DBOS (Formerly Database-Oriented Operating System, now just DBOS) is an open source durable workflow execution software library written for the Python, TypeScript, Java, and Go programming languages. DBOS arose from a joint open source project from MIT and Stanford, after a discussion between Michael Stonebraker and Matei Zaharia on how to scale and improve scheduling and performance of millions of Apache Spark tasks. Today it is a commercial company that offers an open source system to add durable computing to any software, built on concepts derived from the joint research project. == History == === 2020: Academic R&D Project === DBOS originated in 2020 as a joint open source project between MIT, Stanford, and Carnegie Mellon. The project explored the idea of operating system services built atop a distributed database - a database-oriented operating system meant to simplify and improve the scalability, security and resilience of large-scale distributed applications. The basic concept was to run a multi-node multi-core, transactional, highly-available distributed database, such as VoltDB, as the only application for a microkernel, and then to implement scheduling, messaging, file systems and other operating system services on top of the database. The architectural philosophy is described by this quote from the abstract of their initial preprint: All operating system state should be represented uniformly as database tables, and operations on this state should be made via queries from otherwise stateless tasks. This design makes it easy to scale and evolve the OS without whole-system refactoring, inspect and debug system state, upgrade components without downtime, manage decisions using machine learning, and implement sophisticated security features. A prototype was built with competitive performance to existing systems. ==
Couchbase Server
Couchbase Server, originally known as Membase, is a source-available, distributed (shared-nothing architecture) multi-model NoSQL document-oriented database software package optimized for interactive applications. These applications may serve many concurrent users by creating, storing, retrieving, aggregating, manipulating and presenting data. In support of these kinds of application needs, Couchbase Server is designed to provide easy-to-scale key-value, or JSON document access, with low latency and high sustainability throughput. It is designed to be clustered from a single machine to very large-scale deployments spanning many machines. Couchbase Server provided client protocol compatibility with memcached, but added disk persistence, data replication, live cluster reconfiguration, rebalancing and multitenancy with data partitioning. == Product history == Membase was developed by several leaders of the memcached project, who had founded a company, NorthScale, to develop a key-value store with the simplicity, speed, and scalability of memcached, but also the storage, persistence and querying capabilities of a database. The original membase source code was contributed by NorthScale, and project co-sponsors Zynga and Naver Corporation (then known as NHN) to a new project on membase.org in June 2010. On February 8, 2011, the Membase project founders and Membase, Inc. announced a merger with CouchOne (a company with many of the principal players behind CouchDB) with an associated project merger. The merged company was called Couchbase, Inc. In January 2012, Couchbase released Couchbase Server 1.8. In September of 2012, Orbitz said it had changed some of its systems to use Couchbase. In December of 2012, Couchbase Server 2.0 (announced in July 2011) was released and included a new JSON document store, indexing and querying, incremental MapReduce and replication across data centers. == Architecture == Every Couchbase node consists of a data service, index service, query service, and cluster manager component. Starting with the 4.0 release, the three services can be distributed to run on separate nodes of the cluster if needed. In the parlance of Eric Brewer's CAP theorem, Couchbase is normally a CP type system meaning it provides consistency and partition tolerance, or it can be set up as an AP system with multiple clusters. === Cluster manager === The cluster manager supervises the configuration and behavior of all the servers in a Couchbase cluster. It configures and supervises inter-node behavior like managing replication streams and re-balancing operations. It also provides metric aggregation and consensus functions for the cluster, and a RESTful cluster management interface. The cluster manager uses the Erlang programming language and the Open Telecom Platform. ==== Replication and fail-over ==== Data replication within the nodes of a cluster can be controlled with several parameters. In December of 2012, support was added for replication between different data centers. === Data manager === The data manager stores and retrieves documents in response to data operations from applications. It asynchronously writes data to disk after acknowledging to the client. In version 1.7 and later, applications can optionally ensure data is written to more than one server or to disk before acknowledging a write to the client. Parameters define item ages that affect when data is persisted, and how max memory and migration from main-memory to disk is handled. It supports working sets greater than a memory quota per "node" or "bucket". External systems can subscribe to filtered data streams, supporting, for example, full text search indexing, data analytics or archiving. ==== Data format ==== A document is the most basic unit of data manipulation in Couchbase Server. Documents are stored in JSON document format with no predefined schemas. Non-JSON documents can also be stored in Couchbase Server (binary, serialized values, XML, etc.) ==== Object-managed cache ==== Couchbase