Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-scale Strategy Based 3D Dual-Encoder Brain Tumor Segmentation Network with Attention Mechanism

Magnetic resonance imaging (MRI) is a widely used diagnostic technique in the initial evaluation of patients with primary brain tumor. Automatic segmentation algorithms for brain tumor plays an important role in diagnosis of tumor subregions and treatments for patients. In this paper, we propose a novel 3D convolutional neural network for segmentation of brain tumor, which is based on the traditional encoder-decoder architecture and inspired by multi-scale strategy. The proposed network employs multi-scale strategy with the designed dualencoder structure which can be considered as two scales streams to extract features from two scales inputs respectively. To integrate the output features from two scales encoders, a multiscale attention is model designed to weight and integrated the multi-scale features. Moreover, with the purpose of modeling long-range feature dependencies more efficiently, hidden features from different positions of stream are also fused and then utilized by attention mechanism in each scale encoder. To validate the performance of the proposed network, we conduct experiments on the brain tumor segmentation dataset BraTS2019 against several state-of-art methods. The results show that proposed method has the effective and competitive performance in brain tumor segmentation.
Geometric Control and Experimental Validation for a Quadrotor UAV Transporting a Payload

This paper presents a control system for a quadrotor unmanned aerial vehicle transporting a payload connected by a link. This system is considered as a multibody system evolving a nonlinear configuration manifold to construct an intrinsic global formulation of the dynamics. Based on this, a geometric control system is proposed such that the payload follows an arbitrary desired trajectory. In particular, the effects of the mass distribution of the link connecting the quadrotor and the payload are explicitly considered in the dynamic model and the stability analysis. Furthermore, the control system is designed to eliminate the adverse effects of disturbances. The efficacy of the proposed geometric tracking control system is illustrated by both a numerical example and an indoor flight experiment.
Secure Cognitive MIMO Wiretap Networks With Different Antenna Transmission Schemes

This paper investigates a secure transmission in the multiple-input multiple-output (MIMO) cognitive wiretap networks, where a secondary transmitter (Alice) sends data to a secondary receiver (Bob) in the presence of an eavesdropper (Eve). In order to solve the problems of inter channel interference and inter antenna synchronization encountered by traditional MIMO technologies, the antenna transmission scheme is adopted at the transmitter. As a comparison, we design two different antenna transmission schemes, namely transmit antenna selection maximal-ratio combining (TAS-MRC) scheme and differential spatial modulation maximal-ratio combining (DSM-MRC) scheme, respectively. Moreover, due to outdated channel state information (CSI) of the interference link from Alice to the primary user (PU), we propose power control mechanism to protect the quality of service (QoS) of PU. Furthermore, the closed-form for the secrecy outage probability and the secrecy throughput with TAS-MRC and DSM-MRC schemes are derived to evaluate the secrecy performance, respectively. What’s more, we explore the security diversity gain and coding gain based on the asymptotic secrecy outage probability. As the results, it demonstrates that DSM-MRC requires less CSI and is convenient for modulation and demodulation, but sacrifices some secrecy performance gains between two proposed schemes.
Spatial and seasonal evolution of trihalomethanes in water distribution systems in Lebanon

Spatial and seasonal changes in trihalomethane concentrations were investigated in various water distribution systems in Lebanon supplied by different water sources. A seasonal sampling program was initiated during which trihalomethanes and various contributing water quality and operational parameters were monitored at points along the distribution system. The majority of samples collected from points within the distribution network exhibited higher trihalomethane concentrations (1–13 folds) when compared to total trihalomethane levels detected in samples collected directly after chlorination. Recorded trihalomethane concentrations in Lebanon were highest in the spring, followed by the summer and winter. Moreover, total trihalomethane concentrations were higher when the abstraction sources were surface waters compared to ground water sources. Also, higher mean trihalomethane values were recorded in chlorinated waters originating from sources subjected to semi-conventional water treatment before the application of chlorine compared to mean trihalomethane values established in chlorinated waters originating from sources subjected to direct chlorination.
Coronary Heart Disease Prediction Based on Combined Reinforcement Multitask Progressive Networks

