Miracle-dz/UniFAR-FTUs huggingface dataset:
Entwicklung eines Kalman-Filters zur Bestimmung kurzzeitiger Variationen des Erdschwerefeldes aus Daten der Satellitenmission GRACE

Development of a Kalman filter to derive short-term variations of the Earth's gravity field from GRACE data Summary The gravitational field and its temporal variations represent an important observable for the monitoring of the Earth's system. The satellite mission GRACE (Gravity Recovery And Climate Experiment) is, for the first time, able to measure gravity field variations with homogeneous global coverage. Nevertheless, the predicted GRACE accuracy has not been reached yet, partly due to the insufficient representation of the temporal variations in terms of monthly mean fields. In this thesis, an approach is presented which allows the calculation of daily GRACE solutions with the goal of modeling short-term gravity field variations. This time series can on the one hand be used to improve the knowledge of the underlying geophysical processes. On the other hand the daily GRACE solutions can also be used to improve the monthly mean fields. Increasing the temporal resolution is accompanied by a loss of accuracy due to insufficient data coverage. Therefore, additional information in terms of temporal and spatial correlations of the expected gravity signal is introduced into the analysis process. The combination of the GRACE observations and the correlation patterns is then performed within a Kalman filter framework. In a simulation study, the performance of the approach at hand is investigated and, in a second step, applied to real GRACE L1B data. Comparisons to independant data sets, i.e. vertical displacements of GPS stations and ocean bottom pressure time series, reveal that the daily GRACE time series contains geophysically interpretable signal. Furthermore, the daily GRACE time series can be used to significantly reduce the effects of temporal aliasing in the processing of monthly mean gravity field solutions. 2 1. Einleitung und Motivation
Path Tracking and Direct Yaw Moment Coordinated Control Based on Robust MPC With the Finite Time Horizon for Autonomous Independent-Drive Vehicles

It is a striking fact that the characteristics of parametric uncertainties, external disturbance, time-varying and nonlinearities are available in the constructed model of autonomous independent-drive vehicles; therefore, in this paper, the robust model predictive control (MPC) with the finite time horizon is proposed to realize the coordinated path tracking and direct yaw moment control (DYC) for autonomous four in-wheel motor independent-drive electric vehicles (AMIDEV). Firstly, considering the time-varying and uncertain feature of the tire cornering stiffness and the vehicle velocity in the state space equation constructed by 2 degrees of freedom (DoF) vehicle model and the path tracking preview model, the linear parameter varying (LPV) discrete model with four polytypic vertexes is constructed. Then, based on the linear matrix inequality (LMI) method, the novel robust MPC theory with the finite time horizon is put forward to solve the min-max optimization problem after updating four polytypic vertexes in real time, which could deal with the inevitable model mismatch problem caused by the time-varying, uncertain vehicle dynamic characteristics and external disturbance. Finally, the simulation and experimental results have verified that the proposed novel robust MPC theory could emerge from the stranglehold exercised by the conservativeness of the traditional robust MPC theory with the infinite time horizon, which strengthens the robustness of this control system as well as achieves better path tracking accuracy and handling ability of AMIDEV.
Computer Vision and Fuzzy Logic for Sustainable Indonesian Fisheries

Tuna fishing in Indonesia is excessive and if left unchecked, some types of tuna will become extinct within 3-10 years. There is an urgent need to help MMAF for producing more accurate fish catches data. This paper proposed a system that utilizes computer vision, fuzzy logic and Global Positioning System (GPS) for supporting a sustainableIndonesian fishery by recognizing and measuring fish length, determine fish age and where is the catch location. The system can help authority to manage sustainable fisheries management in Indonesia.
Efficient on-the-fly data race detection in multithreaded C++ programs

Data race detection is highly essential for debugging multithreaded programs and assuring their correctness. Nevertheless, there is no single universal technique capable of handling the task efficiently, since the data race detection problem is computationally hard in the general case. Thus, all currently available tools, when applied to some general case program, usually result in excessive false alarms or in a large number of undetected races. Another major drawback of currently available tools is that they are restricted, for performance reasons, to detection units of fixed size. Thus, they all suffer from the same problem - choosing a small unit might result in missing some of the data races, while choosing a large one might lead to false detection. We present a novel testing tool, called MultiRace, which combines improved versions of Djit and Lockset - two very powerful on-the-fly algorithms for dynamic detection of apparent data races. Both extended algorithms detect races in multithreaded programs that may execute on weak consistency systems, and may use two-way as well as global synchronization primitives. By employing novel technologies, MultiRace adjusts its detection to the native granularity of objects and variables in the program under examination. In order to monitor all accesses to each of the shared locations, MultiRace instruments the C++ source code of the program. It lets the user fine-tune the detection process, but otherwise is completely automatic and transparent. This paper describes the algorithms employed in MultiRace, gives highlights of its implementation issues, and suggests some optimizations. It shows that the overheads imposed by MultiRace are often much smaller (orders of magnitude) than those obtained by other existing tools.
Left ventricular and atrial segmentation of 2D echocardiography with convolutional neural networks

