Optimization in Machine Learning:a Distribution-Space Approach

We present the viewpoint that optimization problems encountered in machine learning can often be interpreted as minimizing a convex functional over a function space,but with a non-convex constraint set introduced by model parameterization.This observation allows us to repose such problems via a suitable relaxation as convex optimization problems in the space of distributions over the training parameters.We derive some simple relationships between the distribution-space problem and the original problem,e.g.,a distribution-space solution is at least as good as a solution in the original space.Moreover,we develop a numerical algorithm based on mixture distributions to perform approximate optimization directly in the distribution space.Consistency of this approximation is established and the numerical efficacy of the proposed algorithm is illustrated in simple examples.In both theory and practice,this formulation provides an alternative approach to large-scale optimization in machine learning.

Recent advances in protein conformation sampling by combining machine learning with molecular simulation

The rapid advancement and broad application of machine learning(ML)have driven a groundbreaking revolution in computational biology.One of the most cutting-edge and important applications of ML is its integration with molecular simulations to improve the sampling efficiency of the vast conformational space of large biomolecules.This review focuses on recent studies that utilize ML-based techniques in the exploration of protein conformational landscape.We first highlight the recent development of ML-aided enhanced sampling methods,including heuristic algorithms and neural networks that are designed to refine the selection of reaction coordinates for the construction of bias potential,or facilitate the exploration of the unsampled region of the energy landscape.Further,we review the development of autoencoder based methods that combine molecular simulations and deep learning to expand the search for protein conformations.Lastly,we discuss the cutting-edge methodologies for the one-shot generation of protein conformations with precise Boltzmann weights.Collectively,this review demonstrates the promising potential of machine learning in revolutionizing our insight into the complex conformational ensembles of proteins.

Convergence analysis for complementary-label learning with kernel ridge regression

Complementary-label learning(CLL)aims at finding a classifier via samples with complementary labels.Such data is considered to contain less information than ordinary-label samples.The transition matrix between the true label and the complementary label,and some loss functions have been developed to handle this problem.In this paper,we show that CLL can be transformed into ordinary classification under some mild conditions,which indicates that the complementary labels can supply enough information in most cases.As an example,an extensive misclassification error analysis was performed for the Kernel Ridge Regression(KRR)method applied to multiple complementary-label learning(MCLL),which demonstrates its superior performance compared to existing approaches.

Automatic Generation of Artificial Space Weather Forecast Product Based on Sequence-to-sequence Model

Both analyzing a large amount of space weather observed data and alleviating personal experience bias are significant challenges in generating artificial space weather forecast products.With the use of natural language generation methods based on the sequence-to-sequence model,space weather forecast texts can be automatically generated.To conduct our generation tasks at a fine-grained level,a taxonomy of space weather phenomena based on descriptions is presented.Then,our MDH(Multi-Domain Hybrid)model is proposed for generating space weather summaries in two stages.This model is composed of three sequence-to-sequence-based deep neural network sub-models(one Bidirectional Auto-Regressive Transformers pre-trained model and two Transformer models).Then,to evaluate how well MDH performs,quality evaluation metrics based on two prevalent automatic metrics and our innovative human metric are presented.The comprehensive scores of the three summaries generating tasks on testing datasets are 70.87,93.50,and 92.69,respectively.The results suggest that MDH can generate space weather summaries with high accuracy and coherence,as well as suitable length,which can assist forecasters in generating high-quality space weather forecast products,despite the data being starved.

Sim-to-Real: A Performance Comparison of PPO, TD3, and SAC Reinforcement Learning Algorithms for Quadruped Walking Gait Generation

The performance of the state-of-the-art Deep Reinforcement algorithms such as Proximal Policy Optimization, Twin Delayed Deep Deterministic Policy Gradient, and Soft Actor-Critic for generating a quadruped walking gait in a virtual environment was presented in previous research work titled “A Comparison of PPO, TD3, and SAC Reinforcement Algorithms for Quadruped Walking Gait Generation”. We demonstrated that the Soft Actor-Critic Reinforcement algorithm had the best performance generating the walking gait for a quadruped in certain instances of sensor configurations in the virtual environment. In this work, we present the performance analysis of the state-of-the-art Deep Reinforcement algorithms above for quadruped walking gait generation in a physical environment. The performance is determined in the physical environment by transfer learning augmented by real-time reinforcement learning for gait generation on a physical quadruped. The performance is analyzed on a quadruped equipped with a range of sensors such as position tracking using a stereo camera, contact sensing of each of the robot legs through force resistive sensors, and proprioceptive information of the robot body and legs using nine inertial measurement units. The performance comparison is presented using the metrics associated with the walking gait: average forward velocity (m/s), average forward velocity variance, average lateral velocity (m/s), average lateral velocity variance, and quaternion root mean square deviation. The strengths and weaknesses of each algorithm for the given task on the physical quadruped are discussed.

