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The Magnetic Observatory of Tatuoca (TTB) was installed by Observatório Nacional (ON) in 1957, near Belém city in the state of Pará, Brazilian Amazon. Its history goes back to 1933, when a Danish mission used this location to collect data, due to its privileged position near the terrestrial equator. Between 1957 and 2007, TTB produced 18,000 magnetograms on paper using photographic variometers, and other associated documents like absolute value forms and yearbooks. Data was obtained manually from these graphs with rulers and grids, taking 24 average readings per day, that is, one per hour. In 2017, the Federal University of Pará (UFPA in the Portuguese acronym) and ON collaborated to rescue this physical archive. In 2022 UFPA took a step forward and proposed not only digitizing the documents but also developing an intelligent agent capable of reading and extracting the information of the curves with a resolution better than an hour, being this the central goal of the project. If the project succeeds, it will rescue 50 years of data imprisoned in paper, increasing measurement sensitivity far beyond what these sources used to give. This will also open the possibility of applying the same AI to similar documents in other observatories or disciplines like seismography. This article recaps the project, and the complex challenges faced in articulating Archival Science principles with AI and Geoscience.

Rock Paper Scissors is one of the most popular games in the world, because of their easy and simple way to play among young and elderly people. The point of this game is to do the draw or just to find out who loses or wins. The pandemic conditions made people unable to meet face-to-face and could only play this game virtually. To carry out this activity in a virtual way, this research facilitates a model in the form of image classification to distinguish the hand gestures s in the form of rock, paper, and scissors. This classification process utilizes the Convolutional Neural Network (CNN) method. This method is one type of artificial neural network in terms of image classification. CNN uses three stages, namely convolutional layer, pooling layer, and fully connected layer. The implementation of this method for hand gesture classification in the form of rock, scissors, and paper images in this study shows an increased average accuracy towards the previous study from 97.66% to 99%.

To explore whether the brain contains pattern differences in the rock–paper–scissors (RPS) imagery task, this paper attempts to classify this task using fNIRS and deep learning. In this study, we designed an RPS task with a total duration of 25 min and 40 s, and recruited 22 volunteers for the experiment. We used the fNIRS acquisition device (FOIRE-3000) to record the cerebral neural activities of these participants in the RPS task. The time series classification (TSC) algorithm was introduced into the time-domain fNIRS signal classification. Experiments show that CNN-based TSC methods can achieve 97% accuracy in RPS classification. CNN-based TSC method is suitable for the classification of fNIRS signals in RPS motor imagery tasks, and may find new application directions for the development of brain–computer interfaces (BCI).

In daily clinical practice, clinicians integrate available data to ascertain the diagnostic and prognostic probability of a disease or clinical outcome for their patients. For patients with suspected or known cardiovascular disease, several anatomical and functional imaging techniques are commonly performed to aid this endeavor, including coronary computed tomography angiography (CCTA) and nuclear cardiology imaging. Continuous improvement in positron emission tomography (PET), single-photon emission computed tomography (SPECT), and CT hardware and software has resulted in improved diagnostic performance and wide implementation of these imaging techniques in daily clinical practice. However, the human ability to interpret, quantify, and integrate these data sets is limited. The identification of novel markers and application of machine learning (ML) algorithms, including deep learning (DL) to cardiovascular imaging techniques will further improve diagnosis and prognostication for patients with cardiovascular diseases. The goal of this position paper of the European Association of Nuclear Medicine (EANM) and the European Association of Cardiovascular Imaging (EACVI) is to provide an overview of the general concepts behind modern machine learning-based artificial intelligence, highlights currently prefered methods, practices, and computational models, and proposes new strategies to support the clinical application of ML in the field of cardiovascular imaging using nuclear cardiology (hybrid) and CT techniques.

The excellent performance of deep neural networks and machine learning algorithms is pushing the industry to adopt such a technology in several application domains, including safety-critical ones, as self-driving vehicles, autonomous robots, and diagnosis support systems for medical applications. However, most of the AI methodologies available today have not been designed to work in safety-critical environments and several issues need to be solved, at different architecture levels, to make them trustworthy. This paper presents some of the major problems existing today in AI-powered embedded systems, highlighting possible solutions and research directions to support them, increasing their security, safety, and time predictability., OASIcs, Vol. 98, Third Workshop on Next Generation Real-Time Embedded Systems (NG-RES 2022), pages 1:1-1:14

To improve the accuracy of user implicit rating prediction, we combine the traditional latent factor model (LFM) and bidirectional gated recurrent unit neural network (BiGRU) model to propose a hybrid model that deeply mines the latent semantics in the unstructured content of the text and generates a more accurate rating matrix. First, we utilize the user’s historical behavior (favorites records) to build a user rating matrix and decompose the matrix to obtain the latent factor vectors of users and literature. We also apply the BERT model for word embedding of the research papers to obtain the sequence of word vectors. Then, we apply the BiGRU with the user attention mechanism to mine the research paper textual content and to generate the new literature latent feature vectors that are used to replace the original literature latent factor vectors decomposed from the rating matrix. Finally, a new rating matrix is generated to obtain users’ ratings of noninteractive research papers and to generate the recommendation list according to the user latent factor vector. We design experiments on the real datasets and verify that the research paper recommendation model is superior to traditional recommendation models in terms of precision, recall, F1-value, coverage, popularity and diversity.

Knowledge bases are typically incomplete, meaning that they are missing information that we would expect to be there. Recent years have seen two main approaches to guess missing facts: Rule Mining and Knowledge Graph Embeddings. The first approach is symbolic, and finds rules such as "If two people are married, they most likely live in the same city". These rules can then be used to predict missing statements. Knowledge Graph Embeddings, on the other hand, are trained to predict missing facts for a knowledge base by mapping entities to a vector space. Each of these approaches has their strengths and weaknesses, and this article provides a survey of neuro-symbolic works that combine embeddings and rule mining approaches for fact prediction., OASIcs, Vol. 99, International Research School in Artificial Intelligence in Bergen (AIB 2022), pages 4:1-4:30

These data were used to build an object detection model to locate Relict Charcoal Hearths (RCH) as described in the paper “When Computers Dream of Charcoal: Using Deep Learning, Open Tools and Open Data to Identify Relict Charcoal Hearths in and around State Game Lands in Pennsylvania” [1]. This is the first grouping of data for the paper above. The second grouping is also available in this journal, see “Object detection model, image data and results from the “When Computers Dream of Charcoal: Using Deep Learning, Open Tools and Open Data to Identify Relict Charcoal Hearths in and around State Game Lands in Pennsylvania” paper”. These files consist of georeferenced Digital Elevation Model, Hillshade and Slope files derived from LiDAR for the State Game Lands (SGL). Included is a Shapefile and GeoJSON of State Game Land borders as well as the program used for downloading the LiDAR files. These data are stored on Zenodo.org .

