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Quantifying sea salt sensitivity.
Analytic Efficiency of Xpert MTB/RIF Analysis throughout Bronchoalveolar Lavage Water for Tracheobronchial T . b: The Retrospective Analysis.
We demonstrate the superiority of our approach for the tasks of COVID-19 and liver tumor pathology identification.Medical images contain various abnormal regions, most of which are closely related to the lesions or diseases. The abnormality or lesion is one of the major concerns during clinical practice and therefore becomes the key in answering questions about medical images. However, the recent efforts still focus on constructing a generic Visual Question Answering framework for medical-domain tasks, which is not adequate for practical medical requirements and applications. In this paper, we present two novel medical-specific modules named multiplication anomaly sensitive module and residual anomaly sensitive module to utilize weakly supervised anomaly localization information in medical Visual Question Answering. Firstly, the proposed multiplication anomaly sensitive module designed for anomaly-related questions can mask the feature of the whole image according to the anomaly location map. Secondly, the residual anomaly sensitive module could learn a flexible anomaly feature while preserving the information of the original questioned image, which is more helpful in answering anomaly-unrelated questions. Thirdly, the transformer decoder and multi-task learning strategy are combined to further enhance the question-reasoning ability and the model generalization performance. JTZ951 Finally, qualitative and quantitative experiments on a variety of medical datasets exhibit the superiority of the proposed approaches compared to the state-of-the-art methods.This article addresses global stabilization via disparate event-triggered output feedback for a class of uncertain nonlinear systems. Typically, the systems allow unknown control directions and unmeasurable-state dependent growth simultaneously. Actually, in the context of the latter ingredient, there has been no any continuous control strategy that has allowed the former ingredient so far. Hence, one cannot solve the event-triggered control problem based on corresponding continuous feedback as done in the emulation-based method. In view of the unsolvability, we pursue a nonemulation-based strategy, directly conducting event-triggered control design. First, a parameterized output feedback controller incorporating a dynamic high gain is designed, which would globally stabilize the system once the adjustable parameter therein is suitable. Then, an event-triggering mechanism is developed to not only decide when the controller is sampled/executed but also determine which constant value the adjustable parameter takes. Just due to the instantly varying (discontinuous) adjustable parameter, the feedback ability of the controller is large enough, making it possible to solve the control design problem in the event-triggered framework. A simulation example is provided to verify the effectiveness and advantage of the proposed approach.In this article, we address the asynchronous H∞ control problem of a class of hidden Markov jump systems (HMJSs) subject to actuator saturation in the continuous-time domain. A bunch of convex hulls is utilized to represent the saturated nonlinearity. Considering that there is an asynchronous mode mismatch between the system and the controller, we establish a hidden Markov model (HMM) to simulate the situation. By means of the Lyapunov theory, sufficient conditions are presented to ensure that the resultant closed-loop HMJS is stochastically mean square stable within the domain of attraction with a prescribed H∞ performance index. Furthermore, the state feedback gain matrix and the estimation of the domain of attraction are given by solving an optimization problem, which is constructed via linear matrix inequality (LMI) techniques. Finally, the reliability and validity of the derived results are examined by a numerical example.Broad learning system (BLS), an efficient neural network with a flat structure, has received a lot of attention due to its advantages in training speed and network extensibility. However, the conventional BLS adopts the least square loss, which treats each sample equally and thus is sensitivity to noise and outliers. To address this concern, in this article we propose a self-paced BLS (SPBLS) model by incorporating the novel self-paced learning (SPL) strategy into the network for noisy data regression. With the assistance of the SPL criterion, the model output is used as feedback to learn appropriate priority weight to readjust the importance of each sample. Such a reweighting strategy can help SPBLS to distinguish samples from "easy" to "difficult" in model training, equipping the model robust to noise and outliers while maintaining the characteristics of the original system. Moreover, two incremental learning algorithms associated to SPBLS have also been developed, with which the system can be updated quickly and flexibly without retraining the entire model when new training samples are added or the network needs to be expanded. Experiments conducted on various datasets demonstrate that the proposed SPBLS can achieve satisfying performance for noisy data regression.Symbolic regression (SR) is an important problem with many applications, such as automatic programming tasks and data mining. Genetic programming (GP) is a commonly used technique for SR. In the past decade, a branch of GP that utilizes the program behavior to guide the search, called semantic GP (SGP), has achieved great success in solving SR problems. However, existing SGP methods only focus on the tree-based chromosome representation and usually encounter the bloat issue and unsatisfactory