SprayPrinter is a device that attaches to aerosol paint cans whereby users can print images via Bluetooth from a smartphone onto a wall or almost any surface. == History == The technology behind SprayPrinter was developed by Mihkel Joala. He explained in a 2016 interview with New Atlas that his idea was inspired by the modern car engine and the Nintendo Wii console. "Engines nowadays use extremely fast valves to spray fuel to [the] combustion chamber," says Joala. "I realized I can use them to shoot paint with pinpoint accuracy." As of December 2021, the company appears to be no longer selling products. == Awards and Recognitions == In 2015, SprayPrinter received €8,000 from the Estonian prototyping contest Prototron for its initial prototype. In 2016, the SprayPrinter team won the grand prize of €30,000 from the televised pitching competition Ajujaht.
Inductive programming
Inductive programming (IP) is a special area of automatic programming, covering research from artificial intelligence and programming, which addresses learning of typically declarative (logic or functional) and often recursive programs from incomplete specifications, such as input/output examples or constraints. Depending on the programming language used, there are several kinds of inductive programming. Inductive functional programming, which uses functional programming languages such as Lisp or Haskell, and most especially inductive logic programming, which uses logic programming languages such as Prolog and other logical representations such as description logics, have been more prominent, but other (programming) language paradigms have also been used, such as constraint programming or probabilistic programming. == Definition == Inductive programming incorporates all approaches which are concerned with learning programs or algorithms from incomplete (formal) specifications. Possible inputs in an IP system are a set of training inputs and corresponding outputs or an output evaluation function, describing the desired behavior of the intended program, traces or action sequences which describe the process of calculating specific outputs, constraints for the program to be induced concerning its time efficiency or its complexity, various kinds of background knowledge such as standard data types, predefined functions to be used, program schemes or templates describing the data flow of the intended program, heuristics for guiding the search for a solution or other biases. Output of an IP system is a program in some arbitrary programming language containing conditionals and loop or recursive control structures, or any other kind of Turing-complete representation language. In many applications the output program must be correct with respect to the examples and partial specification, and this leads to the consideration of inductive programming as a special area inside automatic programming or program synthesis, usually opposed to 'deductive' program synthesis, where the specification is usually complete. In other cases, inductive programming is seen as a more general area where any declarative programming or representation language can be used and we may even have some degree of error in the examples, as in general machine learning, the more specific area of structure mining or the area of symbolic artificial intelligence. A distinctive feature is the number of examples or partial specification needed. Typically, inductive programming techniques can learn from just a few examples. The diversity of inductive programming usually comes from the applications and the languages that are used: apart from logic programming and functional programming, other programming paradigms and representation languages have been used or suggested in inductive programming, such as functional logic programming, constraint programming, probabilistic programming, abductive logic programming, modal logic, action languages, agent languages and many types of imperative languages. == History == The early works of Plotkin, and his "relative least general generalization (rlgg)", had an enormous impact in inductive logic programming. There were some encouraging results on learning recursive Prolog programs such as quicksort from examples together with suitable background knowledge, for example with GOLEM. However, after initial success, the community got disappointed by limited progress about the induction of recursive programs with ILP less and less focusing on recursive programs and leaning more and more towards a machine learning setting with applications in relational data mining and knowledge discovery. In parallel to work in ILP, Koza proposed genetic programming in the early 1990s as a generate-and-test based approach to learning programs. The idea of genetic programming was further developed into the inductive programming system ADATE and the systematic-search-based system MagicHaskeller. Here again, functional programs are learned from sets of positive examples together with an output evaluation (fitness) function which specifies the desired input/output behavior of the program to be learned. The early work in grammar induction (also known as grammatical inference) is related to inductive programming, as rewriting systems or logic programs can be used to represent production rules. In fact, early works in inductive inference considered grammar induction and Lisp program inference as basically the same problem. The results in terms of learnability were related to classical concepts, such as identification-in-the-limit, as introduced in the seminal work of Gold. More recently, the language learning problem was addressed by the inductive programming community. In the recent years, the classical approaches have been resumed and advanced with great success. Therefore, the synthesis problem has been reformulated on the background of constructor-based term rewriting systems taking into account modern techniques of functional programming, as well as moderate use