Large-scale self-normalizing neural networks

Self-normalizing neural networks(SNN)regulate the activation and gradient flows through activation functions with the self-normalization property.As SNNs do not rely on norms computed from minibatches,they are more friendly to data parallelism,kernel fusion,and emerging architectures such as ReRAM-based accelerators.However,existing SNNs have mainly demonstrated their effectiveness on toy datasets and fall short in accuracy when dealing with large-scale tasks like ImageNet.They lack the strong normalization,regularization,and expression power required for wider,deeper models and larger-scale tasks.To enhance the normalization strength,this paper introduces a comprehensive and practical definition of the self-normalization property in terms of the stability and attractiveness of the statistical fixed points.It is comprehensive as it jointly considers all the fixed points used by existing studies:the first and second moment of forward activation and the expected Frobenius norm of backward gradient.The practicality comes from the analytical equations provided by our paper to assess the stability and attractiveness of each fixed point,which are derived from theoretical analysis of the forward and backward signals.The proposed definition is applied to a meta activation function inspired by prior research,leading to a stronger self-normalizing activation function named‘‘bi-scaled exponential linear unit with backward standardized’’(bSELU-BSTD).We provide both theoretical and empirical evidence to show that it is superior to existing studies.To enhance the regularization and expression power,we further propose scaled-Mixup and channel-wise scale&shift.With these three techniques,our approach achieves 75.23%top-1 accuracy on the ImageNet with Conv MobileNet V1,surpassing the performance of existing self-normalizing activation functions.To the best of our knowledge,this is the first SNN that achieves comparable accuracy to batch normalization on ImageNet.

Effect of Nonlinear Dynamic Process on Formation and Breakdown of Blocking

With the L-P approximate method(variation of parameter method), a barotropic channel model in β-plane is used to study the effect of nonlinear interaction between two waves with different scales on the formation of blocking. The approximate analytical solution, which can describe the process of the blocking formation, maintenance and breakdown, has been obtained by using the method of aproximate expansion. The importance of nonlinear interaction between two waves with different scales is stressed in the solution. The result suggests that the nonlinear interaction is the main dynamic process of the blocking formation. Some required conditions of blocking formation are also discussed.

Reversible Data Hiding Based on Pixel-Value-Ordering and Pixel Block Merging Strategy

With the reversible data hiding method based on pixel-value-ordering,data are embedded through the modification of the maximum and minimum values of a block.A significant relationship exists between the embedding performance and the block size.Traditional pixel-value-ordering methods utilize pixel blocks with a fixed size to embed data;the smaller the pixel blocks,greater is the embedding capacity.However,it tends to result in the deterioration of the quality of the marked image.Herein,a novel reversible data hiding method is proposed by incorporating a block merging strategy into Li et al.’s pixel-value-ordering method,which realizes the dynamic control of block size by considering the image texture.First,the cover image is divided into non-overlapping 2×2 pixel blocks.Subsequently,according to their complexity,similarity and thresholds,these blocks are employed for data embedding through the pixel-value-ordering method directly or after being emerged into 2×4,4×2,or 4×4 sized blocks.Hence,smaller blocks can be used in the smooth region to create a high embedding capacity and larger blocks in the texture region to maintain a high peak signal-to-noise ratio.Experimental results prove that the proposed method is superior to the other three advanced methods.It achieves a high embedding capacity while maintaining low distortion and improves the embedding performance of the pixel-value-ordering algorithm.

Identification,characteristics and classification of cryogenic block streams

Cryogenic block streams consist of a stream of rocks superficially resembling a stream deposit but lacking a matrix, usually occurring on a valley or gully floor or on slopes that are less steep than the maximum angle of repose of coarse sediments. They are usually formed on perennially frozen ground, but can also occur as relict landforms. There are three main active kinds forming today, viz., Siberian and Tibetan dynamic rock streams and lag block streams. During their formation, the blocks in the active Siberian and Tibetan dynamic block streams move downslope at up to 1 rn/a. They are forming today on the Tibetan Plateau and in the more arid parts of south-central Siberia, although the processes involved in the movement are different. In the case of the Tibetan type, individual blocks slide downslope over the substrate in winter on an icy coating in areas of minimal winter precipitation. The Siberian type develops in areas of 15-80 cm of winter snow cover and an MAAT （mean annual air temperature） of-4 ~C to -17 ~C. The movement is due to creep of snow and ice and collapse of the blocks downslope during thawing. Lag block streams are formed by meltwater flowing over the surface of sediment consisting primarily of larger blocks with a limited amount of interstitial sediment. The erosion of the matrix is primarily in the spring in areas of higher winter precipitation on 10^-30~ slopes. The blocks remain stationary, but the interstitial sediment is washed out by strong seasonal flows of meltwater or rain to form an alluvial fan. The boulders undergo weathering and become more rounded in the process. Lag block streams can also develop without the presence of permafrost in areas with cold climates or glaciers. Block streams also occur as relict deposits in older deposits under various climatic regimes that are unsuitable for their formation today. An example of relict lag block streams with subangular to subrounded blocks occurs in gullies on the forested mountainsides at Felsen in Germany, and is the original ＂felsenmeer＂. Similar examples occur near Vitosha Mountain in Bulgaria. The ＂stone runs＂ in the Falkland Islands are examples of the more angular relict lag block streams. In both Tasmania and the Falkland Islands, they mask a more complex history, the underlying soils indicating periods of tropical and temperate soil formation resulting from weathering during and since the Tertiary Period. Block streams have also been reported from beneath cold-based glaciers in Sweden, and below till in Canada, and when ex- humed, can continue to develop.

Adaptive block dynamic surface control for integrated missile guidance and autopilot

A novel integrated guidance and autopilot design method is proposed for homing missiles based on the adaptive block dynamic surface control approach. The fully integrated guidance and autopilot model is established by combining the nonlinear missile dynamics with the nonlinear dynamics describing the pursuit situation of a missile and a target in the three-dimensional space. The integrated guidance and autopilot design problem is further converted to a state regulation problem of a time-varying nonlinear system with matched and unmatched uncertainties. A new and simple adaptive block dynamic surface control algorithm is proposed to address such a state regulation problem. The stability of the closed-loop system is proven based on the Lyapunov theory. The six degrees of freedom （6DOF） nonlinear numerical simulation results show that the proposed integrated guidance and autopilot algorithm can ensure the accuracy of target interception and the robust stability of the closed-loop system with respect to the uncertainties in the missile dynamics.

Impact of Wavelength-Routed Network Physical Topology on Blocking Probability Using a Dynamic Traffic Growth Model

We investigate the impact of network topology on blocking probability in wavelength-routed networks using a dynamic traffic growth model. The dependence of blocking on different physical parameters is assessed.