Miracle-dz/UniFAR-FTUs huggingface dataset:
SENSOR: Data-driven Construction of Sketch-based Visual Query Interfaces for Time Series Data

Sketching is a common approach to visually query time series data. However, a recent study reported that sketching a pattern for querying is "often ineffective on its own" in practice due to lack of "representative objects" to facilitate bottom-up search. In this demonstration, we present a novel data-driven sketch-based visual query interface (VQI) construction system called SENSOR to alleviate this challenge. Given a time series dataset, SENSOR automatically constructs its VQI by populating different components from the underlying data. Specifically, it discovers and exposes a set of representative objects in the form of VST-aware shapelets to facilitate query formulation. Such data-driven construction has several potential benefits such as empowering efficient top-down and bottom-up search and portability of the interface across different application domains and sources.
OceanBase: A 707 Million tpmC Distributed Relational Database System

We have designed and developed OceanBase, a distributed relational database system from the very basics for a decade. Being a scale-out multi-tenant system, OceanBase is cross-region fault tolerant, which is based on the shared-nothing architecture. Besides sharing many similar goals with alternative distributed DBMS, such as horizontal scalability, fault-tolerance, etc., our design has been driven by the demands of typical RDBMS compatibility as well as both on-premise and off-premise deployments. OceanBase has fulfilled its design goal. It implements the salient features of certain mainstream classical RDBMS, and most applications on them can run on OceanBase, with or without a few minor modifications. Tens of thousands of OceanBase servers have been deployed in Alipay.com as well as many other commercial organizations. It has also successfully passed the TPC-C benchmark test and seized the first place with more than 707 million tpmC. This paper presents the goals, design criteria, infrastructure, and key components of OceanBase including its engines for storage and transaction processing. Further, it details how OceanBase achieves the above leading TPC-C benchmark in a distributed cluster with more than 1,500 servers from 3 zones. It also describes lessons what we have learnt in building OceanBase for more than a decade.
TencentCLS: The Cloud Log Service with High Query Performances

With the trend of cloud computing, the cloud log service is becoming increasingly important, as it plays a critical role in tasks such as root cause analysis, service monitoring and security audition. To meet these needs, we provide Tencent Cloud Log Service (TencentCLS), a one-stop solution for log collection, storage, analysis and dumping. It currently hosts more than a million tenants, of which the largest ones can generate up to PB-level logs per day. The most important challenge that TencentCLS faces is to support both low-latency and resource-efficient queries on such large quantities of log data. To address that challenge, we propose a novel search engine based upon Lucene. The system features a novel procedure for querying logs within a time range, an indexing technique for the time field, as well as optimized query algorithms dedicated to multiple critical and common query types. As a result, the search engine at TencentCLS gains significant performance improvements against Lucene. It achieves 20x performance increase with standard queries, and 10x performance increase with histogram queries in massive log query scenarios. In addition, TencentCLS also supports storing and querying with microsecond-level time precision, as well as the microsecond-level time order preservation capability.
ByteGraph: A High-Performance Distributed Graph Database in ByteDance

Most products at ByteDance, e.g., TikTok, Douyin, and Toutiao, naturally generate massive amounts of graph data. To efficiently store, query and update massive graph data is challenging for the broad range of products at ByteDance with various performance requirements. We categorize graph workloads at ByteDance into three types: online analytical, transaction, and serving processing, where each workload has its own characteristics. Existing graph databases have different performance bottlenecks in handling these workloads and none can efficiently handle the scale of graphs at ByteDance. We developed ByteGraph to process these graph workloads with high throughput, low latency and high scalability. There are several key designs in ByteGraph that make it efficient for processing our workloads, including edge-trees to store adjacency lists for high parallelism and low memory usage, adaptive optimizations on thread pools and indexes, and geographic replications to achieve fault tolerance and availability. ByteGraph has been in production use for several years and its performance has shown to be robust for processing a wide range of graph workloads at ByteDance.
A Bayesian Nonparametric Approach to Multi-Task Learning for Contextual Bandits in Mobile Health

