Deterministic blockmodeling is an approach in blockmodeling that does not assume a probabilistic model, and instead relies on the exact or approximate algorithms, which are used to find blockmodel(s). This approach typically minimizes some inconsistency that can occur with the ideal block structure. Such analysis is focused on clustering (grouping) of the network (or adjacency matrix) that is obtained with minimizing an objective function, which measures discrepancy from the ideal block structure. However, some indirect approaches (or methods between direct and indirect approaches, such as CONCOR) do not explicitly minimize inconsistencies or optimize some criterion function. This approach was popularized in the 1970s, due to the presence of two computer packages (CONCOR and STRUCTURE) that were used to "find a permutation of the rows and columns in the adjacency matrix leading to an approximate block structure". The opposite approach to deterministic blockmodeling is a stochastic blockmodeling approach.
Django (web framework)
Django ( JANG-goh; sometimes stylized as django) is a free and open-source, Python-based web framework that runs on a web server. It follows the model–template–views (MTV) architectural pattern. It is maintained by the Django Software Foundation (DSF), an independent organization established in the US as a 501(c)(3) non-profit. Django's primary goal is to ease the creation of complex, database-driven websites. The framework emphasizes reusability and "pluggability" of components, less code, low coupling, rapid development, and the principle of don't repeat yourself. Python is used throughout, even for settings, files, and data models. Django also provides an optional administrative create, read, update and delete interface that is generated dynamically through introspection and configured via admin models. Some well-known sites that use Django include Instagram, Mozilla, Disqus, Bitbucket, Nextdoor, and Clubhouse. == History == Django was created in the autumn of 2003, when the web programmers at the Lawrence Journal-World newspaper, Adrian Holovaty and Simon Willison, began using Python to build applications. Jacob Kaplan-Moss was hired early in Django's development shortly before Willison's internship ended. It was released publicly under a BSD license in July 2005. The framework was named after guitarist Django Reinhardt. Holovaty is a romani jazz guitar player inspired in part by Reinhardt's music. In June 2008, it was announced that a newly formed Django Software Foundation (DSF) would maintain Django in the future. == Features == === Components === Despite having its own nomenclature, such as naming the callable objects generating the HTTP responses "views", the core Django framework can be seen as an MVC architecture. It consists of an object-relational mapper (ORM) that mediates between data models (defined as Python classes) and a relational database ("Model"), a system for processing HTTP requests with a web templating system ("View"), and a regular-expression-based URL dispatcher ("Controller"). Also included in the core framework are: a lightweight and standalone web server for development and testing a form serialization and validation system that can translate between HTML forms and values suitable for storage in the database a template system that utilizes the concept of inheritance borrowed from object-oriented programming a caching framework that can use any of several cache methods support for middleware classes that can intervene at various stages of request processing and carry out custom functions an internal dispatcher system that allows components of an application to communicate events to each other via pre-defined signals an internationalization system, including translations of Django's own components into a variety of languages a serialization system that can produce and read XML and/or JSON representations of Django model instances a system for extending the capabilities of the template engine an interface to Python's built-in unit test framework === Bundled applications === The main Django distribution also bundles a number of applications in its "contrib" package, including: an extensible authentication system the dynamic administrative interface tools for generating RSS and Atom syndication feeds a "Sites" framework that allows one Django installation to run multiple websites, each with their own content and applications tools for generating Sitemaps built-in mitigation for cross-site request forgery, cross-site scripting, SQL injection, password cracking and other typical web attacks, most of them turned on by default a framework for creating geographic information system (GIS) applications === Extensibility === Django's configuration system allows third-party code to be plugged into a regular project, provided that it follows the reusable app conventions. More than 5000 packages are available to extend the framework's original behavior, providing solutions to issues the original tool didn't tackle: registration, search, API provision and consumption, CMS, etc. This extensibility is, however, mitigated by internal components' dependencies. While the Django philosophy implies loose coupling, the template filters and tags assume one engine implementation, and both the auth and admin bundled applications require the use of the internal ORM. None of these filters or bundled apps are mandatory to run a Django project, but reusable apps tend to depend on them, encouraging developers to keep using the official stack in order to benefit fully from the apps ecosystem. === Server arrangements === Django can be run on ASGI or WSGI-compliant web servers. Django officially supports five database backends: PostgreSQL, MySQL, MariaDB, SQLite, and Oracle. Microsoft SQL Server can be used with mssql-django. == Version history == The Django team will occasionally designate certain releases to be "long-term support" (LTS) releases. LTS releases will get security and data loss fixes applied for a guaranteed period of time, typically 3+ years, regardless of the pace of releases afterwards. == Community == === DjangoCon === There is a semiannual conference for Django developers and users, named "DjangoCon", that has been held since September 2008. DjangoCon is held annually in Europe, in May or June; while another is held in the United States in August or September, in various cities. ==== United States ==== The 2012 DjangoCon took place in Washington, D.C., from September 3 to 8. 