Nowadays, recommendation systems play an indispensable role in many fields, including e-commerce, finance, economy, and gaming. There is emerging research on publication venue recommendation systems to support researchers when submitting their scientific work. Several publishers such as IEEE, Springer, and Elsevier have implemented their submission recommendation systems only to help researchers choose appropriate conferences or journals for submission. In this work, we present a demo framework to construct an effective recommendation system for paper submission. With the input data (the title, the abstract, and the list of possible keywords) of a given manuscript, the system recommends the list of top relevant journals or conferences to authors. By using state-of-the-art techniques in natural language understanding, we combine the features extracted with other useful handcrafted features. We utilize deep learning models to build an efficient recommendation engine for the proposed system. Finally, we present the User Interface (UI) and the architecture of our paper submission recommendation system for later usage by researchers.

Over the past few years, what might not unreasonably be described as a true revolution has taken place in the field of machine vision, radically altering the way many things had previously been done and offering new and exciting opportunities for those able to quickly embrace and master the new techniques. Rapid developments in machine learning, largely enabled by faster GPU-equipped computing hardware, has facilitated an explosion of machine vision applications into hitherto extremely challenging or, in many cases, previously impossible to automate industrial tasks. Together with developments towards an internet of things and the availability of big data, these form key components of what many consider to be the fourth industrial revolution. This transformation has dramatically improved the efficacy of some existing machine vision activities, such as in manufacturing (e.g. inspection for quality control and quality assurance), security (e.g. facial biometrics) and in medicine (e.g. detecting cancers), while in other cases has opened up completely new areas of use, such as in agriculture and construction (as well as in the existing domains of manufacturing and medicine). Here we will explore the history and nature of this change, what underlies it, what enables it, and the impact it has had - the latter by reviewing several recent indicative applications described in the research literature. We will also consider the continuing role that traditional or classical machine vision might still play. Finally, the key future challenges and developing opportunities in machine vision will also be discussed.

Question answering systems are mainly concerned with fulfilling an information query written in natural language, given a collection of documents with relevant information. They are key elements in many popular application systems as personal assistants, chat-bots, or even FAQ-based online support systems. This paper describes an exploratory work carried out to come up with a state-of-the-art model for question-answering tasks, for the Portuguese language, based on deep neural networks. We also describe the automatic construction of a data-set for training and testing the model. The final model is not trained in any specific topic or context, and is able to handle generic documents, achieving 50% accuracy in the testing data-set. While the results are not exceptional, this work can support further development in the area, as both the data-set and model are publicly available., OASIcs, Vol. 94, 10th Symposium on Languages, Applications and Technologies (SLATE 2021), pages 18:1-18:5

In this work, we describe a new approach that uses deep neural networks (DNN) to obtain regularization parameters for solving inverse problems. We consider a supervised learning approach, where a network is trained to approximate the mapping from observation data to regularization parameters. Once the network is trained, regularization parameters for newly obtained data can be computed by efficient forward propagation of the DNN. We show that a wide variety of regularization functionals, forward models, and noise models may be considered. The network-obtained regularization parameters can be computed more efficiently and may even lead to more accurate solutions compared to existing regularization parameter selection methods. We emphasize that the key advantage of using DNNs for learning regularization parameters, compared to previous works on learning via optimal experimental design or empirical Bayes risk minimization, is greater generalizability. That is, rather than computing one set of parameters that is optimal with respect to one particular design objective, DNN-computed regularization parameters are tailored to the specific features or properties of the newly observed data. Thus, our approach may better handle cases where the observation is not a close representation of the training set. Furthermore, we avoid the need for expensive and challenging bilevel optimization methods as utilized in other existing training approaches. Numerical results demonstrate the potential of using DNNs to learn regularization parameters., 27 pages, 16 figures

Researchers and scientists read articles to improve their studies. Researchers spend a lot of time trying to find the suitable article from among a large number of publications. The purpose of the article recommendation system is to shorten the working time and to present the research articles to the researchers. Classic article recommendation systems do not consider the user’s information. They show the same results in the same sort for each researcher.

Objectives The International Society for Bipolar Disorders Big Data Task Force assembled leading researchers in the field of bipolar disorder (BD), machine learning, and big data with extensive experience to evaluate the rationale of machine learning and big data analytics strategies for BD. Method A task force was convened to examine and integrate findings from the scientific literature related to machine learning and big data based studies to clarify terminology and to describe challenges and potential applications in the field of BD. We also systematically searched PubMed, Embase, and Web of Science for articles published up to January 2019 that used machine learning in BD. Results The results suggested that big data analytics has the potential to provide risk calculators to aid in treatment decisions and predict clinical prognosis, including suicidality, for individual patients. This approach can advance diagnosis by enabling discovery of more relevant data-driven phenotypes, as well as by predicting transition to the disorder in high-risk unaffected subjects. We also discuss the most frequent challenges that big data analytics applications can face, such as heterogeneity, lack of external validation and replication of some studies, cost and non-stationary distribution of the data, and lack of appropriate funding. Conclusion Machine learning-based studies, including atheoretical data-driven big data approaches, provide an opportunity to more accurately detect those who are at risk, parse-relevant phenotypes as well as inform treatment selection and prognosis. However, several methodological challenges need to be addressed in order to translate research findings to clinical settings.