Design space exploration of neural network accelerator based on transfer learning

With the increasing demand of computational power in artificial intelligence(AI)algorithms,dedicated accelerators have become a necessity.However,the complexity of hardware architectures,vast design search space,and complex tasks of accelerators have posed significant challenges.Tra-ditional search methods can become prohibitively slow if the search space continues to be expanded.A design space exploration(DSE)method is proposed based on transfer learning,which reduces the time for repeated training and uses multi-task models for different tasks on the same processor.The proposed method accurately predicts the latency and energy consumption associated with neural net-work accelerator design parameters,enabling faster identification of optimal outcomes compared with traditional methods.And compared with other DSE methods by using multilayer perceptron(MLP),the required training time is shorter.Comparative experiments with other methods demonstrate that the proposed method improves the efficiency of DSE without compromising the accuracy of the re-sults.

Reinforcement learning method for machining deformation control based on meta-invariant feature space

Precise control of machining deformation is crucial for improving the manufacturing quality of structural aerospace components.In the machining process,different batches of blanks have different residual stress distributions,which pose a significant challenge to machining deformation control.In this study,a reinforcement learning method for machining deformation control based on a meta-invariant feature space was developed.The proposed method uses a reinforcement-learning model to dynamically control the machining process by monitoring the deformation force.Moreover,combined with a meta-invariant feature space,the proposed method learns the internal relationship of the deformation control approaches under different stress distributions to achieve the machining deformation control of different batches of blanks.Finally,the experimental results show that the proposed method achieves better deformation control than the two existing benchmarking methods.

Development of New Capabilities Using Machine Learning for Space Weather Prediction

With the development of space exploration and space environment measurements,the numerous observations of solar,solar wind,and near Earth space environment have been obtained in last 20 years.The accumulation of multiple data makes it possible to better use machine learning technique,which has achieved unforeseen results in industrial applications in last decades,for developing new approaches and models in space weather investigation and prediction.In this paper,the efforts on the forecasting methods for space weather indices,events,and parameters using machine learning are briefly introduced based on the study works in recent years.These investigations indicate that machine learning,especially deep learning technique can be used in automatic characteristic identification,solar eruption prediction,space weather forecasting for solar and geomagnetic indices,and modeling of space environment parameters.

Day-ahead scheduling based on reinforcement learning with hybrid action space

Driven by the improvement of the smart grid,the active distribution network(ADN)has attracted much attention due to its characteristic of active management.By making full use of electricity price signals for optimal scheduling,the total cost of the ADN can be reduced.However,the optimal dayahead scheduling problem is challenging since the future electricity price is unknown.Moreover,in ADN,some schedulable variables are continuous while some schedulable variables are discrete,which increases the difficulty of determining the optimal scheduling scheme.In this paper,the day-ahead scheduling problem of the ADN is formulated as a Markov decision process(MDP)with continuous-discrete hybrid action space.Then,an algorithm based on multi-agent hybrid reinforcement learning(HRL)is proposed to obtain the optimal scheduling scheme.The proposed algorithm adopts the structure of centralized training and decentralized execution,and different methods are applied to determine the selection policy of continuous scheduling variables and discrete scheduling variables.The simulation experiment results demonstrate the effectiveness of the algorithm.

Research on Learning Space Management Transformation

Learning space management transformation is an inevitable guidance for learners’increasingly abundant learning needs and technological innovation.Learning space management should be transformed for students,teachers,and schools to form a new pattern that centers on learners,which is led by professional teachers,and breaks the inherent shape of schools.The development of learning space management transformation needs top level design from top to bottom and basic level exploration from bottom to top,meantime combining the overall construction with key breakthroughs.The learning space sharing mechanism proposed in this research will provide references for the learning space management transformation.

