Preface

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Double electron-attachment equation-of-motion coupled-cluster methods with up to 4-particle–2-hole excitations: improved implementation and application to singlet–triplet gaps in <i>ortho-</i>, <i>meta-</i>, and <i>para-</i>benzyne isomers

Efficient new codes enabling double electron-attachment equation-of-motion coupled-cluster (DEA-EOMCC) calculations with up to 4-particle–2-hole (4p-2h) excitations, treated with active orbitals, have been developed. They can be applied to medium size diradicals using larger basis sets and modest computational resources. The new codes have been used to determine the singlet–triplet (S–T) gaps characterising the ortho-, meta-, and para-benzyne isomers using the correlation consistent cc-pVxZ basis sets with x=D, T, and Q and complete-basis-set (CBS) extrapolations. Our best estimates of the S–T gaps in the ortho-, meta-, and para-benzynes obtained in the DEA-EOMCC calculations with an active-space treatment of 4p-2h terms, extrapolated to the CBS limit and corrected for vibrations, are 37.4, 20.7, and 4.5 kcal/mol, respectively, in excellent agreement with experiment.</p

Children’s E-Learning Interactions and Perceived Outcomes with Educational Key Opinion Leaders in China

Classroom teaching has been undergoing a digital transformation in the last decade and is now being amplified by Educational Key Opinion Leaders (Edu-KOLs). This research aims to investigate the relationship between learners’ perceived outcomes, motivation, and the selection preferences of Edu-KOLs. This paper presents insights gained from a two-phase study. We conducted research in the first phase through an online questionnaire completed by 186 parents in China whose children are studying or have recently studied online. In the second phase, we interviewed parents to deep dive into their thinking process behind their choices of Edu-KOLs. By utilizing the PLS-SEM method, this research has proposed and verified six hypotheses asserting that e-learning platforms, student engagement scores, and perceived outcomes strongly correlate with the perception of Edu-KOLs. However, parents’ educational level or occupation has less impact on the choices of Edu-KOLs. There are also positive relationships among Edu-KOLs, customer advocacy, and future purchase intention

Novel E-Learning Experience and Perceptions with Impacts from Educational Key Opinion Leaders

In recent years, an increasing number of school-age children and adult learners are flocking to e-learning platforms or mobile Apps for personal or professional development. This research compared two studies that were built upon the constructivism model to investigate the parents, whose children are studying or have recently studied online, and also the adult learners' satisfaction, perceived learning outcomes, and recommendations towards Educational Key Opinion Leaders (Edu-KOLs). A two-phase study was designed specifically for both studies. We adopted the quantitative research approach using partial least squares structural equation modelling (PLS-SEM) to interpret the collected data. The findings revealed that for both learning cohorts, Edu-KOLs' knowledge level and course content has a significant influence on learners' perceived learning outcomes and customer advocacy and that higher engagement and interaction levels are favourably associated with their perception of Edu-KOLs. However, the e-learning platform played a positive role in selecting Edu-KOLs for parents but was not significant for adult learners. Perceived outcomes are critical for adult learners, whereas parents are satisfied as long as children are engaged, regardless of what they have learned

Logit Calibration for Non-IID and Long-Tailed Data in Federated Learning

Federated learning (FL) strives to enable collaborative training of deep models on the distributed clients of different data without centrally aggregating raw data and hence improving data privacy. Nevertheless, a central challenge in training classification models in the federated system is learning with non-IID data. Most of the existing work is dedicated to eliminating the heterogeneous influence of non-IID data in a federated system. However, in many real-world FL applications, the co-occurrence of data heterogeneity and long-tailed distribution is unavoidable. The universal class distribution is long-tailed, causing them to become easily biased towards head classes, which severely harms the global model performance. In this work, we also discovered an intriguing fact that the classifier logit vector (i.e., pre-softmax output) introduces a heterogeneity drift during the learning process of local training and global optimization, which harms the convergence as well as model performance. Therefore, motivated by the above finding, we propose a novel logit calibration FL method to solve the joint problem of non-IID and long-tailed data in federated learning, called Federated Learning with Logit Calibration (FedLC). First, we presented a method to mitigate the local update drift by calculating the Wasserstein distance among adjacent client logits and then aggregating similar clients to regulate local training. Second, based on the model ensemble, a new distillation method with logit calibration and class weighting was proposed by exploiting the diversity of local models trained on heterogeneous data, which effectively alleviates the global drift problem under long-tailed distribution. Finally, we evaluated FedLC using a highly non-IID and long-tailed experimental setting, comprehensive experiments on several benchmark datasets demonstrated that FedLC achieved superior performance compared with state-of-the-art FL methods, which fully illustrated the effectiveness of the logit calibration strategy

Addressing strong correlation by approximate coupled-pair methods with active-space and full treatments of three-body clusters