Coronary heart disease is the first killer of human health. At present, the common way of coronary heart disease diagnosis is coronary angiography. This method is a kind of surgery that could cause some physical damage to the patient, and it could cause some complications and adverse reactions. Furthermore, coronary angiography is expensive. However, the heart color Doppler echocardiography report, blood biochemical indicators and other basic information can reflect the degree of heart damage in patients. Therefore, this paper proposes a combined reinforcement multitask progressive networks (CRMPN) model to predict the grade of coronary heart disease through heart color Doppler echocardiography report, blood biochemical indicators and ten basic body information items about the patient. In this model, the first step is performing deep reinforcement learning (DRL) pre-training through asynchronous advantage actor-critic (A3C). Training data is adopted to optimize the recurrent neural networks (RNN) that parameterizes the stochastic policy. In the second step, we use soft parameter sharing module, hard parameter sharing module and progressive deep network to predict coronary heart disease. The experimental results show that after DRL pre-training, the multiple tasks in the model interact with each other and learn together to achieve satisfactory results and outperform other state-of-the-art methods.
Decision making through stochastic maneuver validation for overtaking on country roads

Driver assistance systems have become more and more important in recent years due to the increasing degree of automation in road traffic – especially with regard to safety. Often a driver’s perception of a situation is prone to inaccuracy, particularly on country roads. Consequently, there is a high mortality rate due to frontal collisions with oncoming traffic (among others). One of the most dangerous maneuvers that leads to such conflicts is overtaking. Overtaking involves other traffic participants and therefore uncertainties exist. We cope with this challenge by introducing a system that evaluates the options a vehicle has during overtaking (completing or aborting the maneuver) using stochastic models of the surrounding traffic. The stochastic models are used to predict the movements of surrounding road users. As a next step, the general feasibility of the possible maneuvers is checked and rated. The results of this analysis can either be directly used for longitudinal control, be forwarded to a low level controller, or serve as a guideline for the decision-making process of a human driver.
Alphabet reduction and distributed vector representation based method for classification of antimicrobial peptides

Antimicrobial peptides(AMPs) also known as host defence peptides are an essential part of innate immunity. AMPs are emerging as promising agents to multidrug resistant pathogens owing to their size, toxicity and biological activities. Effective identification of AMPs using computational method will be helpful in designing new antimicrobial agents for further study. Sequence based analysis for AMPs have been there for a while, where different methods have been proposed using amino acid composition and pseudo amino acid composition methods for inferring the activity of AMPs. In this paper, we demonstrate the use of machine learning models using alphabet reduction and distributed vector representation for classifying a sequence as AMP and non-AMP. The alphabet reduction is based on various physico-chemical properties of peptide sequences such as hydropathy index, contact energies between amino acids, conformation similarity, substitution matrix, amino acid charges etc. Alphabet reduction along with distributed vector representation (ProtVec) gives promising results and is also found to be computationally inexpensive as compared to other sequence based methods. Antimicrobial peptides are predicted using a binary classifier which gives a n a ccuracy of 97.94% a long with MCC 0.94.
FCM-Based Fast Texture CU Size Decision Algorithm for 3D-HEVC Inter-Coding

3D-HEVC is an extension of High Efficiency Video Coding (HEVC) standard. The Coding Unit (CU) sizes in 3D-HEVC, namely, depth levels are splitting on different block sizes from 64×64 to 8×8 in texture video and depth map associated, which brings a great computational complexity. In order to reduce the complexity caused by variable sizes of CU, a fast CU size decision approach is proposed for 3D-HEVC based on texture video homogeneity. The proposed algorithm consists of three steps. First, feature extraction from each texture CU size. Then, these features are used as input for Fuzzy C-Means (FCM) clustering algorithm. Finally, the FCM algorithm is used to define the quadtree splitting of CU and find suitable thresholds for early termination of CU partition. The experimental results show that the proposed algorithm reduces a significant encoding time, while the loss in coding efficiency is negligible.
Characterization of Nano-Structured Multiferroic Bismuth Ferrite Produced via Solid State Reaction Route

