labelling serves as an empirical estimation of its more comprehensive underlying formulation. Following this insight, we present a full generalisation of pseudo-labels under Bayes’ theorem, termed Bayesian Pseudo Labels. Subsequently, we introduce a variational approach to generate these Bayesian Pseudo Labels, involving the learning of a threshold to automatically select high-quality pseudo labels. In the remainder of the paper, we showcase the applications of pseudo-labelling and its generalised form, Bayesian Pseudo-Labelling, in the semi-supervised segmentation of medical images. Specifically, we focus on: (1) 3D binary segmentation of lung vessels from CT volumes; (2) 2D multi-class segmentation of brain tumours from MRI volumes; (3) 3D binary segmentation of whole brain tumours from MRI volumes; and (4) 3D binary segmentation of prostate from MRI volumes. We further demonstrate that pseudo-labels can enhance the robustness of the learned representations. The code is released in the following GitHub repository: https://github.com/moucheng2017/EMSSL.
Graph neural networks for clinical risk prediction based on electronic health records: A survey

Authors:
raw_author_name: Ali Amirahmadi
raw_affiliation_string: School of Information Technology, Halmstad University, Kristian IV:s väg 3, Halmstad, 301 18, Sweden
raw_author_name: Edison Pignaton de Freitas
raw_affiliation_string: Institute of Informatics, Universidade Federal do Rio Grande do Sul, Avenida Bento Gonçalves, 9500, Porto Alegre, 91501-970, RS, Brazil
raw_author_name: Amira Soliman
raw_affiliation_string: School of Information Technology, Halmstad University, Kristian IV:s väg 3, Halmstad, 301 18, Sweden
raw_author_name: Kobra Etminani
raw_affiliation_string: School of Information Technology, Halmstad University, Kristian IV:s väg 3, Halmstad, 301 18, Sweden
raw_author_name: Stefan Byttner
raw_affiliation_string: School of Information Technology, Halmstad University, Kristian IV:s väg 3, Halmstad, 301 18, Sweden
raw_author_name: Mariana Recamonde-Mendoza
raw_affiliation_string: Bioinformatics Core, Hospital de Clínicas de Porto Alegre (HCPA), Av. Protásio Alves, 211, Bloco C, Porto Alegre, 90035-903, RS, Brazil; Institute of Informatics, Universidade Federal do Rio Grande do Sul, Avenida Bento Gonçalves, 9500, Porto Alegre, 91501-970, RS, Brazil

Abstract: This study aims to comprehensively review the use of graph neural networks (GNNs) for clinical risk prediction based on electronic health records (EHRs). The primary goal is to provide an overview of the state-of-the-art of this subject, highlighting ongoing research efforts and identifying existing challenges in developing effective GNNs for improved prediction of clinical risks. A search was conducted in the Scopus, PubMed, ACM Digital Library, and Embase databases to identify relevant English-language papers that used GNNs for clinical risk prediction based on EHR data. The study includes original research papers published between January 2009 and May 2023. Following the initial screening process, 50 articles were included in the data collection. A significant increase in publications from 2020 was observed, with most selected papers focusing on diagnosis prediction (n = 36). The study revealed that graph attention networks (GAT) (n = 19) was the most prevalent architecture, and MIMIC-III (n = 23) was the most common data resource. GNNs are relevant tools for predicting clinical risk by accounting for the relational aspects among medical events and entities and managing large volumes of EHR data. Future studies in this area may address challenges such as EHR data heterogeneity, multimodality, and model interpretability, aiming to develop more holistic GNN models that can produce more accurate predictions, be effectively implemented in clinical settings, and ultimately improve patient care.
Deep learning-Aided Dull Grading Scheme for Drill Bits

Authors:
raw_author_name: Jia Sun
raw_affiliation_string: Drilling Mechanical Department, CNPC Engineering Technology R&D Company Limited, Beijing, China
raw_author_name: Baodong Wang
raw_affiliation_string: Drilling Mechanical Department, CNPC Engineering Technology R&D Company Limited, Beijing, China

