Tribute is an American video-sharing website headquartered in Brooklyn. Created in 2014 by Andrew Horn and Rory Petty, the platform lets customers create video montages (called "tributes") for occasions including weddings, birthdays, anniversaries, get well soon, and memorials. Tribute.co allows users to record video messages, request submissions from friends and family, insert photos, add music, and send the resulting video tribute montage to a recipient. == Overview == Tribute's collaborative technology starts with inviting people to contribute via email, SMS or social media. Participants receive a prompt to record a short video via their phone, computer or tablet. The site's video editing software allows users to drag and drop the clips in their desired order without prior video editing experience. == History == When Andrew Horn turned twenty-seven, his girlfriend, Miki Agrawal surprised him with a video montage containing clips of his family and closest friends explaining why they loved him. This resulted in Andrew's idea to create Tribute–a "living eulogy" video-compilation service that he co-founded with software engineer Rory Petty. Founded in 2014, Tribute's activity accelerated in 2020 due to the COVID-19 pandemic, and it had sent over 5 million videos as of December 2021. While social distance restrictions were in effect, the site provided a way for people to connect while in-person celebrations were put on hold. For each video sold, Tribute makes one available to hospitals for free and has partnered with Cleveland Clinic Cancer Center in Ohio, Lurie Children's Hospital in Illinois and CarePoint Health in New Jersey.
Inverse consistency
In image registration, inverse consistency measures the consistency of mappings between images produced by a registration algorithm. The inverse consistency error, introduced by Christiansen and Johnson in 2001, quantifies the distance between the composition of the mappings from each image to the other, produced by the registration procedure, and the identity function, and is used as a regularisation constraint in the loss function of many registration algorithms to enforce consistent mappings. Inverse consistency is necessary for good image registration but it is not sufficient, since a mapping can be perfectly consistent but not register the images at all. == Definition == Image registration is the process of establishing a common coordinate system between two images, and given two images I 1 : Ω 1 → R I 2 : Ω 2 → R {\displaystyle {\begin{aligned}I_{1}:\Omega _{1}\to \mathbb {R} \\I_{2}:\Omega _{2}\to \mathbb {R} \end{aligned}}} registering a source image I 1 {\displaystyle I_{1}} to a target image I 2 {\displaystyle I_{2}} consists of determining a transformation f 1 : Ω 2 → Ω 1 {\displaystyle f_{1}:\Omega _{2}\to \Omega _{1}} that maps points from the target space to the source space. An ideal registration algorithm should not be sensitive to which image in the pair is used as source or target, and the registration operator should be antisymmetric such that the mappings f 1 : Ω 2 → Ω 1 f 2 : Ω 1 → Ω 2 {\displaystyle {\begin{aligned}f_{1}:\Omega _{2}\to \Omega _{1}\\f_{2}:\Omega _{1}\to \Omega _{2}\end{aligned}}} produced when registering I 1 {\displaystyle I_{1}} to I 2 {\displaystyle I_{2}} and I 2 {\displaystyle I_{2}} to I 1 {\displaystyle I_{1}} respectively should be the inverse of each other, i.e. f 2 = f 1 − 1 {\displaystyle f_{2}=f_{1}^{-1}} and f 1 = f 2 − 1 {\displaystyle f_{1}=f_{2}^{-1}} or, equivalently, f 2 ∘ f 1 = id Ω 2 {\displaystyle f_{2}\circ f_{1}=\operatorname {id} _{\Omega _{2}}} and f 1 ∘ f 2 = id Ω 1 {\displaystyle f_{1}\circ f_{2}=\operatorname {id} _{\Omega _{1}}} , where ∘ {\displaystyle \circ } denotes the function composition operator. Real algorithms are not perfect, and when swapping the role of source and target image in a registration problem the so obtained transformations are not the inverse of each other. Inverse consistency can be enforced by adding to the loss function of the registration a symmetric regularisation term that penalises inconsistent transformations ∫ Ω 2 ‖ f 2 ( f 1 ( x ) ) − x ‖ 2 d x + ∫ Ω 1 ‖ f 1 ( f 2 ( x ) ) − x ‖ 2 d x . {\displaystyle \int _{\Omega _{2}}\left\Vert f_{2}(f_{1}(x))-x\right\Vert ^{2}\mathrm {d} x+\int _{\Omega _{1}}\left\Vert f_{1}(f_{2}(x))-x\right\Vert ^{2}\mathrm {d} x.