[ES] En este trabajo, se aborda el problema de la detección de expresiones matemáticas en artículos científicos. Primero, se da una breve introducción al problema de la detección de objetos general, junto a las técnicas y métodos de evaluación más actuales. Luego, se presentan dos modelos del estado del arte (YOLO y SSD), y se realiza un estudio comparativo en el que se analizan los problemas encontrados cuando estos son aplicados a la detección de expresiones matemáticas. Finalmente, el trabajo termina con una discusión sobre cómo abordar estos problemas en modelos futuros y se ahonda en la posibilidad de derivar este trabajo hacia el problema del reconocimiento de expresiones matemáticas., [EN] In this work, we address the problem of detecting mathematical expressions in scientific papers. First, a brief introduction to the problem of general-object detection is given, along with state-of-the-art techniques and evaluation methods. Then, two state-of-the-art models (YOLO and SSD) are presented, and a comparative study is carried out where we analyze the problems and weaknesses found when they are applied to this specific task. Finally, the work ends with a discussion on how to tackle these problems in future models and the possibility of deriving this work towards the mathematical expression recognition problem.

This paper provides an overview of the main developments of the Tech United Eindhoven RoboCup @Home team. Tech United uses an advanced world modeling system called the Environment Descriptor. It allows straightforward implementation of localization, navigation, exploration, object detection & recognition, object manipulation and robot-robot cooperation skills based on the most recent state of the world. Other important features include object and people detection via deep learning methods, a GUI, speech recognition, natural language interpretation and a chat interface combined with a conversation engine. Recent developments that aided with obtaining the victory during RoboCup 2019 include pointing detection, usage of HSR’s display, a people detector and the addition of a custom keyboard in the chat interface.

Paleographical features can be extracted from digitized images by applying deep machine learning. This paper presents an unsupervised deep-learning method for determining script types and modes for medieval Hebrew manuscripts. The results would be presented vs. the previously published results of supervised deep learning for the same datasets.

This project explores computational uses of pose analysis to investigate questions of directorial style and actorly interpretation in theatrical performances. The process involves developing methods to help performing arts scholars and professionals use pose-data models to capture movement from performances, as well as record, analyze, recreate and remix those movements.

The contribution highlights the importance of developing DH methods for large-scale investigation of depicted materials and surface qualities in medieval painting. Preliminary findings from our project, which is developing methods for AI-based recognition of materials in digitized paintings and evaluating their application to address art historical research questions, are presented.

In this presentation, I aim to demonstrate how the recently introduced CLIP-model can be used as a valuable tool to access and analyse digitised manuscripts without the need of providing manual transcriptions first. This entails a shift which would allow the efficient browsing of digital images of any handwritten document.

The past two decades have seen impressive progress in a variety of areas of AI, particularly NLP, through the application of machine learning methods to a wide range of tasks. With the intensive use of deep learning methods in recent years this work has produced significant improvements in the coverage and accuracy of NLP systems in such domains as speech recognition, topic identification, semantic interpretation, and image description generation. While deep learning is opening up exciting new approaches to long standing, difficult problems in computational linguistics, it also raises important foundational questions. Specifically, we do not have a clear formal understanding of why multi-level recursive deep neural networks achieve the success in learning and classification that they are delivering. It is also not obvious whether they should displace more traditional, logically driven methods, or be combined with them. Finally, we need to explore the extent, if any, to which both logical models and machine learning methods offer insights into the cognitive foundations of natural language. The aim of the Conference on Logic and Machine Learning in Natural Language (LAML) was to initiate a dialogue between these two approaches, where they have traditionally remained separate and in competition. The conference and this publication was supported by a grant from the Swedish Research Council (VR project 2014-39) for the establishment of the Centre for Linguistic Theory and Studies in Probability (CLASP) at Department of Philosophy, Linguistics and Theory of Science (FLoV), University of Gothenburg.

COVID-19 is still a fatal disease, which has threatened all people by affecting the human lungs. Chest X-Ray or computed tomography imaging is commonly used to make a fast and reliable medical investigation to detect the COVID-19 virus. These medical images are remarkably challenging because it is a full-time job and prone to human errors. In this paper, a new normalization algorithm that consists of Mean–Variance-Softmax-Rescale (MVSR) processes respectively is proposed to provide facilitation pre-assessment and diagnosis Covid-19 disease. In order to show the effect of MVSR normalization technique, the algorithm of proposed method is applied to chest X-ray and Sars-Cov-2 computed tomography images dataset. The normalized X-ray images with MVSR are used to recognize Covid-19 virus via Convolutional Neural Network (CNN) model. At the implementation stage, the MVSR algorithm is executed on MATLAB environment, then all the arithmetic operations of the MVSR normalization are coded in VHDL with the help of fixed-point fractional number representation format on FPGA platform. The experimental platform consists of Zynq-7000 Development FPGA Board and VGA monitor to display the both original and MVSR normalized chest X-ray images. The CNN model is constructed and executed using Anaconda Navigator interface with python language. Based on the results of this study, infections of Covid-19 disease can be easily diagnosed with MVSR normalization technique. The proposed MVSR normalization technique increased the classification accuracy of the CNN model from 83.01, to 96.16% for binary class of chest X-ray images.

In this paper we proposed a novel Deep Learning approach to realize a Word Embeddings (WEs) similarity based search tool, considering the medical literature as case study. Using the compositional properties of the WEs we defined a methodology to aggregate the information coming from each word to obtain a vector corresponding to the abstracts of each PubMed article. Through this paradigm it is possible to capture the semantic content of the papers and, consequently, to evaluate and rank the similarity among them. The preliminary results with the proposed approach are obtained analysing a subset of the whole the PubMed collection. The results correctness has been verified by human domain experts, showing that the methodology is promising.

Ongoing improvements in AI, particularly concerning deep learning techniques, are assisting to identify, classify, and quantify patterns in clinical images. Deep learning is the quickest developing field in artificial intelligence and is effectively utilized lately in numerous areas, including medication. A brief outline is given on studies carried out on the region of application: neuro, brain, retinal, pneumonic, computerized pathology, bosom, heart, breast, bone, stomach, and musculoskeletal. For information exploration, knowledge deployment, and knowledge-based prediction, deep learning networks can be successfully applied to big data. In the field of medical image processing methods and analysis, fundamental information and state-of-the-art approaches with deep learning are presented in this paper. The primary goals of this paper are to present research on medical image processing as well as to define and implement the key guidelines that are identified and addressed.

