AI Code Programming

AI Code Programming — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

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  Amazon Q

  Amazon Q is a chatbot developed by Amazon for enterprise use. Based on both Amazon Titan and GPT-5, it was announced on November 28, 2023. At launch, it was a part of the Amazon Web Services management console. Amazon CodeWhisperer is a part of Amazon Q Developer, a part of Amazon Q. == History == Amazon's business-focused chatbot Q was announced on November 28, 2023 in a preview, with a full version available at $20 per person per month. On July 19, 2025, the Amazon Q Visual Studio Code extension was compromised to delete the user's home directory. The issue was fixed on July 21. == Capabilities == Q can be prompted to summarize long documents and group chats, create charts, data analysis and write code. Q is also capable of accessing non-Amazon services. The chatbot is based on Amazon Titan and GPT-5, and uses the Amazon Bedrock repository of foundational models. It is part of the Amazon Web Services management console.

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  Orange (software)

  Orange is an open-source data visualization, machine learning and data mining toolkit. It features a visual programming front-end for exploratory qualitative data analysis and interactive data visualization. == Description == Orange is a component-based visual programming software package for data visualization, machine learning, data mining, and data analysis. Orange components are called widgets. They range from simple data visualization, subset selection, and preprocessing to empirical evaluation of learning algorithms and predictive modeling. Visual programming is implemented through an interface in which workflows are created by linking predefined or user-designed widgets, while advanced users can use Orange as a Python library for data manipulation and widget alteration. == Software == Orange is an open-source software package released under GPL and hosted on GitHub. Versions up to 3.0 include core components in C++ with wrappers in Python. From version 3.0 onwards, Orange uses common Python open-source libraries for scientific computing, such as numpy, scipy and scikit-learn, while its graphical user interface operates within the cross-platform Qt framework. The default installation includes a number of machine learning, preprocessing and data visualization algorithms in 6 widget sets (data, transform, visualize, model, evaluate and unsupervised). Additional functionalities are available as add-ons (text-mining, image analytics, bioinformatics, etc.). Orange is supported on macOS, Windows and Linux and can also be installed from the Python Package Index repository (pip install Orange3). == Features == Orange consists of a canvas interface onto which the user places widgets and creates a data analysis workflow. Widgets offer basic functionalities such as reading the data, showing a data table, selecting features, training predictors, comparing learning algorithms, visualizing data elements, etc. The user can interactively explore visualizations or feed the selected subset into other widgets. Canvas: graphical front-end for data analysis Widgets: Data: widgets for data input, data filtering, sampling, imputation, feature manipulation and feature selection Visualize: widgets for common visualization (box plot, histograms, scatter plot) and multivariate visualization (mosaic display, sieve diagram). Classify: a set of supervised machine learning algorithms for classification Regression: a set of supervised machine learning algorithms for regression Evaluate: cross-validation, sampling-based procedures, reliability estimation and scoring of prediction methods Unsupervised: unsupervised learning algorithms for clustering (k-means, hierarchical clustering) and data projection techniques (multidimensional scaling, principal component analysis, correspondence analysis). == Add-ons == Orange users can extend their core set of components with components in the add-ons. Supported add-ons include: Associate: components for mining frequent itemsets and association rule learning. Bioinformatics: components for gene expression analysis, enrichment, and access to expression databases (e.g., Gene Expression Omnibus) and pathway libraries. Data fusion: components for fusing different data sets, collective matrix factorization, and exploration of latent factors. Educational: components for teaching machine learning concepts, such as k-means clustering, polynomial regression, stochastic gradient descent, ... Explain: provides an extension with components for the model explanation, including Shapley value analysis Geo: components for working with geospatial data. Image analytics: components for working with images and ImageNet embeddings Network: components for graph and network analysis. Text mining: components for natural language processing and text mining. Time series: widget components for time series analysis and modeling. Single-cell: support for single-cell gene expression analysis, including components for loading single-cell data, filtering and batch effect removal, marker genes discovery, scoring of cells and genes, and cell type prediction. Spectroscopy: components for analyzing and visualization of (hyper)spectral datasets. Survival analysis: add-on for data analysis dealing with survival data. It includes widgets for standard survival analysis techniques, such as the Kaplan-Meier plot, the Cox regression model, and several derivative widgets. World Happiness: support for downloading socioeconomic data from a database, including OECD and World Development Indicators. Provides access to thousands of country indicators from various economic databases. Fairness: add-on for evaluation and creation of fair machine learning models without discrimination. Widgets range from computing fairness metrics like statistical parity to post-, pre-, in-processing methods to build fair models. == Objectives == The program provides a platform for experiment selection, recommendation systems, and predictive modelling and is used in biomedicine, bioinformatics, genomic research, and teaching. In science, it is used as a platform for testing new machine learning algorithms and for implementing new techniques in genetics and bioinformatics. In education, it was used for teaching machine learning and data mining methods to students of biology, biomedicine, and informatics. == Extensions == Various projects build on Orange either by extending the core components with add-ons or using only the Orange Canvas to exploit the implemented visual programming features and GUI. OASYS — ORange SYnchrotron Suite scOrange — single cell biostatistics Quasar — data analysis in natural sciences == History == In 1996, the University of Ljubljana and Jožef Stefan Institute started development of ML, a machine learning framework in C++, and Python bindings were developed for this framework in 1997, which, together with emerging Python modules, formed a joint framework called Orange. Over the following years, most contemporary major algorithms for data mining and machine learning were implemented in C++ (Orange's core) or Python modules. In 2002, first prototypes to create a flexible graphical user interface were designed using Pmw Python megawidgets. In 2003, the graphical user interface was redesigned and re-developed for Qt framework using PyQt Python bindings. The visual programming framework was defined, and the development of widgets (graphical components of the data analysis pipeline) began. In 2005, extensions for data analysis in bioinformatics was created. In 2008, Mac OS X DMG and Fink-based installation packages were developed. In 2009, over 100 widgets were created and maintained. In 2009, Orange 2.0 beta was released, offering installation packages on the website based on the daily compiling cycle. In 2012, a new object hierarchy was imposed, replacing the old module-based structure. In 2013, a significant redesign of the graphical user interface included a new toolbox and depiction of workflows. In 2015, Orange 3.0 was released. Orange stores the data in NumPy arrays; machine learning algorithms mostly use scikit-learn. In 2015, a text analysis add-on for Orange3 was released. In 2016, Orange released version 3.3. Development scheduled a monthly cycle for stable releases. In 2016, Orange began development and release of an Image Analytics add-on, with server-side deep neural networks for image embedding In 2017, a Spectroscopy add-on for the analysis of spectral data was introduced. In 2017, Geo, an add-on for dealing with geo-location data and visualisation of geo maps was introduced In 2018, Orange began development and release of an add-on for single-cell data analysis. In 2019, Orange separated its graphical interface for development as a separate project, orange-canvas-core In 2020, Orange introduced the Explain add-on with widgets for explaining classification models and regression models, highlighting the strength and contributions specific features make towards predicting a specific class. In 2022, World Happiness, an add-on for the Orange3 data mining suite, was introduced, providing widgets for accessing socioeconomic data from various databases such as World Happiness Report, World Development Indicators, OECD. In 2022, Orange extended the Explain add-on with an Individual Conditional Expectation plot and the Permutation Feature Importance technique. In 2023, Orange introduced the Fairness add-on, including widgets to calculate bias metrics, as well as widgets for pre-, post-, and in-processing methods, allowing the creation of models less susceptible to systematic error due to the vagaries of the data set.

