title: Ozone (O3) and SARS-CoV-2: Physiological Bases and Their Therapeutic Possibilities According to COVID-19 Evolutionary Stage

text: To date, there is no definitive treatment for the new SARS-CoV-2 pandemic. Three evolutionary stages in SARS-CoV-2 infection are recognized (early infection, pulmonary phase, and systemic hyper inflammation), with characteristic clinical signs and symptoms. There are 80 international experimental trials underway seeking effective treatment for the COVID-19 pandemic. Of these, there are only three that consider ozone therapy (major auto hemotherapy) as an alternative option. There is no study that evaluates rectal ozone insufflation, despite being a safe, cheap, risk-free technique. That technique is a systemic route of ozone administration (95–96%) and that could be extrapolated to the use of SARS-CoV-2, given the excellent results observed in the management of Ebola. Ozone has four proven biological properties that could allow its use as an alternative therapy in the different phases of SARS-CoV-2 infection. Ozone could inactivate the virus by direct (O3) or indirect oxidation (ROS and LOPs) and could stimulate the cellular and humoral immune systems, being useful in the early COVID-19 infection phase (stages 1 and 2a). Ozone improves gas exchange, reduces inflammation, and modulates the antioxidant system, so it would be useful in the hyper inflammation or “cytokine storm” phase, and in the hypoxemia and/or multi-organ failure phase (stage 2b and stage 3). Given the current pandemic, it is urgent to carry out an experimental study that confirms or rules out the biological properties of ozone and thus allows it to be an alternative or compassionate therapy for the effective management of SARS-Cov-2 infection. The Ethical Committee at our Hospital has authorized the use of this technique for compassionate management of SARS-CoV-2 infection, considering the four biological Ozone properties exposed previously.


Role of frailty in COVID-19 patients


docid: xwpxwdrl

title: Coronavirus and surgery


title: Management of Pregnant Women in Times of Covid-19: A Review of Current Literature

text: COVID-19 is a Public Health Emergency of International Concern. Its impact on pregnant women is not yet clear owing to limited data and the knowledge is evolving in several aspects. Based on the available evidences, various clinical guidelines for management of COVID-19 have been formulated. This article intends to compile and summarise guidelines from esteemed organisations, along with their implication in the Indian scenario, and offers an easy tool for clinicians managing pregnant women in times of COVID-19.


Mother and Son Suicide Pact Due to COVID-19-Related Online Learning Issues in Bangladesh: An Unusual Case Report


title: Changes in provider Fidelity after introducing a new model of intervention

text: Given the impact of implementation fidelity on community-based outcomes, it is important to understand how fidelity may change over time as providers learn an intervention. Attachment and Biobehavioral Catch-up is an evidence-based early intervention that assesses fidelity during weekly supervision. Providers are first trained in the infant model, with toddler model training considered to be a separate, specialized opportunity. The current study examined changes in fidelity, measured by “in-the-moment” commenting, as providers moved from infant to toddler certification. An initial drop, with a subsequent increase, in commenting fidelity over the training year was expected. Results were consistent with our hypotheses, demonstrating a main effect of time, with most indices of commenting data initially decreasing and then increasing. These findings are consistent with research suggesting that fluctuation in fidelity is typical within community dissemination and suggests that ongoing supervision after the initial training is useful in facilitating successful skill development.


docid: pcvjtiry

title: Does behavior simulation based on augmented reality improve moral imagination?

text: Innovative learning techniques are needed, to match the expectations of the current audience to improve business ethics education for the twenty-first century. One of the innovative technologies which is believed to have a big effect on a bachelor’s degree is augmented reality (AR). Incorporating AR into the modern education system is expected to produce optimal augmentation in the learning and teaching environment. This will have a large impact on increasing students’ moral imagination. This study examines the use of AR-based behavior simulation, as an innovative technique for learning ethics, to improve moral imagination. This study employs a 3 × 2 experiment method, three training modes (AR-based behavioral simulation, paper-based-behavioral simulation, and no training) and two times (time one and time two) between- and within-subject factorial design. The subjects are 147 students on a business ethics course. The result of this study reveals that the use of AR-based behavior simulation can improve moral imagination.


title: Histogram analysis for bedside respiratory monitoring in not critically ill preterm neonates: a proposal for a new way to look at the monitoring data

text: Despite robust evidence in favour of maintaining optimal oxygen saturation targets in the preterm infants, the titration of oxygen is largely dependent on manual observations and transcription. Similarly, notwithstanding the gaining popularity of non-invasive modalities like high-flow nasal therapy, the practices of weaning and escalating support are largely individualized and based on point of care observations. These are often erroneous and lack objectivity. Histogram analysis from patient monitors is an easy and objective way of quantifying vital parameters and their trends. We review the technology and evidence available behind this practice. Conclusions: Though there are no randomized controlled trials on this practice solely, we identify several quality improvement studies implementing this into practice with benefit. We also cite studies which have implemented histogram analysis in methodology, thus concluding that this is a useful clinical tool worth incorporating into clinical practice to reduce manual errors and bring more objectivity into decisions.


title: Designing dynamic reverse logistics network for post-sale service

text: The paper addresses the problem of designing a multi-country production–distribution network that also provides services such as repairs and remanufacturing. The proposed work concentrates primarily on post-sale service provided by the firm under warranty returns. The proposed model assumes that existing warehouses can also serve as collection centres or repair centres for reverse logistics. In addition, the model also explores the possibility of establishing a new facility. Hybrid facilities are considered because of their huge cost-cutting potential due to equipment sharing and space sharing. The capacity of hybrid facilities can be expanded to a predefined limit to process returned products without hampering forward logistics operations. However, if a product cannot be repaired at the warehouse, it is transported to the plant for remanufacturing. The model optimizes the overall configuration and operation cost of the production–distribution network. The production–distribution model developed in the paper is a mixed-integer nonlinear program (MINLP) that is later transformed to a mixed-integer linear program to reduce the solution time. The usefulness of the model is illustrated using a randomly generated dataset. The model identifies (a) the optimal locations/allocations of the existing/new facilities, (b) the distribution of returned products for refurbishing and remanufacturing, and (c) the capacity expansion of the existing plants and warehouses to facilitate remanufacturing and repair services.


title: A brief review on lavatory cleaning devices and their feasibility in public toilets in developing countries

text: The purpose of this article is twofold, one is to provide a brief review on various lavatory cleaning devices while the other is to study the feasibility of using these devices in public toilets in developing countries. The article presents a literature review on various lavatory cleaning devices, focusing on various designs available as patent documents and commercially available toilet cleaning robots. The issues that prevent these devices from being deployed in multi-lavatory public toilet complexes in developing countries are discussed. Design considerations of a simple, cost-effective, semi-automated lavatory cleaning robot that may serve as a solution to these issues are also covered in this article.


Highlights of the issue 7, 2020


title: Object Detector Combination for Increasing Accuracy and Detecting More Overlapping Objects

text: Object detection is considered as the cornerstone of many modern applications such as Drone vision and Self-driven cars. Object detectors, mainly those which are based on Convolutional Neural Net-works (CNNs) have received great attention from many researchers because they were able to yield remarkable results. However, most of them fail when it comes to detecting overlapping and small objects in images. There are two families of detectors: the first family detects more objects but with imprecise bounding boxes, while those of the second family do the opposite. In this paper, we propose a solution to this problem by combining the two families, in a way similar to classifier combination. Our solution has been validated through the combination of two famous detectors, Faster R-CNN which detects more objects and YOLO which produces accurate bounding boxes. However, it is more general and it can be applied to other detectors. The evaluation of our method has been applied to the PASCAL VOC dataset and it gave promising results.


title: Vine Disease Detection by Deep Learning Method Combined with 3D Depth Information

text: Vine disease detection (VDD) is an important asset to predict a probable contagion of virus or fungi. Diseases that spreads through the vineyard has a huge economic impact, therefore it is considered as a challenge for viticulture. Automatic detection and mapping of vine disease in earlier stage can help to limit its impact and reduces the use of chemicals. This study deals with the problem of locating symptomatic areas in images from an unmanned aerial vehicle (UAV) using the visible and infrared domains. This paper, proposes a new method, based on segmentation by a convolutional neuron network SegNet and a depth map (DM), to delineate the asymptomatic regions in the vine canopy. The results obtained showed that SegNet combined with the depth information give better accuracy than a SegNet segmentation alone.


docid: cmexddcz

title: Classification Results for Hyperovals of Generalized Quadrangles

text: A hyperoval of a point-line geometry is a nonempty set of points meeting each line in either 0 or 2 points. We discuss a combination of theoretical and practical techniques that are helpful for classifying hyperovals of generalized quadrangles. These techniques are based on the connection between hyperovals, even sets and pseudo-embeddings of point-line geometries.


title: The Program Generation in the Software Package QextNewEdition

text: This paper is devoted to the program Generation which is a self-containing console application for classification of linear codes. It can be used for codes over fields with [Formula: see text] elements and with wide-range parameters. The base of the implemented algorithm is the concept of canonical augmentation.