Multiferroic bismuth ferrite, BiFeO3, was synthesized via conventional solid-state reaction method using Bi2O3, Fe2O3 as starting materials. Effects of Bi2O3/Fe2O3 molar ratio and calcination temperature on the phase composition, morphology and magnetic properties of produced powders were systematically studied using XRD, FESEM/EDS and VSM techniques, respectively. The results revealed that BiFeO3 phase with rhombohedral R3c structure with a mean particle size of 40 nm was formed in the sample processed with a Bi2O3/Fe2O3 molar ratio of 1:1 after calcination at 800 °C. Rietveld analysis which was applied to the x-ray diffraction data via MAUD software indicated high purity of 95%wt for the above sample. Deviation from the stoichiometric molar ratio (Bi2O3/Fe2O3: 0.9, 1.1, 1.2) yielded higher content of the intermediate phases of Bi2Fe4O9 and Bi25FeO40. FESEM studies showed that the mean particle size was increased from 40 to 62 nm by increasing calcination temperature from 800 to 850 °C. VSM results for 1:1 molar ratio samples indicated that increasing the calcination temperature from 800 to 850 °C increased saturation magnetization (Ms) from 0.087 to 0.116 emu/g and also coercive field (Hc) from 60 to 100 Oe.
Multi-State Border Gateway Protocol for Multi-Domain Software Defined Networking–Based Gateways

Border Gateway Protocol (BGP) is a path-vector routing protocol used to exchange routing and reachability information between autonomous system (AS) that offers flexibility and scalability. BGP is capable of handling scalability issues using classless inter-domain routing and is more efficient than alternatives. However, BGP related challenges have been encountered, including growing routing tables, load balancing issues, hijacking, and AS transit delays, leading to increased convergence delay. Convergence delay is the time gap between the commencement of the best-path selection process and when the router is updated. Convergence delay has become a significant issue for larger networks because frequent routing path updates can lead to network instability. Network instability causes packet loss, delayed packet delivery and occasional loss of network connectivity. The research aims to incorporate Software Defined Networking (SDN) technology with gateway operation to reduce the BGP convergence time.
Opportunities and Challenges of Bridging the Digital Divide using 5G enabled High Altitude Platforms and TVWS spectrum

Like with the past generations of mobile cellular networks, deployment of 5G services in rural areas are expected to be financially and technically challenging. Currently, researchers around the globe explore the possibility of employing the underutilized/unused portion of TV spectrum referred to as TV White Spaces (TVWS) as a low-cost alternative to traditional licensed type of wired/wireless broadband networks and possibly as a way to bridge the gap of broadband service availability between rural and urban areas. Delivering TVWS services from High Altitude Platforms (HAPs) is an option that many developed /developing countries can use to provide broadband services over many of their rural and low-income population. This work presents the advantages of employing TVWS spectrum from HAPs as well as the challenges of this type of communication architecture. More specifically, this work proposes a novel communication type of architecture where HAPs are delivering broadband services using the TVWS spectrum. Taking advantage of their position in the sky and the centralized nature of the communication system, they deliver communications over a wide area while monitoring and optimizing radio resource allocation. The paper evaluates the performance of such a system based on the IEEE 802.22 standard and the free space path-loss model ITV - R P.452.
Unsupervised Biometric Anti-spooﬁng using Generative Adversarial Networks

