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%.


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.


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.


title: Challenges of providing healthcare worker education and training in protracted conflict: a focus on non-government controlled areas in north west Syria

text: Without healthcare workers (HCWs), health and humanitarian provision in Syria cannot be sustained either now or in the post-conflict phase. The protracted conflict has led to the exodus of more than 70% of the healthcare workforce. Those remaining work in dangerous conditions with insufficient resources and a healthcare system that has been decimated by protracted conflict. For many HCWs, particularly those in non-government-controlled areas (NGCAs) of Syria, undergraduate education and postgraduate training has been interrupted with few opportunities to continue. In this manuscript, we explore initiatives present in north west Syria at both undergraduate and postgraduate level for physician and non-physician HCWs. Conclusion: Challenges to HCW education in north west Syria can be broadly divided into 1. Organisational (local healthcare leadership and governance, coordination and collaboration between stakeholders, competition between stakeholders and insufficient funding.) 2. Programmatic (lack of accreditation or recognition of qualifications, insufficient physical space for teaching, exodus of faculty affecting teaching and training, prioritisation of physicians over non-physicians, informally trained healthcare workers.) 3. Healthcare system related (politicisation of healthcare system, changing healthcare needs of the population, ongoing attacks on healthcare.) Locally implementable strategies including dedicated funding are key to supporting retention of HCWs and return during post-conflict reconstruction.


title: Poor quality research and clinical practice during COVID-19

text: Breathe Chief Editor @ClaudiaCDobler on how #COVID19 amplifies flaws in clinical research and practice https://bit.ly/3cX0jpO


title: Increased Use of Quaternary Ammonium Compounds during the SARS-CoV-2 Pandemic and Beyond: Consideration of Environmental Implications

text: [Image: see text] Quaternary ammonium compounds (QACs) are active ingredients in over 200 disinfectants currently recommended by the U.S. EPA for use to inactivate the SARS-CoV-2 (COVID-19) virus. The amounts of these compounds used in household, workplace, and industry settings has very likely increased, and usage will continue to be elevated given the scope of the pandemic. QACs have been previously detected in wastewater, surface waters, and sediments, and effects on antibiotic resistance have been explored. Thus, it is important to assess potential environmental and engineering impacts of elevated QAC usage, which may include disruption of wastewater treatment unit operations, proliferation of antibiotic resistance, formation of nitrosamine disinfection byproducts, and impacts on biota in surface waters. The threat caused by COVID-19 is clear, and a reasonable response is elevated use of QACs to mitigate spread of infection. Exploration of potential effects, environmental fate, and technologies to minimize environmental releases of QACs, however, is warranted.


title: The effect of Voki application on students’ academic achievements and attitudes towards English course

text: The purpose of this research is to assess the effect of Voki, a tool of web 2.0 on students’ academic achievement and attitudes towards English courses. The study group of this research, using a quasi-experimental design with a pretest-posttest control group, consists of 5th-grade students at a public secondary school in the Merkezefendi district of Denizli province, Turkey. In the experiment group, the “Party Time” unit was taught via Voki-supported activities while the same unit was taught according to the current curriculum without using Voki in the control group. The entire process of the experiment lasted 6 weeks. In the study, the academic achievement test and attitude scale towards English courses (Cronbach’s Alpha = 0.92) were used to measure dependent variables. The normality distributions of the data were examined after the data were collected, and it was decided to analyze the data through the mean, standard deviation, and independent-sample t-test. According to the results of the research, there was an increase in the achievement test scores of both groups in the English lesson. However, a statistically significant difference was observed in the speaking section of the experimental group in the achievement test. There was no significant difference between the attitudes of the groups towards English courses. It is therefore recommended that English teachers should use Voki application in speaking activities.