title: Image-Based Place Recognition Using Semantic Segmentation and Inpainting to Remove Dynamic Objects

text: Place recognition is an important step in intelligent driving, allowing the vehicle recognize where it is to plan its route. Obtaining distinguishable features can ensure the success of image-based place recognition. However, generating robust features across drastically appearance changing images is still a challenging problem. Deep features are frequently chosen instead of local features in the tasks of place recognition following the development of convolutional neural networks. But even the deep features generated by powerful neural models can cause unsatisfactory recognition results. This is perhaps due to a lack of information selecting process. The technology of semantic segmentation allows recognizing and classifying image information. Semantic segmentation followed by image inpainting provide a possibility of detecting, deleting and reconstructing annoying information. This paper proves that dynamic information present in images such as vehicles and pedestrians damages the performance of place recognition and proposes a feature extraction system that includes a step to decrease the presence of dynamic information of an image. This system is composed of two stages: 1) dynamic objects detection and removing, 2) image inpainting to reconstruct the background of removed regions. Objects detection and removing consists of deleting unstable objects recognized by semantic segmentation method from images. Image inpainting and reconstructing deals with generating inpaint-images by repairing missing regions through image inpainting method. The robustness of the proposed approach is evaluated by comparing to the non-selecting deep feature based place recognition approaches over three datasets.


title: Web Based Notebooks for Teaching, an Experience at Universidad de Zaragoza

text: Since 2012, web based notebooks have been used as interface for computer algebra systems in teaching mathematics courses at Universidad de Zaragoza. We present an overview of the experience, detailing the advantages and problems that have been noticed during this time.


title: Mathematical World Knowledge Contained in the Multilingual Wikipedia Project

text: The purpose of this project is to test and evaluate an approach for Formula Concept Discovery (FCD). FCD aims at retrieving a formula concept (in the form of a Wikidata item) together with its defining formula within documents, in this case 100 English Wikipedia articles. To correctly identify the defining formula of a Wikipedia article, this approach searches for shared formulae across Wikipedia articles available in different languages. The formula shared in the most languages is then assumed to be the defining formula. The results show that neither this approach alone nor a combination with an existing approach that considers the order of the formulae inside an article leads to satisfying results. It is thus concluded that the number of times a formula is shared across a Wikipedia article in different languages is not a good indicator to determine the defining formula with the current approach. Consequently, several ideas for further research are proposed which could improve the results.


title: Multispectral Dynamic Codebook and Fusion Strategy for Moving Objects Detection

text: The Codebook model is one of the popular real-time models for background subtraction to detect moving objects. In this paper, we propose two techniques to adapt the original Codebook algorithm to multispectral images: dynamic mechanism and fusion strategy. For each channel, only absolute spectral value is used to calculate the spectral similarity between the current frame pixel and reference average value in the matching process, which can simplify the matching equations. Besides, the deciding boundaries are obtained based on statistical information extracted from the data and always adjusting themselves to the scene changes. Results demonstrate that with the proposed techniques, we can acquire a comparable accuracy with other methods using the same multispectral dataset for background subtraction.


title: Variable Ordering Selection for Cylindrical Algebraic Decomposition with Artificial Neural Networks

text: Cylindrical algebraic decomposition (CAD) is a fundamental tool in computational real algebraic geometry. Previous studies have shown that machine learning (ML) based approaches may outperform traditional heuristic ones on selecting the best variable ordering when the number of variables [Formula: see text]. One main challenge for handling the general case is the exponential explosion of number of different orderings when n increases. In this paper, we propose an iterative method for generating candidate variable orderings and an ML approach for selecting the best ordering from them via learning neural network classifiers. Experimentations show that this approach outperforms heuristic ones for [Formula: see text].


title: Evaluating Fractional Derivatives of the Riemann Zeta Function

text: We present a method for evaluating the reverse Grünwald-Letnikov fractional derivatives of the Riemann Zeta function [Formula: see text] and use it to explore the location of zeros of integral and fractional derivatives on the left half plane.


title: Classifying Simplicial Dissections of Convex Polyhedra with Symmetry

text: A convex polyhedron is the convex hull of a finite set of points in [Formula: see text] A triangulation of a convex polyhedron is a decomposition into a finite number of 3-simplices such that any two intersect in a common face or are disjoint. A simplicial dissection is a decomposition into a finite number of 3-simplices such that no two share an interior point. We present an algorithm to classify the simplicial dissections of a regular polyhedron under the symmetry group of the prolyhedron.


title: A Dataset to Support Sexist Content Detection in Arabic Text

text: Social media have become a viral source of information. This huge amount of data offers an opportunity to study the feelings and opinions of the crowds toward any subject using Sentiment Analysis, which is a struggling area for Arabic Language. In this research, we present our approach to build a thematic training set by combining manual and automatic annotation of Arabic texts addressing Discrimination and Violence Against Women.


title: Classification of Linear Codes by Extending Their Residuals

text: An approach for classification of linear codes with given parameters starting from their proper residual codes or subcodes is presented. The base of the algorithm is the concept of canonical augmentation which is important for parallel implementations. The algorithms are implemented in the programs LengthExtension and DimExtension of the package QextNewEdition. As an application, the nonexistence of binary [41, 14, 14] codes is proved.


title: Archiving and Referencing Source Code with Software Heritage

text: Software, and software source code in particular, is widely used in modern research. It must be properly archived, referenced, described and cited in order to build a stable and long lasting corpus of scientific knowledge. In this article we show how the Software Heritage universal source code archive provides a means to fully address the first two concerns, by archiving seamlessly all publicly available software source code, and by providing intrinsic persistent identifiers that allow to reference it at various granularities in a way that is at the same time convenient and effective. We call upon the research community to adopt widely this approach.


title: Chordality Preserving Incremental Triangular Decomposition and Its Implementation

text: In this paper, we first prove that the incremental algorithm for computing triangular decompositions proposed by Chen and Moreno Maza in ISSAC’ 2011 in its original form preserves chordality, which is an important property on sparsity of variables. On the other hand, we find that the current implementation in Triangularize command of the RegularChains library in Maple may not always respect chordality due to the use of some simplification operations. Experimentation show that modifying these operations, together with some other optimizations, brings significant speedups for some super sparse polynomial systems.


docid: wmyvcgim

title: A Numerical Approach for Computing Euler Characteristics of Affine Varieties

text: We develop a numerical nonlinear algebra approach for computing the Euler characteristic of an affine variety. Our approach is to relate Euler characteristics of a smooth affine variety with the number of critical points using Morse theory. In general, we stratify a variety into the union of smooth affine varieties to obtain results on singular varieties.


title: Phase Portraits of Bi-dimensional Zeta Values

text: In this extended abstract, we present how to compute and visualize phase portraits of bi-dimensional Zeta Values. Such technology is useful to explore bi-dimensional Zeta Values and in long-term quest to discover a 2D-Riemann hypothesis. To reach this goal, we need two preliminary steps: [Formula: see text] the notion of phase portraits and a general tool to visualize phase portrait based on interactive Jupyter widgets. [Formula: see text] the ability to compute numerical approximations of bi-dimensional Zeta values, using mpmath, a Python library for arbitrary-precision floating-point arithmetic. To this end, we develop a theory to numerically compute double sums and produce the first algorithm to compute bi-dimensional Zeta Values with complex parameters.


title: DermoNet: A Computer-Aided Diagnosis System for Dermoscopic Disease Recognition

text: The research of skin lesion diseases is currently one of the hottest topics in the medical research fields, and has gained a lot of attention on the last few years. However, the existing skin lesion methods are mainly relying on conventional Convolutional Neural Network (CNN) and the performance of skin lesion recognition is far from satisfactory. Therefore, to overcome the aforementioned drawbacks of traditional methods, we propose a novel Computer-Aided Diagnosis (CAD) system, named DermoNet, based on Multi-Scale Feature Level (MSFL) blocks and Multi-Level Feature Fusion (MLFF). Further, the DermoNet approach yields a significant enhancement in terms of dealing with the challenge of small training data sizes in the dermoscopic domain and avoiding high similarity between classes and overfitting issue. Extensive experiments are conducted on the public dermoscopic dataset, and the results demonstrate that DermoNet outperforms the state-of-the-art approaches. Hence, DermoNet can achieve an excellent diagnostic efficiency in the auxiliary diagnosis of skin lesions.


docid: lrsqyeqw

title: The GAP Package LiePRing

text: A symbolic Lie p-ring defines a family of Lie rings with [Formula: see text] elements for infinitely many different primes p and a fixed positive integer n. Symbolic Lie p-rings are used to describe the classification of isomorphism types of nilpotent Lie rings of order [Formula: see text] for all primes p and all [Formula: see text]. This classification is available as the LiePRing package of the computer algebra system GAP. We give a brief description of this package, including an approach towards computing the automorphism group of a symbolic Lie p-ring.


title: Markov Transition Matrix Analysis of Mathematical Expression Input Models

text: Computer software interfaces for mathematics collaboration and problem solving rely, as all interfaces do, on user identification and recognition of symbols (via icons and other contextual widgets). In this paper we examine the results of a short study which examined users interacting with mathematics software (Mathematics Classroom Communicator, MC[Formula: see text]) designed for education, real-time communication and collaboration. Videos were recorded of 14 users working through seven comprehensive problems in the MC[Formula: see text] interface. Extensive second-by-second coding was completed of the user’s actions and status throughout their work, and a set of transition matrices were tabulated, estimating transition probabilities between symbols, operators and other aspects of mathematical expressions. We discuss the results of these matrices, and their implications in the translation of abstract mathematical concepts into software interfaces, and further conclude with a brief discussion of suggestions for mathematical software interface design. This study also has applications in mathematical software usability and accessibility.