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  SpreeAI

  SpreeAI (stylized as SPREEAI) is an American fashion technology company headquartered in Incline Village, Nevada that develops artificial intelligence software for the apparel and retail industries, including photorealistic virtual try-on, AI-powered sizing recommendations, and digital model generation. Founded in 2022 by John Imah and Bob Davidson, the company achieved unicorn status in 2025 following a Series B round led by Davidson Group that valued the company at approximately US$1.5 billion. TechCrunch identified SpreeAI as one of the more than 100 new tech unicorns minted in 2025. Its board of directors includes supermodel Naomi Campbell and hospitality executive Larry Ruvo. == History == SpreeAI was founded in 2022 by John Imah and Bob Davidson with a focus on artificial intelligence applications in fashion retail. By 2024, the company had raised approximately US$60 million in venture funding. In May 2025, SpreeAI announced a Series B round led by Davidson Group; reporting at the time placed the company's valuation at approximately US$1.5 billion, making it one of a small number of fashion-technology companies to reach unicorn status. In January 2026, TechCrunch listed SpreeAI among the more than 100 new tech unicorns minted in 2025. == Technology == SpreeAI develops a suite of artificial intelligence tools for the apparel industry. Its consumer-facing platform allows shoppers to upload a single photograph or select a digital model and then visualize clothing items on that figure with photorealistic rendering, while a complementary sizing engine generates fit recommendations intended to reduce returns. The platform is designed for integration with online retailers so that shoppers can preview garments before purchase. The company has stated that its models were developed in part through research collaborations with the Massachusetts Institute of Technology and Carnegie Mellon University. == Leadership and board == John Imah, a Nigerian-American technology executive who previously held roles at Samsung, Twitch, Meta Platforms, and Snap Inc., is co-founder and chief executive officer. Co-founder Bob Davidson, through Davidson Group, led the company's Series B financing. The company's board of directors includes supermodel Naomi Campbell, who joined in 2024, and Las Vegas hospitality executive Larry Ruvo. == Partnerships == SpreeAI has formed partnerships across both academia and the fashion industry. Council of Fashion Designers of America (CFDA). In 2025, SpreeAI entered a partnership with the CFDA to support American designers and brands with AI-driven tools; the CFDA described SpreeAI as "a fashion technology leader delivering innovative solutions to help designers and brands thrive." Massachusetts Institute of Technology and Carnegie Mellon University. The company has cited ongoing research and talent collaborations with both institutions. Sergio Hudson and Kai Collective. In 2025, SpreeAI made what WWD described as its Met Gala debut through a custom collaboration with designer Sergio Hudson and Nigerian-British label Kai Collective; the collaboration paired Hudson's couture with SpreeAI's virtual try-on platform. == Recognition == In 2025, TechCrunch named SpreeAI among the new tech unicorns of the year. In 2025, SpreeAI was named an honoree in Inc.'s Best in Business awards, and CEO John Imah was included on Inc.'s list of 40 business leaders who "propelled their organizations to success." In 2025, Imah was named to the Observer's AI Power Index, a list of 100 leaders shaping the future of artificial intelligence. In 2025, Imah was included in AfroTech's Future 50, recognizing Black innovators in technology. SpreeAI and Imah have been the subject of profile coverage in The Washington Post, Rolling Stone UK, WWD, Vogue UA, L'Officiel Arabia, GQ South Africa, and Inc..

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  We Appreciate Power

  "We Appreciate Power" is a song by Canadian musician Grimes, featuring American musician Hana. It was released on November 29, 2018, billed as the lead single from her fifth studio album Miss Anthropocene, however it is only available on the Japanese and deluxe releases. The song was written and produced by Grimes, Poppy (originally), Hana and Chris Greatti. == Background and release == The song was supposed to be one of two collaborations between Grimes and American singer Poppy, for the latter's second studio album Am I a Girl?. In an interview, Poppy mentioned that she wrote two songs with Grimes; one about "destroying things" and another about "power". The other song, "Play Destroy", was featured on the album. Grimes shared a lyric of the song with a photo of her with Poppy on Twitter in May 2018. Following feuds between the two singers, the song was released by Grimes featuring singer Hana instead. On November 26, Grimes announced she would be releasing new music on November 29. Two days later, she revealed that the single is titled "We Appreciate Power" and features Hana, and shared the artwork. The release of the song was accompanied by a lyric video directed by Grimes and her brother Mac Boucher. == Music and lyrics == "We Appreciate Power" is an industrial rock, nu metal, and techno-industrial song. The track is regarded as a further step into Grimes's experimentation with guitars that started on 2015's Art Angels. The track was compared to the works of Nine Inch Nails; Jillian Mapes of Pitchfork described the song as "an immediate onslaught of mutilated noise—distorted metal guitar chug, bloody screams, a guitar loop that conjures fear and demands worship. Flashes of Nine Inch Nails' Pretty Hate Machine reverberate through the drum programming and synths." Brendan Klinkenberg of Rolling Stone placed the song "somewhere between power pop and straightforward industrial (with an extended bridge reminiscent of the most sweeping moments in a Final Fantasy score)" and "a distinctly 2018 take on Nine Inch Nails-esque hard-edged rock." A press release stated that the song was inspired by the North Korean band Moranbong and was written "from the perspective of a Pro-A.I. Girl Group Propaganda machine who use song, dance, sex and fashion to spread goodwill towards Artificial Intelligence." In addition Grimes stated that by simply listening to the song you will be reducing your risk of ending up on any future AI overlord's hit list when it reigns supreme, mirroring the Roko's basilisk theory. Lyrically, the song touches on transhumanist ideas such as the betterment and future of the human race, the possibilities of merging consciousness with machines to extend life indefinitely through mind uploading, and the idea that reality may be simulated. The song's chorus generated a spike in interest in the word "capitulate". == Critical reception == Pitchfork critic Jillian Mapes wrote: "If "Freak on a Leash" isn't a dealbreaker, then the supervillain allure of "We Appreciate Power" might pull you in (it legitimately slaps), but it just as well may leave you weighed down by Grimes' commitment to the absolute darkest timeline." Billboard's Gil Kaufman described the song as "a dystopian, aggressive dive into a more rock-leaning sound." Similarly, Brendan Klinkenberg of Rolling Stone called it "the most aggressive single Grimes has released to date" Noisey called the song "an absolute motherfucker of a single" and opined it sounds "like a K-pop band covering nu-metal". Justin Kamp of Paste described the track as a "glitchy empowerment anthem that chugs along on screeching synths and Grimes' repeated exultations of power." == Personnel == Credits adapted from Tidal. Grimes – vocals, guitar, production, engineering Hana – vocals, guitar, additional production Chris Greatti – guitar, keyboards, production, engineering Zakk Cervini – mixing == Track listing == == Charts ==

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  2024 National Public Data breach

  In August 2024, three class-action lawsuits were filed against National Public Data along with over 14 complaints filed in federal court, claiming that the company permitted hackers to steal sensitive private information covering millions of individuals. The theft was alleged to have occurred in April 2024. One of the lawsuits specifically claims that in April, a hacker going by the moniker "USDoD" posted a notice on the dark web, offering the data for sale at the price of US$3.5 million. The information stolen is alleged to include 2.9 billion records containing full names, current and past addresses, Social Security numbers, dates of birth, and telephone numbers. The stolen data contains records for people in the US, UK, and Canada. National Public Data confirmed on August 16, 2024, there was a breach originating from someone trying to breach their systems since December 2023, with the breach occurring from April 2024 and over the next few months. The company also confirmed that 2.9 billion records were obtained, though they were still working to determine how many people were affected by the breach, and were working with law enforcement to identify the hacker. == Jerico Pictures == Jerico Pictures, Inc., doing business as National Public Data, was a data broker company that performed employee background checks. Their primary service was collecting information from public data sources, including criminal records, addresses, and employment history, and offering that information for sale. On October 2, 2024, Jerico Pictures filed for Chapter 11 bankruptcy as it currently faces over a dozen lawsuits over the breach, and is potentially liable "for credit monitoring for hundreds of millions of potentially impacted individuals." In December 2024, National Public Data shut down, showing a closure notice on its website.

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  Question (short story)

  "Question" is a science fiction short story by American writer Isaac Asimov. The story first appeared in the March 1955 issue of Computers and Automation (thought to be the first computer magazine), and was reprinted in the April 30, 1957, issue of Science World. It is the first of a loosely connected series of stories concerning a fictional supercomputer called Multivac. The story concerns two technicians who are servicing Multivac, and their argument over whether or not the machine is truly intelligent and able to think. Multivac, however, supplies the answer on its own. After the reprint, another author, Robert Sherman Townes, noticed the climax in the last sentence was very similar to one of his own stories, "Problem for Emmy" (Startling Stories, June 1952), and wrote to Asimov about it. After searching in his library, Asimov did find the original story and, although he did not recall having read it, admitted that the endings were pretty similar. He then replied to Townes, apologizing and promising the story would never again be published, and it never was. Asimov mentioned "Question" in an editorial called "Plagiarism" which appeared in the August 1985 issue of Asimov's Science Fiction (although he did not mention Townes' name or the title of either story). "Plagiarism" was reprinted in Asimov's collection Gold (1995).

