A Comprehensive Grammar of the English Language is a descriptive grammar of English written by Randolph Quirk, Sidney Greenbaum, Geoffrey Leech, and Jan Svartvik. It was first published by Longman in 1985. In 1991, it was called "The greatest of contemporary grammars, because it is the most thorough and detailed we have," and "It is a grammar that transcends national boundaries." The book relies on elicitation experiments as well as three corpora: a corpus from the Survey of English Usage, the Lancaster-Oslo-Bergen Corpus (UK English), and the Brown Corpus (US English). == Reviews == In 1988, Rodney Huddleston published a very critical review. He wrote:[T]here are some respects in which it is seriously flawed and disappointing. A number of quite basic categories and concepts do not seem to have been thought through with sufficient care; this results in a remarkable amount of unclarity and inconsistency in the analysis, and in the organization of the grammar. Aarts, F. G. A. M. (April 1988). "A Comprehensive Grammar of the English Language: The great tradition continued". English Studies. 69 (2): 163–173. doi:10.1080/00138388808598565.
Business process automation
Business process automation (BPA), also known as business automation, refers to the technology-enabled automation of business processes. == Development approaches == There are three main approaches to developing BPA: traditional business process automation involves developing BPA software in a programming language for integrating relevant applications in the digital ecosystem to execute a given process; robotic process automation uses software robots (also called agents, bots, or workers) to emulate human-computer interaction for executing a combination of processes, activities, transactions, and tasks in one or more unrelated software systems; hyperautomation (also called intelligent automation (IA), intelligent process automation (IPA), integrated automation platform (IAP), and cognitive automation (CA) combines business process automation, artificial intelligence (AI), and machine learning (ML) to discover, validate, and execute organizational processes automatically with no or minimal human intervention. == Deployment == BPA toolsets vary in capability. With the increasing adoption of artificial intelligence (AI), organizations are implementing AI-driven technologies that can process natural language, interpret unstructured datasets, and interact with users. These systems are designed to adapt to new types of problems with reduced reliance on human intervention. == Business process management implementation == A business process management system differs from BPA. However, it is possible to implement automation based on a BPM implementation. The methods to achieve this vary, from writing custom application code to using specialist BPA tools. == Robotic process automation == Robotic process automation (RPA) involves the deployment of attended or unattended software agents in an organization's environment. These software agents, or robots, are programmed to perform predefined structured and repetitive sets of business tasks or processes. Robotic process automation is designed to streamline workflows by delegating repetitive tasks to software agents, allowing human workers to focus on more complex and strategic activities. BPA providers typically focus on different industry sectors, but the underlying approach is generally similar in that they aim to provide the shortest route to automation by interacting with the user interface rather than modifying the application code or database behind it. == Use of artificial intelligence == Artificial intelligence software robots are used to handle unstructured data sets (like images, texts, audios) and are often deployed after implementing robotic process automation. They can, for instance, generate an automatic transcript from a video. The combination of automation and artificial intelligence (AI) enables autonomy for robots, along with the capability to perform cognitive tasks. At this stage, robots can learn and improve processes by analyzing and adapting them.
Social collaboration
Social collaboration refers to processes that help multiple people or groups interact and share information to achieve common goals. Such processes find their 'natural' environment on the Internet, where collaboration and social dissemination of information are made easier by current innovations and the proliferation of the web. Sharing concepts on a digital collaboration environment often facilitates a "brainstorming" process, where new ideas may emerge due to the varied contributions of individuals. These individuals may hail from different walks of life, different cultures and different age groups, their diverse thought processes help in adding new dimensions to ideas, dimensions that previously may have been missed. A crucial concept behind social collaboration is that 'ideas are everywhere.' Individuals are able to share their ideas in an unrestricted environment as anyone can get involved and the discussion is not limited to only those who have domain knowledge. Social collaboration is also known as enterprise social networking, and the products to support it are often branded enterprise social networks (ESNs). It is important that we understand the rhythm of social collaboration. There needs to be a balance, with ease to move from focused solitary work to brainstorming for problem solving in group work. This critical balance can be achieved by creating structures or a work environment where it is not too rigid to prevent brainstorming in group work nor too loose to result in total chaos. Social collaboration should happen at the edge of chaos. Work practices should support social collaboration. The most effective environment is one that supports opportunistic planning. Opportunistic planning provides a general plan but then