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AI Assistant Jetbrains Plugin — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

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  Carrier cloud

  In cloud computing, a carrier cloud is a class of cloud that integrates wide area networks (WAN) and other attributes of communications service providers’ carrier-grade networks to enable the deployment of highly-complex applications in the cloud. In contrast, classic cloud computing focuses on the data center and does not address the network connecting data centers and cloud users. This may result in unpredictable response times and security issues when business-critical data are transferred over the Internet. == History == The advent of virtualization technology, cost-effective computing hardware, and ubiquitous Internet connectivity have enabled the first wave of cloud services starting in the early years of the 21st century. But many businesses and other organizations hesitated to move to more demanding applications, from on-premises dedicated hardware to private or public clouds. As a response, communications service providers started in the 2010/2011 time frame to develop carrier clouds that address perceived weaknesses in existing cloud services. Cited weaknesses vary but often include possible downtime, security issues, high cost of custom software and data transfer, inflexibility of some cloud apps, poor customer and nonfulfillment of service level agreements (SLAs). == Characteristics == To enable the deployment of time-sensitive and business critical applications in the cloud, the carrier cloud is designed to match or even exceed the characteristics of on-premises deployments. Therefore, the carrier cloud is characterized by some or all of the following items: Configurable, elastic network performance: Typical cloud computing solutions use the best effort of the public Internet to connect cloud users and data centers. This approach provides instant connectivity but does not offer control over network capacities, latencies, and jitter. Carrier clouds address these gaps with content delivery networks and/or dedicated virtual private networks (VPN) at OSI layers 1 (optical wavelengths), 2 (data link layer), and 3 (network layer). These VPNs can be configured to offer the desired performance parameters and exhibit the same type of elasticity for the network that regular clouds provide for servers and storage. To achieve the requested performance parameters, such as low latency, cloud applications can be (automatically) allocated to distributed data centers that are close enough to the cloud users. Automatic resource placement: For a cloud with multiple data centers, information about both the data center and the connecting network is relevant for a decision of where to place cloud images and storage volumes. For this decision, carrier clouds can obtain relevant information about the network, e.g., using the Application-Layer Traffic Optimization (ALTO) protocol. High level of security and governance: Cloud application providers are subject to general and domain specific security, privacy, and governance requirements and regulations, such as the European Data Protection Directive and the U.S. Health Insurance Portability and Accountability Act. For added security, the wide area network of the carrier cloud can provide segregated encrypted or unencrypted network links that are not accessible from the general Internet. At the data center, the carrier cloud provides e.g. virtual private servers, management processes, logs, and documentation to fulfill security and governance rules. Location control: Fundamentally, cloud users should not be concerned with the geographic location of their cloud resources. However, privacy and other regulations may mandate that certain types of data must not be sent outside a national jurisdiction or other geographical region. Open APIs: Carrier clouds provide graphical user interfaces and Web application programming interfaces that allow cloud application providers to set up, manage, and monitor both, the data center and the WAN, of their cloud services. == Architecture == Carrier clouds encompass data centers at different network tiers and wide area networks that connect multiple data centers to each other as well as to the cloud users. Links between data centers are used for failover, overflow, backup, and geographic diversity. Carrier clouds can be set up as public, private, or hybrid clouds. The carrier cloud federates these cloud entities by using a single management system to orchestrate, manage, and monitor data center and network resources as a single system.