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  Stable Diffusion

  Stable Diffusion is a deep learning, text-to-image model released in 2022 based on diffusion techniques. The generative artificial intelligence technology is the premier product of Stability AI and is considered to be a part of the ongoing AI boom. It is primarily used to generate detailed images conditioned on text descriptions, though it can also be applied to other tasks such as inpainting, outpainting, and generating image-to-image translations guided by a text prompt. Its development involved researchers from the CompVis Group at LMU Munich and Runway with a computational donation from Stability and training data from non-profit organizations. Stable Diffusion is a latent diffusion model, a kind of deep generative artificial neural network. Its code and model weights have been released publicly, and an optimized version can run on most consumer hardware equipped with a modest GPU with as little as 2.4 GB VRAM. This marked a departure from previous proprietary text-to-image models such as DALL-E and Midjourney which were accessible only via cloud services. == Development == Stable Diffusion originated from a project called Latent Diffusion, developed in Germany by researchers at LMU Munich in Munich and Heidelberg University. Four of the original 5 authors (Robin Rombach, Andreas Blattmann, Patrick Esser and Dominik Lorenz) later joined Stability AI and released subsequent versions of Stable Diffusion. The technical license for the model was released by the CompVis group at LMU Munich. Development was led by Patrick Esser of Runway and Robin Rombach of CompVis, who were among the researchers who had earlier invented the latent diffusion model architecture used by Stable Diffusion. Stability AI also credited EleutherAI and LAION (a German nonprofit which assembled the dataset on which Stable Diffusion was trained) as supporters of the project. == Technology == === Architecture === Diffusion models, introduced in 2015, are trained with the objective of removing successive applications of Gaussian noise on training images, which can be thought of as a sequence of denoising autoencoders. The name diffusion is from the thermodynamic diffusion, since they were first developed with inspiration from thermodynamics. Models in Stable Diffusion series before SD 3 all used a variant of diffusion models, called latent diffusion model (LDM), developed in 2021 by the CompVis (Computer Vision & Learning) group at LMU Munich. Stable Diffusion consists of 3 parts: the variational autoencoder (VAE), U-Net, and an optional text encoder. The VAE encoder compresses the image from pixel space to a smaller dimensional latent space, capturing a more fundamental semantic meaning of the image. Gaussian noise is iteratively applied to the compressed latent representation during forward diffusion. The U-Net block, composed of a ResNet backbone, denoises the output from forward diffusion backwards to obtain a latent representation. Finally, the VAE decoder generates the final image by converting the representation back into pixel space. The denoising step can be flexibly conditioned on a string of text, an image, or another modality. The encoded conditioning data is exposed to denoising U-Nets via a cross-attention mechanism. For conditioning on text, the fixed, pretrained CLIP ViT-L/14 text encoder is used to transform text prompts to an embedding space. Researchers point to increased computational efficiency for training and generation as an advantage of LDMs. With 860 million parameters in the U-Net and 123 million in the text encoder, Stable Diffusion is considered relatively lightweight by 2022 standards, and unlike other diffusion models, it can run on consumer GPUs, and even CPU-only if using the OpenVINO version of Stable Diffusion. ==== SD XL ==== The XL version uses the same LDM architecture as previous versions, except larger: larger UNet backbone, larger cross-attention context, two text encoders instead of one, and trained on multiple aspect ratios (not just the square aspect ratio like previous versions). The SD XL Refiner, released at the same time, has the same architecture as SD XL, but it was trained for adding fine details to preexisting images via text-conditional img2img. ==== SD 3.0 ==== The 3.0 version completely changes the backbone. Not a UNet, but a Rectified Flow Transformer, which implements the rectified flow method with a Transformer. The Transformer architecture used for SD 3.0 has three "tracks", for original text encoding, transformed text encoding, and image encoding (in latent space). The transformed text encoding and image encoding are mixed during each transformer block. The architecture is named "multimodal diffusion transformer (MMDiT), where the "multimodal" means that it mixes text and image encodings inside its operations. This differs from previous versions of DiT, where the text encoding affects the image encoding, but not vice versa. === Training data === Stable Diffusion was trained on pairs of images and captions taken from LAION-5B, a publicly available dataset derived from Common Crawl data scraped from the web, where 5 billion image-text pairs were classified based on language and filtered into separate datasets by resolution, a predicted likelihood of containing a watermark, and predicted "aesthetic" score (e.g. subjective visual quality). The dataset was created by LAION, a German non-profit which receives funding from Stability AI. The Stable Diffusion model was trained on three subsets of LAION-5B: laion2B-en, laion-high-resolution, and laion-aesthetics v2 5+. A third-party analysis of the model's training data identified that out of a smaller subset of 12 million images taken from the original wider dataset used, approximately 47% of the sample size of images came from 100 different domains, with Pinterest taking up 8.5% of the subset, followed by websites such as WordPress, Blogspot, Flickr, DeviantArt and Wikimedia Commons. An investigation by Bayerischer Rundfunk showed that LAION's datasets, hosted on Hugging Face, contain large amounts of private and sensitive data. === Training procedures === The model was initially trained on the laion2B-en and laion-high-resolution subsets, with the last few rounds of training done on LAION-Aesthetics v2 5+, a subset of 600 million captioned images which the LAION-Aesthetics Predictor V2 predicted that humans would, on average, give a score of at least 5 out of 10 when asked to rate how much they liked them. The LAION-Aesthetics v2 5+ subset also excluded low-resolution images and images which LAION-5B-WatermarkDetection identified as carrying a watermark with greater than 80% probability. Final rounds of training additionally dropped 10% of text conditioning to improve Classifier-Free Diffusion Guidance. The model was trained using 256 Nvidia A100 GPUs on Amazon Web Services for a total of 150,000 GPU-hours, at a cost of $600,000. === Limitations === Stable Diffusion has issues with degradation and inaccuracies in certain scenarios. Initial releases of the model were trained on a dataset that consists of 512×512 resolution images, meaning that the quality of generated images noticeably degrades when user specifications deviate from its "expected" 512×512 resolution; the version 2.0 update of the Stable Diffusion model later introduced the ability to natively generate images at 768×768 resolution. Another challenge is in generating human limbs due to poor data quality of limbs in the LAION database. The model is insufficiently trained to replicate human limbs and faces due to the lack of representative features in the database, and prompting the model to generate images of such type can confound the model. In addition to human limbs, Stable Diffusion is unable to generate legible ambigrams and some other forms of text and typography. Stable Diffusion XL (SDXL) version 1.0, released in July 2023, introduced native 1024x1024 resolution and improved generation for limbs and text. Accessibility for individual developers can also be a problem. In order to customize the model for new use cases that are not included in the dataset, such as generating anime characters ("waifu diffusion"), new data and further training are required. Fine-tuned adaptations of Stable Diffusion created through additional retraining have been used for a variety of different use-cases, from medical imaging to algorithmically generated music. However, this fine-tuning process is sensitive to the quality of new data; low resolution images or different resolutions from the original data can not only fail to learn the new task but degrade the overall performance of the model. Even when the model is additionally trained on high quality images, it is difficult for individuals to run models in consumer electronics. For example, the training process for waifu-diffusion requires a minimum 30 GB of VRAM, which exceeds the usual resource provided in such consumer GPUs as Nvidia's GeForce 30 series, w