The pandemic caused by the COVID-19 virus affects the world widely and heavily. When examining the CT, X-ray, and ultrasound images, radiologists must first determine whether there are signs of COVID-19 in the images. That is, COVID-19/Healthy detection is made. The second determination is the separation of pneumonia caused by the COVID-19 virus and pneumonia caused by a bacteria or virus other than COVID-19. This distinction is key in determining the treatment and isolation procedure to be applied to the patient. In this study, which aims to diagnose COVID-19 early using X-ray images, automatic two-class classification was carried out in four different titles: COVID-19/Healthy, COVID-19 Pneumonia/Bacterial Pneumonia, COVID-19 Pneumonia/Viral Pneumonia, and COVID-19 Pneumonia/Other Pneumonia. For this study, 3405 COVID-19, 2780 Bacterial Pneumonia, 1493 Viral Pneumonia, and 1989 Healthy images obtained by combining eight different data sets with open access were used. In the study, besides using the original X-ray images alone, classification results were obtained by accessing the images obtained using Local Binary Pattern (LBP) and Local Entropy (LE). The classification procedures were repeated for the images that were combined with the original images, LBP, and LE images in various combinations. 2-D CNN (Two-Dimensional Convolutional Neural Networks) and 3-D CNN (Three-Dimensional Convolutional Neural Networks) architectures were used as classifiers within the scope of the study. Mobilenetv2, Resnet101, and Googlenet architectures were used in the study as a 2-D CNN. A 24-layer 3-D CNN architecture has also been designed and used. Our study is the first to analyze the effect of diversification of input data type on classification results of 2-D/3-D CNN architectures. The results obtained within the scope of the study indicate that diversifying X-ray images with tissue analysis methods in the diagnosis of COVID-19 and including CNN input provides significant improvements in the results. Also, it is understood that the 3-D CNN architecture can be an important alternative to achieve a high classification result.

ETDSeer (electronic thesis and dissertation digital library connected with SeerSuite) will build on 15 years of collaboration between teams at Virginia Tech (VT) and Penn State University (PSU), since both have been leaders in the worldwide digital library (DL) community. VT helped launch the national and international efforts for ETDs more than 20 years ago, which have been led by the Networked Digital Library of Theses and Dissertations (NDLTD, directed by PI Fox); its Union Catalog has increased to 5 million records. PSU hosts CiteSeerX, which co-PI Giles launched almost 20 years ago, and which is connected with a wide variety of research results under the SeerSuite family. ETDs, typically in PDF, are a largely untapped international resource. Digital libraries with advanced services can effectively address the broad needs to discover and utilize ETDs of interest. Our research will leverage SeerSuite methods that have been applied mostly to short documents, plus a variety of exploratory studies at VT, and will yield a “web of graduate research”, rich knowledge bases, and a digital library with effective interfaces. References will be analyzed and converted to canonical forms, figures and tables will be recognized and re-represented for flexible searching, small sections (acknowledgments, biographical sketches) will be mined, and aids for researchers will be built especially from literature reviews and discussions of future work. Entity recognition and disambiguation will facilitate flexible use of a large graph of linked open data. ETDseerReport.pdf -- Final report in PDF format ETDseerPresentation.pptx -- Presentation slides in pptx format ETDseerPresentation.pdf -- Presentation slides in PDF format ETDseerLaTeX.zip -- LaTeX source files for final report

In this work, we present a novel module to perform fusion of heterogeneous data using fully convolutional networks for semantic labeling. We introduce residual correction as a way to learn how to fuse predictions coming out of a dual stream architecture. Especially, we perform fusion of DSM and IRRG optical data on the ISPRS Vaihingen dataset over a urban area and obtain new state-of-the-art results., Comment: Joint Urban Remote Sensing Event (JURSE), Mar 2017, Dubai, United Arab Emirates. Joint Urban Remote Sensing Event 2017

The novel coronavirus infection (COVID-19) first appeared in Wuhan, China, in December 2019. COVID-19 declared as a global pandemic by the WHO was the most rapidly spreading disease all across the world. India, the second most populated nation in the world, is still fighting it, when coronavirus reached the stage where community transmission takes place at an exponential rate. Therefore, it is crucial to examine the future trends of COVID-19 in India and anticipate how it will affect economic and social growth in a short run. In this paper, a new deep learning framework using CNN and stacked Bi-GRU has been developed for predicting and analyzing the COVID-19 cases in India. The proposed model can predict the next 30 days' new positive cases, new death cases, recovery rate and containment and health index values with high accuracy. The proposed method is compared against Gaussian process regression (GPR) model on COVID-19 datasets. The experimental result shows that the proposed framework is highly reliable for COVID-19 prediction over the GPR model.

Atomic force microscopy (AFM) has been an important tool for nanoscale imaging and characterization with atomic and subatomic resolution. Theoretical investigations are getting highly important for the interpretation of AFM images. Researchers have used molecular simulation to examine the AFM imaging mechanism. With a recent flurry of researches applying machine learning to AFM, AFM images obtained from molecular simulation have also been used as training data. However, the simulation is incredibly time consuming. In this paper, we apply super-resolution methods, including compressed sensing and deep learning methods, to reconstruct simulated images and to reduce simulation time. Several molecular simulation energy maps under different conditions are presented to demonstrate the performance of reconstruction algorithms. Through the analysis of reconstructed results, we find that both presented algorithms could complete the reconstruction with good quality and greatly reduce simulation time. Moreover, the super-resolution methods can be used to speed up the generation of training data and vary simulation resolution for AFM machine learning.