gives enough room for flexibility to change activities and tasks until the last moment. This way, people are able to cope up with unforeseen developments and not throwing away everything with one grand plan. == Comparison to social networking == Social collaboration is related to social networking, with the distinction that while social networking is individual-centric, social collaboration is entirely group-centric. Generally speaking, social networking means socializing for personal, professional or entertainment purposes, for example, LinkedIn and Facebook. Social collaboration, on the other hand, means working socially to achieve a common goal, for example, GitHub and Quora. Social networking services generally focus on individuals sharing messages in a more-or-less undirected way and receiving messages from many sources into a single personalized activity feed. Social collaboration services, on the other hand, focus on the identification of groups and collaboration spaces in which messages are explicitly directed at the group and the group activity feed is seen the same way by everyone. Social collaboration may refer to time-bound collaborations with an explicit goal to be completed or perpetual collaborations in which the goal is knowledge sharing (e.g. community of practice, online community). == Comparison to crowdsourcing == Social collaboration is similar to crowdsourcing as it involves individuals working together towards a common goal. Crowdsourcing is a method for harnessing specific information from a large, diverse group of people. Unlike social collaboration, which involves much communication and cooperation among a large group of people, crowdsourcing is more like individuals working towards the common goal relatively independently. Therefore, the process of working involves less communication. Andrea Grover, curator of a crowdsourcing art show, explained that collaboration among individuals is an appealing experience, because participation is "a low investment, with the possibility of a high return." == Social collaboration software == Notable social collaboration software includes Glip messaging, Google Apps, Knowledge Plaza Electronic Document System and Social Intranet, Microsoft Lync social collaboration tool for businesses, Slack, Weekdone for managers, and Wrike. == Future == Social collaboration is going to be used as a tool in companies to enhance productivity. Social workers could be able to use social collaboration tools to manage personal tasks, professional projects and social networks with other colleagues within the same organization. Social collaboration will serve as a platform to get people involved and connected. This kind of platform provides a spiritual training practice for social workers. Social collaboration software could help enhance the communication between customers and employees and build trust in the organization. When we need real-time chat, it would be excellent to include every participant in a shared and archived forum which keeps a record of important information and logs. So collaborators need not worry about losing important records while working towards the common goal. The interactive communication and synchronous environment promote understanding among colleagues. Collaboration helps in building strong relationships between workers, which in turn leads to faster problem solving. The close connection between workers and customers creates a scalable organization which naturally increases the trust and faith that customers have in the company. Therefore, the interactive customer relationship levels up customer satisfaction in ways that traditional collaboration methods cannot. Apart from its effect on the way work will be conducted in the future, social collaboration will also affect society. In the coming years social collaboration will be the driving force in societal change as more and more people work together to get their vision across to governments and governing agencies. An example of this is Change.org, an online petition tool where users can help bring their government's attention to pressing social issues that need to be addressed.
Radical trust
Radical trust is the confidence that any structured organization, such as a government, library, business, religion, or museum, has in collaboration and empowerment within online communities. Specifically, it pertains to the use of blogs, wiki and online social networking platforms by organizations to cultivate relationships with an online community that then can provide feedback and direction for the organization's interest. The organization 'trusts' and uses that input in its management. One of the first appearances of the notion of radical trust appears in an info graphic outlining the base principles of web 2.0 in Tim O'Reilly's weblog post "What is Web 2.0". Radical Trust is listed as the guiding example of trusting the validity of consumer generated media. This concept is considered to be an underlying assumption of Library 2.0. The adoption of radical trust by a library would require its management let go of some of its control over the library and building an organization without an end result in mind. The direction a library would take would be based on input provided by people through online communities. These changes in the organization may merely be anecdotal in nature, making this method of organization management dramatically distinct from data-based or evidence based management. In marketing, Collin Douma further describes the notion of radical trust as a key mindset required for marketers and advertisers to enter the social media marketing space. Conventional marketing dictates and maintains control of messages to cause the greatest persuasion in consumer decisions, but Douma argued that in the social media space, brands would need to cede that control in order to build brand loyalty.