title: Considerably Improving Clustering Algorithms Using UMAP Dimensionality Reduction Technique: A Comparative Study

text: Dimensionality reduction is widely used in machine learning and big data analytics since it helps to analyze and to visualize large, high-dimensional datasets. In particular, it can considerably help to perform tasks like data clustering and classification. Recently, embedding methods have emerged as a promising direction for improving clustering accuracy. They can preserve the local structure and simultaneously reveal the global structure of data, thereby reasonably improving clustering performance. In this paper, we investigate how to improve the performance of several clustering algorithms using one of the most successful embedding techniques: Uniform Manifold Approximation and Projection or UMAP. This technique has recently been proposed as a manifold learning technique for dimensionality reduction. It is based on Riemannian geometry and algebraic topology. Our main hypothesis is that UMAP would permit to find the best clusterable embedding manifold, and therefore, we applied it as a preprocessing step before performing clustering. We compare the results of many well-known clustering algorithms such ask-means, HDBSCAN, GMM and Agglomerative Hierarchical Clustering when they operate on the low-dimension feature space yielded by UMAP. A series of experiments on several image datasets demonstrate that the proposed method allows each of the clustering algorithms studied to improve its performance on each dataset considered. Based on Accuracy measure, the improvement can reach a remarkable rate of 60%.


title: Real Tropical Hyperfaces by Patchworking in polymake

text: An implementation of Viro’s patchworking in polymake is presented, and a census of Betti numbers of real tropical surfaces serves as a showcase. The latter is relevant in the context of Hilbert’s 16th Problem.


title: A Flow-Based Programming Environment for Geometrical Construction

text: In this article, we show a flow-based programming environment for interactive geometry software. Flow-based programming is one of the programming paradigms. All of the processes and data are represented as nodes, and we connect processes and data with edges. We call the figure with nodes and edges graph because the figure looks like a planar graph. There is a lot of software implementing flow-based programming. However, there are few mathematical software based on a flow-based programming environment. So, we develop experimental interactive geometry software to generate kaleidoscope patterns based on flow-based programming. The software shows us some advantages of flow-based programming. First, it is easy to understand the procedure of construction. Second, flow-based programming is flexible. Third, flow-based programming has high extensibility. We seek possibilities of practical use of the geometrical construction software with flow-based programming.


title: A Bottom-Up Approach for Pig Skeleton Extraction Using RGB Data

text: Animal behavior analysis is a crucial task for the industrial farming. In an indoor farm setting, extracting Key joints of animals is essential for tracking the animal for a longer period of time. In this paper, we proposed a deep network that exploits transfer learning to train the network for the pig skeleton extraction in an end to end fashion. The backbone of the architecture is based on an hourglass stacked dense-net. In order to train the network, keyframes are selected from the test data using K-mean sampler. In total, 9 Keypoints are annotated that gives a brief detailed behavior analysis in the farm setting. Extensive experiments are conducted and the quantitative results show that the network has the potential of increasing the tracking performance by a substantial margin.


docid: dvtlxtcq

title: Isomorphism and Invariants of Parallelisms of Projective Spaces

text: We consider the computer-aided constructive classification of parallelisms with predefined automorphism groups in small finite projective spaces. The usage of a backtrack search algorithm makes it very important to filter away equivalent partial solutions as soon as possible and to use a fast method for checking for isomorphism of any two parallelisms. The rejection of most of the equivalent solutions can be done by a test which uses the normalizer of the predefined automorphism group. We consider the applicability and effectiveness of such a test, and present sensitive invariants of resolutions of Steiner 2-designs. They can be used to facilitate any type of test for isomorphism of parallelisms.


docid: jkofmrsx

title: A Design and an Implementation of an Inverse Kinematics Computation in Robotics Using Gröbner Bases

text: The solution and a portable implementation of the inverse kinematics computation of a 3 degree-of-freedom (DOF) robot manipulator using Gröbner bases are presented. The main system was written Python with computer algebra system SymPy. Gröbner bases are computed with computer algebra system Risa/Asir, called from Python via OpenXM infrastructure for communicating mathematical software systems. For solving a system of algebraic equations, several solvers (both symbolic and numerical) are used from Python, and their performance has been compared. Experimental results with different solvers for solving a system of algebraic equations are shown.


title: Algorithms for Pfaffian Systems and Cohomology Intersection Numbers of Hypergeometric Integrals

text: In the theory of special functions, a particular kind of multidimensional integral appears frequently. It is called the Euler integral. In order to understand the topological nature of the integral, twisted de Rham cohomology theory plays an important role. We propose an algorithm of computing an invariant cohomology intersection number of a given basis of the twisted cohomology group. We also develop an algorithm of computing the Paffian system that a given basis satisfies. These algorithms are based on the fact that the Euler integral satisfies GKZ system and utilizes algorithms to find rational function solutions of differential equations. We provide software to perform this algorithm.


title: A Machine Learning Based Software Pipeline to Pick the Variable Ordering for Algorithms with Polynomial Inputs

text: We are interested in the application of Machine Learning (ML) technology to improve mathematical software. It may seem that the probabilistic nature of ML tools would invalidate the exact results prized by such software, however, the algorithms which underpin the software often come with a range of choices which are good candidates for ML application. We refer to choices which have no effect on the mathematical correctness of the software, but do impact its performance. In the past we experimented with one such choice: the variable ordering to use when building a Cylindrical Algebraic Decomposition (CAD). We used the Python library Scikit-Learn (sklearn) to experiment with different ML models, and developed new techniques for feature generation and hyper-parameter selection. These techniques could easily be adapted for making decisions other than our immediate application of CAD variable ordering. Hence in this paper we present a software pipeline to use sklearn to pick the variable ordering for an algorithm that acts on a polynomial system. The code described is freely available online.


title: Employing C++ Templates in the Design of a Computer Algebra Library

text: We discuss design aspects of the open-source Basic Polynomial Algebra Subprograms (BPAS) library. We build on standard C++11 template mechanisms to improve ease of use and accessibility. The BPAS computer algebra library looks to enable end-users to do work more easily and efficiently through optimized C code wrapped in an object-oriented and user-friendly C++ interface. Two key aspects of this interface to be discussed are the encoding of the algebraic hierarchy as a class hierarchy and a mechanism to support the combination of algebraic types as a new type. Existing libraries, if encoding the algebraic hierarchy at all, use runtime value checks to determine if two elements belong to the same ring for an incorrect false sense of type safety in an otherwise statically-typed language. On the contrary, our template metaprogramming mechanism provides true compile-time type safety and compile-time code generation. The details of this mechanism are transparent to end-users, providing a very natural interface for an end-user mathematician.


title: Graph-Based Image Retrieval: State of the Art

text: The paper deals with the problem of semantic Image Retrieval. Indeed, the image has recently gained popularity in several domains such as medical domain, marketing, etc. Image plays a very vital role in documentation. However, finding visual and relevant information in an image is a huge task for Image Retrieval community and a very discussed issue in digital image processing. In fact, image can be extracted from a big collection of images, in the purpose of responding to user’s need. Image Retrieval processes based on classical techniques may not be sufficient to user. For several years, great efforts have been devoted to integrate semantic aspect, in order to enhance relevance of the result and ensure high-level content consideration in image. This paper presents a state of the art of Image Retrieval approaches using graph theory due to the growing interest given to graphs in terms of performance, representation and its ability to ingrate semantic aspect. We review a number of recently available graph-based approaches in Image Retrieval aiming to determine factors adding semantic aspect in Image Retrieval system.


title: Approach to Analysis the Surface Geometry Change in Cultural Heritage Objects

text: The three-dimensional digitization of the cultural heritage objects during different stages of the conservation process is an important tool for objective documentation. Further data analysis is also important to monitor, estimate and understand any possible change as accurately as possible. In this work, the cultural heritage (CH) objects were selected for 3D scanning, analysis and visualisation of the change or degradation on their surface over time. The main goal of this work is to develop analysis, and visualization methods for CH object to assess local change in their surface geometry to support conservation processes documentation. The analysis was based on geometrical analysis of change in global distance between before and after chemical cleaning for a chosen object. The new local neighborhood distance histogram has been proposed as a local measure of surface change based on optimized k-neighborhood search algorithm to assess the local geometry change of a focus point.


title: Speech Enhancement Based on Deep AutoEncoder for Remote Arabic Speech Recognition

text: Remote applications that deal with speech need the speech signal to be compressed. First, speech coding transforms the continuous waveform into a numerical form. Then, the digitized signal is compressed with or without loss of information. This transformation affects the original waveform and degrades performances for further recognition of the speech signal. Meanwhile, the transmission is another source of speech degradation. To restore the original “clean” speech, speech enhancement (SE) is widely used, and deep learning algorithms are state-of-the-art, nowadays. In this paper, the target application is a remote Arabic speech recognition system, and the aim of using SE is to improve the accuracy of the speech recognizer. For that purpose, a Deep Auto Encoder (DAE) is used. The effect of the DAE-based SE is studied through different configurations, and the performances are evaluated through accuracy. The results showed an improvement of about 3.17 between the accuracy prior to the SE and that computed with the enhanced speech.


title: Machine Learning-Based Classification of Powdery Mildew Severity on Melon Leaves

text: Precision agriculture faces challenges related to plant disease detection. Plant phenotyping assesses the appearance to select the best genotypes that resist to varying environmental conditions via plant variety testing. In this process, official plant variety tests are currently performed in vitro by visual inspection of samples placed in a culture media. In this communication, we demonstrate the potential of a computer vision approach to perform such tests in a much faster and reproducible way. We highlight the benefit of fusing contrasts coming from front and back light. To the best of our knowledge, this is illustrated for the first time on the classification of the severity of the presence of a fungi, powdery mildew, on melon leaves with 95% of accuracy.


