Learned Distributed Query Optimizer:Architecture and Challenges

The query processing in distributed database management systems(DBMS)faces more challenges,such as more operators,and more factors in cost models and meta-data,than that in a single-node DMBS,in which query optimization is already an NP-hard problem.Learned query optimizers(mainly in the single-node DBMS)receive attention due to its capability to capture data distributions and flexible ways to avoid hard-craft rules in refinement and adaptation to new hardware.In this paper,we focus on extensions of learned query optimizers to distributed DBMSs.Specifically,we propose one possible but general architecture of the learned query optimizer in the distributed context and highlight differences from the learned optimizer in the single-node ones.In addition,we discuss the challenges and possible solutions.

RSscore:Reaction superiority learned from reaction mapping hypergraph

The selection of chemical reactions is directly related to the quality of synthesis pathways,so a reasonable reaction evaluation metric plays a crucial role in the design and planning of synthesis pathways.Since reaction conditions also need to be considered in synthesis pathway design,a reaction metric that combines reaction time,temperature,and yield is required for chemical reactions of different reaction agents.In this study,a chemical reaction graph descriptor which includes the atom-atom mapping relationship is proposed to effectively describe reactions.Then,through pre-training using graph contrastive learning and fine-tuning through supervised learning,we establish a model for generating the probability of reaction superiority(RSscore).Finally,to validate the effectiveness of the current evaluation index,RSscore is applied in two applications,namely reaction evaluation and synthesis routes analysis,which proves that the RSscore provides an important agents-considered evaluation criterion for computer-aided synthesis planning(CASP).

Exercise and hippocampal neurogenesis: a dogma re-examined and lessons learned

Exercise is a potent force of nature with significant potential for extending longevity and boosting physical fitness. It is also be- ing increasingly used as a prophylactic and curative measure for various physical ailments, such as cardiovascular diseases and diabetes.

Delivering forestry courses online:experiences,lessons learned,and future of forestry online education in the Asia Pacifi c

Innovation in forestry education is needed to address changing contexts of the positionality of forests.This is particularly signifi cant in the Asia–Pacifi c region,where deforestation and degradation are high.However,the accessibility of high-quality forestry education to address changing regional and global contexts is lacking.A series of innovative sustainable forest management(SFM)open education resource(OER)courses were developed and implemented to improve the accessibility of SFM education to enhance teaching quality,curriculum,and research capacity of universities in the Asia-Pacifi c Region.To evaluate the SFM-OER program in terms of student experiences,this study investigated student achievement,perceived success of the pedagogical approach and instructional design,and perceived eff ectiveness of the learning activities in promoting active and transformative learning through the assessment of a 1,191-course feedback survey between 2018 and 2020,including the global pandemic.This study revealed that the program attracted diverse student demographics,including a higher proportion of female students majoring in forestry,ecology,and other environmental studies.Their primary motivation to participate in the courses was to gain international experience,followed by the fl exibility of online learning,mandatory course requirements,and earning course credits.Students were satisfi ed with the Canvas learning management system.Most students spent less than 5 to 10 h of their weekly time in the course and agreed or strongly agreed that the workloads were manageable.Students refl ected positively on various learning activities and assignments,such as watching lecture videos,taking quizzes,reading and summarizing,having discussions,and peer review writing.However,they did not clearly prefer specifi c learning activities,signifying the importance of using diverse learning activities to satisfy diverse individual learning styles in online settings.This analysis contributes to the further development of student-centered pedagogical development for online learning and provides insight into the ways forward for online higher forestry education,while repurposing existing OER courses in a post-Covid-19 era.

Multimodal spontaneous affect recognition using neural networks learned with hints

A multimodal fusion classifier is presented based on neural networks （NNs） learned with hints for automatic spontaneous affect recognition. In case that different channels can provide com- plementary information, features are utilized from four behavioral cues： frontal-view facial expres- sion, profile-view facial expression, shoulder movement, and vocalization （audio）. NNs are used in both single cue processing and multimodal fusion. Coarse categories and quadrants in the activation- evaluation dimensional space are utilized respectively as the heuristic information （hints） of NNs during training, aiming at recognition of basic emotions. With the aid of hints, the weights in NNs could learn optimal feature groupings and the subtlety and complexity of spontaneous affective states could be better modeled. The proposed method requires low computation effort and reaches high recognition accuracy, even if the training data is insufficient. Experiment results on the Semaine nat- uralistic dataset demonstrate that our method is effective and promising.