of search-based strategies and usage of background knowledge as well as automatic invention of subprograms. Many new and successful applications have recently appeared beyond program synthesis, most especially in the area of data manipulation, programming by example and cognitive modelling (see below). Other ideas have also been explored with the common characteristic of using declarative languages for the representation of hypotheses. For instance, the use of higher-order features, schemes or structured distances have been advocated for a better handling of recursive data types and structures; abstraction has also been explored as a more powerful approach to cumulative learning and function invention. One powerful paradigm that has been recently used for the representation of hypotheses in inductive programming (generally in the form of generative models) is probabilistic programming (and related paradigms, such as stochastic logic programs and Bayesian logic programming). == Application areas == The first workshop on Approaches and Applications of Inductive Programming (AAIP) Archived 2016-03-03 at the Wayback Machine held in conjunction with ICML 2005 identified all applications where "learning of programs or recursive rules are called for, [...] first in the domain of software engineering where structural learning, software assistants and software agents can help to relieve programmers from routine tasks, give programming support for end users, or support of novice programmers and programming tutor systems. Further areas of application are language learning, learning recursive control rules for AI-planning, learning recursive concepts in web-mining or for data-format transformations". Since then, these and many other areas have shown to be successful application niches for inductive programming, such as end-user programming, the related areas of programming by example and programming by demonstration, and intelligent tutoring systems. Other areas where inductive inference has been recently applied are knowledge acquisition, artificial general intelligence, reinforcement learning and theory evaluation, and cognitive science in general. There may also be prospective applications in intelligent agents, games, robotics, personalisation, ambient intelligence and human interfaces.
International Speech Communication Association
The International Speech Communication Association (ISCA) is a non-profit organization and one of the two main professional associations for speech communication science and technology, the other association being the IEEE Signal Processing Society. == Purpose == The purpose of the International Speech Communication Association (ISCA) is to promote the study and application of automatic speech processing, including speech recognition and synthesis, as well as related areas such as speaker recognition and speech compression. The association's activities cover all aspects of speech processing, including computational, linguistic, and theoretical aspects. The primary goal of the International Speech Communication Association (ISCA) is to advance the field of automatic speech processing and communication technology through research, education, and collaboration. By promoting the study and application of speech technologies such as speech recognition, speech synthesis, speaker recognition, and speech compression, ISCA aims to foster innovation and development in the areas of human-computer interaction, telecommunications, and multimedia applications. ISCA serves as a platform for researchers, academics, industry professionals, and students to exchange knowledge, share best practices, and foster interdisciplinary dialogue in the field of speech communication science. Through conferences, workshops, publications, and educational initiatives, ISCA seeks to enhance the understanding of speech processing mechanisms, improve the accuracy and efficiency of speech technologies, and explore new frontiers in the realm of human language communication. Furthermore, ISCA plays a crucial role in promoting international collaboration and networking among professionals in the speech communication community. By facilitating partnerships and cooperation between individuals and organizations worldwide, ISCA seeks to drive global progress in speech technology research and application, ultimately contributing to the advancement of communication systems, accessibility tools, and interactive interfaces that benefit society as a whole. == Conferences == ISCA organizes yearly the Interspeech conference. Most recent Interspeech: 2013 Lyon, France 2014 Singapore 2015 Dresden, Germany 2016 San Francisco, US 2017 Stockholm, Sweden 2018 Hyderabad, India 2019 Graz, Austria 2020 Shanghai, China (fully virtual) 2021 Brno, Czechia (hybrid) 2022 Incheon, South Korea 2023 Dublin, Ireland 2023 Kos Island, Greece Forthcoming Interspeech: 2025 Rotterdam, the Netherlands == ISCA board == The ISCA president for 2023-2025 is Odette Scharenborg. The vice president is Bhuvana Ramabhadran and the other members are professionals in the field. == History of ISCA == The precursor to Interspeech was a conference called Eurospeech, first held in 1989 and organised by Jean-Pierre Tubach. It was the conference of the European Speech Communication Association (ESCA), itself the precursor of the International Speech Communication Association (ISCA). A year later another conference on speech science and technology was started: the International Conference on Spoken Language Processing (ICSLP), which was founded in 1990 by Hiroya Fujisaki. The first ISCA (vs. ESCA) event was the merging of Eurospeech and ICSLP to create ICSLP-Interspeech, held in Beijing, China in 2000. This was followed by Eurospeech-Interspeech, which was held in Aalborg, Denmark in 2001. In 2007, the Eurospeech and ICSLP parts of the conference names were dropped and Interspeech became the name of the yearly conference (first Interspeech location: Antwerp, Belgium).