title: Excluded futures: the continuity bias in scenario assessments

text: Global scenario assessments in support of climate, biodiversity, energy and other international policy deliberations tend to focus on a narrow bandwidth of possibilities: futures that unfold gradually from current patterns and trends. This “continuity bias” downplays the real risks (and opportunities) of structural discontinuity in the evolution of the global social-ecological system. The inclination to focus on mathematically tractable representations and conventional futures preferred by decision-makers is understandable, but constrains the scientific imagination and the scope of policy guidance. Earlier studies spotlighted discontinuous global futures, thereby revealing a broader spectrum of possibilities and repertoire of actions than found in contemporary scenario analysis. The paper revisits three types of futures introduced 25 years ago; examines three truths they convey about the contemporary moment; and points to three courses of action they suggest. Contemporary assessments centre on incrementally changing Conventional Worlds, yet varieties of global disruption (Barbarization) and progressive transformation (Great Transition) remain plausible alternatives. Corresponding to this triad, three synergistic action prongs—reform (incremental policies), remediation (emergency preparedness and prevention), and redesign (deep cultural and institutional change)—come into focus. Recovering a comprehensive perspective on the global possible would reinvigorate debate on the kind of transformation needed, broaden the action agenda, and stimulate innovative research for illuminating our indeterminate future. The COVID-19 pandemic, a concrete illustration of historical discontinuity, underscores the critical importance of emphasizing nonconventional futures in policy assessments.


title: Prone positioning in severe ARDS requiring extracorporeal membrane oxygenation

text: BACKGROUND: Prone positioning (PP) has shown to improve survival in patients with severe acute respiratory distress syndrome (ARDS). To this point, it is unclear if PP is also beneficial for ARDS patients treated with veno-venous extracorporeal membrane oxygenation (VV ECMO) support. METHODS: We report retrospective data of a single-centre registry of patients with severe ARDS requiring VV ECMO support between October 2010 and May 2018. Patients were allocated to the PP group if PP was performed during VV ECMO treatment or the supine positioning group. VV ECMO weaning success and hospital survival were analysed before and after propensity score matching. RESULTS: A total of 158 patients could be analysed, and 38 patients (24.1%) received PP. There were no significant differences in VV ECMO weaning rate (47.4% vs. 46.7%, p = 0.94) and hospital survival (36.8% vs. 36.7%, p = 0.98) between the prone and supine groups, respectively. The analysis of 38 propensity score matched pairs also showed no difference in hospital survival (36.8% vs. 36.8%, p = 1.0) or VV ECMO weaning rate (47.4% vs. 44.7%, p = 0.82). Hospital survival was superior in the subgroup of patients treated with early PP (cutoff < 17 h via Youden’s Index) as compared to late or no PP (81.8% vs. 33.3%, p = 0.02). CONCLUSION: In this propensity score matched cohort of severe ARDS patients requiring VV ECMO support, prone positioning at any time was not associated with improved weaning or survival. However, early initiation of prone positioning was linked to a significant reduction of hospital mortality.


title: Proteomic analysis of Rickettsia akari proposes a 44 kDa-OMP as a potential biomarker for Rickettsialpox diagnosis

text: BACKGROUND: Rickettsialpox is a febrile illness caused by the mite-borne pathogen Rickettsia akari. Several cases of this disease are reported worldwide annually. Nevertheless, the relationship between the immunogenicity of R. akari and disease development is still poorly understood. Thus, misdiagnosis is frequent. Our study is aiming to identify immunogenic proteins that may improve disease recognition and enhance subsequent treatment. To achieve this goal, two proteomics methodologies were applied, followed by immunoblot confirmation. RESULTS: Three hundred and sixteen unique proteins were identified in the whole-cell extract of R. akari. The most represented protein groups were found to be those involved in translation, post-translational modifications, energy production, and cell wall development. A significant number of proteins belonged to amino acid transport and intracellular trafficking. Also, some proteins affecting the virulence were detected. In silico analysis of membrane enriched proteins revealed 25 putative outer membrane proteins containing beta-barrel structure and 11 proteins having a secretion signal peptide sequence. Using rabbit and human sera, various immunoreactive proteins were identified from which the 44 kDa uncharacterized protein (A8GP63) has demonstrated a unique detection capability. It positively distinguished the sera of patients with Rickettsialpox from other rickettsiae positive human sera. CONCLUSION: Our proteomic analysis certainly contributed to the lack of knowledge of R. akari pathogenesis. The result obtained may also serve as a guideline for a more accurate diagnosis of rickettsial diseases. The identified 44 kDa uncharacterized protein can be certainly used as a unique marker of rickettsialpox or as a target molecule for the development of more effective treatment.


