bluuebunny/crossref_metadata_2025 huggingface dataset:
HOMOEOPATHIC MANAGEMENT OF OVARIAN CYST – A CASE REPORT
Authors: Arunava Nath*  , Deb Kumar Palit  , Nivedita Kundu
Published: July 2020

Ovarian enlargements, cystic or solid, may occur at any age. Functional and inflammatory enlargements of the ovary develop almost exclusively during the childbearing years. They may be asymptomatic or produce local discomfort, menstrual disturbances, infertility, or in rare cases cause acute symptoms due to complications like haemorrhage, rupture or torsion. A case on dysmenorrhoea along with right ovarian cyst measuring about 52x45 mm has been presented which was treated with constitutional homoeopathic medicine Lycopodium clavatum. Literature review suggested spontaneous regression of cysts within 3 months with oral combined pills but in the presented case the subject had been suffering for last one year and was under hormonal treatment with no favourable results. In the case of simple cysts more than 5 cm in diameter and complex cysts, surgical removal of the mass is most often recommended in an attempt to preserve viable ovarian tissue. With homoeopathic approach the treatment lasted for nearly one year with gradual reduction in pain intensity and sonographically no detectable abnormality was noted after treatment.
METABOLIC SYNDROME IN PATIENTS OF ACUTE CORONARY SYNDROME
Authors: Iqbal Zahid , Shahid Muhammad , Qadir Fawad , Saeed Hadi Yousuf , Mohyudin Muhammad Tahir , Saad Abu Bakar Ali
Published: August 2020

Objective: To determine the prevalence of metabolic syndrome (MetS) in patients of acute coronary syndrome. Methodology: This cross-sectional study was conducted in department of cardiology, CPE institute of cardiology Multan. A total of 150 patients of ACS who were admitted in the hospital from Oct-2018 to March-2019 were included. Patients demographics, reason for admission in hospital were noted. Waist circumference, body mass index (BMI), serum lipid profile, and fasting blood sugar was measured in all patients. Results: Metabolic syndrome (MetS) was diagnosed in 53 (35.3%). Frequency of heart failure was lower in Non-MetS group; 17 (17.5%) versus 15 (28.3%) in MetS group (p-value 0.12). STEMI was 10 (18.8%) in MetS group whereas it was 28 (28.8%) in Non-MetS group (p-value 0.18). Arrhythmias occurred in 5 (9.4%) in MetS group; as compared to 5 (5.15%) in Non-MetS group (p-value 0.32). Conclusion: The prevalence of MetS in present study was 35.3% and these patients were mostly female and obese. These were more likely admitted because of heart failure and arrhythmias. Key Words: Metabolic syndrome, Acute coronary syndrome, heart failure, myocardial infarction
0.034” GUIDEWIRE ENTRAPMENT DURING RADIAL APPROACH FOR CORONARY ANGIOGRAPHY. A RARE COMPLICATION
Authors: Dastgir Noor , Iqbal Ahsan , Masood Arslan , Akram Zubair
Published: August 2020

The radial artery approach for cardiac catheterization has now become the preferred route for arterial access. The main reason behind this is reduced access site complications as compared to femoral artery punctures. Radial artery access also allows the advantage of early mobilization of the patient and improved patient comfort. Lesser complications in turn result in shorter hospital stay and discharge on the same day is also possible. This in turn significantly decreases the overall cost and bed occupancy as well. However, as with every procedure complications do occur and it is how we deal with them defines our success. This is a case report in which a 0.034” radial guidewire used routinely for angiography was entrapped and how it was then tackled and recovered.
ROLE OF IMAGING TECHNOLOGY AND CURRENT STATUS OF CORONAVIRUS DISEASE 2019 (COVID-19): A CRITICAL REVIEW
Authors: Shahzeb Muhammad , Khan Areena , Muhammad Anees
Published: August 2020

Lately, there was an outbreak of Coronavirus disease (COVID-19) in Wuhan, a city of province Hubei, China. The infectious and transferrable virus originated in bats, seafood and animals. COVID-19 belongs to Coronviradae family. Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS) also belong to this family and their outbreak occurred in China and Saudi Arabia back in 2003 and 2012. The number of deaths due to COVID-19 is much greater than that of SARS and MERS. The clinical symptoms of infected individuals from virus are almost the same but diagnostically they are different from one another. Reverse transcription polymerase chain reaction (RT-PCR) laboratory technique is a standard test for evaluation and confirmation of COVID-19 disease. In early stages, imaging modalities can be used for screening purposes. X-Ray is used to identify the gross changes within the chest region while Computed Tomography Scanning (CT Scan) chest is used for detailed modification which occurs due to coronavirus. CT scan evaluates the different characteristics in the  individuals infected from COVID-19 such as presence of bilateral ground glass opacities, presence of diffuse consolidation, presence of nodules with halo sign, enlarge thoracic lymph nodes, presence of thicken inter-lobar septa, presence of bronchiectasis and pleural effusion. Each lung lobe was assigned different score on the basis of lobe area involvement. A lobe having no involvement is of score 0, minimal involvement lobe score is 1, mild involvement lobe score is 2, moderate involvement lobe score is 3, and severe involvement lobe score is 4. The lobes are assessed for different degree of involvement and then are divided as none (0%), minimal (1% to 25%), mild (26% to 50%), moderate (51% to 75%) and severe 76% to above level. These all techniques are very effective and responsible for the exact location of the area involved and also show the extension of pathology that can only be possible on CT scan. CT scan not only evaluates the disease but it also provides the assessment of the disease severity. Keywords: Computed Tomography scan; Coronavirus; Real time polymerase chain reaction; SARS; SARS-CoV-2; MERS
PERBANDINGAN PROJECT BASED LEARNING DAN GUIDED INQUIRY PADA PENGEMBANGAN PEMAHAMAN KONSEP PESERTA DIDIK SMA
Authors: Kaharudin La Ode , Rosnawati Veni
Published: July 2020