title: A Random Forest-Cellular Automata Modeling Approach to Predict Future Forest Cover Change in Middle Atlas Morocco, Under Anthropic, Biotic and Abiotic Parameters

text: This study aims to predict forest species cover changes in the Sidi M’Guild Forest (Mid Atlas, Morocco). Used approach combines remote sensing and GIS and is based on training Cellular Automata and Random Forest (RF) regression model for predicting species cover transition. Five covariates that precludes such transition have been chosen according to Pearson’s test. The model was trained and validated based on the use of forest cover stratum transition probabilities between 1990 and 2004 and then validated using 2018 forest species cover map. Validation of the predicted map with that of 2018 shows an overall agreement between the two maps (72%) for each number of RF’s trees used. The 2032 projected forest species cover map indicate a strong regression of Cedar atlas and thuriferous juniper cover and a medium regression of mixture holm oak and thuriferous juniper, mixture of atlas cedar and thuriferous juniper, and sylvatic and asylvatic vacuums, a very strong progression of holm oak, and of mixture atlas cedar, holm oak and thuriferous juniper and medium progression of mixture of atlas cedar and holm oak. These findings provide important insights to planners, natural resource managers and policy-makers to reconsider their strategies to ensure the sustainability goals.


title: Visualizing Blood Flow of Palm in Different Muscle Tense State Using High-Speed Video Camera

text: In this paper, we propose a method to visualize blood flow of palm in different muscle tense state using RGB high-speed camera. Recently, new modalities are needed to develop a more accurate system to non-contact multi-modal affect analysis. Then, we focus on muscle tense. The muscle tense is caused by stress. Hence, the muscle tense is one of the effective modalities for non-contact multi-modal affect analysis. However, it is very difficult to measure muscle tense in the real environment because it requires a contact-type sensor. Therefore, we use iPPG to visualize the pulse wave during muscle tense from the skin video taken with the RGB video camera. As a result of this experiment, we found that it was possible to recognize the difference in pulse wave during muscle tense from the video that visualized the pulse wave. From this result, the realization of non-contact measurement of muscle tense can be expected.


title: Image Watermarking Based on Fourier-Mellin Transform

text: Geometric attacks are still challenging issues in image watermarking. In this paper, the robustness of different insertion position and shape of the watermark are evaluated in watermarking scheme based on Fourier-Mellin transform. We propose diagonal, rectangular, and circular insertion of the mark. The robustness of these techniques against geometric deformations such as rotation, scaling and translation (RST) is evaluated. Results show that the circular insertion performs better for translation and scaling attacks, while the diagonal insertion is better for rotations and RST attacks. The last point makes the diagonal insertion to be preferred in industrial applications since the combination of RST attacks often occurs in many applications such as printing the image on a physical support, and scanning it (print-scan attack).


title: FunGrim: A Symbolic Library for Special Functions

text: We present the Mathematical Functions Grimoire (FunGrim), a website and database of formulas and theorems for special functions. We also discuss the symbolic computation library used as the backend and main development tool for FunGrim, and the Grim formula language used in these projects to represent mathematical content semantically.


title: Proposed Integration Algorithm to Optimize the Separation of Audio Signals Using the ICA and Wavelet Transform

text: In the present work, an integration of two combined methodologies is developed for the blind separation of mixed audio signals. The mathematical methodologies are the independent component analysis (ICA) and the discrete Wavelet transform (DWT). The DWT optimizes processing time by decreasing the amount of data, before that signals are processed by ICA. A traditional methodology for signal processing such as Wavelet is combined with a statistical process as ICA, which assumes that the source signals are mixed and they are statistically independent of each other. The problem refers to very common situations where the human being listens to several sound sources at the same time. The human brain being able to pay attention to the message of a particular signal. The results are very satisfactory, effectively achieving signal separation, where only a small background noise and a attenuation in the amplitude of the recovered signal are noticed, but that nevertheless the signal message is identified in such a way.


title: A New Sparse Blind Source Separation Method for Determined Linear Convolutive Mixtures in Time-Frequency Domain

text: This paper presents a new Blind Source Separation method for linear convolutive mixtures, which exploits the sparsity of source signals in the time-frequency domain. This method especially brings a solution to the artifacts problem that affects the quality of signals separated by existing time-frequency methods. These artifacts are in fact introduced by a time-frequency masking operation, used by all these methods. Indeed, by focusing on the case of determined mixtures, we show that this problem can be solved with much less restrictive sparsity assumptions than those of existing methods. Test results show the superiority of our new proposed method over existing ones based on time-frequency masking.


title: Implementing the Tangent Graeffe Root Finding Method

text: The tangent Graeffe method has been developed for the efficient computation of single roots of polynomials over finite fields with multiplicative groups of smooth order. It is a key ingredient of sparse interpolation using geometric progressions, in the case when blackbox evaluations are comparatively cheap. In this paper, we improve the complexity of the method by a constant factor and we report on a new implementation of the method and a first parallel implementation.


title: Applying Machine Learning to Heuristics for Real Polynomial Constraint Solving

text: This paper considers the application of machine learning to automatically generating heuristics for real polynomial constraint solvers. We consider a specific choice-point in the algorithm for constructing an open Non-uniform Cylindrical Algebraic Decomposition (NuCAD) for a conjunction of constraints, and we learn a heuristic for making that choice. Experiments demonstrate the effectiveness of the learned heuristic. We hope that the approach we take to learning this heuristic, which is not a natural fit to machine learning, can be applied effectively to other choices in constraint solving algorithms.


title: Microcontrollers on the Edge – Is ESP32 with Camera Ready for Machine Learning?

text: For most machine learning tasks big computing power is needed, but some tasks can be done with microcontrollers. In this paper well-known SoC ESP32 has been analyzed. It is usually used in IoT devices for data measurement, but some authors started to use simple machine learning algorithms with them. Generally, this paper will analyze the possibility of using ESP32 with a built-in camera for machine learning algorithms. Focus of research will be on durations of photographing and photograph processing, because that can be a bottleneck of a machine learning tasks. For this purpose, logistic regression has been implemented on ESP32 with camera. It has been used to differentiate two handwritten letters on the greyscale pictures (“o” and “x”). Logistic regression weights have been calculated on the cloud, but then they have been transferred to an ESP32. The output results have been analyzed. The duration of photographing and processing were analyzed as well as the impact of implemented PSRAM memory on performances. It can be concluded that ESP32 with camera can be used for some simple machine learning tasks and for camera picture taking and preparing for other more powerful processors. Arduino IDE still does not provide enough level of optimization for implemented PSRAM memory.


title: Towards the Tactile Discovery of Cultural Heritage with Multi-approach Segmentation

text: This paper presents a new way to access visual information in museums through tactile exploration, and related techniques to efficiently transform visual data into tactile objects. Accessibility to cultural heritage and artworks for people with visual impairments requires the segmentation of images and paintings to extract and classify their contents into meaningful elements which can then be presented through a tactile medium. In this paper, we investigate the feasibility and how to optimize the tactile discovery of an image. First, we study the emergence of image comprehension through tactile discovery, using 3D-printed objects extracted from paintings. Later, we present a dynamic Force Feedback Tablet (F2T) used to convey the 2D shape and texture information of objects through haptic feedback. We then explore several image segmentation methods to automate the extraction of meaningful objects from selected artworks, to be presented to visually impaired people through the F2T. Finally, we evaluate how to best combine the F2T’s haptic effects in order to convey the extracted objects and features to the users, with the aim of facilitating the comprehension of the represented objects and their affordances.


title: Equality Checking for General Type Theories in Andromeda 2

text: We designed a user-extensible judgemental equality checking algorithm for general type theories that supports computation rules and extensionality rules. The user needs only provide the equality rules they wish to use, after which the algorithm devises an appropriate notion of normal form. The algorithm is a generalization of type-directed equality checking for Martin-Löf type theory, and we implemented it in the Andromeda 2 prover.


title: Nilpotent Quotients of Associative [Formula: see text]-Algebras and Augmentation Quotients of Baumslag-Solitar Groups

text: We describe the functionality of the package zalgs for the computer algebra system GAP. The package contains an implementation of the nilpotent quotient algorithm for finitely presented associative [Formula: see text]-algebras described in [3]. As an application of this algorithm we calculate augmentation quotients, i.e. successive quotients of powers of the augmentation ideal I(G) of the integral group ring [Formula: see text], where G is a finitely presented group. We apply these methods to obtain conjectures for augmentation quotients of the Baumslag-Solitar groups BS(m, n) with [Formula: see text] equal to 0, 1 or a prime p.


title: Diagnosing Tuberculosis Using Deep Convolutional Neural Network

text: One of the global topmost causes of death is Tuberculosis (TB) which is caused by mycobacterium bacillus. The increase rate of infected people and the recorded deaths from TB disease is as a result of its transmissibility, lack of early diagnosis, and inadequate professional radiologist in developing regions where TB is more prevalent. Tuberculosis is unquestionably curable but needs to be detected early for necessary treatment to be effective. Many screening techniques are available, but chest radiograph has proven to be valuable for screening pulmonary diseases but hugely dependent on the interpretational skill of an expert radiologist. We propose a Computer-Aided Detection model using Deep Convolutional Neural Networks to automatically detect TB from Montgomery County (MC) Tuberculosis radiographs. Our proposed model performed at 87.1% validation accuracy and evaluated using confusion matrix and accuracy as metrics.


title: Logo Detection Based on FCM Clustering Algorithm and Texture Features

text: Logo detection methods usually depend on logo shapes and need for training data or a-priori information on the processed images. This limits their effectiveness to real-world applications. In this paper, we tackle these challenges by exploring the textural information. Specifically we propose a novel approach for administrative logo detection based on a fuzzy classification with a multi-fractal texture feature, capable of automatically characterizing texture measures describing logo and non-logo regions. Experimental results, using two real datasets, confirm the feasibility of the proposed method for degraded administrative documents. Extensive comparative evaluations demonstrate the superiority of this approach over the state-of-the-art methods.