title: Spitzengastronomie und die Corona-Krise

text: Die Corona-Pandemie hat die Spitzengastronomie massiv getroffen. Wie gingen Köchinnen und Köche zu Beginn des Shutdowns mit den unerwarteten Herausforderungen um? Wie verändert der Corona-Shutdown die Situation der (Spitzen-)Restaurants in Deutschland? Wie reagieren diese auf die Krise? Diese Fragen haben wir im Rahmen einer Studie untersucht. Die zentralen Ergebnisse stellen wir in diesem Kapitel vor.


title: Estimating the costs for the airport operator and airlines of a drone-related shutdown: an application to Frankfurt international airport

text: Commercially-acquired drones threaten airport operations due to limited knowledge of airspace safety regulations or deliberate action by drone operators. This study aims to determine whether the investment cost of a drone-defence system can be justified in relation to the financial cost of a drone-related shutdown. To that end, a case study of Frankfurt Airport is carried out with simulations of different disruptions during a peak-activity period similar to the 2018 Gatwick drone incident. With data on passenger traffic and airline schedules, we developed a passenger recovery algorithm to determine the amount of delays caused by the disruptions and the costs for the airport operator and the airlines. Results show that the investment in a drone-defence system is offset by the costs of a 48-h continued closure or several smaller closures, but since the largest share of costs is borne by the airlines, investments should be shared between both stakeholders.


Operational excellence for improving sustainable supply chain performance


title: Toward a Curry-Howard Equivalence for Linear, Reversible Computation: Work-in-Progress

text: In this paper, we present a linear and reversible language with inductive and coinductive types, together with a Curry-Howard correspondence with the logic [Image: see text] : linear logic extended with least and greatest fixed points allowing inductive and coinductive statements. Linear, reversible computation makes an important sub-class of quantum computation without measurement. In the latter, the notion of purely quantum recursive type is not yet well understood. Moreover, models for reasoning about quantum algorithms only provide complex types for classical datatypes: there are usually no types for purely quantum objects beside tensors of quantum bits. This work is a first step towards understanding purely quantum recursive types.


title: Encoding Reversing Petri Nets in Answer Set Programming

text: Reversing Petri nets (RPNs) have been proposed as a reversible approach to Petri nets, which allows the transitions of a net to be reversed. This work presents an approach towards an implementation of RPNs to support their simulation and analysis. Specifically, we define how to model RPNs in Answer Set Programming (ASP), a declarative programming framework with competitive solvers. We highlight how the methodology can be used to reason about the behavior of RPN models.


title: ReverCSP: Time-Travelling in CSP Computations

text: This paper presents reverCSP, a tool to animate both forward and backward CSP computations. This ability to reverse computations can be done step by step or backtracking to a given desired state of interest. reverCSP allows us to reverse computations exactly in the same order in which they happened, or also in a causally-consistent way. Therefore, reverCSP is a tool that can be especially useful to comprehend, analyze, and debug computations. reverCSP is an open-source project publicly available for the community. We describe the tool and its functionality, and we provide implementation details so that it can be reimplemented for other languages.


title: Reversible Occurrence Nets and Causal Reversible Prime Event Structures

text: One of the well-known results in concurrency theory concerns the relationship between event structures and occurrence nets: an occurrence net can be associated with a prime event structure, and vice versa. More generally, the relationships between various forms of event structures and suitable forms of nets have been long established. Good examples are the close relationship between inhibitor event structures and inhibitor occurrence nets, or between asymmetric event structures and asymmetric occurrence nets. Several forms of event structures suited for the modelling of reversible computation have recently been developed; also a method for reversing occurrence nets has been proposed. This paper bridges the gap between reversible event structures and reversible nets. We introduce the notion of reversible occurrence net, which is a generalisation of the notion of reversible unfolding. We show that reversible occurrence nets correspond precisely to a subclass of reversible prime event structures, the causal reversible prime event structures.