In the last few years, the deep learning (DL) computing paradigm has been deemed the Gold Standard in the machine learning (ML) community. Moreover, it has gradually become the most widely used computational approach in the field of ML, thus achieving outstanding results on several complex cognitive tasks, matching or even beating those provided by human performance. One of the benefits of DL is the ability to learn massive amounts of data. The DL field has grown fast in the last few years and it has been extensively used to successfully address a wide range of traditional applications. More importantly, DL has outperformed well-known ML techniques in many domains, e.g., cybersecurity, natural language processing, bioinformatics, robotics and control, and medical information processing, among many others. Despite it has been contributed several works reviewing the State-of-the-Art on DL, all of them only tackled one aspect of the DL, which leads to an overall lack of knowledge about it. Therefore, in this contribution, we propose using a more holistic approach in order to provide a more suitable starting point from which to develop a full understanding of DL. Specifically, this review attempts to provide a more comprehensive survey of the most important aspects of DL and including those enhancements recently added to the field. In particular, this paper outlines the importance of DL, presents the types of DL techniques and networks. It then presents convolutional neural networks (CNNs) which the most utilized DL network type and describes the development of CNNs architectures together with their main features, e.g., starting with the AlexNet network and closing with the High-Resolution network (HR.Net). Finally, we further present the challenges and suggested solutions to help researchers understand the existing research gaps. It is followed by a list of the major DL applications. Computational tools including FPGA, GPU, and CPU are summarized along with a description of their influence on DL. The paper ends with the evolution matrix, benchmark datasets, and summary and conclusion.

COVID-19 is an infectious disease, which has adversely affected public health and the economy across the world. On account of the highly infectious nature of the disease, rapid automated diagnosis of COVID-19 is urgently needed. A few recent findings suggest that chest X-rays and CT scans can be used by machine learning for the diagnosis of COVID-19. Herein, we employed semi-supervised learning (SSL) approaches to detect COVID-19 cases accurately by analyzing digital chest X-rays and CT scans. On a relatively small COVID-19 radiography dataset, which contains only 219 COVID-19 positive images, 1341 normal and 1345 viral pneumonia images, our algorithm, COVIDCon, which takes advantage of data augmentation, consistency regularization, and multicontrastive learning, attains 97.07% average class prediction accuracy, with 1000 labeled images, which is 7.65% better than the next best SSL method, virtual adversarial training. COVIDCon performs even better on a larger COVID-19 CT Scan dataset that contains 82,767 images. It achieved an excellent accuracy of 99.13%, at 20,000 labels, which is 6.45% better than the next best pseudo-labeling approach. COVIDCon outperforms other state-of-the-art algorithms at every label that we have investigated. These results demonstrate COVIDCon as the benchmark SSL algorithm for potential diagnosis of COVID-19 from chest X-rays and CT-Scans. Furthermore, COVIDCon performs exceptionally well in identifying COVID-19 positive cases from a completely unseen repository with a confirmed COVID-19 case history. COVIDCon, may provide a fast, accurate, and reliable method for screening COVID-19 patients.

Purpose: Earlier work showed that IVIM-NET orig, an unsupervised physics-informed deep neural network, was faster and more accurate than other state-of-the-art intravoxel-incoherent motion (IVIM) fitting approaches to diffusion-weighted imaging (DWI). This study presents a substantially improved version, IVIM-NET optim, and characterizes its superior performance in pancreatic cancer patients. Method: In simulations (signal-to-noise ratio [SNR] = 20), the accuracy, independence, and consistency of IVIM-NET were evaluated for combinations of hyperparameters (fit S0, constraints, network architecture, number of hidden layers, dropout, batch normalization, learning rate), by calculating the normalized root-mean-square error (NRMSE), Spearman’s ρ, and the coefficient of variation (CV NET), respectively. The best performing network, IVIM-NET optim was compared to least squares (LS) and a Bayesian approach at different SNRs. IVIM-NET optim’s performance was evaluated in an independent dataset of 23 patients with pancreatic ductal adenocarcinoma. Fourteen of the patients received no treatment between two repeated scan sessions and nine received chemoradiotherapy between the repeated sessions. Intersession within-subject standard deviations (wSD) and treatment-induced changes were assessed. Results: In simulations (SNR = 20), IVIM-NET optim outperformed IVIM-NET orig in accuracy (NRMSE(D) = 0.177 vs 0.196; NMRSE(f) = 0.220 vs 0.267; NMRSE(D*) = 0.386 vs 0.393), independence (ρ(D*, f) = 0.22 vs 0.74), and consistency (CV NET(D) = 0.013 vs 0.104; CV NET(f) = 0.020 vs 0.054; CV NET(D*) = 0.036 vs 0.110). IVIM-NET optim showed superior performance to the LS and Bayesian approaches at SNRs < 50. In vivo, IVIM-NET optim showed significantly less noisy parameter maps with lower wSD for D and f than the alternatives. In the treated cohort, IVIM-NET optim detected the most individual patients with significant parameter changes compared to day-to-day variations. Conclusion: IVIM-NET optim is recommended for accurate, informative, and consistent IVIM fitting to DWI data.

Deep reinforcement learning has the advantage of being able to encode fairly complex behaviors by collecting and learning empirical information. In the current study, we have proposed a framework for reinforcement learning in decentralized collision avoidance where each agent independently makes its decision without communication with others. In an environment exposed to various kinds of dynamic obstacles with irregular movements, mobile robot agents could learn how to avoid obstacles and reach a target point efficiently. Moreover, a path planner was integrated with the reinforcement learning-based obstacle avoidance to solve the problem of not finding a path in a specific situation, thereby imposing path efficiency. The robots were trained about the policy of obstacle avoidance in environments where dynamic characteristics were considered with soft actor critic algorithm. The trained policy was implemented in the robot operating system (ROS), tested in virtual and real environments for the differential drive wheel robot to prove the effectiveness of the proposed method. Videos are available at https://youtu.be/xxzoh1XbAl0 .

COVID-19 is an acronym for coronavirus disease 2019. Initially, it was called 2019-nCoV, and later International Committee on Taxonomy of Viruses (ICTV) termed it SARS-CoV-2. On 30th January 2020, the World Health Organization (WHO) declared it a pandemic. With an increasing number of COVID-19 cases, the available medical infrastructure is essential to detect the suspected cases. Medical imaging techniques such as Computed Tomography (CT), chest radiography can play an important role in the early screening and detection of COVID-19 cases. It is important to identify and separate the cases to stop the further spread of the virus. Artificial Intelligence can play an important role in COVID-19 detection and decreases the workload on collapsing medical infrastructure. In this paper, a deep convolutional neural network-based architecture is proposed for the COVID-19 detection using chest radiographs. The dataset used to train and test the model is available on different public repositories. Despite having the high accuracy of the model, the decision on COVID-19 should be made in consultation with the trained medical clinician.

