Super-resolution optical fluctuation imaging

Super-resolution optical fluctuation imaging (SOFI) is a post-processing method for the calculation of super-resolved images from recorded image time series that is based on the temporal correlations of independently fluctuating fluorescent emitters. SOFI has been developed for super-resolution of biological specimen that are labelled with independently fluctuating fluorescent emitters (organic dyes, fluorescent proteins). In comparison to other super-resolution microscopy techniques such as STORM or PALM that rely on single-molecule localization and hence only allow one active molecule per diffraction-limited area (DLA) and timepoint, SOFI does not necessitate a controlled photoswitching and/ or photoactivation as well as long imaging times. Nevertheless, it still requires fluorophores that are cycling through two distinguishable states, either real on-/off-states or states with different fluorescence intensities. In mathematical terms SOFI-imaging relies on the calculation of cumulants, for what two distinguishable ways exist. For one thing an image can be calculated via auto-cumulants that by definition only rely on the information of each pixel itself, and for another thing an improved method utilizes the information of different pixels via the calculation of cross-cumulants. Both methods can increase the final image resolution significantly although the cumulant calculation has its limitations. Actually SOFI is able to increase the resolution in all three dimensions. == Principle == Likewise to other super-resolution methods SOFI is based on recording an image time series on a CCD- or CMOS camera. In contrary to other methods the recorded time series can be substantially shorter, since a precise localization of emitters is not required and therefore a larger quantity of activated fluorophores per diffraction-limited area is allowed. The pixel values of a SOFI-image of the n-th order are calculated from the values of the pixel time series in the form of a n-th order cumulant, whereas the final value assigned to a pixel can be imagined as the integral over a correlation function. The finally assigned pixel value intensities are a measure of the brightness and correlation of the fluorescence signal. Mathematically, the n-th order cumulant is related to the n-th order correlation function, but exhibits some advantages concerning the resulting resolution of the image. Since in SOFI several emitters per DLA are allowed, the photon count at each pixel results from the superposition of the signals of all activated nearby emitters. The cumulant calculation now filters the signal and leaves only highly correlated fluctuations. This provides a contrast enhancement and therefore a background reduction for good measure. As it is implied in the figure on the left the fluorescence source distribution: ∑ k = 1 N δ ( r → − r → k ) ⋅ ε k ⋅ s k ( t ) {\displaystyle \sum _{k=1}^{N}\delta ({\vec {r}}-{\vec {r}}_{k})\cdot \varepsilon _{k}\cdot s_{k}(t)} is convolved with the system's point spread function (PSF) U(r). Hence the fluorescence signal at time t and position r → {\displaystyle {\vec {r}}} is given by F ( r → , t ) = ∑ k = 1 N U ( r → − r → k ) ⋅ ε k ⋅ s k ( t ) . {\displaystyle F({\vec {r}},t)=\sum _{k=1}^{N}U({\vec {r}}-{\vec {r}}_{k})\cdot \varepsilon _{k}\cdot s_{k}(t).