Segmentation of heart substructures in 2D echocardiography images is an important step in diagnosis and management of cardiovascular disease. Given the ubiquity of echocardiography in routine cardiology practice, the time-consuming nature of manual segmentation, and the high degree of inter-observer variability, fully automatic segmentation is a goal common to both clinicians and researchers. The recent publication of the annotated CA- MUS dataset will help catalyze these efforts. In this work we develop and validate against this dataset a deep fully convolutional neural network architecture for the multi-structure segmentation of echocardiography, in- cluding the left ventricular endocardium and epicardium, and the left atrium. In ten-fold cross validation with data augmentation, we obtain mean Dice overlaps of 0.93, 0.95, and 0.89 on the three structures respectively, representing state of the art on this dataset. We further report small biases and narrow limits of agreement between the automatic and manual segmentations in derived clinical indices, including median absolute errors for left ventricular diastolic (7.4mL) and systolic (4.8mL) volumes, and ejection fraction (4.1%), within previously reported inter-observer variability. These encouraging results must still be validated against large-scale independent clinical data.
A NOVEL 3D-CNN BASED FEATURE EXTRACTION BASED CLASSIFICATION FOR DIABETIC RETINOPATHY (DR) DETECTION

Diabetic retinopathy (DR) is one of the eye diseases that results in vision loss if not diagnosed earlier. The automated computer aided models on the DR images help in accurate treatment disease prevention. Microaneurysms (MA) and red spots are the indicators of DR for disease diagnosis. Many DR classification approaches have been proposed in the literature with deep learning framework and non-linear functionality. Also, these models are not applicable to large feature space due to high true negative rate. To optimize these problems, a hybrid feature selection based deep learning classifier is used to detect the MA and red spots disease severity on the large image dataset. In this paper, a new feature extraction approach is implemented to find the essential positive bag features to the deep learning framework. A hybrid SVM classification model is used to classify the disease patterns with high true positive rate. Experimental results are simulated on different DR image class labels; results show that the hybrid deep learning classification model is better than the traditional models under various statistical metrics on large dataset.
Contributions to Semiparametric Methods for Incomplete Data

Chapter 1: The effect of treatment on the treated (ETT) is a common parameter of interest in causal inference. Identification of ETT typically relies on an assumption of no unobserved confounding. When information on a subset of potential confounders is not observed in a main study, external data from a validation study with more detailed confounding information may be used, under certain assumptions, to help mitigate confounding. In the absence of missing data, a common approach to account for confounding is based on the propensity score. Recently such methods have been extended to address missing confounder data in a main-validation study context; however existing methods rely on overly restrictive assumptions that are unlikely to hold in practice. To address this problem, we develop a novel approach which entails constructing an extended propensity score (EPS) which preserves essential properties of a standard propensity score, but with the additional advantage that it can be evaluated even for subjects with missing confounders. The approach is universal in the sense that it applies to virtually any outcome scale, whether binary, polytomous, or continuous. The finite sample performance of the proposed approach is carefully evaluated and compared to several existing methods in extensive simulation studies. The proposed EPS approach is also illustrated in an application examining the effect of surgical resection on survival time among 14,312 Medicare beneficiaries with malignant neoplasm of the brain using 2,391 patients in SEER-Medicare for the validation study. Chapter 2: Missing data and confounding are two problems researchers face in observational studies for comparative effectiveness. Williamson et al (2012) recently proposed a unified approach to handle both issues concurrently using a multiply-robust (MR) methodology under the assumption that confounders are missing at random. Their approach considers a union of models in which any submodel has a parametric component while the remaining models are unrestricted. We show that while their estimating function is MR in theory, the possibility for multiply robust inference is complicated by the fact that parametric models for different components of the union model are not variation independent and therefore the MR property is unlikely to hold in practice. To address this, we propose an alternative transparent parametrization of the likelihood function, which makes explicit the model dependencies between various nuisance functions needed to evaluate the MR efficient score. The proposed method is genuinely
Internal structure recognition of EPS composite soil using fully convolutional network