title: Reversible Computations in Logic Programming

text: In this work, we say that a computation is reversible if one can find a procedure to undo the steps of a standard (or forward) computation in a deterministic way. While logic programs are often invertible (e.g., one can use the same predicate for adding and for subtracting natural numbers), computations are not reversible in the above sense. In this paper, we present a so-called Landauer embedding for SLD resolution, the operational principle of logic programs, so that it becomes reversible. A proof-of-concept implementation of a reversible debugger for Prolog that follows the ideas in this paper has been developed and is publicly available.


title: Involutory Turing Machines

text: An involutory function, also called involution, is a function [Formula: see text] that is its own inverse, i.e., [Formula: see text] holds whenever [Formula: see text] is defined. This paper presents a computational model of involution as a variant of Turing machines, called an involutory Turing machine. The computational model is shown to be complete in the sense that not only does an involutory Turing machine always compute an involution but also every involutory computable function can be computed by an involutory Turing machine. As any involution is injective (hence reversible), any involutory Turing machine forms a standard reversible Turing machine that is backward deterministic. Furthermore, the existence of a universal involutory Turing machine is shown under an appropriate redefinition of universality given by Axelsen and Glück for reversible Turing machines. This work is motivated by characterizing bidirectional transformation languages.


title: Fractional Types: Expressive and Safe Space Management for Ancilla Bits

text: In reversible computing, the management of space is subject to two broad classes of constraints. First, as with general-purpose computation, every allocation must be paired with a matching de-allocation. Second, space can only be safely de-allocated if its contents are restored to their initial value from allocation time. Generally speaking, the state of the art provides limited partial solutions, either leaving both constraints to programmers’ assertions or imposing a stack discipline to address the first constraint and leaving the second constraint to programmers’ assertions. We propose a novel approach based on the idea of fractional types. As a simple intuitive example, allocation of a new boolean value initialized to [Image: see text] also creates a value [Image: see text] that can be thought of as a garbage collection (GC) process specialized to reclaim, and only reclaim, storage containing the value [Image: see text]. This GC process is a first-class entity that can be manipulated, decomposed into smaller processes and combined with other GC processes. We formalize this idea in the context of a reversible language founded on type isomorphisms, prove its fundamental correctness properties, and illustrate its expressiveness using a wide variety of examples. The development is backed by a fully-formalized Agda implementation (https://github.com/DreamLinuxer/FracAncilla).


title: Telemedicine and the COVID-19 Pandemic: Are We Ready To Go Live?

text: Telemedicine use in the field of wound care had been increasing in popularity when the novel coronavirus 2019 paralyzed the globe in early 2020. To combat the constraints of healthcare delivery during this time, the use of telemedicine has been further expanded. Though many limitations of telemedicine are still being untangled, the benefits of virtual care are being realized in both inpatient and outpatient settings. In this article, the advantages and disadvantages of telemedicine are discussed through two case examples that highlight the promise of implementation during and beyond the pandemic.


title: Connectivity guided theta burst transcranial magnetic stimulation versus repetitive transcranial magnetic stimulation for treatment-resistant moderate to severe depression: study protocol for a randomised double-blind controlled trial (BRIGhTMIND)

text: INTRODUCTION: The BRIGhTMIND study aims to determine the clinical effectiveness, cost-effectiveness and mechanism of action of connectivity guided intermittent theta burst stimulation (cgiTBS) versus standard repetitive transcranial magnetic stimulation (rTMS) in adults with moderate to severe treatment resistant depression. METHODS AND ANALYSIS: The study is a randomised double-blind controlled trial with 1:1 allocation to either 20 sessions of (1) cgiTBS or (2) neuronavigated rTMS not using connectivity guidance. A total of 368 eligible participants with a diagnosis of current unipolar major depressive disorder that is both treatment resistant (defined as scoring 2 or more on the Massachusetts General Hospital Staging Score) and moderate to severe (scoring >16 on the 17-item Hamilton Depression Rating Scale (HDRS-17)), will be recruited from primary and secondary care settings at four treatment centres in the UK. The primary outcome is depression response at 16 weeks (50% or greater reduction in HDRS-17 score from baseline). Secondary outcomes include assessments of self-rated depression, anxiety, psychosocial functioning, cognition and quality of life at 8, 16 and 26 weeks postrandomisation. Cost-effectiveness, patient acceptability, safety, mechanism of action and predictors of response will also be examined. ETHICS AND DISSEMINATION: Ethical approval was granted by East Midlands Leicester Central Research Ethics Committee (ref: 18/EM/0232) on 30 August 2018. The results of the study will be published in relevant peer-reviewed journals, and then through professional and public conferences and media. Further publications will explore patient experience, moderators and mediators of outcome and mechanism of action. TRIAL REGISTRATION NUMBER: ISRCTN19674644