Teleradiology
Teleradiology is the transmission of radiological patient images from procedures such as x-rays, Computed tomography (CT), and MRI imaging, from one location to another for the purposes of sharing studies with other radiologists and physicians. Teleradiology allows radiologists to provide services without actually having to be at the location of the patient. This is particularly important when a sub-specialist such as an MRI radiologist, neuroradiologist, pediatric radiologist, or musculoskeletal radiologist is needed, since these professionals are generally only located in large metropolitan areas working during daytime hours. Teleradiology allows for specialists to be available at all times. Teleradiology utilizes standard network technologies such as the Internet, telephone lines, wide area networks, local area networks (LAN) and the latest advanced technologies such as medical cloud computing. Specialized software is used to transmit the images and enable the radiologist to effectively analyze potentially hundreds of images of a given study. Technologies such as advanced graphics processing, voice recognition, artificial intelligence, and image compression are often used in teleradiology. Through teleradiology and mobile DICOM viewers, images can be sent to another part of the hospital or to other locations around the world with equal effort. Teleradiology is a growth technology given that imaging procedures are growing approximately 15% annually against an increase of only 2% in the radiologist population. == Reports == Teleradiology services commonly provide either preliminary or final interpretations of medical imaging studies. Preliminary reads are frequently used in emergency settings to support immediate clinical decisions and may include direct communication of critical findings to the referring physician. Some providers report turnaround times of approximately 30 minutes for emergency cases, with faster processing for time-sensitive conditions such as stroke. Final reads are definitive and used in official patient records and billing. These reports typically include all relevant findings and may require access to prior imaging and clinical data. Teleradiology is also employed to provide off-hour or overflow coverage for healthcare institutions lacking continuous on-site radiology staffing. == Subspecialties == Some teleradiologists are fellowship trained and have a wide variety of subspecialty expertise including such difficult-to-find areas as neuroradiology, pediatric neuroradiology, thoracic imaging, musculoskeletal radiology, mammography, and nuclear cardiology. There are also various medical practitioners who are not radiologists that take on studies in radiology to become sub specialists in their respected fields, an example of this is dentistry where oral and maxillofacial radiology allows those in dentistry to specialize in the acquisition and interpretation of radiographic imaging studies performed for diagnosis of treatment guidance for conditions affecting the maxillofacial region. == Teleultrasound == Teleradiology infrastructure has also been adapted to support point-of-care ultrasound (POCUS) in remote and austere environments. In teleultrasound—also known as telementored ultrasound—a remote expert guides a non-specialist in real time during image acquisition. This technique has been successfully demonstrated in extreme settings, including aboard the International Space Station, on Mount Everest, and during helicopter flight. == Regulations == In the United States, Medicare and Medicaid laws require the teleradiologist to be on U.S. soil in order to qualify for reimbursement of the Final Read. In addition, advanced teleradiology systems must also be HIPAA compliant, which helps to ensure patients' privacy. HIPAA (Health Insurance Portability and Accountability Act of 1996) is a uniform, federal floor of privacy protections for consumers. It limits the ways that entities can use patients' personal information and protects the privacy of all medical information no matter what form it is in. Quality teleradiology must abide by important HIPAA rules to ensure patients' privacy is protected. Also State laws governing the licensing requirements and medical malpractice insurance coverage required for physicians vary from state to state. Ensuring compliance with these laws is a significant overhead expense for larger multi-state teleradiology groups. Medicare (Australia) has identical requirements to that of the United States, where the guidelines are provided by the Department of Health and Ageing, and government based payments fall under the Health Insurance Act. The regulations in Australia are also conducted at both federal and state levels, ensuring that strict guidelines are adhered to at all times, with regular yearly updates and amendments are introduced (usually around March and November of every year), ensuring that the legislation is kept up to date with changes in the industry. One of the most recent changes to Medicare and radiology / teleradiology in Australia was the introduction of the Diagnostic Imaging Accreditation Scheme (DIAS) on 1 July 2008. DIAS was introduced to further improve the quality of Diagnostic Imaging and to amend the Health Insurance Act. == Industry growth == Until the late 1990s teleradiology was primarily used by individual radiologists to interpret occasional emergency