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  Managed private cloud

  Managed private cloud (also known as "hosted private cloud" or "single-tenant SaaS") refers to a principle in software architecture where a single instance of the software runs on a server, serves a single client organization (tenant), and is managed by a third party. The third-party provider is responsible for providing the hardware for the server and also for preliminary maintenance. This is in contrast to multitenancy, where multiple client organizations share a single server, or an on-premises deployment, where the client organization hosts its software instance. Managed private clouds also fall under the larger umbrella of cloud computing. == Adoption == The need for private clouds arose due to enterprises requiring a dedicated service and infrastructure for their cloud computing needs, such as for business-critical operations, improved security, and better control over their resources. Managed private cloud adoption is a popular choice among organizations. It has been on the rise due to enterprises requiring a dedicated cloud environment and preferring to avoid having to deal with management, maintenance, or future upgrade costs for the associated infrastructure and services. Such operational costs are unavoidable in on-premises private cloud data centers. == Advantages and challenges of managed private cloud == A managed private cloud cuts down on upkeep costs by outsourcing infrastructure management and maintenance to the managed cloud provider. It is easier to integrate an organization's existing software, services, and applications into a dedicated cloud hosting infrastructure which can be customized to the client's needs instead of a public cloud platform, whose hardware or infrastructure/software platform cannot be individualized to each client. Customers who choose a managed private cloud deployment usually choose them because of their desire for efficient cloud deployment, but also have the need for service customization or integration only available in a single-tenant environment. This chart shows the key benefits of the different types of deployments, and shows the overlap between these cloud solutions. This chart shows key drawbacks. Since deployments are done in a single-tenant environment, it is usually cost-prohibitive for small and medium-sized businesses. While server upkeep and maintenance are handled by the service provider, including network management and security, the client is charged for all such services. It is up to the potential client to determine if a managed private cloud solution aligns with their business objectives and budget. While the service provider maintains the upkeep of servers, network, and platform infrastructure, sensitive data is typically not stored on managed private clouds as it may leave business-critical information prone to breaches via third-party attacks on the cloud service provider. Common customizations and integrations include: Active Directory Single Sign-on Learning Management Systems Video Teleconferencing == Deployment strategies and service providers == Software companies have taken a variety of strategies in the Managed Private Cloud realm. Some software organizations have provided managed private cloud options internally, such as Microsoft. Companies that offer an on-premises deployment option, by definition, enable third-party companies to market Managed Private Cloud solutions. A few managed private cloud service providers are: Adobe Connect: Adobe Connect may be purchased for on-premises deployment, multi-tenant hosted deployment, managed private cloud as ACMS, or managed by third-party managed private cloud provider ConnectSolutions. Rackspace CenturyLink Microsoft licenses for Lync, SharePoint and Exchange may be purchased for on-premises deployment, a multi-tenant hosted deployment via Office 365, or managed by third-party cloud hosting from Azaleos, ConnectSolutions and others.

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  ZygoteBody

  ZygoteBody, formerly Google Body, is a web application by Zygote Media Group that renders manipulable 3D anatomical models of the human body. Several layers, from muscle tissues down to blood vessels, can be removed or made transparent to allow better study of individual body parts. Most of the body parts are labelled and are searchable. == Technology == The human models are based on data from the Zygote Media Group. The website uses JavaScript and WebGL technology to display 3D images inside the web browser without requiring the installation of external browser plug-ins. == History == ZygoteBody was launched as Google Body on December 15, 2010. On April Fools' Day 2011, users were greeted with the anatomy of a cow on the home page. The cow model is still available as part of the open-3d-viewer open source project. As part of the wind down on Google Labs, it was announced that Google Body will be shut down but will continue to be maintained by Zygote as ZygoteBody. On October 13, 2011, the Google Body site was shut down. Then, on January 9, 2012, ZygoteBody was launched and core code base (with the Google Cow model as a demo) was made available as an open source project called open-3d-viewer.