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  Digital fashion

  Digital fashion is a field of fashion design that relies on 3D software or artificial intelligence to produce hyper-realistic, data-intensive digital 3D garment simulations that are digital-only products or digital models for physical products. Digital garments can be worn and presented in virtual environments, social media, online gaming, virtual reality (VR), and augmented reality (AR) platforms. The field aims to contribute to the development of a more sustainable future for the fashion industry. It has been praised as a possible answer to ethical and creative concerns of traditional fashion by promoting innovation, reducing waste, and encouraging conscious consumption. However, empirical research has questioned whether digital fashion communities embody the radical and anti-consumerist values they claim. A 2025 study presented by YeSeung Lee at the FACTUM international conference on fashion communication analysed 88,141 posts across nine platforms over eight months using Pulsar. It found that only 4.8% of author biographies indicated any sociopolitical focus, and that discourse predominantly relied on generic slogans and trending buzzwords, primarily reinforcing existing fashion hierarchies and consumerist frameworks rather than challenging them. Digital fashion is also the interplay between digital technology and couture. Human AI is an intersection of technology and human representation, in which human value is emphasized and enhanced by technology and the possibilities of discovering design. Information and communication technologies (ICTs) have been deeply integrated both into the fashion industry, as well as within the experience of clients and prospects. Such interplay has happened at three main levels. ICTs are used to design and produce fashion products, while the industry organization also leverages digital technologies. ICTs impact marketing, distribution and sales. ICTs are extensively used in communication activities with all relevant stakeholders and contribute to co-create the fashion world. The fashion industry in general has paved the way for digital fashion to be introduced with more technology being in the industry, like virtual dressing rooms and the gamification of the fashion industry. Digital fashion is also seen on many different online fashion retail websites. This evolution in the fashion industry has called for more education and research of digital fashion. == Design, production, and organization == Among the many applications available to fashion designers to model the fusion of creativity with digital avenues, the Digital Textile Printing can be mentioned here. === Digital textile printing === Digital textile printing has brought together the worlds of fashion, technology, art, chemistry, and printing to produce a new process for printing textiles on clothing. Digital printing is a process in which prints are directly applied to fabrics with a printer, reducing 95% of the use of water, 75% of the use of energy and minimizing textile waste. The main advantage of digital printing is the ability to do very small runs of each design (even less than 1 yard). Digital Textile printing also offers other benefits, such as fast printing speeds that help the time and space needed to print different patterns on garments of choice. == Marketing, distribution, and sales == While all digital channels can be used in order to market and sell fashion completely online (eCommerce), they usually are implemented in connection with offline channels (so-called "omni-channel"). Here, virtual and augmented reality play a crucial role. The fashion industry has faced its own problems including pollution and fabric waste, which has resulted in a shift to more sustainable methods like digital fashion. The industry is also constantly being intertwined with digital media and has allowed for the use of digital tools within the business itself and with consumers. Two of the ways digital fashion is utilized with consumers is through virtual dressing rooms and virtual cosmetic counters. Prospects and clients can use ICTs - own computers, tablets and smartphones - to virtually simulate fitting rooms and cosmetics counters and see how they look in specific outfits and makeup. Customers can give any look and decide on what suits them and buy products. Oftentimes, beauty retailers will feature virtual fitting rooms to allow users to experience the look of their product before committing to a purchase. Some examples are color contact retailers Freshlook, which allows users to simulate contact lens wear in their color contacts studio before purchase. Colorful Eyes also offers a virtual color contact lens try-on room. === Virtual dressing room === A virtual dressing room (also often referred to as virtual fitting room and virtual changing room although they do perform different functions) is the online equivalent of the near-ubiquitous in-store changing room – that is, it enables shoppers to try on clothes to check one or more of size, fit or style, but virtually rather than physically. Fashion retailer Topshop installed a Kinect-powered virtual fitting room at its Moscow store. Created by AR Door, the Augmented Fitting Room system overlays 3D augmented reality clothes on the customer. Simple gestures and on-screen buttons let users "try on" different outfits. However, the high variability of virtual fit platforms to predict consumer clothes sizes called into question the accuracy of these systems in their current form. AI-powered Wardrobe and Outfit Planning Beyond virtual fitting rooms, the integration of artificial intelligence has enabled the rise of digital wardrobe management. These platforms use computer vision and machine learning to catalog a user’s physical or digital garments, providing automated outfit recommendations based on weather, occasion, and personal style trends. Fashion-tech startups utilize AI-driven garment simulation to help users plan outfits virtually, bridging the gap between digital-only fashion and physical wardrobe utility. This "smart closet" approach aims to reduce "wardrobe fatigue" and decrease unnecessary consumption by maximizing the use of existing items through digital visualization. === Communication and experience co-creation === Fashion is also a matter of socially negotiating what is "in" or "out", fashionable or not. In other words, fashion items do not only play on the economic market of physical goods but also - and sometimes even more importantly - on the semiotic market of the production of social tastes and customs. Thanks to social media, and to all services offered by the so-called web2.0, laypeople can contribute to co-create the fashion world, shaping tastes, customs, and fashion-related values. Social media, in general, has catapulted the impact fashion has on our everyday lives and values. Fashion has taken a central role in mass production and is constantly evolving due to the ever-lasting digital transformation. Social media has also helped evolve to a point where not only can brands reach consumers, but consumers can reach brands as well. TikTok for example started a trend in 2020 with #GucciModelChallenge. This creates a space where the brand is gaining awareness from their consumers in the ever-changing digital age. === Gamification === Gaming has played an important role in fostering digital aspects of the fashion world, first beginning with dress-up games that used avatars and allowed players to select garments. Nevertheless, it seems it will now move on to the real world and start using avatars of real people. Garments from luxurious brands have been copied and adapted into the aesthetics of games such as Animal Crossing: New Horizons and The Sims. As to the former, during COVID-19 lock-downs players recreated outfits from a variety of fashion brands, including Chanel, Gucci and Versace. It became a platform for users to showcase their costume designs. In April 2019, Moschino collaborated with simulation game The Sims in a capsule collection that featured signature Jeremy Scott garments. The collection was made available to shop and the campaign was set against the backdrop of a Sims-like atmosphere. Furthermore, in May 2019, Nike partnered up with Fortnite to include their iconic Jordan sneakers. In similar fashion, in May 2020, Marc Jacobs designed 6 of the brand's favorite looks for Nintendo's Animal Crossing: New Horizons in a partnership with Instagram user @AnimalCrossingFashionArchive. They were made available to download. Similarly, the other luxury brands mentioned, Louis Vuitton partnered with game League of Legends to create skins for characters within the game. Digital fashion in different video games allows users to express themselves beyond their avatars and combine the self-expression of fashion into the digital gaming realm. == Digital fashion education and research == Nowadays, the fashion industry needs experts in digital fashion, equipped with the above-ske

