Derivation of Feynman Rules for Higher Order Poles Using Cross-ratio Identities in CHY Construction

In order to generalize the integration rules to general CHY integrands which include higher order poles, algorithms are proposed in two directions. One is to conjecture new rules, and the other is to use the cross-ratio identity method. In this paper,we use the cross-ratio identity approach to re-derive the conjectured integration rules involving higher order poles for several special cases: the single double pole, single triple pole and duplex-double pole. The equivalence between the present formulas and the previously conjectured ones is discussed for the first two situations.Comment: 29 pages, 11 figure

On Multi-step BCFW Recursion Relations

In this paper, we extensively investigate the new algorithm known as the multi-step BCFW recursion relations. Many interesting mathematical properties are found and understanding these aspects, one can find a systematic way to complete the calculation of amplitude after finite, definite steps and get the correct answer, without recourse to any specific knowledge from field theories, besides mass dimension and helicities. This process consists of the pole concentration and inconsistency elimination. Terms that survive inconsistency elimination cannot be determined by the new algorithm. They include polynomials and their generalizations, which turn out to be useful objects to be explored. Afterwards, we apply it to the Standard Model plus gravity to illustrate its power and limitation. Ensuring its workability, we also tentatively discuss how to improve its efficiency by reducing the steps.Comment: 38 pages, 13 figures, 3 appendice

A Semiblind Two-Way Training Method for Discriminatory Channel Estimation in MIMO Systems

Discriminatory channel estimation (DCE) is a recently developed strategy to enlarge the performance difference between a legitimate receiver (LR) and an unauthorized receiver (UR) in a multiple-input multiple-output (MIMO) wireless system. Specifically, it makes use of properly designed training signals to degrade channel estimation at the UR which in turn limits the UR's eavesdropping capability during data transmission. In this paper, we propose a new two-way training scheme for DCE through exploiting a whitening-rotation (WR) based semiblind method. To characterize the performance of DCE, a closed-form expression of the normalized mean squared error (NMSE) of the channel estimation is derived for both the LR and the UR. Furthermore, the developed analytical results on NMSE are utilized to perform optimal power allocation between the training signal and artificial noise (AN). The advantages of our proposed DCE scheme are two folds: 1) compared to the existing DCE scheme based on the linear minimum mean square error (LMMSE) channel estimator, the proposed scheme adopts a semiblind approach and achieves better DCE performance; 2) the proposed scheme is robust against active eavesdropping with the pilot contamination attack, whereas the existing scheme fails under such an attack.Comment: accepted for publication in IEEE Transactions on Communication

Optical clearing-aided photoacoustic microscopy with enhanced resolution and imaging depth

Due to strong light scattering in tissue, both the spatial resolution and maximum penetration depth of optical-resolution photoacoustic microscopy (OR-PAM) deteriorate sharply with depth. To reduce tissue scattering, we propose to use glycerol as an optical clearing agent in OR-PAM. Our results show that the imaging performance of OR-PAM can be greatly enhanced by optical clearing both in vitro and in vivo

Learning to Auto Weight: Entirely Data-driven and Highly Efficient Weighting Framework

Example weighting algorithm is an effective solution to the training bias problem, however, most previous typical methods are usually limited to human knowledge and require laborious tuning of hyperparameters. In this paper, we propose a novel example weighting framework called Learning to Auto Weight (LAW). The proposed framework finds step-dependent weighting policies adaptively, and can be jointly trained with target networks without any assumptions or prior knowledge about the dataset. It consists of three key components: Stage-based Searching Strategy (3SM) is adopted to shrink the huge searching space in a complete training process; Duplicate Network Reward (DNR) gives more accurate supervision by removing randomness during the searching process; Full Data Update (FDU) further improves the updating efficiency. Experimental results demonstrate the superiority of weighting policy explored by LAW over standard training pipeline. Compared with baselines, LAW can find a better weighting schedule which achieves much more superior accuracy on both biased CIFAR and ImageNet.Comment: Accepted by AAAI 202

Apparatus and method for intra-layer modulation of the material deposition and assist beam and the multilayer structure produced therefrom

A method of producing a multilayer structure that has reduced interfacial roughness and interlayer mixing by using a physical-vapor deposition apparatus. In general the method includes forming a bottom layer having a first material wherein a first plurality of monolayers of the first material is deposited on an underlayer using a low incident adatom energy. Next, a second plurality of monolayers of the first material is deposited on top of the first plurality of monolayers of the first material using a high incident adatom energy. Thereafter, the method further includes forming a second layer having a second material wherein a first plurality of monolayers of the second material is deposited on the second plurality of monolayers of the first material using a low incident adatom energy. Next, a second plurality of monolayers of the second material is deposited on the first plurality of monolayers of the second material using a high incident adatom energy