Comparison of Project Based Learning and Guided Inquiry in the Development of Understanding of High School Student Concepts. This study aims to compare the understanding of concepts in the Excretion System material between students taught using Project Based Learning (PjBL) and Guided Inquiry (GI). This research is in the form of a quasi experiment with a pretest-posttest non-equivalent control group design. The population of this research is the XI grade students of SMA Negeri 1 Kendari, 2018/2019 Academic Year. The sample of this study was students of Class XI MIPA 1 as an experimental group taught by the PjBL model and students of Class XI MIPA 2, taught by the GI model. Data were analyzed descriptively and inferentially using the SPSS program. The conclusions of this study are (1) There is a significant difference in the understanding of concepts in the excretion system material between students taught with project based learning models and students taught with guided inquiry models as evidenced by differences in the average value of students' posttest and G-gain value in each indicator of concept understanding, (2) Students of the project based learning model class gain significantly higher concept understanding than the group of students in the guided inquiry model class.
Effect of heat flow via glazing on the productivity of a solar still
Authors: Khechekhouche Abderrahmane , . Zied Driss, Benjamin Durakovic
Published: August 2020

In many parts of the world, particularly in the Greater Maghreb, desalination of water by solar energy is practiced with incredible abundance. Drinking water shortage has become a major problem. Improving the efficiency of solar distillers in several laboratories around the world is still one of the major concerns of research topics. In this work we want to show that the heat flow through the glazing can also be considered as an index on the productivity of pure water from a conventional solar still.
δ13C values in stalagmites from tropical South America for the last two millennia
Authors: Novello Valdir Felipe , da Cruz Francisco William , Vuille Mathias , Campos José Leandro Pereira Silveira , Stríkis Nicolás Misailidis , Apáestegui James , Moquet Jean Sebastien , Azevedo Vitor , Ampuero Angela , Utida Giselle , Wang Xianfeng , Paula-Santos Gustavo Macedo , Jaqueto Plinio , Pessenda Luiz Carlos Ruiz , Breecker Daniel O. , Karmann Ivo
Published: July 2020

Abstract. Due to the many factors controlling δ13C values in stalagmites, complicating their paleoclimatic and paleoenvironmental interpretation, most studies do not present δ13C values, but instead focus mainly on δ18O values. This is also the case for most cave studies from tropical South America, where many new δ18O stalagmite records covering the last millennia were recently published. Here, we test the influence of local hydroclimate, altitude, temperature and changing vegetation types on δ13C values in stalagmites, by employing a new dataset (named δ13C_2k_SA, https://doi.pangaea.de/10.1594/PANGAEA.919050) composed of published and unpublished carbon isotope records from various sites in tropical South America. Most locations were dominated by C3 plants over the last two millennia and are characterized by speleothem δ13C values more depleted than −6 ‰. The main factors influencing δ13C values are associated with the local hydroclimate, followed by minor effects from temperature. Most of the isotopic records show a significant correlation between the δ13C and δ18O values, indicating a close relationship between local hydroclimate and atmospheric convective processes related to the South American Monsoon System.
PDE-NetGen 1.0: from symbolic partial differential equation (PDE) representations of physical processes to trainable neural network representations
Authors: Pannekoucke Olivier , Fablet Ronan
Published: July 2020

Abstract. Bridging physics and deep learning is a topical challenge. While deep learning frameworks open avenues in physical science, the design of physically consistent deep neural network architectures is an open issue. In the spirit of physics-informed neural networks (NNs), the PDE-NetGen package provides new means to automatically translate physical equations, given as partial differential equations (PDEs), into neural network architectures. PDE-NetGen combines symbolic calculus and a neural network generator. The latter exploits NN-based implementations of PDE solvers using Keras.
With some knowledge of a problem, PDE-NetGen is a plug-and-play tool to generate physics-informed NN architectures. They provide computationally efficient yet compact representations to address a variety of issues, including, among others, adjoint derivation, model calibration, forecasting and data assimilation as well as uncertainty quantification.
As an illustration, the workflow is first presented for the 2D diffusion equation,
then applied to the data-driven and physics-informed identification of uncertainty dynamics for the Burgers equation.
AN IMPROVED AUTONOMOUS EXPLORATION FRAMEWORK FOR INDOOR MOBILE ROBOTICS USING REDUCED APPROXIMATED GENERALIZED VORONOI GRAPHS
Authors: Zuo X. , Yang F. , Liang Y. , Gang Z. , Su F. , Zhu H. , Li L.
Published: August 2020