Learned Index和B-Tree在不同分布数据上的性能对比及优化

Learned Index是一种通过训练模型来建立输入数据和存储位置之间映射关系的索引,它能学习到数据间分布的信息,而不同的数据分布将影响模型训练准确率和模型复杂度之间的平衡。为了探索Learned Index适用的场景,使用不同分布、不同数据量的数据对它和加以优化的可更新的自适应学习索引(ALEX)进行性能测试,并与B-Tree进行对比,最终发现Learned Index构建大批量数据的索引时间比B-Tree短,读操作性能、存储空间大小有明显的优势,但写操作性能较差,因此得出Learned Index更适用于大数据情景下的在线分析处理(OLAP)数据库,用于静态数据的存储和查询操作的结论。基于B-Tree的索引结构,对初版Learned Index的结构进行了优化和调整,最终使优化后Learned Index在大批量数据的读写操作性能上有明显提高,其中读操作最高达到原版Learned Index的2倍,写操作最高达到原版的3倍。

Responding to fire in an intensive care unit: Management and lessons learned

Healthcare institutions are vulnerable to disruptionfrom events such as earthquakes, fires, and floods, andthe damage incurred can endanger the lives of patientsin the hospital.[1] In this type of scenario hospital staffhave primary responsibility for the hospitalized patients＇safety, since patients are neither fit to respond to sucha disaster, nor do they know how to respond.[2,3] Thesituation becomes more difficult and challenging if thedisaster occurs in critical care areas such as intensivecare units （ICUs） and operating rooms.

Lessons learned from the TIMI trials in rescue interventions for elderly patients after failed fibrinolytic therapy:look beyond the TIMI flow

In ST-segment elevation myocardial infarction (STEMI), acute reperfusion of the infarct-related artery (IRA)is the main goal in the early minutes after the patient seeks medical attention. Fibrinolytic therapy (FT) and/or primary coronary intervention (PCI) were proven to be effective in opening the IRA.

The COVID-19 Pandemic and Higher Education: Common Interdisciplinary Issues and Lessons Learned

Background: Coronavirus-19 (COVID-19) dramatically impacted institutions of higher education. The effect was acute in the practice disciplines such as medicine, medical laboratory science, and nursing. The purpose is to describe how an interdisciplinary team, led by nursing faculty, adapted to the changes driven by COVID-19 and the lessons that were learned for nursing and other disciplines in higher education. Method: The interdisciplinary group created a comprehensive list which captured the impact of COVID-19 on their academic disciplines. Similarities and differences between the disciplines regarding faculty experiences, teaching, and responding to student concerns were discovered. Results: Collective review resulted in the identification of four inclusive thematic categories and several sub-categories. These were: academic considerations (didactic, lab, clinical), perceptions (faculty, student), ethical considerations, and social determinants affecting the learning environment. Lessons Learned: This project utilized an innovative interdisciplinary approach to identify common COVID-19 effects on higher education. Conclusions: Nursing and other health-related disciplines should pursue interdisciplinary collaboration to address common academic issues that arise during the COVID.

The Genetic Epidemiology of Lung Cancer-What have we learned?

With the completion of the Human Genome Project new opportunities have been arisen to more fully characterize the genomic factor contributing to human susceptibility to chemical and pharmacological toxicity. Over 6 million single nucleotide polymorphisms（SNPs） have been identified and cataloged in public databases. Research efforts are now underway to identify which SNPs are associated with variation in disease risk, chemical sensitivity, drug toxicity, as well as drug responsiveness.

Best Practices and Lessons Learned in the Implementation of Global Framework for Climate Services—Adaptation Programme in Africa: Tanzania-Experience

This paper provides an analysis and a description of the best practices and lessons learned in the implementation of the Global Framework for Climate Services Adaptation Program in Africa (GFCS-APA) focusing on Tanzania country’s activities. GFCS-APA was the first multi-agency initiative implemented under the Global Framework for Climate Services (GFCS) in two African countries, namely Tanzania and Malawi with funding from the Royal Government of Norway. In Tanzania, the programme was implemented in two phases from the year 2014 to 2021 in the three pilot districts of Kondoa, Longido and Kiteto located in Dodoma, Arusha and Manyara regions, respectively. The overarching goal of the programme was to enable better management of the risks caused by climate variability and change at all levels, from end-users to policy level, through development and incorporation of science-based climate information and prediction services into planning, policy and practice. The programme focused on bridging the gap between providers and users of climate information and products through development of user-driven climate services for food security, health and disaster risk reduction. This paper aimed to analyze lessons learned and best practices in the course of the implementation of the GFCS-APA phase I and II in Tanzania. A qualitative approach was employed to analyze the lessons learned and best practices, by extracting them and exploring further on their contribution to the enhancement of climate services, as well as their applicability and potentiality for scaling out in other regions within Tanzania, and in other countries. The results indicate the identified best practices and lessons learned contributed significantly in enhancing climate services, particularly in understanding, availability, accessibility, utilization, ownership and sustainability of climate services among users (farmers and pastoralists) of various gender, as well as intermediaries and decision-makers. Results also indicate the lessons learned and the documented best practices could influence the effectiveness of climate services in other areas, to address existing challenges in access, uptake and sustainability of climate services. The best practices and lessons learned could be considered for integration in the future projects or operational activities in other regions within the country and other countries, particularly in the developing world, including Sub-Saharan Africa.