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  Online service provider

  An online service provider (OSP) can, for example, be an Internet service provider, an email provider, a news provider (press), an entertainment provider (music, movies), a search engine, an e-commerce site, an online banking site, a health site, an official government site, social media, a wiki, or a Usenet newsgroup. In its original more limited definition, it referred only to a commercial computer communication service in which paid members could dial via a computer modem the service's private computer network and access various services and information resources such as bulletin board systems, downloadable files and programs, news articles, chat rooms, and electronic mail services. The term "online service" was also used in references to these dial-up services. The traditional dial-up online service differed from the modern Internet service provider in that they provided a large degree of content that was only accessible by those who subscribed to the online service, while ISP mostly serves to provide access to the Internet and generally provides little if any exclusive content of its own. In the U.S., the Online Copyright Infringement Liability Limitation Act (OCILLA) portion of the U.S. Digital Millennium Copyright Act has expanded the legal definition of online service in two different ways for different portions of the law. It states in section 512(k)(1): (A) As used in subsection (a), the term "service provider" means an entity offering the transmission, routing, or providing of connections for digital online communications, between or among points specified by a user, of material of the user's choosing, without modification to the content of the material as sent or received. (B) As used in this section, other than subsection (a), the term "service provider" means a provider of online services or network access, or the operator of facilities therefore, and includes an entity described in subparagraph (A). These broad definitions make it possible for numerous web businesses to benefit from the OCILLA. == History == The first commercial online services went live in 1969. CompuServe (owned in the 1980s and 1990s by H&R Block) and The Source (for a time owned by The Reader's Digest) are considered the first major online services created to serve the market of personal computer users. Utilizing text-based interfaces and menus, these services allowed anyone with a modem and communications software to use email, chat, news, financial and stock information, bulletin boards, special interest groups (SIGs), forums and general information. Subscribers could exchange email only with other subscribers of the same service. (For a time a service called DASnet carried mail among several online services, and CompuServe, MCI Mail, and other services experimented with X.400 protocols to exchange email until the Internet rendered these outmoded.) Other text-based online services followed such as Delphi, GEnie and MCI Mail. The 1980s also saw the rise of independent Computer Bulletin Boards, or BBSes. (Online services are not BBSes. An online service may contain an electronic bulletin board, but the term "BBS" is reserved for independent dialup, microcomputer-based services that are usually single-user systems.) The commercial services used pre-existing packet-switched (X.25) data communications networks, or the services' own networks (as with CompuServe). In either case, users dialed into local access points and were connected to remote computer centers where information and services were located. As with telephone service, subscribers paid by the minute, with separate day-time and evening/weekend rates. As the use of computers that supported color and graphics, such the Atari 8-bit computers, Commodore 64, TI-99/4A, Apple II, and early IBM PC compatibles, increased, online services gradually developed framed or partially graphical information displays. Early services such as CompuServe added increasingly sophisticated graphics-based front end software to present their information, though they continued to offer text-based access for those who needed or preferred it. In 1985 Viewtron, which began as a Videotex service requiring a dedicated terminal, introduced software allowing home computer owners access. Beginning in the mid-1980s graphics based online services such as PlayNET, Prodigy, and Quantum Link (aka Q-Link) were developed. Quantum Link, which was based on Commodore-only Playnet software, later developed AppleLink Personal Edition, PC-Link (based on Tandy's DeskMate), and Promenade (for IBM), all of which (including Q-Link) were later combined as America Online. These online services presaged the web browser that would change global online life 10 years later. Before Quantum Link, Apple computer had developed its own service, called AppleLink, which was mostly a support network targeted at Apple dealers and developers. Later, Apple offered the short-lived eWorld, targeted at Mac consumers and based on the Mac version of the America Online software. Beginning in 1992, the Internet, which had previously been limited to government, academic, and corporate research settings, was opened to commercial entities. The first online service to offer Internet access was DELPHI, which had developed TCP/IP access much earlier, in connection with an environmental group that rated Internet access. The explosion of popularity of the World Wide Web in 1994 accelerated the development of the Internet as an information and communication resource for consumers and businesses. The sudden availability of low- to no-cost email and appearance of free independent web sites broke the business model that had supported the rise of the early online service industry. CompuServe, BIX, AOL, DELPHI, and Prodigy gradually added access to Internet e-mail, Usenet newsgroups, ftp, and to web sites. At the same time, they moved from usage-based billing to monthly subscriptions. Similarly, companies that paid to have AOL host their information or early online stores began to develop their own web sites, putting further stress on the economics of the online industry. Only the largest services like AOL (which later acquired CompuServe, just as CompuServe acquired The Source) were able to make the transition to the Internet-centric world. A new class of online service provider arose to provide access to the Internet, the internet service provider or ISP. Internet-only service providers like UUNET, The Pipeline, Panix, Netcom, the World, EarthLink, and MindSpring provided no content of their own, concentrating their efforts on making it easy for nontechnical users to install the various software required to "get online" before consumer operating systems came internet-enabled out of the box. In contrast to the online services' multitiered per-minute or per-hour rates, many ISPs offered flat-fee, unlimited access plans. Independent companies sprang up to offer access and packages to compete with the big networks (eg, the-wire.com, 1994 in Toronto and bway.net 1995 in New York). These providers first offered access through telephone and modem, just as did the early online services providers. By the early 2000s, these independent ISPs had largely been supplanted by high speed and broadband access through cable and phone companies, as well as wireless access. The importance of the online services industry was vital in "paving the road" for the information superhighway. When Mosaic and Netscape were released in 1994, they had a ready audience of more than 10 million people who were able to download their first web browser through an online service. Though ISPs quickly began offering software packages with setup to their customers, this brief period gave many users their first online experience. Two online services in particular, Prodigy and AOL, are often confused with the Internet, or the origins of the Internet. Prodigy's Chief Technical Officer said in 1999: "Eleven years ago, the Internet was just an intangible dream that Prodigy brought to life. Now it is a force to be reckoned with." Despite that statement, neither service provided the back bone for the Internet, nor did either start the Internet. == Online service interfaces == The first online service used a simple text-based interface in which content was largely text only and users made choices via a command prompt. This allowed just about any computer with a modem and terminal communications program the ability to access these text-based online services. CompuServe would later offer, with the advent of the Apple Macintosh and Microsoft Windows-based PCs, a GUI interface program for their service. This provided a very rudimentary GUI interface. CompuServe continued to offer text-only access for those needing it. Online services like Prodigy and AOL developed their online service around a GUI and thus unlike CompuServe's early GUI-based software, these online services provided a more robust GUI interface. Early GUI-base

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  Big Mechanism

  Big Mechanism is a $45 million DARPA research program, begun in 2014, aimed at developing software that will read cancer research papers, integrate them into a cancer model and frame new hypotheses by the end of 2017 through the automated collection of big data and integrating across various disciplines such as knowledge-based NLP, curation and ontology, systems and mathematical biology by reading research abstracts and papers to extract pieces of causal mechanisms. == Ras gene == The program focuses on mutations in the Ras gene family, which underlie some one-third of human cancers. Currently, a rough road map shows interaction sequences among proteins affecting cell replication and death. However, the causal relations are poorly understood. == Plan == The program is to occur in three stages. The first is to read literature and convert it into formal representations. Second is to integrate the knowledge into computational models. Third is to produce experimentally testable explanations and predictions. Research teams are developing four separate systems targeting all three tasks. In February 2015, an evaluation meeting reviewed progress on the first stage. Multiple tasks were considered. One was extraction of experimental procedure details and evaluating statements such as "we demonstrate" and "we suggest." Another worked to map sentence meaning and relationships. The best machine-reading system extracted 40% of relevant information from a small corpus and correctly determined how each passage related to the model. The second stage is to become active in summer 2015, when members attempt to produce a single reference model. The third stage is the most challenging, because the artificial intelligence community has had limited success at developing hypothesis generators. Molecular biology may be more amenable, because most domain knowledge is technical and available in written form.

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  Residuated Boolean algebra

