Spatiotemporal Observer Design for Predictive Learning of High-Dimensional Data

Although deep learning-based methods have shown great success in spatiotemporal predictive learning, the framework of those models is designed mainly by intuition. How to make spatiotemporal forecasting with theoretical guarantees is still a challenging issue. In this work, we tackle this problem by applying domain knowledge from the dynamical system to the framework design of deep learning models. An observer theory-guided deep learning architecture, called Spatiotemporal Observer, is designed for predictive learning of high dimensional data. The characteristics of the proposed framework are twofold: firstly, it provides the generalization error bound and convergence guarantee for spatiotemporal prediction; secondly, dynamical regularization is introduced to enable the model to learn system dynamics better during training. Further experimental results show that this framework could capture the spatiotemporal dynamics and make accurate predictions in both one-step-ahead and multi-step-ahead forecasting scenarios.Comment: Under review by IEEE Transactions on Pattern Analysis and Machine Intelligenc

Down-regulation of F-actin and paxillin by N-(3-(1Htetrazol- 1-yl)phenyl) isonicotinamide derivative inhibits proliferation of prostate cancer cells

Purpose: To investigate the effect of N-(3-(1H-tetrazol-1-yl)phenyl) isonicotinamide derivative (TPIN) on prostate cancer cells, and the mechanism involved.Methods: The cytotoxicity of TPIN in DU145 and PC3 cells was determined using Cell Counting Kit-8, while apoptosis induction was assayed by flow cytometry using Annexin V-fluorescein isothiocyanate dye. Changes in expressions of F-actin, RAC-α and paxillin were determined by western blot assay.Results: Cell proliferation was effectively inhibited by TPIN in the concentration range of 0.75-15 μM. The values of half-minimum inhibitory concentration (IC50) of TPIN for DU145 and PC3 cells at 48 h were 5.6 and 10.2 μM, respectively (p < 0.05). Treatment with 5.6 μM TPIN increased apoptosis to 59.64 % in DU145 cells, and 54.21% in PC3 cells. Cleaved caspase-3 and caspase-9 levels were increased by TPIN treatment in both cell lines (p < 0.05). Moreover, the levels of F-actin and paxillin were significantly downregulated by TPIN treatment in DU145 and PC3 cells (p < 0.05). In TPIN-treated DU145 and PC3 cells, cofilin-1expression was up-regulated, relative to control cells.Conclusion: TPIN exhibits cytotoxic effect on prostate cancer cells via activation of apoptosis. It elevates cofilin-1 and the expressions of targets F-actin and paxillin in prostate cancer cells. Thus, TPIN is a potential chemotherapeutic agent for prostate cancer. However, further investigations, including clinical trials are required to authenticate these findings. Keywords: Prostate cancer, F-actin, Paxillin, Apoptosis, Caspase

Adversarial Examples Detection with Bayesian Neural Network

In this paper, we propose a new framework to detect adversarial examples motivated by the observations that random components can improve the smoothness of predictors and make it easier to simulate the output distribution of a deep neural network. With these observations, we propose a novel Bayesian adversarial example detector, short for BATer, to improve the performance of adversarial example detection. Specifically, we study the distributional difference of hidden layer output between natural and adversarial examples, and propose to use the randomness of the Bayesian neural network to simulate hidden layer output distribution and leverage the distribution dispersion to detect adversarial examples. The advantage of a Bayesian neural network is that the output is stochastic while a deep neural network without random components does not have such characteristics. Empirical results on several benchmark datasets against popular attacks show that the proposed BATer outperforms the state-of-the-art detectors in adversarial example detection

Changes and prognosis of coupling between heart and brain in ischemic stroke rats

Objective To study the changes of heart-brain coupling indexes after cerebral ischemia in a rat model of middle cerebral artery occlusion (MCAO) and the relationship with prognosis in the early stage. Methods Twenty male SD rats(7~8 weeks old, body weight 290±25 g) were randomly divided into 2 groups (n=10): the cerebral ischemia group (MCAO group) and the control group (Sham group). Lead Ⅱ electrocardiography (ECG) and dual-channel electroencephalogram (EEG) were recorded at the baseline and 0~4 h after the surgery. Neurofunctional recovery was evaluated by neurological deficit score(NDS) via a series of behavior tests every 24 h and survival time was recorded. The heart rate variability (HRV) indicators were extracted by the collected ECG signals, including RR interval (RRI), low frequency (LF), high frequency (HF) and LF/HF ratio. The power spectrum of δ, θ, α and β waves were calculated from EEG signal. The heart-brain network was constructed based on the indicators mentioned, and the transfer entropy algorithm was used to quantify the coupling strength applied to the prognosis for survival which was analyzed and compared by the area under curve (AUC) among nodes of different networks. Results There was a bidirectional interaction between the brain and the heart, and the strength of two-way coupling increased after ischemia. There was obviously increased coupling between HRV LF and EEG δ wave. Transfer entropy (AUC=0.717, P=0.010) was superior to the HRV (AUC=0.571, P=0.404) and EEG power spectrum (AUC=0.583, P=0.329) in prognostication. Conclusion The low-frequency coupling between heart and brain enhanced after cerebral ischemia, and the coupling index of heart and brain can improve the prognostic performance