Explanatory Multi-Scale Adversarial Semantic Embedding Space Learning for Zero-Shot Recognition

The goal of zero-shot recognition is to classify classes it has never seen before, which needs to build a bridge between seen and unseen classes through semantic embedding space. Therefore, semantic embedding space learning plays an important role in zero-shot recognition. Among existing works, semantic embedding space is mainly taken by user-defined attribute vectors. However, the discriminative information included in the user-defined attribute vector is limited. In this paper, we propose to learn an extra latent attribute space automatically to produce a more generalized and discriminative semantic embedded space. To prevent the bias problem, both user-defined attribute vector and latent attribute space are optimized by adversarial learning with auto-encoders. We also propose to reconstruct semantic patterns produced by explanatory graphs, which can make semantic embedding space more sensitive to usefully semantic information and less sensitive to useless information. The proposed method is evaluated on the AwA2 and CUB dataset. These results show that our proposed method achieves superior performance.

Lotus LearningSpace在高等特殊教育教学领域的应用

Lotus LearningSpace是Lotus在知识管理策略基础上推出的最新教学解决方案,旨在提供一个可协作、可按计划、辅助指导、分布式的虚拟学习环境。Lotus LearningSpace简体中文版,为国内有关高等特殊教育的研究机构提供了一个很好的参考。斯塔福德郡大学的实验表明,相关软件开发必须要了解各类残疾人群的特点,考虑残疾学生的主要缺陷,开发专门适合残疾学生的、适用领域广泛的虚拟学习系统。

应用Learning Space4实现生理学的远程教育

实现远程教育的关键是有机地组织各类教育资源和高效率的双向通信。 L earning Space4是可以有效地解决高效率有机组织教育资源、跟踪、评估学生的学习状况、非实时和实时教学等关键问题的一个优秀的网络远程教学和管理平台系统。介绍应用 L earning Space4创建远程教育教程的基本方法 ,并以《生理学》第四版为例介绍应用 L

Combination of effective color information and machine learning for rapid prediction of soil water content

Soil water content(SWC)is one of the critical indicators in various fields such as geotechnical engineering and agriculture.To avoid the time-consuming,destructive,and laborious drawbacks of conventional SWC measurements,the image-based SWC prediction is considered based on recent advances in quantitative soil color analysis.In this study,a promising method based on the Gaussian-fitting gray histogram is proposed for extracting characteristic parameters by analyzing soil images,aiming to alleviate the interference of complex surface conditions with color information extraction.In addition,an identity matrix consisting of 32 characteristic parameters from eight color spaces is constituted to describe the multi-dimensional information of the soil images.Meanwhile,a subset of 10 parameters is identified through three variable analytical methods.Then,four machine learning models for SWC prediction based on partial least squares regression(PLSR),random forest(RF),support vector machines regression(SVMR),and Gaussian process regression(GPR),are established using 32 and 10 characteristic parameters,and their performance is compared.The results show that the characteristic parameters obtained by Gaussian-fitting can effectively reduce the interference from soil surface conditions.The RGB,CIEXYZ,and CIELCH color spaces and lightness parameters,as the inputs,are more suitable for the SWC prediction models.Furthermore,it is found that 10 parameters could also serve as optimal and generalizable predictors without considerably reducing prediction accuracy,and the GPR model has the best prediction performance(R^(2)≥0.95,RMSE≤2.01%,RPD≥4.95,and RPIQ≥6.37).The proposed image-based SWC predictive models combined with effective color information and machine learning can achieve a transient and highly precise SWC prediction,providing valuable insights for mapping soil moisture fields.

IAACS: Image aesthetic assessment through color composition and space formation

Background Determining how an image is visually appealing is a complicated and subjective task. This motivates the use of a machine-learning model to evaluate image aesthetics automatically by matching the aesthetics of the general public. Although deep learning methods have successfully learned good visual features from images,correctly assessing the aesthetic quality of an image remains a challenge for deep learning. Methods To address this, we propose a novel multiview convolutional neural network to assess image aesthetics assessment through color composition and space formation(IAACS). Specifically, from different views of an image––including its key color components and their contributions, the image space formation, and the image itself––our network extracts the corresponding features through our proposed feature extraction module(FET) and the Image Net weight-based classification model. Result By fusing the extracted features, our network produces an accurate prediction score distribution for image aesthetics. The experimental results show that we have achieved superior performance.