Motivation The automatic discovery of sparse biomarkers that are associated with an outcome of interest is a central goal of bioinformatics. In the context of high-throughput sequencing (HTS) data, and compositional data (CoDa) more generally, an important class of biomarkers are the log-ratios between the input variables. However, identifying predictive log-ratio biomarkers from HTS data is a combinatorial optimization problem, which is computationally challenging. Existing methods are slow to run and scale poorly with the dimension of the input, which has limited their application to low- and moderate-dimensional metagenomic datasets. Results Building on recent advances from the field of deep learning, we present CoDaCoRe, a novel learning algorithm that identifies sparse, interpretable and predictive log-ratio biomarkers. Our algorithm exploits a continuous relaxation to approximate the underlying combinatorial optimization problem. This relaxation can then be optimized efficiently using the modern ML toolbox, in particular, gradient descent. As a result, CoDaCoRe runs several orders of magnitude faster than competing methods, all while achieving state-of-the-art performance in terms of predictive accuracy and sparsity. We verify the outperformance of CoDaCoRe across a wide range of microbiome, metabolite and microRNA benchmark datasets, as well as a particularly high-dimensional dataset that is outright computationally intractable for existing sparse log-ratio selection methods. Availability and implementation The CoDaCoRe package is available at https://github.com/egr95/R-codacore. Code and instructions for reproducing our results are available at https://github.com/cunningham-lab/codacore. Supplementary information Supplementary data are available at Bioinformatics online.

A majority of breast cancers (BC) are age-related and we seek to determine what cellular and molecular changes occur in breast tissue with age that make women more susceptible to cancer initiation. Immune-epithelial cell interactions are important during mammary gland development and the immune system plays an important role in BC progression. The composition of human immune cell populations is known to change in peripheral blood with age and in breast tissue during BC progression. Less is known about changes in immune populations in normal breast tissue and how their interactions with mammary epithelia change with age. We quantified densities of T cells, B cells, and macrophage subsets in pathologically normal breast tissue from 122 different women who ranged in age from 24 to 74 years old. Donor-matched peripheral blood from a subset of 20 donors was analyzed by flow cytometry. Tissue immune cell densities and localizations relative to the epithelium were quantified in situ with machine learning-based image analyses of multiplex immunohistochemistry-stained tissue sections. In situ results were corroborated with flow cytometry analyses of peri-epithelial immune cells from primary breast tissue preparations and transcriptome analyses of public data from bulk tissue reduction mammoplasties. Proportions of immune cell subsets in breast tissue and donor-matched peripheral blood were not correlated. Density (cells/mm2) of T and B lymphocytes in situ decreased with age. T cells and macrophages preferentially localized near or within epithelial bilayers, rather than the intralobular stroma. M2 macrophage density was higher than M1 macrophage density and this difference was due to higher density of M2 in the intralobular stroma. Transcriptional signature analyses suggested age-dependent decline in adaptive immune cell populations and functions and increased innate immune cell activity. T cells and macrophages are so intimately associated with the epithelia that they are embedded within the bilayer, suggesting an important role for immune-epithelial cell interactions. Age-associated decreased T cell density in peri-epithelial regions, and increased M2 macrophage density in intralobular stroma suggests the emergence of a tissue microenvironment that is simultaneously immune-senescent and immunosuppressive with age. Supplementary Information The online version contains supplementary material available at 10.1007/s10911-021-09495-2.

Introduction Convolutional neural networks gained popularity due to their ability to detect and classify objects in images and videos. It gives also an opportunity to use them for medical tasks in such specialties like dermatology, radiology or ophthalmology. The aim of this study was to investigate the ability of convolutional neural networks to classify malignant melanoma in dermoscopy images. Aim To examine the usefulness of deep learning models in malignant melanoma detection based on dermoscopy images. Material and methods Four convolutional neural networks were trained on open source dataset containing dermoscopy images of seven types of skin lesions. To evaluate the performance of artificial neural networks, the precision, sensitivity, F1 score, specificity and area under the receiver operating curve were calculated. In addition, an ensemble of all neural networks' ability of proper malignant melanoma classification was compared with the results achieved by every single network. Results The best convolutional neural network achieved on average 0.88 precision, 0.83 sensitivity, 0.85 F1 score and 0.99 specificity in the classification of all skin lesion types. Conclusions Artificial neural networks might be helpful in malignant melanoma detection in dermoscopy images.

Motivation Phosphorylation is one of the most studied post-translational modifications, which plays a pivotal role in various cellular processes. Recently, deep learning methods have achieved great success in prediction of phosphorylation sites, but most of them are based on convolutional neural network that may not capture enough information about long-range dependencies between residues in a protein sequence. In addition, existing deep learning methods only make use of sequence information for predicting phosphorylation sites, and it is highly desirable to develop a deep learning architecture that can combine heterogeneous sequence and protein–protein interaction (PPI) information for more accurate phosphorylation site prediction. Results We present a novel integrated deep neural network named PhosIDN, for phosphorylation site prediction by extracting and combining sequence and PPI information. In PhosIDN, a sequence feature encoding sub-network is proposed to capture not only local patterns but also long-range dependencies from protein sequences. Meanwhile, useful PPI features are also extracted in PhosIDN by a PPI feature encoding sub-network adopting a multi-layer deep neural network. Moreover, to effectively combine sequence and PPI information, a heterogeneous feature combination sub-network is introduced to fully exploit the complex associations between sequence and PPI features, and their combined features are used for final prediction. Comprehensive experiment results demonstrate that the proposed PhosIDN significantly improves the prediction performance of phosphorylation sites and compares favorably with existing general and kinase-specific phosphorylation site prediction methods. Availability and implementation PhosIDN is freely available at https://github.com/ustchangyuanyang/PhosIDN. Supplementary information Supplementary data are available at Bioinformatics online.