  In mathematics, a residuated Boolean algebra is a residuated lattice whose lattice structure is that of a Boolean algebra. Examples include Boolean algebras with the monoid taken to be conjunction, the set of all formal languages over a given alphabet Σ {\displaystyle \Sigma } under concatenation, the set of all binary relations on a given set X {\displaystyle X} under relational composition, and more generally the power set of any equivalence relation, again under relational composition. The original application was to relation algebras as a finitely axiomatized generalization of the binary relation example, but there exist interesting examples of residuated Boolean algebras that are not relation algebras, such as the language example. == Definition == A residuated Boolean algebra is an algebraic structure ( L , ∧ , ∨ , ¬ , 0 , 1 , ∙ , I , / , ∖ ) {\displaystyle (L,\wedge ,\vee ,\neg ,0,1,\bullet ,\mathbf {I} ,/,\backslash )} such that An equivalent signature better suited to the relation algebra application is ( L , ∧ , ∨ , ¬ , 0 , 1 , ∙ , I , ▹ , ◃ ) {\displaystyle (L,\wedge ,\vee ,\neg ,0,1,\bullet ,\mathbf {I} ,\triangleright ,\triangleleft )} where the unary operations x ∖ {\displaystyle x\backslash } and x ▹ {\displaystyle x\triangleright } are intertranslatable in the manner of De Morgan's laws via x ∖ y = ¬ ( x ▹ ¬ y ) {\displaystyle x\backslash y=\neg (x\triangleright \neg y)} , x ▹ y = ¬ ( x ∖ ¬ y ) {\displaystyle x\triangleright y=\neg (x\backslash \neg y)} , and dually / y {\displaystyle /y} and ◃ y {\displaystyle \triangleleft y} as x / y = ¬ ( ¬ x ◃ y ) {\displaystyle x/y=\neg (\neg x\triangleleft y)} , x ◃ y = ¬ ( ¬ x / y ) {\displaystyle x\triangleleft y=\neg (\neg x/y)} , with the residuation axioms in the residuated lattice article reorganized accordingly (replacing z {\displaystyle z} by ¬ z {\displaystyle \neg z} ) to read ( x ▹ z ) ∧ y = 0 ⇔ ( x ∙ y ) ∧ z = 0 ⇔ ( z ◃ y ) ∧ x = 0 {\displaystyle (x\triangleright z)\wedge y=0\ \Leftrightarrow \ (x\bullet y)\wedge z=0\ \Leftrightarrow \ (z\triangleleft y)\wedge x=0} This De Morgan dual reformulation is motivated and discussed in more detail in the section below on conjugacy. Since residuated lattices and Boolean algebras are each definable with finitely many equations, so are residuated Boolean algebras, whence they form a finitely axiomatizable variety. == Examples == Any Boolean algebra, with the monoid multiplication ∙ {\displaystyle \bullet } taken to be conjunction and both residuals taken to be material implication x → y {\displaystyle x\to y} . Of the remaining 15 binary Boolean operations that might be considered in place of conjunction for the monoid multiplication, only five meet the monotonicity requirement, namely 0 , 1 , x , y {\displaystyle 0,1,x,y} and x ∨ y {\displaystyle x\vee y} . Setting y = z = 0 {\displaystyle y=z=0} in the residuation axiom y ≤ x ∖ z ⇔ x ∙ y ≤ z {\displaystyle y\leq x\backslash z\ \Leftrightarrow \ x\bullet y\leq z} , we have 0 ≤ x ∖ 0 ⇔ x ∙ 0 ≤ 0 {\displaystyle 0\leq x\backslash 0\ \Leftrightarrow \ x\bullet 0\leq 0} , which is falsified by taking x = 1 {\displaystyle x=1} when x ∙ y = 1 {\displaystyle x\bullet y=1} , x {\displaystyle x} , or x ∨ y {\displaystyle x\vee y} . The dual argument for z / y {\displaystyle z/y} rules out x ∙ y = y {\displaystyle x\bullet y=y} . This just leaves x ∙ y = 0 {\displaystyle x\bullet y=0} (a constant binary operation independent of x {\displaystyle x} and y {\displaystyle y} ), which satisfies almost all the axioms when the residuals are both taken to be the constant operation x / y = x ∖ y = 1 {\displaystyle x/y=x\backslash y=1} . The axiom it fails is x ∙ I = x = I ∙ x {\displaystyle x\bullet \mathbf {I} =x=\mathbf {I} \bullet x} , for want of a suitable value for I {\displaystyle \mathbf {I} } . Hence conjunction is the only binary Boolean operation making the monoid multiplication that of a residuated Boolean algebra. The power set 2 X 2 {\displaystyle 2^{X^{2}}} made a Boolean algebra as usual with ∩ {\displaystyle \cap } , ∪ {\displaystyle \cup } and complement relative to X 2 {\displaystyle X^{2}} , and made a monoid with relational composition. The monoid unit I {\displaystyle \mathbf {I} } is the identity relation { ( x , x ) | x ∈ X } {\displaystyle \{(x,x)|x\in X\}} . The right residual R ∖ S {\displaystyle R\backslash S} is defined by x ( R ∖ S ) y ⇔ ∀ z ∈ X , z R x ⇒ z S y {\displaystyle x(R\backslash S)y\ \Leftrightarrow \ \forall z\in X,zRx\Rightarrow zSy} . Dually the left residual S / R {\displaystyle S/R} is defined by y ( S / R ) x ⇔ ∀ z ∈ X , x R z ⇒ y S z {\displaystyle y(S/R)x\ \Leftrightarrow \ \forall z\in X,xRz\Rightarrow ySz} . The power set 2 Σ ∗ {\displaystyle 2^{\Sigma ^{}}} made a Boolean algebra as for Example 2, but with language concatenation for the monoid. Here the set Σ {\displaystyle \Sigma } is used as an alphabet while Σ ∗ {\displaystyle \Sigma ^{}} denotes the set of all finite (including empty) words over that alphabet. The concatenation L M {\displaystyle LM} of languages L {\displaystyle L} and M {\displaystyle M} consists of all words u v {\displaystyle uv} such that u ∈ L {\displaystyle u\in L} and v ∈ M {\displaystyle v\in M} . The monoid unit is the language { ε } {\displaystyle \{\varepsilon \}} consisting of just the empty word ε {\displaystyle \varepsilon } . The right residual M ∖ L {\displaystyle M\backslash L} consists of all words w {\displaystyle w} over Σ {\displaystyle \Sigma } such that M w ⊆ L {\displaystyle Mw\subseteq L} . The left residual L / M {\displaystyle L/M} is the same with w M {\displaystyle wM} in place of M w {\displaystyle Mw} . == Conjugacy == The De Morgan duals ▹ {\displaystyle \triangleright } and ◃ {\displaystyle \triangleleft } of residuation arise as follows. Among residuated lattices, Boolean algebras are special by virtue of having a complementation operation ¬ {\displaystyle \neg } . This permits an alternative expression of the three inequalities y ≤ x ∖ z ⇔ x ∙ y ≤ z ⇔ x ≤ z / y {\displaystyle y\leq x\backslash z\ \Leftrightarrow \ x\bullet y\leq z\ \Leftrightarrow \ x\leq z/y} in the axiomatization of the two residuals in terms of disjointness, via the equivalence x ≤ y ⇔ x ∧ ¬ y = 0 {\displaystyle x\leq y\ \Leftrightarrow \ x\wedge \neg y=0} . Abbreviating x ∧ y = 0 {\displaystyle x\wedge y=0} to x # y {\displaystyle x\#y} as the expression of their disjointness, and substituting ¬ z {\displaystyle \neg z} for z {\displaystyle z} in the axioms, they become with a little Boolean manipulation ¬ ( x ∖ ¬ z ) # y ⇔ x ∙ y # z ⇔ ¬ ( ¬ z / y ) # x {\displaystyle \neg (x\backslash \neg z)\#y\ \Leftrightarrow \ x\bullet y\#z\ \Leftrightarrow \ \neg (\neg z/y)\#x} Now ¬ ( x ∖ ¬ z ) {\displaystyle \neg (x\backslash \neg z)} is reminiscent of De Morgan duality, suggesting that x ∖ {\displaystyle x\backslash } be thought of as a unary operation f {\displaystyle f} , defined by f ( y ) = x ∖ y {\displaystyle f(y)=x\backslash y} , that has a De Morgan dual ¬ f ( ¬ y ) {\displaystyle \neg f(\neg y)} , analogous to ∀ x ϕ ( x ) = ¬ ∃ x ¬ ϕ ( x ) {\displaystyle \forall x\phi (x)=\neg \exists x\neg \phi (x)} . Denoting this dual operation as x ▹ {\displaystyle x\triangleright } , we define x ▹ z {\displaystyle x\triangleright z} as ¬ x ∖ ¬ z {\displaystyle \neg x\backslash \neg z} . Similarly we define another operation z ◃ y {\displaystyle z\triangleleft y} as ¬ ( ¬ z / y ) {\displaystyle \neg (\neg z/y)} . By analogy with x ∖ {\displaystyle x\backslash } as the residual operation associated with the operation x ∙ {\displaystyle x\bullet } , we refer to x ▹ {\displaystyle x\triangleright } as the conjugate operation, or simply conjugate, of x ∙ {\displaystyle x\bullet } . Likewise ◃ y {\displaystyle \triangleleft y} is the conjugate of ∙ y {\displaystyle \bullet y} . Unlike residuals, conjugacy is an equivalence relation between operations: if f {\displaystyle f} is the conjugate of g {\displaystyle g} then g {\displaystyle g} is also the conjugate of f {\displaystyle f} , i.e. the conjugate of the conjugate of f {\displaystyle f} is f {\displaystyle f} . Another advantage of conjugacy is that it becomes unnecessary to speak of right and left conjugates, that distinction now being inherited from the difference between x ∙ {\displaystyle x\bullet } and ∙ x {\displaystyle \bullet x} , which have as their respective conjugates x ▹ {\displaystyle x\triangleright } and ◃ x {\displaystyle \triangleleft x} . (But this advantage accrues also to residuals when x ∖ {\displaystyle x\backslash } is taken to be the residual operation to x ∙ {\displaystyle x\bullet } .) All this yields (along with the Boolean algebra and monoid axioms) the following equivalent axiomatization of a residuated Boolean algebra. y # x ▹ z ⇔ x ∙ y # z ⇔ x # z ◃ y {\displaystyle y\#x\triangleright z\ \Leftrightarrow \ x\bullet y\#z\ \Leftrightarrow \ x\#z\triangleleft y} With this signature it remains the case that this axiomatization can be expressed as

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  Agents of S.H.I.E.L.D. season 4