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  Deconvolution

  In mathematics, deconvolution is the inverse of convolution. Both operations are used in signal processing and image processing. For example, it may be possible to recover the original signal after a filter (convolution) by using a deconvolution method with a certain degree of accuracy. Due to the measurement error of the recorded signal or image, it can be demonstrated that the worse the signal-to-noise ratio (SNR), the worse the reversing of a filter will be; hence, inverting a filter is not always a good solution as the error amplifies. Deconvolution offers a solution to this problem. The foundations for deconvolution and time-series analysis were largely laid by Norbert Wiener of the Massachusetts Institute of Technology in his book Extrapolation, Interpolation, and Smoothing of Stationary Time Series (1949). The book was based on work Wiener had done during World War II but that had been classified at the time. Some of the early attempts to apply these theories were in the fields of weather forecasting and economics. == Description == In general, the objective of deconvolution is to find the solution f of a convolution equation of the form: f ∗ g = h {\displaystyle fg=h\,} Usually, h is some recorded signal, and f is some signal that we wish to recover, but has been convolved with a filter or distortion function g, before we recorded it. Usually, h is a distorted version of f and the shape of f can't be easily recognized by the eye or simpler time-domain operations. The function g represents the impulse response of an instrument or a driving force that was applied to a physical system. If we know g, or at least know the form of g, then we can perform deterministic deconvolution. However, if we do not know g in advance, then we need to estimate it. This can be done using methods of statistical estimation or building the physical principles of the underlying system, such as the electrical circuit equations or diffusion equations. There are several deconvolution techniques, depending on the choice of the measurement error and deconvolution parameters: === Raw deconvolution === When the measurement error is very low (ideal case), deconvolution collapses into a filter reversing. This kind of deconvolution can be performed in the Laplace domain. By computing the Fourier transform of the recorded signal h and the system response function g, you get H and G, with G as the transfer function. Using the convolution theorem, F = H / G {\displaystyle F=H/G\,} where F is the estimated Fourier transform of f. Finally, the inverse Fourier transform of the function F is taken to find the estimated deconvolved signal f. Note that G is at the denominator and could amplify elements of the error model if present. === Deconvolution with noise === In physical measurements, the situation is usually closer to ( f ∗ g ) + ε = h {\displaystyle (fg)+\varepsilon =h\,} In this case ε is noise that has entered our recorded signal. If a noisy signal or image is assumed to be noiseless, the statistical estimate of g will be incorrect. In turn, the estimate of ƒ will also be incorrect. The lower the signal-to-noise ratio, the worse the estimate of the deconvolved signal will be. That is the reason why inverse filtering the signal (as in the "raw deconvolution" above) is usually not a good solution. However, if at least some knowledge exists of the type of noise in the data (for example, white noise), the estimate of ƒ can be improved through techniques such as Wiener deconvolution. == Applications == === Seismology === The concept of deconvolution had an early application in reflection seismology. In 1950, Enders Robinson was a graduate student at MIT. He worked with others at MIT, such as Norbert Wiener, Norman Levinson, and economist Paul Samuelson, to develop the "convolutional model" of a reflection seismogram. This model assumes that the recorded seismogram s(t) is the convolution of an Earth-reflectivity function e(t) and a seismic wavelet w(t) from a point source, where t represents recording time. Thus, our convolution equation is s ( t ) = ( e ∗ w ) ( t ) . {\displaystyle s(t)=(ew)(t).\,} The seismologist is interested in e, which contains information about the Earth's structure. By the convolution theorem, this equation may be Fourier transformed to S ( ω ) = E ( ω ) W ( ω ) {\displaystyle S(\omega )=E(\omega )W(\omega )\,} in the frequency domain, where ω {\displaystyle \omega } is the frequency variable. By assuming that the reflectivity is white, we can assume that the power spectrum of the reflectivity is constant, and that the power spectrum of the seismogram is the spectrum of the wavelet multiplied by that constant. Thus, | S ( ω ) | ≈ k | W ( ω ) | . {\displaystyle |S(\omega )|\approx k|W(\omega )|.\,} If we assume that the wavelet is minimum phase, we can recover it by calculating the minimum phase equivalent of the power spectrum we just found. The reflectivity may be recovered by designing and applying a Wiener filter that shapes the estimated wavelet to a Dirac delta function (i.e., a spike). The result may be seen as a series of scaled, shifted delta functions (although this is not mathematically rigorous): e ( t ) = ∑ i = 1 N r i δ ( t − τ i ) , {\displaystyle e(t)=\sum _{i=1}^{N}r_{i}\delta (t-\tau _{i}),} where N is the number of reflection events, r i {\displaystyle r_{i}} are the reflection coefficients, t − τ i {\displaystyle t-\tau _{i}} are the reflection times of each event, and δ {\displaystyle \delta } is the Dirac delta function. In practice, since we are dealing with noisy, finite bandwidth, finite length, discretely sampled datasets, the above procedure only yields an approximation of the filter required to deconvolve the data. However, by formulating the problem as the solution of a Toeplitz matrix and using Levinson recursion, we can relatively quickly estimate a filter with the smallest mean squared error possible. We can also do deconvolution directly in the frequency domain and get similar results. The technique is closely related to linear prediction. === Optics and other imaging === In optics and imaging, the term "deconvolution" is specifically used to refer to the process of reversing the optical distortion that takes place in an optical microscope, electron microscope, telescope, or other imaging instrument, thus creating clearer images. It is usually done in the digital domain by a software algorithm, as part of a suite of microscope image processing techniques. Deconvolution is also practical to sharpen images that suffer from fast motion or jiggles during capturing. Early Hubble Space Telescope images were distorted by a flawed mirror and were sharpened by deconvolution. The usual method is to assume that the optical path through the instrument is optically perfect, convolved with a point spread function (PSF), that is, a mathematical function that describes the distortion in terms of the pathway a theoretical point source of light (or other waves) takes through the instrument. Usually, such a point source contributes a small area of fuzziness to the final image. If this function can be determined, it is then a matter of computing its inverse or complementary function, and convolving the acquired image with that. The result is the original, undistorted image. In practice, finding the true PSF is impossible, and usually an approximation of it is used, theoretically calculated or based on some experimental estimation by using known probes. Real optics may also have different PSFs at different focal and spatial locations, and the PSF may be non-linear. The accuracy of the approximation of the PSF will dictate the final result. Different algorithms can be employed to give better results, at the price of being more computationally intensive. Since the original convolution discards data, some algorithms use additional data acquired at nearby focal points to make up some of the lost information. Regularization in iterative algorithms (as in expectation-maximization algorithms) can be applied to avoid unrealistic solutions. When the PSF is unknown, it may be possible to deduce it by systematically trying different possible PSFs and assessing whether the image has improved. This procedure is called blind deconvolution. Blind deconvolution is a well-established image restoration technique in astronomy, where the point nature of the objects photographed exposes the PSF thus making it more feasible. It is also used in fluorescence microscopy for image restoration, and in fluorescence spectral imaging for spectral separation of multiple unknown fluorophores. The most common iterative algorithm for the purpose is the Richardson–Lucy deconvolution algorithm; the Wiener deconvolution (and approximations) are the most common non-iterative algorithms. For some specific imaging systems such as laser pulsed terahertz systems, PSF can be modeled mathematically. As a result, as shown in the figure, deconvolution of the modeled PS