Reinforcement learning algorithms have found utility in a number of digital interventions, such as in mobile health (mHealth), wherein N users are followed and sequentially treated over T timesteps with the aim of optimizing a health outcome. In the precision medicine paradigm, the aim of the intervention (algorithm) is to learn a personalized, optimal treatment policy for each of the N users that optimizes the health outcome of interest. Learning such a policy can be prohibitively slow due to sparse and noisy data for each user; in practice, users can disengage from the intervention before a good policy is learned. To address this problem, we aim to pool information across users to speed learning while preserving individualized treatment. However, pooling data across dissimilar users can lead to disastrous treatment decisions and outcomes. These challenges underscore the need for rigorous, model-based approaches to defining similarity across users before pooling. We model the N-user mHealth setting with a contextual bandit environment and formalize similarity across users using a Dirichlet Process mixture model. We offer a variant of blocked Gibbs sampling to infer clusters among users; we further propose DPMM-Pooling, an integrated intervention algorithm to learn clusters among users and share data within clusters, in order to speed the learning of optimal and individualized treatment policies. We evaluate DPMM-Pooling in simulated mHealth settings, across a range of parameters such as the number of ground-truth clusters, noise in observed outcomes, and the time of pooling. We find and analyze key bias-variance tradeoffs in pooling pertaining to parameters of the environment. We also find that DPMM-Pooling is relatively robust to likely forms of mild and extreme model misspecification. Finally, we outline the implications of our results for the design of pooling-based mHealth interventions in practice.
CDI-E: An Elastic Cloud Service for Data Engineering

We live in the gilded age of data-driven computing. With public clouds offering virtually unlimited amounts of compute and storage, enterprises collecting data about every aspect of their businesses, and advances in analytics and machine learning technologies, data driven decision making is now timely, cost-effective, and therefore, pervasive. Alas, only a handful of power users can wield today's powerful data engineering tools. For one thing, most solutions require knowledge of specific programming interfaces or libraries. Furthermore, running them requires complex configurations and knowledge of the underlying cloud for cost-effectiveness. We decided that a fundamental redesign is in order to democratize data engineering for the masses at cloud scale. The result is Informatica Cloud Data Integration - Elastic (CDI-E). Since the early 1990s, Informatica has been a pioneer and industry leader in building no-code data engineering tools. Non-experts can express complex data engineering tasks using a graphical user interface (GUI). Informatica CDI-E is built to incorporate the simplicity of GUI in the design layer with an elastic and highly scalable run time to handle data in any format without little to no user input using automated optimizations. Users upload their data to the cloud in any format and can immediately use them in conjunction with their data management and analytic tools of choice using CDI-E GUI. Implementation began in the Spring of 2017, and Informatica CDI-E has been generally available since the Summer of 2019. Today, CDI-E is used in production by a growing number of small and large enterprises to make sense of data in arbitrary formats. In this paper, we describe the architecture of Informatica CDI-E and its novel no-code data engineering interface. The paper highlights some of the key features of CDI-E: simplicity without loss in productivity and extreme elasticity. It concludes with lessons we learned and an outlook of the future.
In-Page Shadowing and Two-Version Timestamp Ordering for Mobile DBMSs

Increasing the concurrency level in mobile database systems has not received much attention, mainly because the concurrency requirements of mobile workloads has been regarded to be low. Contrary to popular belief, mobile workloads require higher concurrency. In this work, we propose novel journaling and concurrency mechanisms for mobile DBMSs, both of which build upon one common concept - In-Page Shadowing (IPS). We design and implement a novel In-Page Shadowing recovery method for SQLite to resolve the journaling of journal anomaly, which is known to quadruple the I/O traffic in mobile devices. IPS unions the previous and the next versions of a database page in the same physical page. Using the consolidated two versions of database page, we design Two-Version Timestamp-Ordering (2VTO) protocol that enables non-blocking reads as in multi-version concurrency control, but reduces the garbage collection overhead. Designed with mobile environments in mind, IPS and 2VTO are high-performant and resource-efficient transactional solutions. Our performance study shows that IPS and 2VTO outperform state-of-the-art logging methods and an optimistic concurrency control protocol for real mobile workloads.
Molecular dynamics analysis of N-acetyl-D-glucosamine against specific SARS-CoV-2’s 1 pathogenicity factors