title: Multistage Deep Neural Network Framework for People Detection and Localization Using Fusion of Visible and Thermal Images

text: In Computer vision object detection and classification are active fields of research. Applications of object detection and classification include a diverse range of fields such as surveillance, autonomous cars and robotic vision. Many intelligent systems are built by researchers to achieve the accuracy of human perception but could not quite achieve it yet. Convolutional Neural Networks (CNN) and Deep Learning architectures are used to achieve human like perception for object detection and scene identification. We are proposing a novel method by combining previously used techniques. We are proposing a model which takes multi-spectral images, fuses them together, drops the useless images and then provides semantic segmentation for each object (person) present in the image. In our proposed methodology we are using CNN for fusion of Visible and thermal images and Deep Learning architectures for classification and localization. Fusion of visible and thermal images is carried out to combine informative features of both images into one image. For fusion we are using Encoder-decoder architecture. Fused image is then fed into Resnet-152 architecture for classification of images. Images obtained from Resnet-152 are then fed into Mask-RCNN for localization of persons. Mask-RCNN uses Resnet-101 architecture for localization of objects. From the results it can be clearly seen that Fused model for object localization outperforms the Visible model and gives promising results for person detection for surveillance purposes. Our proposed model gives the Miss Rate of 5.25% which is much better than the previous state of the art method applied on KAIST dataset.


title: Deep Transfer Learning Models for Tomato Disease Detection

text: Vegetable crops in Morocco and especially in the Sous-Massa region are exposed to parasitic diseases and pest attacks which affect the quantity and the quality of agricultural production. Precision farming is introduced as one of the biggest revolutions in agriculture, which is committed to improving crop protection by identifying, analyzing and managing variability delivering effective treatment in the right place, at the right time, and with the right rate. The main purpose of this study is to find the most suitable machine learning model to detect tomato crop diseases in standard RGB images. To deal with this problem we consider the deep learning models DensNet, 161 and 121 layers and VGG16 with transfer learning. Our study is based on images of infected plant leaves divided into 6 types of infections pest attacks and plant diseases. The results were promising with an accuracy up to 95.65% for DensNet161, 94.93% for DensNet121 and 90.58% for VGG16.


title: Homomorphic Encryption and Some Black Box Attacks

text: This paper is a compressed summary of some principal definitions and concepts in the approach to the black box algebra being developed by the authors [6–8]. We suggest that black box algebra could be useful in cryptanalysis of homomorphic encryption schemes [11], and that homomorphic encryption is an area of research where cryptography and black box algebra may benefit from exchange of ideas.


title: Slack Ideals in Macaulay2

text: Recently Gouveia, Thomas and the authors introduced the slack realization space, a new model for the realization space of a polytope. It represents each polytope by its slack matrix, the matrix obtained by evaluating each facet inequality at each vertex. Unlike the classical model, the slack model naturally mods out projective transformations. It is inherently algebraic, arising as the positive part of a variety of a saturated determinantal ideal, and provides a new computational tool to study classical realizability problems for polytopes. We introduce the package SlackIdeals for Macaulay2, that provides methods for creating and manipulating slack matrices and slack ideals of convex polytopes and matroids. Slack ideals are often difficult to compute. To improve the power of the slack model, we develop two strategies to simplify computations: we scale as many entries of the slack matrix as possible to one; we then obtain a reduced slack model combining the slack variety with the more compact Grassmannian realization space model. This allows us to study slack ideals that were previously out of computational reach. As applications, we show that the well-known Perles polytope does not admit rational realizations and prove the non-realizability of a large simplicial sphere.


title: CNN-SVM Learning Approach Based Human Activity Recognition

text: Although it has been encountered for a long time, the human activity recognition remains a big challenge to tackle. Recently, several deep learning approaches have been proposed to enhance the recognition performance with different areas of application. In this paper, we aim to combine a recent deep learning-based method and a traditional classifier based hand-crafted feature extractors in order to replace the artisanal feature extraction method with a new one. To this end, we used a deep convolutional neural network that offers the possibility of having more powerful extracted features from sequence video frames. The resulting feature vector is then fed as an input to the support vector machine (SVM) classifier to assign each instance to the corresponding label and bythere, recognize the performed activity. The proposed architecture was trained and evaluated on MSR Daily activity 3D dataset. Compared to state of art methods, our proposed technique proves that it has performed better.


title: Practical Volume Estimation of Zonotopes by a New Annealing Schedule for Cooling Convex Bodies

text: We study the problem of estimating the volume of convex polytopes, focusing on zonotopes. Although a lot of effort is devoted to practical algorithms for polytopes given as an intersection of halfspaces, there is no such method for zonotopes. Our algorithm is based on Multiphase Monte Carlo (MMC) methods, and our main contributions include: (i) a new uniform sampler employing Billiard Walk for the first time in volume computation, (ii) a new simulated annealing generalizing existing MMC by making use of adaptive convex bodies which fit to the input, thus drastically reducing the number of phases. Extensive experiments on zonotopes show our algorithm requires sub-linear number of oracle calls in the dimension, while the best theoretical bound is cubic. Moreover, our algorithm can be easily generalized to any convex body. We offer an open-source, optimized C++ implementation, and analyze its performance. Our code tackles problems intractable so far, offering the first efficient algorithm for zonotopes which scales to high dimensions (e.g. one hundred dimensions in less than 1 h).


title: Extraction and Recognition of Bangla Texts from Natural Scene Images Using CNN

text: The semantic information presents in the scene images may be the useful information for the viewers who is searching for a specific location or any specific shop and address. This type of information can also be useful in licenseplate detection, controlling the vehicle on the road, robot navigation, and assisting visually impaired persons. An efficient method is presented in this paper to detect and extract Bangla texts from scene images based on a connected component approach along with rule-based filtering and vertical scanning scheme. Next, extracted characters are recognized by using Convolutional Neural Network (CNN). The method consists of the four basic consecutive steps such as detection and extraction of the Region of Interest (ROI), segmentation of the words, extraction of characters, and recognition of the extracted characters. After extracting the ROI from the input image, connected component(CC) analysis and bounding box technology are used for segmentation of Bangla words. To separate and extract Bangla characters from the segmented Bangla words, vertical scanning based method along with a dynamic threshold value has been applied. Finally, character recognition is carried out using CNN. The proposed algorithm is applied to 600 scene images of different writing styles and colors, and we have obtained 89.25% accuracy in text detection and 94.50% accuracy in the extraction of characters. We have achieved an accuracy of 99.30% and 95.76% in recognition of Bangla digits and characters respectively. By combining both the digits and characters, obtained recognition accuracy is 95.39%.


docid: vbuqnxma

title: ECG Signal Analysis on an Embedded Device for Sleep Apnea Detection

text: Low cost embedded devices with computational power have the potential to revolutionise detection and management of many diseases. This is especially true in the case of conditions like sleep apnea, which require continuous long term monitoring. In this paper, we give details of a portable, cost-effective and customisable Electrocardiograph(ECG) Signal analyser for real time sleep apnea detection. We have developed a data analysis pipeline using which we can identify sleep apnea using a single lead ECG signal. Our method combines steps including dataset extraction, segmentation, signal cleaning, filtration and finally apnea detection using Support Vector Machines (SVM). We analysed our proposed implementation through a complete run on the MIT-Physionet dataset. Due to the low computational complexity of our proposed method, we find that it is well suited for deployment on embedded devices such as the Raspberry Pi.


title: MORLAB – A Model Order Reduction Framework in MATLAB and Octave

text: When synthesizing feedback controllers for large-scale dynamical systems, often a reduction of the plant model by model order reduction is required. This is a typical task in computer-aided control system design environments. Therefore, in the last years, model order reduction became an essential tool for the practical use of mathematical models in engineering processes. For the integration of established model order reduction methods into those processes, software solutions are needed. In this paper, we describe the MORLAB (Model Order Reduction LABoratory) toolbox as such a software solution in MathWorks MATLAB(®) and GNU Octave, and its featured integration into established software tools used in simulations and controller design. We give benchmark examples for two important extensions of the toolbox.


title: Hyperplane Arrangements in polymake

text: Hyperplane arrangements form the latest addition to the zoo of combinatorial objects dealt with by polymake. We report on their implementation and on a algorithm to compute the associated cell decomposition. The implemented algorithm performs significantly better than brute force alternatives, as it requires fewer convex hulls computations. The implementation is included in polymake since release 4.0.


docid: gjkwfkox

title: Discrete p-bilaplacian Operators on Graphs

text: In this paper, we first introduce a new family of operators on weighted graphs called p-bilaplacian operators, which are the analogue on graphs of the continuous p-bilaplacian operators. We then turn to study regularized variational and boundary value problems associated to these operators. For instance, we study their well-posedness (existence and uniqueness). We also develop proximal splitting algorithms to solve these problems. We finally report numerical experiments to support our findings.


title: A Deep CNN-LSTM Framework for Fast Video Coding

text: High Efficiency Video Coding (HEVC) doubles the compression rates over the previous H.264 standard for the same video quality. To improve the coding efficiency, HEVC adopts the hierarchical quadtree structured Coding Unit (CU). However, the computational complexity significantly increases due to the full search for Rate-Distortion Optimization (RDO) to find the optimal Coding Tree Unit (CTU) partition. Here, this paper proposes a deep learning model to predict the HEVC CU partition at inter-mode, instead of brute-force RDO search. To learn the learning model, a large-scale database for HEVC inter-mode is first built. Second, to predict the CU partition of HEVC, we propose as a model a combination of a Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) network. The simulation results prove that the proposed scheme can achieve a best compromise between complexity reduction and RD performance, compared to existing approaches.