When the number of strongly correlated electrons becomes larger, the single-reference coupled-cluster (CC) CCSD, CCSDT, etc. hierarchy displays an erratic behaviour, while traditional multi-reference approaches may no longer be applicable due to enormous dimensionalities of the underlying model spaces. These difficulties can be alleviated by the approximate coupled-pair (ACP) theories, in which selected (T2)2 diagrams in the CCSD amplitude equations are removed, but there is no generally accepted and robust way of incorporating connected triply excited (T3) clusters within the ACP framework. It is also not clear if the specific combinations of (T2)2 diagrams that work well for strongly correlated minimum-basis-set model systems are optimum when larger basis sets are employed. This study explores these topics by considering a few novel ACP schemes with the active-space and full treatments of T3 correlations and schemes that scale selected (T2)2 diagrams by factors depending on the numbers of occupied and unoccupied orbitals. The performance of the proposed ACP approaches is illustrated by examining the symmetric dissociations of the H6 and H10 rings using basis sets of the triple- and double-ζ quality and the H50 linear chain treated with a minimum basis, for which the conventional CCSD and CCSDT methods fail.</p

Distributional Knowledge Transfer for Heterogeneous Federated Learning

Federated learning (FL) produces an effective global model by aggregating multiple client weights trained on their private data. However, it is common that the data are not independently and identically distributed (non-IID) across different clients, which greatly degrades the performance of the global model. We observe that existing FL approaches mostly ignore the distribution information of client-side private data. Actually, the distribution information is a kind of structured knowledge about the data itself, and it also represents the mutual clustering relations of data examples. In this work, we propose a novel approach, namely Federated Distribution Knowledge Transfer (FedDKT), that alleviates heterogeneous FL by extracting and transferring the distribution knowledge from diverse data. Specifically, the server learns a lightweight generator to generate data and broadcasts it to the sampled clients, FedDKT decouples the feature representations of the generated data and transfers the distribution knowledge to assist model training. In other words, we exploit the similarity and shared parts of the generated data and local private data to improve the generalization ability of the FL global model and promote representation learning. Further, we also propose the similarity measure and attention measure strategies, which implement FedDKT by capturing the correlations and key dependencies among data examples, respectively. The comprehensive experiments demonstrate that FedDKT significantly improves the performance and convergence rate of the FL global model, especially when the data are extremely non-IID. In addition, FedDKT is also effective when the data are identically distributed, which fully illustrates the generalization and effectiveness of the distribution knowledge

A Document-Level Relation Extraction Framework with Dynamic Pruning

Relation extraction (RE) has been a fundamental task in natural language processing (NLP) as it identifies semantic relations among entity pairs in texts. Because sentence-level RE can only capture intra-connections within a sentence rather than inter-connections between or among sentences, researchers shift their attentions to document-level RE to obtain richer and complex relations which may involve logic inference. Prior works on document-level RE suffer from inflexible pruning rules and lack of sentence-level features, which lead to the missing of valuable information. In this paper, we propose a document-level relation extraction framework with both dynamic pruning mechanism and sentence-level attention. Specifically, a weight-based flexible pruning mechanism is applied on the document-level dependency tree to remove non-relational edges dynamically and obtain the weight dependency tree (WDT). Moreover, a graph convolution network (GCN) then is employed to learn syntactic representations of the WDT. Furthermore, the sentence-level attention and gating selection module are applied to capture the intrinsic interactions between sentence-level and document-level features. We evaluate our framework on three benchmark datasets: DocRED, CDR, and GDA. Experiment results demonstrate that our approach outperforms the baselines and achieves the state-of-the-art performance

Movement Patterns of Pedestrians and Cyclists at Signalized Segregated Crosswalks: A Case Study in Nanjing, China

Interactions and conflicts between vulnerable road users, mainly pedestrians and cyclists, are frequently observed on crosswalks, especially in urban areas with relatively high traffic volume. To alleviate the potential safety risks, one possible measure is to adopt the segregated crosswalk to provide separate crossing space for pedestrians and cyclists. However, the crossing behaviours of pedestrians and cyclists at segregated crosswalks have rarely been investigated. To fill the gap, this study aims to investigate the movement patterns of pedestrians and cyclists at signalized segregated crosswalks. Field observations and data collection are conducted at a typical intersection with segregated crosswalks in Nanjing CBD, China. In total, trajectory data of 659 pedestrians and 1,212 (e-)cyclists is collected and analysed. The route choice and crossing speed of pedestrians and cyclists are explored, and multiple influencing factors are analysed. The research findings indicate that road users, especially cyclists, in the minor direction violate the segregation rule more frequently compared to road users in the major direction. It is recommended to adopt the segregated crosswalks mainly for unidirectional pedestrian and cyclist flow

Towards an integrated framework for developing blockchain systems

Although the expanding applications of blockchain technologies have been widely explored in the IS literature, a noticeable gap exists in understanding information systems development methods (ISDMs) that facilitate the implementation of systems leveraging these technologies. A conceptual foundation that cohesively organizes an ISDM along with its facets associated with the development lifecycle for this class of systems is lacking. Applying a Design Science Research approach and borrowing ideas from method engineering, we describe a comprehensive framework for the development of blockchain systems. A series of qualitative in-depth applicability checks with domain experts and case studies lend credence to the core framework fragments. The evaluation results demonstrate the utility of the proposed framework as a conceptual anchor to simplify the understanding of the complex nature of developing blockchain systems and lead researchers to suggest future research agendas in their quests using our framework. The framework can aid practitioners in comparing or designing new ISDMs to satisfy the requirements of blockchain development