Abstract. In the field of autonomous navigation for robotics, one of the most challenging issues is to locate the Next-Best-View and to guide robotics through a previously unknown environment. Existing methods based on generalized Voronoi graphs (GVGs) have presented feasible solutions but require excessive computation to construct GVGs from metric maps, and the GVGs are usually redundant. This paper proposes a reduced approximated GVG (RAGVG), which provides a topological representation of the explored space with a smaller graph. To be specific, a fast and practical algorithm for constructing RAGVGs from metric maps is presented, and a RAGVG-based autonomous robotic exploration framework is designed and implemented. The proposed method for constructing RAGVGs is validated with two known common maps, while the RAGVG-based autonomous exploration framework is tested on two simulation and one real-world museum. The experimental results show that the proposed algorithm is efficient in constructing RAGVGs, and indicate that the mobile robot controlled by the RAGVG-based autonomous exploration framework, compared with famous frontiers-based method, reduced the total time by approximately 20% for the given tasks.
CNN SEMANTIC SEGMENTATION TO RETRIEVE PAST LAND COVER OUT OF HISTORICAL ORTHOIMAGES AND DSM: FIRST EXPERIMENTS
Authors: Le Bris A. , Giordano S. , Mallet C.
Published: August 2020

Abstract. Images from archival aerial photogrammetric surveys are a unique and relatively unexplored means to chronicle 3D land-cover changes occurred since the mid 20th century. They provide a relatively dense temporal sampling of the territories with a very high spatial resolution. Thus, they offer time series data which can answer a large variety of long-term environmental monitoring studies. Besides, they are generally stereoscopic surveys, making it possible to derive 3D information (Digital Surface Models). In recent years, they have often been digitized, making them more suitable to be considered in automatic analyses processes. Some photogrammetric softwares make it possible to retrieve their geometry (pose and camera calibration) and to generate corresponding DSM and orthophotomosaic. Thus, archival aerial photogrammetric surveys appear as being a powerful remote sensing data source to study land use/cover evolution over the last century. However, several difficulties have to be faced to be able to use them in automatic analysis processes. Indeed, surveys available on a study area can exhibit very different characteristics: survey pattern, focal, spatial resolution, modality (panchromatic, colour, infrared…). Planimetric and altimetric accuracies of derived products strongly depend on these characteristics. Thus, analysis processes have to cope with these uncertainties. Another important gap states in the lack of training data. Deep learning methods and especially Convolutional Neural Networks (CNN) are at present the most efficient semantic segmentation methods as long as a sufficient training dataset is available. However, temporal gaps can be very important between existing available databases and archival data. In this study, two custom variants of simple yet effective U-net - Deconv-Net inspired DL architectures are developed to process ortho-image and DSM based information. They are then trained out of a groundtruth derived out of a recent database to process archival datasets.
GUIDED FEATURE MATCHING FOR MULTI-EPOCH HISTORICAL IMAGE BLOCKS POSE ESTIMATION
Authors: Zhang L. , Rupnik E. , Pierrot-Deseilligny M.
Published: August 2020

Abstract. Historical aerial imagery plays an important role in providing unique information about evolution of our landscapes. It possesses many positive qualities such as high spatial resolution, stereoscopic configuration and short time interval. Self-calibration reamains a main bottleneck for achieving the intrinsic value of historical imagery, as it involves certain underdeveloped research points such as detecting inter-epoch tie-points. In this research, we present a novel algorithm to detecting inter-epoch tie-points in historical images which do not rely on any auxiliary data. Using SIFT-detected keypoints we perform matching across epochs by interchangeably estimating and imposing that points follow two mathematical models: at first a 2D spatial similarity, then a 3D spatial similarity. We import GCPs to quantitatively evaluate our results with Digital Elevation Models (DEM) of differences (abbreviated as DoD) in absolute reference frame, and compare the results of our method with other 2 methods that use either the traditional SIFT or few virtual GCPs. The experiments show that far more correct inter-epoch tie-points can be extracted with our guided technique. Qualitative and quantitative results are reported.
LOCK-FREE MULTITHREADED SEMI-GLOBAL MATCHING WITH AN ARBITRARY NUMBER OF PATH DIRECTIONS
Authors: Frommholz D.
Published: August 2020

Abstract. This paper describes an efficient implementation of the semi-global matching (SGM) algorithm on multi-core processors that allows a nearly arbitrary number of path directions for the cost aggregation stage. The scanlines for each orientation are discretized iteratively once, and the regular substructures of the obtained template are reused and shifted to concurrently sum up the path cost in at most two sweeps per direction over the disparity space image. Since path overlaps do not occur at any time, no expensive thread synchronization will be needed. To further reduce the runtime on high counts of path directions, pixel-wise disparity gating is applied, and both the cost function and disparity loop of SGM are optimized using current single instruction multiple data (SIMD) intrinsics for two major CPU architectures. Performance evaluation of the proposed implementation on synthetic ground truth reveals a reduced height error if the number of aggregation directions is significantly increased or when the paths start with an angular offset. Overall runtime shows a speedup that is nearly linear to the number of available processors.
A WORLDWIDE 3D GCP DATABASE INHERITED FROM 20 YEARS OF MASSIVE MULTI-SATELLITE OBSERVATIONS
Authors: Chandelier L. , Coeurdevey L. , Bosch S. , Favé P. , Gachet R. , Orsoni A. , Tilak T. , Barot A.
Published: August 2020