} Inverse consistency can be used as a quality metric to evaluate image registration results. The inverse consistency error ( I C E {\displaystyle ICE} ) measures the distance between the composition of the two transforms and the identity function, and it can be formulated in terms of both average ( I C E a {\displaystyle ICE_{a}} ) or maximum ( I C E m {\displaystyle ICE_{m}} ) over a region of interest Ω {\displaystyle \Omega } of the image: I C E a = 1 ∫ Ω d x ∫ Ω ‖ f 2 ( f 1 ( x ) ) − x ‖ d x I C E m = max x ∈ Ω ‖ f 2 ( f 1 ( x ) ) − x ‖ . {\displaystyle {\begin{aligned}ICE_{a}&={\frac {1}{\int _{\Omega }\mathrm {d} x}}\int _{\Omega }\left\Vert f_{2}(f_{1}(x))-x\right\Vert \mathrm {d} x\\ICE_{m}&=\max _{x\in \Omega }\left\Vert f_{2}(f_{1}(x))-x\right\Vert .\end{aligned}}} While inverse consistency is a necessary property of good registration algorithms, inverse consistency error alone is not a sufficient metric to evaluate the quality of image registration results, since a perfectly consistent mapping, with no other constraint, may be not even close to correctly register a pair of images.
Is an AI Photo Editor Worth It in 2026?
Shopping for the best AI photo editor? An AI photo editor is software that uses machine learning to help you get more done — it keeps getting smarter as the underlying models improve. Pricing, accuracy, and the size of the model behind the tool are the three factors that most affect daily usefulness. Whether you are a beginner or a pro, the right AI photo editor slots into your workflow and pays for itself fast. Below we compare features, pricing, and real output so you can choose with confidence.
Machine translation in China
Machine translation in China is the history of machine translation systems developed in China. China became the fourth country that began machine translation (MT) research following USA, UK, and the Soviet Union. In 1957, the Language Institute of Chinese Academy of Sciences took the initiative in Russian-Chinese MT research program and set up an MT research group. From then on the research activities were directed and applied for academic purposes in Universities. The turning point of MT systems launching initiatives in market began from 1990s. MT systems went into blossom into the market. Among these systems, there were commercialized MT systems. To be more specific, Transtar was the first commercialized MT system and has been constantly upgraded. What's more, IMC/EC MT system which was developed by Computer Institute of Chinese Academy of Sciences has further made great advancement. Meanwhile, the practical MT system MT-IT-EC specific to communication domain was also striking to notice, for it has greatly improved the efficiency and productivity in the issue of publications. Government funding is a critical component and support in the development of market-oriented machine translation in China. It is evident to see that since Chinese opened up to the outside world and joined the WTO, the vigorous import and export trade generate opportunities for machine translation to transfer technical terms of products into the readable target information. Facing the increasing demand of sophisticated state-of -the -art translation technology, the academic area including research institute and universities are even launching bachelors’ and master's programs regarding machine translation. Thus, strong evidence illustrates the promising field of machine translation in the future market of China.
AI Copywriting Tools: Free vs Paid (2026)
Comparing the best AI copywriting tool? An AI copywriting tool is software that uses machine learning to help you get more done — it lowers the barrier so anyone can produce professional output. Privacy matters too: check whether your data trains the model and whether a no-log or enterprise tier is available. Whether you are a beginner or a pro, the right AI copywriting tool slots into your workflow and pays for itself fast. We tested the leading options and ranked them by quality, value, and ease of use.