title: Incep-EEGNet: A ConvNet for Motor Imagery Decoding

text: The brain-computer interface consists of connecting the brain with machines using the brainwaves as a mean of communication for several applications that help to improve human life. Unfortunately, Electroencephalography that is mainly used to measure brain activities produces noisy, non-linear and non-stationary signals that weaken the performances of Common Spatial Pattern (CSP) techniques. As a solution, deep learning waives the drawbacks of the traditional techniques, but it still not used properly. In this paper, we propose a new approach based on Convolutional Neural Networks (ConvNets) that decodes the raw signal to achieve state-of-the-art performances using an architecture based on Inception. The obtained results show that our method outperforms state-of-the-art filter bank common spatial patterns (FBCSP) and ShallowConvNet on based on the dataset IIa of the BCI Competition IV.


title: A Formalization of Properties of Continuous Functions on Closed Intervals

text: Formal mathematics is getting increasing attention in mathematics and computer science. In particular, the formalization of calculus has important applications in engineering design and analysis. In this paper, we present a formal proof of some fundamental theorems of continuous functions on closed intervals based on the Coq proof assistant. In this formalization, we build a real number system referring to Landau’s Foundations of Analysis. Then we complete the formalization of the basic definitions of interval, function, and limit and formally prove the theorems including completeness theorem, intermediate value theorem, uniform continuity theorem and others in Coq. The proof process is normalized, rigorous and reliable.


title: Big Data and Reality Mining in Healthcare: Promise and Potential

text: Nowadays individuals are creating a huge amount of data; with a cell phone in every pocket, a laptop in every bag and wearable sensors everywhere, the fruits of the information are easy to see but less noticeable is the information itself. This data could be particularly useful in making people’s lives healthier and easier, by contributing not only to understand new diseases and therapies but also to predict outcomes at earlier stages and make real-time decisions. In this paper, we explain the potential benefits of big data to healthcare and explore how it improves treatment and empowers patients, providers and researchers. We also describe the capabilities of reality mining in terms of individual health, social network mapping, behavior patterns and treatment monitoring. We illustrate the benefits of reality mining analytics that lead to promote patients’ health, enhance medicine, reduce cost and improve healthcare value and quality. Furthermore, we highlight some challenges that big data analytics faces in healthcare.


title: Discovering Unknown Diseases with Explainable Automated Medical Imaging

text: Deep neural network (DNN) classifiers have attained remarkable performance in diagnosing known diseases when the models are trained on a large amount of data from known diseases. However, DNN classifiers trained on known diseases usually fail when they confront new diseases such as COVID-19. In this paper, we propose a new deep learning framework and pipeline for explainable medical imaging that can classify known diseases as well as detect new/unknown diseases when the models are only trained on known disease images. We first provide in-depth mathematical analysis to explain the overconfidence phenomena and present the calibrated confidence that can mitigate the overconfidence. Using calibrated confidence, we design a decision engine to determine if a medical image belongs to some known diseases or a new disease. At last, we introduce a new visual explanation to further reveal the suspected region inside each image. Using both Skin Lesion and Chest X-Ray datasets, we validate that our framework significantly improves the accuracy of new disease discovery, i.e., distinguish COVID-19 from pneumonia without seeing any COVID-19 data during training. We also qualitatively show that our visual explanations are highly consistent with doctors’ ground truth. While our work was not designed to target COVID-19, our experimental validation using the real world COVID-19 cases/data demonstrates the general applicability of our pipeline for different diseases based on medical imaging.


title: Use of Imaging Techniques as a Support for the Preventive Conservation Strategy of Wall Paintings: Application to the Medieval Decors of the Château de Germolles

text: Imaging techniques were used to document and monitor physical damage to the unique wall paintings at the Château de Germolles, Burgundy, France. Photogrammetry combined with scanning are the most appropriate techniques to monitor the evolution of microcrack networks in the cornice overhanging the paintings and preserved as a witness to 19th century additions. However, the application of these techniques was challenged due to the given constraints of a working height of 4 m and the required accuracy of a tenth of a millimetre. A special effort was therefore necessary to ensure sufficient stability of the acquisition protocol and to make it relevant during the four measurement campaigns planned over the two years of the project. The analysis of photogrammetric data has made it possible to document certain macro-deformations of the cornice according to the seasons of the year. The microcracks could be visualized and monitored from transformed 3D models of each segment for the different campaigns. The results obtained show only local movements, mainly on the walls that are most exposed to the specific climatic conditions of each season.


title: Evaluating and Differentiating a Polynomial Using a Pseudo-witness Set

text: Polynomials which arise via elimination can be difficult to compute explicitly. By using a pseudo-witness set, we develop an algorithm to explicitly compute the restriction of a polynomial to a given line. The resulting polynomial can then be used to evaluate the original polynomial and directional derivatives along the line at any point on the given line. Several examples are used to demonstrate this new algorithm including examples of computing the critical points of the discriminant locus for parameterized polynomial systems.


title: Segmentation of Microscopic Image of Colorants Using U-Net Based Deep Convolutional Networks for Material Appearance Design

text: In this study, U-Net based deep convolutional networks are used to achieve the segmentation of particle regions in a microscopic image of colorants. The material appearance of products is greatly affected by the distribution of the particle size. From that fact, it is important to obtain the distribution of the particle size to design the material appearance of products. To obtain the particle size distribution, it is necessary to segment particle regions in the microscopic image of colorants. Conventionally, this segmentation is performed manually using simple image processing. However, this manual processing leads to low reproducibility. Therefore, in this paper, to extract the particle region with high reproducibility, segmentation is performed using U-Net based deep convolutional networks. We improved deep convolutional U-Net type networks based on the feature maps trained for a microscopic image of colorants. As a result, we obtained more accurate segmentation results using the improved network than conventional U-Net.


title: Algebraic Polytopes in Normaliz

text: We describe the implementation of algebraic polyhedra in Normaliz. In addition to convex hull computation/vertex enumeration, Normaliz computes triangulations, volumes, lattice points, face lattices and automorphism groups. The arithmetic is based on the package e-antic by V. Delecroix.


title: Curtains in CAD: Why Are They a Problem and How Do We Fix Them?

text: This paper is part of our ongoing research on the adaptation of Lazard’s CAD to benefit from equational constraints in formulae. In earlier work we combined the CAD methods of McCallum and Lazard so as to produce an efficient algorithm for decomposing a hypersurface rather than the whole of [Formula: see text] (exploiting an equational constraint [Formula: see text]). That method, however, fails if f is nullified (in McCallum’s terminology): we call the set where this happens a curtain. Here we provide a further modification which, at the cost of a trade off in terms of complexity, is valid for any hypersurface, including one containing curtains.


title: Convolutional Neural Networks Backbones for Object Detection

text: Detecting objects in images is an extremely important step in many image and video analysis applications. Object detection is considered as one of the main challenges in the field of computer vision, which focuses on identifying and locating objects of different classes in an image. In this paper, we aim to highlight the important role of deep learning and convolutional neural networks in particular in the object detection task. We analyze and focus on the various state-of-the-art convolutional neural networks serving as a backbone in object detection models. We test and evaluate them in the common datasets and benchmarks up-to-date. We Also outline the main features of each architecture. We demonstrate that the application of some convolutional neural network architectures has yielded very promising state-of-the-art results in image classification in the first place and then in the object detection task. The results have surpassed all the traditional methods, and in some cases, outperformed the human being’s performance.


title: Computations with Algebraic Surfaces

text: Computations with algebraic number fields and algebraic curves have been carried out for a long time. They resulted in many interesting examples and the formation of various conjectures. The aim of this talk is to report on some computations with algebraic surfaces that are currently possible.


title: Handwriting Based Gender Classification Using COLD and Hinge Features

text: Gender Classification from handwriting is still considered to be challenging due to homogeneous vision comparing male and female handwritten documents. This paper presents a new method based on Cloud of Line Distribution (COLD) and Hinge feature for distinguishing the gender from handwriting. The SVM classifier combination decides the assigned class based on the maximum of the two decisions values resulting from COLD and Hinge feature. The proposed approach is evaluated on the standard QUWI dataset and following the framework protocol described in the ICFHR 2016 competition. Obtained results are promising regarding the classification rates announced in the literature.


title: A New Texture Descriptor: The Homogeneous Local Binary Pattern (HLBP)

text: This paper presents a simple and novel descriptor named Homogeneous Local Binary Pattern (HLBP) for texture analysis. The purpose of this description is to improve the Local Binary Pattern (LBP) approach basing on the impact of criterion homogeneous region using General Adaptive Neighborhood (GAN) principle. HLBP method is generated by using the criterion homogeneity which helps to represent a significant feature based on relationships between neighboring pixels. The main idea of HLBP is to threshold the distance between the current pixel and each of its neighbors with a homogeneity tolerance value which correspond more to the underlying spatial structures consequently allow extracting highly distinctive invariant features of the image. To assess the performance of the our proposed descriptor, we use “Outex" database and compared with the basic (LBPs). The experimental results show that the proposed Homogeneous Local Binary Pattern gives a good performance in term of classification accuracy.


