The Basal Thermal Sensitivity of the TRPV1 Ion Channel Is Determined by PKCβII

Copyright © 2014 the authors 0270-6474/14/348246-13$15.00/0.Peer reviewedPublisher PD

Efficient randomized-adaptive designs

Response-adaptive randomization has recently attracted a lot of attention in the literature. In this paper, we propose a new and simple family of response-adaptive randomization procedures that attain the Cramer--Rao lower bounds on the allocation variances for any allocation proportions, including optimal allocation proportions. The allocation probability functions of proposed procedures are discontinuous. The existing large sample theory for adaptive designs relies on Taylor expansions of the allocation probability functions, which do not apply to nondifferentiable cases. In the present paper, we study stopping times of stochastic processes to establish the asymptotic efficiency results. Furthermore, we demonstrate our proposal through examples, simulations and a discussion on the relationship with earlier works, including Efron's biased coin design.Comment: Published in at http://dx.doi.org/10.1214/08-AOS655 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

SGTR+: End-to-end Scene Graph Generation with Transformer

Scene Graph Generation (SGG) remains a challenging visual understanding task due to its compositional property. Most previous works adopt a bottom-up, two-stage or point-based, one-stage approach, which often suffers from high time complexity or suboptimal designs. In this work, we propose a novel SGG method to address the aforementioned issues, formulating the task as a bipartite graph construction problem. To address the issues above, we create a transformer-based end-to-end framework to generate the entity and entity-aware predicate proposal set, and infer directed edges to form relation triplets. Moreover, we design a graph assembling module to infer the connectivity of the bipartite scene graph based on our entity-aware structure, enabling us to generate the scene graph in an end-to-end manner. Based on bipartite graph assembling paradigm, we further propose a new technical design to address the efficacy of entity-aware modeling and optimization stability of graph assembling. Equipped with the enhanced entity-aware design, our method achieves optimal performance and time-complexity. Extensive experimental results show that our design is able to achieve the state-of-the-art or comparable performance on three challenging benchmarks, surpassing most of the existing approaches and enjoying higher efficiency in inference. Code is available: https://github.com/Scarecrow0/SGTRComment: Accepted by TPAMI: https://ieeexplore.ieee.org/document/1031523

A Low-latency Collaborative HARQ Scheme for Control/User-plane Decoupled Railway Wireless Networks

ArticleThe recently proposed Control/User (C/U) plane decoupled railway wireless network is a promising new network architecture to meet the communication demands of both train control systems and onboard passengers by completely separating the C-plane and U-plane into different network nodes operating at different frequency bands. Although the system capacity of this network architecture can be highly increased, the forwarding latency of X3 interfaces to link the C-plane and U-plane becomes a serious concern, especially for hybrid automatic repeat request (HARQ) protocols which demand frequent interactions between the C-plane and U-plane. This concern becomes more pronounced for latency sensitive train control. To address this challenging problem, in this paper we propose a low-latency collaborative HARQ scheme. Through a newly designed collaborative transmission framework, the possible spare resources on lower frequency bands of macro cells by excluding those used by C-plane transmissions are utilized to help small cells relay erroneously received data. Compared to the conventional HARQ scheme, to reach the same transmission reliability, the proposed scheme requires fewer retransmissions on average, thereby mitigating the latency problem caused by HARQ retransmissions. Correspondingly, the channel mapping is also redesigned to conform to the proposed collaborative transmission framework. In the theoretical analysis, the expression of the average retransmission times related to the sum of independent Gamma variables is developed. Finally, simulation results show that a great decrease in the retransmission latency is gained by the proposed scheme, but at the sacrifice of few average system transmission rate

Federated Learning with Manifold Regularization and Normalized Update Reaggregation

Federated Learning (FL) is an emerging collaborative machine learning framework where multiple clients train the global model without sharing their own datasets. In FL, the model inconsistency caused by the local data heterogeneity across clients results in the near-orthogonality of client updates, which leads to the global update norm reduction and slows down the convergence. Most previous works focus on eliminating the difference of parameters (or gradients) between the local and global models, which may fail to reflect the model inconsistency due to the complex structure of the machine learning model and the Euclidean space's limitation in meaningful geometric representations. In this paper, we propose FedMRUR by adopting the manifold model fusion scheme and a new global optimizer to alleviate the negative impacts. Concretely, FedMRUR adopts a hyperbolic graph manifold regularizer enforcing the representations of the data in the local and global models are close to each other in a low-dimensional subspace. Because the machine learning model has the graph structure, the distance in hyperbolic space can reflect the model bias better than the Euclidean distance. In this way, FedMRUR exploits the manifold structures of the representations to significantly reduce the model inconsistency. FedMRUR also aggregates the client updates norms as the global update norm, which can appropriately enlarge each client's contribution to the global update, thereby mitigating the norm reduction introduced by the near-orthogonality of client updates. Furthermore, we theoretically prove that our algorithm can achieve a linear speedup property for non-convex setting under partial client participation.Experiments demonstrate that FedMRUR can achieve a new state-of-the-art (SOTA) accuracy with less communication

Named Entity Recognition Using BERT BiLSTM CRF for Chinese Electronic Health Records

As the generation and accumulation of massive electronic health records (EHR), how to effectively extract the valuable medical information from EHR has been a popular research topic. During the medical information extraction, named entity recognition (NER) is an essential natural language processing (NLP) task. This paper presents our efforts using neural network approaches for this task. Based on the Chinese EHR offered by CCKS 2019 and the Second Affiliated Hospital of Soochow University (SAHSU), several neural models for NER, including BiLSTM, have been compared, along with two pre-trained language models, word2vec and BERT. We have found that the BERT-BiLSTM-CRF model can achieve approximately 75% F1 score, which outperformed all other models during the tests