SEMAT
SEMAT (Software Engineering Method and Theory) is an initiative to reshape software engineering such that software engineering qualifies as a rigorous discipline. The initiative was launched in December 2009 by Ivar Jacobson, Bertrand Meyer, and Richard Soley with a call for action statement and a vision statement. The initiative was envisioned as a multi-year effort for bridging the gap between the developer community and the academic community and for creating a community giving value to the whole software community. The work is now structured in four different but strongly related areas: Practice, Education, Theory, and Community. The Practice area primarily addresses practices. The Education area is concerned with all issues related to training for both the developers and the academics including students. The Theory area is primarily addressing the search for a General Theory in Software Engineering. Finally, the Community area works with setting up legal entities, creating websites and community growth. It was expected that the Practice area, the Education area and the Theory area would at some point in time integrate in a way of value to all of them: the Practice area would be a "customer" of the Theory area, and direct the research to useful results for the developer community. The Theory area would give a solid and practical platform for the Practice area. And, the Education area would communicate the results in proper ways. == Practice area == The first step was here to develop a common ground or a kernel including the essence of software engineering – things we always have, always do, always produce when developing software. The second step was envisioned to add value on top of this kernel in the form of a library of practices to be composed to become specific methods, specific for all kinds of reasons such as the preferences of the team using it, kind of software being built, etc. The first step is as of this writing just about to be concluded. The results are a kernel including universal elements for software development – called the Essence Kernel, and a language – called the Essence Language - to describe these elements (and elements built on top of the kernel (practices, methods, and more). Essence, including both the kernel and language, has been published as an OMG standard in beta status in July 2013 and is expected to become a formally adopted standard in early 2014. The second step has just started, and the Practice area will be divided into a number of separate but interconnected tracks: the practice (library track), the tool track are so far identified and work has started or is about to get started. The practice track is currently working on a Users Guide. == Education area == The area focuses on leveraging the work of SEMAT in software engineering education, both within academia and industry. It promotes global education based on a common ground called Essence. The area's target groups are instructors such as university professors and industrial coaches as well as their students and learning practitioners. The goal of the area is to create educational courses and course materials that are internationally viable, identify pedagogical approaches that are appropriate and effective for specific target groups and disseminate experience and lessons learned. The area includes members from a number of universities and institutes worldwide. Most members have already been involved in leveraging aspects of SEMAT in the context of their software engineering courses. They are gathering their resources and starting a common venture towards defining a new generation of SEMAT-powered software engineering curricula. As of 2018, some studies of utilizing Essence in educational settings exist. One example of the use of Essence in university education was a software engineering course carried out in Norwegian University of Science and Technology. A study was conducted by introducing Essence into a project-based software engineering course, with the aim of understanding what difficulties the students faced in using Essence, and whether they considered it to have been useful. The results indicated that Essence could also be useful for novice software engineers by (1) encouraging them to look up and study new practices and methods in order to create their own, (2) encouraging them to adjust their way-of-working reflectively and in a situation-specific manner, (3) helping them structure their way of working. The findings of another study introducing students to Essence through a digital game supported these findings: the students felt that Essence will be useful to them in future, real-world projects, and that they wish to utilize it in them. == Theory area == An important part of SEMAT is that a general theory of software engineering is planned to emerge with significant benefits. A series of workshops held under the title SEMAT Workshop on a General Theory of Software Engineering (GTSE) are a key component in awareness building around general theories. In addition to community awareness building, SEMAT also aims to contribute with a specific general theory of software engineering. This theory should be solidly based on the SEMAT Essence language and kernel, and should support software engineering practitioners' goal-oriented decision making. As argued elsewhere, such support is predicated on the predictive capabilities of the theory. Thus, the SEMAT Essence should be augmented to allow the prediction of critical software engineering phenomena. The GTSE workshop series assists in the development of the SEMAT general software engineering theory by engaging a larger community in the search for, development of, and evaluation of promising theories, which may