} Within the above equations N is the amount of emitters, located at the positions r → k {\displaystyle {\vec {r}}_{k}} with a time-dependent molecular brightness ε k ⋅ s k {\displaystyle \varepsilon _{k}\cdot s_{k}} where ε k {\displaystyle \varepsilon _{k}} is a variable for the constant molecular brightness and s k ( t ) {\displaystyle s_{k}(t)} is a time-dependent fluctuation function. The molecular brightness is just the average fluorescence count-rate divided by the number of molecules within a specific region. For simplification it has to be assumed that the sample is in a stationary equilibrium and therefore the fluorescence signal can be expressed as a zero-mean fluctuation: δ F ( r → , t ) = F ( r → , t ) − ⟨ F ( r → , t ) ⟩ t {\displaystyle \delta F({\vec {r}},t)=F({\vec {r}},t)-\langle F({\vec {r}},t)\rangle _{t}} where ⟨ ⋯ ⟩ t {\displaystyle \langle \cdots \rangle _{t}} denotes time-averaging. The auto-correlation here e.g. the second-order can then be described deductively as follows for a certain time-lag τ {\displaystyle \tau } : δ F ( r → , t ) = ⟨ δ F ( r → , t + τ ) ⋅ δ F ( r → , t ) ⟩ t {\displaystyle \delta F({\vec {r}},t)=\langle \delta F({\vec {r}},t+\tau )\cdot \delta F({\vec {r}},t)\rangle _{t}} From these equations it follows that the PSF of the optical system has to be taken to the power of the order of the correlation. Thus in a second-order correlation the PSF would be reduced along all dimensions by a factor of 2 {\displaystyle {\sqrt {2}}} . As a result, the resolution of the SOFI-images increases according to this factor. === Cumulants versus correlations === Using only the simple correlation function for a reassignment of pixel values, would ascribe to the independency of fluctuations of the emitters in time in a way that no cross-correlation terms would contribute to the new pixel value. Calculations of higher-order correlation functions would suffer from lower-order correlations for what reason it is superior to calculate cumulants, since all lower-order correlation terms vanish. == Cumulant-calculation == === Auto-cumulants === For computational reasons it is convenient to set all time-lags in higher-order cumulants to zero so that a general expression for the n-th order auto-cumulant can be found: A C n ( r → , τ 1 … n − 1 = 0 ) = ∑ k = 1 N U n ( r → − r → k ) ε k n w k ( 0 ) {\displaystyle AC_{n}({\vec {r}},\tau _{1\ldots n-1}=0)=\sum _{k=1}^{N}U^{n}({\vec {r}}-{\vec {r}}_{k})\varepsilon _{k}^{n}w_{k}(0)} w k {\displaystyle w_{k}} is a specific correlation based weighting function influenced by the order of the cumulant and mainly depending on the fluctuation properties of the emitters. Albeit there is no fundamental limitation in calculating very high orders of cumulants and thereby shrinking the FWHM of the PSF there are practical limitations according to the weighting of the values assigned to the final image. Emitters with a higher molecular brightness will show a strong increase in terms of the pixel cumulant value assigned at higher-orders as well as this performance can be expected from a diverse appearance of fluctuations of different emitters. A wide intensity range of the resulting image can therefore be expected and as a result dim emitters can get masked by bright emitters in higher-order images:. The calculation of auto-cumulants can be realized in a very attractive way in a mathematical sense. The n-th order cumulant can be calculated with a basic recursion from moments K n ( r → ) = μ n ( r → ) − ∑ i = 1 n − 1 ( n − 1 i ) K n − i ( r → ) μ i ( r → ) {\displaystyle K_{n}({\vec {r}})=\mu _{n}({\vec {r}})-\sum _{i=1}^{n-1}{\begin{pmatrix}n-1\\i\end{pmatrix}}K_{n-i}({\vec {r}})\mu _{i}({\vec {r}})} where K is a cumulant of the index's order, likewise μ {\displaystyle \mu } represents the moments. The term within the brackets indicates a binomial coefficient. This way of computation is straightforward in comparison with calculating cumulants with standard formulas. It allows for the calculation of cumulants with only little time of computing and is, as it is well implemented, even suitable for the calculation of high-order cumulants on large images. === Cross-cumulants === In a more advanced approach cross-cumulants are calculated by taking the information of several pixels into account. Cross-cumulants can be described as follows: C C n ( r → , τ 1 … n − 1 = 0 ) = ∏ j < l n U ( r → j − r → l n ) ⋅ ∑ i = 1 N U n ( r → i − ∑ k n r → k n ) ε i n w i ( 0 ) {\displaystyle CC_{n}({\vec {r}},\tau _{1\ldots n-1}=0)=\prod _{j

