Land-Atmosphere Interaction in the Southwestern Karst Region of China

Land-atmosphere interaction in the southwestern Karst region of China was investigated from two aspects: response of land cover to climate change and climatic effects of Karst rocky desertification. The first part focused on the temporal-spatial variation of growing-season normalized difference vegetation index (NDVI) and its relationship with climate variables. The relationships between growing-season NDVI with temperature and precipitation were both positive, indicating its limiting role on the distribution and dynamic of vegetation cover in the study area. The second part was designed to investigate whether the changed vegetation cover and land surface processes in the Karst regions was capable of modifying the summer climate simulation over East Asia. It was shown that land desertification resulted in the reduced net radiation and evaporation in the degraded areas. The East Asian summer monsoon was weakened after land degradation. Such circulation differences favored the increase in moisture flux and clouds, and thereby causing more precipitation in southeast coastal areas. Based on the above findings, it can be concluded that vegetation cover in Karst region was sensitive to climate change at larger scale, and on the other hand, there was significant feedback of vegetation cover change to regional climate by altering water and energy balance

Secure Wireless Communication via Movable-Antenna Array

Movable antenna (MA) array is a novel technology recently developed where positions of transmit/receive antennas can be flexibly adjusted in the specified region to reconfigure the wireless channel and achieve a higher capacity. In this letter, we, for the first time, investigate the MA array-assisted physical-layer security where the confidential information is transmitted from a MA array-enabled Alice to a single-antenna Bob, in the presence of multiple single-antenna and colluding eavesdroppers. We aim to maximize the achievable secrecy rate by jointly designing the transmit beamforming and positions of all antennas at Alice subject to the transmit power budget and specified regions for positions of all transmit antennas. The resulting problem is highly non-convex, for which the projected gradient ascent (PGA) and the alternating optimization methods are utilized to obtain a high-quality suboptimal solution. Simulation results demonstrate that since the additional spatial degree of freedom (DoF) can be fully exploited, the MA array significantly enhances the secrecy rate compared to the conventional fixed-position antenna (FPA) array

Mechanism of phosphate release from actin filaments

-phosphate is hydrolyzedwithin seconds and dissociates over minutes. We used all-atom molecular dynamics simulations to sample the release of phosphate from filaments and study residues that gate release. Dissociation of phosphate from Mg2+ is rate limiting and associated with an energy barrier of 20 kcal/mol, consistent with experimental rates of phosphate release. Phosphate then diffuses within an internal cavity toward a gate formed by R177, as suggested in prior computational studies and cryo-EM structures. The gate is closed when R177 hydrogen bonds with N111 and is open when R177 forms a salt bridge with D179. Most of the time, interactions of R177 with other residues occlude the phosphate release pathway. Machine learning analysis reveals that the occluding interactions fluctuate rapidly, underscoring the secondary role of backdoor gate opening in Pi release, in contrast with the previous hypothesis that gate opening is the primary event

Parallel Multistage Wide Neural Network

Deep learning networks have achieved great success in many areas such as in large scale image processing. They usually need large computing resources and time, and process easy and hard samples inefficiently in the same way. Another undesirable problem is that the network generally needs to be retrained to learn new incoming data. Efforts have been made to reduce the computing resources and realize incremental learning by adjusting architectures, such as scalable effort classifiers, multi-grained cascade forest (gc forest), conditional deep learning (CDL), tree CNN, decision tree structure with knowledge transfer (ERDK), forest of decision trees with RBF networks and knowledge transfer (FDRK). In this paper, a parallel multistage wide neural network (PMWNN) is presented. It is composed of multiple stages to classify different parts of data. First, a wide radial basis function (WRBF) network is designed to learn features efficiently in the wide direction. It can work on both vector and image instances, and be trained fast in one epoch using subsampling and least squares (LS). Secondly, successive stages of WRBF networks are combined to make up the PMWNN. Each stage focuses on the misclassified samples of the previous stage. It can stop growing at an early stage, and a stage can be added incrementally when new training data is acquired. Finally, the stages of the PMWNN can be tested in parallel, thus speeding up the testing process. To sum up, the proposed PMWNN network has the advantages of (1) fast training, (2) optimized computing resources, (3) incremental learning, and (4) parallel testing with stages. The experimental results with the MNIST, a number of large hyperspectral remote sensing data, CVL single digits, SVHN datasets, and audio signal datasets show that the WRBF and PMWNN have the competitive accuracy compared to learning models such as stacked auto encoders, deep belief nets, SVM, MLP, LeNet-5, RBF network, recently proposed CDL, broad learning, gc forest etc. In fact, the PMWNN has often the best classification performance

Fluid Antennas-Enabled Multiuser Uplink: A Low-Complexity Gradient Descent for Total Transmit Power Minimization

We investigate multiuser uplink communication from multiple single-antenna users to a base station (BS), which is equipped with a movable-antenna (MA) array and adopts zero-forcing receivers to decode multiple signals. We aim to optimize the MAs' positions at the BS, to minimize the total transmit power of all users subject to the minimum rate requirement. After applying transformations, we show that the problem is equivalent to minimizing the sum of each eigenvalue's reciprocal of a matrix, which is a function of all MAs' positions. Subsequently, the projected gradient descent (PGD) method is utilized to find a locally optimal solution. In particular, different from the latest related work, we exploit the eigenvalue decomposition to successfully derive a closed-form gradient for the PGD, which facilitates the practical implementation greatly. We demonstrate by simulations that via careful optimization for all MAs' positions in our proposed design, the total transmit power of all users can be decreased significantly as compared to competitive benchmarks