Server includes a built-in multi-threaded object-managed cache that implements memcached compatible APIs such as get, set, delete, append, prepend etc. ==== Storage engine ==== Couchbase Server has a tail-append storage design that is immune to data corruption, OOM killers or sudden loss of power. Data is written to the data file in an append-only manner, which enables Couchbase to do mostly sequential writes for update, and provide an optimized access patterns for disk I/O. === Performance === A performance benchmark done by Altoros in 2012, compared Couchbase Server with other technologies. Cisco Systems published a benchmark that measured the latency and throughput of Couchbase Server with a mixed workload in 2012. == Licensing and support == Couchbase Server is a packaged version of Couchbase's open source software technology and is available in a community edition without recent bug fixes with an Apache 2.0 license and an edition for commercial use. Couchbase Server builds are available for Ubuntu, Debian, Red Hat, SUSE, Oracle Linux, Microsoft Windows and macOS operating systems. Couchbase has supported software developers' kits for the programming languages .NET, PHP, Ruby, Python, C, Node.js, Java, Go, and Scala. == SQL++ == A query language called SQL++ (formerly called N1QL), is used for manipulating the JSON data in Couchbase, just like SQL manipulates data in RDBMS. It has SELECT, INSERT, UPDATE, DELETE, MERGE statements to operate on JSON data. It was initially announced in March 2015 as "SQL for documents". The SQL++ data model is non-first normal form (N1NF) with support for nested attributes and domain-oriented normalization. The SQL++ data model is also a proper superset and generalization of the relational model. === Example === Like query SELECT FROM `bucket` WHERE email LIKE "%@example.org"; Array query SELECT FROM `bucket` WHERE ANY x IN friends SATISFIES x.name = "Pavan" END; == Couchbase Mobile == Couchbase Mobile / Couchbase Lite is a mobile database providing data replication. Couchbase Lite (originally TouchDB) provides native libraries for offline-first NoSQL databases with built-in peer-to-peer or client-server replication mechanisms. Sync Gateway manages secure access and synchronization of data between Couchbase Lite and Couchbase Server. Couchbase Lite added support for Vector Search in version 3.2, allowing cloud to edge support for vector search in mobile applications. == Uses == Couchbase began as an evolution of Memcached, a high-speed data cache, and can be used as a drop-in replacement for Memcached, providing high availability for memcached application without code changes. Couchbase is used to support applications where a flexible data model, easy scalability, and consistent high performance are required, such as tracking real-time user activity or providing a store of user preferences or online applications. Couchbase Mobile, which stores data locally on devices (usually mobile devices) is used to create “offline-first” applications that can operate when a device is not connected to a network and synchronize with Couchbase Server once a network connection is re-established. The Catalyst Lab at Northwestern University uses Couchbase Mobile to support the Evo application, a healthy lifestyle research program where data is used to help participants improve dietary quality, physical activity, stress, or sleep. Amadeus uses Couchbase with Apache Kafka to support their “open, simple, and agile” strategy to consume and integrate data on loyalty programs for airline and other travel partners. High scalability is needed when disruptive travel events create a need to recognize and compensate high value customers. Starting in 2012, it played a role in LinkedIn's caching systems, including backend caching for recruiter and jobs products, counters for security defense mechanisms, for internal applications. == Alternatives == For caching, Couchbase competes with Memcached and Redis. For document databases, Couchbase competes with other document-oriented database systems. It is commonly compared with MongoDB, Amazon DynamoDB, Oracle RDBMS, DataStax, Google Bigtable, MariaDB, IBM Cloudant, Redis Enterprise, SingleStore, and MarkLogic.
AppyStore
AppyStore is a comprehensive learning videos and games app for kids up to the age of 8 years. The platform developed by Mauj Mobile, a mobile value-added services (VAS) provider curates content to help in child development by leveraging technology. Mauj is funded by Sequoia Capital, Westbridge Capital and Intel Capital. == Background == AppyStore was launched in 2014 as a platform providing content for kids between the ages of 1.5 and 6 years. AppyStore subsequently extended its services for kids up to 8 years of age. The company operates on a subscription-based model and claims to have 5,000 learning games and videos segregated in 18 learning areas developed to help children gain optimal skills and qualities. According to an article published in Business Standard, the application is claimed to be one of the top 5 apps that help to enhance the logical and imaginative capabilities of children. AppyStore was awarded the Best app for kids by Google Play in December 2017. == Service == The company provides content via a website and an Android app. The website and android app provide learning games, rhymes, phonics, reading, stories, science, numbers, maths, logic videos comprising puzzles, worksheets, videos and fun activities and the premium subscription also includes physical worksheets which are home delivered. This content is educational and has been handpicked by teachers and experts with an understanding of the major areas of child development milestones for children up to 8 years of age. The mobile application also allows parents to track the progress of their child on the basis of the number of videos viewed.