title: Social Media Effects: Hijacking Democracy and Civility in Civic Engagement

text: Perceived as an equalizing force for disenfranchised individuals without a voice, the importance of social networks as agents of change cannot be ignored. However, in some societies, social networks have evolved into a platform for fake news and propaganda, empowering disruptive voices, ideologies, and messages. Social networks such as Twitter, Facebook, and Google hold the potential to alter civic engagement, thus essentially hijacking democracy, by influencing individuals toward a particular way of thinking.


title: Multiorgan Failure With Emphasis on Acute Kidney Injury and Severity of COVID-19: Systematic Review and Meta-Analysis

text: BACKGROUND: Abnormalities in hematologic, biochemical, and immunologic biomarkers have been shown to be associated with severity and mortality in Coronavirus Disease 2019 (COVID-19). Therefore, early evaluation and monitoring of both liver and kidney functions, as well as hematologic parameters, are pivotal to forecast the progression of COVID-19. OBJECTIVES: In this study, we performed a systematic review and meta-analysis to investigate the association between several complications, including acute kidney injury (AKI), acute liver injury (ALI), and coagulopathy, with poor outcomes in COVID-19. DESIGN: Systematic review and meta-analysis SETTING: Observational studies reporting AKI, ALI, and coagulopathy along with the outcomes of clinically validated death, severe COVID-19, or intensive care unit (ICU) care were included in this study. The exclusion criteria were abstract-only publications, review articles, commentaries, letters, case reports, non-English language articles, and studies that did not report key exposures or outcomes of interest. PATIENTS: Adult patients diagnosed with COVID-19. MEASUREMENTS: Data extracted included author, year, study design, age, sex, cardiovascular diseases, hypertension, diabetes mellitus, respiratory comorbidities, chronic kidney disease, mortality, severe COVID-19, and need for ICU care. METHODS: We performed a systematic literature search from PubMed, SCOPUS, EuropePMC, and the Cochrane Central Database. AKI and ALI follow the definition of the included studies. Coagulopathy refers to the coagulopathy or disseminated intravascular coagulation defined in the included studies. The outcome of interest was a composite of mortality, need for ICU care, and severe COVID-19. We used random-effects models regardless of heterogeneity to calculate risk ratios (RRs) for dichotomous variables. Heterogeneity was assessed using I(2). Random effects meta-regression was conducted for comorbidities and the analysis was performed for one covariate at a time. RESULTS: There were 3615 patients from 15 studies. The mean Newcastle-Ottawa scale of the included studies was 7.3 ± 1.2. The AKI was associated with an increased the composite outcome (RR: 10.55 [7.68, 14.50], P < .001; I(2): 0%). Subgroup analysis showed that AKI was associated with increased mortality (RR: 13.38 [8.15, 21.95], P < .001; I(2): 24%), severe COVID-19 (RR: 8.12 [4.43, 14.86], P < .001; I(2): 0%), and the need for ICU care (RR: 5.90 [1.32, 26.35], P = .02; I(2): 0%). The ALI was associated with increased mortality (RR: 4.02 [1.51, 10.68], P = .005; I(2): 88%) in COVID-19. Mortality was higher in COVID-19 with coagulopathy (RR: 7.55 [3.24, 17.59], P < .001; I(2): 69%). The AKI was associated with the composite outcome and was not influenced by age (P = .182), sex (P = .104), hypertension (P = .788), cardiovascular diseases (P = .068), diabetes (P = .097), respiratory comorbidity (P = .762), and chronic kidney disease (P = .77). LIMITATIONS: There are several limitations of this study. Many of these studies did not define the extent of AKI (grade), which may affect the outcome. Acute liver injury and coagulopathy were not defined in most of the studies. The definition of severe COVID-19 differed across studies. Several articles included in the study were published at preprint servers and are not yet peer-reviewed. Most of the studies were from China; thus, some patients might overlap across the reports. Most of the included studies were retrospective in design. CONCLUSIONS: This meta-analysis showed that the presence of AKI, ALI, and coagulopathy was associated with poor outcomes in patients with COVID-19.