Microstructure evolution and tensile strength of Al/Cu inertia friction welded joint

To save production costs and reduce the weight of structures, it is one of the common ways to replace copper (Cu) with aluminum (Al) in some industries such as the refrigeration industry. The high-quality welding of Al to Cu determines the application of the Al/Cu hybrid structure. The inertia friction welding process was used to weld Al pipe to Cu pipe and the Taguchi experiment method was used to study the effects of inertia friction welding parameters on the mechanical properties of the welded joints, and the results show that the initial speed has the greatest influence on the tensile strength of welded joints. Meanwhile, the analyses of the microstructures of the joint show that Al–Cu IMCs formed at the friction interface to realize the metallurgical bonding of the welded joints. The formation sequence of Al–Cu IMCs in the friction welding process is Al2Cu, Al4Cu9, and AlCu. Whereas the type and thickness of IMCs are closely related to the tensile strength of the joints. When the welded joint forms Al2Cu and Al4Cu9, the interfacial misfit between the IMCs layer and base materials is minimized and the tensile strength of the joint is optimized

A graphene oxide-modified biosensor for non-invasive glucose monitoring in college athletes

The study aims to address the need for accurate and real-time monitoring of glucose levels in college athletes during physical activities. This work reports on the development of an electrochemical sensor that uses glucose oxidase (GOx) immobilized on PdO nanoparticles to reduce graphene oxide (rGO) nanocomposite printed on a cellulose substrate (GOx/PdO-rGO/C-PE). The successful reduction of GO to rGO, the production of the PdO-rGO nanocomposite, and the electropolymerization of GOx on the PdO-rGO nanocomposite were all validated by the material characterization. The biosensor's electrochemical response investigation showed that its detection limit was 0.046 μM and its sensitivity was 0.03239 μA/μM. Excellent stability, reproducibility, and glucose selectivity were shown by the GOx/PdO-rGO/C-PE, which makes it a viable option for consistent and dependable glucose sensing in real-world applications. The real sample analysis assessed how well the combination of GOx/PdO-rGO/C-PE could identify glucose in human serum. Furthermore, under a variety of real-world conditions, such as during various physical activities and at different times of the day, the sensor demonstrated outstanding performance in real-time glucose monitoring. These findings imply that the GOx/PdO-rGO/C-PE offers accurate and dependable readings in the field of non-invasive glucose monitoring, which will be especially helpful for college and professional athletes

Performance analysis of different pixel-wise processing methods for depth imaging with single photon detection data

We establish a long-range single photon counting three-dimensional (3D) imaging system based on cage optical structure. Five different pixel-wise processing methods for time-of-flight (TOF) photon counting data are compared with data collected by our 3D imaging system for ranges 40-700 m and a suitable representation model for photon counting data is proposed for pixel-wise processing. Experimental results show that these methods exploit the instrumental response function (IRF), yielding a high-quality 3D image. When the signal photon counts are greater than 13 per pixel, the resulting mean absolute error (MAE) values of the IRF-based methods are better than results from the non-IRF-based methods. Regarding IRF-based methods, the union of subspace (UOS) model-based approach and cross correlation are more suitable than the Markov chain Monte Carlo (MCMC) method in the condition of a small number of return signal photons. These results offer valuable information to promote the implementation of photon counting 3D imaging in real applications

Passive Vibration Control of a Semi-Submersible Floating Offshore Wind Turbine

Floating offshore wind turbines have the potential to commercially convert the vast wind resource in deep-water area. Compared with fixed-bottom wind turbines, motions of the floating foundation complicate vibrations and loads of the wind turbine in offshore environment. To alleviate the responses of the wind turbine, this study investigates the use of fore–aft tuned mass damper (TMD) in nacelle/tower for passive control of a semi-submersible offshore wind turbine. A simplified structural model, considering the degree-of-freedom of platform pitch and surge, tower tilt and TMD translation, is proposed in the light of motion features of semi-submersible platform. After identifying ten unknown parameters, the correctness of the deterministic model is validated by pitch free decay responses. The mass, stiffness and damping of TMD are optimized using both method of exhaustion and genetic algorithm to avoid local minimum. Six optimized TMD devices are evaluated under three kinds of realistic environment conditions. The control effectiveness is assessed by the extreme and fatigue response reduction ratios. It is found that the high stiffness TMDs that directly dissipate the energy of tower oscillation exhibit an overall stable performance. Similar to the spar-type foundation, the TMDs in the nacelle/tower are capable of extending the service life of floating wind turbines