Dyme (company)
Dyme is a Dutch fintech start-up and subscription management app that allows users to cancel and renegotiate their recurring costs. In 2019, Dyme was the first independent Dutch company to receive a PSD2 licence from the Netherlands' central bank (DNB). == History == Dyme was founded in 2018 by Joran Iedema, David Knap, David Schogt and Wouter Florijn. The four had previously founded Cycleswap, a bicycle rental platform launched in 2015 and sold to the American platform Spinlister in 2016. The company gained notability in the Netherlands in 2020 when it appeared on Dutch television in Dragons Den, where Pieter Schoen made a €750,000 bid in an attempt to acquire 51.01% of the company. Dyme's Joran Iedema rejected the deal. == Recognition == Wired described Dyme as one of the "hottest start-ups in Europe" in 2021. As of 2021, the company reportedly had 350,000 registered users in the Netherlands and Great Britain.
Bitstrips
Bitstrips, Inc. was a Canadian media and technology company based in Toronto, founded in 2007 by Jacob Blackstock, David Kennedy, Shahan Panth, Dorian Baldwin, and Jesse Brown. The company created and offered a web application, Bitstrips.com, which allowed users to create comic strips using personalized avatars, and preset templates and poses. Brown and Blackstock explained that the service was meant to enable self-expression without the need to have artistic skills. Bitstrips was first presented in 2008 at South by Southwest in Austin, Texas, and the service later piloted and launched a version designed for use as educational software. The service achieved increasing prominence following the launch of versions for Facebook and mobile platforms. In 2014, Bitstrips launched a spin-off app known as Bitmoji, which allows users to create personalized stickers for use in instant messaging. In July 2016, Snapchat Inc. announced that it had acquired the company; the Bitstrips comic service was shut down, but Bitmoji remains operational, and has subsequently been given greater prominence within Snapchat's overall platform. == History == Bitstrips was co-developed by Toronto-based comic artist Jacob Blackstock and his high school friend, journalist Jesse Brown. The service was originally envisioned as a means to allow anyone to create their own comic strip without needing artistic skills. Brown explained that "it's so difficult and time-consuming to tell a story in comic book form, drawing the same characters again and again in these tiny little panels, and just the amount of craftsmanship required. And even if you can do it well, which I never could, it takes years to make a story." Brown stated that the service would be "groundwork for a whole new way to communicate", and went as far as describing the service as being a "YouTube for comics". Blackstock explained that the concept of Bitstrips was influenced by his own use of comics as a form of socialization; a student, Blackstock and his friends drew comics featuring each other and shared them during classes. He felt that Bitstrips was a "medium for self-expression", stating that "It's not just about you making the comics, but since you and your friends star in these comics, it's like you're the medium. The visual nature of comics just speaks so much louder than text." The service was publicly unveiled at South by Southwest in 2008. In 2009, the service introduced a version oriented towards the educational market, Bitstrips for Schools, which was initially piloted at a number of schools in Ontario. The service was praised by educators for being engaging to students, especially within language classes. Brown noted that students were using the service to create comics outside of class as well, stating that it was "so gratifying and shocking what people do with your tool to make their own stories in ways that you never would have anticipated. Some of them are just brilliant." In December 2012, Bitstrips launched a version for Facebook; by July 2013, Bitstrips had 10 million unique users on Facebook, having created over 50 million comics. In October 2013, Bitstrips launched a mobile app; in two months, Bitstrips became a top-downloaded app in 40 countries, and over 30 million avatars had been created with it. In November 2013, Bitstrips secured a round of funding from Horizons Ventures and Li Ka-shing. In October 2014, Bitstrips launched Bitmoji, a spin-off app that allows users to create stickers featuring Bitstrips characters in various templates. In July 2016, following unconfirmed reports earlier in the year, Snapchat Inc. announced that it had acquired Bitstrips. The company's staff continue to operate out of Toronto, but the original Bitstrips comic service was shut down in favour of focusing exclusively on Bitmoji, leaving many Bitstrips users to call for a reboot of the comic service.
