We introduce a novel continued pre-training method, MELT (MatEriaLs-aware continued pre-Training), specifically designed to efficiently adapt the pre-trained language models (PLMs) for materials science. Unlike previous adaptation strategies that solely focus on constructing domain-specific corpus, MELT comprehensively considers both the corpus and the training strategy, given that materials science corpus has distinct characteristics from other domains. To this end, we first construct a comprehensive materials knowledge base from the scientific corpus by building semantic graphs. Leveraging this extracted knowledge, we integrate a curriculum into the adaptation process that begins with familiar and generalized concepts and progressively moves toward more specialized terms. We conduct extensive experiments across diverse benchmarks to verify the effectiveness and generality of MELT. A comprehensive evaluation convincingly supports the strength of MELT, demonstrating superior performance compared to existing continued pre-training methods. The in-depth analysis also shows that MELT enables PLMs to effectively represent materials entities compared to the existing adaptation methods, thereby highlighting its broad applicability across a wide spectrum of materials science.

Software developers often struggle to update APIs, leading to manual, time-consuming, and error-prone processes. We introduce MELT, a new approach that generates lightweight API migration rules directly from pull requests in popular library repositories. Our key insight is that pull requests merged into open-source libraries are a rich source of information sufficient to mine API migration rules. By leveraging code examples mined from the library source and automatically generated code examples based on the pull requests, we infer transformation rules in \comby, a language for structural code search and replace. Since inferred rules from single code examples may be too specific, we propose a generalization procedure to make the rules more applicable to client projects. MELT rules are syntax-driven, interpretable, and easily adaptable. Moreover, unlike previous work, our approach enables rule inference to seamlessly integrate into the library workflow, removing the need to wait for client code migrations. We evaluated MELT on pull requests from four popular libraries, successfully mining 461 migration rules from code examples in pull requests and 114 rules from auto-generated code examples. Our generalization procedure increases the number of matches for mined rules by 9x. We applied these rules to client projects and ran their tests, which led to an overall decrease in the number of warnings and fixing some test cases demonstrating MELT's effectiveness in real-world scenarios.

ETL and ELT are data integration pipelines that transfer data from multiple sources to a single centralized source and perform some transformation and processing steps to it. The difference between these two is ETL transforms the data before loading, and ELT transforms the data after loading. But before diving deeply into them, let's first understand the meaning of E, L, and T. T for Transform - Transforming the data is a process of cleaning and modifying the data in a format so that it can be used for business analysis. L for Loading - It involves loading data to a target system, which may be a data warehouse or a database. ETL is the first standardized data integration method that emerged in the 1970s due to the evolution of disk storage.

By Vikram Labhe It is a truism at this point to talk of the centrality of data for organisations. According to IDC, the global datasphere will rise at a compound annual growth rate (CAGR) of 23% between 2020-2025, highlighting the importance of responding to the surge in storage demand. For businesses to leverage data insights and drive growth, they must coordinate the dependencies and execute the different tasks on their data journey in the desired order, all while ensuring minimal impact from potential errors. Whether an organisation favours extract, transform, load (ETL) or extract, load, transform (ELT) will depend on their specific needs. Orchestration is fundamental for modern data processes, but for many businesses a modern data stack makes specific orchestration tools redundant.

The traditional data transformation procedure of extract, transform and load (ETL) is rapidly being turned on its head in a modern twist enabled by cloud technologies. The Cloud's lower costs, its flexibility and scalability, and the huge processing capability of cloud data warehouses, have driven a major change: the ability to load all data into the cloud, before transforming it. This trend means that ETL itself has been transformed--into extract, load and transform, or ELT. ELT offers several advantages, including retention of data granularity, reduced need for expensive software engineers and significantly reduced project turnaround times. Data is vital for organizations, who use it to understand their customers, identify new opportunities and support decision-makers with mission-critical and up-to-date information.

How we store and manage data has completely changed over the last decade. We moved from an ETL world to an ELT world, with companies like Fivetran pushing the trend. However, we don't think it is going to stop there; ELT is a transition in our mind towards EL(T) (with EL decoupled from T). And to understand this, we need to discern the underlying reasons for this trend, as they might show what's in store for the future. This is what we will be doing in this article. Historically, the data pipeline process consisted of extracting, transforming, and loading data into a warehouse or a data lake.

When designing and building data pipelines to load data into data warehouses you might have heard of the common ETL and ELT paradigms. This post goes over what they mean, their differences and which paradigm you might want to choose. If you are wondering why we have a staging area click here. ELT is very similar but the data is loaded into a table before being transformed to a final table which is used by users. As you can see it has fewer components compared to the ETL approach.

The problem of ranking arises ubiquitously in almost every aspect of life, and in particular in Machine Learning/Information Retrieval. A statistical model for ranking predicts how humans rank subsets V of some universe U. In this work we define a statistical model for ranking that satisfies certain desirable properties. The model automatically gives rise to a logistic regression based approach to learning how to rank, for which the score and comparison based approaches are dual views. This offers a new generative approach to ranking which can be used for IR.

The problem of ranking arises ubiquitously in almost every aspect of life, and in particular in Machine Learning/Information Retrieval. A statistical model for ranking predicts how humans rank subsets V of some universe U. In this work we define a statistical model for ranking that satisfies certain desirable properties. The model automatically gives rise to a logistic regression based approach to learning how to rank, for which the score and comparison based approaches are dual views. This offers a new generative approach to ranking which can be used for IR.

The problem of ranking arises ubiquitously in almost every aspect of life, and in particular in Machine Learning/Information Retrieval. A statistical model for ranking predicts how humans rank subsets V of some universe U. In this work we define a statistical model for ranking that satisfies certain desirable properties. The model automatically gives rise to a logistic regression based approach to learning how to rank, for which the score and comparison based approaches are dual views. This offers a new generative approach to ranking which can be used for IR.