studies from offsite locations, often in the radiologists home. The connections were made through standard analog phone lines. Teleradiology expanded rapidly as the growth of the internet and broad band combined with new CT scanner technology to become an essential tool in trauma cases in emergency rooms throughout the country. The occasional 2–3 x-ray studies a week soon became 3–10 CT scans, or more, a night. Because ER physicians are not trained to read CT scans or MRIs, radiologists went from working 8–10 hours a day, five and half days a week to a schedule of 24 hours a day, 7 days a week coverage. This became a particularly acute challenge in smaller rural facilities that only had one solo radiologist with no other to share call. These circumstances spawned a post-dot.com boom of firms and groups that provided medical outsourcing, off-site teleradiology on-call services to hospitals and Radiology Groups around the country. As an example, a teleradiology firm might cover trauma at a hospital in Indiana with doctors based in Texas. Some firms even used overseas doctors in locations like Australia and India. Nighthawk, founded by Paul Berger, was the first to station U.S. licensed radiologists overseas (initially Australia and later Switzerland) to maximize the time zone difference to provide nightcall in U.S. hospitals. Currently, teleradiology firms are facing pricing pressures. Industry consolidation is likely as there are more than 500 of these firms, large and small, throughout the United States.
Template matching
Template matching is a technique in digital image processing for finding small parts of an image which match a template image. It can be used for quality control in manufacturing, navigation of mobile robots, or edge detection in images. The main challenges in a template matching task are detection of occlusion, when a sought-after object is partly hidden in an image; detection of non-rigid transformations, when an object is distorted or imaged from different angles; sensitivity to illumination and background changes; background clutter; and scale changes. == Feature-based approach == The feature-based approach to template matching relies on the extraction of image features, such as shapes, textures, and colors, that match the target image or frame. This approach is usually achieved using neural networks and deep-learning classifiers such as VGG, AlexNet, and ResNet.Convolutional neural networks (CNNs), which many modern classifiers are based on, process an image by passing it through different hidden layers, producing a vector at each layer with classification information about the image. These vectors are extracted from the network and used as the features of the image. Feature extraction using deep neural networks, like CNNs, has proven extremely effective has become the standard in state-of-the-art template matching algorithms. This feature-based approach is often more robust than the template-based approach described below. As such, it has become the state-of-the-art method for template matching, as it can match templates with non-rigid and out-of-plane transformations, as well as high background clutter and illumination changes. == Template-based approach == For templates without strong features, or for when the bulk of a template image constitutes the matching image as a whole, a template-based approach may be effective. Since template-based matching may require sampling of a large number of data points, it is often desirable to reduce the number of sampling points by reducing the resolution of search and template images by the same factor before performing the operation on the resultant downsized images. This pre-processing method creates a multi-scale, or pyramid, representation of images, providing a reduced search window of data points within a search image so that the template does not have to be compared with every viable data point. Pyramid representations are a method of dimensionality reduction, a common aim of machine learning on data sets that suffer the curse of dimensionality. == Common challenges == In instances where the template may not provide a direct match, it may be useful to implement eigenspaces to create templates that detail the matching object under a number of different conditions, such as varying perspectives, illuminations, color contrasts, or object poses. For example, if an algorithm is looking for a face, its template eigenspaces may consist of images (i.e., templates) of faces in different positions to the camera, in different lighting conditions, or with different expressions (i.e., poses). It is also possible for a matching image to be obscured or occluded by an object. In these cases, it is unreasonable to provide a multitude of templates to cover each possible occlusion. For example, the search object may be a playing card, and in some of the search images, the card is obscured by the fingers of someone holding the card, or by another card on top of it, or by some other object in front of the camera. In cases where the object is malleable or poseable, motion becomes an additional problem, and problems involving both motion and occlusion become ambiguous. In these cases, one possible solution is to divide the template image into multiple sub-images and perform matching on each subdivision. == Deformable templates in computational anatomy == Template matching is a central tool in