As a geotechnical backfill material, expanded polystyrene (EPS) composite soil has the advantages of low weight, high strength and easy in-site handling. This study applies the fully convolutional network (FCN) to identify each individual EPS bead on the cross-section planes of EPS composite soil samples. FCN is a powerful deep learning architecture that can make pixel-level classification of images. This paper introduces the structure of the FCN model, the data preparation and model training for the EPS bead identification task. The trained FCN model is capable of recognizing and assisting quantitative assessment of the internal structure of EPS composite soil.
ARDUINO INTEGRATED DEVELOPMENT ENVIRONMENT

Arduino is a brand of hardware and software for building simple automation systems and robotics, a imed at non-professional users. Arduino IDE is an application that allows you to write programs in a convenient text editor, compile them into machine code, and download them to all versions of Arduino. The hardware is a set of mounted printed circuit boards. The Arduino board connects to the computer via USB, where it connects to the Arduino development environment (IDE)
Learning from Cross-Modal Behavior Dynamics with Graph-Regularized Neural Contextual Bandit

Contextual multi-armed bandit algorithms have received significant attention in modeling users’ preferences for online personalized recommender systems in a timely manner. While significant progress has been made along this direction, a few major challenges have not been well addressed yet: (i) a vast majority of the literature is based on linear models that cannot capture complex non-linear inter-dependencies of user-item interactions; (ii) existing literature mainly ignores the latent relations among users and non-recommended items: hence may not properly reflect users’ preferences in the real-world; (iii) current solutions are mainly based on historical data and are prone to cold-start problems for new users who have no interaction history. To address the above challenges, we develop a Graph Regularized Cross-modal (GRC) learning model, a general framework to exploit transferable knowledge learned from user-item interactions as well as the external features of users and items in online personalized recommendations. In particular, the GRC framework leverage a non-linearity of neural network to model complex inherent structure of user-item interactions. We further augment GRC with the cooperation of the metric learning technique and a graph-constrained embedding module, to map the units from different dimensions (temporal, social and semantic) into the same latent space. An extensive set of experiments are conducted on two benchmark datasets as well as a large scale proprietary dataset from a major search engine demonstrates the power of the proposed GRC model in effectively capturing users’ dynamic preferences under different settings by outperforming all baselines by a large margin.
Sound Recognition of Four Stroke Manual Transmission Motorcycle Engine’s Damage

Sound recognition that learns about the source of a sound, which is then used as a reference in learning. Sound recognition referred to in this study is the sound produced from the sound of a motorcycle engine. From the sound of the engine, the sound is then studied more and the results of the recorded sound are then used as objects of research. The management of motorcycle engine sound is carried out using Matlab and assisted by using the HMM (Hidden Markov Model) method with GMM (Gaussian Mixture Model) where HMM is used as chain pattern formation and GMM is used as a sound matching pattern. In this study indeed really learns from every sound of a motorcycle engine that will be used as a sample in the laboratory. From the sound of a motorcycle engine that is heard by the human ear can already indicate the conditions and eligibility limits of the motorcycle. Therefore, from this study trying to find answers to each condition and type of damage from a motorcycle through the sound of a motorcycle engine. With this research, it can help the vehicle owner, because the results of this study warn the vehicle owner to immediately conduct a scale check at the nearest official workshop and from this study also provide benefits and benefits for researchers and motorcycle vehicle owners to find out the condition of the damage early vehicles and help prevent them so as not to cause severe damage which can add air pollution in the city of Dili, East Timor. And the results of existing research are very helpful both for researchers, mechanics and motorcycle vehicle owners.
Development of a Biologically Immortalized Equine Stem Cell Line

Bone repair in horses implies invasive surgeries and increased cost. Research on musculoskeletal disorders therapy in horses includes cell-based therapy with mesenchymal stromal cells (MSCs). Mesenchymal stromal cells can be obtained from bone marrow (BMMSCs). Unfortunately, BMMSCs have limited cell replication in vitro. The objective of this study was to develop a biologically immortalized equine stem cell line derived from bone marrow, with unlimited in-vitro proliferation and the ability to differentiate into bone cells. Equine BMMSCs were transfected and immortalized with human telomerase reverse transcriptase (hTERT) gene. Cell passages from equine immortal BMMSCs were characterized by the presence of stemness CD markers and expression of multi-potent differentiation genes (OCT-4, SOX2, and NANOG). Equine immortal BMMSCs were incubated in osteogenic medium and bone cell differentiation was determined by alkaline phosphatase and von Kossa staining, and osteogenic gene expression (osteocalcin, Runx2, and osterix). Telomerase activity was determined by telomeric repeat amplification technique. Results showed that equine immortal BMMSCs were able to replicate in-vitro up to passage 50 and maintain stem cell characteristics by the presence of CD90 and expression of multi-potent genes. Equine immortal BMMSCs were able to differentiate into bone cells, which was confirmed by the positive osteogenic staining and gene expression. Equine BMMSCs were successfully immortalized and maintained characteristics of stem cells and readily differentiated into osteogenic cells. Extending the life span of equine BMMSCs by transfection of the hTERT gene will revolutionize the clinical use of MSCs by making them available to orthopedic surgeons "off the shelf. "
Object recognition on patrol ship using image processing and convolutional neural network (CNN)