Abstract. High location accuracy is a major requirement for satellite image users. Target performance is usually achieved thanks to either specific on-board satellite equipment or an auxiliary registration reference dataset. Both methods may be expensive and with certain limitations in terms of performance. The Institut national de l’information géographique et forestière (IGN) and Airbus Defence and Space (ADS) have worked together for almost 20 years, to build reference data for improving image location using multi-satellite observations. The first geometric foundation created has mainly used SPOT 5 High Resolution Stereoscopic (HRS) imagery, ancillary Ground Control Points (GCP) and Very High Resolution (VHR) imagery, providing a homogenous location accuracy of 10m CE90 almost all over the world in 2010.Space Reference Points (SRP) is a new worldwide 3D GCP database, built from a plethoric SPOT 6/7 multi-view archive, largely automatically processed, with cloud-based technologies. SRP aims at providing a systematic and reliable solution for image location (Unmanned Aerial Vehicle, VHR satellite imagery, High Altitudes Pseudo-Satellite…) and similar topics thanks to a high-density point distribution with a 3m CE90 accuracy.This paper describes the principle of SRP generation and presents the first validation results. A SPOT 6/7 smart image selection is performed to keep only relevant images for SRP purpose. The location of these SPOT 6/7 images is refined thanks to a spatiotriangulation on the worldwide geometric foundation, itself improved where needed. Points making up the future SRP database are afterward extracted thanks to classical feature detection algorithms and with respect to the expected density. Different filtering methods are applied to keep the best candidates. The last step of the processing chain is the formatting of the data to the delivery format, including metadata. An example of validation of SRP concept and specification on two tests sites (Spain and China) is then given. As a conclusion, the on-going production is shortly presented.
CLASSIFICATION OF TREE SPECIES AND STANDING DEAD TREES BY FUSING UAV-BASED LIDAR DATA AND MULTISPECTRAL IMAGERY IN THE 3D DEEP NEURAL NETWORK POINTNET++
Authors: Briechle S. , Krzystek P. , Vosselman G.
Published: August 2020

Abstract. Knowledge of tree species mapping and of dead wood in particular is fundamental to managing our forests. Although individual tree-based approaches using lidar can successfully distinguish between deciduous and coniferous trees, the classification of multiple tree species is still limited in accuracy. Moreover, the combined mapping of standing dead trees after pest infestation is becoming increasingly important. New deep learning methods outperform baseline machine learning approaches and promise a significant accuracy gain for tree mapping. In this study, we performed a classification of multiple tree species (pine, birch, alder) and standing dead trees with crowns using the 3D deep neural network (DNN) PointNet++ along with UAV-based lidar data and multispectral (MS) imagery. Aside from 3D geometry, we also integrated laser echo pulse width values and MS features into the classification process. In a preprocessing step, we generated the 3D segments of single trees using a 3D detection method. Our approach achieved an overall accuracy (OA) of 90.2% and was clearly superior to a baseline method using a random forest classifier and handcrafted features (OA = 85.3%). All in all, we demonstrate that the performance of the 3D DNN is highly promising for the classification of multiple tree species and standing dead trees in practice.
STATE OF THE ART IN DIGITAL SURFACE MODELLING FROM MULTI-VIEW HIGH-RESOLUTION SATELLITE IMAGES
Authors: Han Y. , Wang S. , Gong D. , Wang Y. , Wang Y. , Ma X.
Published: August 2020

Abstract. Data from the optical satellite imaging sensors running 24/7, is collecting in embarrassing abundance nowadays. Besides more suitable for large-scale mapping, multi-view high-resolution satellite images (HRSI) are cheaper when comparing to Light Detection And Ranging (LiDAR) data and aerial remotely sensed images, which are more accessible sources for digital surface modelling and updating. Digital Surface Model (DSM) generation is one of the most critical steps for mapping, 3D modelling, and semantic interpretation. Computing DSM from this dataset is relatively new, and several solutions exist in the market, both commercial and open-source solutions, the performances of these solutions have not yet been comprehensively analyzed. Although some works and challenges have focused on the DSM generation pipeline and the geometric accuracy of the generated DSM, the evaluations, however, do not consider the latest solutions as the fast development in this domain. In this work, we discussed the pipeline of the considered both commercial and opensource solutions, assessed the accuracy of the multi-view satellite image-based DSMs generation methods with LiDAR-derived DSM as the ground truth. Three solutions, including Satellite Stereo Pipeline (S2P), PCI Geomatica, and Agisoft Metashape, are evaluated on a WorldView-3 multi-view satellite dataset both quantitatively and qualitatively with the LiDAR ground truth. Our comparison and findings are presented in the experimental section.
USING SEMANTICALLY PAIRED IMAGES TO IMPROVE DOMAIN ADAPTATION FOR THE SEMANTIC SEGMENTATION OF AERIAL IMAGES
Authors: Gritzner D. , Ostermann J.
Published: August 2020