title: GeoLogic – Graphical Interactive Theorem Prover for Euclidean Geometry

text: Domain of mathematical logic in computers is dominated by automated theorem provers (ATP) and interactive theorem provers (ITP). Both of these are hard to access by AI from the human-imitation approach: ATPs often use human-unfriendly logical foundations while ITPs are meant for formalizing existing proofs rather than problem solving. We aim to create a simple human-friendly logical system for mathematical problem solving. We picked the case study of Euclidean geometry as it can be easily visualized, has simple logic, and yet potentially offers many high-school problems of various difficulty levels. To make the environment user friendly, we abandoned strict logic required by ITPs, allowing to infer topological facts from pictures. We present our system for Euclidean geometry, together with a graphical application GeoLogic, similar to GeoGebra, which allows users to interactively study and prove properties about the geometrical setup.


title: A New Method of Image Reconstruction for PET Using a Combined Regularization Algorithm

text: Positron emission tomography (PET), is a medical imaging technique that provides functional information about physiological processes. The goal of PET is to reconstruct the distribution of the radioisotopes in the body by measuring the emitted photons. The computer methods are designed to solve the inverse problem known as “image reconstruction from projections.” In this paper, an iterative image reconstruction algorithm ART was regularized by combining Tikhonov and total variation regularizations. In the first step, combined regularization algorithm of total variation and Tikhonov regularization was applied to the image obtained by ART algorithm in each iteration for background noise removal with preserving edges. The quality measurements and visual inspections show a significant improvement in image quality compared to other algorithms.


title: FlexRiLoG—A SageMath Package for Motions of Graphs

text: In this paper we present the SageMath package FlexRiLoG (short for flexible and rigid labelings of graphs). Based on recent results the software generates motions of graphs using special edge colorings. The package computes and illustrates the colorings and the motions. We present the structure and usage of the package.


title: Detection of Elliptical Traffic Signs

text: Detection of elliptical features is a challenging and important task in computer vision. In fact, ellipses can describe many objects in real images like manufactured objects, cells, ball, or traffic signs. Furthermore, an ellipse is defined by five parameters: the center coordinates, the semi major axe, the semi minor axe and the orientation, which require more computational power to estimate them. Some non-ideal ellipses cause also difficulties for detection such as occlusion, appearance of multiple ellipses at same time and non-parallel ellipse. In this paper, we are interested in detecting elliptical traffic signs. We present a method for detecting different cases of ellipses: simple, partially occluded, non-parallel, multiple ellipses in images. The method selects three lines to find the ellipse center, and then it calculates the value of the semi minor and major axes of the ellipse. Experiments show that the proposed method performs well on real images in the presence or not of noises.


title: Semantic Segmentation of Diabetic Foot Ulcer Images: Dealing with Small Dataset in DL Approaches

text: Foot ulceration is the most common complication of diabetes and represents a major health problem all over the world. If these ulcers are not adequately treated in an early stage, they may lead to lower limb amputation. Considering the low-cost and prevalence of smartphones with a high-resolution camera, Diabetic Foot Ulcer (DFU) healing assessment by image analysis became an attractive option to help clinicians for a more accurate and objective management of the ulcer. In this work, we performed DFU segmentation using Deep Learning methods for semantic segmentation. Our aim was to find an accurate fully convolutional neural network suitable to our small database. Three different fully convolutional networks have been tested to perform the ulcer area segmentation. The U-Net network obtained a Dice Similarity Coefficient of 97.25% and an intersection over union index of 94.86%. These preliminary results demonstrate the power of fully convolutional neural networks in diabetic foot ulcer segmentation using a limited number of training samples.


title: Operational Research Literature as a Use Case for the Open Research Knowledge Graph

text: The Open Research Knowledge Graph (ORKG) provides machine-actionable access to scholarly literature that habitually is written in prose. Following the FAIR principles, the ORKG makes traditional, human-coded knowledge findable, accessible, interoperable, and reusable in a structured manner in accordance with the Linked Open Data paradigm. At the moment, in ORKG papers are described manually, but in the long run the semantic depth of the literature at scale needs automation. Operational Research is a suitable test case for this vision because the mathematical field and, hence, its publication habits are highly structured: A mundane problem is formulated as a mathematical model, solved or approximated numerically, and evaluated systematically. We study the existing literature with respect to the Assembly Line Balancing Problem and derive a semantic description in accordance with the ORKG. Eventually, selected papers are ingested to test the semantic description and refine it further.


title: Fuzzy-Based Approach for Assessing Traffic Congestion in Urban Areas

text: The very rapid evolution of urban areas leads to a reflection on the citizens’ mobility inside the cities. This mobility problem is highlighted by the increase in terms of time, distance and social and economic costs, whereas the congestion management approach implemented rarely meets the road users’ expectations. To overcome this problem, a novel approach for evaluating urban traffic congestion is proposed. Factors such as the imprecision of traffic records, the user’s perception of the road’s level of service provided and variation in sample data are mandatory to describe the real traffic condition. To respond to these requirements, a fuzzy inference-based method is suggested. It combines three independent congestion measures which are: speed ratio, volume to capacity ratio and decreased speed ratio into a single composite measure which is the congestion index. To run the proposed fuzzy model, the traffic dataset of Austin-Texas is used. Although it is still not possible to determine the best congestion measure, the proposed approach gives a composite aspect of traffic congestion by combining and incorporating the uncertainty of the three independent measures.


title: Polymake.jl: A New Interface to polymake

text: We present the Julia interface [Image: see text] to polymake, a software for research in polyhedral geometry. We describe the technical design and how the integration into Julia makes it possible to combine polymake with state-of-the-art numerical software.


title: A Spectral Hazy Image Database

text: We introduce a new database to promote visibility enhancement techniques intended for spectral image dehazing. SHIA (Spectral Hazy Image database for Assessment) is composed of two real indoor scenes M1 and M2 of 10 levels of fog each and their corresponding fog-free (ground-truth) images, taken in the visible and the near infrared ranges every 10 nm starting from 450 to 1000 nm. The number of images that form SHIA is 1540 with a size of [Formula: see text] pixels. All images are captured under the same illumination conditions. Three of the well-known dehazing image methods based on different approaches were adjusted and applied on the spectral foggy images. This study confirms once again a strong dependency between dehazing methods and fog densities. It urges the design of spectral-based image dehazing able to handle simultaneously the accurate estimation of the parameters of the visibility degradation model and the limitation of artifacts and post-dehazing noise. The database can be downloaded freely at http://chic.u-bourgogne.fr.


title: Certifying Irreducibility in [Formula: see text]

text: We consider the question of certifying that a polynomial in [Formula: see text] or [Formula: see text] is irreducible. Knowing that a polynomial is irreducible lets us recognise that a quotient ring is actually a field extension (equiv. that a polynomial ideal is maximal). Checking that a polynomial is irreducible by factorizing it is unsatisfactory because it requires trusting a relatively large and complicated program (whose correctness cannot easily be verified). We present a practical method for generating certificates of irreducibility which can be verified by relatively simple computations; we assume that primes and irreducibles in [Formula: see text] are self-certifying.


title: Making Presentation Math Computable: Proposing a Context Sensitive Approach for Translating LaTeX to Computer Algebra Systems

text: Scientists increasingly rely on computer algebra systems and digital mathematical libraries to compute, validate, or experiment with mathematical formulae. However, the focus in digital mathematical libraries and scientific documents often lies more on an accurate presentation of the formulae rather than providing uniform access to the semantic information. But, presentational math formats do not provide exclusive access to the underlying semantic meanings. One has to derive the semantic information from the context. As a consequence, the workflow of experimenting and publishing in the Sciences often includes time-consuming, error-prone manual conversions between presentational and computational math formats. As a contribution to improve this workflow, we propose a context-sensitive approach that extracts semantic information from a given context, embeds the information into the given input, and converts the semantically enhanced expressions to computer algebra systems.


title: Prototyping Controlled Mathematical Languages in Jupyter Notebooks

text: The Grammatical Logical Framework (GLF) is a framework for prototyping the translation of natural language sentences into logic. The motivation behind GLF was to apply it to mathematical language, as the classical compositional approach to semantics construction seemed most suitable for a domain where high precision was mandatory—even at the price of limited coverage. In particular, software for formal mathematics (such as proof checkers) require formal input languages. These are typically difficult to understand and learn, raising the entry barrier for potential users. A solution is to design input languages that closely resemble natural language. Early results indicate that GLF can be a useful tool for quickly prototyping such languages. In this paper, we will explore how GLF can be used to prototype such languages and present a new Jupyter kernel that4 adds visual support for the development of GLF-based syntax/semantics interfaces.


title: A Content Dictionary for In-Object Comments

text: It is observed that some OpenMath objects may benefit from containing comments. A content dictionary with suitable attribution symbols is proposed. This content dictionary also provides application symbols for constructing comments that are somewhat more than just plain text strings.


title: A Convex Programming Approach to Solve Posynomial Systems

text: We exhibit a class of classical or tropical posynomial systems which can be solved by reduction to linear or convex programming problems. This relies on a notion of colorful vectors with respect to a collection of Newton polytopes. This extends the convex programming approach of one player stochastic games.


title: Method to Create Multiple Choice Exercises for Computer Algebra System

text: When studying mathematics, it is important to solve exercises of an appropriate level. Recently, web-based assessment systems with a computer algebra system (CAS), e.g., Moodle with Stack and the Möbius platform with Maple, have become popular. Such web-based systems are convenient; however, they have some problems relative to inputting and evaluating mathematical formulas. In addition, when considering and solving mathematical problems, handwriting mathematics is important. We want management system of paper-oriented exercises. Auto multiple choice (AMC), which was developed by Alexis Bienvenüe, is open source software for creating and managing multiple choice questionnaires with automated marking. [Image: see text] is the native AMC language for questionnaire descriptions. We propose to combine AMC and CAS using Lua [Image: see text] , which is a [Image: see text] -based computer typesetting system with an embedded Lua scripting engine. We can embed CAS scripts into Lua [Image: see text] source, and, by creating exercises with CAS, we can generate various problems with random coefficients or terms. By providing various patterns of practice problems and facilitating discussions with each student, we expect sufficient educational benefits of providing opportunities to communicate about mathematical concepts and algorithms among students.