computational anatomy (CA). In this field, a deformable template model is used to model the space of human anatomies and their orbits under the group of diffeomorphisms, functions which smoothly deform an object. Template matching arises as an approach to finding the unknown diffeomorphism that acts on a template image to match the target image. Template matching algorithms in CA have come to be called large deformation diffeomorphic metric mappings (LDDMMs). Currently, there are LDDMM template matching algorithms for matching anatomical landmark points, curves, surfaces, volumes. == Template-based matching explained using cross correlation or sum of absolute differences == A basic method of template matching sometimes called "Linear Spatial Filtering" uses an image patch (i.e., the "template image" or "filter mask") tailored to a specific feature of search images to detect. This technique can be easily performed on grey images or edge images, where the additional variable of color is either not present or not relevant. Cross correlation techniques compare the similarities of the search and template images. Their outputs should be highest at places where the image structure matches the template structure, i.e., where large search image values get multiplied by large template image values. This method is normally implemented by first picking out a part of a search image to use as a template. Let S ( x , y ) {\displaystyle S(x,y)} represent the value of a search image pixel, where ( x , y ) {\displaystyle (x,y)} represents the coordinates of the pixel in the search image. For simplicity, assume pixel values are scalar, as in a greyscale image. Similarly, let T ( x t , y t ) {\textstyle T(x_{t},y_{t})} represent the value of a template pixel, where ( x t , y t ) {\textstyle (x_{t},y_{t})} represents the coordinates of the pixel in the template image. To apply the filter, simply move the center (or origin) of the template image over each point in the search image and calculate the sum of products, similar to a dot product, between the pixel values in the search and template images over the whole area spanned by the template. More formally, if ( 0 , 0 ) {\displaystyle (0,0)} is the center (or origin) of the template image, then the cross correlation T ⋆ S {\displaystyle T\star S} at each point ( x , y ) {\displaystyle (x,y)} in the search image can be computed as: ( T ⋆ S ) ( x , y ) = ∑ ( x t , y t ) ∈ T T ( x t , y t ) ⋅ S ( x t + x , y t + y ) {\displaystyle (T\star S)(x,y)=\sum _{(x_{t},y_{t})\in T}T(x_{t},y_{t})\cdot S(x_{t}+x,y_{t}+y)} For convenience, T {\displaystyle T} denotes both the pixel values of the template image as well as its domain, the bounds of the template. Note that all possible positions of the template with respect to the search image are considered. Since cross correlation values are greatest when the values of the search and template pixels align, the best matching position ( x m , y m ) {\displaystyle (x_{m},y_{m})} corresponds to the maximum value of T ⋆ S {\displaystyle T\star S} over S {\displaystyle S} . Another way to handle translation problems on images using template matching is to compare the intensities of the pixels, using the sum of absolute differences (SAD) measure. To formulate this, let I S ( x s , y s ) {\displaystyle I_{S}(x_{s},y_{s})} and I T ( x t , y t ) {\displaystyle I_{T}(x_{t},y_{t})} denote the light intensity of pixels in the search and template images with coordinates ( x s , y s ) {\displaystyle (x_{s},y_{s})} and ( x t , y t ) {\displaystyle (x_{t},y_{t})} , respectively. Then by moving the center (or origin) of the template to a point ( x , y ) {\displaystyle (x,y)} in the search image, as before, the sum of absolute differences between the template and search pixel intensities at that point is: S A D ( x , y ) = ∑ ( x t , y t ) ∈ T | I T ( x t , y t ) − I S ( x t + x , y t + y ) | {\displaystyle SAD(x,y)=\sum _{(x_{t},y_{t})\in T}\left\vert I_{T}(x_{t},y_{t})-I_{S}(x_{t}+x,y_{t}+y)\right\vert } With this measure, the lowest SAD gives the best position for the template, rather than the greatest as with cross correlation. SAD tends to be relatively simple to implement and understand, but it also tends to be relatively slow to execute. A simple C++ implementation of SAD template matching is given below. == Implementation == In this simple implementation, it is assumed that the above described method is applied on grey images: This is why Grey is used as pixel intensity. The final position in this implementation gives the top left location for where the template image best matches the search image. One way to perform template matching on color images is to decompose the pixels into their color components and measure the quality of match between the color template and search image using the sum of the SAD computed for each color separately. == Speeding up the process == In the past, this type of spatial filtering was normally only used in dedicated hardware solutions because of the computational complexity of the operation, however we can lessen this complexity b
Purged cross-validation