Abstract. Modern machine learning, especially deep learning, which is used in a variety of applications, requires a lot of labelled data for model training. Having an insufficient amount of training examples leads to models which do not generalize well to new input instances. This is a particular significant problem for tasks involving aerial images: often training data is only available for a limited geographical area and a narrow time window, thus leading to models which perform poorly in different regions, at different times of day, or during different seasons. Domain adaptation can mitigate this issue by using labelled source domain training examples and unlabeled target domain images to train a model which performs well on both domains. Modern adversarial domain adaptation approaches use unpaired data. We propose using pairs of semantically similar images, i.e., whose segmentations are accurate predictions of each other, for improved model performance. In this paper we show that, as an upper limit based on ground truth, using semantically paired aerial images during training almost always increases model performance with an average improvement of 4.2% accuracy and .036 mean intersection-over-union (mIoU). Using a practical estimate of semantic similarity, we still achieve improvements in more than half of all cases, with average improvements of 2.5% accuracy and .017 mIoU in those cases.
USING REDUNDANT INFORMATION FROM MULTIPLE AERIAL IMAGES FOR THE DETECTION OF BOMB CRATERS BASED ON MARKED POINT PROCESSES
Authors: Kruse C. , Rottensteiner F. , Heipke C.
Published: August 2020

Abstract. Many countries were the target of air strikes during World War II. Numerous unexploded bombs still exist in the ground. These duds can be tracked down with the help of bomb craters, indicating areas where unexploded bombs may be located. Such areas are documented in so-called impact maps based on detected bomb craters. In this paper, a stochastic approach based on marked point processes (MPPs) for the automatic detection of bomb craters in aerial images taken during World War II is presented. As most areas are covered by multiple images, the influence of redundant image information on the object detection result is investigated: we compare the results generated based on single images with those obtained by our new approach that combines the individual detection results of multiple images covering the same location. The object model for the bomb craters is represented by circles. Our MPP approach determines the most likely configuration of objects within the scene. The goal is reached by minimizing an energy function that describes the conformity with a predefined model by Reversible Jump Markov Chain Monte Carlo sampling in combination with simulated annealing. Afterwards, a probability map is generated from the automatic detections via kernel density estimation. By setting a threshold, areas around the detections are classified as contaminated or uncontaminated sites, respectively, which results in an impact map. Our results show a significant improvement with respect to its quality when redundant image information is used.
SPATIAL PROCESSING OF SENTINEL IMAGERY FOR MONITORING OF ACACIA FOREST DEGRADATION IN LAKE NAKURU RIPARIAN RESERVE
Authors: Osio A. , Pham M. T. , Lefèvre S.
Published: August 2020

Abstract. Tree degradation in National Parks poses a serious risk to the birds and animals and to a larger extent the general ecosystem. The essence of Forest degradation mapping is to detect the extent of damage on the trees over time, hence providing stakeholders with a basis for forest rehabilitation and intervention. The study proposes a workflow for detection and classification of degrading acacia vegetation along Lake Nakuru riparian reserve. Inspired by previous research on the use of Dual Polarized Sentinel 1 Ground Range Detected (GRD) data for vegetation detection, a set of six Sentinel 1 GRD and Sentinel 2 MSI of corresponding dates (2018–2019) were used. Our study confirms the existing correlation between vegetation indices derived from optical sensors and the backscatter indices from S1 SAR image of the same land cover classes. Factors that were used in validating the results include some comparisons between pixelwise and object-based classification, with a focus on the underlying segmentation and classification algorithms, the polarimetric attributes (VV+VH intensity bands) and the reflectance bands (NIR, SWIR & GREEN), the Haralick features (GLCM) vs. some geometric attributes (area & moment of inertia). Classification carried out on the temporal datasets considering geometric attributes and the Random Forest classifier yielded the highest Overall Accuracy (OA) with 94.25 %, and a Kappa coefficient of 0.90.
A JITTER DETECTION METHOD BASED ON THE INTEGRATION IMAGING MODEL
Authors: Ye G. , Pan J. , Zhu Y. , Jin S.
Published: August 2020

Abstract. Satellite jitter is a random error source which leads to image degradation. This paper proposes a method to detect the time-variant jitter using multispectral images. In the method, multispectral images are adopted for their large overlap to obtain the parallax map. The imaging process is analyzed in details, and an integration imaging model is constructed, which takes fully into account the time-variant jitter property and builds the relationship between object space with image space. Besides, multispectral images of ZY-3 satellite were used for experiments, and results show that the presented method obtains the jitter curve with the error of amplitude, frequency and phase not more than 0.0591 px, 0.0006 Hz and 0.007 rad, respectively. Results demonstrate the performance of the presented method in jitter detection.
SEMCITY TOULOUSE: A BENCHMARK FOR BUILDING INSTANCE SEGMENTATION IN SATELLITE IMAGES
Authors: Roscher R. , Volpi M. , Mallet C. , Drees L. , Wegner J. D.
Published: August 2020