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  International Conference on Language Resources and Evaluation

  The International Conference on Language Resources and Evaluation is an international conference organised by the ELRA Language Resources Association every other year (on even years) with the support of institutions and organisations involved in Natural language processing. The series of LREC conferences was launched in Granada in 1998. == History of conferences == The survey of the LREC conferences over the period 1998-2013 was presented during the 2014 conference in Reykjavik as a closing session. It appears that the number of papers and signatures is increasing over time. The average number of authors per paper is higher as well. The percentage of new authors is between 68% and 78%. The distribution between male (65%) and female (35%) authors is stable over time. The most frequent technical term is "annotation", then comes "part-of-speech". == The LRE Map == The LRE Map was introduced at LREC 2010 and is now a regular feature of the LREC submission process for both the conference papers and the workshop papers. At the submission stage, the authors are asked to provide some basic information about all the resources (in a broad sense, i.e. including tools, standards and evaluation packages), either used or created, described in their papers. All these descriptors are then gathered in a global matrix called the LRE Map. This feature has been extended to several other conferences.

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  Server.com

  Server.com is a domain name that was owned by software as a service (SaaS) company Server Corporation. They offered a suite of services from 1996 until 2007. It was the first SaaS site to offer a variety of services and the first to use the term WebApp to describe its services. It was selected as an Incredibly Useful Site by Yahoo! Internet Life magazine. net magazine listed Server.com among the 100 most influential websites of all time. Server.com launched in 1996 offering the first online personal information manager. In 1997, they rolled out the first threaded message board service; the first web based mailing list manager; one of the first online calendar services; and one of the first online form builders. In 2000, Server.com partnered with NBCi and became server.snap.com until 2001. In 2001, Server.com was serving 100 million monthly pageviews. Media Life declared it one of the 20 biggest ad domains on the Web. In 2002, Server.com developed one of the first web-based RSS aggregators. In 2007, all services were moved to YourWebApps.com. The domain name Server.com was sold in 2009 for $770,000.

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  Clone tool

  The clone tool, as it is known in Adobe Photoshop, Inkscape, GIMP, and Corel PhotoPaint, is used in digital image editing to replace information for one part of a picture with information from another part. In other image editing software, its equivalent is sometimes called a rubber stamp tool or a clone brush. == Applications == The clone tool can remove objects by copying a nearby background. The user selects a matching location as the source, then paints over the element to be hidden. A typical use for the tool is in object removal – more colloquially, "airbrushing" or "photoshopping" out an unwanted part of the image. If a part of an image is removed simply by cutting it out, then a hole is left in the background. The Clone tool can fill in this hole convincingly with a copy of the existing background from elsewhere in the image. A common use for this tool is to retouch skin, particularly in portraits, to remove blemishes and make skin tones more even. Cloning can also be used to remove other unwanted elements, such as telephone wires, an unwanted bird in the sky, and the like. A more automated method of object removal uses texture synthesis to fill in gaps. Of these, patch-based texture synthesis or "image quilting" is essentially an automated application of the clone tool, choosing the optimal source area so as to patch over with a minimal seam. In some cases, the undesired object is mixed with the remainder of the image, and a simple circular brush, even with feathering, would not work. For these cases, some programs allow an object to be selected by color/outline so other areas are not affected. Other programs allow edge/color sensitive brushes to deal with such objects. == Healing tool == A similar tool is the healing tool, which occurs in variants such as the healing brush or spot healing tool. These incorporate the existing texture, rather than painting it over.

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  Comparison gallery of image scaling algorithms

  This gallery shows the results of numerous image scaling algorithms. == Scaling methods == An image size can be changed in several ways. Consider resizing a 160x160 pixel photo to the following 40x40 pixel thumbnail and then scaling the thumbnail to a 160x160 pixel image. Also consider doubling the size of the following image containing text. == Examples of enlarged images == Below are examples of various images enlarged 4x using each scaling algorithm.

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  Clean Email

  Clean Email is an automated software as a service email management application which identifies and clears junk mail from inboxes. The service uses a subscription business model with a free trial for the first 1,000 emails. and is available on macOS, iOS, Android, and on the web. == History == Clean Email is a self-funded company headquartered in Los Angeles, California. Initially developed by the founder for personal use, the service was designed to address the growing issue of inbox clutter and privacy concerns. In 2017, John Gruber recognized Clean Email as a trustworthy alternative to Unroll.me after the latter was found to be selling user data. == Features == Clean Email uses algorithms to identify and categorize emails, enabling users to group, remove, label, and archive email messages in bulk. Its Unsubscriber tool consolidates all subscriptions and newsletters into a single view for quick management, allowing users to bulk unsubscribe or temporarily pause mail. Its Screener feature transforms the inbox into an "opt-in" system, enabling users to pre-approve mail from new senders. Cleaning Suggestions identifies frequently cleaned mail, recommending actions accordingly. Additional functionalities include automatic deletion of aging emails, delivery of messages to specified folders, and options to mute or block senders.