- With the advent of new technologies, the meth-ods of presentation attacks as well as the security measures taken against it is diversifying with each passing day and are competing with each other. The imposter can make access to a system illegally by deceiving the security through the use of material containing artiﬁcial biometrics traits like a printed photo, display, etc. Therefore, we propose a novel presentation attack detection algorithm which can ensure security against unknown presentation attacks without any prior knowledge of fake samples. Moreover, our proposed algo-rithm can detect presentation attack with a single static image only. The essential tasks are divided into two parts, creating a smooth manifold of live samples and determining whether the manifolds includes the query image. In this paper, we utilize one class system such as SVM(Support Vector Machine) and DCGAN(Deep Convolutional Generative Adversarial Network) to learn the manifold of live samples. For DCGAN we propose a liveness scoring scheme based on the AnoGAN(Anomaly Generative Adversarial Network) Framework. Based on these, we utilize the proposed method to palm presentation attack detection. Through our experiment, we were able to produce decent results by using palm live/fake image dataset.
Full Stack Swarm Architecture

—Robot swarms are homogeneous multi-robot systems that form collective behaviour from decentralized local interactions. Swarms are a favorable choice for solving various problems in robotics as they are robust and fault tolerant in nature, the individual swarm agents themselves being cost effective alternatives to the solution. Developing swarm technology aids in large scale data collection and environmental exploration, package delivery, warehouse management, military reconnais-sance, and search and rescue. With the growing interest in developing swarm systems, much researcher and developer time is spent migrating, integrating, and coordinating these various swarm solutions that become deprecated and isolated from the community in frequent cycle. Furthermore, despite the afore-mentioned swarm architectures provided insights into speciﬁc design considerations, there still lacks a generalized architecture that outlines a full swarm pipeline, and is modular enough in design to be readily interchanged with new components as both research and industry advance. We present an architecture for developing full stack swarm systems. Such a system must allow for easy design, deployment, interaction, and evaluation. Using our proposed architecture, we then implemented a framework titled: CMUSWARM, on the ROS platform using Gazebo with irobot create for simulation. We then conduct an evaluation of our architecture by comparing two simple swarm control-laws within the framework.
Proposed Structure for Calcium Silicate Hydrate Gel

Calcium silicate hydrate gel is a very poorly crystalline material that is the main product of reaction of portland cement with water. Evidence is presented for a disordered layer structure, in which most of the layers are structurally imperfect ones of jennite (Ca9Si6O32H22), and others are similarly related to 1.4-nm tobermorite (Ca5Si6O26H18), both structures being modified by omission of many of their silicate tetrahedra. The evidence comprises conditions of formation, silicate anion type, Ca/Si ratio, H2O/Ca ratios and densities for various drying conditions, thermogravimetric curve, X-ray powder and selected-area electron diffraction patterns, and results of analytical electron microfcopy.
The Polish views on climate policy [EN] | Pol-Int

There are few topics that unite the key political actors in Poland as much as the reluctance to EU climate policy. One of the recent cases exemplifying this view is the opinion [1] issued by the Council for Social Dialogue (including the largest trade unions, the association of employers and the government). It contains a harsh critique of the more ambitious EU climate policy proposed by the European Commission (EC). The Council stated sharply that in its view, the EC proposal has a destructive impact on the Polish economy and may lead to an increase of energy poverty. Although parliamentary debate is more diversified (see the parliamentary debate on restructuring the coal mining sector) [2], none of the major political parties openly express their support for EU climate policy goals.
Construction , packaging and identification of RNAi lentiviral vectors carrying the decoy receptor 3 gene