Background: The causative agent of virus disease outbreak in the world identified as SARS- 15 CoV-2 leads to a severe respiratory illness similar to that of SARS and MERS. The pathogen 16 outbreak was declared as a pandemic and by the time of this article, it has claimed 2.19 17 million lives. Computational biology research has provided a lot of insight into many of the 18 structural and functional proteins of the virus and the efforts continue. 19 Methods: We performed molecular docking of several SARS-CoV-2 proteins that are 20 responsible for its pathogenicity against N-acetyl-D-glucosamine via AutoDock Vina and 21 further analyzed the docked poses with molecular dynamics, we also performed a similar 22 experiment for both SARS-CoV-2 and anti- Staphylococcus aureus neutralizing antibodies to 23 establish the potential N-acetyl-D-glucosamine hold in inducing the immune system against 24 the virus. 25 Results: Our new molecular docking and molecular dynamic analysis results have confirmed 26 that SARS-CoV-2 spike receptor-binding domain (PDB: 6M0J), crystal structure of RNA- 27 binding domain of nucleocapsid phosphoprotein from SARS-CoV-2 monoclinic crystal form (PDB: strong protein-ligand interaction of N-acetyl-D-glucosamine. These results were confirmed 34 with molecular dynamics studies and the conformational modeling could be used to predict 35 the possible protein-ligand interactions. Additionally, docking analysis against the D614G 36 mutant of the virus have shown that N-acetyl-D-glucosamine affinity was not affected by 37 mutations in the virus’ receptor binding domain. Furthermore, our analysis on the affinity of 38 D-GlcNAc towards human antibodies has shown that it could potentially bind to both SARS- 39 CoV-2 proteins and antibodies hence further induce the immune system against the viral 40 infection. 41 Conclusion: Based on our predictive modelling work, N-acetyl-D-glucosamine holds the 42 potential to inhibit several SARS-CoV-2 proteins as well as induce an immune response 43 against the virus in host system. 44
Robustness scores in fattening pigs estimated from phenotypes measured routinely: definition and genetic parameters

constructive discussion on this project. We also the technicians AXIOM’s testing station at La Garenne for their implication for data collection. ABSTRACT The objective of this study was to define robustness scores based on data collected routinely on farm to allow phenotyping of this trait in fattening pigs. A total of 7 256 pigs, from 2 Piétrain paternal lines (Pie and Pie NN), were controlled at the Axiom boar testing station (Azay-sur-Indre, France), in 2019-2021. During the fattening period (from 75 to 150 days of age), individual performance indicators were recorded (growth, backfat, loin depth, feed intake, feed conversion ratio) together with indicators such as mortality, clinical signs, antibiotic and anti-inflammatory injections. These indicators were combined to estimate a set of three categorical robustness traits: R1, R2 and R3. Genetic parameters were estimated using an animal linear model. The robustness score R2 (selectable or not selectable animal) that combines information from status at testing and mortality presented the most interesting heritability, from 0.12 (±0.03) to 0.13 (±0.02) depending of the line. The score R3 that combines information from the score R2 with antibiotic and anti-inflammatory injections presented slightly lower heritability estimates (0.08 ±0.02 to 0.11±0.03). Genetic correlations between R2 and R3 were high and favorable (0.93 ±0.04 to 0.95 ±0.03). These two robustness scores were also highly and favorably genetically correlated with initial body weight, average daily gain and daily feed intake (ranging from 0.73 ±0.06 to 0.90 ±0.08). Estimates of genetic correlations of R2 and R3 with backfat depth and feed conversion ratio were moderate and unfavorable (0.20 ±0.13 to 0.46±0.20). A part of these genetic correlations, that are of low precision due to the number of data available, have to be confirmed on larger datasets. The results showed the interest of using routine phenotypes collected on farm to build simple robustness indicators that can be applied in breeding.
Original Article Adaptation strategies of coastal ﬁshing communities as species shift poleward