generalization ability. To address these issues, we propose a new semantic linear GP (SLGP) algorithm. In SLGP, we design a new chromosome representation to encode the programs and semantic information in a linear fashion. To utilize the semantic information more effectively, we further propose a novel semantic genetic operator, namely, mutate-and-divide propagation, to recursively propagate the semantic error within the linear program. The empirical results show that the proposed method has better training and test errors than the state-of-the-art algorithms in solving SR problems and can achieve a much smaller program size.This article investigates optimal regulation scheme between tumor and immune cells based on the adaptive dynamic programming (ADP) approach. The therapeutic goal is to inhibit the growth of tumor cells to allowable injury degree and maximize the number of immune cells in the meantime. The reliable controller is derived through the ADP approach to make the number of cells achieve the specific ideal states. First, the main objective is to weaken the negative effect caused by chemotherapy and immunotherapy, which means that the minimal dose of chemotherapeutic and immunotherapeutic drugs can be operational in the treatment process. Second, according to the nonlinear dynamical mathematical model of tumor cells, chemotherapy and immunotherapeutic drugs can act as powerful regulatory measures, which is a closed-loop control behavior. Finally, states of the system and critic weight errors are proved to be ultimately uniformly bounded with the appropriate optimization control strategy and the simulation results are shown to demonstrate the effectiveness of the cybernetics methodology.Modern facial age estimation systems can achieve high accuracy when training and test datasets are identically distributed and captured under similar conditions. However, domain shifts in data, encountered in practice, lead to a sharp drop in accuracy of most existing age estimation algorithms. In this work, we propose a novel method, namely RAgE, to improve the robustness and reduce the uncertainty of age estimates by leveraging unlabelled data through a subject anchoring strategy and a novel consistency regularisation term. First, we propose an similarity-preserving pseudo-labelling algorithm by which the model generates pseudo-labels for a cohort of unlabelled images belonging to the same subject, while taking into account the similarity among age labels. In order to improve the robustness of the system, a consistency regularisation term is then used to simultaneously encourage the model to produce invariant outputs for the images in the cohort with respect to an anchor image. We propose a novel consistency regularisation term the noise-tolerant property of which effectively mitigates the so-called confirmation bias caused by incorrect pseudo-labels. Experiments on multiple benchmark ageing datasets demonstrate substantial improvements over the state-of-the-art methods and robustness to confounding external factors, including subject's head pose, illumination variation and appearance of expression in the face image.Electrical stimulation is widely used in nerve regulation and treatment. JTZ951 The detection of the distribution of stimulation current in tissues is of great significance to improve the accuracy of electrical stimulation, but the current technical means are still difficult to achieve the non-invasive detection of stimulation current. This study proposes a non-invasive detection method of electrical stimulation current based on the magneto-acoustic (MA) effect, which has the advantages of high spatial resolution and high spatial contrast. In this study, continuous sine waves with the frequency of 20kHz are used to stimulate samples. The MA signal generated by the stimulation current in the samples in a stable magnetic field is detected by lock-in amplifier, and the two-dimensional distribution of sound source is extracted. The current density distribution of samples is simulated by the method of finite element analysis, and the simulation results are verified by experiments. The results show that under the electrical stimulation of the order of 0.01A, the location measurement of two-dimensional surface sound source with millimeter accuracy can be achieved non-invasively in isolated pork and pig brain, and the measurement accuracy of weak MA signal can reach 10-7Pa. In short, the stimulation current detection method based on MA effect can achieve the non-invasive and high-precision detection of electrical stimulation current distribution, which is of great significance to improve the stimulation accuracy and study the neural regulation mechanism of electrical stimulation.
We propose a tactile-induced-oscillation approach to reduce the calibration time in somatosensory brain-computer interfaces (BCI).

Based on the similarity between tactile induced event-related desynchronization (ERD) and imagined sensation induced ERD activation, we extensively evaluated BCI performance when using a conventional and a novel calibration strategy. In the conventional calibration, the tactile imagined data was used, while in the sensory calibration model sensory stimulation data was used. Subjects were required to sense the tactile stimulus when real tactile was applied to the left or right wrist and were required to perform imagined sensation tasks in the somatosensory BCI paradigm.

The sensory calibration led to a significantly better performance than the conventional calibration when tested on the same imagined sensation dataset ( [Formula see text]=10.89, P=0.0038), with an average 5.1% improvement in accuracy. Moreover, the sensory calibration was 39.3% faster in reaching a performance level of above 70% accuracy.
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