Abstract: Drill bit design plays a key role in drilling operations. A suitable choice of the drill bit parameters (such as the profile shape, the orientation, and the distribution of cutters, etc.) will significantly improve the durability and stability of the drilling process, increase the rate of penetration (ROP) and enhance the drilling efficiency. Traditional strategies of the drill bit design usually involve a trial-and-error process to adjust the parameters, which is usually time-consuming and contains an element of subjectivity. Alternatively, machine learning techniques provide powerful tools to make full use of the collected data, and analyze the relation between the design parameters and the performance of the drill bits on specified rock mechanics data. In the present work, we aim at building a machine learning-aided scheme to help design the parameters of the drill bits. A machine learning-aided scheme is proposed to perform the drill bit design. First, a dull grading system is established to evaluate the wear extent of the drill bits from images. The grading system includes a cutter localization model to separate the cutters from drill bit images, a reconstruction model to regenerate the 3D geometries of the cutters from several 2D images, a classifier to evaluate the wear extent of the cutters, and an aggregative algorithm to grade the worn drill bits. The grading system is then applied to build the data set of the worn drill bits. Finally, an individual deep learning model is adopted to learn the relation between the design parameters and the wear extent, and a sensitivity analysis is performed to optimize the design parameters. The research results showed that: (1) the proposed machine learning-aided scheme could produce instructive guides on the drill bit design, reduce the analysis cost and provide accurate prediction on the impact of the design parameters. (2) The 3D reconstruction algorithm could efficiently extract the geometry feature of the drill bits from 2D data, and provide detailed geometry parameters of each cutter. The performance of the 3D reconstruction model is mainly related to the number of corresponding 2D images of a given drill bit and the coincidence degree among them. (3) The classifier based on convolutional neural networks (CNN) could provide accurate wear extent assessments of drill bits. In this paper a novel machine learning-aided drill bit design scheme is proposed to effectively enhance the efficiency of the evaluation and optimization of the design parameters. A 3D reconstruction method is established to extract the 3D geometry parameters of the drill bits from several 2D images. The deep learning-based dull grading model is proposed to accurately evaluate the wear extent of the drill bits.
Research on the Construction of Petroleum Software Cloud Platform Based on Distributed Architecture

Authors:
raw_author_name: Mengxin Song
raw_affiliation_string: Research Institute of Petroleum Exploration & Development, CNPC, Beijing, China
raw_author_name: Mei Feng
raw_affiliation_string: Research Institute of Petroleum Exploration & Development, CNPC, Beijing, China
raw_author_name: Hongping Miao
raw_affiliation_string: Research Institute of Petroleum Exploration & Development, CNPC, Beijing, China
raw_author_name: Defu Feng
raw_affiliation_string: Research Institute of Petroleum Exploration & Development, CNPC, Beijing, China

Abstract: Abstract Researchers in exploration and development (E&amp;D) field need several professional software resources for data processing and analysis. Enterprises can achieve cloud-based usage and shared management of petroleum exploration and development software through the deployment of cloud platforms. With the increasing demand for remote and overseas collaborative research, the single-node cloud platform deployment mode no longer meets the needs of collaborative research. This paper proposes a construction scheme of cloud platform for petroleum software based on distributed architecture, which realizes the unified management of software research environment, software licenses, and data. The proposed architecture has been implemented in a division of CNPC, realizing the cloudification of more than 20 mainstream professional E&amp;D software applications such as Petrel and LandMark. By establishing central sites in Beijing and branch sites in Dubai and Hangzhou, the researchers are provided with a cross-regional collaborative research environment on the cloud, thereby enhancing the research efficiency, improving the efficiency of cross-regional collaborative research, and facilitating the unified data management and the digital transformation of petroleum enterprises in the cloud environment.