Adaptation strategies of coastal ﬁshing communities as species shift poleward. In this period of environmental change, understanding how resource users respond to such changes is critical for effective resource management and adaptation planning. Extensive work has focused on natural resource responses to environmental changes, but less has examined the response of resource users to such changes. We used an interdisciplinary approach to analyse changes in resource use among commercial trawl ﬁshing communities in the northwest Atlantic, a region that has shown poleward shifts in harvested ﬁsh species. We found substantial community-level changes in ﬁshing patterns since 1996: southern trawl ﬂeets of larger vessels with low catch diversity ﬁshed up to 400 km further north, while trawl ﬂeets of smaller vessels with low catch diversity shrank or disappeared from the data set over time. In contrast, trawl ﬂeets (of both large and small vessels) with higher catch diversity neither changed ﬁshing location dramatically or nor disappeared as often from the data set. This analysis suggests that catch diversity and high mobility may buffer ﬁshing communities from effects of environmental change. Particularly in times of rapid and uncertain change, constructing diverse portfolios and allowing for ﬂeet mobility may represent effective adaptation strategies.
Managing a maxillary premolar with calcified canals- A case report

1,2,6 Pulpal calcifications or calcific metamorphosis can present a great challenge for successfully performing root canal treatment. These calcifications can prevent achieving straight line access and glide path to the apical thirds of the roots. Pulpal calcifications can have variable presentations like pulp stones, calcified canals or other calcified masses in the pulpal tissue. There is a range of causative factors including dental trauma, orthodontic treatment, genetic and developmental abnormalities, age and pulpal necrosis. Different methods can be used to overcome this problem during root canal treatment. This article explains the use of Cone beam CT (CBCT) and ultrasonic instruments to remove the calcified tissue and gain access to the root canal system for effectively performing the endodontic treatment.
A consistent operator splitting algorithm and a two-metric variant: Application to topology optimization

In this work, we explore the use of operator splitting algorithms for solving regularized structural topology optimization problems. The context is the classical structural design problems (e.g., compliance minimization and compliant mechanism design), parameterized by means of density functions, whose ill-posendess is addressed by introducing a Tikhonov regularization term. The proposed forward-backward splitting algorithm treats the constituent terms of the cost functional separately which allows suitable approximations of the structural objective. We will show that one such approximation, inspired by the optimality criteria algorithm and reciprocal expansions, improves the convergence characteristics and leads to an update scheme that resembles the well-known heuristic sensitivity filtering method. We also discuss a two-metric variant of the splitting algorithm that removes the computational overhead associated with bound constraints on the density field without compromising convergence and quality of optimal solutions. We present several numerical results and investigate the influence of various algorithmic parameters.
Strategic Drift and Firm Performance: A Review of Literature