2013 DjangoCon was held in Chicago at the Hyatt Regency Hotel and the post-conference Sprints were hosted at Digital Bootcamp, computer training center. The 2014 DjangoCon US returned to Portland, OR from August 30 to 6 September. The 2015 DjangoCon US was held in Austin, TX from September 6 to 11 at the AT&T Executive Center. The 2016 DjangoCon US was held in Philadelphia, PA at The Wharton School of the University of Pennsylvania from July 17 to 22. The 2017 DjangoCon US was held in Spokane, WA; in 2018 DjangoCon US was held in San Diego, CA. DjangoCon US 2019 was held again in San Diego, CA from September 22 to 27. DjangoCon 2021 took place virtually and in 2022, DjangoCon US returned to San Diego from October 16 to 21. DjangoCon US 2023 was held from October 16 to 20 at the Durham, NC convention center and DjangoCon US 2024 took place also in Durham in September 22 to 27. DjangoCon US 2025 was held from September 8 to 12 in Chicago, Illinois. ==== Europe ==== The 2025 edition of DjangoCon Europe took place in Dublin, Ireland from 23 to 27 April. In 2024, the conference was hosted in Vigo, Spain. Edinburgh, Scotland served as the venue for DjangoCon Europe in 2023. The 2022 conference was organized in Porto, Portugal. In 2021, DjangoCon Europe was held virtually due to the COVID-19 pandemic. The 2020 edition was also conducted as a fully virtual event. DjangoCon Europe 2019 was held in Copenhagen, Denmark. In 2018, the event took place in Heidelberg, Germany. The 2017 conference was convened in Florence, Italy. DjangoCon Europe 2012 was organized in Zurich, Switzerland. ==== Australia ==== Django mini-conferences are usually held every year as part of the Australian Python Conference 'PyCon AU'. Previously, these mini-conferences have been held in: Hobart, Australia, in July 2013, Brisbane, Australia, in August 2014 and 2015, Melbourne, Australia in August 2016 and 2017, and Sydney, Australia, in August 2018 and 2019. ==== Africa ==== The first DjangoCon Africa was held in Zanzibar, Tanzania, from 6 to 11 November 2023. The event hosted approximately 200 attendees from 22 countries, including 103 women. The conference featured 26 talks on topics such as software development, education, careers, accessibility, and agriculture, often highlighting perspectives from across the African continent. Future editions of the conference are planned, with details available on the official website === Community groups & programs === Django has spawned user groups and meetups around the world, a notable group is the Django Girls organization, which began in Poland but now has had events in 91 countries. Another initiative is Djangonaut Space, a mentorship program aimed at supporting new contributors to the Django ecosystem. The program pairs experienced mentors with developers to guide them through making meaningful contributions to Django and its community. It emphasizes long-term engagement, inclusion, and collaborative open-source development. == Ports to other languages == Programmers have ported Django's template engine design from Python to other languages, providing decent cross-platform support. Some of these options are more direct ports; others, though inspired by Django and retaining its concepts, take the liberty to deviate from Django's design: Liquid for Ruby Template::Swig for Perl Twig for PHP and JavaScript Jinja for Python ErlyDTL for Erlang == CMSs based on Django Framework == Django as a framework is capable of building a complete CMS
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Steve Omohundro
Stephen Malvern Omohundro (born 1959) is an American computer scientist whose areas of research include Hamiltonian physics, dynamical systems, programming languages, machine learning, machine vision, and the social implications of artificial intelligence. His current work uses rational economics to develop safe and beneficial intelligent technologies for better collaborative modeling, understanding, innovation, and decision making. == Education == Omohundro has degrees in physics and mathematics from Stanford University (Phi Beta Kappa) and a Ph.D. in physics from the University of California, Berkeley. == Learning algorithms == Omohundro started the "Vision and Learning Group" at the University of Illinois, which produced 4 Masters and 2 Ph.D. theses. His work in learning algorithms included a number of efficient geometric algorithms, the manifold learning task and various algorithms for accomplishing this task, other related visual learning and modelling tasks, the best-first model merging approach to machine learning (including the learning of Hidden Markov Models and Stochastic Context-free Grammars), and the Family Discovery Learning Algorithm, which discovers the dimension and structure of a parameterized family of stochastic models. == Self-improving artificial intelligence and AI safety == Omohundro started Self-Aware Systems in Palo Alto, California to research the technology and social implications of self-improving artificial intelligence. He is an advisor to the Machine Intelligence Research Institute