On Improving Students' Learning Efficiency in English Teaching

In our country,traditional English teaching attaches excessive importance to the explanation of vocabulary and grammar,causing students' disability in language practice.This paper provides some strategies to improve students' learning efficiency.

Low rank optimization for efficient deep learning:making a balance between compact architecture and fast training

Deep neural networks(DNNs)have achieved great success in many data processing applications.However,high computational complexity and storage cost make deep learning difficult to be used on resource-constrained devices,and it is not environmental-friendly with much power cost.In this paper,we focus on low-rank optimization for efficient deep learning techniques.In the space domain,DNNs are compressed by low rank approximation of the network parameters,which directly reduces the storage requirement with a smaller number of network parameters.In the time domain,the network parameters can be trained in a few subspaces,which enables efficient training for fast convergence.The model compression in the spatial domain is summarized into three categories as pre-train,pre-set,and compression-aware methods,respectively.With a series of integrable techniques discussed,such as sparse pruning,quantization,and entropy coding,we can ensemble them in an integration framework with lower computational complexity and storage.In addition to summary of recent technical advances,we have two findings for motivating future works.One is that the effective rank,derived from the Shannon entropy of the normalized singular values,outperforms other conventional sparse measures such as the?_1 norm for network compression.The other is a spatial and temporal balance for tensorized neural networks.For accelerating the training of tensorized neural networks,it is crucial to leverage redundancy for both model compression and subspace training.

Near is more:learning efficiency in research and development innovation among interlocking firms

Research question/issue:This study examines whether geographic proximity produces a proximity preference as interlocking firms observe each other and learn innovative behaviors through information transmission among interlocking directors.Research findings/insights:We study the performance of A-share-listed companies in China from 2007 to 2017 on the basis of resource dependence theory,agglomeration effect theory,and Porter’s competitive theory.When target firms learn about research and development–related innovation behaviors from interlocking firms closer to them,they experience more efficient learning effects and have improved convergent traits.Moreover,this proximity advantage increases the willingness of the target firm to communicate with and learn from interlocking firms closer to them.Highly developed areas and research and development–intensive industries positively affect the learning efficiency of interlocking firms.Theoretical/academic implications:Our conclusion is consistent with resource dependence theory;target firms in highly developed areas are more willing to imitate and study nearby interlocking firms to maintain their peer relations,innovation potential,and competitiveness.Our conclusion is also consistent with competition theory,which states that the exchange of information between target firms in highly research and development–intensive industries and distant interlocking firms increases innovation differentiation,innovation potential,and competitiveness,even when such exchange has a high cost.Practitioner/policy implications:The results support resource dependence theory and peers’effects.The information obtained by interlocking directorates through external social relations guides firm decision-making,and closer distances reveal more obvious effects.

Joint User Selection and Resource Allocation for Fast Federated Edge Learning

By periodically aggregating local learning updates from edge users, federated edge learning (FEEL) is envisioned as a promising means to reap the benefit of local rich da?ta and protect users'privacy. However, the scarce wireless communication resource greatly limits the number of participated users and is regarded as the main bottleneck which hin?ders the development of FEEL. To tackle this issue, we propose a user selection policy based on data importance for FEEL system. In order to quantify the data importance of each user, we first analyze the relationship between the loss decay and the squared norm of gradi?ent. Then, we formulate a combinatorial optimization problem to maximize the learning effi?ciency by jointly considering user selection and communication resource allocation. By problem transformation and relaxation, the optimal user selection policy and resource alloca?tion are derived, and a polynomial-time optimal algorithm is developed. Finally, we deploy two commonly used deep neural network (DNN) models for simulation. The results validate that our proposed algorithm has strong generalization ability and can attain higher learning efficiency compared with other traditional algorithms.

An Overview of Autonomous Learning and Reflections on EFL Teaching

This paper mainly focuses on the development of autonomous learning worldwide and its reflections on English teachingin China.Autonomous learning,which is beneficial to one’s lifelong journey,is the ability to take responsibility for one’s learning.The final goal of education is to cultivate students with autonomous learning skills.As for EFL teachers,three steps are as followed.Firstly,EFL teachers need to be trained to learn autonomously.Secondly,EFL teachers should construct some online autonomouslearning platforms.Thirdly,EFL teachers need to motivate learners to listen and watch more about English to arouse students’ in-terests in the target language.

Efficient Pose: Efficient human pose estimation with neural architecture search

Human pose estimation from image and video is a key task in many multimedia applications.Previous methods achieve great performance but rarely take efficiency into consideration,which makes it difficult to implement the networks on lightweight devices.Nowadays,real-time multimedia applications call for more efficient models for better interaction.Moreover,most deep neural networks for pose estimation directly reuse networks designed for image classification as the backbone,which are not optimized for the pose estimation task.In this paper,we propose an efficient framework for human pose estimation with two parts,an efficient backbone and an efficient head.By implementing a differentiable neural architecture search method,we customize the backbone network design for pose estimation,and reduce computational cost with negligible accuracy degradation.For the efficient head,we slim the transposed convolutions and propose a spatial information correction module to promote the performance of the final prediction.In experiments,we evaluate our networks on the MPII and COCO datasets.Our smallest model requires only0.65 GFLOPs with 88.1%PCKh@0.5 on MPII and our large model needs only 2 GFLOPs while its accuracy is competitive with the state-of-the-art large model,HRNet,which takes 9.5 GFLOPs.

Energy efficiency enhancement in heterogeneous networks:a joint resource allocation approach

To support the drastic growth of wireless multimedia services and the requirements of ubiquitous access, numerous wireless infrastructures which consume enormous energy, such as macrocell, small cell, distributed antenna systems and wireless sensor networks, have been deployed. Under the background of environmental protection, improving the energy efficiency（EE） in wireless networks is becoming more and more important. In this paper, an EE enhancement scheme in heterogeneous networks（Het Nets） by using a joint resource allocation approach is proposed. The Het Nets consists of a mix of macrocell and small cells. Firstly, we model this strategic coexistence as a multi-agent system in which decentralized resource management inspired from Reinforcement Learning are devised. Secondly, a Q-learning based joint resource allocation algorithm is designed. Meanwhile, with the consideration of the time-varying channel characteristics, we take the long-term learning reward into account. At last, simulation results show that the proposed decentralized algorithm can approximate to centralized algorithm with low-complexity and obtain high spectral efficiency（SE） in the meantime.