Autonomous patrol vessels are state border patrol vessels equipped with cameras and image processing capabilities to detect objects around them. This prototype of ship can recognize a detected object; it used an image classification method called Convolutional Neural Network (CNN). So, it will minimize the occurrence of accidents on patrol boats. Input image in the form of RGB will experience feature extraction using a convolution layer. In the classification layer there is an artificial neural network with backpropagation to classify objects against a predetermined dataset. The detection value of the obtained vessel is operated by a predetermined actuator. In the final classification results the object recognition in the form of ships have a quite high accuracy. The average accuracy value is 96.59 percent with a sufficient light condition and RGB image input is taken in real-time.
Designing for cross-device interactions

Driven by technological advancements, we now own and operate an ever-growing number of digital devices, leading to an increased amount of digital data we produce, use, and maintain. However, while there is a substantial increase in computing power and availability of devices and data, many tasks we conduct with our devices are not well connected across multiple devices. We conduct our tasks sequentially instead of in parallel, while collaborative work across multiple devices is cumbersome to set up or simply not possible. To address these limitations, this thesis is concerned with cross-device computing. In particular it aims to conceptualise, prototype, and study interactions in cross-device computing. This thesis contributes to the field of Human-Computer Interaction (HCI)—and more specifically to the area of cross-device computing—in three ways: first, this work conceptualises previous work through a taxonomy of cross-device computing resulting in an in-depth understanding of the field, that identifies underexplored research areas, enabling the transfer of key insights into the design of interaction techniques. Second, three case studies were conducted that show how cross-device interactions can support curation work as well as augment users’ existing devices for individual and collaborative work. These case studies incorporate novel interaction techniques for supporting cross-device work. Third, through studying cross-device interactions and group collaboration, this thesis provides insights into how researchers can understand and evaluate multi- and cross-device interactions for individual and collaborative work. We provide a visualization and querying tool that facilitates interaction analysis of spatial measures and video recordings to facilitate such evaluations of cross-device work. Overall, the work in this thesis advances the field of cross-device computing with its taxonomy guiding research directions, novel interaction techniques and case studies demonstrating cross-device interactions for curation, and insights into and tools for effective evaluation of cross-device systems.
Technical Note on the quality of DNA sequencing for the molecular characterisation of genetically modified plants

Abstract As part of the risk assessment (RA) requirements for genetically modified (GM) plants, according to Regulation (EU) No 503/2013 and the EFSA guidance on the RA of food and feed from GM plants (EFSA GMO Panel, 2011), applicants need to perform a molecular characterisation of the DNA sequences inserted in the GM plant genome. The European Commission has mandated EFSA to develop a technical note to the applicants on, and checking of, the quality of the methodology, analysis and reporting covering complete sequencing of the insert and flanking regions, insertion site analysis of the GM event, and generational stability and integrity. This Technical Note puts together requirements and recommendations for when DNA sequencing is part of the molecular characterisation of GM plants, in particular for the characterisation of the inserted genetic material at each insertion site and flanking regions, the determination of the copy number of all detectable inserts, and the analysis of the genetic stability of the inserts, when addressed by Sanger sequencing or NGS. This document reflects the current knowledge in scientific‐technical methods for generating and verifying, in a standardised manner, DNA sequencing data in the context of RA of GM plants. From 1 October 2018, this Technical Note will replace the JRC guideline of 2016 (updated April 2017) related to the verification and quality assessment of the sequencing of the insert(s) and flanking regions. It does not take into consideration the verification and validation of the detection method which remains under the remit of the JRC.
Video production for agriculture - A guide for farmers, advisors and researchers (Annex to Deliverable 4.3)

Video trainings for farmers and consultants were carried out during the PLAID project. The experiences of these trainings form the basis of this guide. It provides tips on how to produce informative videos using simple means. This guide follows the three steps of each video production: planning, shooting editing. The focus is on the specific requirements of videos for agriculture. The guide is targeted to beginners: farmers, consultants, but also researchers in national and international projects who would like to produce simple instructional videos themselves as part of their dissemination activities.