: The dynamic and uncertain business environment globally has resulted in increased competition among organizations. This has been caused by several factors among them innovation and technological changes, globalization, changing demographics, evolving customers’ demands and preferences and supply chain dynamics. Increased competition is affecting firm performance with many organizations failing to meet the planned strategies. It is essential for firms to understand and address strategic drift to achieve superior and sustained performance. The firm performance is likely to be influenced by strategic drift. Strategic drift is a concept of strategic management referring to the response of organization when going through a changing environment and has been found to cause a decline in competitive advantage of organizations through managerial inertia, an increase in operating costs and the decline of innovation and market adaptability and thus negatively affecting the performance of organizations. The study’s general objective was to review literature on strategic drift with a view to highlight the knowledge gaps suitable to form basis of future research works. Specifically, the study has reviewed the existing conceptual and theoretical literature on the construct of strategic drift and it’s connected phenomena, reviewed relevant empirical literature on the construct of the firm performance and it’s connected phenomena, identified emerging conceptual theoretical and empirical gaps from the reviewed literature and proposed a suitable theoretical framework in respect to the identified gaps for guiding future research. The study is anchored on the path dependency theory, organizational learning theory, theory of constraints, dynamics capability theory and open systems theory. Empirical review on leadership complacency, marketing myopia, logical incrementalism and organizational commitment and firm performance has been done. Finally, the study proposes a suitable conceptual model in respect to identified gaps and guide future research on the constructs of strategic drift and its connected phenomena, on firm performance. The study’s findings are significant to the top management of firms, as they will help them to understand the concept of strategic drift and its implications on performance and therefore be able to develop strategies that will achieve the firm’s competitive advantage and superior performance.
Building Large Lexicalized Ontologies from Text: a Use Case in Indexing Biotechnology Related Patents

This paper presents TyDI, a cooperative tool for building termino-ontologies from corpus in specific domains. The built resource is aimed at fulfilling the fine-grained indexing requirements of semantic search applications. TyDI provides facilities to allow a knowledge engineer and a domain expert to collaborate to validate, organize and conceptualize corpus extracted terms. A use case on patent search in the biotechnology domain demonstrates TyDI strengths and limits. The quality of the biotechnology termino-ontology has been positively evaluated through the patent retrieval application.
INVITED REVIEW: PART OF AN INVITED ISSUE ON TREE NUTRITION The in ﬂ uence of mixed tree plantations on the nutrition of individual species: a review

Summary Productivity of tree plantations is a function of the supply, capture and e ﬃ ciency of use of resources, as out-lined in the Production Ecology Equation. Species interactions in mixed-species stands can in ﬂ uence each of these variables. The importance of resource-use e ﬃ ciency in determining forest productivity has been clearly demonstrated in monocultures; however, substantial knowledge gaps remain for mixtures. This review examines how the physiology and morphology of a given species can vary depending on whether it grows in a mixture or monoculture. We outline how physiological and morphological shifts within species, resulting from interactions in mixtures, may in ﬂ uence the three variables of the Production Ecology Equation, with an emphasis on nutrient resources [nitrogen (N) and phosphorus (P)]. These include (i) resource availability, including soil nutrient mineralization, N 2 ﬁ xation and litter decomposition; (ii) proportion of resources captured, resulting from shifts in spatial, temporal and chemical patterns of root dynamics; (iii) resource-use e ﬃ ciency. We found that more than 50% of mixed-species studies report a shift to greater above-ground nutrient content of species grown in mixtures compared to monocultures, indicating an increase in the proportion of resources captured from a site. Secondly, a meta-analysis showed that foliar N concentrations signi ﬁ cantly increased for a given species in a mixture containing N 2 - ﬁ xing species, compared to a monoculture, suggesting higher rates of photosynthesis and greater resource-use e ﬃ ciency. Signi ﬁ cant shifts in N- and P-use e ﬃ ciencies of a given species, when grown in a mixture compared to a monoculture, occurred in over 65% of studies where resource-use e ﬃ ciency could be calculated. Such shifts can result from changes in canopy photosynthetic capacities, changes in carbon allocation or changes to foliar nutrient residence times of species in a mixture. We recommend that future research focus on individual species' changes, particularly with respect to resource-use ef- ﬁ ciency (including nutrients, water and light), when trees are grown in mixtures compared to monocultures. A better understanding of processes responsible for changes to tree productivity in mixed-species tree plantations can improve species, and within-species, selection so that the long-term outcome of mixtures is more predictable.