Abstract. In order to reach the goal of reliably solving Earth monitoring tasks, automated and efficient machine learning methods are necessary for large-scale scene analysis and interpretation. A typical bottleneck of supervised learning approaches is the availability of accurate (manually) labeled training data, which is particularly important to train state-of-the-art (deep) learning methods. We present SemCity Toulouse, a publicly available, very high resolution, multi-spectral benchmark data set for training and evaluation of sophisticated machine learning models. The benchmark acts as test bed for single building instance segmentation which has been rarely considered before in densely built urban areas. Additional information is provided in the form of a multi-class semantic segmentation annotation covering the same area plus an adjacent area 3 times larger. The data set addresses interested researchers from various communities such as photogrammetry and remote sensing, but also computer vision and machine learning.
Norwegian Sea net community production estimated from O2
and prototype CO2 optode measurements on a Seaglider
Authors: Possenti Luca , Skjelvan Ingunn , Atamanchuk Dariia , Tengberg Anders , Humphreys Matthew P. , Loucaides Socratis , Fernand Liam , Kaiser Jan
Published: July 2020

Abstract. We report on a pilot study using a CO2 optode deployed on a Seaglider in the Norwegian Sea for 8 months (March to October 2014). The optode measurements required drift- and lag-correction, and in situ calibration using discrete water samples collected in the vicinity. We found the optode signal correlated better with the concentration of CO2, c(CO2), than with its partial pressure, p(CO2). Using the calibrated c(CO2) and a regional parameterisation of total alkalinity (AT) as a function of temperature and salinity, we calculated total dissolved inorganic carbon concentrations, CT, which had a standard deviation of 10 µmol kg−1 compared with direct CT measurements. The glider was also equipped with an oxygen (O2) optode. The O2 optode was drift-corrected and calibrated using a c(O2) climatology for deep samples (R2 = 0.89; RMSE = 0.009 µmol kg−1). The calibrated data enabled the calculation of CT – and oxygen-based net community production, N(CT) and N(O2). To derive N, CT and O2 inventory changes over time were combined with estimates of air-sea gas exchange and entrainment of deeper waters. Glider-based observations captured two periods of increased Chl a inventory in late spring (May) and a second one in summer (June). For the May period, we found N(CT) = (24±5) mmol m−2 d−1, N(O2) = (61±14) mmol m−2 d−1 and an (uncalibrated) Chl a peak concentration of craw(Chl a) = 3 mg m−3. During the June period, craw(Chl a) increased to a summer maximum of 4 mg m−3, which drove N(CT) to (64±67) mmol m−2 d−1 and N(O2) to (166±75) mmol m−2 d−1. The high-resolution dataset allowed for quantification of the changes in N before, during and after the periods of increased Chl a inventory. After the May period, the remineralisation of the material produced during the period of increased Chl a inventory decreased N(CT) to (−80±107) mmol m−2 d−1 and N(O2) to (−15±27) mmol m−2 d−1. The survey area was a source of O2 and a sink of CO2 for most of the summer. The deployment captured two different surface waters: the Norwegian Atlantic Current (NwAC) and the Norwegian Coastal Current (NCC). The NCC was characterised by lower c(O2) and CT than the NwAC, as well as lower N(O2), N(CT) and craw(Chl a). Our results show the potential of glider data to simultaneously capture time and depth-resolved variability in CT and O2.
Implications of surface flooding on airborne thickness measurements
of snow on sea ice
Authors: Rösel Anja , Farrell Sinead Louise , Nandan Vishnu , Richter-Menge Jaqueline , Spreen Gunnar , Divine Dimitry V. , Gallet Jean-Charles , Gerland Sebastian
Published: August 2020

Abstract. Snow thickness observations from airborne snow radars, such as the NASA’s Operation IceBridge (OIB) mission, have recently been used in altimeter-derived sea ice thickness estimates, as well as for model parameterization. A number of validation studies comparing airborne and in situ snow thickness measurements have been conducted in the western Arctic Ocean, demonstrating the utility of the airborne data. However, there have been no validation studies in the Atlantic sector of the Arctic. Recent observations in this region suggest a significant and predominant shift towards a snow-ice regime, caused by deep snow on thin sea ice. During the Norwegian young sea ICE expedition (N-ICE2015) in the area north of Svalbard, a validation study was conducted on March 19, 2015, during which ground truth data were collected during an OIB overflight. Snow and ice thickness measurements were obtained across a two dimensional (2-D) 400 m × 60 m grid. Additional snow and ice thickness measurements collected in situ from adjacent ice floes helped to place the measurements obtained at the gridded survey field site into a more regional context. Widespread negative freeboards and flooding of the snow pack were observed during the N-ICE2015 expedition, due to the general situation of thick snow on relatively thin sea ice. These conditions caused brine wicking and saturation into the basal snow layers, causing more diffuse scattering and influenced the airborne radar signal to detect the radar main scattering horizon well above the snow/sea ice interface, resulting in a subsequent underestimation of total snow thickness, if only radar-based information is used. The average airborne snow thickness was 0.16 m thinner than that measured in situ at the 2-D survey field. Regional data within 10 km of the 2-D survey field suggested however a smaller deviation between average airborne and in situ snow thickness, a 0.06 m underestimate in snow thickness by the airborne radar, which is close to the resolution limit of the OIB snow radar system. Our results also show a broad snow thickness distribution, indicating a large spatial variability in snow across the region. Differences between the airborne snow radar and in situ measurements fell within the standard deviation of the in situ data (0.15–0.18 m). Our results suggest that, with frequent flooding of the snow-ice interface in specific regions of the Arctic in the future, it may result in an underestimate of snow thickness or an overestimate of ice freeboard, measured from radar altimetry, thereby affecting the accuracy of sea ice thickness estimates.
“Otherness” in America: Hemingway, Hungarians, and Transnationalism
Authors: Dömötör Teodóra
Published: July 2020

