Hendriksenvega8481

Nearly 100% differentiation between normal and COVID-19 radiographs is observed for the three-class classification scheme using the lung area-specific Kaggle radiographs. While Recall of 72.65 ± 6.83 and specificity of 77.72 ± 8.06 is observed for the COVIDGR dataset.With the advent of data science, the analysis of network or graph data has become a very timely research problem. A variety of recent works have been proposed to generalize neural networks to graphs, either from a spectral graph theory or a spatial perspective. The majority of these works, however, focus on adapting the convolution operator to graph representation. At the same time, the pooling operator also plays an important role in distilling multiscale and hierarchical representations, but it has been mostly overlooked so far. In this article, we propose a parameter-free pooling operator, called iPool, that permits to retain the most informative features in arbitrary graphs. With the argument that informative nodes dominantly characterize graph signals, we propose a criterion to evaluate the amount of information of each node given its neighbors and theoretically demonstrate its relationship to neighborhood conditional entropy. This new criterion determines how nodes are selected and coarsened graphs are constructed in the pooling layer. The resulting hierarchical structure yields an effective isomorphism-invariant representation of networked data on arbitrary topologies. The proposed strategy achieves superior or competitive performance in graph classification on a collection of public graph benchmark data sets and superpixel-induced image graph data sets.Efficient neural architecture search (ENAS) achieves novel efficiency for learning architecture with high-performance via parameter sharing and reinforcement learning (RL). In the phase of architecture search, ENAS employs deep scalable architecture as search space whose training process consumes most of the search cost. Moreover, time-consuming model training is proportional to the depth of deep scalable architecture. Through experiments using ENAS on CIFAR-10, we find that layer reduction of scalable architecture is an effective way to accelerate the search process of ENAS but suffers from a prohibitive performance drop in the phase of architecture estimation. In this article, we propose a broad neural architecture search (BNAS) where we elaborately design broad scalable architecture dubbed broad convolutional neural network (BCNN) to solve the above issue. On the one hand, the proposed broad scalable architecture has fast training speed due to its shallow topology. Moreover, we also adopt RL and parameter ageNet just using 3.9 million parameters.Recently, deep learning-based approaches have achieved superior performance on object detection applications. However, object detection for industrial scenarios, where the objects may also have some structures and the structured patterns are normally presented in a hierarchical way, is not well investigated yet. In this work, we propose a novel deep learning-based method, hierarchical graphical reasoning (HGR), which utilizes the hierarchical structures of trains for train component detection. CADD522 mouse HGR contains multiple graphical reasoning branches, each of which is utilized to conduct graphical reasoning for one cluster of train components based on their sizes. In each branch, the visual appearances and structures of train components are considered jointly with our proposed novel densely connected dual-gated recurrent units (Dense-DGRUs). To the best of our knowledge, HGR is the first kind of framework that explores hierarchical structures among objects for object detection. We have collected a data set of 1130 images captured from moving trains, in which 17 334 train components are manually annotated with bounding boxes. Based on this data set, we carry out extensive experiments that have demonstrated our proposed HGR outperforms the existing state-of-the-art baselines significantly. The data set and the source code can be downloaded online at https//github.com/ChengZY/HGR.We present a novel, movement-based haptic illusion called the "snake effect." Unlike apparent motion or sensory saltation, the snake effect feels wavy and creepy as though the belly of a slithering snake is making and breaking contact with the skin. This illusion is achieved by modulating the amplitudes of vibrotactile pulses sent successively to an array of tactors. Pilot testing established the following signal parameters for creating the snake effect a minimal pulse duration of 1.69 s, carrier frequency in the range of 200-300 Hz, amplitude modulation of the carrier with a sine, sine-squared or Gaussian waveform (shown to be more effective than a linear up-and-down ramp), and a peak amplitude of 30 dB above detection threshold. The main experiment examined the most effective signal onset asynchrony (SOA) ranges by estimating the upper and lower SOA thresholds using a one-up one-down adaptive procedure with interleaved ascending and descending series. The results indicate an optimal SOA range from 271.5 ms to 798 ms with a midpoint of 535 ms. The snake effect is a vivid illusion that can be used as a distinctive signal for encoding information and to enhance immersion and engagement in gaming and entertainment.Through nonlinear effects, airborne ultrasound phased arrays enable mid-air tactile presentations, as well as auditory presentation and acoustic levitation. To create workplaces flexibly, we have developed a scalable phased array system in which multiple modules can be connected via Ethernet cables and controlled from a PC or other host device. Each module has 249 transducers and the software used can individually specify the phase and amplitude of each of the connected transducers. Using EtherCAT for communication, the system achieves high accuracy synchronization among the connected modules. In this paper, we describe the details of the hardware and software architecture of the developed system and evaluate it. We experimentally confirmed the synchronization of 20 modules within an accuracy of 0.1 's and the phase and amplitude can be specified at 8 bits resolution. In addition, using nine modules, we confirmed that we could make a focal point of the size consistent with the theory at 500 mm above the array surface.