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  Object model

  In computing, object model has two related but distinct meanings: The properties of objects in general in a specific computer programming language, technology, notation or methodology that uses them. Examples are the object models of Java, the Component Object Model (COM), or Object-Modeling Technique (OMT). Such object models are usually defined using concepts such as class, generic function, message, inheritance, polymorphism, and encapsulation. There is an extensive literature on formalized object models as a subset of the formal semantics of programming languages. A collection of objects or classes through which a program can examine and manipulate some specific parts of its world. In other words, the object-oriented interface to some service or system. Such an interface is said to be the object model of the represented service or system. For example, the Document Object Model (DOM) is a collection of objects that represent a page in a web browser, used by script programs to examine and dynamically change the page. There is a Microsoft Excel object model [1] for controlling Microsoft Excel from another program, and the ASCOM Telescope Driver is an object model for controlling an astronomical telescope. == Features == An object model consists of the following important features: === Object reference === Objects can be accessed via object references. To invoke a method in an object, the object reference and method name are given, together with any arguments. === Interfaces === An interface provides a definition of the signature of a set of methods without specifying their implementation. An object will provide a particular interface if its class contains code that implement the method of that interface. An interface also defines types that can be used to declare the type of variables or parameters and return values of methods. === Actions === An action in object-oriented programming (OOP) is initiated by an object invoking a method in another object. An invocation can include additional information needed to carry out the method. The receiver executes the appropriate method and then returns control to the invoking object, sometimes supplying a result. === Exceptions === Programs can encounter various errors and unexpected conditions of varying seriousness. During the execution of the method many different problems may be discovered. Exceptions provide a clean way to deal with error conditions without complicating the code. A block of code may be defined to throw an exception whenever particular unexpected conditions or errors arise. This means that control passes to another block of code that catches the exception.

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  WiPay

  WiPay is a Caribbean-based payment technology company that specializes in electronic payments for businesses. WiPay was founded in 2016 by Aldwyn Wayne Jr., a Trinidadian businessman and graduate of Georgia Tech Institute. In September 2019, WiPay partnered with MasterCard. As a result, WiPay became the only licensed Payment Facilitator (PAYFAC) on both the MasterCard and Visa networks in the region.