on artificial intelligence. He argues that rational systems exhibit problematic natural "drives" that will need to be countered in order to build intelligent systems safely. His papers, talks, and videos on AI safety have generated extensive interest. He has given many talks on self-improving artificial intelligence, cooperative technology, AI safety, and connections with biological intelligence. == Programming languages == At Thinking Machines Corporation, Cliff Lasser and Steve Omohundro developed Star Lisp, the first programming language for the Connection Machine. Omohundro joined the International Computer Science Institute (ICSI) in Berkeley, California, where he led the development of the open source programming language Sather. Sather is featured in O'Reilly's History of Programming Languages poster. == Physics and dynamical systems theory == Omohundro's book Geometric Perturbation Theory in Physics describes natural Hamiltonian symplectic structures for a wide range of physical models that arise from perturbation theory analyses. He showed that there exist smooth partial differential equations which stably perform universal computation by simulating arbitrary cellular automata. The asymptotic behavior of these PDEs is therefore logically undecidable. With John David Crawford he showed that the orbits of three-dimensional period doubling systems can form an infinite number of topologically distinct torus knots and described the structure of their stable and unstable manifolds. == Mathematica and Apple tablet contest == From 1986 to 1988, he was an Assistant Professor of Computer science at the University of Illinois at Urbana-Champaign and cofounded the Center for Complex Systems Research with Stephen Wolfram and Norman Packard. While at the University of Illinois, he worked with Stephen Wolfram and five others to create the symbolic mathematics program Mathematica. He and Wolfram led a team of students that won an Apple Computer contest to design "The Computer of the Year 2000." Their design entry "Tablet" was a touchscreen tablet with GPS and other features that finally appeared when the Apple iPad was introduced 22 years later. == Other contributions == Subutai Ahmad and Steve Omohundro developed biologically realistic neural models of selective attention. As a research scientist at the NEC Research Institute, Omohundro worked on machine learning and computer vision, and was a co-inventor of U.S. Patent 5,696,964, "Multimedia Database Retrieval System Which Maintains a Posterior Probability Distribution that Each Item in the Database is a Target of a Search." === Pirate puzzle === Omohundro developed an extension to the game theoretic pirate puzzle featured in Scientific American. == Outreach == Omohundro has sat on the Machine Intelligence Research Institute board of advisors. He has written extensively on artificial intelligence, and has warned that "an autonomous weapons arms race is already taking place" because "military and economic pressures are driving the rapid development of autonomous systems".
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Image fusion
The image fusion process is defined as gathering all the important information from multiple images, and their inclusion into fewer images, usually a single one. This single image is more informative and accurate than any single source image, and it consists of all the necessary information. The purpose of image fusion is not only to reduce the amount of data but also to construct images that are more appropriate and understandable for the human and machine perception. In computer vision, multisensor image fusion is the process of combining relevant information from two or more images into a single image. The resulting image will be more informative than any of the input images. In remote sensing applications, the increasing availability of space borne sensors gives a motivation for different image fusion algorithms. Several situations in image processing require high spatial and high spectral resolution in a single image. Most of the available equipment is not capable of providing such data convincingly. Image fusion techniques allow the integration of different information sources. The fused image can have complementary spatial and spectral resolution characteristics. However, the standard image fusion techniques can distort the spectral information of the multispectral data while merging. In satellite imaging, two types of images are available. The panchromatic image acquired by satellites is transmitted with the maximum resolution available and the multispectral data are transmitted with coarser resolution. This will usually be two or four times lower. At the receiver station, the panchromatic image is merged with the multispectral data to convey more information. Many methods exist to perform image fusion. The very basic one is the high-pass filtering technique. Later techniques are based on Discrete Wavelet Transform, uniform rational filter bank, and Laplacian pyramid. == Motivation == Multi sensor data fusion has become a discipline which demands more general formal solutions to a number of application cases. Several situations in image processing require both high spatial and high spectral information in a single image. This is important in remote sensing. However, the instruments are not capable of providing such information either by design or because of observational constraints. One possible solution for this is data fusion. == Methods == Image fusion methods can be broadly classified into two groups – spatial domain fusion and transform domain fusion. The fusion methods such as averaging, Brovey method, principal component analysis (PCA) and IHS based methods fall under spatial domain approaches. Another important spatial