To construct, package and identify RNA interference (RNAi) lentiviral vectors carrying the decoy receptor 3 (DcR3) gene, 3 short hairpin RNA interference sequences for DcR3 gene were designed, synthesized and constructed into the lentiviral vector plasmids. The lentiviral vector plasmids were then transformed into competent Escherichia coli cells, and the positive clones were identified by PCR. The identified recombinant vectors were used to infect 293T cells. The titer of packaged virus was determined using real-time quantitative PCR (RT-qPCR) 48 h after infection. PCR analysis showed that the amplified target gene was inserted in the pLKD-CMV-G&PR-U6-shRNA vector. Digestion analysis showed that the reconstructed plasmids were consistent with the theoretical fragment, and the sequencing results showed that the positive fragment was exactly the same as the target gene. Forty-eight hours after infection, more than 80% of cells displayed green fluorescence. RT-qPCR assay showed that the packaged viral titer was 6.58×10, 4.60×10, 4.63×10 IU/mL, respectively. RT-qPCR assay showed that the lentiviral vector 3 knockdowned about 61.44% of the expression of DcR3 mRNA.
THE OCCURRENCE OF IMPORTANT PATHOGENS IN LUMPFISH (CYCLOPTERUS LUMPUS) FROM A WILD POPULATION FISHED FOR BROOD FISH

3 Table of contents . 4 1.0 Introduction . 6 1.1 Lumpfish in aquaculture . 7 Diseases . 7 1.2 Viruses . 8 Infectious pancreatic necrosis virus (IPNV) . 8 Nervous necrosis virus, NNV . 9 Viral haemorrhagic septicaemia virus (VHSV) . 10 Lumpfish flavivirus . 11 Ranavirus . 12 1.3 Bacterial infections . 12 1.4 Parasites . 13 Coccidians . 13 Kudoa islandica . 14 Nucleospora cyclopteri . 15 1.5 Aims . 17 2.0 Materials and methods . 18 2.1 Location . 18 2.2 Samples . 19 2.3 Dissection . 21 Histology . 23 2.4 Bacteriology . 23 2.5 RNA isolation . 23 2.6 Quantitative Reverse Transcriptase PCR, RT-qPCR (here: qPCR) . 24 Assay efficiency . 26 2.7 DNA isolation . 27 2.8 PCR . 28 Sequencing . 29 2.9 Data analysis . 29 Sequence identification . 29 Normalized expression . 30
A New Three Phase 5-Level Converter by Cascading a Flying Capacitor Inverter and an H-Bridge

Multilevel converters have been widely used in motor drive applications for the advantages of high-power capacity, low total harmonic distortion, low switching frequency, and so. As an answer to the increasing demand for drives which can increase the power factor and reduce the switching frequency, the five-level converter has come into being. The five-level neutral-point-clamped (5L-NPC) converter and the five-level floating-capacitor (5L-FC) converter are both the expansions of typical three-level topologies. However, both of them have the difficulty in balancing the voltages of capacitors. The voltage balancing can affect in producing the desire voltage levels of the inverter. Hence the performance of the such inverters are been highly depended up on the voltage balancing schemes. To overcome the issues , a new three-phase, five-level inverter topology with a single-dc source is presented. The proposed topology is obtained by cascading a three-level flying capacitor inverter with a flying Hbridge power cell in each phase. This topology has redundant switching states for generating different pole voltages. By selecting appropriate switching states using SVPWM, the capacitor voltages can be balanced instantaneously (as compared to the fundamental) in any direction of the current, irrespective of the load power factor. Another important feature of this topology is that if any H-bridge fails, it can be bypassed and the configuration can still operate as a three-level inverter at its full power rating. Simulation results are obtained using MATLAB/ Simulink environs for value of the study.
User manual T-Beg : A Tool for Behavioural Equivalence Games (cid:63)

Behavioural equivalences can be characterized via bisimulation, modal logics, and spoiler-duplicator games. In T-Beg we work in the general setting of coalgebra and focus on generic algorithms for computing the winning strategies of both players in a bisimulation game. The winning strategy of the spoiler (if it exists) is then transformed into a modal formula that distinguishes the given non-bisimilar states. The modalities required for the formula are also synthesized on-the-fly, and we present a recipe for re-coding the formula with different modalities, given a separating set of predicate liftings (for theoretical details we refer to [6, 5]). In the next section we give an overview of the tool and the main design decisions. After that we explain based on an example how to use the tool including a brief description how T-Beg has been tested. Finally, we give an outlook on the optimization of T-Beg.