Purged cross-validation is a variant of k-fold cross-validation designed to prevent look-ahead bias in time series and other structured data, developed in 2017 by Marcos López de Prado at Guggenheim Partners and Cornell University. It is primarily used in financial machine learning to ensure the independence of training and testing samples when labels depend on future events. It provides an alternative to conventional cross-validation and walk-forward backtesting methods, which often yield overly optimistic performance estimates due to information leakage and overfitting. == Motivation == Standard cross-validation assumes that observations are independently and identically distributed (IID), which often does not hold in time series or financial datasets. If the label of a test sample overlaps in time with the features or labels in the training set, the result may be data leakage and overfitting. Purged cross-validation addresses this issue by removing overlapping observations and, optionally, adding a temporal buffer ("embargo") around the test set to further reduce the risk of leakage. The figure below illustrates standard 5 Fold Cross-Validation == Purging == Purging removes from the training set any observation whose timestamp falls within the time range of formation of a label in the test set. This can be the case for train set observations before and after the test set. Their removal ensures that the algorithm cannot learn during train time information that will be used to assess the performance of the algorithm. See the figure below for an illustration of purging. == Embargoing == Embargoing addresses a more subtle form of leakage: even if an observation does not directly overlap the test set, it may still be affected by test events due to market reaction lag or downstream dependencies. To guard against this, a percentage-based embargo is imposed after each test fold. For example, with a 5% embargo and 1000 observations, the 50 observations following each test fold are excluded from training. Unlike purging, embargoing can only occur after the test set. The figure below illustrates the application of embargo: == Applications == Purged and embargoed cross-validation has been useful in: Backtesting of trading strategies Validation of classifiers on labeled event-driven returns Any machine learning task with overlapping label horizons == Example == To illustrate the effect of purging and embargoing, consider the figures below. Both diagrams show the structure of 5-fold cross-validation over a 20-day period. In each row, blue squares indicate training samples and red squares denote test samples. Each label is defined based on the value of the next two observations, hence creating an overlap. If this overlap is left untreated, test set information leaks into the train set. The second figure applies the Purged CV procedure. Notice how purging removes overlapping observations from the training set and the embargo widens the gap between test and training data. This approach ensures that the evaluation more closely resembles a true out-of-sample test and reduces the risk of backtest overfitting. == Combinatorial Purged Cross-Validation == Walk-forward backtesting analysis, another common cross-validation technique in finance, preserves temporal order but evaluates the model on a single sequence of test sets. This leads to high variance in performance estimation, as results are contingent on a specific historical path. Combinatorial Purged Cross-Validation (CPCV) addresses this limitation by systematically constructing multiple train-test splits, purging overlapping samples, and enforcing an embargo period to prevent information leakage. The result is a distribution of out-of-sample performance estimates, enabling robust statistical inference and more realistic assessment of a model's predictive power. === Methodology === CPCV divides a time-series dataset into N sequential, non-overlapping groups. These groups preserve the temporal order of observations. Then, all combinations of k groups (where k < N) are selected as test sets, with the remaining N − k groups used for training. For each combination, the model is trained and evaluated under strict controls to prevent leakage. To eliminate potential contamination between training and test sets, CPCV introduces two additional mechanisms: Purging: Any training observations whose label horizon overlaps with the test period are excluded. This ensures that future information does not influence model training. Embargoing: After the end of each test period, a fixed number of observations (typically a small percentage) are removed from the training set. This prevents leakage due to delayed market reactions or auto-correlated features. Each data point appears in multiple test sets across different combinations. Because test groups are drawn combinatorially, this process produces multiple backtest "paths," each of which simulates a plausible market scenario. From these paths, practitioners can compute a distribution of performance statistics such as the Sharpe ratio, drawdown, or classification accuracy. === Formal definition === Let N be the number of sequential groups into which the dataset is divided, and let k be the number of groups selected as the test set for each split. Then: The number of unique train-test combinations is given by the binomial coefficient: ( N k ) {\displaystyle {\binom {N}{k}}} Each observation is used in k {\displaystyle k} test sets and contributes to φ [ N , k ] {\displaystyle \varphi [N,k]} unique backtest paths: φ [ N , k ] = k N ( N k ) {\displaystyle \varphi [N,k]={\frac {k}{N}}{\binom {N}{k}}} This yields a distribution of performance metrics rather than a single point estimate, making