A deep learning neural network has been developed to efficiently and accurately clean the background noise in experimental lab-based X-ray diffraction images. A better spot segmentation is obtained and thus grain mapping in 3D is improved compared with the conventional method., Laboratory X-ray diffraction contrast tomography (LabDCT) is a novel imaging technique for non-destructive 3D characterization of grain structures. An accurate grain reconstruction critically relies on precise segmentation of diffraction spots in the LabDCT images. The conventional method utilizing various filters generally satisfies segmentation of sharp spots in the images, thereby serving as a standard routine, but it also very often leads to over or under segmentation of spots, especially those with low signal-to-noise ratios and/or small sizes. The standard routine also requires a fine tuning of the filtering parameters. To overcome these challenges, a deep learning neural network is presented to efficiently and accurately clean the background noise, thereby easing the spot segmentation. The deep learning network is first trained with input images, synthesized using a forward simulation model for LabDCT in combination with a generic approach to extract features of experimental backgrounds. Then, the network is applied to remove the background noise from experimental images measured under different geometrical conditions for different samples. Comparisons of both processed images and grain reconstructions show that the deep learning method outperforms the standard routine, demonstrating significantly better grain mapping.

Vertebral compression fracture is a deformity of vertebral bodies found on lateral spine images. To diagnose vertebral compression fracture, accurate measurement of vertebral compression ratio is required. Therefore, rapid and accurate segmentation of vertebra is important for measuring the vertebral compression ratio. In this study, we used 339 data of lateral thoracic and lumbar vertebra images for training and testing a deep learning model for segmentation. The result of segmentation by the model was compared with the manual measurement, which is performed by a specialist. As a result, the average sensitivity of the dataset was 0.937, specificity was 0.995, accuracy was 0.992, and dice similarity coefficient was 0.929, area under the curve of receiver operating characteristic curve was 0.987, and the precision recall curve was 0.916. The result of correlation analysis shows no statistical difference between the manually measured vertebral compression ratio and the vertebral compression ratio using the data segmented by the model in which the correlation coefficient was 0.929. In addition, the Bland–Altman plot shows good equivalence in which VCR values are in the area within average ± 1.96. In conclusion, vertebra segmentation based on deep learning is expected to be helpful for the measurement of vertebral compression ratio.

Engaging deep neural networks for textual sentiment analysis is an extensively practiced domain of research. Textual sentiment classification harnesses the full computational potential of deep learning models. Typically, these research works are carried either with a popular open-source data corpus, or self-extracted short phrase texts from Twitter, Reddit, or web-scrapped text data from other resources. Rarely do we see a large amount of data on a current ongoing event is being collected and cultured further. Also, an even more complex task would be to model the data from a currently ongoing event, not only for scaling the sentiment accuracy but also for making a predictive analysis for the same. In this paper, we propose a novel approach for achieving sentiment evaluation accuracy by using a deep neural network on live-streamed tweets on Coronavirus and future case growth prediction. We develop a large tweet corpus exclusively based on the Coronavirus tweets. We split the data into train and test sets, alongside we perform polarity classification and trend analysis. The refined outcome from the trend analysis helps to train the data to provide an incremental learning curvature for our neural network, and we obtain an accuracy of 90.67%. Finally, we provide a statistical-based future prediction for Coronavirus cases growth. Not only our model outperforms several previous state-of-art experiments in overall sentiment accuracy comparison for similar tasks, but it also maintains a throughout performance stability among all the test cases when tested with several popular open-source text corpora.

Having content in an archive is of limited value if it cannot be read and used. As a case study of extricating information from obsolete media, making it readable once again through deep learning techniques, we examine the Cauzin Softstrip: one of the first two-dimensional bar codes, released in 1985 by Cauzin Systems, which could be used for encoding all manner of digital data. Softstrips occupy a curious middle ground, as they were both physical and digital. The bar codes were printed on paper, and in that sense are no different in an archival way than any printed material. Softstrips can be found in old computer magazines, computer books, and booklets of software Cauzin produced. However, managing the digital nature of these physical artifacts falls within the scope of digital curation. To make the information on them readable and useful, the digital information needs to be extracted, which originally would have occurred using a physical Cauzin Softstrip reader. Obtaining a working Softstrip reader is already extremely difficult and will most likely be impossible in the coming years. In order to extract the encoded data, we created a digital Softstrip reader, making Softstrip data accessible without needing a physical reader. Our decoding strategy is able to decode over 91% of the 1229 Softstrips in our Softstrip corpus; this rises to 99% if we only consider Softstrip images produced under controlled conditions. Furthermore, we later acquired another set of 117 Softstrips and we were able to decode nearly 95% of them with no adjustments to the decoder. These excellent results underscore the fact that technology like deep learning is readily accessible to non-experts; we obtained these results using a convolutional neural network, even though neither of the authors are expert in the area.

The COVID-19, novel coronavirus or SARS-Cov-2, has claimed hundreds of thousands of lives and affected millions of people all around the world with the number of deaths and infections growing exponentially. Deep convolutional neural network (DCNN) has been a huge milestone for image classification task including medical images. Transfer learning of state-of-the-art models have proven to be an efficient method of overcoming deficient data problem. In this paper, a thorough evaluation of eight pre-trained models is presented. Training, validating, and testing of these models were performed on chest X-ray (CXR) images belonging to five distinct classes, containing a total of 760 images. Fine-tuned models, pre-trained in ImageNet dataset, were computationally efficient and accurate. Fine-tuned DenseNet121 achieved a test accuracy of 98.69% and macro f1-score of 0.99 for four classes classification containing healthy, bacterial pneumonia, COVID-19, and viral pneumonia, and fine-tuned models achieved higher test accuracy for three-class classification containing healthy, COVID-19, and SARS images. The experimental results show that only 62% of total parameters were retrained to achieve such accuracy.

