Beijing University of Posts and Telecommunications

About: Beijing University of Posts and Telecommunications is a education organization based out in Beijing, Beijing, China. It is known for research contribution in the topics: MIMO & Quality of service. The organization has 39576 authors who have published 41525 publications receiving 403759 citations. The organization is also known as: BUPT.

An O $(\sqrtn L)$ Iteration Primal-dual Path-following Method, Based on Wide Neighborhoods and Large Updates, for Monotone LCP

Abstract: In this paper we propose a new class of primal-dual path-following interior point algorithms for solving monotone linear complementarity problems. At each iteration, the method would select a target on the central path with a large update from the current iterate, and then the Newton method is used to get the search directions, followed by adaptively choosing the step sizes, which are, e.g., the largest possible steps before leaving a neighborhood that is as wide as the given ${\cal N}^-_{\infty}$ neighborhood. The only deviation from the classical approach is that we treat the classical Newton direction as the sum of two other directions, corresponding to, respectively, the negative part and the positive part of the right-hand side. We show that if these two directions are equipped with different and appropriate step sizes, then the method enjoys the low iteration bound of $O(\sqrt{n}\log L)$, where $n$ is the dimension of the problem and $L=\frac{(x^0)^Ts^0}{\ep}$ with $\ep$ the required precision and $(x^0,s^0)$ the initial interior solution. For a predictor-corrector variant of the method, we further prove that, besides the predictor steps, each corrector step also reduces the duality gap by a rate of $1-1/O(\sqrt{n})$. Additionally, if the problem has a strict complementary solution, then the predictor steps converge Q-quadratically.

All-Oxide NiO/Ga2O3 p–n Junction for Self-Powered UV Photodetector

Abstract: Recently, Ga2O3-based self-powered ultraviolet photodetectors have aroused great interest due to their potential applications in civil, medical, and environmental monitoring fields. So far, most p–...

Robust Biometrics Based Authentication and Key Agreement Scheme for Multi-Server Environments Using Smart Cards

Abstract: Biometrics authenticated schemes using smart cards have attracted much attention in multi-server environments. Several schemes of this type where proposed in the past. However, many of them were found to have some design flaws. This paper concentrates on the security weaknesses of the three-factor authentication scheme by Mishra et al. After careful analysis, we find their scheme does not really resist replay attack while failing to provide an efficient password change phase. We further propose an improvement of Mishra et al.’s scheme with the purpose of preventing the security threats of their scheme. We demonstrate the proposed scheme is given to strong authentication against several attacks including attacks shown in the original scheme. In addition, we compare the performance and functionality with other multi-server authenticated key schemes.

Dense Relation Distillation with Context-aware Aggregation for Few-Shot Object Detection

Abstract: Conventional deep learning based methods for object detection require a large amount of bounding box annotations for training, which is expensive to obtain such high quality annotated data. Few-shot object detection, which learns to adapt to novel classes with only a few annotated examples, is very challenging since the fine-grained feature of novel object can be easily overlooked with only a few data available. In this work, aiming to fully exploit features of annotated novel object and capture fine-grained features of query object, we propose Dense Relation Distillation with Context-aware Aggregation (DCNet) to tackle the few-shot detection problem. Built on the meta-learning based framework, Dense Relation Distillation module targets at fully exploiting support features, where support features and query feature are densely matched, covering all spatial locations in a feed-forward fashion. The abundant usage of the guidance information endows model the capability to handle common challenges such as appearance changes and occlusions. Moreover, to better capture scale-aware features, Context-aware Aggregation module adaptively harnesses features from different scales for a more comprehensive feature representation. Extensive experiments illustrate that our proposed approach achieves state-of-the-art results on PASCAL VOC and MS COCO datasets. Code will be made available at https://github.com/hzhupku/DCNet.