Volatility regarding negotiated subject positions features prominently in Hemingway’s works. Yet, his portrayal of Hungarians in the vignette of Chapter VIII and the short story entitled “The Revolutionist” (both found in the collection of In Our Time, 1925) underlines 1920s America’s unwillingness to modify preconceived stereotypes about the “other.” Both stories have attracted considerable attention among scholars who have analyzed these texts from such perspectives as political ideology and the arts. Aiming to fill a gap in literary criticism, I shall examine the narrative representation of stereotypical approaches to the Hungarian minority with emphasis on societal expectations set by white, Anglo-Saxon, middle-class men in the United States during the 1920s. The values they propagated in society illustrate that the Roaring Twenties was an openly discriminatory decade in which ignoring and sometimes literally attacking the “other” for deviating from the prescribed norms of the era was acceptable. Anxiety about the “other” uncovers a great deal of national insecurity; America’s battle with foreigners merges into a battle with itself.
The Controversy About 1944 in Hungary and the Escape of Budapest’s Jews from Deportation. A Response.
Authors: Jeszenszky Géza
Published: July 2020

The purpose of this Note is to clarify the interpretation of the volume, July 1944: Deportation of the Jews of Budapest Foiled (Reno, NV: Helena History Press, 2018), put forth by Peter Pastor in his book review, “A New Historical Myth from Hungary: The Legend of Colonel Ferenc Koszorús as the Wartime Saviour of the Jews of Budapest,” that was published in the 2019 issue of Hungarian Cultural Studies. Rather than making any attempt to remove or lessen blame for the acts committed following the German occupation of Hungary in March 1944, this collection of studies aims to shed light on whether Regent Horthy’s order to Colonel Ferenc Koszorús prevented the deportation of the remaining, nearly 300,000 Hungarian Jews who lived (or were just hiding) in Budapest.
Stenge, Csaba B. 2019. Forgotten Heroes: Aces of the Royal Hungarian Air Force in the Second World War. London: Helion & Company. 438 pp.
Authors: Szentkirályi Endre
Published: July 2020

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Survey Among Medical Students During COVID-19 Lockdown: The Online Class Dilemma
Authors: Thomas Andrew , Shenoy Mohan T. , K. T. Shenoy , Suresh Kumar Sruthi , Sidheeque Aboobakker , C. Khovidh  , Pillai Jayakumar Parameshwaran , Pillai Pramod Murukan , CH Shana Sherin , Mathew Anna , Zakkir Twinkle , Dileep Sreelakshmi , Mekha Victory , Raju Sony , Junaid K. Mohammed , P. Sivendu
Published: August 2020

Background: In view of COVID-19 lockdown in India, many colleges started online classes. This study aimed to evaluate the attitude of and the factors affecting medical students attending online classes during lockdown.
Methods: We designed an online questionnaire with open-ended, close-ended, and Linkert scale questions. Links to the questionnaires were shared with the medical undergraduate students who have attended at least one online class during the COVID-19 lockdown period. Respondents were 1061 participants from 30 medical colleges from the states of Kerala and Tamil Nadu in India.
Results: The majority of students – 94 % (955/1016) – used smartphones to attend online classes. ZOOM/ Skype – by 57.1 % (580/1016) – and Google platforms – by 54.4 % (553/1016) – were commonly used. Learning at leisure – 44.5 % (452/1016) – was the top reason why students liked online classes, whereas network problems – 85.8 % (872/1016) – was the top reason why students disliked them. Lack of sufficient interaction – 61.1% (621/1016) and more than half the participants – 51.7% (526/1016) – did not want to continue online classes after COVID-19 lockdown. More students – 55% (558/1016) – favored regular classes than online classes.
Conclusion: Students in our survey did not seem favorably disposed to online classes. Network problems experienced by students should be addressed. Furthermore, teachers should try to make the classes more interactive and educational institutions should address the problem pointed out by the students in order to make online classes more effective in the future.
“I NEVER USE HEADSETS”: WOMEN, WARINESS, AND HYPERVIGILANCE FOR THE INEVITABILITY OF ONLINE HARASSMENT IN GAMING CULTURE
Authors: Butt Mahli-Ann
Published: August 2020

The gaming community has been contoured by divisive issues around the exacerbation of sexism, racism, and harassment. These tensions culminated in 2014 in the shape of #gamergate: a decentralised online harassment campaign against women and feminism in gaming. Gamergate continues to intensify a heightened climate of hostility especially felt by women and minorities.This ongoing feminist ethnography has emerged from an imperative to create interventions into the increasing normalisation of online and offline harassment. In it, this research analyses the affective labour of how people navigate and ‘cope’ with discrimination in gaming cultures.