Objectives To develop and test the performance of computerized ultrasound image analysis using deep neural networks (DNNs) in discriminating between benign and malignant ovarian tumors and to compare its diagnostic accuracy with that of subjective assessment (SA) by an ultrasound expert. Methods We included 3077 (grayscale, n = 1927; power Doppler, n = 1150) ultrasound images from 758 women with ovarian tumors, who were classified prospectively by expert ultrasound examiners according to IOTA (International Ovarian Tumor Analysis) terms and definitions. Histological outcome from surgery (n = 634) or long‐term (≥ 3 years) follow‐up (n = 124) served as the gold standard. The dataset was split into a training set (n = 508; 314 benign and 194 malignant), a validation set (n = 100; 60 benign and 40 malignant) and a test set (n = 150; 75 benign and 75 malignant). We used transfer learning on three pre‐trained DNNs: VGG16, ResNet50 and MobileNet. Each model was trained, and the outputs calibrated, using temperature scaling. An ensemble of the three models was then used to estimate the probability of malignancy based on all images from a given case. The DNN ensemble classified the tumors as benign or malignant (Ovry‐Dx1 model); or as benign, inconclusive or malignant (Ovry‐Dx2 model). The diagnostic performance of the DNN models, in terms of sensitivity and specificity, was compared to that of SA for classifying ovarian tumors in the test set. Results At a sensitivity of 96.0%, Ovry‐Dx1 had a specificity similar to that of SA (86.7% vs 88.0%; P = 1.0). Ovry‐Dx2 had a sensitivity of 97.1% and a specificity of 93.7%, when designating 12.7% of the lesions as inconclusive. By complimenting Ovry‐Dx2 with SA in inconclusive cases, the overall sensitivity (96.0%) and specificity (89.3%) were not significantly different from using SA in all cases (P = 1.0). Conclusion Ultrasound image analysis using DNNs can predict ovarian malignancy with a diagnostic accuracy comparable to that of human expert examiners, indicating that these models may have a role in the triage of women with an ovarian tumor. © 2020 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology., This article's abstract has been translated into Spanish and Chinese. Follow the links from the abstract to view the translations.

This paper describes a method for determining an atomic model of a protein complex using moderate-resolution cryoEM data and distance predictions from deep learning., Cryo-electron microscopy of protein complexes often leads to moderate resolution maps (4–8 Å), with visible secondary-structure elements but poorly resolved loops, making model building challenging. In the absence of high-resolution structures of homologues, only coarse-grained structural features are typically inferred from these maps, and it is often impossible to assign specific regions of density to individual protein subunits. This paper describes a new method for overcoming these difficulties that integrates predicted residue distance distributions from a deep-learned convolutional neural network, computational protein folding using Rosetta, and automated EM-map-guided complex assembly. We apply this method to a 4.6 Å resolution cryoEM map of Fanconi Anemia core complex (FAcc), an E3 ubiquitin ligase required for DNA interstrand crosslink repair, which was previously challenging to interpret as it comprises 6557 residues, only 1897 of which are covered by homology models. In the published model built from this map, only 387 residues could be assigned to the specific subunits with confidence. By building and placing into density 42 deep-learning-guided models containing 4795 residues not included in the previously published structure, we are able to determine an almost-complete atomic model of FAcc, in which 5182 of the 6557 residues were placed. The resulting model is consistent with previously published biochemical data, and facilitates interpretation of disease-related mutational data. We anticipate that our approach will be broadly useful for cryoEM structure determination of large complexes containing many subunits for which there are no homologues of known structure.

Colorectal cancer lymph node metastasis, which is highly associated with the patient’s cancer recurrence and survival rate, has been the focus of many therapeutic strategies that are highly associated with the patient’s cancer recurrence and survival rate. The popular methods for classification of lymph node metastasis by neural networks, however, show limitations as the available low-level features are inadequate for classification, and the radiologists are unable to quickly review the images. Identifying lymph node metastasis in colorectal cancer is a key factor in the treatment of patients with colorectal cancer. In the present work, an automatic classification method based on deep transfer learning was proposed. Specifically, the method resolved the problem of repetition of low-level features and combined these features with high-level features into a new feature map for classification; and a merged layer which merges all transmitted features from previous layers into a map of the first full connection layer. With a dataset collected from Harbin Medical University Cancer Hospital, the experiment involved a sample of 3,364 patients. Among these samples, 1,646 were positive, and 1,718 were negative. The experiment results showed the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were 0.8732, 0.8746, 0.8746 and 0.8728, respectively, and the accuracy and AUC were 0.8358 and 0.8569, respectively. These demonstrated that our method significantly outperformed the previous classification methods for colorectal cancer lymph node metastasis without increasing the depth and width of the model., Graphical abstract

We address the problem of exposure correction of dark, blurry and noisy images captured in low-light conditions in the wild. Classical image-denoising filters work well in the frequency space but are constrained by several factors such as the correct choice of thresholds, frequency estimates etc. On the other hand, traditional deep networks are trained end-to-end in the RGB space by formulating this task as an image-translation problem. However, that is done without any explicit constraints on the inherent noise of the dark images and thus produce noisy and blurry outputs. To this end we propose a DCT/FFT based multi-scale loss function, which when combined with traditional losses, trains a network to translate the important features for visually pleasing output. Our loss function is end-to-end differentiable, scale-agnostic, and generic; i.e., it can be applied to both RAW and JPEG images in most existing frameworks without additional overhead. Using this loss function, we report significant improvements over the state-of-the-art using quantitative metrics and subjective tests., Comment: Published version 8 pages. SIViP (2021)

Purpose Motivated by recent advances in deep learning, the purpose of this study was to investigate a deep learning method in automatic segment and reconstruct applicators in computed tomography (CT) images for cervix brachytherapy treatment planning. Material and methods U-Net model was developed for applicator segmentation in CT images. Sixty cervical cancer patients with Fletcher applicator were divided into training data and validation data according to ratio of 50 : 10, and another 10 patients with Fletcher applicator were employed to test the model. Dice similarity coefficient (DSC) and 95th percentile Hausdorff distance (HD95) were used to evaluate the model. Segmented applicator coordinates were calculated and applied into RT structure file. Tip error and shaft error of applicators were evaluated. Dosimetric differences between manual reconstruction and deep learning-based reconstruction were compared. Results The averaged overall 10 test patients' DSC, HD95, and reconstruction time were 0.89, 1.66 mm, and 17.12 s, respectively. The average tip error was 0.80 mm, and the average shaft error was less than 0.50 mm. The dosimetric differences between manual reconstruction and automatic reconstruction were 0.29% for high-risk clinical target volume (HR-CTV) D90%, and less than 2.64% for organs at risk D2cc at a scenario of doubled maximum shaft error. Conclusions We proposed a deep learning-based reconstruction method to localize Fletcher applicator in three-dimensional CT images. The achieved accuracy and efficiency confirmed our method as clinically attractive. It paves the way for the automation of brachytherapy treatment planning.