Yao's test
In cryptography and the theory of computation, Yao's test is a test defined by Andrew Chi-Chih Yao in 1982, against pseudo-random sequences. A sequence of words passes Yao's test if an attacker with reasonable computational power cannot distinguish it from a sequence generated uniformly at random. == Formal statement == === Boolean circuits === Let P {\displaystyle P} be a polynomial, and S = { S k } k {\displaystyle S=\{S_{k}\}_{k}} be a collection of sets S k {\displaystyle S_{k}} of P ( k ) {\displaystyle P(k)} -bit long sequences, and for each k {\displaystyle k} , let μ k {\displaystyle \mu _{k}} be a probability distribution on S k {\displaystyle S_{k}} , and P C {\displaystyle P_{C}} be a polynomial. A predicting collection C = { C k } {\displaystyle C=\{C_{k}\}} is a collection of boolean circuits of size less than P C ( k ) {\displaystyle P_{C}(k)} . Let p k , S C {\displaystyle p_{k,S}^{C}} be the probability that on input s {\displaystyle s} , a string randomly selected in S k {\displaystyle S_{k}} with probability μ ( s ) {\displaystyle \mu (s)} , C k ( s ) = 1 {\displaystyle C_{k}(s)=1} , i.e. Moreover, let p k , U C {\displaystyle p_{k,U}^{C}} be the probability that C k ( s ) = 1 {\displaystyle C_{k}(s)=1} on input s {\displaystyle s} a P ( k ) {\displaystyle P(k)} -bit long sequence selected uniformly at random in { 0 , 1 } P ( k ) {\displaystyle \{0,1\}^{P(k)}} . We say that S {\displaystyle S} passes Yao's test if for all predicting collection C {\displaystyle C} , for all but finitely many k {\displaystyle k} , for all polynomial Q {\displaystyle Q} : === Probabilistic formulation === As in the case of the next-bit test, the predicting collection used in the above definition can be replaced by a probabilistic Turing machine, working in polynomial time. This also yields a strictly stronger definition of Yao's test (see Adleman's theorem). Indeed, one could decide undecidable properties of the pseudo-random sequence with the non-uniform circuits described above, whereas BPP machines can always be simulated by exponential-time deterministic Turing machines.
LakeFS
lakeFS is an open-source data version control system for managing data stored in object storage. It provides Git-like operations such as branching, committing, merging, and reverting for large-scale data stored in systems including Amazon S3, Azure Blob Storage, and Google Cloud Storage, as well as other S3-compatible object storage platforms. lakeFS is used in data engineering and machine learning workflows to manage changes to data, support reproducibility, and enable data governance across data lakes. The software is available as an open-source project, as well as in enterprise and managed service offerings, including lakeFS Cloud. == History == lakeFS was created in 2020 by Einat Orr and Oz Katz at Treeverse. Its first public release, version 0.8.1, appeared in August 2020 and introduced Git-style operations with support for Amazon S3. In 2021, Treeverse raised $23 million in a Series A funding round led by Dell Technologies Capital, Norwest Venture Partners, and Zeev Ventures. The same year, lakeFS was included in InfoWorld’s Best of Open Source Software (Bossie) awards. In June 2022, Treeverse introduced lakeFS Cloud, a managed service providing hosted lakeFS deployments for cloud-based data lakes. Version 1.0 was released in October 2023, adding integrations with platforms such as Databricks and Apache Iceberg, as well as support for orchestration tools including Apache Airflow. Public case studies and conference materials have described usage of lakeFS by organizations such as Microsoft, Volvo, and NASA. In July 2025, Treeverse announced an additional $20 million in growth funding to support further development of lakeFS. In November 2025, Treeverse announced the acquisition of the open-source data version control project DVC. == Software == === Overview === lakeFS provides Git-like operations such as branching, committing, merging, and reverting for datasets stored in object storage. These operations are used to manage changes to data, test modifications in isolation, reproduce specific data states, and recover from errors or unintended updates. === Architecture === lakeFS operates as a metadata layer on top of object storage systems such as Amazon S3, Azure Blob Storage, and Google Cloud Storage. It stores repository metadata describing commits, branches, and tags, enabling versioned views of data without copying underlying objects. The system provides access through multiple interfaces, including a web user interface, command-line tools, a REST API, and software development kits. It is designed to integrate with existing data engineering and machine learning workflows, and can be deployed either in self-hosted environments or as a managed service. === Functions === lakeFS provides version control functionality for data stored in object storage–based data lakes. Core features include: Atomic commits and version tracking for datasets, supporting reproducibility and auditability. Branching and merging mechanisms that allow isolated development and testing without duplicating data. Configurable hooks that can validate data or trigger external processes during commit and merge operations. The ability to revert repositories to earlier states to recover from data errors or failed changes. Recording of commit history and associated metadata for lineage tracking. Support for managing data across multiple object storage systems, including Amazon S3, Azure Blob Storage, Google Cloud Storage, and MinIO. Use of fixed data versions to reproduce experiments and machine learning model training. === Integrations === Coverage of lakeFS has described integrations with platforms such as Databricks and Apache Iceberg, as well as support for environments including Red Hat OpenShift. Additional materials describe its use with Trino, including validation of data changes prior to merging in versioned data workflows, as well as compatibility with orchestration tools such as Apache Airflow.
G.9963
Recommendation G.9963 is a home networking standard under development at the International Telecommunication Union standards sector, the ITU-T. It was begun in 2010 by ITU-T to add multiple-input and multiple-output (known as MIMO) capabilities to the G.hn standard originally defined in Recommendation G.9960. The standard is also known as "G.hn-mimo". As part of the family of G.hn standards, G.9963 was endorsed by the HomeGrid Forum.