This paper will present vignettes of the larger research project of how online harassment has coloured people’s lived experiences, through thick-descriptions of in-depth semi-structured interviews conducted with 7 women who play videogames with their romantic partners. These interviews gesture towards a rich complexity of affective relationships at the nexus of gaming, romantic relationships, and the everyday lived experiences of women. To avoid bringing attention to their gender, harassment, and unwanted confrontations, women are hypervigilant. In similar ways to self-defence tactics, women constantly avoid using headsets to communicate with other players, keep clear of conversations about playing videogames, and minimise the performance of their femininity in public gaming spaces.This paper critically examines the dynamics of how the increasing normalisation and public gamification of online harassment impacts women’s engagement with gaming, as well as how ‘online’ harassment may invade into their intimate relationships and domestic ‘private’ spheres.
CHEMSEX: DIGITAL, CHEMICAL AND COMMUNAL INFRASTRUCTURE OF DISINHIBITION
Authors: Hakim Jamie , Møller Kristian , Florêncio João , Murphy Dean , Race Kane , Pienaar Kiran , Lea Toby
Published: August 2020

Since the early days of digital media studies, it has been widely understood that trust in digital technologies has been indispensable for the operation of queer communities. Attaining access to _disinhibition_ is central to queer world-making: not only through coming “out”; and dancing at the gay club, but also by chemically altering your state of mind through alcohol and drug consumption. This panel explores queer ‘digital infrastructures of disinhibition’ through a digitally mediated sexual practice that has attracted significant attention over the last decade: ‘chemsex’, when gay and bisexual men use locative social media such as hook-up apps to organise group sex encounters where certain recreational drugs are consumed.

Chemsex is a socio-sexual practice that to an overwhelming degree is _constructed_, _negotiated_, _enacted_, _maintained_ and _critiqued_ via digital platforms. Hook-up apps are key to facilitating chemsex encounters because they allow for instant access to nearby subjects who can join the chemsex event; porn platforms and video conferencing tools lend their affordances to the visual culture and consumption of chemsex. At the same time as digital media intervenes in chemsex subjectivity, so do the recreational drugs.

Instead of treating the “effects” of media and drugs on the participating bodies as separated elements, this panel explores what can be achieved analytically if we think of them as co-constituting chemsex. To do so we conceptualize contemporary gay culture as an emerging assemblage depending on _digital_, _chemical_, and _communal_ infrastructures and the ways they are practiced, experienced, policed and transformed.

The panel explores both the emergence and practice of chemsex, as well as what kinds of queer futurity chemsex events might offer, what “ways of feeling” emerge, and the subjectivities it produces, including the extent to which they might be considered to disturb, disrupt or, conversely, consolidate homonormative sociabilities and realities.
IN EACH OTHER WE (STRATEGICALLY) TRUST: THE DISCURSIVE NETWORKS OF TRUST IN TWITTER DISCUSSION OF IMMIGRATION IN AUSTRAILIA
Authors: Dehghan Eshan
Published: August 2020

This study investigates the discourses around the topic of immigration in the Australian Twittersphere, especially with regards to the discursive role of trust in the dynamics of the debates. Methodologically, this paper draws from social media analytics, network analysis, corpus linguistics, and discourse-theoretical analysis. The findings show how Twitter users strategically and discursively use the platform’s affordances and choose who and what to trust, what hashtags to use, and what discourse to amplify, in order to both form agonistic networks of discursive alliances and to intensify antagonisms against the rival discourses. In this sense, the paper argues that trust, whether it is trusting the information sources or other users, becomes a discursive strategy employed in the hegemonic struggle over the issue of immigration in the Australian Twittersphere.
PRINCIPLES OF GOOD DATA
Authors: Devitt Susannah Kate , Mann Monique , Daly Angela
Published: August 2020

In recent years, there has been an exponential increase in the collection, aggregation and automated analysis of information by government and private actors that disproportionately disadvantages the underrepresented, marginalized and unheard. In response to this there has been significant critique regarding what could be termed ‘bad’ data practices in the globalised digital economy. Considerations of ‘bad data’ practices often only provide critiques rather than engaging constructively with a new vision of how digital technologies and data can be used productively and justly to promote social, economic, cultural and politically progressive goals. In this paper we consider the fundamentals of Good Data to increase trust. We begin by conceptual considerations of the nature of ‘data’ and ‘goodness’. We align our principles with the Data Information Knowledge Wisdom (DIKW) model and use the term ‘data’ as a proxy for the whole DIKW model. Given the limits of our knowledge of moral facts (should they exist) and in light of colonial and post-colonial data practices we assume a hybrid moral theory—where we allow that some moral facts may be objective (e.g. ‘tolerance’ or ‘openness’) and others relative. We advocate an ethic of active seeking, openness and tolerance to diverse views on ‘the good’ particularly consultation with the underrepresented, marginalised and unheard. We go on to defend fifteen principles of good data under four banners: Community, Rights, Usability & Politics in order to ultimately progress a more just digital economy and society.
VIDEOGAME ANALYTICS, SURVEILLANCE CAPITALISM AND THE RETENTIONAL ECONOMY OF PLAY
Authors: Egliston Ben
Published: August 2020

Data analytics tools are increasingly prevalent in videogames and are reliant on the surveillant capture and relay of user data. In this paper I present some conceptual work and preliminary analysis of the analytics tool ‘DotaPlus’ used in Dota 2. Through my analysis, I frame DotaPlus as a site of ‘surveillance capitalism’, using data derived from various modes of surveillance to generate potentials for commercially desirable gameplay experience.