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  Wix.com

  Wix.com Ltd. (Hebrew: וויקס.קום, romanized: wix.com) or simply Wix is an Israeli software company, publicly listed in the US, that provides cloud-based web development services. It offers tools for creating HTML5 websites for desktop and mobile platforms using online drag-and-drop editing. Along with its headquarters and other offices in Israel, Wix also has offices in Brazil, Canada, Germany, India, Ireland, Japan, Lithuania, Poland, the Netherlands, the United States, Ukraine, and Singapore. Users can add applications for social media, e-commerce, online marketing, contact forms, e-mail marketing, and community forums to their websites. The Wix website builder is built on a freemium business model, earning its revenues through premium upgrades. According to the W3Techs technology survey website, Wix was used by 2.5% of websites as of September 2023; at the end of May 2025, it was 3.8%. == History == === Corporate affairs === Wix was founded in 2006 by Israeli developers Avishai Abrahami, Nadav Abrahami, and Giora Kaplan. With its main offices in Tel Aviv, Wix was backed by investors Insight Venture Partners, Mangrove Capital Partners, Bessemer Venture Partners, DAG Ventures, and Benchmark Capital. By April 2010, Wix had 3.5 million users and raised US$10 million in Series C funding provided by Benchmark Capital and existing investors Bessemer Venture Partners and Mangrove Capital Partners. In March 2011, Wix had 8.5 million users and raised US$40 million in Series D funding, bringing its total funding to that date to US$61 million. By August 2013, the Wix platform had more than 34 million registered users. On 5 November 2013, Wix had an initial public offering on NASDAQ, raising about US$127 million for the company and some share holders. In 2016, Mark Tluszcz became the chair of the board of directors. In 2020, Wix's revenue increased to $989 million, a 30% rise year-on-year, primarily due to the shift of businesses online during the coronavirus pandemic. The company added over 31 million new registered users in 2020, reaching a total of 196.7 million by year's end. Wix added approximately 1 million net new premium subscriptions in 2020, surpassing $1 billion in annual collections for the first time. By the end of the year, there were 5.5 million premium subscriptions, a 22% increase compared to the end of 2019. As of its most recent reporting in June 2024, Wix has over 260 million users worldwide. === Product development === ==== 2000s ==== Wix entered an open beta phase in 2007 using a platform based on Adobe Flash. ==== 2010s ==== In June 2011, Wix launched the Facebook store module, making its first step into social commerce. In March 2012, Wix launched a new HTML5 site builder, replacing the Adobe Flash technology. In October 2012, Wix launched an app market for users to sell applications built with the company's automated web development technology. In August 2014, Wix launched Wix Hotels, a booking system for hotels, bed and breakfasts, and vacation rentals that use Wix websites. In June 2016, Wix introduced Wix ADI (Artificial Design Intelligence), a platform that uses artificial intelligence to design websites. ==== 2020s ==== In 2020, Wix launched an additional CMS, EditorX, which included additional CSS features to the original builder. In July 2023, Wix announced that it would be building on its ADI technology to create an AI powered website generator In October 2023, Wix launched the Wix Studio website builder. Co-founder and CEO, Avishai Abrahami described the platform as a “product for agencies”. In March 2024, the AI web builder, which uses a chatbot to help users create content was launched to the public. In March 2025, the digital publisher CNET has identified Wix as the "Best overall website builder overall." In August 2025, Wix announced it would launch banking services—including checking accounts and loans for small businesses—via a partnership with Israeli fintech Unit Finance, as it sought to diversify amid what it described as threats to its core website-building business from artificial intelligence. In January 2026, Wix launched Wix Harmony. Wix harmony is an AI website builder that uses agentic technology, generative design and vibe coding—with manual editing features for additional control. In May 2026, Wix announced layoffs affecting approximately 1,000 employees, or 20% of its workforce. CEO Avishai Abrahami cited two factors: the need to restructure around artificial intelligence and the appreciation of the Israeli shekel against the US dollar, which increased the cost of its Israel-based workforce relative to its dollar-denominated revenue. === Acquisitions === In April 2014, Wix announced the acquisition of Appixia, an Israeli startup for creating native mobile commerce (mCommerce) apps. In October 2014, Wix announced its acquisition of OpenRest, a developer of online ordering systems for restaurants. In April 2015, Wix acquired Moment.me, a mobile website builder for events and marketing tools for social lead generation. On 23 February 2017, Wix acquired the online art community DeviantArt for US$36 million. In January 2017, the company acquired Flok, a provider of customer loyalty programs tools. In February 2020, Wix acquired Inkfrog for eBay sellers, a web design company that provides customized business management software for eBay sellers. On 2 March 2021, Wix acquired SpeedETab, a Miami-based restaurant online technology provider. In May 2021, Wix acquired Rise.ai, a gift card and customer re-engagement package for online brands. A month later, Wix acquired Modalyst, a marketplace and drop-shipping platform. In May 2025, Wix acquired Hour One, a startup specializing in AI-powered video creation tools, to enhance its generative AI capabilities. In June 2025, the company acquired Base44, owned by independent entrepreneur Maor Shlomo, with the intention of integrating Base44's artificial intelligence capabilities and conversational interface into Wix's website and app building platform. == Description == Wix uses a freemium business model. Users can create websites for free then must purchase premium packages to connect their sites to their own domains, remove Wix ads, access the form builder, add e-commerce capabilities, or buy extra data storage and bandwidth. Wix provides customizable website templates and a drag-and-drop HTML5 website builder that includes apps, graphics, image galleries, fonts, vectors, animations, and other options. Users also may opt to create their web sites from scratch. In October 2013, Wix introduced a mobile editor for mobile viewing customization. Wix App Market offers both free and subscription-based applications, with a revenue split of 80% for the developer and 20% for Wix. Customers can integrate third-party applications into their own web sites, such as photograph feeds, blogging, music playlists, online community, e-mail marketing, and file management. Custom JavaScript code can be inserted into Wix webpages using the Velo API. == Controversies == === Use of WordPress code === In October 2016, there was a controversy over Wix's use of WordPress's GPL-licensed code. In response, Avishai Abrahami, Wix's CEO, published a response describing which open-source code was used and how Wix says it collaborates with the open-source community. However, it was subsequently noted that collaboration with the open-source community was not sufficient under the terms of the GPL license, which requires any code built on GPL-licensed code to be released under the same license. === Censorship === On 31 May 2021, 2021 Hong Kong Charter, a Wix-hosted website run by exiled Hong Kong activists, was shut down at the request of the Hong Kong Police. This was the first known case of Hong Kong's National Security Law being used to censor content on an overseas website. Wix later apologized for "mistakenly removing the website" and reinstated the website after it had been down for four days. In October 2023, Wix fired an employee in Dublin, Ireland, for having made social media posts critical of Israel. This incident led to criticism of Wix from members of the Oireachtas (the Irish parliament) and from the head of the Irish government, Taoiseach Leo Varadkar, who said it was "not okay to dismiss somebody because of their political views". Deputy head of government, Tánaiste Micheál Martin, also condemned their dismissal, stating "we tolerate debate with freedom of speech, freedom of opinion, and people have different opinions on these issues." The dismissed employee, Courtney Carey, successfully sued the company for unfair dismissal. Wix did not contest the charge, admitting liability. === Outreach abroad === In October 2023, The Irish Times reported that an Israeli advertising agency advised Wix staff how they can tailor posts for "outreach abroad". This included advice for Wix employees to “show Westernity” in social media posts supporting Israel, stating that “unlike the Gazans,