it possible to apply Monte Carlo-based or probabilistic techniques to assess model robustness. === Illustrative example === Consider the case where N = 6 and k = 2. The number of possible test set combinations is ( 6 2 ) = 15 {\displaystyle {\binom {6}{2}}=15} . Each of the six groups appears in five test splits. Consequently, five distinct backtest paths can be constructed, each incorporating one appearance from every group. ==== Test group assignment matrix ==== This table shows the 15 test combinations. An "x" indicates that the corresponding group is included in the test set for that split. ==== Backtest path assignment ==== Each group contributes to five different backtest paths. The number in each cell indicates the path to which the group's result is assigned for that split. === Advantages === Combinatorial Purged Cross-Validation offers several key benefits over conventional methods: It produces a distribution of performance metrics, enabling more rigorous statistical inference. The method systematically eliminates lookahead bias through purging and embargoing. By simulating multiple historical scenarios, it reduces the dependence on any single market regime or realization. It supports high-confidence comparisons between competing models or strategies. CPCV is commonly used in quantitative strategy research, especially for evaluating predictive models such as classifiers, regressors, and portfolio optimizers. It has been applied to estimate realistic Sharpe ratios, assess the risk of overfitting, and support the use of statistical tools such as the Deflated Sharpe Ratio (DSR). === Limitations === The main limitation of CPCV stems from its high computational cost. However, this cost can be managed by sampling a finite number of splits from the space of all possible combinations.
Prequel (mobile application)
Prequel, Inc. is an American technology company and mobile app developer known for developing the Prequel mobile application, which enables editing photos and videos with filters and effects generated using artificial intelligence. Prequel was founded in 2018 by Serge Aliseenko and Timur Khabirov, who currently serves as the company's CEO. It is headquartered in New York City. As of August 2022, it had been downloaded more than 100 million times. == History == In 2016, entrepreneur Timur Khabirov and investor Serge Aliseenko registered a US corporation named AIAR Labs Inc, which was developing AR solutions as an outsourced contractor. Of several proprietary products, Prequel was selected for beta-testing as a product focused on editing photos and videos. In 2018, Prequel was released on the Apple App Store. The launch cost $3 million USD, financed with the founders’ personal funds. The first release included approximately 10 filters for photos and the same amount of effects that augmented images with rose petals, rain and snow, VHS and film reel simulations, glitch, grain, sun puddles, and lomography. By June 2020, the app had also been released for Android. In 2021, Prequel founders Timur Khabirov and Serge Aliseenko launched a venture studio for startups working with artificial, computer vision, and AR-based visual art. In December 2022, Prequel reached the number 14 slot on the global rankings for Apple App Store’s Top Charts and the number 5 slot on the App Store’s U.S. charts. In March 2023, Prequel launched a new app called Artique, which is an AI-powered image editing app for businesses. Artique provides advertising and marketing graphic design using ready-made templates that users can customize, while giving suggestions and visual cues through artificial intelligence. Prequel was also one of the companies participating in discussions about artificial intelligence at SXSW 2023. == Features == Prequel describes its app as an "Aesthetic Pic Editor. The app uses artificial intelligence to create and edit content. Prequel can be used to touch up faces on images and videos and can also tie various decorative elements to certain points on the human body and face. Prequel filters include the "Cartoon" filter, which converts selfies into cartoon-style pictures. Other filters include Kidcore, Dust, Grain, Fisheye, Retro Style, Miami, Disco, and VHS-style filters, as well as the ability to create Renaissance-style pictures. Prequel also gives users the ability to apply color correction tools and to make moving images with 3D effects out of 2D images. Prequel allows users to take photos and videos directly through the app and apply filters and effects in real time. The app also comes with manual editing options for photos, such as adjusting the brightness and/or exposure and cropping photos, as well as an option to automatically apply adjustments. The Prequel app uses the Core ML, MNN, and TFLight frameworks to work with its neural networks. Some AI solutions are launched server-side, and some on the user's mobile device. A resulting photo or video edited with the app is called "a prequel." The app daily generates over 2 million such prequels, which are published by users in Instagram, TikTok, and other social media. As of 2022, the app has more than 800 filters and effects, along with video templates and support for GIFs and stickers. Prequel is free-to-use, but has a premium version that gives users access to more effects, filters, and beauty tools. Since its launch in 2018, Prequel has been downloaded more than 100 million times.