Proteinase-3-(cANCA)-assoziierte Colitis ulcerosa mit möglicher entzündlicher leukenzephalopathischer ZNS‑Beteiligung


title: QCD axion from a spontaneously broken B − L gauge symmetry

text: In this paper, we show that the Peccei-Quinn (PQ) symmetry with a good quality can be realized in a simple B −L extension of the minimal supersymmetric standard model. The PQ symmetry is a remnant of the B − L gauge symmetry at the renormalizable level. Besides, the sufficient quality of the PQ symmetry is preserved by a non anomalous discrete gauged R-symmetry and a small gravitino mass m(3/2) ≪ 100 GeV. A viable mass range is [Formula: see text] eV which allows a high reheating temperature and many baryogenesis scenarios typified by the thermal leptogenesis without any astrophysical and cosmological problems. Such a light gravitino may be tested in the future 21cm line observations.


title: The future of Europe and the role of Eastern Europe in its past, present and future

text: Thirty years after the fall of the Berlin Wall, and over a decade after its reunification, the European Union (EU) is experiencing increasingly more challenges toward its unity. The EU has experienced a number of crises in the early 2000s, the breakaway of one of its members in 2019, and is challenged by the Covid-19 pandemic in 2020. The latter crisis exhibits, on the one hand, the need for social coherence and unified policies, and on the other, has prompted the physical closure of borders, and divergent responses by domestic political elite. One such reaction—the adopted strengthening of power for Hungary’s Prime Minister—has prompted an international outcry and re-heated the debate of the democratic backtracking of some of the new EU member-states. Analyzing the process of European Enlargement and the changing sentiments about European Integration in a number of East European countries, this symposium brings to the fore important questions about the relationship between Eastern and Western Europe. Although there is a general consensus that both the East and the West have benefited and continue to benefit from their reunion, it is nevertheless the case that the quick assimilation of liberal values has led to policies seen as threatening the liberal democracy model of the EU that we need to address in order to preserve the stability of the Union. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1057/s41304-020-00264-z) contains supplementary material, which is available to authorized users.


title: Scaling Logic Locking Schemes to Multi-module Hardware Designs

text: The involvement of third parties in the integrated circuit design and fabrication flow has introduced severe security concerns, including intellectual property piracy, reverse engineering and the insertion of malicious circuits known as hardware Trojans. Logic locking has emerged as a prominent technique to counter these security threats by protecting the integrity of integrated circuits through functional and structural obfuscation. In recent years, a great number of locking schemes has been introduced, thereby focusing on a variety of security objectives and the resiliency against different attacks. However, several major pitfalls can be identified in the existing proposals: (i) the focus on isolated and often small circuit components, (ii) the assumption of unrealistic attack models that enable powerful attacks on logic locking and (iii) the design of very specific locking schemes targeted towards achieving resilience against specific attacks. These observations strongly impair the practicality of logic locking. Therefore, in this paper we present a holistic framework for scaling logic locking schemes to common multi-module hardware designs, thereby showcasing an industry-ready pathway of applying logic locking in a realistic design flow. The framework represents an enhancement of the previously published Inter-Lock methodology, offering several algorithmic improvements as well as toolflow implementation details to facilitate the applicability of the framework to large multi-module designs. The framework is tested and evaluated on a real-life 64-bit RISC-V core.