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  Microscope image processing

  Microscope image processing is a broad term that covers the use of digital image processing techniques to process, analyze and present images obtained from a microscope. Such processing is now commonplace in a number of diverse fields such as medicine, biological research, cancer research, drug testing, metallurgy, etc. A number of manufacturers of microscopes now specifically design in features that allow the microscopes to interface to an image processing system. == Image acquisition == Until the early 1990s, most image acquisition in video microscopy applications was typically done with an analog video camera, often simply closed circuit TV cameras. While this required the use of a frame grabber to digitize the images, video cameras provided images at full video frame rate (25-30 frames per second) allowing live video recording and processing. While the advent of solid state detectors yielded several advantages, the real-time video camera was actually superior in many respects. Today, acquisition is usually done using a CCD camera mounted in the optical path of the microscope. The camera may be full colour or monochrome. Very often, very high resolution cameras are employed to gain as much direct information as possible. Cryogenic cooling is also common, to minimise noise. Often digital cameras used for this application provide pixel intensity data to a resolution of 12-16 bits, much higher than is used in consumer imaging products. Ironically, in recent years, much effort has been put into acquiring data at video rates, or higher (25-30 frames per second or higher). What was once easy with off-the-shelf video cameras now requires special, high speed electronics to handle the vast digital data bandwidth. Higher speed acquisition allows dynamic processes to be observed in real time, or stored for later playback and analysis. Combined with the high image resolution, this approach can generate vast quantities of raw data, which can be a challenge to deal with, even with a modern computer system. While current CCD detectors allow very high image resolution, often this involves a trade-off because, for a given chip size, as the pixel count increases, the pixel size decreases. As the pixels get smaller, their well depth decreases, reducing the number of electrons that can be stored. In turn, this results in a poorer signal-to-noise ratio. For best results, one must select an appropriate sensor for a given application. Because microscope images have an intrinsic limiting resolution, it often makes little sense to use a noisy, high resolution detector for image acquisition. A more modest detector, with larger pixels, can often produce much higher quality images because of reduced noise. This is especially important in low-light applications such as fluorescence microscopy. Moreover, one must also consider the temporal resolution requirements of the application. A lower resolution detector will often have a significantly higher acquisition rate, permitting the observation of faster events. Conversely, if the observed object is motionless, one may wish to acquire images at the highest possible spatial resolution without regard to the time required to acquire a single image. == 2D image techniques == Image processing for microscopy application begins with fundamental techniques intended to most accurately reproduce the information contained in the microscopic sample. This might include adjusting the brightness and contrast of the image, averaging images to reduce image noise and correcting for illumination non-uniformities. Such processing involves only basic arithmetic operations between images (i.e. addition, subtraction, multiplication and division). The vast majority of processing done on microscope image is of this nature. Another class of common 2D operations called image convolution are often used to reduce or enhance image details. Such "blurring" and "sharpening" algorithms in most programs work by altering a pixel's value based on a weighted sum of that and the surrounding pixels (a more detailed description of kernel based convolution deserves an entry for itself) or by altering the frequency domain function of the image using Fourier Transform. Most image processing techniques are performed in the Frequency domain. Other basic two dimensional techniques include operations such as image rotation, warping, color balancing etc. At times, advanced techniques are employed with the goal of "undoing" the distortion of the optical path of the microscope, thus eliminating distortions and blurring caused by the instrumentation. This process is called deconvolution, and a variety of algorithms have been developed, some of great mathematical complexity. The end result is an image far sharper and clearer than could be obtained in the optical domain alone. This is typically a 3-dimensional operation, that analyzes a volumetric image (i.e. images taken at a variety of focal planes through the sample) and uses this data to reconstruct a more accurate 3-dimensional image. == 3D image techniques == Another common requirement is to take a series of images at a fixed position, but at different focal depths. Since most microscopic samples are essentially transparent, and the depth of field of the focused sample is exceptionally narrow, it is possible to capture images "through" a three-dimensional object using 2D equipment like confocal microscopes. Software is then able to reconstruct a 3D model of the original sample which may be manipulated appropriately. The processing turns a 2D instrument into a 3D instrument, which would not otherwise exist. In recent times this technique has led to a number of scientific discoveries in cell biology. == Analysis == Analysis of images will vary considerably according to application. Typical analysis includes determining where the edges of an object are, counting similar objects, calculating the area, perimeter length and other useful measurements of each object. A common approach is to create an image mask which only includes pixels that match certain criteria, then perform simpler scanning operations on the resulting mask. It is also possible to label objects and track their motion over a series of frames in a video sequence.