be used as a base for the SEMAT theory. == Organizational structure == === Main organization === SEMAT is chaired by Sumeet S. Malhotra of Tata Consultancy Services. The CEO of the organization is Ste Nadin of Fujitsu. The Executive Management Committee of SEMAT are Ivar Jacobson, Ste Nadin, Sumeet S. Malhotra, Paul E. McMahon, Michael Goedicke and Cecile Peraire. === Japan Chapter === Japan Chapter was established in April 2013, and it has more than 250 members as of November 2013. Member activities include carrying out seminars about SEMAT, considering utilization of SEMAT Essence for integrating different requirements engineering techniques and body of knowledges (BoKs), and translating articles into Japanese. === Korea Chapter === The chapter was inaugurated with about 50 members in October 2013. Member activities include: 2e Consulting started rewriting their IT service engagement methods using the Essence kernel, and uEngine Solutions started developing a tool to orchestrate Essence-kernel based practices into a project method. Korean government supported KAIST to conduct research in Essence. === Latin American Chapter === Semat Latin American Chapter was created in August 2011 in Medellin (Colombia) by Ivar Jacobson during the Latin American Software Engineering Symposium. This Chapter has 9 Executive Committee members from Colombia, Venezuela, Peru, Brazil, Argentina, Chile, and Mexico, chaired by Dr. Carlos Zapata from Colombia. More than 80 people signed the initial declaration of the Chapter and nowadays the Chapter members are in charge of disseminating the Semat ideas in all Latin America. Chapter members have participated in various Latin American conferences, including the Latin American Conference on Informatics (CLEI), the Ibero American Software Engineering and Knowledge Engineering Journeys (JIISIC), the Colombian Computing Conference (CCC), and the Chilean Computing Meeting (ECC). The Chapter contributed in the submission sent in response to the OMG call for proposals and currently studies didactic strategies for teaching the Semat kernel by games, theoretical studies about some kernel elements, and practical representations of several software development and quality methods by using the Semat kernel. Some of the members also translated the Essence book and some other Semat materials and papers into Spanish. === Russia Chapter === Russian Chapter has about 20 members. A few universities have incorporated SEMAT in their training courses , including Moscow State University, Moscow Institute of Physics and Technology, Higher School of Economics, Moscow State University of Economics, Statistics, and Informatics. The chapter and some commercial companies are carrying out seminars about SEMAT. INCOSE Russian Chapter is working on an extension of SEMAT to systems engineering. EC-leasing is working on an extension of the Kernel for Software Life Cycle. Russian Chapter attended in two conferences: Actual Problems of System and Software Engineering and SECR with SEMAT section and articles. Translation of the Essence book into Russian is in progress. == Practical Applications of SEMAT == Ideas developed by the SEMAT community have been applied by both industry and ac
Generalized nondeterministic finite automaton
In the theory of computation, a generalized nondeterministic finite automaton (GNFA), also known as an expression automaton or a generalized nondeterministic finite state machine, is a variation of a nondeterministic finite automaton (NFA) where each transition is labeled with any regular expression. The GNFA reads blocks of symbols from the input which constitute a string as defined by the regular expression on the transition. There are several differences between a standard finite state machine and a generalized nondeterministic finite state machine. A GNFA must have only one start state and one accept state, and these cannot be the same state, whereas an NFA or DFA both may have several accept states, and the start state can be an accept state. A GNFA must have only one transition between any two states, whereas a NFA or DFA both allow for numerous transitions between states. In a GNFA, a state has a single transition to every state in the machine, although often it is a convention to ignore the transitions that are labelled with the empty set when drawing generalized nondeterministic finite state machines. == Formal definition == A GNFA can be defined as a 5-tuple, (S, Σ, T, s, a), consisting of a finite set of states (S); a finite set called the alphabet (Σ); a transition function (T : (S ∖ {\displaystyle \setminus } {a}) × (S ∖ {\displaystyle \setminus } {s}) → R); a start state (s ∈ S); an accept state (a ∈ S); where R is the collection of all regular expressions over the alphabet Σ. The transition function takes as its argument a pair of two states and outputs a regular expression (the label of the transition). This differs from other finite state machines, which take as input a single state and an input from the alphabet (or the empty string in the case of nondeterministic finite state machines) and outputs the next state (or the set of possible states in the case of nondeterministic finite state machines). A DFA or NFA can easily be converted into a GNFA and then the GNFA can be easily converted into a regular expression by repeatedly collapsing parts of it to single edges until S = {s, a}. Similarly, GNFAs can be reduced to NFAs by changing regular expression operators into new edges until each edge is labelled with a regular expression matching a single string of length at most 1. NFAs, in turn, can be reduced to DFAs using the powerset construction. This shows that GNFAs recognize the same set of formal languages as DFAs and NFAs.