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.

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.

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。

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.

Deep Learned Singular Residual Network for Super Resolution Reconstruction

Single image super resolution(SISR)techniques produce images of high resolution(HR)as output from input images of low resolution(LR).Motivated by the effectiveness of deep learning methods,we provide a framework based on deep learning to achieve super resolution(SR)by utilizing deep singular-residual neural network(DSRNN)in training phase.Residuals are obtained from the difference between HR and LR images to generate LR-residual example pairs.Singular value decomposition(SVD)is applied to each LR-residual image pair to decompose into subbands of low and high frequency components.Later,DSRNN is trained on these subbands through input and output channels by optimizing the weights and biases of the network.With fewer layers in DSRNN,the influence of exploding gradients is reduced.This speeds up the learning process and also improves accuracy by using skip connections.The trained DSRNN parameters yield residuals to recover the HR subbands in the testing phase.Experimental analysis shows that the proposed method results in superior performance to existingmethods in terms of subjective quality.Extensive testing results on popular benchmark datasets such as set5,set14,and urban100 for a scaling factor of 4 show the effectiveness of the proposed method across different qualitative evaluation metrics.

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.

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.

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.

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？

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.

Multiagent reinforcement learning through merging individually learned value functions

In cooperative multiagent systems, to learn the optimal policies of multiagents is very difficult. As the numbers of states and actions increase exponentially with the number of agents, their action policies become more intractable. By learning these value functions, an agent can learn its optimal action policies for a task. If a task can be decomposed into several subtasks and the agents have learned the optimal value functions for each subtask, this knowledge can be helpful for the agents in learning the optimal action policies for the whole task when they are acting simultaneously. When merging the agents’ independently learned optimal value functions, a novel multiagent online reinforcement learning algorithm LU-Q is proposed. By applying a transformation to the individually learned value functions, the constraints on the optimal value functions of each subtask are loosened. In each learning iteration process in algorithm LU-Q, the agents’ joint action set in a state is processed. Some actions of that state are pruned from the available action set according to the defined multiagent value function in LU-Q. As the items of the available action set of each state are reduced gradually in the iteration process of LU-Q, the convergence of the value functions is accelerated. LU-Q’s effectiveness, soundness and convergence are analyzed, and the experimental results show that the learning performance of LU-Q is better than the performance of standard Q learning.