Artificial intelligence in fraud detection

Artificial intelligence is used by many different businesses and organizations. It is widely used in the financial sector, especially by accounting firms, to help detect fraud. In 2022, PricewaterhouseCoopers reported that fraud has impacted 46% of all businesses in the world. The shift from working in person to working from home has brought increased access to data. According to an FTC (Federal Trade Commission) study from 2022, customers reported fraud of approximately $5.8 billion in 2021, an increase of 70% from the year before. The majority of these scams were imposter scams and online shopping frauds. Furthermore, artificial intelligence plays a crucial role in developing advanced algorithms and machine learning models that enhance fraud detection systems, enabling businesses to stay ahead of evolving fraudulent tactics in an increasingly digital landscape. == Tools == === Expert systems === Expert systems were first designed in the 1970s as an expansion into artificial intelligence technologies. Their design is based on the premise of decreasing potential user error in decision-making and emulating mental reasoning used by experts in a particular field. They differentiate themselves from traditional linear reasoning models by separating identified points in data and processing them individually at the same time. Though, these systems do not rely purely on machine-learned intelligence. Information regarding rules, practices, and procedures in the form of "if-then" statements are implemented into the programming of the system. Users interact with the system by feeding information into the system either through direct entry or import of external data. An inference system compares the information provided by the user with corresponding rules that are believed to specifically apply to the situation. Using this information and the corresponding rules will be used to create a solution to the user's query. Expert systems will generally not operate properly when the common procedures for a specified situation are ambiguous due to the need for well-defined rules. Implementation of expert systems in accounting procedures is feasible in areas where professional judgment is required. Situations where expert systems are applicable include investigations into transactions that involve potential fraudulent entries, instances of going concern, and the evaluation of risk in the planning stages of an audit. === Continuous auditing === Continuous auditing is a set of processes that assess various aspects of information gathered in an audit to classify areas of risk and potential weaknesses in financial Internal controls at a more frequent rate than traditional methods. Instead of analyzing recorded transactions and journal entries periodically, continuous auditing focuses on interpreting the character of these actions more frequently. The frequency of these processes being undertaken as well as highlighting areas of importance is up to the discretion of their implementer, who commonly makes such decisions based on the level of risk in the accounts being evaluated and the goals of implementing the system. Performance of these processes can occur as frequently as being nearly instantaneous with an entry being posted. The processes involved with analyzing financial data in continuous auditing can include the creation of spreadsheets to allow for interactive information gathering, calculation of financial ratios for comparison with previously created models, and detection of errors in entered figures. A primary goal of this practice is to allow for quicker and easier detection of instances of faulty controls, errors, and instances of fraud. === Machine learning and deep learning === The ability of machine learning and deep learning to swiftly and effectively sort through vast volumes of data in the forms of various documents relevant to companies and documents being audited makes them applicable to the domains of audit and fraud detection. Examples of this include recognizing key language in contracts, identifying levels of risk of fraud in transactions, and assessing journal entries for misstatement. == Applications == === 'Big 4' Accounting Firms === Deloitte created an Al-enabled document-reviewing system in 2014. The system automates the method of reviewing and extracting relevant information from different business documents. Deloitte claims that this innovation has made a difference by reducing time spent going through lawful contract documents, invoices, money-related articulations, and board minutes by up to 50%. Working with IBM's Watson, Deloitte is developing cognitive-technology-enhanced commerce arrangements for its clients. LeasePoint is fueled by IBM TRIRIGA (this product evolved into IBM Maximo Real Estate and Facilities) and uses Deloitte's industrial information to create an end-to-end leasing portfolio. Automated Cognitive Resource Assessment employs IBM's Maximo innovation to progress the proficiency of asset inspection. Ernst and Young (EY) connected Al to the investigation of lease contracts. EY (Australia) has also received Al-enabled auditing technology. Collaborating with H20.ai, PwC developed an Al-enabled framework (GL.ai) capable of analyzing reports and preparing reports. PwC claims to have made a significant investment in normal dialect processing (NLP), an Al-enabled innovation to process unstructured information efficiently. KPMG built a portfolio of Al instruments, called KPMG Ignite, to upgrade trade decisions and forms. Working with Microsoft and IBM Watson, KPMG is creating instruments to coordinate Al, data analytics, Cognitive Technologies, and RPA. == Advantages == === Efficiency === The process of auditing an entity in an attempt to detect fraudulent activity requires the repeating of investigatory processes until an error or misstatement may be identified. Under traditional methods, these processes would be carried out by a human being. Proponents of artificial intelligence in fraud detection have stated that these traditional methods are inefficient and can be more quickly accomplished with the aid of an intelligent computing system. A survey of 400 chief executive officers created by KPMG in 2016 found that approximately 58% believed that artificial intelligence would play a key role in making audits more efficient in the future. === Data interpretation === Higher levels of fraud detection entail the use of professional judgement to interpret data. Supporters of artificial intelligence being used in financial audits have claimed that increased risks from instances of higher data interpretation can be minimized through such technologies. One necessary element of an audit of financial statements that requires professional judgement is the implementation of thresholds for materiality. Materiality entails the distinction between errors and transactions in financial statements that would impact decisions made by users of those financial statements. The threshold for materiality in an audit is set by the auditor based on various factors. Artificial intelligence has been used to interpret data and suggest materiality thresholds to be implemented through the use of expert systems. === Decreased costs === Those in favor of using artificial intelligence to complete investigations of fraud have stated that such technologies decrease the amount of time required to complete tasks that are repetitive. The claim further states that such efficiencies allow for lowered resource requirements, which can then be further spent on tasks that have not been fully automated. The audit firm Ernst & Young has posited these claims by declaring that their deep learning systems have been used to reduce time spent on administrative tasks by analyzing relevant audit documents. According to the firm, this has allowed their employees to focus more on judgement and analysis. == Disadvantages == === Job Displacement === The inescapable reception of computer based intelligence and robotization advancements might prompt critical work relocation across different enterprises. As artificial intelligence frameworks become more equipped for performing undertakings customarily completed by people, there is a worry that specific work jobs could become out of date, prompting joblessness and financial imbalance. === Initial investment requirement === Along with a knowledge of coding and building systems through computer programs, we are seeing the advantages of these systems, but since they are so new, they require a large investment to start building such a system. Any firm that is planning on implementing an AI system to detect fraud must hire a team of data scientists, along with upgrading their cloud system and data storage. The system must be consistently monitored and updated to be the most efficient form of itself, otherwise the likelihood of fraud being involved in those transactions increases. If one does not initially invest in such a syst