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  Camera interface

  The Camera Interface block or CAMIF is the hardware block that interfaces with different image sensor interfaces and provides a standard output that can be used for subsequent image processing. A typical Camera Interface would support at least a parallel interface although these days many camera interfaces are beginning to support the Mobile Industry Processor Interface (MIPI) Camera Serial Interface (CSI) interface. == Electrical connections == The camera interface's parallel interface consists of the following lines: 8 to 12 bits parallel data line These are parallel data lines that carry pixel data. The data transmitted on these lines change with every Pixel Clock (PCLK). Horizontal Sync (HSYNC) This is a special signal that goes from the camera sensor or ISP to the camera interface. An HSYNC indicates that one line of the frame is transmitted. Vertical Sync (VSYNC) This signal is transmitted after the entire frame is transferred. This signal is often a way to indicate that one entire frame is transmitted. Pixel Clock (PCLK) This is the pixel clock and it would change on every pixel. NOTE: The above lines are all treated as input lines to the Camera Interface hardware.

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  PerfKitBenchmarker

  PerfKit Benchmarker is an open source benchmarking tool used to measure and compare cloud offerings. PerfKit Benchmarker is licensed under the Apache 2 license terms. PerfKit Benchmarker is a community effort involving over 500 participants including researchers, academic institutions and companies together with the originator, Google. == General == PerfKit Benchmarker (PKB) is a community effort to deliver a repeatable, consistent, and open way of measuring Cloud Performance. It supports a growing list of cloud providers including: Alibaba Cloud, Amazon Web Services, CloudStack, DigitalOcean, Google Cloud Platform, Kubernetes, Microsoft Azure, OpenStack, Rackspace, IBM Bluemix (Softlayer). In addition to Cloud Providers to supports container orchestration including Kubernetes [1] and Mesos [2] and local "static" workstations and clusters of computers [3]. The goal is to create an open source living benchmark [framework] that represents how Cloud developers are building applications, evaluating Cloud alternatives, learning how to architect applications for each cloud. Living because it will change and morph quickly as developers change. PerfKit Benchmarker measures the end to end time to provision resources in the cloud, in addition to reporting on the most standard metrics of peak performance, e.g.: latency, throughput, time-to-complete, IOPS. PerfKit Benchmarker reduces the complexity in running benchmarks on supported cloud providers by unified and simple commands. It's designed to operate via vendor provided command line tools. PerfKit Benchmarker contains a canonical set of public benchmarks. All benchmarks are running with default/initial state and configuration (Not tuned to in favor of any providers). This provides a way to benchmark across cloud platforms, while getting a transparent view of application throughput, latency, variance, and overhead. == History == PerfKit Benchmarker (PKB) was started by Anthony F. Voellm, Alain Hamel, and Eric Hankland at Google in 2014. Once an initial "alpha" was in place Anthony F. Voellm and Ivan Santa Maria Filho built a community including ARM, Broadcom, Canonical, CenturyLink, Cisco, CloudHarmony, CloudSpectator, EcoCloud@EPFL, Intel, Mellanox, Microsoft, Qualcomm Technologies, Inc., Rackspace, Red Hat, Tradeworx Inc., and Thesys Technologies LLC. This community worked together behind the scenes in a private GitHub project to create an open way to measure cloud performance. This community released the first public "beta" was released on February 11, 2015, and announced in a blog post at which point the GitHub project was open to everyone. After almost a year and with large adaption (600+ participants on GitHub) the V1.0.0 was released along with a detailed architectural design on December 10, 2015. == Benchmarks == A list of available benchmarks from PerfKitBenchmarker: (The latest set of benchmarks can be found at GitHub readme file.) == Industry participants == Since Google open sourced the PerfKitBenchmarker, it became a community effort from over 30 leading researchers, academic schools and industry companies. Those organizations include: ARM, Broadcom, Canonical, CenturyLink, Cisco, CloudHarmony, Cloud Spectator, EcoCloud@EPFL, Intel, Mellanox, Microsoft, Qualcomm Technologies, Rackspace, Red Hat, and Thesys Technologies. In addition, Stanford and MIT are leading quarterly discussions on default benchmarks and settings proposed by the community. EcoCloud@EPFL is integrating CloudSuite into PerfKit Benchmarker. == Example runs == On Google Cloud Platform On AWS On Azure On Rackspace On a local machine

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