Neural computation
Neural computation is the information processing performed by networks of neurons. Neural computation is affiliated with the philosophical tradition of computationalism, which advances the thesis that neural computation explains cognition. Warren McCulloch and Walter Pitts were the first to propose an account of neural activity as being computational in their seminal 1943 paper "A Logical Calculus of the Ideas Immanent in Nervous Activity." There are three general branches of computationalism, including classicism, connectionism, and computational neuroscience. All three branches agree that cognition is computation, however, they disagree on what sorts of computations constitute cognition. The classicism tradition believes that computation in the brain is digital, analogous to digital computing. Both connectionism and computational neuroscience do not require that the computations that realize cognition are necessarily digital computations. However, the two branches greatly disagree upon which sorts of experimental data should be used to construct explanatory models of cognitive phenomena. Connectionists rely upon behavioral evidence to construct models to explain cognitive phenomena, whereas computational neuroscience leverages neuroanatomical and neurophysiological information to construct mathematical models that explain cognition. When comparing the three main traditions of the computational theory of mind, as well as the different possible forms of computation in the brain, it is helpful to define what we mean by computation in a general sense. Computation is the processing of information, otherwise known as variables or entities, according to a set of rules. A rule in this sense is simply an instruction for executing a manipulation on the current state of the variable, in order to produce a specified output. In other words, a rule dictates which output to produce given a certain input to the computing system. A computing system is a mechanism whose components must be functionally organized to process the information in accordance with the established set of rules. The types of information processed by a computing system determine which type of computations it performs. Traditionally in cognitive science, there have been two proposed types of computation related to neural activity, digital and analog, with the vast majority of theoretical work incorporating a digital understanding of cognition. Computing systems that perform digital computation are functionally organized to execute operations on strings of digits with respect to the type and location of the digit on the string. It has been argued that neural spike train signaling implements some form of digital computation, since neural spikes may be considered as discrete units or digits, like 0 or 1—the neuron either fires an action potential or it does not. Accordingly, neural spike trains could be seen as strings of digits. Alternatively, analog computing systems perform manipulations on non-discrete, irreducibly continuous variables, that is, entities that vary continuously as a function of time. These sorts of operations are characterized by systems of differential equations. Neural computation can be studied by, for example, building models of neural computation. Work on artificial neural networks has been somewhat inspired by knowledge of neural computation.
Luminance HDR
Luminance HDR, formerly Qtpfsgui, is graphics software used for the creation and manipulation of high-dynamic-range images. Released under the terms of the GPL, it is available for Linux, Microsoft Windows, and Mac OS X (Intel only). Luminance HDR supports several High Dynamic Range (HDR) as well as Low Dynamic Range (LDR) file formats. == Functionality == Prerequisite of HDR photography are several narrow-range digital images with different exposures. Luminance HDR combines these images and calculates a high-contrast image. In order to view this image on a regular computer monitor, Luminance HDR can convert it into a displayable LDR image format using a variety of methods, such as tone mapping. Currently fifteen different tone mapping operators (algorithms) are available, each one with its tunable parameters. Different image processing techniques can be applied to the generated HDR images, such as resizing, cropping, rotating and a number of projective transformations. The software also provides batch processing functionality for creating HDR images and for tone mapping them in a non-interactive way. A module for copying Exif data among sets of images is also provided. For users who prefers the command line, a non-GUI, non-graphical interface is also available on all supported platforms. A common problem with HDR photography is that images need to be aligned exactly. If the subject is static, this can be achieved using a tripod or a stable surface on which the camera is placed. In the case of image data that does not align exactly, an automatic alignment can be performed using a tool provided by the Hugin project. If this automation doesn't provide the desired result, the user may improve it manually. == Supported formats == HDR images are images with a high dynamic range and, using Luminance HDR, they can be created as well as edited. The following HDR graphic formats are supported: OpenEXR Radiance HDR Tag Image File Format (TIFF) Format: 16 Bit, 32 Bit (Float) and LogLuv Raw PFS native Luminance HDR can create an HDR image from several LDR images and tonemap an HDR into an LDR. The following LDR formats are supported: JPG PNG Portable Pixmap (PPM) Portable Bitmap (PBM) TIFF (8 Bit)