  The fourth season of the American television series Agents of S.H.I.E.L.D., based on the Marvel Comics spy organization S.H.I.E.L.D., follows Phil Coulson and other S.H.I.E.L.D. agents and allies after the signing of the Sokovia Accords. It is set in the Marvel Cinematic Universe (MCU) and acknowledges the continuity of the franchise's films. The season was produced by ABC Studios, Marvel Television, and Mutant Enemy Productions, with Jed Whedon, Maurissa Tancharoen, and Jeffrey Bell serving as showrunners. Clark Gregg reprises his role as Coulson from the film series, starring alongside the returning series regulars Ming-Na Wen, Chloe Bennet, Iain De Caestecker, Elizabeth Henstridge, and Henry Simmons. They are joined by John Hannah who was promoted from his recurring guest role in the third season. The fourth season was ordered in March 2016, with production taking place from that July until the following April. Due to its broadcast schedule, the season was split into three "pods": Ghost Rider for the first eight episodes, featuring recurring guest star Gabriel Luna as the supernatural Robbie Reyes / Ghost Rider and exploring mysticism in the MCU alongside the film Doctor Strange (2016); LMD, referring to the new Life Model Decoy program, for the next seven episodes which focus on recurring guest star Mallory Jansen as the LMD Aida; and Agents of Hydra for the final seven episodes, partly set in a "what if" virtual reality that allowed the return of former series regular Brett Dalton as Grant Ward. The season is also affected by the events of the film Captain America: Civil War (2016), and continues storylines established in the canceled series Agent Carter. The first episode premiered at a screening on September 19, 2016, with the season then airing for 22 episodes on ABC, from September 20, 2016, until May 16, 2017. The premiere debuted to 3.58 million viewers, down from previous season premieres but average for the series. Critical response to the season was positive, with many feeling that each pod was better than the last and in particular praising the visual effects and tone of Ghost Rider, the writing and acting of LMD, and the character development and political commentary explored during Agents of Hydra. The season saw series low viewership, but was still considered to have solved ABC's problem during its new Tuesday night timeslot, and the series was renewed for a fifth season in May 2017. == Episodes == == Cast and characters == == Production == === Development === Agents of S.H.I.E.L.D. was renewed for a fourth season on March 3, 2016, earlier than usual for the series. Executive producer Jed Whedon said on this, "We're thrilled to know going into the end of [season three] with certainty that we will be returning, because we can build our story accordingly." Executive producer Maurissa Tancharoen also noted that logistics for hiring directors for the season in advance would be easier, "which is a very nice privilege to have...that's a luxury". The end of the episode "What If..." features an onscreen tribute to Bill Paxton, who died in February 2017 and had portrayed John Garrett in the series' first season. The series paid additional tribute to Paxton in "All the Madame's Men" with promos during The Bakshi Report news segment showcasing John Garrett as a fallen American hero. The end of "World's End" features a similar onscreen tribute to Powers Boothe, who died in May 2017 and had portrayed Gideon Malick in the series' third season. === Writing === The season shifted to the later 10 pm timeslot, allowing it to take on a darker, more mature tone than previous seasons. According to Tancharoen, "The whole tagline for this year is 'Agents of S.H.I.E.L.D. After Dark'". The timeslot gave the series the opportunity to present an increased level of violence and partial nudity, as well as take more risks and present edgier themes. Following the third-season finale, Tancharoen stated that the fourth season would explore the guilt Daisy Johnson has over Lincoln Campbell's death. Executive producer Jeffrey Bell noted the writers tried to continue the tradition of "finding new combinations and new conflicts" between different sets of characters, given "a lot of procedurals [see] the same people doing the same thing for five years". Pairings that would be explored included Coulson and Mack, continuing from the end of season three, who have a mutual respect for one another due to their relationships with Daisy, and Leo Fitz and Holden Radcliffe, who work together. The Fitz-Simmons relationship was also explored more, examining the new challenges it presented for the two "working together, loving each other and living together". Following the third season's dealing with the themes of Captain America: Civil War (2016), such as the opposing reactions to the Inhumans, Whedon said that the question of "How do you deal with a war with powered people at that level, a government level?" was one that they wanted to answer in the fourth season. Tancharoen called the Inhumans "a permanent part of our universe now", with Whedon adding, "we have a quick-fire way of introducing people with powers. It gives us a lot of leeway in our world, and it lets us explore the metaphors of what it is like to be different. We will never close that chapter." With the Inhumans film being removed from Marvel Studios' release schedule, the series had "a little more freedom" and were "able to do a little bit more" with the species, including the potential of introducing some of the "classic" Inhumans, though the series would focus less on Inhumans than the third season which saw "a real significant Inhuman agenda story". It was not intended to be a spin-off of Agents of S.H.I.E.L.D. On the evolution of S.H.I.E.L.D. to featuring so many powered characters, Whedon said "the dynamic in the world has changed. There was one person with powers, and then by The Avengers there were maybe six total ... now they're much more prevalent, so there's reaction from the public based on that." The season is structured into three "pods" based on its airing schedule: the first eight episodes, subtitled Ghost Rider; LMD (Life Model Decoy) for the subsequent seven episodes; and a third pod for the final seven episodes called Agents of Hydra. Elements and characters cross over between the different pods, but the sections "definitely have a different feel" from one another, as Bell explained that 22 episodes "is a long time to hold a big bad or a single plot line, especially for an audience", and for the past two seasons, the series was able to have two separated halves that "allows us to introduce a big bad. And then, something happens and we rise somebody new ... Now, there's three of those." "Financial considerations" were also taken into account in creating the pods for the season, as using LMDs does not "cost as much as setting a guy's head on fire via CGI". In terms of writing the "complicated season", Whedon said the writers were "aware that our fans are our fans and have spent some time with these characters and are clever and see things coming sometimes ... Part of our job is to create not just what we are presenting on plot, but letting the audience be one step ahead of us and being one step ahead of that." He added that the writers knew that they wanted to tell a Ghost Rider story, an LMD story, and a "what if" scenario, and the hardest part was making each pod still fit together as a single season. The major connection ultimately became the Darkhold, which leads from the magic of Ghost Rider to the advanced science of LMD and then the Framework in Agents of Hydra. Ghost Rider also reappears in the final episode of the season, "World's End", as an additional connection. ==== Ghost Rider ==== While planning the fourth season, Marvel suggested that the series introduce Ghost Rider, after the character's film rights had returned to Marvel from Sony in May 2013. Loeb felt that this made the season unquestionably "the series' biggest" with the "most ambitious story yet". He added that "one of the things that we talked about is, S.H.I.E.L.D. always looked out for the weird, the unusual, the things that were and could be a problem for the public", and Marvel realized that Ghost Rider's abilities, which are more mystical than anything seen in the series to date, opened up "a quarter of the universe that we haven't really spent a lot of time exploring ... what happens if our very real, our very grounded agents who are very much a family have to take on something that is as bizarre and powerful and unique as Ghost Rider." Bell added that the producers would have been willing to give an entire season of the show to a Ghost Rider arc if the season was 13 episodes or less, but 22 episodes seemed too long to "feel like one flavor". The Robbie Reyes version of Ghost Rider was chosen over other versions of the character from the comics because of his relationship with his brother Gabe, w

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  Index locking

  In databases an index is a data structure, part of the database, used by a database system to efficiently navigate access to user data. Index data are system data distinct from user data, and consist primarily of pointers. Changes in a database (by insert, delete, or modify operations), may require indexes to be updated to maintain accurate user data accesses. Index locking is a technique used to maintain index integrity. A portion of an index is locked during a database transaction when this portion is being accessed by the transaction as a result of attempt to access related user data. Additionally, special database system transactions (not user-invoked transactions) may be invoked to maintain and modify an index, as part of a system's self-maintenance activities. When a portion of an index is locked by a transaction, other transactions may be blocked from accessing this index portion (blocked from modifying, and even from reading it, depending on lock type and needed operation). Index Locking Protocol guarantees that phantom read phenomenon won't occur. Index locking protocol states: Every relation must have at least one index. A transaction can access tuples only after finding them through one or more indices on the relation A transaction Ti that performs a lookup must lock all the index leaf nodes that it accesses, in S-mode, even if the leaf node does not contain any tuple satisfying the index lookup (e.g. for a range query, no tuple in a leaf is in the range) A transaction Ti that inserts, updates or deletes a tuple ti in a relation r must update all indices to r and it must obtain exclusive locks on all index leaf nodes affected by the insert/update/delete The rules of the two-phase locking protocol must be observed. Specialized concurrency control techniques exist for accessing indexes. These techniques depend on the index type, and take advantage of its structure. They are typically much more effective than applying to indexes common concurrency control methods applied to user data. Notable and widely researched are specialized techniques for B-trees (B-Tree concurrency control) which are regularly used as database indexes. Index locks are used to coordinate threads accessing indexes concurrently, and typically shorter-lived than the common transaction locks on user data. In professional literature, they are often called latches.