title: [Image: see text]-Integral Points on a Mordell Curve

text: We use an extension of quadratic Chabauty to number fields, recently developed by the author with Balakrishnan, Besser and Müller, combined with a sieving technique, to determine the integral points over [Image: see text] on the Mordell curve [Formula: see text].


title: Towards Efficient Normalizers of Primitive Groups

text: We present the ideas behind an algorithm to compute normalizers of primitive groups with non-regular socle in polynomial time. We highlight a concept we developed called permutation morphisms and present timings for a partial implementation of our algorithm. This article is a collection of results from the author’s PhD thesis.


title: On detecting urgency in short crisis messages using minimal supervision and transfer learning

text: Humanitarian disasters have been on the rise in recent years due to the effects of climate change and socio-political situations such as the refugee crisis. Technology can be used to best mobilize resources such as food and water in the event of a natural disaster, by semi-automatically flagging tweets and short messages as indicating an urgent need. The problem is challenging not just because of the sparseness of data in the immediate aftermath of a disaster, but because of the varying characteristics of disasters in developing countries (making it difficult to train just one system) and the noise and quirks in social media. In this paper, we present a robust, low-supervision social media urgency system that adapts to arbitrary crises by leveraging both labeled and unlabeled data in an ensemble setting. The system is also able to adapt to new crises where an unlabeled background corpus may not be available yet by utilizing a simple and effective transfer learning methodology. Experimentally, our transfer learning and low-supervision approaches are found to outperform viable baselines with high significance on myriad disaster datasets.


Beyond JAAD - October 2020


Aging Science Talks: The role of miR-181a in age-related loss of muscle mass and function


Development and implementation of virtual grand rounds in surgery


Extracorporeal Membrane Oxygenation in Coronavirus Disease 2019-associated Acute Respiratory Distress Syndrome: An Initial US Experience at a High-volume Centre


title: Coronavirus Disease 2019: Where are we and Where are we Going? Intersections Between Coronavirus Disease 2019 and the Heart

text: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19), which has become a pandemic affecting every country in the world. In the province of Bergamo, Italy, more than 2,200 cases of COVID-19 have been reported, which include more than 300 deaths. Most hospitalisations have been at the Papa Giovanni XXIII Hospital. This has imposed a significant burden on our hospital in terms of healthcare personnel, dedicated spaces (including intensive care areas) and time spent by clinicians, who are committed to assisting COVID-19 patients. In this short expert opinion, the authors will focus on new insights related to COVID-19 and the cardiovascular system, and try to investigate the grey areas and uncertainties in this field.


title: SARS-CoV-2 Transmission in a Dental Practice in Spain: After the Outbreak

text: The World Health Organization declared a pandemic on March 11, 2020, due to a virus named SARS-CoV-2 discovered in Wuhan, China, in December 2019. Many countries have been hit hard including Spain, with the highest number of healthcare workers being infected (>50,000). A lack of personal protective equipment and protocols at the time of the outbreak led to many fatalities. Although few of these healthcare workers are dental professionals, this community required protective measures as well. Fortunately, there are no reported cases of SARS-CoV-2 transmission in dental practices. Dental professionals were advised only to treat dental emergencies, and such cases were screened via telephone to maintain social distancing. Nevertheless, new protocols and measures are needed as dental professionals return to normal practice after weeks of confinement in many countries. Relatively, few articles have discussed the management of dental practice during the SARS-CoV-2 with no known articles on postpandemic outbreak guidelines. Though some protocols and measures are the same, there are also many differences. Here, we describe protocols and measures for dental practice in Spain in accordance with the Spanish Health Ministry.


title: A Silent Infection Pandemic of COVID-19: Epidemiological Investigation and Hypothetical Models

text: To explore the epidemic mode of COVID-19, we made an epidemiological investigation, set up hypothetical models, and compared them with hepatitis A virus (HAV) age-specific epidemic characteristic. In the epidemiological investigation, we reported the first familial COVID-19 silent infection in the world. A 19-year-old healthy female COVID-19 virus carrier without any symptoms caused two mild and one severe pneumonia. In hypothetical models, the silent infection rate ranges from 60% to 80% based on 3 sources: China mainland, evacuation of 4 nationals, and the ship “Diamond Princess,” respectively. In comparison with HAV, COVID-19 shows the same infection mode in children (aged 0–9 years), but significant difference in young adults (aged 10–44 years) and the elderly (aged 45 years or older). Therefore, we prejudged that COVID-19 is a silent infection pandemic mainly in young adults but threatens the elderly.


title: Clinical efficacy and safety in patients treated with teicoplanin with a target trough concentration of 20 μg/mL using a regimen of 12 mg/kg for five doses within the initial 3 days

text: BACKGROUND: A trough concentration (C(min)) ≥20 μg/mL of teicoplanin is recommended for the treatment of serious methicillin-resistant Staphylococcus aureus (MRSA) infections. However, sufficient clinical evidence to support the efficacy of this target C(min) has not been obtained. Even though the recommended high C(min) of teicoplanin was associated with better clinical outcome, reaching the target concentration is challenging. METHODS: Pharmacokinetics and adverse events were evaluated in all eligible patients. For clinical efficacy, patients who had bacteremia/complicated MRSA infections were analyzed. The primary endpoint for clinical efficacy was an early clinical response at 72–96 h after the start of therapy. Five dosed of 12 mg/kg or 10 mg/kg was administered as an enhanced or conventional high loading dose regimen, respectively. The C(min) was obtained at 72 h after the first dose. RESULTS: Overall, 512 patients were eligible, and 76 patients were analyzed for treatment efficacy. The proportion of patients achieving the target C(min) range (20–40 μg/mL) by the enhanced regimen was significantly higher than for the conventional regimen (75.2% versus 41.0%, p < 0.001). In multivariate analysis, C(min) ≥ 20 μg/mL was an independent factor for an early clinical response (odds ratio 3.95, 95% confidence interval 1.25–12.53). There was no significant difference in the occurrence of adverse events between patients who did or did not achieve a C(min) ≥ 20 μg/mL. CONCLUSION: A target C(min) ≥ 20 μg/mL might improve early clinical responses during the treatment of difficult MRSA infections using 12 mg/kg teicoplanin for five doses within the initial 3 days.


title: Recombinant tandem epitope vaccination provides cross protection against Actinobacillus pleuropneumoniae challenge in mice

text: Actinobacillus pleuropneumoniae (A. pleuropneumoniae/APP) is the pathogen that causes porcine contagious pleuropneumonia. Actinobacillus pleuropneumoniae is divided into 18 serovars, and the cross protection efficacy of epitopes is debatable, which has resulted in the slow development of a vaccine. Consequently, epitope-based vaccines conferring Actinobacillus pleuropneumoniae cross protection have rarely been reported. In this study, B cell epitopes in the head domain of trimeric autotransporter adhesin were predicted, and 6 epitopes were selected. Then, the predicted epitopes (Ba1, Bb5, C1, PH1 and PH2) were connected by linkers to construct a recombinant tandem antigen (rta) gene. The RTA protein encoded by the recombinant rta gene was expressed, and finally the ICR mice were immunized with the RTA protein with or without inactivated Actinobacillus pleuropneumoniae (serovars 1 and 5b) and challenged with Actinobacillus pleuropneumoniae to evaluate the protective effect of the epitope-based vaccine and combined vaccine. The mice in the RTA-immunized group and RTA plus inactivated Actinobacillus pleuropneumoniae vaccine group had a significant improvement in clinical symptoms and a higher level of antibody in the serum than those in the control group. The RTA immune group had a 40% survival rate after Actinobacillus pleuropneumoniae infection, whereas the combination of RTA and inactivated Actinobacillus pleuropneumoniae produced very strong cross immune protection in mice, at least 50% (RTA IB1 + C5) and at most 100% (RTA IB5 + C1), whereas no cross immunoprotection was found in the solo Actinobacillus pleuropneumoniae immune group. Overall, the combination of the RTA protein and inactivated bacteria significantly enhanced the cross protection effects. This implies that RTA protein in combination with a suitable inactivated Actinobacillus pleuropneumoniae strain could be a candidate vaccine for porcine contagious pleuropneumonia.


title: Extracellular vesicles: novel communicators in lung diseases

text: The lung is the organ with the highest vascular density in the human body. It is therefore perceivable that the endothelium of the lung contributes significantly to the circulation of extracellular vesicles (EVs), which include exosomes, microvesicles, and apoptotic bodies. In addition to the endothelium, EVs may arise from alveolar macrophages, fibroblasts and epithelial cells. Because EVs harbor cargo molecules, such as miRNA, mRNA, and proteins, these intercellular communicators provide important insight into the health and disease condition of donor cells and may serve as useful biomarkers of lung disease processes. This comprehensive review focuses on what is currently known about the role of EVs as markers and mediators of lung pathologies including COPD, pulmonary hypertension, asthma, lung cancer and ALI/ARDS. We also explore the role EVs can potentially serve as therapeutics for these lung diseases when released from healthy progenitor cells, such as mesenchymal stem cells.