U(1)-decoupling, KK and BCJ relations in N=4\mathcal{N}=4 SYM

We proved the color reflection relation, U(1)-decoupling, Kleiss-Kuijf and Bern-Carrasco-Johansson relation for color-ordered $\mathcal{N}=4$ Super Yang-Mills theory using $\mathcal{N}=4$ SYM version BCFW recursion relation, which depends only on the general properties of super-amplitudes. This verified the conjectured matter fields BCJ relation. We also show that color reflection relation and U(1)-decoupling relation are special cases of KK relation, if we consider the KK relation as a general relation, then the former two relations come out naturally as the special cases.Comment: 17 page

Moisture-triggered physically transient electronics

Physically transient electronics, a form of electronics that can physically disappear in a controllable manner, is very promising for emerging applications. Most of the transient processes reported so far only occur in aqueous solutions or biofluids, offering limited control over the triggering and degradation processes. We report novel moisture-triggered physically transient electronics, which exempt the needs of resorption solutions and can completely disappear within well-controlled time frames. The triggered transient process starts with the hydrolysis of the polyanhydride substrate in the presence of trace amounts of moisture in the air, a process that can generate products of corrosive organic acids to digest various inorganic electronic materials and components. Polyanhydride is the only example of polymer that undergoes surface erosion, a distinct feature that enables stable operation of the functional devices over a predefined time frame. Clear advantages of this novel triggered transience mode include that the lifetime of the devices can be precisely controlled by varying the moisture levels and changing the composition of the polymer substrate. The transience time scale can be tuned from days to weeks. Various transient devices, ranging from passive electronics (such as antenna, resistor, and capacitor) to active electronics ( such as transistor, diodes, optoelectronics, and memories), and an integrated system as a platform demonstration have been developed to illustrate the concept and verify the feasibility of this design strategy

Local Manifold Augmentation for Multiview Semantic Consistency

Multiview self-supervised representation learning roots in exploring semantic consistency across data of complex intra-class variation. Such variation is not directly accessible and therefore simulated by data augmentations. However, commonly adopted augmentations are handcrafted and limited to simple geometrical and color changes, which are unable to cover the abundant intra-class variation. In this paper, we propose to extract the underlying data variation from datasets and construct a novel augmentation operator, named local manifold augmentation (LMA). LMA is achieved by training an instance-conditioned generator to fit the distribution on the local manifold of data and sampling multiview data using it. LMA shows the ability to create an infinite number of data views, preserve semantics, and simulate complicated variations in object pose, viewpoint, lighting condition, background etc. Experiments show that with LMA integrated, self-supervised learning methods such as MoCov2 and SimSiam gain consistent improvement on prevalent benchmarks including CIFAR10, CIFAR100, STL10, ImageNet100, and ImageNet. Furthermore, LMA leads to representations that obtain more significant invariance to the viewpoint, object pose, and illumination changes and stronger robustness to various real distribution shifts reflected by ImageNet-V2, ImageNet-R, ImageNet Sketch etc