Huawei

About: Huawei is a company organization based out in Shenzhen, China. It is known for research contribution in the topics: Terminal (electronics) & Signal. The organization has 41417 authors who have published 44698 publications receiving 343496 citations. The organization is also known as: Huawei Technologies & Huawei Technologies Co., Ltd..

Message interworking method, system, entity and message delivery report processing method, system, the entity, terminal for message interworking

Abstract: The message interworking method and system of SIP message and conventional short message, using an entity SMI AS for message interworking to execute the authorization of the interworking service of SIP message and conventional network message, to transform the message format and to store and forward the message. Herein, SMI AS can be a new added network entity, or a new added function module in the exiting network entity, and it provides service for users by the third registration of users. Applying present invention enables the IMS-only terminal, which does not support the conventional short message service, to achieve the interworking of the message service with the conventional terminal, enriching the variety of the service. The entity for message interworking is also provided. Additionally, a message delivery report processing method and system are also provided.

Gradually Vanishing Bridge for Adversarial Domain Adaptation

Abstract: In unsupervised domain adaptation, rich domain-specific characteristics bring great challenge to learn domain-invariant representations. However, domain discrepancy is considered to be directly minimized in existing solutions, which is difficult to achieve in practice. Some methods alleviate the difficulty by explicitly modeling domain-invariant and domain-specific parts in the representations, but the adverse influence of the explicit construction lies in the residual domain-specific characteristics in the constructed domain-invariant representations. In this paper, we equip adversarial domain adaptation with Gradually Vanishing Bridge (GVB) mechanism on both generator and discriminator. On the generator, GVB could not only reduce the overall transfer difficulty, but also reduce the influence of the residual domain-specific characteristics in domain-invariant representations. On the discriminator, GVB contributes to enhance the discriminating ability, and balance the adversarial training process. Experiments on three challenging datasets show that our GVB methods outperform strong competitors, and cooperate well with other adversarial methods. The code is available at this https URL.

A Step Toward 5G in 2020: Low-cost OTA performance evaluation of massive MIMO base stations.

Abstract: Massive multiple-input, multiple-output (MIMO) is seen as an enabling technology to fulfill dramatic improvements in spectral efficiency for fifth-generation (5G) deployment in 2020. For massive MIMO systems, the learning loop from early-stage prototype design to final-stage performance validation is expected to be slow and ineffective. There is a strong need to evaluate massive MIMO base station (BS) performance with over-the-air (OTA) methods. Until now, such OTA solutions have not been discussed for massive MIMO BS systems.

Exact and Consistent Interpretation for Piecewise Linear Neural Networks: A Closed Form Solution

Abstract: Strong intelligent machines powered by deep neural networks are increasingly deployed as black boxes to make decisions in risk-sensitive domains, such as finance and medical. To reduce potential risk and build trust with users, it is critical to interpret how such machines make their decisions. Existing works interpret a pre-trained neural network by analyzing hidden neurons, mimicking pre-trained models or approximating local predictions. However, these methods do not provide a guarantee on the exactness and consistency of their interpretations. In this paper, we propose an elegant closed form solution named $OpenBox$ to compute exact and consistent interpretations for the family of Piecewise Linear Neural Networks (PLNN). The major idea is to first transform a PLNN into a mathematically equivalent set of linear classifiers, then interpret each linear classifier by the features that dominate its prediction. We further apply $OpenBox$ to demonstrate the effectiveness of non-negative and sparse constraints on improving the interpretability of PLNNs. The extensive experiments on both synthetic and real world data sets clearly demonstrate the exactness and consistency of our interpretation.

Transformer in Transformer

Abstract: Transformer is a new kind of neural architecture which encodes the input data as powerful features via the attention mechanism. Basically, the visual transformers first divide the input images into several local patches and then calculate both representations and their relationship. Since natural images are of high complexity with abundant detail and color information, the granularity of the patch dividing is not fine enough for excavating features of objects in different scales and locations. In this paper, we point out that the attention inside these local patches are also essential for building visual transformers with high performance and we explore a new architecture, namely, Transformer iN Transformer (TNT). Specifically, we regard the local patches (e.g., 16$\times$16) as "visual sentences" and present to further divide them into smaller patches (e.g., 4$\times$4) as "visual words". The attention of each word will be calculated with other words in the given visual sentence with negligible computational costs. Features of both words and sentences will be aggregated to enhance the representation ability. Experiments on several benchmarks demonstrate the effectiveness of the proposed TNT architecture, e.g., we achieve an $81.5%$ top-1 accuracy on the ImageNet, which is about $1.7%$ higher than that of the state-of-the-art visual transformer with similar computational cost. The PyTorch code is available at this https URL, and the MindSpore code is at this https URL.