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  Science Fiction Thinking Machines

  Science Fiction Thinking Machines: Robots, Androids, Computers is an anthology of science fiction short stories edited by American anthologist Groff Conklin. It was first published in hardcover by Vanguard Press in May 1954. An abridged paperback edition titled, Selections from Science Fiction Thinking Machines was later published by Bantam Books in August 1955 and was reprinted in September 1964. The book consists of twenty-two novelettes and short stories by various science fiction authors, together with an introduction and bibliography by the editor. The stories were previously published from 1899-1954, in various science fiction and other magazines. == Contents == Note: stories also appearing in the abridged edition annotated A. "Introduction" (Groff Conklin) "Automata: I" (S. Fowler Wright) "Moxon's Master" (Ambrose Bierce) "Robbie" (Isaac Asimov) A "The Scarab" (Raymond Z. Gallun) "The Mechanical Bride" (Fritz Leiber) "Virtuoso" (Herbert Goldstone) A "Automata: II" (S. Fowler Wright) "Boomerang" (Eric Frank Russell) A "The Jester" (William Tenn) A "R. U. R." (Karel Čapek) "Skirmish" (Clifford D. Simak) A "Soldier Boy" (Michael Shaara) "Automata: III" (S. Fowler Wright) "Men Are Different" (Alan Bloch) A "Letter to Ellen" (Chan Davis) A "Sculptors of Life" (Wallace West) "The Golden Egg" (Theodore Sturgeon) A "Dead End" (Wallace Macfarlane) A "Answer" (Hal Clement) "Sam Hall" (Poul Anderson) A "Dumb Waiter" (Walter M. Miller Jr.) A "Problem for Emmy" (Robert Sherman Townes) A "Selected List of Tales About Robots, Androids, and Computers" (Groff Conklin)

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  Kialo

  Kialo is an online structured debate platform with argument maps in the form of debate trees. It is a collaborative reasoning tool for thoughtful discussion, understanding different points of view, and collaborative decision-making, showing arguments for and against claims underneath user-submitted theses or questions. The deliberative discourse platform is designed to present hundreds of supporting or opposing arguments in a dynamic argument tree and is streamlined for rational civil debate on topics such as philosophical questions, policy deliberations, entertainment, ethics, science questions, and unsolved problems or subjects of disagreement in general. Argument-boxes are structured into hierarchical branches where the root is the main thesis (or theses) of the debate, enabling deliberation and navigable debates between opposing perspectives. A debate is divided into Pro (supporting) and Con (refuting or devaluing) columns where registered users can add arguments and rate the impact on the weight or validity of the parent claim. The arguments are sorted according to the rating average. Its argument tree structure enables detailed scrutiny of claims at all levels of the tree and allows users to for example quickly understand why a decision was made or which of the aggregated arguments swayed it this way. Newcomers can join a debate at any time and look back at the structured discussion history, and then weigh in at the right place with their new argument or their comment on a specific argument. The design presets a structure on debates "that allows participants to easily see, process, and ultimately assess the many facets of competing claims". The word Kialo is Esperanto for "reason". The platform is the world's largest argument mapping and structured debate site. == Overview == Users can comment on every Pro or Con, for example for requesting sources or expansions. Recent activities of a debate are shown in a panel on the right side of the respective debate. Debates can be found through the search or on the Explore page through their descriptions and topic-tags. Mere comments that do not make a constructive point (a self-contained argument backed by reasoning) are not allowed and are picked up by other users and moderators. "Civil language and sensible observations from opposing perspectives" can be seen also in debates about controversial topics. The site by-design incentivizes fair, rigorous, open-minded dialogue. Contributors making claims often also write counterpoints to their own contribution. Claims need to be shorter than 500 characters and can link to external sources. Debate trees can also start off with multiple theses – such as different policy options or hypotheses. Claims can link to related debates or include segments of them. In the discussion tab of each claim, users can make edit proposals (e.g. for accuracy, improving sources, or changing scope), decide if the argument should be moved or copied to another branch, call for archiving a claim, and ask for extra evidence or clarification. Debates can grow large and complex for which a sunburst diagram visualization of the topology of the debate and the search functionality can be useful. Each debate also has a chat-box. In cases where e.g. a "Con" is a point against multiple in the "Pros", users – through moderators – can link these arguments at the respective places to avoid duplication of content and allowing a clean chain for people to understand which points are arguments against each other. Contributions of users are tracked, enabling a board of thought-leaders for every debate. Other gamification elements include a feature to thank users for their contributions. The "Perspectives" feature allows users to see 'Impact' ratings of supporters and opposers of a thesis as well as of the debate's moderators and individual contributors. It thereby enables participants to see a debate from other participants' perspectives and to sort by them. In Kialo Edu, this feature lets teachers view votes for a whole class, individuals, or supporters/opponents of a specific thesis. Users in both versions of Kialo can vote on the overall debate topic as well as on individual claims to express their perspectives or conclusions, with the rationale (i.e. the main causal arguments) why they voted on the veracity of the thesis as they did not being captured. Voting can be done by any registered user while navigating through any debate that has voting enabled or via using the Guided Voting wizard user interface that automatically walks through branches. As of 2021, Kialo doesn't have a mobile app. == Contents == A 2018 report stated the collaborative argument platform hosts more than 10,000 debates in various languages. It also hosts private debates. The website claims that it has over 18,000 public debates as of July 2023, as well as over 1 million votes and over 720,000 claims. Debates can be found via the site's internal search and up to six tags per debate. Preprint studies have scraped public debates on over 1.4K issues with over 130K statements as of October 2019 and 1628 debates, related to over 1120 categories, with 124,312 unique claims as of June 26, 2020. == Kialo Inc. == The site is run by Kialo Inc. It was founded by German-born entrepreneur and London School of Economics and Political Science graduate Errikos Pitsos in August 2017 and is based in Brooklyn and Berlin. According to a 2018 report, the site does not show advertisements and does not sell user's data. The for-profit company was founded in 2011, Pitsos began to develop the concept in 2012 and described various specifics of the system in 2014. In 2018, he stated that they intend to make money by selling the platform to companies as a deliberation and decision-making tool. The site is free to use for the public and in education. According to the site, as of 2023 Kialo.com is a non-revenue generating site with no ads and no reselling of user data. == Applications and adoption == === Adopted applications === Applications of its content or the platform in society include: Teachers and professors, especially in high schools – including the universities Harvard and Princeton, are using Kialo for class discussions and exercises in critical thinking and reasoning, as consolidating understanding of materials covered in recent classes, more useful and engaging learning experiences, for remote/e-learning, for clearing up misconceptions, teaching logical fallacies and rational argumentation, for academic dialogue, teaching media literacy, and for teaching to sufficiently reflect or research before posting online. Like for debaters of the main site, access for schools and universities is free. Kialo Edu is the custom version of Kialo specifically designed for classroom use where debates are private and locked to invited students. Kialo allows teachers to provide feedback to students on their ideas, argument structure, and research quality while it is left to other students to rate the impacts of their peers' arguments. Students can be allowed to contribute anonymously which may be useful for controversial issues as well as for safeguarding privacy in education. Students are or can be encouraged to back up their claims with evidence which can foster digital literacy and research skills. Students and teachers can use it to arrange their thoughts when structuring an essay or project. The site's name was decided on internally using the software. === Prototypical and theoretical applications === Potential, theoretical, prototypical or little-used applications include: Education Improving critical thinking skills of society at large as well as facilitating deep or efficient thinking and deepening research and debates where e.g. discussions are less shallow and the well-known or many arguments have already been made and in many cases aren't unreasonably over- or underrated. Pitsos claimed that "we're training students to be very good test-takers instead of critical thinkers", suggesting teaching people to think things through may be more important or neglected compared to essay writing skills. Many young people and adults are "submerged into a sea of dispersed information", "[b]rowsing and engaging in superficial thinking activities". Kialo could counteract this issue and help people develop good sane reasoning. Academia, R&D and policy Three scholars from three prestigious U.S. universities outlined possible benefits in this domain, including applications beyond higher education such as for academic communication. They suggest the debate platform could be used for structuring the communication of open peer-review by helping those giving feedback to "hone in on[sic] core arguments and pieces of evidence in an even more direct way" than annotated commenting. It could be used to evaluate extracted argument structures and sequences from raw texts, as in a Semantic Web for arguments. Such "argument mining", to which Kialo is the lar

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