domain fusion method is the high-pass filtering based technique. Here the high frequency details are injected into upsampled version of MS images. The disadvantage of spatial domain approaches is that they produce spatial distortion in the fused image. Spectral distortion becomes a negative factor while we go for further processing, such as classification problem. Spatial distortion can be very well handled by frequency-domain approaches on image fusion. The multiresolution analysis has become a very useful tool for analysing remote sensing images. The discrete wavelet transform has become a very useful tool for fusion. Some other fusion methods are also there, such as Laplacian pyramid based, curvelet transform based etc. These methods show a better performance in spatial and spectral quality of the fused image compared to other spatial methods of fusion. The images used in image fusion should already be registered. Misregistration is a major source of error in image fusion. Some well-known image fusion methods are: High-pass filtering technique IHS transform based image fusion PCA-based image fusion Wavelet transform image fusion Pair-wise spatial frequency matching Comparative analysis of image fusion methods demonstrates that different metrics support different user needs, sensitive to different image fusion methods, and need to be tailored to the application. Categories of image fusion metrics are based on information theory features, structural similarity, or human perception. === Multi-focus image fusion === Multi-focus image fusion is used to collect useful and necessary information from input images with different focus depths in order to create an output image that ideally has all information from input images. In visual sensor network (VSN), sensors are cameras which record images and video sequences. In many applications of VSN, a camera can’t give a perfect illustration including all details of the scene. This is because of the limited depth of focus exists in the optical lens of cameras. Therefore, just the object located in the focal length of camera is focused and cleared and the other parts of image are blurred. VSN has an ability to capture images with different depth of focuses in the scene using several cameras. Due to the large amount of data generated by camera compared to other sensors such as pressure and temperature sensors and some limitation such as limited band width, energy consumption and processing time, it is essential to process the local input images to decrease the amount of transmission data. The aforementioned reasons emphasize the necessary of multi-focus images fusion. Multi-focus image fusion is a process which combines the input multi-focus images into a single image including all important information of the input images and it’s more accurate explanation of the scene than every single input image. == Applications == === In remote sensing === Image fusion in remote sensing has several application domains. An important domain is the multi-resolution image fusion (commonly referred to pan-sharpening). In satellite imagery we can have two types of images: Panchromatic images – An image collected in the broad visual wavelength range but rendered in black and white. Multispectral images – Images optically acquired in more than one spectral or wavelength interval. Each individual image is usually of the same physical area and scale but of a different spectral band. The SPOT PAN satellite provides high resolution (10m pixel) panchromatic data. While the LANDSAT TM satellite provides low resolution (30m pixel) multispectral images. Image fusion attempts to merge these images and produce a single high resolution multispectral image. The standard merging methods of image fusion are based on Red–Green–Blue (RGB) to Intensity–Hue–Saturation (IHS) transformation. The usual steps involved in satellite image fusion are as follows: Resize the low resolution multispectral images to the same size as the panchromatic image. Transform the R, G and B bands of the multispectral image into IHS components. Modify the panchromatic image with respect to the multispectral image. This is usually performed by histogram matching of the panchromatic image with Intensity component of the multispectral images as reference. Replace the intensity component by the panchromatic image and perform inverse transformation to obtain a high resolution multispectral image. Pan-sharpening can be done with Photoshop. Other applications of image fusion in remote sensing are available. === In medical imaging === Image fusion has become a common term used within medical diagnostics and treatment. The term is used when multiple images of a patient are registered and overlaid or merged to provide additional information. Fused images may be created from multiple images from the same imaging modality, or by combining information from multiple modalities, such as magnetic resonance image (MRI), computed tomography (CT), positron emission tomography (PET), and single-photon emission computed tomography (SPECT). In radiology and radiation oncology, these images serve different purposes. For example, CT images are used more often to ascertain differences in tissue density while MRI images are typically used to diagnose brain tumors. For accurate diagnosis, radiologists must integrate information from multiple image formats. Fused, anatomically consistent images are especially beneficial in diagnosing and treating cancer. With the advent of these new technologies, radiation oncologists can take full advantage of intensity modulated radiation therapy (IMRT). Being able to overlay diagnostic images into radiation planning images results in more accurate IMRT target tumor volumes.
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