Bjerringadler4409

The universal feature space is constructed by leveraging the relatednesses among features. We propose a generative graphical model to model the construction process, and show that learning from the universal feature space can effectively improve the performance with theoretical guarantees. The experimental results demonstrate that GLSC achieves conspicuous performance on both synthetic and real data sets.Multitask Gaussian processes (MTGPs) are a powerful approach for modeling dependencies between multiple related tasks or functions for joint regression. Current kernels for MTGPs cannot fully model nonlinear task correlations and other types of dependencies. In this article, we address this limitation. We focus on spectral mixture (SM) kernels and propose an enhancement of this type of kernels, called multitask generalized convolution SM (MT-GCSM) kernel. The MT-GCSM kernel can model nonlinear task correlations and dependence between components, including time and phase delay dependence. Each task in MT-GCSM has its GCSM kernel with its number of convolution structures, and dependencies between all components from different tasks are considered. Another constraint of current kernels for MTGPs is that components from different tasks are aligned. Here, we lift this constraint by using inner and outer full cross convolution between a base component and the reversed complex conjugate of another base component. Extensive experiments on two synthetic and three real-life data sets illustrate the difference between MT-GCSM and previous SM kernels as well as the practical effectiveness of MT-GCSM.On a touch surface, providing a local vibrotactile feedback enables multiuser and multitouch interactions. While the vibration propagation usually impedes this localization, we show in this paper that narrow strip-shaped plates constitute waveguides in which bending waves below a cut-off frequency do not propagate. We provide a theoretical explanation of the phenomenon and experimental validations. We thus show that vibrations up to a few kHz are well confined on top of the actuated area with vibration amplitude over 1 micrometer. The principle was validated with piezoelectric actuators of various shapes and a linear resonant actuator (LRA). Investigation of the effect of a fingertip load on the system through theory and experimentation was conducted and revealed that almost no attenuation was brought by the fingertip when using low frequency evanescent waves. Finally, a perceptual validation was conducted and showed dynamic stimuli with a large frequency spectrum could be felt and distinguished.Echocardiography (echo) is a critical tool in diagnosing various cardiovascular diseases. Despite its diagnostic and prognostic value, interpretation and analysis of echo images are still widely performed manually by echocardiographers. A plethora of algorithms has been proposed to analyze medical ultrasound data using signal processing and machine learning techniques. These algorithms provided opportunities for developing automated echo analysis and interpretation systems. The automated approach can significantly assist in decreasing the variability and burden associated with manual image measurements. In this paper, we review the state-of-the-art automatic methods for analyzing echocardiography data. Gefitinib in vivo Particularly, we comprehensively and systematically review existing methods of four major tasks echo quality assessment, view classification, boundary segmentation, and disease diagnosis. Our review covers three echo imaging modes, which are B-mode, M-mode, and Doppler. We also discuss the challenges and limitations of current methods and outline the most pressing directions for future research. In summary, this review presents the current status of automatic echo analysis and discusses the challenges that need to be addressed to obtain robust systems suitable for efficient use in clinical settings or point-of-care testing.The pandemic of coronavirus disease 2019 (COVID-19) is spreading all over the world. Medical imaging such as X-ray and computed tomography (CT) plays an essential role in the global fight against COVID-19, whereas the recently emerging artificial intelligence (AI) technologies further strengthen the power of the imaging tools and help medical specialists. We hereby review the rapid responses in the community of medical imaging (empowered by AI) toward COVID-19. For example, AI-empowered image acquisition can significantly help automate the scanning procedure and also reshape the workflow with minimal contact to patients, providing the best protection to the imaging technicians. Also, AI can improve work efficiency by accurate delineation of infections in X-ray and CT images, facilitating subsequent quantification. Moreover, the computer-aided platforms help radiologists make clinical decisions, i.e., for disease diagnosis, tracking, and prognosis. In this review paper, we thus cover the entire pipeline of medical imaging and analysis techniques involved with COVID-19, including image acquisition, segmentation, diagnosis, and follow-up. We particularly focus on the integration of AI with X-ray and CT, both of which are widely used in the frontline hospitals, in order to depict the latest progress of medical imaging and radiology fighting against COVID-19.This paper presents a SAR converter based mixed-signal multiplier for the feature extraction of neural signals using quadratic operators. After a thorough analysis of design principles and circuit-level aspects, the proposed architecture is explored for the implementation of two quadratic operators often used for the characterization of neural activity, the moving average energy operator and the nonlinear energy operator. Programmable chips for both operators have been implemented in a HV-180 nm CMOS process. Experimental results confirm their suitability for energy computation and action potential detection and the accomplished areapower performance is compared to prior art. The NEO prototype with no added delay consumes 178 nW and digitizes both the input neural signal and the operator outcome, with no need for digital multipliers.