Lessons learned from the hospital to home community care program in Singapore and the supporting AI multiple readmissions prediction model

In a prior practice and policy article published in Healthcare Science,we introduced the deployed application of an artificial intelligence(AI)model to predict longer‐term inpatient readmissions to guide community care interventions for patients with complex conditions in the context of Singapore's Hospital to Home(H2H)program that has been operating since 2017.In this follow on practice and policy article,we further elaborate on Singapore's H2H program and care model,and its supporting AI model for multiple readmission prediction,in the following ways:(1)by providing updates on the AI and supporting information systems,(2)by reporting on customer engagement and related service delivery outcomes including staff‐related time savings and patient benefits in terms of bed days saved,(3)by sharing lessons learned with respect to(i)analytics challenges encountered due to the high degree of heterogeneity and resulting variability of the data set associated with the population of program participants,(ii)balancing competing needs for simpler and stable predictive models versus continuing to further enhance models and add yet more predictive variables,and(iii)the complications of continuing to make model changes when the AI part of the system is highly interlinked with supporting clinical information systems,(4)by highlighting how this H2H effort supported broader Covid‐19 response efforts across Singapore's public healthcare system,and finally(5)by commenting on how the experiences and related capabilities acquired from running this H2H program and related community care model and supporting AI prediction model are expected to contribute to the next wave of Singapore's public healthcare efforts from 2023 onwards.For the convenience of the reader,some content that introduces the H2H program and the multiple readmissions AI prediction model that previously appeared in the prior Healthcare Science publication is repeated at the beginning of this article.

A Life Lesson That I Learned

All of us joined together to fight against the disease when COVID-19 hit the whole world in asudden early this jear.It was a big challenge to everyone,andno one held back.Many people dovoted themselves to fighting against it.The government advised us staying at home and avoid going.

Ebola Lessons Learned

THE Ebola virus has made another appearance on the African continent. The World Health Organization （WHO） announced on May 11 that they were informed about an outbreak of the vLrus by the Ministry of Health of the Democratic Republic of the Congo （DRC）. At least nine people are suspected of being infected, and four have died as of May 21. The Ebola virus disease is often fatal in humans, transmitted from wild animals and spread via human-to-human transmission. In 2014, the West African countries of Guinea, Sierra Leone and Liberia were at the center of the largest Ebola epidemic in history, resulting in 11.310 deaths, and now they have been Ebola-free since June 2016 according to the WHO.

A Lesson to Be Learned

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Lessons Learned

Five years after the devastating 8.0-magnitude Wenchuan earthquake struck southwest China＇s Sichuan Province, the same fault zone produced another quake on April 20, this time with a preliminary magnitude of 7.0.

Some Things Are Best Learned in a Storm

I think the 20s are the hardest years of adulthood. While it＇s no doubt also tough for the elderly with all the pressures of coping up with the stress of declining years, I think the bur- den in your early 20s comes from you being expected to behave like a grown-up, earn your own living, be successful, yet probably still think and feel like a teenager. The agony is figuring out who I am and what do I want to be？

USTC-Pickers:a Unified Set of seismic phase pickers Transfer learned for China

Current popular deep learning seismic phase pickers like PhaseNet and EQTransformer suffer from performance drop in China.To mitigate this problem,we build a unified set of customized seismic phase pickers for different levels of use in China.We first train a base picker with the recently released DiTing dataset using the same U-Net architecture as PhaseNet.This base picker significantly outperforms the original PhaseNet and is generally suitable for entire China.Then,using different subsets of the DiTing data,we fine-tune the base picker to better adapt to different regions.In total,we provide 5 pickers for major tectonic blocks in China,33 pickers for provincial-level administrative regions,and 2 special pickers for the Capital area and the China Seismic Experimental Site.These pickers show improved performance in respective regions which they are customized for.They can be either directly integrated into national or regional seismic network operation or used as base models for further refinement for specific datasets.We anticipate that this picker set will facilitate earthquake monitoring in China.

Lessons learned from workers' suicide tragedy

A series of suicides recently at the Foxconn companyin Shenzhen have deeply distressed the nation.Given thefact that such suicide cases happened in succession, in ashort time span, profound social ills must lie behind thescene.