Fansly

Fansly is a subscription-based social media platform that allows content creators to monetize exclusive content, including photos, videos, live streams, and direct messages. Operated by Select Media LLC, the platform is headquartered in Baltimore, Maryland. While the platform hosts a variety of content genres, it is primarily known for adult content and is frequently compared to OnlyFans. == History == Fansly was launched in 2020 by Micheal Etelis under Select Media LLC, which was incorporated in February 2020. The platform also operates through CY Media LTD, registered in Kamares, Cyprus, established in May 2021. The company has remained privately held with no disclosed external funding rounds or official valuation, operating as a bootstrapped entity. Based on Fansly's social media presence, which was created in November 2020, the platform did not begin gaining traction until early 2021 when creators started to become concerned about potential content policy changes at OnlyFans. In August 2021, OnlyFans announced it would ban sexually explicit content effective October 2021, citing pressure from banks involved in its payment processing. Although OnlyFans reversed the decision six days later, the announcement triggered a massive influx of users to Fansly; the platform received nearly 4,000 new creator applications in a single hour, causing its servers to crash from the surge in traffic. By August 21, 2021, Fansly had reached 2.1 million users. == Features and business model == Fansly operates as a B2C marketplace, taking a 20% commission on all transactions conducted on the platform, with creators retaining the remaining 80%. This commission rate is the same as that charged by its main competitor, OnlyFans. A distinguishing feature of Fansly is its tiered subscription model, which allows creators to set multiple subscription levels at different price points, each offering different perks such as exclusive content, chat access, or custom requests. By contrast, OnlyFans historically relied on a single-tier subscription model. Revenue streams on the platform include recurring subscriptions, one-time pay-per-view content purchases, tips, paid messaging, and live-streaming fees. The platform also features an algorithmic "For You" feed that helps users discover new creators, addressing a limitation of competitors that lack internal content promotion mechanisms. Additional features include content watermarking, geolocation blocking to control where content is visible, two-factor authentication, community polls, 24-hour stories, and social media integration with platforms such as Twitter and Twitch. Payouts are processed within one to two business days and support multiple methods, including bank transfers, Skrill, Paxum, and cryptocurrency. In December 2025, Fansly expanded its live-streaming capabilities, introducing ticketed access, private list gating, configurable chat permissions, stream goals, and interactive device integration. == Controversies == === OnlyFans anti-competitive allegations === In August 2022, a series of lawsuits were filed in the United States alleging that OnlyFans had bribed employees of Meta Platforms to place Instagram accounts of creators who also sold content on competitor platforms, including Fansly, onto a terrorist blacklist. The lawsuits alleged that adult performers had traffic driven away from their Instagram accounts after being falsely tagged as terror-related. OnlyFans denied awareness of such activity. The plaintiffs withdrew the bribery claim in July 2023, and the case was dismissed in August 2023. === Privacy class action === In June 2025, Select Media LLC (operating as Fansly) was the subject of a digital privacy class action lawsuit filed in Massachusetts District Court. The lawsuit alleged that the platform secretly collected and shared users' sensitive viewing data with Google and other third parties without consent. The case was brought on behalf of an estimated class of over 10,000 users across multiple states.