L2 Teaching and Learning in the Classroom Space

L2 teaching and learning is a way of using language,but it happens in a particular space—the classroom space,which,to some extent,has a restriction to language using.This paper provides a valuable sight into L2 teaching and learning in the classroom space,and discusses the viewpoint of how to make an actual learning of L2 under the way of teaching.

基于Cite Space的我国护理专业案例教学研究可视化分析

目的探讨我国护理专业案例教学研究现状与发展趋势,为护理专业开展案例教学提供借鉴与指导。方法以万方和中国知网为数据源,运用Cite Space软件可视化分析1993年1月—2022年3月护理专业案例教学研究现状及其趋势。结果共提取到2878篇有效文献,发文量处于快速上升阶段,《卫生职业教育》载文量最多(215篇),尚未形成核心期刊,基金支持占10.67%,形成了以魏志名、吴伟、伊晓峰和郭大英为代表的高产作者群体,核心作者群和机构群尚未形成,研究热点主要集中在教学方法、问题式学习(PBL)、护理带教、教学效果、应用效果、情景模拟、临床带教和教学改革,高频关键词形成了11个聚类,研究前沿主要集中在教学方法、PBL、护理带教、教学效果、应用效果、情景模拟、临床带教和教学改革。结论近年来护理专业案例教学受到了护理教育者的重视,核心作者和核心机构群尚未形成,研究质量有待进一步提升。

Word Embeddings and Semantic Spaces in Natural Language Processing

One of the critical hurdles, and breakthroughs, in the field of Natural Language Processing (NLP) in the last two decades has been the development of techniques for text representation that solves the so-called curse of dimensionality, a problem which plagues NLP in general given that the feature set for learning starts as a function of the size of the language in question, upwards of hundreds of thousands of terms typically. As such, much of the research and development in NLP in the last two decades has been in finding and optimizing solutions to this problem, to feature selection in NLP effectively. This paper looks at the development of these various techniques, leveraging a variety of statistical methods which rest on linguistic theories that were advanced in the middle of the last century, namely the distributional hypothesis which suggests that words that are found in similar contexts generally have similar meanings. In this survey paper we look at the development of some of the most popular of these techniques from a mathematical as well as data structure perspective, from Latent Semantic Analysis to Vector Space Models to their more modern variants which are typically referred to as word embeddings. In this review of algoriths such as Word2Vec, GloVe, ELMo and BERT, we explore the idea of semantic spaces more generally beyond applicability to NLP.

Prescribed performance neural control to guarantee tracking quality for near space kinetic kill vehicle

A prescribed performance neural controller to guarantee tracking quality is addressed for the near space kinetic kill vehicle (NSKKV) to meet the state constraints caused by side window detection. Different from the traditional prescribed performance control in which the shape of the performance function is constant, this paper exploits new performance functions which can change the shape of their function according to different symbols of initial errors and can ensure the error convergence with a small overshoot. The neural backstepping control and the minimal learning parameters (MLP) technology are employed for exploring a prescribed performance controller (PPC) that provides robust tracking attitude reference trajectories. The highlight is that the transient performance of tracking errors is satisfactory and the computational load of neural approximation is low. The pseudo rate (PSR) modulator is used to shape the continuous control command to pulse or on-off signals to meet the requirements of the thruster. Numerical simulations show that the proposed method can achieve state constraints, pseudo-linear operation and high accuracy.

基于Transformer的街道停车位数据补全和预测

随着城市汽车数量的持续增长,街道停车难已经成为一个热点问题。解决街道停车问题的关键在于准确预测街道未来的停车位信息。移动群智感知方式(CrowdSensing)通过在车辆上安装声呐以感知路边的停车位情况,是一种低成本、高效益的感知停车位的方式,然而这种方式感知的停车位数据在时间上存在高稀疏性问题,传统模型无法直接用于预测。针对此问题,提出了一种基于Transformer的停车位序列补全和预测网络,此网络通过编码器生成缺失停车位序列的记忆,进而解码器以自回归的方式补全停车位序列中缺失的部分,同时预测出未来的停车位信息。实验结果表明,所提方法在两个高缺失的街道停车位数据集上的补全和预测效果都优于传统的机器学习和深度学习方法。