The Culture of Connectivity

The Culture of Connectivity: A Critical History of Social Media is a book by José van Dijck published by Oxford University Press in 2013 on social media platforms and their history. The author considers the histories of five social media platforms: Facebook, Twitter, Flickr, YouTube, and Wikipedia. She focuses on how their technological, social and cultural dimensions contribute to their current status.

FaceApp

FaceApp is a photo and video editing application for iOS and Android developed by FaceApp Technology Limited, a company based in Cyprus. The app generates highly realistic transformations of human faces in photographs by using neural networks. The app can transform a face to make it smile, look younger, look older, or change gender. == History == FaceApp was launched on iOS in January 2017 and on Android in February 2017. It was developed by Yaroslav Goncharov, a former executive at Yandex, and created by the Russian company Wireless Lab. == Features == There are multiple options to manipulate the photo uploaded such as editor options of adding an impression, make-up, smiles, hair colors, hairstyles, glasses, age or beards. Filters, lens blur and backgrounds along with overlays, tattoos, and vignettes are also a part of the app. The gender change transformations of FaceApp have attracted particular interest from the LGBT and transgender communities, due to their ability to realistically simulate the appearance of a person as the opposite gender. == Criticism == In 2017, FaceApp faced criticism for a "hot" filter that appeared to lighten users' skin tones, prompting accusations of racial bias. The feature was briefly renamed "spark" before being removed. Founder Yaroslav Goncharov attributed the issue to training data bias and apologized. In August of that year, more criticism arose when it featured "ethnicity filters" depicting "White", "Black", "Asian", and "Indian". The filters were immediately removed from the app. In 2019, FaceApp faced criticism over its handling of user data, including concerns that it stored users' photos on its servers and could use them for commercial purposes. Founder Yaroslav Goncharov stated that images were processed on cloud servers like Google Cloud Platform and Amazon Web Services, not transferred to Russia, and were temporarily stored only to support editing functions before being deleted. U.S. Senator Chuck Schumer raised concerns about data privacy and called for an FBI investigation.

GitHub Codespaces

GitHub Codespaces is a cloud-based online integrated development environment developed by GitHub. It allows users to create and manage development environments directly within the browser or through Visual Studio Code desktop. Codespaces is tightly integrated with GitHub repositories and enables on-demand coding, debugging, and testing in a full-featured development container hosted in the cloud. == Features == Instant development environments integrated with GitHub Browser-based and desktop access via Visual Studio Code Configurable Dockerfile or devcontainer.json environments Built-in support for GitHub Copilot, extensions, snippets, and SSH. == Licensing == GitHub Codespaces is proprietary software and available to GitHub users under various subscription plans. Codespaces includes a monthly usage quota for free tier users of 120 hours, and expanded access for GitHub education, Pro, Team, and GitHub Enterprise plans. == GitHub Classroom == GitHub Classroom is an educational tool developed by GitHub to streamline the process of managing programming assignments and coursework. Integrated with GitHub repositories, it allows instructors to distribute starter code, automate grading workflows, and track student progress. GitHub Classroom is widely used in computer science education and supports integration with GitHub Codespaces for cloud-based development environments. == Programming languages supported == == Extensions == Some of the popular extensions include:

Proximedia Group

Proximedia Group is a Belgian media group. == History == Proximedia Belgium was founded in 1998, by Fabrice Wuyts and Eric Glachant. The company specializes in providing websites for SMEs. The Proximedia Group SA was founded in 1999 and became the coordinating organization of Proximedia Belgium, Online, Bizbook Channel, Globule Bleu bvba, Click+, Proximedia France, Proximedia Nederland, and Proximedia Spain. The Proximedia Group has been listed at the Free Market of Euronext Brussels since 2005. In 2007, the Proximedia Group founded the Bizbook Channel. This branch specialized in creating corporate videos. In 2008, Proximedia SA took over the web agency Globule Bleu. The following year, Proximedia launched the brand BeUP. They were also elected ‘Enterprise of The Year 2009’ by Ernst & Young. Proximedia launched two new services in 2011: Videobiz and Promobook. In 2012, the Bizbook Channel was launched. Proximedia was acquired by Publicis Groupe S.A. in July 2014. == Branches == Proximedia Belgium: the oldest branch of the Proximedia Group. It makes websites and provides support for their customers. Similar branches are Proximedia France and Proximedia Nederland. Batibouw +: specialized in bringing contractors and clients together. Bizbook Channel: specialized in creating corporate videos for SMEs. Click+: offers the management of Google AdWords campaigns. This contains advertising in Google's search results. Globule Bleu: specialized in digital campaigns for larger companies or organisations. Online: an Internet Service Provider (ISP) that provides internet access, domain names, hosting of websites and data centers, email service, etc. Bizbook: an online guestbook where users can post reviews on products and services of a company. Promobook: an online service which can be used to print promotions and coupons. == Key figures == == Sale tactics and lawsuits == There are a lot of websites, forums and blogs that warn for Proximedia. This is because of the long duration of the contract, the inability to terminate the contract and the alleged aggressive approach of Proximedia and the alleged low quality of service that Proximedia offers. Also, there are a lot of lawsuits every month, some of which are customers that wish to terminate the contract, others that allege Proximedia of misguiding. List of some example lawsuits: Mitigation of contractual termination compensation on the basis of article 6:248 paragraph 2 of the Dutch Civil Code A clause on the basis of which a termination fee is claimed can be considered a penalty clause. Mitigation of the penalty based on article 6:94 of the Dutch Civil Code? Performance claim rejected; successful appeal to breach of contract; dissolution; restitution claim awarded. Agreement for IT services. Contents of the agreement. No reflex effect of the Door-to-Door Sales Act for small entrepreneurs. Implementation Act of the Consumer Rights Directive. Breach of contract? Unreasonably onerous clause? Cassation: ECLI:NL:HR:2016:996, (Partial) annulment with referral. Final judgment: ECLI:NL:GHSHE:2014:4228 Error. Reflex effect of the unfair commercial practices law? Compelling evidentiary force of written agreement. (No summary provided by court) Proximedia case. No valid defense against the claim concerning a number of monthly invoices. Article 7.1 of the agreement (containing a termination fee) is a general term in the sense of article 6:231 introductory text and under a of the Dutch Civil Code. No "reflex effect" of article 6:237 introductory text and under i of the Dutch Civil Code. Insufficiently argued why article 7.1 would be unreasonably onerous in the sense of article 6:233 of the Dutch Civil Code and that granting the claim would be unacceptable according to standards of reasonableness and fairness. Termination fee is not a penalty in the sense of article 6:91 of the Dutch Civil Code. A retailer (sole proprietorship) is approached by a representative of a company and enters into an "agreement for IT services" with a term of four years, which includes a dissolution fee of 60% of the not yet due monthly payments. The retailer is instructed to prove that, at the time of entering the agreement, the company promised him that he could terminate the agreement without any further obligations if he terminated his business. The retailer is considered to have succeeded in the burden of proof, and the company's claim for payment of the dissolution fee is rejected.